tag:blogger.com,1999:blog-33643847064203693382024-03-05T01:37:45.883-08:00Eggonomics: hard numbers over easyChandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.comBlogger20125tag:blogger.com,1999:blog-3364384706420369338.post-31817012021672939302020-10-15T14:02:00.010-07:002020-10-15T16:31:45.096-07:00California ballot propositions for November elections (pt. 1 of 2)<p>As a Californian, I'm used to the barrage of propositions that enter into our ballots every election season. For those less familiar with California state government: there are a number of institutional elements in our government that make it more of a direct democracy than other state governments in the U.S. One of these elements is the initiative statute which allows members of the public with sufficient signatories to place statutes - or propositions - on the ballot to be passed into law with a simple majority of votes. The initiative statute and other elements allow for greater citizen participation in the lawmaking process. This right to greater citizen participation is accompanied by greater responsibility on the part of voters to be informed on the often extensive slate of propositions that can have important political, social, and financial ramifications for California residents. Acknowledging these responsibilities, my family and I gather together every two years once we've received our sample ballots to discuss the merits of the propositions and come up with a voting strategy. Given the number of important propositions up for consideration this year and interest from other friends, I decided to open up the discussion around this year's propositions beyond the confines of our kitchen. </p><p><b>Background on direct democracy elements in state government</b></p><p>Direct democracy allows for greater citizen participation in the lawmaking process. In some circumstances it can be viewed as a positive given that it allows for the majority opinion to be heard and legislated. In other circumstances it can be viewed as a negative given that the legislation of some issues may be better left to those who are more knowledgeable and more efficient at governing. In other words, mob rule is not necessarily the most enlightened rule. The balance between these elements is what led the founders of American government to construct a representative republic in which popular participation was used only for the election of representatives to the legislature. The institutional elements of California government that I discuss here - instituted in 1911 - are initiative statute, initiative constitutional amendment, recall, and referendum. </p><p><a href="https://www.law.berkeley.edu/wp-content/uploads/2019/07/92_3_Carrillo_557.pdf">The article here</a> in the Southern California Law Review provides a more extensive legal discussion of these elements that I discuss here. Note firstly that - the outdated - Table 1 in the article here on California government in the Southern California Law Review indicates that, while California was well ahead of the third-place contender Colorado on the list of states with the highest number of ballot initiatives between 1904-2000, it was also still behind Oregon. It serves as a reminder that California is not the only state with a history of using direct democracy tools. <a href="https://ballotpedia.org/States_with_initiative_or_referendum">Here is a map</a> that illustrates the states that do have initiative or veto referendum processes. </p><p>Initiative statute - the element that I discuss here when I say ballot propositions - is a way for citizens to place statutes on the ballot by soliciting a <a href="https://ballotpedia.org/Signature_requirements_for_ballot_measures_in_California">minimum number of signatories</a>. These statutes are then voted on and signed into law if a majority of voters accept them. Two years ago we even voted to go Daylight-Savings time all year round, which made me feel simultaneously that: of course a decision of this importance should only be made through majority voting (if someone is going to tell me when to wake up and go to bed everyday shouldn't it be the majority?) but at the same time question whether this issue is even important enough to merit the level of introspection granted to it by placing it on the ballot for 21 million registered voters to read (does it really matter that much?). </p><p>Jokes about Daylight-Savings time aside, anyone that grew up in the U.S. likely watched <a href="https://www.youtube.com/watch?v=FFroMQlKiag&ab_channel=DisneyEducationalProductions">this Schoolhouse Rock video</a> in elementary school on how bills are legislated. This element of direct democracy bypasses this process - legislators are not needed to pass bills, rather, citizens can obtain the signatories to place statutes on the ballot and these statutes can be voted into law with a simple majority. It should be noted, however, that issues with this form of citizen lawmaking that doesn't require legislative input have led to recent reforms to the initiative process. For example, 2014 legislation now requires the secretary of state to notify lawmakers when a 25 percent threshold for signatures has been reached for ballot measures. This then allows lawmakers to engage with the lawmaking process by offering their suggestions and amendments to sponsors of the ballot measures. </p><p><a href="https://www.latimes.com/opinion/op-ed/la-oe-gardels-ballot-initiatives-20190110-story.html">This op-ed</a> in the L.A. Times outlines the problems with the initiative process including inadequate review of legislation, disconnect between a myriad of fiscal policies all voted on by the public (it has always amazed me that California voters can decide whether to undertake projects financed by bond sales even though the magnitude of these finances are probably a small share of the state's GDP), and a process that is subject to hijacking by special interest groups that can spend outsized amounts of money to convince voters of their proposals. Once initiative measures are passed, they cannot be altered by the legislature nor are they subject to an executive veto. </p><p><b>Ballot measures for November elections</b></p><p>Hopefully the above has alerted you to the responsibilities associated with a California ballot. This November there are 12 propositions to be voted on at the state-level. One important resource for information on the ballot measures is the <a href="https://calmatters.org/election-2020-guide/">CalMatters 2020 Election Guide</a>. This non-partisan, journalist driven site provides a high level summary of each of the propositions, who's sponsoring them, and who's in favor/opposed. Given the role that special interest groups play in propelling these measures to the ballot box, it's useful to follow the money (see the "how is this being bankrolled?" section on each proposition). </p><p>For those looking for a more detailed analysis from the non-partisan California Legislative Analyst's Office see <a href="https://lao.ca.gov/BallotAnalysis/Propositions?date=11%2F3%2F2020">here</a>. </p><p><b>My takes on the propositions</b></p><p>These are my takes on the propositions. <span style="font-family: inherit;">For those with a dash rather than Yes/No, I have yet to complete a thorough analysis of the proposition. Note that these mostly consist of 3 measures that have to do with property taxes as a revenue collection mechanism for state and local government: Props 15, 19, and 21. My main concern with these that I am still weighing the costs and benefits is the uncertainty of the real estate and housing markets in light of the ongoing pandemic <span style="background-color: white;">and that these should be thought about in conjunction with one another (since they all relate to property tax collection) rather than independent of one another so that we have a more cohesive fiscal policy</span>. </span></p><p><span style="font-family: inherit;">Given how new the California privacy law - CCPA - is, I am unsure whether a measure that is on the ballot due to citizen signatures should be considered so soon in the process of our government legislating on this issue. </span></p><p><span style="font-family: inherit;">However, for those which I've taken a stance - including the 3 measures related to criminal justice system - this indicates how I would be voting on these.</span> </p><p><b>(No) Prop 14: Stem cell research </b></p><p>( - ) Prop 15: Commercial property tax assessment based on market value</p><p>( - ) Prop 16: Affirmative action</p><p><b>(Yes) Prop 17: Voting for parolees</b></p><p><b>(No) Prop 18: Voting at age 17</b></p><p>( - ) Prop 19: Property tax break for Californians 55+</p><p><b>(No) Prop 20: Increase penalties for property crimes </b></p><p>( - ) Prop 21: Allow cities to enact their own rent control measures on housing over 15 years old</p><p><b>(No) Prop 22: Keep workers in "gig" jobs classified as contractors and limit their benefits</b></p><p>( - ) Prop 23: Requirements on dialysis clinics</p><p>( - ) Prop 24: Expand on recent CCPA data privacy law</p><p><b>(No) Prop 25: Replace cash bail with an algorithm that determines the likelihood of a person showing up to trial and grants bail based on this likelihood</b></p><p>Following is a more thorough analysis that I've done so far on three of the propositions. I aim to continue but thought this would be a good starting point. </p><p><b>(No) Prop 14: Stem cell research </b></p><p><b>Prop summary: </b>California will continue to fund stem cell research by selling bonds worth $5.5 billion to be repaid with interest over a 30-year period for an estimated cost of $7.8 billion. </p><p><b>Background: </b>This proposition is a follow up to a proposition that passed in 2006. Prop 71 created the California Institute for Regenerative Medicine (CIRM) with $3 billion in funding from bonds. It was passed with a 59-41 percent majority. This financing was doled out in grants to California public universities, Stanford, research institutes, and for-profit companies working on stem cell cures. <a href="https://projects.sfchronicle.com/2018/stem-cells/politics/">This article </a>in the <i>SF Chronicle</i> details how the financing was spent between 2006-2019. </p><p>The major critiques of Prop 71 are that: </p><p></p><ol style="text-align: left;"><li>Public was misled about how long the time-horizon was for cures and how big the ROI would be for investing in this research: Campaign promises made to incentivize voters on Prop 71 - that clinical trials were right around the corner and that the results could address a myriad of incurable diseases - were rolled back almost immediately. Would the public show up to vote as fervently for cures that would be at least 30 years down the line? Likely not. Almost half of the funding (nearly 40 percent or $1.1 billion) was spent on training programs and basic research indicating that the research was not as close to clinical trials as the campaign promises suggested. In fact only four clinical trials have taken place under the grants from Prop 71. As a result of this long time-horizon, the ROI thus far has been very low. As stated in the article: "The state, once told to expect as much as $1.1 billion in royalties from CIRM-backed discoveries within 35 years, so far has received just a tiny fraction of that amount: a single payment of $190,000 from the City of Hope medical research center in Los Angeles County." </li><li>Limited public oversight of CIRM and its governing board: The language of Prop 71 ensured that a change in the structure of the organization and the governing board would require another voter initiative or a 70 percent vote in both houses of the California state legislature and governor's approval. It also ensured that the leadership of the organization was composed of members of the institutions that were receiving the majority of the grants. This is a conflict of interest concern and it is well-documented in the <i>SF Chronicle</i> article. </li><li>(Critique of Prop 14) Prop 71 was passed at a time when there was a federal ban on funding for embryonic stem cell research and state-level funding: This ban was rolled back under the Obama administration and the climate around stem cell research is less hostile than it was in the early 2000s. Furthermore, a significant amount of the research funded by Prop 71 was adult rather than embryonic stem cell research with the former being far less controversial than the latter. Therefore there are more funds available today for this area of research than in 2006.</li></ol><p></p><p><b>My take:</b> Campaign promises are regularly utilized to get legislation passed by special interest groups (even public universities with an interest in curing cancer can be special interest groups though of a less-nefarious variety). </p><p>What I am more concerned about is the conflicts of interest within the organization combined with a lack of oversight of a publicly-funded organization that should be responsive to taxpayers. It would be one thing if the originally legislation allowed for amendments to be made in subsequent years but the rigidity of a lot of propositions - see my subsequent comments to come on the rigidity embedded in Prop 22 - are in my opinion arguments against their passage. </p><p>It is possible that the financial ROI is higher for Prop 14 funds than Prop 71 funds but financially it seems less of an investment to me and more of a grant (i.e. we should not expect to see substantive returns given that we are not seeing the financials and results of the companies that are being financed). Given the public's role in subsidizing this research for the past 15-year period, my opinion would be to see how private financing can be shored up for promising R&D over the next few years before providing more public financing. This is particularly given the current fiscal status: above average, coronavirus-induced public expenditures and below average, recession-induced public revenues. </p><p><b>( - ) Prop 15: Commercial property tax assessment based on market value</b></p><p><b>Prop summary: </b>Change the way commercial property taxes on businesses over $3 million in California property (farm land exempt) are assessed. Currently commercial property taxes are assessed based on price that was originally paid for the real estate and annual tax hikes from that base value are capped (according to 1978 Prop 13). Prop 15 would assess commercial property taxes based on the market value of the real estate for corporations with over $3 million in commercial property only. This would not directly impact residential property owners, commercial property owners with under $3 million in commercial property, or owners of farm land. The assessment would be done at least every three years rather than at the time of sale only. </p><p><b>Background: </b>Property tax assessments are currently made under the stipulations in Prop 13. Critics of this provision on commercial real estate in Prop 13 argue that while real estate prices soar, holders of said real estate are taxed based only on the initial price paid for the asset rather than its market value. Changing the way these assessments are done could raise an estimated $8-$12.5 billion in revenue annually and the proposition specifies that 60 percent of this revenue would go to cities and counties and the remaining 40 percent to schools. Given that property taxes would be tied to real estate prices, one can imagine that fluctuations in the latter would lead to fluctuations in the former as well which would require greater planning and forecasting on the part of commercial property owners to set aside the appropriate amount in property tax. </p><p><b>My take:</b> The reason that I am uncertain about this proposition is the uncertain nature of the state economy during and in the aftermath of the pandemic. If this were not a turning point for businesses in other ways - specifically the assessment by businesses on whether they would want to invest in commercial real estate in the first place given the remote nature of work for a significant number of Californians at the moment - then I would agree that now would be the time to change the way commercial property taxes are assessed. </p><p>I agree that the mode of property tax assessments are out-of-date and out-of-touch with the realities of the revenue collection and expenditures of our state government and this is particularly true during the pandemic (a time when local governments and schools have higher than average expenditures). My concern would be that $3 million is too low a figure given the already-high property values in California. Proponents of the Prop 15 claim that 92 percent of the new taxes will be paid by 10 percent of commercial real estate owners but 10 percent is a high figure and may lead to opportunistic behavior by firms within that 10 percent to avoid the large hikes they will be facing. I tend to disagree that these taxes will be passed on to the rental market - a concern raised by opponents to Prop 15 - but I am concerned about the design of the measure and that there is a hard threshold rather than a phasing in of higher property taxes across the distribution. </p><p><b>(No) Prop 22: </b><b>Keep workers in "gig" jobs classified as contractors and limit their benefits</b></p><p><b>Summary: </b>Prop 22 is a response sponsored by platform companies, including Uber and Lyft, that hire employees to recently legislated AB5. AB5 mandated that companies that hire independent contractors re-classify these workers as employees with some exceptions. The exceptions would take place through a test that would prove workers are independent contractors and not employees. What does it mean for companies to be mandated to treat their workers as employees rather than contractors? Employees are entitled to worker's compensation, unemployment benefits, paid sick and family leave, and health insurance, in addition to other benefits that are normally accorded to full-time employees. </p><p>Given that I've been part of an email back-and-forth among friends and family on this proposition, I can provide a more detailed summary of this proposition. </p><p>Prop 22 specifically states that gig workers should not fall under the mandates of AB5 (this means all workers for Uber, Lyft, etc... regardless of the hours that they work and if they work approximately full-time). However, it offers certain benefits to gig workers:</p><p></p><ol style="text-align: left;"><li>120 percent of the minimum wage in the area per "engaged" time - time that you spend driving on a particular ride - plus 30 cents per "engaged" mile. Wages are calculated per pay period (at most 2 weeks) meaning that it impacts the gig companies' bottom line only when revenues made by drivers are lower than the minimum wage calculation over an entire 2-week period. The current IRS 2020 mileage rate is 57.5 cents per mile.</li><li>Companies will pay 82 percent of the Obamacare Bronze premium for only the driver (no dependents) if the driver has 25 "engaged" hours per week for a quarter. The health insurance payments are retrospective and made once the quarter is over meaning that gig workers who use employment through Uber or Lyft as full-time would have to save up for their own health insurance payments, face uncertainty over whether they would qualify for a reimbursement, and be reimbursed at the end of a 3-month period. </li><li>Drivers may have the option to purchase accidental medical coverage, disability coverage, and death benefits but this is also denominated based on "engaged" time. </li></ol><p></p><p>It also specifies that the proposition if passed will override local laws and regulations, that it will restrict labor organizing on the part of gig workers at these companies. and amendments to this proposition will only made if 7/8 of the membership of both houses vote for those amendments. </p><p><b>My take:</b> First of all, this proposition is a prime example of how citizen initiatives (bankrolled by private interests) can be extremely powerful given that within the text of the proposition it effectively guarantees that the proposition cannot be repealed or amended. It should be noted that requiring 7/8 majority in both houses of the legislature is an extremely strong mandate (beyond even a 2/3 majority in the federal legislature on constitutional amendments). </p><p>The main issue here seems to be whether the workers at Uber and Lyft and other ridesharing companies are working at close to full-time and being shortchanged on benefits that have been traditionally accorded to full-time employees. How many of them fall into this category versus the category that ridesharing companies want us to believe most people are in (workers who use this form of employment to supplement income, benefits, etc... that they already get from a different job)? This would be the million-dollar question. It should be noted that the guaranteed "minimum wage" accorded in Prop 22 would be <a href="https://laborcenter.berkeley.edu/the-uber-lyft-ballot-initiative-guarantees-only-5-64-an-hour-2/">nowhere close to minimum wage</a> in actuality when analyzed by economists/analysts at the UC Berkeley Labor Center and this would impact drivers whether they are the full-time equivalent that Uber and Lyft use for their classification (25 "engaged" hours per week) or not. </p><p>Second, the pandemic has shown that gig workers that are not eligible for traditional unemployment insurance because their employers do not pay into the unemployment insurance pool on their behalf. However these employers have been encouraging their independent contractors to apply for <a href="https://www.npr.org/2020/03/27/822169893/gig-workers-would-get-unemployment-safety-net-in-rescue-package">relief packages that have opened up specifically for gig workers</a> thereby shirking from any financial responsibility over contractors that may in fact be working at or close to full-time. This shirking of financial responsibility is not only detrimental to employees but also to taxpayers who are the ones to shoulder the burden of relief packages outside of traditional unemployment insurance programs. </p><p>Third, the tax differential between contractors and employees is not insignificant. Self-employed workers pay 15.3 percent in taxes on every dollar net of income whereas traditional W2 employees pay only 7.65 percent. The change in classification can be impactful if only from a tax standpoint for individuals who are working exclusively for one ridesharing company, which brings me to my next point: to what extent is the exclusivity of working for a single company already in effect today? While proponents of the legislation claim that the ability to work across ridesharing companies as a contractor is a major benefit of the existing system, it it unclear without further evidence as to how many ridesharing companies' employees exercise this option in the first place. I.e. is preventing drivers from driving for both Uber and Lyft actually imposing a binding constraint? The flexibility option is touted as a "give" in Prop 22 but I'm curious whether it is symbolic or useful in practice. There are already a number of existing promotions that incentivize drivers to drive exclusively for one or the other, for example if you hit a certain mark in a certain number of days you would get a bonus, indicating there is already a degree of exclusivity in practice. </p><p>While we don't have a lot of data on these questions, it is clear that due to the restrictive and stringent nature of the proposition - in terms of amendments and repeals - and its very limited concessions to labor it is not the appropriate choice to amend AB5 or its treatment of gig companies. There may be a significant number of workers who are treating their "gigs" at ridesharing companies as just that. But I suspect given broader trends in the U.S. workforce that there are many people who are also effectively employees but classified as "contractors" for opportunistic purposes on the part of their employers. While AB5 may not be a sufficient middle-ground on this issue, Prop 22 does not appear to be an answer that is respectful of labor and its due benefits. Given the large precedent that AB5 and Prop 22 will set going forward with independent contractors, this issue should be treated more carefully.</p><div><b><br /></b></div>Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0tag:blogger.com,1999:blog-3364384706420369338.post-15345972747466539722019-08-08T10:59:00.001-07:002019-08-08T10:59:05.976-07:00Place-based economic policies (pt. 2 of 3): How effective are tax incentives for investments in low-income communities <div dir="ltr" style="text-align: left;" trbidi="on">
In this post, I discuss tax incentives for investments in low-income communities. In the U.S., these incentives often have bipartisan support. Democratic presidential contender and Senator Cory Booker and Republican Senator Tim Scott were the primary architects of the provision in the recent Trump tax bill (Tax Cuts and Jobs Act or TCJA) that allows investors to forego capital gains taxes on long-term investments made in low-income Census tracts that they designate as "opportunity zones". These incentives are by no means recent or unique. <a href="https://www.leg.state.mn.us/docs/2005/other/050167.pdf">This</a> policy paper from the Minnesota House of Representatives provides a concise summary of the history and implementation of enterprise zones in the U.S. and evidence from the literature - through 2005 - on their effectiveness.<br />
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In theory, economic barriers prevent qualifying low-income communities from reaching their full potential (lack of transportation, lack of access to capital, lack of skilled labor, social problems, environmental problems) however tax incentives can outweigh the costs to investors of investing in these areas. As stated aptly in the paper: "In the language of economics, the first, best solution is to find a subsidy that equates the marginal social benefits with the marginal social costs to doing business within the zone. Deciding upon the value of the social benefits is a difficult task, let alone determining how much of a subsidy is needed to attract the needed number of businesses."<br />
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These decisions are made by our elected officials (and therefore indirectly by us). For example, elected officials determine which social benefits matter, the way in which these social benefits are to be quantified (e.g. unemployment rate, job growth rate, quality of jobs created, poverty rate), and they determine the price tag associated with these social benefits (e.g. the capital gains tax that the federal or state government foregoes by incentivizing investments in these communities and any administrative costs).<br />
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The TCJA, for example, indicates that investors will be allowed to delay paying a capital gains tax on any investments that are moved into an opportunity zone fund, will have to pay the capital gains on a smaller proportion of that initial investment depending on the number of years that the investment is held, and will not have to pay any capital gains on the proceeds from the opportunity zone investment. This is aptly summarized in a CNN article: "Here's how it works. Someone who reinvests a capital gain worth $100 in an Opportunity Zone in 2019 gets a 15% step up in basis," which means she has to pay the federal capital gains tax on only $85 of that original income. At a tax rate of 23.8%, that comes to $20 - and she doesn't have to pay it for another 10 years. On top of that, if she holds the investment for at least 10 years, she pays no capital gains taxes on the proceeds from the Opportunity Zone investment."<br />
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Governors were allowed to nominate Census tracts to become opportunity zones so long as they met one of the following criteria: (1) poverty rate of at least 20 percent; (2) median family income of the tract is 80 percent or less of the median family income at the metropolitan or state level; (3) contiguous to a low-income tract and does not exceed 125 percent of the median family income of the neighboring tract. Any tracts selected based on criteria (3) were to make up only 5 percent of the total opportunity zones in a state. There are no restrictions on the types of investments that can be made within opportunity zones other than "sin businesses" that include "liquor stores, gambling facilities, golf courses, country clubs, tanning facilities, and massage parlors".<br />
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But the effectiveness of these policies and whether they even produce the social benefits that we as a society care about is unclear. The decisions are made by elected officials but the degree to which they have been informed by the empirical economic literature is circumspect. Even Jared Bernstein, economist behind this TCJA provision and former chief economist to another Democratic presidential contender, Vice President Joe Biden, <a href="https://www.washingtonpost.com/outlook/2019/01/14/opportunity-zones-can-tax-break-rich-people-really-help-poor-people/?noredirect=on">wrote</a>: "If OZs [opportunity zones] turn out to largely subsidize gentrification, if their funds just go to places where investments would have flowed even without the tax break, or if their benefits fail to reach struggling families and workers in the zones, they will be a failure."<br />
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It is therefore unsurprising that these tax incentives in the Trump tax bill have received significant backlash (see <a href="https://www.wnycstudios.org/story/trump-inc-opportunity-for-rich">here</a>, <a href="https://www.cnn.com/2019/06/14/economy/opportunity-zones-investing-los-angeles/index.html">here</a>, and <a href="https://www.npr.org/2019/07/08/736546264/white-house-touts-help-for-poor-areas-but-questions-endure-over-wholl-benefit">here</a> for examples) if these incentives place a hefty cost to the federal government in foregone capital gains taxes - these capital gains benefits accruing to the wealthy, i.e. the <a href="https://www.taxpolicycenter.org/model-estimates/distribution-individual-income-tax-long-term-capital-gains-and-qualified-30">9.2 percent</a> of taxpayers that report realizing any long-term capital gains at all - while their social benefits are reduced to these big "ifs". The <a href="https://calbudgetcenter.org/resources/the-federal-opportunity-zones-program-and-its-implications-for-california-communities/">California Budget and Policy Center</a> estimates the cost to the federal government as follows: "These lost revenues - mostly benefiting high-income investors - could instead help pay for other services that may have a greater impact on vulnerable communities in California and across the nation. The official cost estimate for the tax incentives is small relative to the total cost of the TCJA - $1.6 billion over 10 years in a package of nearly $2 trillion in tax cuts. However, the long-run costs could be much greater given that this estimate does not include revenue losses from the complete exclusion of gains on QOF investments held for 10 years, which fall outside the 10-year period for which budget estimates were made." These costs and distributional implications are difficult to ignore.<br />
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To inform this debate from an economist's perspective, I focus on the evaluation of an existing tax incentive program for investments in low-income communities. The New Markets Tax Credit (NMTC) has been ongoing since it was legislated in 2000. Like the TCJA it provides incentives for investing in low-income communities but it places more stringent requirements on the ways in which investments are made.<br />
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<b><a href="https://digitalcommons.ilr.cornell.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&httpsredir=1&article=1008&context=ldi">Freedman (2012)</a></b><br />
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The NMTC program is distinct from opportunity zones in the TCJA because it provides tax credits to investors who make equity investments in Community Development Entities (CDEs). According to the federal government, any "domestic corporation or partnership that is an intermediary vehicle for the provision of loans, investments, or financial counseling in Low-Income Communities (LICs)" can qualify to be a CDE. However, in order to qualify these organizations need to have a primary mission of serving LICs and must maintain accountability to the residents of the LICs that they serve. Qualified CDEs can then take the equity investments and make Qualified Low Income Community Investments (QLICs). These requirements are stated very clearly in <a href="https://www.cdfifund.gov/Documents/2018%20Introduction%20to%20the%20NMTC%20Program%20-%20FINAL.PDF">this</a> presentation.<br />
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The NMTC program places more stringent requirements on qualifying investments than does TCJA. Not only does it require the investments to be made through CDEs (organizations must qualify to become a CDE) but it requires that the investments are made by the CDE to qualified active low income community businesses (QALICBs). NMTC does not allow these funds to be invested in businesses that build or rehabilitate residential rental property. This is a notable omission from the TCJA opportunity zone provision given the role that real estate development - particularly for rental property - plays in gentrification. It is discussed in part in <a href="https://www.brickunderground.com/rent/what-causes-gentrification-nyc">this</a> article on gentrification in NYC.<br />
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Freedman (2012) uses quasi-experimental variation to study the effects of NMTC. Because Census tracts at or below 80 percent of median family income of the metropolitan or state median family income qualify for NMTC-subsidized investments and tracts above 80 percent (even if they are 81 percent of the median family income) do not, Freedman uses a regression discontinuity (RD) identification strategy. His RD strategy allows him to estimate the causal effect of NMTC on community economic outcomes (poverty rate, median home value, median household income, unemployment rate, and household turnover) by comparing Census tracts around the 80 percent cutoff. In other words, the RD identification strategy assumes that Census tracts immediately above and immediately below the 80 percent cutoff do not differ based on any observable or unobservable characteristics other than the fact that those below the cutoff are eligible to receive NMTC-subsidized investments.<br />
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Freedman finds that most estimates are statistically indistinguishable from zero. In certain specifications he finds that: from OLS estimates $1 million NMTC-subsidized investment is associated with (1) .01-.03 percent decrease in median home values; (2) .02 percent increase in median household income; from IV estimates $1 million investment is associated with (1) reduction in poverty rates by one percent off a base of 13 percent; (2) reduction in unemployment rate by .33 percent off a base of 6 percent; and (3) increase in household turnover rates by .75 percent off a base of 16 percent. These select estimates are significant but very modest. He states: "Indeed, the results suggest that to the extent that there are benefits associated with subsidizing investment in poor areas, those benefits are limited, and for many outcomes we cannot rule out that there is no effect at all." The issue with Freedman's analysis is that it is conducted at the Census tract-level indicating that the results may be due to a change in the composition of the Census tract rather than an improvement in the economic outcomes of the existing residents. This is hinted at by the statistically significant and positive effect of investment on household turnover (i.e. there is more migration in and out of the tract).<br />
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The questions remain: (1) how does the value for money provided by this program - in terms of poverty reduction or one of its other goals - compare to value for money of other dedicated social programs? (2) how much of these results are driven by changes in the composition of neighborhoods (i.e. gentrification) as opposed to economic improvements seen by existing residents? The latter question is best answered with panel data at the individual or household-level rather than the Census tract-level. The former data would allow researchers to follow the same individuals or households between the pre- and post-investment periods and track the in and outflows from these communities.<br />
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<a href="https://www.sciencedirect.com/science/article/pii/S0166046215000277"><b>Freedman (2014)</b></a><br />
<br />
Question (1) above is arguably more difficult to answer but Freedman (2014) has another more recent paper that provides a partial answer to whether improvements in economic outcomes due to NMTC investments accrue to existing residents of these communities. While he does not use individual or household-level data to study the changing composition of the residents of a given Census tract, he uses administrative data to study the changing proportion of residents who work outside the tract and non-residents who work inside the tract. Given that policymakers would prefer that social benefits of these investments accrue to the residents of these zones rather than non-residents, this addresses an important dimension to the puzzle.<br />
<br />
Freedman states in this paper: "a common feature of these programs is that, while restricting where businesses may locate or invest in order to receive subsidies or tax breaks, they place few constraints on whom subsidized businesses must hire... Further, to the extent that any new jobs subsidized under these programs fall into the hands of residents of distant communities, the local economic benefits of these programs may be diluted and any imbalances between the locations of jobs and housing exacerbated." He notes that only 30 percent of the state enterprise zone programs that he reviewed included an incentive for participating businesses to hire residents of those zones. This is not an incentive included in the NMTC or TCJA either.<br />
<br />
The identification strategy in this paper is the same as in Freedman (2012). While Freedman (2014) does not use individual or household-level data he has data from the OnTheMap database constructed and maintained by the Longitudinal Employer-Household Dynamics (LEHD) program at the Census Bureau that enables him to study the proportion of workers in a tract who live in the same tract. These proportions are further broken down by different earnings categories and industries. This disaggregated data allows the author to connect changes in the number of composition of jobs to change in the proportion of jobs held by residents of the Census tract. He finds a small and - only in some specifications statistically significant - impact of NMTC investment on overall workplace employment. He specifically finds a statistically significant increase in employment in goods-producing industries with no impact on trade, transportation, utilities, or services employment which he indicates is consistent with previous findings (Harger and Ross, 2014) suggesting that NMTC attracted firms in capital-intensive rather than labor-intensive industries. This is suggestive that more limited skills in low-income communities may be deterring labor-intensive industries from locating in these communities.<br />
<br />
Taken in conjunction with the effect on resident employment - a small decline in low-wage resident employment significant at the 10 percent level - these findings suggest that any positive effect of NMTC investments on employment accrue to residents outside of the Census tract. To explore this finding, the author further finds that observed changes in the commuting times of LICs were driven by an increase in commuters who were traveling at least 20 or more miles. As he states in his paper: "Households in these neighborhoods turn out to be not only physically distant, but also socioeconomically distant from households in LICs that receive investment. Indeed... there is a marked gradient in income levels and poverty rates as one moves away from tracts that received NMTC-subsidized investment. The figure shows median family income and poverty rates averaged across treated tracts and tracts at distances up to 30 miles away from treated tracts in the RD sample."<br />
<br />
Taken together, this indicates that any modest benefits in job growth resulting from NMTC investment accrued to non-residents of the tract who were less-likely to be low-income. It should be noted - even acknowledging the positives of an Amazon HQ2 deal in Queens - that concerns like this were made by opponents of this deal. As stated in <a href="https://techcrunch.com/2019/02/14/did-new-york-lose-anything-with-amazons-rejection-its-complicated/">this</a> Tech Crunch article on the issue: "Amazon's promise of 25,000 jobs (high-paying jobs) may have reduced that number [NYC unemployment rate], but there's no guarantee that those jobs would be filled by New Yorkers or Queens residents more specifically - and every indication that they would have gone to Amazon employees coming from somewhere else."<br />
<br />
<b>Policy implications</b><br />
<br />
These are only two papers on the subject (notably both by the same author and employing the same identification strategy). However, the results from these papers are consistent with broader findings from the literature in that the evidence is very mixed and much of it is insignificant. This is similarly stated in the Minnesota House report: "Considering all the studies using regression analysis, the economic effect of enterprise zones remains unclear. Most studies find no significant increase in employment, while a few do. Moreover, the prospect for success seems greatest in already economically viable areas, rather than traditional zone locations - areas with stagnant or declining economies."<br />
<br />
Ultimately, evidence of positive effects is inconclusive and, more importantly, there is at least some evidence of negative effects that would warrant a better investigation into the impact of investments on gentrification and displacement. Individual and household-level panel data can allow for such an investigation because they can provide greater visibility into the trajectory of existing residents of these communities. While the sum spent on this initiative is small relative to the cost of the broader TCJA, it is a large sum to be spent on a set of policies that has had mixed empirical support - and importantly, some of it negative - for several decades. These negative effects may also be exacerbated by the allowance of investments in residential rental property in this bill. Given the mixed evidence, these programs should collect data and evaluate the effectiveness of their program on social benefits but this is perhaps one of the most notable concerns about TCJA opportunity zones: there are <a href="https://morningconsult.com/opinions/restoring-reporting-requirements-key-to-defending-opportunity-zones-promise/">limited reporting requirements</a> and guidelines to ensure that investments are socially impactful. A <a href="https://www.congress.gov/bill/116th-congress/house-bill/2593?q=%7B%22search%22%3A%5B%22H.R.2593%22%5D%7D&s=2&r=1">bill</a> on these requirements has been introduced in the House and should be followed closely.<br />
<br />
<b>References</b><br />
<ol style="text-align: left;">
<li>Freedman, M. (2012). Teaching new markets old tricks: The effects of subsidized investment on low-income neighborhoods. <i>Journal of Public Economics</i>. </li>
<li>Freedman, M. (2014). Place-based programs and the geographic dispersion of employment. <i>Regional Science and Urban Economics</i>. </li>
<li>Harger, K., Ross, A. (2014). Do capital tax incentives attract new businesses? Evidence from across industries from the New Markets Tax Credit. West Virginia University Working Paper. </li>
<li>Hirasuna, D., Michael, J. (2005). Enterprise Zones: A Review of the Economic Theory and Empirical Evidence. Policy Brief: Minnesota House of Representatives Research Department. </li>
</ol>
</div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com2tag:blogger.com,1999:blog-3364384706420369338.post-36664316123105528882019-06-25T22:20:00.000-07:002019-07-01T14:02:45.482-07:00Place-based economic policies (pt. 1 of 3): Motivation<div dir="ltr" style="text-align: left;" trbidi="on">
My goal is to write a series of in-depth posts about geographic inequalities in developed countries and the evidence on the effectiveness of place-based economic policies. There were a few main motivating factors for this discussion: (1) increased attention has been paid to inequality within developed countries since the Great Recession and this discussion would be remiss if it did not address geography; (2) there is no doubt that economic geography has important implications for politics, particularly in the U.S.,where, for example, the <a href="http://www.geographicsociety.org/the-electoral-college-explained/">Electoral College</a> rather than popular vote governs the election of the president; and (3) place-based economic policies, including <a href="https://www.forbes.com/sites/morgansimon/2019/03/30/what-you-need-to-know-about-opportunity-zones/#4255a4ee6ae2">significant tax incentives</a> for investments made in low-income communities introduced in the 2017 Republican tax bill, are gaining bipartisan popularity and should be evaluated as to whether they are effective at improving local labor market outcomes, health, education, and human development.<br />
<br />
This has, however, proven to be a more difficult and time consuming endeavor than originally expected mainly because a conversation on this topic can cover a lot of material and go in many different directions (and is therefore difficult to organize). But - wanting to write something on this topic today on what would be chef, traveler, documentarian, and father Anthony Bourdain's 63rd birthday - this post will have to be the first in a series.<br />
<br />
"Place" continues to be important even in our increasingly globalized world. As much as the world has become more and more connected there is no doubt that place continues to define - particularly for specific socioeconomic and demographic groups - what we can do for fun, what we can eat, where we can work, how much we can earn, and what our living standards are like. It continues to shape our experiences and how we view the world and our places in it. Industry specialization, for example, often defines the labor market opportunities and outcomes that are available in different regions.<br />
<br />
I recommend <a href="https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.103.6.2121">this</a> paper by Autor, Dorn, and Hanson (2015) that illustrates the impact of Chinese import competition on the U.S. labor market outcomes using a local labor market approach. The authors assign a measure of exposure to import competition from China to each local labor market area and utilize an instrumental variable approach to isolate the exogenous variation in this measure across local labor market areas. The reason that there is any variation in exposure to import competition at all is that there are differences in regional industry specialization patterns. These differences have therefore given rise to geographic inequalities within the U.S. whereby regions with import-competing manufacturing were particularly impacted by the growth in trade. These regions are illustrated in the below map taken from the paper. For a presentation on this paper and Chinese import competition you can see Autor's IFS <a href="https://www.ifs.org.uk/publications/9323">Annual Lecture from 2017</a>.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEinwiW9tpzkShoLMBSr1GJIC-Pvh0whkFcL30OmQJUwWGAGDeigWZcSKM9BIfWuZd9FnUUemGq0fkUfTb9l6WlJ50EOexI5SGT0xD11TdGL4lg_YIafEkVrLBffD5pVF8doTUu2ug1ZuC-D/s1600/Screen+Shot+2019-06-24+at+11.54.59+AM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1392" data-original-width="1412" height="315" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEinwiW9tpzkShoLMBSr1GJIC-Pvh0whkFcL30OmQJUwWGAGDeigWZcSKM9BIfWuZd9FnUUemGq0fkUfTb9l6WlJ50EOexI5SGT0xD11TdGL4lg_YIafEkVrLBffD5pVF8doTUu2ug1ZuC-D/s320/Screen+Shot+2019-06-24+at+11.54.59+AM.png" width="320" /></a></div>
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This is only one example of the way location continues to matter. Another particularly striking image comes from Raj Chetty's <a href="http://www.equality-of-opportunity.org/assets/documents/mobility_geo.pdf">work</a> on intergenerational mobility in the U.S. For more on this work, Chetty also presented at the IFS with <a href="https://www.youtube.com/watch?v=HetQt5oz2i0">Annual Lecture from 2014</a>. The figure below illustrates the odds of reaching the top fifth of the income distribution for kids starting from the bottom fifth of the income distribution for metropolitan areas across the country. The differences by region cannot be ignored.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj4gHVu1o9BAZqRxuUbRdXmXP59lmYIDEl3G-O8_iel3bWYiuRiI3JftMG8jbA7QnZ1-uU3G1zFNWjifg-TpvW40yB-DRy9sr4B-cc9QndaU6U7fa7lFGIrfQL1a9bPrGj64uEaulOGLbY0/s1600/Picture1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="547" data-original-width="730" height="239" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj4gHVu1o9BAZqRxuUbRdXmXP59lmYIDEl3G-O8_iel3bWYiuRiI3JftMG8jbA7QnZ1-uU3G1zFNWjifg-TpvW40yB-DRy9sr4B-cc9QndaU6U7fa7lFGIrfQL1a9bPrGj64uEaulOGLbY0/s320/Picture1.jpg" width="320" /></a></div>
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As stated in the <i>New Oxford Handbook on Economic Geography</i>, "This contribution [from economic geographers that economic processes have produced spatial differentiation and inequality] was crucial in counteracting hyper-globalist views, prominent in the 1990s, emphasizing homogenizing forces of globalization, envisaging a global society, and predicting the end of geography in economy, politics, and culture." Today would have been Bourdain's 63rd birthday and it is being celebrated in his memory as Bourdain Day. His show <i>Parts Unknown</i> illustrated the importance of "place" in an increasingly connected world.<br />
<br />
Bourdain had a unique, respectful way of showing us - his global audiences - the local. He acknowledged and reported on the economic, social, and political structures that shaped the places he visited. He exhibited an unaffected empathy for the people he met and the lives that they led and this allowed him to share their stories in a way that we - people sitting thousands of miles away - could relate to. He had an effortless way of communicating with people underneath the layers and putting himself on the same level. At the end of the day he showed his audience the way people live in different places and - like a good social scientist - he mused on the reasons why they lived the way they did and how that was changing. Importantly, he wasn't afraid to call out injustices and criticize the perpetrators. A few of my favorite episodes in <i>Parts Unknown</i> - in Pittsburgh and West Virginia - address structural transformation and regional economies.<br />
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The decline in U.S. manufacturing - one consequence of globalization and to a lesser extent automation on the labor market - is only one of the components of ongoing structural transformation in the U.S. and other developed countries. Another important and related component is the relative decline in wages among those without a high school or college degree. The geographical significance of these changes is stated aptly in the <i>Handbook</i>, "Over the last eighty years, regional per capita income as a percentage of the national average showed signs of converging until the late 1970s. As much as anything, starting in the late 1970s, repeated recessions, major industrial restructuring and both age- and employment-related migration brought an end to the trend of convergence. Incomes and wealth began to concentrate in selected locations while bleeding out of others, reasserting the importance of places of economic power."<br />
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Yet, <a href="https://www.brookings.edu/bpea-articles/understanding-declining-fluidity-in-the-u-s-labor-market/">evidence</a> of low labor mobility in the U.S. makes it more difficult for the population to adjust to changes brought on by structural transformation. Low mobility is <a href="https://www.brookings.edu/opinions/the-us-needs-more-flexible-labor-markets/">hypothesized</a> to to be a function of labor market institutions but it may also be due to changing ties to location and community. The ideas of location and community and what they mean are frequently raised in Bourdain's episodes, particularly in the context of immigrant communities and the decisions that people make to move across cities, states, and countries and their subsequent identities as immigrants. One of my favorite episodes followed Bourdain and fellow celebrity chef Marcus Samuelsson on a trip to Ethiopia. The episode captured the coexistence between Samuelsson's life as a global citizen - born in Ethiopia during the Civil War, adopted by a Swedish family at a young age, and immigrated to the U.S. to apprentice in a New York City restaurant in his early 20s - and his desire to connect with the country of his heritage with which he had little experience as a child.<br />
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Ultimately, much of our lives are still defined by the places that we are born and - whether we have the ability to choose or not - live. This is perhaps an unintended consequence of travel shows but was illustrated in <i>Parts Unknown </i>with unique attention to the "how" and "why". There is much that policymakers can do - and are trying to do - to reduce geographic inequalities where they exist. In many cases these efforts reshape the landscape of a place. These changes were discussed, for example, when Bourdain talked with Pittsburgh locals about local development initiatives and the changing identity of the city as it grows into a tech hub. How do these initiatives impact firm growth and investment decisions? To what extent do they lead to changes in labor market, health, and educational outcomes for existing residents? How do they impact labor mobility both in and out of the area? These are a few of the broad questions for future posts.<br />
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0tag:blogger.com,1999:blog-3364384706420369338.post-29107613211998949702019-05-29T20:04:00.003-07:002019-06-20T15:28:56.708-07:00Jobs in developed economies<div dir="ltr" style="text-align: left;" trbidi="on">
The <i>Economist</i> recently published an <a href="https://www.economist.com/leaders/2019/05/23/the-rich-world-is-enjoying-an-unprecedented-jobs-boom">opinion piece</a> on the status of work and jobs in developed economies that caught my attention. The topic of the piece - the success of today's labor market - is important due to its increased politicization and its implications for democracies in developed countries. Therefore this brief post will fact-check a few of the statements made in the article and provide a more comprehensive and accurate perspective on the labor market in developed economies. First, how well does the data support each of the following statements that were made in the article:<br />
<div>
<ol style="text-align: left;">
<li>Very low unemployment rates in developed economies - TRUE BUT MISLEADING. </li>
<ul>
<li>It is true that many developed economies have very low unemployment rates today. For an interactive look at the unemployment rate in OECD countries over the past several decades see <a href="https://data.oecd.org/unemp/unemployment-rate.htm">here</a>. Below is a screenshot from the interactive chart. This data shows that for many OECD countries, the unemployment rate today is lower today than at any point in the past thirty years. However, France and Southern European countries including Greece, Spain, and Italy are notable exceptions.<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi-Yy1MlZ64KyKW_KSD76tkeLr56WCBl7UinjAzwWbxuWJBxM05i1Jy0Zqr673jurWySRxiZ-436pO0GuQpDELLJp2DEb5pSpiJ68h71aQvy7grx13b0-QpXv1nKzHzAmQGv0R4P1cRnYyw/s1600/Screen+Shot+2019-05-29+at+11.30.53+AM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="882" data-original-width="1410" height="250" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi-Yy1MlZ64KyKW_KSD76tkeLr56WCBl7UinjAzwWbxuWJBxM05i1Jy0Zqr673jurWySRxiZ-436pO0GuQpDELLJp2DEb5pSpiJ68h71aQvy7grx13b0-QpXv1nKzHzAmQGv0R4P1cRnYyw/s400/Screen+Shot+2019-05-29+at+11.30.53+AM.png" width="400" /></a></li>
</ul>
<ul>
<li>However, an analysis of the labor market that focuses on the unemployment rate without considering the labor force participation rate can be very misleading. I.e. is the unemployment rate very low because a significant proportion of the population has taken itself out of the labor market? Would these people accept a job if they were given one, despite not actively looking? <a href="https://data.oecd.org/emp/labour-force-participation-rate.htm">In the U.S.</a>, labor force participation is lower than it was thirty years ago and it took a particularly significant hit in the aftermath of the Great Recession. In 1987, the labor force participation rate was 75 percent and it was 73 percent in 2017. The inactivity rate among working-aged men has risen from 14 to 20 percent over the same time period. Below is a screenshot from the interactive chart for labor force participation. <a href="https://www.reuters.com/article/us-usa-economy-employment/tight-u-s-job-market-not-attracting-new-people-to-the-labor-force-paper-idUSKCN1SQ24S">This</a> article explains why a recent rise in labor force participation this past year reflects a composition effect - meaning fewer people are leaving the labor force - rather than bringing people out of labor force back into it. <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0KTucq5SBwvTDSdBEaiiEOIyS565Drwx8eAZxzoN8x0e4pqP2zRbP31vOQOGLWbOXtqA4TDo_IYPSydWwTEgNf7ZrFdItdnlq59Hx3d3ywdT4wHCHHdFhhKT9-wtCvvq5CTj4H-HWO4ww/s1600/Screen+Shot+2019-05-29+at+11.44.16+AM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="900" data-original-width="1406" height="255" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0KTucq5SBwvTDSdBEaiiEOIyS565Drwx8eAZxzoN8x0e4pqP2zRbP31vOQOGLWbOXtqA4TDo_IYPSydWwTEgNf7ZrFdItdnlq59Hx3d3ywdT4wHCHHdFhhKT9-wtCvvq5CTj4H-HWO4ww/s400/Screen+Shot+2019-05-29+at+11.44.16+AM.png" width="400" /></a></li>
<li>The unemployment rate today is low by historical standards in many OECD countries - again the exceptions should be pointed out and the diversity within these developed economies should be acknowledged - but as an indicator it must be used in conjunction with the employment and labor force participation rates rather than as a standalone metric. </li>
</ul>
<li>"Ever more women work" and women account for almost all the growth in the rich-world employment rate since 2007 - FALSE.</li>
<ul>
<li>In the U.S., a decline in women's labor force participation is in part driving the overall decline mentioned above. This statistic adds a distributional dimension to this analysis that the article overlooks. In the U.S., specifically, labor force participation among women is lower today than in 1999 (which was the high point for women's labor force participation in the U.S.). For more on the declining labor force participation rate in the U.S., see <a href="http://conversableeconomist.blogspot.com/2012/04/falling-labor-force-participation.html">this</a> blog post on the topic. The below graph is taken from a BLS <a href="https://www.dol.gov/wb/stats/NEWSTATS/facts/women_lf.htm#LFPSRE">interactive page</a> on labor force data. <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgV9KuHXUCoU5eKog5CtDNzRPqUCMyPubbyRs-lAtmUeq1XYN4WWNmKsNYlpwqmCe1Z1VpHR35wTtrZv_imbmOn3A3c2pKkqdY6XyOWexabGVJNmUJQymH4Db2vvZ4asK91JZj1krT0f7pI/s1600/LFPR+by+race%252C+ethnicity%252C+and+sex.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="1263" data-original-width="1600" height="315" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgV9KuHXUCoU5eKog5CtDNzRPqUCMyPubbyRs-lAtmUeq1XYN4WWNmKsNYlpwqmCe1Z1VpHR35wTtrZv_imbmOn3A3c2pKkqdY6XyOWexabGVJNmUJQymH4Db2vvZ4asK91JZj1krT0f7pI/s400/LFPR+by+race%252C+ethnicity%252C+and+sex.png" width="400" /></a></li>
</ul>
<li>"As for precariousness, in America traditional full-time jobs made up the same proportion of employment in 2017 as they did in 2005." - FALSE.</li>
<ul>
<li>According to the <a href="https://stats.oecd.org/index.aspx?queryid=54749">OECD</a>, full-time employment was 79 percent for men and 59 percent for women in 2005. The same figures for 2017 were 76 percent and 59 percent. </li>
<li>Furthermore, just as unemployment rate was only one part of the picture to understand the trends in employment status, the full-time employment rate is only one part of the picture to understand labor market insecurity and the degree of job formalization. While this is not a statistic, <a href="https://www.nytimes.com/2017/09/03/upshot/to-understand-rising-inequality-consider-the-janitors-at-two-top-companies-then-and-now.html">this</a> <i>New York Times</i> article from a few years ago exemplifies this point. It discusses the situation of two janitors: one who worked at Kodak in the 1980s and became a CTO of the company and the other who works at Apple today through a contracting company. The article details the aspects of job quality that are not captured by a simple full-time vs. part-time metric. In fact, OECD and ILO among other organizations have worked on measuring job quality. See Table 1 in <a href="http://conference.iza.org/conference_files/JobQual_2016/cazes_s4773.pdf">this</a> document that details the some of the measures of job quality: lifelong learning and career development, safety, ethics, working conditions, collective interest representation, and the stability and security of work. </li>
</ul>
</ol>
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Not only do these statements provide a narrow and therefore misleading perspective on the labor market, building a case for a labor market that works for everyone requires research and evidence. The larger problem with this piece is that it does not put in the research and evidence required. Rather, it claims that now that the unemployment issue has been "settled" in developed countries (a questionable conclusion in and of itself), the public has moved on to a "series of complaints about the quality and direction of work" which are "less tangible and harder to judge than employment statistics." Most of our jobs as economists are to study things that are "less tangible and harder to judge".<br />
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George Akerlof has a forthcoming article in the <i>Journal of Economic Literature</i> entitled <a href="https://assets.aeaweb.org/asset-server/files/9185.pdf">"Sins of Omission and the Practice of Economics"</a> that discusses this bias against "important topics and problems when they are difficult to approach in a 'hard' way" with "hard" being defined as the ease or difficulty of producing precise work on a given topic. He argues that this bias within the discipline often leads to important topics being neglected at the expense of topics that can be more precisely studied. He provides several examples of situations in which the neglect of those topics that are more difficult to study precisely has led to an oversight in understanding. For example, he writes that in the lead-up to the Great Recession there were incentives to study the individual pieces of the recession puzzle but not to study them jointly.<br />
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On the theory side: "Following Caballero (2010), regarding theory, a model with all the pieces could not have been published; it would have been considered too far from precise, simple ideas (such as those that motivate simple new Keynesian or DSGE models); and, in this way, too Soft to merit publication." On the empirical side: "Regarding predictions from empirical evidence, the crucial data would have been of the wrong form... Even if she had uncovered, for instance, AIG's 533 billion dollars of commitments to insure securities such as CDS's, she would have still needed to turn it into the basis for a publishable paper. Those 533 billion dollars indicated tail risk of sufficient size to threaten a gigantic crash of the financial system; but it was only a single number. It was not the statistical evidence that typically underlies empirical papers in economics."<br />
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Akerlof's discussion has interesting parallels to this conversation on jobs in developed economies. Unemployment statistics are telling but they only tell one part of the story. The other part of the story - job quality, wages, and the working-poor rate - will likely better explain the dissatisfaction within developed economies that is driving the political shifts that we've seen in recent years. At the same time this other part of the story may be more complicated to study than unemployment statistics and, importantly for politicians, it may also be more complicated to discuss in the political arena. However, as the <i>Times</i> article poignantly illustrates, it should not be assumed that job quality has improved over the past few decades and the hypothesis that it has deteriorated - particularly for low-wage jobs - is a valid one that should be given its due consideration.</div>
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com2tag:blogger.com,1999:blog-3364384706420369338.post-21531577771001920422019-05-21T16:46:00.000-07:002019-05-29T21:08:35.272-07:00An all-in-one post for the past three months<div dir="ltr" style="text-align: left;" trbidi="on">
Instead of doing a deep dive into one topic today, I have a few different points of discussion. First, thank you to Intelligent Economist for including me again this year in the <a href="https://www.intelligenteconomist.com/economics-blogs/">top economics blog list</a>. Second, I'll be joining a PhD program in Economics this fall and I can share my thoughts on the application procedure and offer whatever limited advice I have and hope/encouragement to those thinking about applying. This is particularly for those who have been out of school for more than a few years in job/grad school and those who found economics a little later in life (both of these apply to me). If I had one general piece of advice about PhD preparation, it is that I've found many people shy away from math and believe that only a few "select" individuals with innate abilities can be good at it (if I had a dollar for every economist I ran into while solving problems in a coffee shop who told me about the one genius in their college real analysis class) but - like anything else in life - I think those who are driven, purposeful, and work hard at it are well-rewarded.<br />
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One of the <a href="http://chandniraja.blogspot.com/2018/12/in-depth-look-at-income-and-wealth-data.html">previous posts</a> on this blog had discussed minimum wage policy. There wasn't enough time to cover all of the implications of minimum wage in that post, but I recently came across an interesting implication that I had not read about before. Specifically, a <a href="http://www-personal.umich.edu/~jwhsu/dettling_hsu_minwage_credit.pdf">paper</a> by Dettling and Hsu (2018) finds that higher minimum wages have significant effects on consumer credit markets (supply of unsecured credit, payday lending, and delinquency on credit payments). Higher minimum wages lead to lower borrowing costs for low income borrowers because they increase the number and favorability of credit card offers and they increase credit limits and decrease delinquencies. As noted in the paper, "labor market outcomes... are just one part of a household's finances. Interactions with consumer credit markets also play a crucial role in many families' economic wellbeing..."<br />
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<b>Ethiopia gender diagnostic</b><br />
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The World Bank's Gender Innovation Lab - the team that I work for within the Office of the Chief Economist for Africa - has published a <a href="http://documents.worldbank.org/curated/en/300021552881249070/Ethiopia-Gender-Diagnostic-Report-Priorities-for-Promoting-Equity">gender diagnostic report for Ethiopia</a>. In this section, these views and interpretations are my own not that of the WB. The report does a few things: it provides evidence of gender gaps in agriculture, self-employment, and wage sectors in Ethiopia based on the <a href="https://blogs.worldbank.org/opendata/ethiopia-socioeconomic-survey-2015-2016-data-and-documentation-now-available">Ethiopia Socioeconomic Surveys</a>; it utilizes an Oaxaca-Blinder decomposition method to connect these gaps to gender gaps in the levels and returns to resources (e.g. fertilizer); and it provides concrete ideas to address the challenges that Ethiopian women face in the labor market. Oaxaca-Blinder decomposition decomposes the gender gap into observable differences in factors of production (endowment effect) and unexplained differences in returns to the same observed factors of production (structural effect). It allows us to determine to what extent differences in productivity are due to differences in the levels of resources versus the impact of those resources on productivity. It should be noted that the report is policy-oriented rather than academic in nature.<br />
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One example of a finding from this report is that the evidenced gender gap in agricultural productivity in Ethiopia is by and large due to unequal levels of productive factors such as land size and quality, fertilizer and other production inputs, formal credit, and farmer extension services (which can serve as a proxy for agricultural knowledge). When these - and other individual- and household-level observable characteristics - are controlled for, the gender gap in agricultural productivity drops from 36 percent to 6 percent. This is not necessarily the case in other countries in Sub-Saharan Africa, where giving female farmers access to the same level of productive factors as male farmers will not close the gender gap. For Ethiopia, we can assess how to close the gaps in factors of production.<br />
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For example, access to formal credit is an issue for not only female farmers but male farmers as well. The <a href="https://openknowledge.worldbank.org/handle/10986/28543">report on myths in African agriculture</a> that I cited in an earlier post indicated that across the African continent only 6 percent of households used credit - formal or informal - to purchase agricultural inputs. It notes that "rural credit markets need to be deepened to serve farmers better, especially with respect to modern input use." On the other hand, the proliferation of farmer extension services is much greater with nearly 40 percent of male plot managers in Ethiopia having attended extension services recently (but only 23 percent of female plot managers having attended). There is a gender gap in both of these resources but while one has high take-up among male farmers, the other does not and therefore may require broader solutions.<br />
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If we focus on women's attendance of extension, we hypothesize based on existing literature and data that there are institutional factors that impact women's attendance and their level of agricultural knowledge more broadly. Namely, women are more time-constrained due to greater responsibilities in the home and are not as mobile due to costs of travel and to safety considerations. Both of these factors - time poverty and more limited mobility - can limit women's access to knowledge because they are not necessarily able to be in a particular place at a particular time to learn.<br />
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It is interesting because this underlying theme runs through many discussions about gender gaps in both the developed and developing worlds. A better understanding of how our existing systems are structured around the needs of specific subsets of our population can allow us to devise solutions that can better suit the needs of the others. For example, we posit that access to mobile phone technology can dramatically improve agricultural knowledge among female farmers because - conditional on their access to the technology - they will be able to access information at the time and place that is convenient for them (in Ethiopia, this is particularly challenging due to the limited competition in the telecommunications sector that has hindered mobile phone and internet penetration). Similarly, as <a href="https://hbswk.hbs.edu/archive/flexibility-key-to-retaining-women">this</a> article in the Harvard Business Review illustrates, women in the developed world are advocating for more flexible working arrangements not to reduce hours but to manage workload at their own time and place where possible.<br />
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However, there are other important solutions as well. For example, investments into technologies that can alleviate the time poverty that women face in the first place. The tasks of collecting firewood, other fuel, and water for household energy consumption often fall on the women of the household and can take several hours per day in rural areas. Yet, there are interesting companies engaged in East Africa that are focused on addressing these energy consumption needs (some of which are highlighted in this report as examples). They provide alternatives to wood-fuel stoves in the form of solar energy or biodegradable biomass. This is just one example of how an evidence-based finding from the report can be developed to identify areas for future academic research, e.g. how successful are these alternative fuel companies and how effective are their alternatives at addressing women's time poverty? For more on these ideas, do check out the report.<br />
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<b>Recession </b><br />
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There has been an upsurge in talk of recession recently in the <a href="https://www.vox.com/policy-and-politics/2019/3/27/18250823/recession-prepare-when-inverted-yield-curve">popular media</a>. It's not a topic that I've had much experience working on but I'll do my best to point out a few resources and start the conversation.<br />
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<a href="https://www.stlouisfed.org/publications/housing-market-perspectives/2018/housing-indicators-to-watch">This</a> article from the Fed lists the points of concern that have gotten analysts, investors, and economists talking in the first place. It lists four important housing market indicators, notes the significance of the housing market has in predicting economic downturns ("based on its forecasting track record - where a housing downturn is necessary but not sufficient for a recession to occur - the risk of broad-based economic recession certainly would be higher if the housing market were to weaken further"), and illustrates that recent trends are consistent with other pre-recessionary periods in 2001 and 2008.<br />
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The key is that current estimates of the four indicators listed - 30-year fixed mortgage rate, home sales rate, home-price change, and residential investment - are compared to their averages over the past three years to determine whether there is significant deviation from the average. For example, the below Fed chart shows the deviation in percentage points of the mortgage rate from the preceding three-year average. In the run up to the recessionary periods in 2001 and 2008 there was a rising deviation of the mortgage rate from the three-year average. The green trend for 2019 indicates the same pattern today.<br />
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<img alt="Percentage-Point Deviation of 30-Year Mortgage Rate from 12-Quarter Average" height="294" src="https://www.stlouisfed.org/~/media/Publications/Housing-Market-Perspectives/2018/issue11/HMP11_fig1_800px.jpg?la=en" width="400" /><br />
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It is unclear in my opinion that the other three indicators track the 2001 and 2008 trendlines as closely as they do here for the mortgage rate but either way I think this is one part of a larger picture. The larger picture is that in 2001 and 2008 these housing market indicators worked in conjunction with a private sector financial deficit (financial deficit from households and firms). This is discussed in an <a href="https://www.goldmansachs.com/insights/podcasts/episodes/03-11-2019-lotfi-karoui.html">episode</a> of the Exchanges at Goldman Sachs podcast - which I highly recommend - on five areas of credit market risk and how they impact the likelihood of recession. It is just one section from a GS report, <a href="https://www.goldmansachs.com/insights/pages/learning-from-a-century-us-recessions/report.pdf">"Learning from a Century of US Recessions."</a><br />
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The report itself provides a high-level summary of the various risk areas that lead to economic downturns but does not provide much detail on the individual risk areas. The podcast does a better job of discussing in detail the primary risk area: financial risk and asset bubbles. The GS viewpoint is that the current private sector surplus differentiates the current situation from 2001 and 2008 where the housing market may have been heating up - as it is today - but at the same time the private sector was running a large deficit. These two deficit periods are indicated in the GS figure below.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj8lsi1MFNmOjLFOQ3q3Jv9sHtcFqjEd9ZpND52Ap60pc9Y4sT4n0_Kd2zKjYW92PX__A3BXSj0nY0dVILB_Ny66ZT1eU6uyUqsfuNB19fUPh_0pl4GMBlfJj7Kv4uPQw9q2vm0Z1eaIZCe/s1600/Screen+Shot+2019-05-21+at+3.13.32+PM.png" imageanchor="1" style="clear: left; display: inline !important; margin-bottom: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="778" data-original-width="1178" height="263" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj8lsi1MFNmOjLFOQ3q3Jv9sHtcFqjEd9ZpND52Ap60pc9Y4sT4n0_Kd2zKjYW92PX__A3BXSj0nY0dVILB_Ny66ZT1eU6uyUqsfuNB19fUPh_0pl4GMBlfJj7Kv4uPQw9q2vm0Z1eaIZCe/s400/Screen+Shot+2019-05-21+at+3.13.32+PM.png" width="400" /></a><br />
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In the podcast, it is also mentioned that debt growth for households in the mortgage market is in decline - 16 percent inflation adjusted decline - that is unprecedented in the past 60 years. I ran a quick chart using the Fed's data to see the trendlines for all mortgage holders (in blue) and one- to four- family residences (in orange) and they do indicate a slow growth in recent years. How much slower than in the rest of the 30-year period shown here (1990-2018) is not clear since the trendline is still rising. However, it is unsurprising that mortgage debt is rising more slowly now than in the pre-2008 period given tighter credit standards in the aftermath of the Great Recession.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh_hThHkHjGPVss7SGeuJ4REKQ4bKcGoEYwVl_x92LLcKttg-2lKBS-MLRBNNtfwsDS27YmCsP2DRKOFGf4hMuWB5Dz93P-CsULyCe8SJz8XPE37q4l7oZeZUWwS4nk977XMqLtr_QI1wYL/s1600/mortgage+debt+outstanding.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" data-original-height="758" data-original-width="1327" height="227" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh_hThHkHjGPVss7SGeuJ4REKQ4bKcGoEYwVl_x92LLcKttg-2lKBS-MLRBNNtfwsDS27YmCsP2DRKOFGf4hMuWB5Dz93P-CsULyCe8SJz8XPE37q4l7oZeZUWwS4nk977XMqLtr_QI1wYL/s400/mortgage+debt+outstanding.png" width="400" /></a><span style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em; text-align: left;">It is possible that an overheating of the housing market and relatively slow mortgage debt growth for households are consistent with one another if mortgage debt and home-ownership are more concentrated today than they were pre-2008. It would be interesting to see whether a smaller segment of the population is driving the uptick in the housing indicators being measured by comparing mortgage debt and home-ownership across the income distribution today and pre-2008. Tighter credit standards and more sluggish recovery among lower- and middle-income households after the 2008 economic downturn, in addition to rising income inequality in the aftermath of the downturn, may explain greater concentration in debt and home-ownership today. This could explain trends in the housing market as well as the private sector financial balance. </span></div>
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com5tag:blogger.com,1999:blog-3364384706420369338.post-47440781994886938562019-02-05T11:59:00.001-08:002019-05-29T21:18:26.090-07:00Review of AEA sessions in Atlanta (Jan 4-5)<div dir="ltr" style="text-align: left;" trbidi="on">
I took last month off from this blog (and most other productive activities) because I was on holiday for three weeks in the Bay Area. I hope all of you had a great holiday season 2018 with friends and family and a refreshing start to the new year 2019. The first topic I wanted to come back to is a review of the webcast sessions from the American Economic Association's annual meetings held in Atlanta from Jan 4-5, 2019. Several of the sessions are webcast <a href="https://www.aeaweb.org/conference/webcasts/2019/">here</a> and you can access lectures on various topics including growth in the developing world, automation and the future of work, public debt, and - returning from last year with an extremely compelling panel - the gender problem in economics and what steps the profession can take to address it. In this post, I discuss two of the panels with an eye to discussing Autor's lecture on the future of work in the next post.<br />
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<b>Growth challenges in the developing world </b><br />
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The AEA convened a "World Bank economists" session consisting of three former World Bank Chief Economists (Justin Lin, Francois Bourguignon, Kaushik Basu), current Chief Economist Pinelopi Goldberg, and moderated by former Acting Chief Economist Shanta Devarajan. The purpose of the panel was to deliberate on the challenges facing the developing world. Given the very broad - arguably too broad - scope of the topic, it is natural that the panelists settled on a narrower topic over the course of the conversation: industrialization and the informality trap facing Africa.<br />
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Historically, industrialization and the rapid job creation in the formal wage sector that accompanies it have been seen as the most effective ways to raise wages and lower the poverty rate in developing countries. Lin cited historical examples of low-income countries' growth trajectories after capturing manufacturing jobs moving from the U.S. to Japan in the aftermath of WWII, Japan to Southeast Asia in 1960s and 1970s, and from Southeast Asia to China in 1980s and 1990s. <span style="font-family: inherit;">Now that wages are increasing in China, many of these manufacturing jobs will be looking for a new home. How can Africa capitalize on these opportunities in coming decades was the question most of these economists were trying to answer. Chapter 2 of <a href="https://www.afdb.org/fileadmin/uploads/afdb/Documents/Publications/2019AEO/AEO_2019-EN.pdf">this policy report</a> from the African Development Bank does a good job of summarizing these issues including evidence of what some economists call "de-industrialization" and the obstacles to small business growth. </span><span style="font-family: inherit;">Given the demographic changes that will add 2 billion to the working age population in the African continent in this century, the creation of jobs in the formal wage sector will be important not only for economic but social and political stability. </span><br />
<ol style="text-align: left;">
<li>The primary point of contention is that it is not clear that "de-industrialized" countries will capture these manufacturing opportunities without concerted policies. E.g. automation is a real threat to manufacturing jobs in certain industries and less so in others (retail incl. clothing, shoes, and furniture). Furthermore, the trade environment is rapidly changing with advanced economies looking to be less hospitable to imports from low-income countries. The second half of the panel asked panelists to comment on different ways of approaching this issue wherein I think the issue of too broad a topic came to light. I think it would have been more useful to showcase specific examples and evidence from recent research. </li>
<li><span style="font-family: inherit;">It wasn't discussed in the panel but it is relevant discuss the impact of a shift from self-employment and agriculture to industrial employment on working populations and whether there is desire on the part of working populations to hold these types of jobs in the first place. Specifically, J-PAL poses the issue in preface to a 2017 <a href="https://www.povertyactionlab.org/fr/node/11103">paper</a> from Chris Blattman and Stefan Dercon that studied the effects of industrial employment on Ethiopian workers: "<span style="background-color: white;">Industrial sector development to boost mass hiring is seen as important to poverty alleviation at the macroeconomic level. But how those jobs, particularly in early stages of industrial sector development, affect the workers themselves and what the workers prefer are less well-understood." The findings from this paper are summarized in this </span></span><i>New York Times</i> <a href="https://www.nytimes.com/2017/04/27/opinion/do-sweatshops-lift-workers-out-of-poverty.html">article</a> with the bottom line being: workers are initially unaware but quickly become aware of the safety hazards and poor wages paid in sweatshop conditions leading to a high turnover rate in these early-stage manufacturing firms. The authors find that particularly when the constraints to self-employment were addressed through cash grants the workers preferred self-employment. </li>
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<li>Does this mean that industrialization is not the best way to raise wages and lower the poverty rate in low-income countries? No. But it indicates that there may be a more efficient equilibria where a set of regulations providing a baseline level of safety for workers that address the issues identified in this study (chemical fumes, repetitive stress injuries, and probability of serious injury) can be beneficial to both employers via a lower turnover rate and to workers who would more likely work there if these health concerns were addressed. Such a set of regulations need not be so stringent that they reduce the comparative advantage of setting up shop in sub-Saharan Africa given the low wages on the continent but they will provide better standards of living for workers expected to drive these changes. </li>
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<b>Gender in the profession</b><br />
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On the panel on gender in the economics profession. The community by now is well aware of statistics indicating the low proportion of women who study economics as undergraduates, the lower proportion who study it as PhD candidates, and the even lower proportion who are tenured faculty at universities. The primary questions now, in my opinion, are (1) whether members of the community believe that these statistics are indicative of gender bias (as opposed to differences in ability or preference between the genders); and (2) whether members of the community believe that they can and should take action to address this bias, particularly when it is implicit and particularly where it requires the buy-in of economists who are neither part of the problem nor the solution.<br />
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Several of the questions posed in the panel revolve around these ideas. First is the need for data and evidence that is reflective of implicit bias to indicate to said economists that there is a problem at hand. Erin Hengel's paper on publication records of male and female economists that I <a href="https://chandniraja.blogspot.com/2018/01/elinor-ostroms-work-on-collective.html#more">discussed last year</a> and Alice Wu's paper on sexism within the Econ Job Market Rumors website which is informal but commonly used among academic economists for job postings and career advice (see <a href="http://calwomenofecon.weebly.com/news_blog/an-interview-with-alice-wu">this interview</a> with Wu on this paper) are two examples of this type of evidence. <a href="http://calwomenofecon.weebly.com/education_resources.html">This</a> webpage put together by the UC Berkeley Women in Economics group offers other useful information.<br />
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From my own anecdotes and research experience within the Gender Innovation Lab at the World Bank, there are a few issues that I think are actionable to address:<br />
<ol style="text-align: left;">
<li>Role models and social networks among women </li>
<li>Gender gap in perceived abilities in STEM fields </li>
<li>Culture and implicit bias within the profession</li>
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Given that the third issue is probably the one that is most difficult to address I think it requires first the buy-in from the community that I mentioned above. Being aware of implicit bias and its effects on the community are important because they are needed to take the next steps. For example, one issue that was talked about in the panel is aggression in economics seminars. It likely impacts women more than men because women tend to do better in collaborative and non-aggressive environments and the aggression tends to be more often directed towards women than it does towards other men (e.g. see Wu's paper on EJMR). But suffice it to say, I think we would all do better - men and women alike - if we were all a bit kinder to one another without compromising the rigor of our work. Specifically, to both acknowledge that we can and should be able to communicate questions and criticisms without resorting to aggression and be willing to learn the techniques to do so. Same with being willing to learn the techniques to recognize and address implicit bias. </div>
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I have been supported in my efforts by peers and role model figures - mostly male - that have been enthusiastic about my ability to succeed in this profession. I have been blessed in not only role models in professional and academic life but also partners in my personal life that have been the most influential factors in my decision to undertake graduate studies. My thoughts on this issue are - in addition to addressing systematic issues within the field - if you can support a young person and believe in their abilities it is probably a determining factor in their decision to pursue higher studies. Whether we have the data or not as of yet (and there is more empirical research being conducted on role model figures and mentoring), we can't underestimate the value of empathy in how people decide whether or not they want to be in a particular location, field, university, firm. </div>
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com6tag:blogger.com,1999:blog-3364384706420369338.post-58906282572068106642018-12-06T18:39:00.000-08:002018-12-06T22:30:47.452-08:00In-depth look at income and wealth data (pt. 3 of 3): Minimum wage policy and the income distribution<div dir="ltr" style="text-align: left;" trbidi="on">
In this final part of this series of posts on income and wealth, I originally intended to discuss the data used to analyze income inequality but I will introduce a more specific topic within income inequality: minimum wage policy and its impact on the wage and income distributions. Given the ongoing public debate over stagnating real wages despite a strong labor market (see <a href="http://www.pewresearch.org/fact-tank/2018/08/07/for-most-us-workers-real-wages-have-barely-budged-for-decades/">this</a> piece by the Pew Research Center for a concise description of the trends and a few of the reasons given by economists for the wage stagnation for workers at the lower end of the earnings distribution), it is particularly relevant to revisit the evidence on minimum wage policy and its impact on income inequality.<br />
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In this post, I focus on two papers - <a href="https://economics.mit.edu/files/3279">Autor, Manning, and Smith (2016)</a> and <a href="http://ftp.iza.org/dp10572.pdf">Dube (2018)</a> both in <i>American Economic Journal: Applied Economics</i> - that discuss the distributional implications of minimum wage policy. These papers have significant differences in both methodology and level of analysis. While AMS (2016) focus on individual wage inequality, Dube (2018) focuses on household income inequality with two iterations on how income is defined. The first is the conventional definition of income that includes both earnings and cash transfers. The second is a broader definition that also includes tax credits and non-cash transfers that enables the author to assess the substitutability between minimum wage earnings and government benefits to derive results that are closer to general equilibrium. The choice and level of the outcome variables measured in these two papers - earnings versus income and at the individual versus household level - are important to treat as distinct and independently informative. As I discussed in an <a href="https://chandniraja.blogspot.com/2018/05/trends-in-household-income-inequality.html">earlier post</a> on household income inequality in the U.S. and Britain, the distributions of individual labor market outcomes and household incomes do not necessarily track one another closely.<br />
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<b>Context</b><br />
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In the U.S., minimum wage policy is determined at several levels of government: federal, state, and as of late even citywide minimum wages exist (in San Francisco, San Jose, Albuquerque, Santa Fe, and Washington, DC). <a href="https://www.cbpp.org/research/economy/policy-basics-the-minimum-wage">This</a> piece on minimum wage policy outlines the basics. In part because the U.S. federal minimum wage declined in real value almost continuously for thirty years between 1979 and 2007 (it was fixed in nominal terms between 1981-1990 and 1997-2007), more than thirty states enacted legislation over the same time period to raise their state minimum wages above the federally mandated level. The below graph tracks the real value of the federal minimum wage (in part indicating the motivation for state and city-level legislation on the issue):<br />
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj5-Cpl-fT1OU2OjBN7hiGbiIKtoWJVRibaMNqiZRtA4vrABhHjPIyggjPvn4cQVBXUvhECqIKM5wvZFQ9porT3C_TsjGbe-MdKjbDc-neJ8TZ0StgYiDzNFdaX_RIB-wDYo17WhJB7P0w/s1600/min_wage_real_value_graph.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="698" data-original-width="900" height="248" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj5-Cpl-fT1OU2OjBN7hiGbiIKtoWJVRibaMNqiZRtA4vrABhHjPIyggjPvn4cQVBXUvhECqIKM5wvZFQ9porT3C_TsjGbe-MdKjbDc-neJ8TZ0StgYiDzNFdaX_RIB-wDYo17WhJB7P0w/s320/min_wage_real_value_graph.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Source: UC Davis Center for Poverty Research (2018)</td></tr>
</tbody></table>
The minimum wage has therefore seen significant state and time-based variation within the U.S. over the past several decades that continues to this day. This variation has been utilized by many empirical studies of the minimum wage including AMS (2016) and Dube (2018). It should be noted that the ratio of real minimum wage to median wage provides some quick information about lower-tail inequality that will be discussed in greater detail below. For example, between 1950-1970 this ratio fluctuated between 45-55 percent but by 1989 it had fallen to 36 percent.<br />
<b><br class="Apple-interchange-newline" />Estimation strategy</b><br />
<b><br /></b>
The challenge with estimates of the impact of minimum wage policy are that both minimum wage legislation and wage inequality levels are impacted by several other unobserved characteristics. For more details on these challenges and how research designs can overcome them, see <a href="http://irle.berkeley.edu/files/2013/Credible-Research-Designs-for-Minimum-Wage-Studies.pdf">Allegretto et al. (2013)</a>. These two papers take two very different approaches: while AMS (2016) employ an instrumental variables strategy, Dube (2018) employs a series of sensitivities and falsification tests to indicate the robustness of his results. The takeaway is that identification of the employment and inequality effects of the minimum wage is challenging and can result in complicated empirical strategies.<br />
<br />
As Dube (2018) indicates of the initial fixed effects model that he presents: "A problem with the two-way fixed effects model [state and time fixed effects] is that there are many potential time varying confounders when it comes to the distribution of family incomes. As shown in Allegretto et al. (2013), high- versus low-minimum wage states over this period are highly spatially clustered, and tend to be differ in terms of growth in income inequality and job polarization, and the severity of business cycles." In other words, the legislation of minimum wage and wage inequality are both impacted by a number of factors that are not controlled for in an OLS or even two-way fixed effects model.<br />
<br />
AMS (2016) also reference potential biases when they present their initial OLS model. They cite confounding evidence that the effective minimum wage is found to be equally significant on both the lower-tail and upper-tail inequality (where it is expected to only have a significant effect on lower-tail inequality since minimum wage policy is only binding for at most the 15th/20th percentile of the wage distribution). The initial OLS model that they present estimates the impact of the "bindingness" of the minimum wage at the state-year level (a variable initially employed in Lee (1999)) - the log difference between the effective minimum wage and the median wage - on the difference between the log real wage at a specific percentile and the log real wage at the median. The former variable on the "bindingness" of the minimum wage is included as a quadratic term because minimum wage is expected to have a larger effect on the part of the wage distribution where it is more binding (i.e. at the lower-tail of the wage distribution) rather than a linear effect. To address potential biases, they employ an instrumental variable strategy and instrument for the observed effective minimum wage.<br />
<br />
Because they include the effective minimum wage as a non-linear term, AMS (2016) utilize a set of three instruments as opposed to just the first one: (1) log of the real statutory minimum wage; (2) square of the log of the real minimum wage; and (3) interaction between log minimum wage and average log median real wage for the state across all periods. While it is not discussed in great detail in the paper, this instrument (1) I would assume is the legislated federal minimum wage and it clearly impacts the state-level effective minimum wage (either the federal or state minimum whichever is higher) but does not impact wage inequality at the state-year level through any channel other than the state-level effective minimum wage.<br />
<br />
Dube (2018) does not employ an IV strategy in his paper on household income distribution but he includes several sensitivities and falsification tests of his original model to indicate the robustness of his results. His original model is the two-way fixed effects model that he critiques in the section above. In his "most saturated" specification he includes in addition to his original controls, division-specific year effects (to capture the effects of regional shocks on minimum wage legislation that may be driving some of the spatial heterogeneity that we see in minimum wage levels), state-specific recession-year dummies (to address the concern that minimum wage legislation is correlated with state business cycle fluctuations), and state-specific linear trends (to capture long-run trend differences across states). It is challenging to assess the effectiveness of these sensitivities and tests at obtaining causal estimates in comparison to quasi-experimental methods (seminal example is Card and Krueger (1993) in their study of the employment effects of minimum wage).<br />
<b><br class="Apple-interchange-newline" />Findings</b><br />
<br />
Based on their instrumental variables empirical strategy, AMS (2016) find that a 10 log points increase in the effective minimum wage leads to a reduction in the 50/10 inequality (inequality between the 50th percentile and 10th percentile in the wage distribution) by 2 log points for women, 0.5 log points for men, and 1.5 log points for the pooled sample. For a better understanding of why this paper uses log points as opposed to percentages, see <a href="https://patrickjuli.us/2016/01/27/how-i-wish-we-measured-percentage-change/">this post</a> on the topic. Women see a larger effect because a greater share of women work at minimum wage (6 percent of women in 2012 compared to 3 percent of men).<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwCrMp3b0yOE8rSkBjGUs4xmnTuUa3TbyONomDzJrAlhHSTJmEHJrBYn2P3SpJLAWnqhnmoQdIqVkkkqQnSshNh_S4cRPCKTk4QmStrweEgeFJgf2-ssp07YokVSxKA6lRnVQRYc2CeqgN/s1600/ted_20130325a.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="360" data-original-width="580" height="198" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwCrMp3b0yOE8rSkBjGUs4xmnTuUa3TbyONomDzJrAlhHSTJmEHJrBYn2P3SpJLAWnqhnmoQdIqVkkkqQnSshNh_S4cRPCKTk4QmStrweEgeFJgf2-ssp07YokVSxKA6lRnVQRYc2CeqgN/s320/ted_20130325a.png" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Source: Bureau of Labor Statistics (2013)</td></tr>
</tbody></table>
They are also able to state that the decline in the real minimum wage explains less than 50 percent of the rise in 50/10 inequality between 1979 and 2012 indicating that the majority of the rise in inequality over this period is due to changes in underlying wage structure (contradicting earlier findings that the decline in the real minimum wage contributed to around 60 percent of the rise in wage inequality over this period). This study indicates that erosion of the real minimum wage played a significant role in the growth of wage inequality over the past several decades but other factors, including skill-biased technological change and increased import competition from low-income countries which compose the "changes in underlying wage structure" that AMS (2016) reference, played a larger role than the decline in the real minimum wage.<br />
<br />
What then of the related outcome of household income inequality? Dube (2018) finds that the poverty rate elasticity with respect to the minimum wage is between -.220 and -.552 indicating that a ten percent increase in the minimum wage yields between a 2.2 and 5.5 percent decrease in the poverty rate. He finds a first order effect of the minimum wage on the family income distribution of a ten percent increase in the minimum wage yielding between 1.5 and 4.9 percent increase in the pretax cash incomes of the 10th and 15th quantiles. This finding is indicated in the graphic below with the green line for cash income. The more interesting finding in my opinion is how much these minimum wage gains are offset by reductions in other government benefits due to ineligibility based on the higher income. He finds that for the bottom fifth of the income distribution, 30 percent of the gains to income resulting from minimum wage are offset by reductions in non-cash transfers and tax credits indicating that the relationship between wage and income (in its broader definition) is not nearly 1:1 for the poorest Americans. This is indicated in the graphic below with the orange line for cash income, tax credits, and non-cash transfers.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOYr5fUeoNgGLx_QGTxk0dz9Zy-r1LA6Vcf3Ih6EqhNAmcRoKVHNGdVQ78_hmBp3DaLDtcE9srEiqNklAQDf54pOyv7dMWoaYGUn6Ge6cU_9_-jGL-xu2qkh72SGGduwVhhJfyC8Wd5Sew/s1600/Dube.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="1000" data-original-width="1508" height="212" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjOYr5fUeoNgGLx_QGTxk0dz9Zy-r1LA6Vcf3Ih6EqhNAmcRoKVHNGdVQ78_hmBp3DaLDtcE9srEiqNklAQDf54pOyv7dMWoaYGUn6Ge6cU_9_-jGL-xu2qkh72SGGduwVhhJfyC8Wd5Sew/s320/Dube.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Source: Washington Center for Equitable Growth (2017)</td></tr>
</tbody></table>
<div>
<b>Implications</b><br />
<ul style="text-align: left;">
<li>These two papers' findings are consistent with one another though they measure different outcomes - individual wage inequality versus household income inequality. They both indicate that minimum wage has a significant impact on inequality (as well as absolute indicators such as the share of Americans living below the poverty line measured in Dube). </li>
<li>It should be noted that the policy debate over minimum wage consists not only of discussion on the benefits of minimum wage policy on inequality, poverty, and other outcomes of interest but the costs in terms of the employment effects (i.e. hiring and firing decisions of firms, indicators of the quality of work, etc...). Economists are far from agreement on the employment effects of minimum wage (not discussed in this post). The extent to which minimum wage gains are offset by public assistance is also an important consideration and Dube's contributions to this question form part of a broader literature on the linkages between minimum wage and the existing social safety net. </li>
<li>An important note about both of these papers is that by construction they reflect not only the mechanical effect of minimum wage (in increasing wages for individuals working at or below minimum wage) but may also reflect spillover effects on those who already work above it. This is an area for future research, however, since AMS (2016) attribute these additional effects not as spillovers but as measurement error of wages for low-wage workers. Specifically, they find that these spillovers do not "represent a true wage effect for workers initially earning above the minimum" rather accepting the null hypothesis that "all of the apparent effect of the minimum wage on percentiles above the minimum is the consequence of measurement error."</li>
<li>Increased trade and automation have yielded dramatic changes to the underlying wage structure of the economy. These changes may not be reflected in the share of workers who work at federal minimum wage (this share declined prior to 2000 and has remained roughly constant between 2000 and 2018 excepting a significant increase at the recession) but perhaps it has increased the share of those in the lower percentiles of the wage distribution. </li>
<ul>
<li>While these jobs in the lower percentiles may not be "minimum wage" jobs they are certainly part of a broader pattern of low-paying jobs in the U.S. These jobs - and the people who hold them - are illustrated in <a href="https://www.nytimes.com/2018/09/11/magazine/americans-jobs-poverty-homeless.html">this</a> article in the <i>New York Times</i>. The article indicates that nearly one-third of workers in the U.S. earn at or below $12/hour with 7.6 million Americans designated as "working poor" meaning they spent at least half of the year in question working or searching for work and were below the poverty line. </li>
</ul>
<li>Minimum wage policy will not - in a mechanical sense - impact these other low-paying jobs nor will it address factors that impact the underlying wage structure of the economy. However, in an environment in which trade and automation have changed the wage structure dramatically, it has been evidenced to address both wage and household income inequality. </li>
</ul>
<div>
<b>Sources</b></div>
</div>
<div>
<ol style="text-align: left;">
<li>Autor, D., Manning, A., Smith, C.L. (2016). The Contribution of the Minimum Wage to US Wage Inequality over Three Decades: A Reassessment. <i>American Economic Journal: Applied Economics. </i></li>
<li>Dube, A. (2018). Minimum wages and the distribution of family incomes in the United States. Forthcoming in<i> American Economic Journal: Applied Economics</i>. </li>
<li>Allegretto, S., Dube, A., Reich, M., Zipperer, B. (2013). Credible Research Designs for Minimum Wage Studies. IRLE Working Paper #148-13. </li>
<li>Card, D., Krueger, A. (1993). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. <i>American Economic Review</i>. </li>
</ol>
</div>
</div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com1tag:blogger.com,1999:blog-3364384706420369338.post-57216383377559834632018-10-05T11:12:00.001-07:002018-10-05T22:05:53.459-07:00In-depth look at income and wealth data (pt. 2.5 of 3): A small note on wealth inequality from the archaeologist's perspective<div dir="ltr" style="text-align: left;" trbidi="on">
I've been working on fellowship applications these past few weeks so naturally I began my research in a germane area of literature and ended up somewhere completely random. And by completely random I mean not even within the field of economics anymore and at best tangential to my original topic of investigation, but fascinating. I stumbled upon <i><a href="https://uapress.arizona.edu/book/ten-thousand-years-of-inequality">Ten Thousand Years of Inequality: The Archaeology of Wealth Differences</a>, </i>a volume on the archaeological studies of wealth inequality, and given its relevance to the posts I've been writing on inequality I figured I would make a small note on how inequality is being measured for a society that lived nearly two thousand years ago.<br />
<br />
I haven't written a formal post on the Gini coefficient but given that this article uses it extensively in the archaeological context I preface by stating a few things: (1) the Gini coefficient is notably a simple measure of inequalities (most commonly income inequality) therefore it has its limitations that are well summarized in <a href="https://en.wikipedia.org/wiki/Gini_coefficient">this</a> Wikipedia post; (2) it has also been subject to revision and extensive debate as well as the creation of alternative measures of inequality including the Atkinson Index that may be more informative if certain contexts; (3) given my lack of knowledge of archaeology (and my lack of knowledge more broadly on the range of applications that the Gini coefficient has had in diverse fields within social science) I don't assess whether or how the Gini coefficient was applied and rather introduce it as a thought-provoking application outside of the realm of economics.<br />
<br />
Feinman, Faulseit, and Nicholas (2018) provide estimates of wealth inequality for the Classic period in the history of the pre-Hispanic Valley of Oaxaca, Mexico based on archaeological house excavations at six pre-Hispanic settlements. They rely principally on architectural constructions and space to proxy for wealth and apply the Gini coefficient to three architectural variables: terrace area, house size, and patio area. They also utilize distribution of artifacts such as obsidian and other rare items.<br />
<br />
The Lorenz curve in the figure below shows the Gini coefficient constructed based on the house sizes for all of the houses in the sample (a total of 36 excavated houses across all six sites in the Valley of Oaxaca) with a coefficient of 0.35 and a 95 percent confidence interval between 0.31 and 0.39. A Gini coefficient of 0 represents perfect equality whereas 1 represents perfect inequality.<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJ3p-TSgFo0HycGrHV2sqouG8FWbNu6EKseidluGg_j8DR59gumT220fiv3itdCjFQjMjS1O-1BU4WzMNQkK6VYNcfX5a_V7ApHjcj9n_53SqzR4xq13UsCNHDfVFlpeTBjCfEPK1Z2hsT/s1600/Screen+Shot+2018-10-04+at+10.22.46+PM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="474" data-original-width="726" height="208" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiJ3p-TSgFo0HycGrHV2sqouG8FWbNu6EKseidluGg_j8DR59gumT220fiv3itdCjFQjMjS1O-1BU4WzMNQkK6VYNcfX5a_V7ApHjcj9n_53SqzR4xq13UsCNHDfVFlpeTBjCfEPK1Z2hsT/s320/Screen+Shot+2018-10-04+at+10.22.46+PM.png" width="320" /></a></div>
The Gini coefficients from all samples (houses, patios, and terraces) and excavation sites are indicated in the figure below. They range from 0.35 to 0.43.<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgrQ05bHtAqh8ev_BXyf3uglj9RnLVGjvbhIjF9wZZgNBL2kszGcDlDzjT3wmt6_MnAlzGf-uELigI8AlZ00f3yb5RJzx_XdbtLMEa8buRrBODJcaEW-5ZmGUpz9tbfuJdPeElYbvS92Ev5/s1600/Screen+Shot+2018-10-04+at+10.25.59+PM.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="746" data-original-width="1274" height="187" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgrQ05bHtAqh8ev_BXyf3uglj9RnLVGjvbhIjF9wZZgNBL2kszGcDlDzjT3wmt6_MnAlzGf-uELigI8AlZ00f3yb5RJzx_XdbtLMEa8buRrBODJcaEW-5ZmGUpz9tbfuJdPeElYbvS92Ev5/s320/Screen+Shot+2018-10-04+at+10.25.59+PM.png" width="320" /></a></div>
<br />
The authors find based on their analysis that wealth inequality during this time was low compared to other urbanized and preindustrial settings (confirming extant evidence).<br />
<br />
While there was notable variation between the periods that the authors link to changes in the socio-political structures of the time, they specifically note that "[t]he consistently low Gini values... are informative, especially as indicators of wealth inequality, because they challenge the long-term notion that archaic states were always starkly divisible into the rulers and the ruled, with dramatic differences in resources and quality of life between the two. This coercive/despotic vantage on archaic states is well ensconced in the historical/social sciences for preindustrial times (e.g. Mann 1977; Wittfogel 1957) but now is being challenged as not uniformly applicable (Blanton 2016; Blanton and Fargher 2008), with some historical polities seen as having had a more collective institutional orientations and lower degrees of wealth inequity (e.g. Mann 2016, for a change from his earlier perspective)."<br />
<br />
A few comments and questions came to mind about the application of Gini coefficient in this context:<br />
<ol style="text-align: left;">
<li>The sets of data used in these analyses of the Classic period in particular are considered by the authors to be large and representative (likely given the difficulties involved in excavations and the number of houses, patios, and terraces that are still intact after thousands of years) but they are still subject to the well-described <a href="https://poseidon01.ssrn.com/delivery.php?ID=494096120005026116006106114118110092056017086086029049025119026074121105066125085004097026027097094097035013011072118118074069076125018078087027027104120102004069095080102076005064115101092&EXT=pdf">small sample size bias </a>associated with the Gini coefficient. Smaller samples are biased towards having smaller Gini coefficients which may make it difficult to compare excavated sites in the graphic below (from the <i>Smithsonian Magazine</i> <a href="https://www.smithsonianmag.com/history/aracheology-wealth-inequality-180968072/">article</a> about this book) with the United States today, which is not based on excavated evidence and has a much, much larger sample size. <a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBTWijhLa93qlYUnor5ahlInHkuVDHQAgB2it-Ltd5H6GgLZ8B_8IwnwUwTGji6_OVzuW9WBGbFvkphvne7Ly-3s8_hyphenhyphenaDOgaaMDhveOn0SY7IzNLmqZ2obxWPIXF9IUa_vHpgbmUJPmU_/s1600/mar2018_f99_prologue-wr.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" data-original-height="666" data-original-width="1600" height="166" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgBTWijhLa93qlYUnor5ahlInHkuVDHQAgB2it-Ltd5H6GgLZ8B_8IwnwUwTGji6_OVzuW9WBGbFvkphvne7Ly-3s8_hyphenhyphenaDOgaaMDhveOn0SY7IzNLmqZ2obxWPIXF9IUa_vHpgbmUJPmU_/s400/mar2018_f99_prologue-wr.jpg" width="400" /></a></li>
<li>Comparisons across past civilizations, though, if they are based on similar sample sizes may be more informative than comparisons between past civilizations that were excavated and modern day societies. Similarly, I would find variation within a given civilization over time (as evidenced in the article) to be informative especially in conjunction with changes in socio-political structures. This is hinted at by the authors when they quote from Piketty (2015) on the impacts of these structures on inequality: "[O]ne should be wary of any economic determinism in regard to inequalities of wealth and income... The history of the distribution of wealth has always been deeply political... How this history plays out depends on how societies view inequalities and what kinds of policies and institutions they adopt." </li>
</ol>
</div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com2tag:blogger.com,1999:blog-3364384706420369338.post-58468823096210692702018-10-03T18:04:00.003-07:002018-10-04T19:27:47.882-07:00In-depth look at income and wealth data (pt. 2 of 3): Wealth<div dir="ltr" style="text-align: left;" trbidi="on">
First, to preface with why wealth as distinct from income is relevant to economists and to policymakers at large. <a href="https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.29.1.47">Kopczuk (2014)</a> discusses the importance of understanding the wealth distribution: "the extent to which the well-off are going to rely on work vs. return to their wealth in the future is clearly important for assessing the extent to which a society will view itself in some way a meritocracy." Wealth is an important determinant of labor force participation and therefore impacts productivity and economic growth. It also has important implications for inequality, intergenerational mobility, and, consequently, implications for democratic institutions whose stability is reliant on a meritocratic society or at least the verisimilitude of a meritocratic society.<br />
<br />
It should be noted that estimates of wealth inequality and the top wealth shares are not as widely agreed upon as estimates of income inequality and labor income shares. There are a few main data sources for estimating wealth inequality that are aptly summarized in <a href="https://www.sciencedirect.com/science/article/abs/pii/S0047272718300288">Alvaredo, Atkinson, and Morelli (2018)</a>:<br />
<div style="text-align: left;">
</div>
<ol style="text-align: left;">
<li>Household surveys including the <a href="https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/debt/methodologies/wealthandassetssurveyqmi" target="_blank">U.K. Wealth and Assets Survey</a> and the <a href="https://www.federalreserve.gov/econres/scfindex.htm?elqCampaignId=5888" target="_blank">U.S. Survey of Consumer Finances</a>;</li>
<li>Administrative data on individual estates at death; </li>
<li>Administrative data on wealth of living from annual wealth taxes; </li>
<li>Administrative data on investment income that are capitalized; and </li>
<li>Lists of large wealth-holders (e.g. Forbes).</li>
</ol>
<div style="text-align: left;">
These data sources are discussed in great detail in Kopczuk (2014)'s "What Do We Know About the Evolution of Top Wealth Shares in the United States?" which specifically discusses the U.S. Survey of Consumer Finances (1), the mortality multiplier method with individual estate data (2), and investment income data (4). Each of these data sources is subject to different concerns. Household surveys and list of the wealthiest individuals are recent phenomena and cannot be used for estimates prior to the 1950s when the household surveys on wealth were first implemented. Administrative data on wealth of the living based on wealth taxes cannot be recouped in most developed countries because only a few developed countries, most notably France and Norway, have a wealth tax to begin with. Therefore, most researchers rely on estate tax records on individual estates at death or on reported taxable capital income.<br />
<br />
The primary concern with estate taxes is that the distribution of estates of the deceased must be projected to the population at large: i.e. a multiplier method must be used in order to answer the question, how does the distribution of wealth among the deceased reflect the distribution of wealth among the living? Mortality multipliers are inverses of mortality rates based on various criteria, for example, wealthy individuals tend to have lower mortality rates and increased longevity compared to less wealthy individuals and therefore a higher mortality multiplier would be applied to the upper estate ranges meaning there are relatively more individuals living within those ranges than lower ones. For more on recent discussions of the relative longevity of the wealthy see <a href="https://gabriel-zucman.eu/files/SaezZucman2016QJE.pdf">Saez and Zucman (2016)</a> and <a href="https://www.ncbi.nlm.nih.gov/pubmed/27063997">Chetty et al. (2016)</a>.<br />
<br />
Kopczuk presents a few interesting stylized facts about wealth that provide a good introduction to the wealth distribution and methods of estimating it:<br />
<ul>
<li>Wealth is highly concentrated (top 10 percent holds between 65 and 85 percent of the total wealth, top 1 percent holds between 20 and 45 percent of total wealth based on time period); </li>
<li>While the methods of estimating the wealth distribution disagree on the timing it is clear that wealth concentration hit its apex prior to the Great Depression and declined after that; </li>
<li>Different methods lead to varying estimates for the top 1% for several reasons: one is that the estate tax multiplier method uses the individual as the unit of observation, surveys use the household, and the capitalization method uses tax units; another is that tax evasion impacts the administrative tax-based methods (estate tax and capitalization) but not the survey-based methods. Some capture debt (estate tax returns) whereas others do not (capitalization). </li>
</ul>
</div>
<div style="text-align: left;">
In a recent issue of the <i>Journal of Public Economics </i>commemorating Tony Atkinson's work, Alvaredo, Atkinson, and Morelli (2018) provides new evidence on the evolution of top wealth shares in the U.K. To choose one of the most interesting facets of the discussion of wealth that they present in the article, it is enlightening to view the top wealth shares compared to the wealth shares excluding housing.<br />
<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRMGBPo_feiwm8bmgsWnipWjLXcO4ttAMbcUxfAxsRgB4_wwCqcgnr2X5IiYQDhFvIAEzQoeuykfZqroJpYTZzSQZvIKtvcZwXA8R3VDi58ajs7nHkVbTLxa9IXxs_XdAcwfevm7nKgXCK/s1600/Screen+Shot+2018-10-03+at+5.32.41+PM.png" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-right: 1em; text-align: center;"><img border="0" data-original-height="900" data-original-width="1144" height="251" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgRMGBPo_feiwm8bmgsWnipWjLXcO4ttAMbcUxfAxsRgB4_wwCqcgnr2X5IiYQDhFvIAEzQoeuykfZqroJpYTZzSQZvIKtvcZwXA8R3VDi58ajs7nHkVbTLxa9IXxs_XdAcwfevm7nKgXCK/s320/Screen+Shot+2018-10-03+at+5.32.41+PM.png" width="320" /></a><br />
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The top 1%'s total wealth share and wealth share excluding housing tracked each other for much of the late 20th century but the authors note the divergence between the two trends in the 21st century, wherein the share of the top 1% of wealth holders of total wealth increased much more rapidly than its share of wealth excluding housing. In other words, the growth of wealth excluding housing is likely to be a more significant contributor to rising inequality than is the growth of housing wealth. In fact, they even mention that increases in housing prices serve an equalizing effect for the top 1%:<br />
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"It appears that housing wealth has moderated a definite tendency for there to be a rise in recent years in top shares in total wealth apart from housing. When people talk about rising wealth concentration in the U.K., then it is probably the latter that they have in mind... The results show how the impact of a general rise in house prices has changed over the period but it is always equalizing for the top 1%. At the beginning of the period a rise of 25% led to a reduction of some 1 percentage point in the share of the top 1% but the effect became smaller over time."<br />
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It should be noted, however, that trends in the housing market - particularly the resurgence in the private landlord and "buy to let" over the past three decades - likely have impacts on other areas of the wealth distribution apart from the top 1% of wealth owners (though these impacts are not addressed in this paper). <a href="https://www.nytimes.com/2017/05/09/magazine/how-homeownership-became-the-engine-of-american-inequality.html">This</a> <i>New York Times </i>article from last year, for example, is a news feature that discusses the role that homeownership plays in propagating existing wealth and income inequalities. These topics and the lower rungs of the wealth distribution more broadly are areas for further investigation, but for the time being Alvaredo, Atkinson, and Morelli (2018) highlight how granularity in wealth data can be used to better identify the causes of growing wealth inequality over the past few decades and, while they utilize estate data and the mortality multiplier method in their analysis, can also be triangulated with other methods and data sources to form a more comprehensive understanding of the wealth distribution.<br />
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<b>Sources</b><br />
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<li style="margin: 0px 0px 0.25em; padding: 0px;">Alvaredo, F., Atkinson, A., Morelli, S. (2018). Top wealth shares in the UK over more than a century. <i>Journal of Public Economics.</i></li>
<li style="margin: 0px 0px 0.25em; padding: 0px;">Kopczuk, W. (2014). What do we know about the evolution of top wealth shares in the United States? NBER Working Paper 20734.</li>
<li style="margin: 0px 0px 0.25em; padding: 0px;">Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., Bergeron, A., Cutler, D. (2016) The association between income and life expectancy in the United States 2001-2014. <i>Journal of American Medical Association</i>.</li>
<li style="margin: 0px 0px 0.25em; padding: 0px;">Saez, E., Zucman, G. (2016) The distribution of US wealth, capital income, and returns since 1913. <i>Quarterly Journal of Economics</i>. </li>
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com1tag:blogger.com,1999:blog-3364384706420369338.post-2589759961194372812018-08-14T14:23:00.001-07:002018-08-14T15:41:31.244-07:00In-depth look at income and wealth data (pt. 1.5 of 3): Non-traditional data and machine learning approaches<div dir="ltr" style="text-align: left;" trbidi="on">
While this was originally meant to be a three-part series on income and wealth data, it would have been an oversight to not include some discussion of the non-traditional data and machine learning approaches to collecting information on poverty. These data are particularly relevant in developing countries where traditional sources of data - administrative data and survey data - are not collected as widely, regularly, or thoroughly. This can be for several reasons: nationally representative surveys are expensive and the costs of data collection too high, challenges associated with data collection in conflict-affected areas (discussed in greater detail in a <a href="https://openknowledge.worldbank.org/bitstream/handle/10986/26799/9781464810640.pdf?sequence=2">previous publication</a> I worked on), and large proportions of the population are employed in the informal economy meaning there is little by way of administrative tax records at the lower end of the income distribution.<br />
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Yet, information on poverty is still needed in these countries to inform evidence-based policymaking by governments, international organizations, and non-profits. A brief article by researcher Joshua Blumenstock published a few years ago in <i>Science, <a href="http://www.jblumenstock.com/files/papers/jblumenstock_2016_science.pdf">"</a></i><a href="http://www.jblumenstock.com/files/papers/jblumenstock_2016_science.pdf">Fighting Poverty with Data"</a>, discusses the frontier of research in this area that aims to supplement the traditional sources of data on wealth and inequality with machine learning approaches. Blumenstock discusses, for example, the rise in use of nightlight data to track economic productivity and growth citing one paper which utilizes nightlight based measures to study the impact of sanctions on North Korea. In fact, a paper that I reviewed earlier in the year on the impact of Chinese aid projects on local corruption used nightlight data to proxy for local economic activity in areas around active and inactive Chinese aid sites.<br />
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More novel and more interestingly, the author cites research in machine learning that uses satellite imagery in conjunction with nightlight data to identify the visual features of relatively wealthier areas (which have brighter nightlight) that would allow researchers to leverage daytime satellite images to better track poverty in developing countries. There are limitations to this approach for example that nightlight is not an ideal measure of activity at the lower end of the income distribution - where all is dark - but with further research these approaches could be very useful in the developing country context.<br />
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Mobile phone data - which was discussed in part in the above article and in greater detail in <a href="http://www.jblumenstock.com/files/papers/jblumenstock_2015_science.pdf">this other <i>Science </i>piece</a> also by Blumenstock - is also promising. Using mobile phone logs, researchers extract statistics including volume, intensity, and timing of phone calls, the structure of the individual's network of contacts, and mobility and migration information based on geospatial markers and whittle down to the statistics that can be used to predict socioeconomic status. In the case cited in this article, the researchers paired consenting individuals' mobile phone data with survey data that they collected on individual income and wealth in order to train the model. It should be noted that mobile phone data is subject to greater ethical and privacy concerns than publicly available data. While the research cited here aimed to obtain macro level statistics to inform policymaking it is clear that attempting to obtain a more granular understanding for specific demographics will be challenging. ICT access and use is far from universal and, often, those who are excluded from its access are the most vulnerable. This is similar to the challenges with using conflict data wherein the data on those who are the most vulnerable and impacted by conflict is the data that is the most challenging to collect and to collect accurately. This is not, however, meant to generalize, given that some of the poorest regions of the world have reasonably high mobile phone penetration but rather a cautionary note when assessing whether data are representative with respect to specific populations.<br />
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For example, with respect to a recent project that I've worked on, there is high mobile phone penetration in sub-Saharan Africa despite low income. Yet, while its neighbors in East Africa have experienced fast growth in mobile phone ownership and usage in the past five years, Ethiopia has fallen behind largely due to government ownership of the nation's telecom monopoly which has limited expansion and service. Further, analyzing the distributional data on mobile phone usage indicates that women are far less likely to own and use mobile phones than men - consistent with the findings in many developing countries - and that any data collected from these devices in a hypothetical scenario would only be representative of a specific demographic.<br />
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And yet, despite the challenges, non-traditional sources of data offer promise particularly in geographic areas where recent, traditional data on wealth and poverty are not available. Research in this interdisciplinary area will be interesting to watch in the near future.</div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0tag:blogger.com,1999:blog-3364384706420369338.post-20609392531338456572018-07-03T09:29:00.001-07:002018-07-03T09:29:42.225-07:00In-depth look at income and wealth data (pt. 1 of 3): Background<div dir="ltr" style="text-align: left;" trbidi="on">
For some time now I have been interested in writing an in-depth post on income and wealth data in order to discuss how the study of inequality - in conjunction with the data and methods that enable this study - has progressed over time. While this was initially intended to be a single post, it quickly became evident that there was too much to discuss within too short a space. In this first post of a three-part series, then, I focus on providing the background for a more granular discussion of wealth and income in the next two parts.<br />
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Given the topic at hand, it is noteworthy that several articles on inequality and tax and redistribution policy were published in a recent special issue of the <i>Journal of Public Economics </i>honoring the late <a href="https://www.tony-atkinson.com/" target="_blank">Tony Atkinson</a>. For an introduction to that series of papers see <a href="https://doi.org/10.1016/j.jpubeco.2018.05.006" target="_blank">here</a>. My <a href="http://chandniraja.blogspot.com/2018/05/trends-in-household-income-inequality.html" target="_blank">previous post</a> on individual and household level inequality is based on a paper within this special issue. Additionally, a recent issue of the <i>Quarterly Journal of Economics </i>features <a href="https://academic.oup.com/qje/article/133/2/553/4430651" target="_blank">an article</a> that combines national accounts data with micro data to produce estimates of inequality in the U.S. that are consistent at the macro level.<br />
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In light of expanding research on inequality, its growing presence in policy debates in developed countries, and the evolution of both data and methods that enable its rigorous study, it is useful to take stock of the existing data sets and methods used by researchers to answer some of the most pressing questions in public economics today: those that deal with the distribution of wealth and income in our societies and the reasons for widening or stagnant inequality levels. We can also assess what types of questions we are now able to answer and how our answers to these and other - yet unasked - questions can become more accurate through improved data collection and methods and how future data collection can fill existing gaps in our knowledge.<br />
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To begin, the <a href="https://wid.world/" target="_blank">World Inequality Database</a> - a database of global wealth and income inequality data co-founded by Tony Atkinson - provides a <a href="https://wid.world/wid-world/" target="_blank">concise description</a> of data and research in this field over the past twenty years. Two important trends:<br />
<ol style="text-align: left;">
<li>Most studies on inequality have until very recently focused on income rather than wealth. The key reason is the greater availability of micro data to study income, which is taxed and therefore observable in administrative data, as opposed to wealth, which in most developed countries is not taxed apart from an estate tax upon death. A secondary reason is that it has not been made evident until recently - likely for similar data reasons - that wealth concentration plays a large role in the inequality we see within developed countries. Piketty (2014)'s <i>Capital in the Twenty-First Century</i> was not the first but perhaps the most prominent description of the growing role of capital in widening divisions between haves and have-nots.</li>
<li>Current efforts are aimed at producing distributed national accounts that combine administrative micro data with national accounts macro data - ledgers of assets and liabilities at the national level - in order to reconcile inequality estimates that are created based on micro data with the national accounting. This publication from the founders of the WID discusses the motivation and methodology for the creation of these "distributed national accounts." It notes the historical background, "[by] combining the macro and micro dimensions of economic measurement, we are of course following a very long tradition. In particular, it is worth recalling that Kuznets was both of the founders of the U.S. national accounts and the author of the first national income series and also the first scholar to combine
national income series and income tax data in order to estimate the evolution of the
share of total income going to top fractiles in the U.S. over the 1913-1948 period (see Kuznets, 1953)." The article cited above from the <i>QJE</i>, Piketty, Saez, and Zucman (2018), presents "distributed national accounts" for the U.S., which they note is distinct from government statistical agencies' work in this area.</li>
</ol>
<b><i>Discussion of the main data types and their roles in inequality studies</i></b><br />
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<b>Administrative micro data</b><br />
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To preface a discussion on administrative tax data for wealth and income studies, I provide context for use of this data for social sciences research more broadly. Administrative data are collected for the purposes of registration, transaction and record keeping, and are often linked to public service delivery. They are typically collected by public sector agencies and can be used in administration systems in education, health, and taxation, among other departments of the public sector. It should be noted that these data are "found" data and are not collected for the purposes of research as survey data are. The social sciences, and economics in particular, have shifted to using administrative data over survey data sources in recent years for several reasons.<br />
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Specifically as noted in <a href="https://eml.berkeley.edu/~saez/card-chetty-feldstein-saezNSF10dataaccess.pdf" target="_blank">this</a> white paper to the National Science Foundation: "Administrative data are highly preferable to survey data along three key dimensions. First, since full population files are generally available, administrative records offer much larger sample sizes... Second, administrative files have an inherent longitudinal structure that enables researchers to follow individuals over time and address many critical policy questions, such as the long term effects of job loss (von Wachter, Song, and Manchester, 2009) or the degree of earnings mobility over the life cycle (Kopczuk, Saez, and Song, 2010). Third administrative data provide much higher quality information than is typically available for survey sources, which suffer from high and rising rates of non-response, attrition, and under-reporting."<br />
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Access to this data is not without its challenges in many developed countries. Nordic countries have been leaders in enabling researchers to access de-identified administrative or "register" data but other countries, such as the U.S., have been relatively slow to follow. Given the central role that administrative data has come to play in social sciences and economics research in particular (see the two charts on the number of publications in leading economics journals that employed administrative data in <a href="http://www.rajchetty.com/chettyfiles/admin_data_trends.pdf" target="_blank">this presentation</a> from researcher Raj Chetty, who also co-authored the white paper cited above), it is clear that access to these data has important implications that are outlined in an <a href="https://www.economist.com/international/2018/05/26/government-data-are-ever-more-important-to-economic-research">article </a>published in the <i>Economist</i> last month on the topic.<br />
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Administrative tax data are widely used in income and wealth inequality studies. For example, wealth inequality is largely studied through either estate tax records - in order to create wealth distributions of wealth at death and to extrapolate from those records the distribution of wealth among the living using the mortality multiplier method - or through taxable capital income (it should be noted that only one-third of total capital income is reported on tax returns which is why it is challenging to estimate wealth based on this quantity). Similarly, income inequality is studied through income tax records. Given the socioeconomic and demographic data contained in these records we are able to answer (or attempt to answer) a wide range of social science research questions based on micro data. Yet, the missing piece is information on movements in the economy at large over time (e.g. increase in fraction of retired individuals or declines in household size) which could have implications for inequality.<br />
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As noted by Piketty, Saez, and Zucman (2018), studies that use micro data exclusively are unable to answer questions such as: (1) what fraction of economic growth accrues to different parts of the income distribution, (2) what fraction of the increase in income inequality is due to changes in share of labor vs. capital in national income as opposed to changes in the distribution within labor or capital earnings, (3) how does government redistribution impact inequality (i.e. we are only able to observe pretax income using micro data series which does not allow us to observe the changes in the income distribution between pre- and posttax). To answer these questions, they argue, merging micro data with national accounts data at the macro level is valuable.<br />
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<b>National accounts macro data</b><br />
<b><br /></b>
On the macro side side, national accounts data aggregate output, expenditure, and income activities of each sector of the economy. While income and consumption measures are important for evaluating standards of living they offer only a static picture of well-being. Specifically, income and consumption reflect current well-being: how much a household or an economy is producing and consuming at present, but they do not provide much insight into a household or economy's long-term or future well-being (beyond making assumptions that current well-being and consumption are highly correlated over time). This is where national accounts data can be useful: data on a household or economy's ownership of marketable assets and contraction of debts can provide insight into long-term or future well-being though it may be cross-sectional rather than longitudinal.<br />
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For a valuable introduction to balance sheets and the national accounts data see <a href="https://www.insee.fr/en/statistiques/3547632?sommaire=3547864#documentation" target="_blank">here</a> for a discussion from the French National Institute of Statistics and Economic Studies (INSEE). It should be noted that the definitions of "assets" and whether or not they provide "economic advantages" refer specifically to those items that have market values. This would exclude, as stated by INSEE, "items that one might expect to see in the accounts (human capital, natural heritage, natural State property, household durables, pension entitlements linked to the allocation system, etc.)" They note as a rule of thumb that only items that are featured in the capital and financial accounts are included as assets in order to maintain internal consistency. The capital account and financial account link the opening and closing balance sheets to one another: they specify what happened to the accumulation of capital based on capital consumption, assets sold and acquired, discoveries and inventions, and nominal holding gains as a result of price fluctuations.<br />
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These data, and specifically the national income measures in these data, may be relied upon to fill the gaps in our knowledge from the tax data. Specifically, there are gaps between the reported income and the national income that are not captured in micro studies: imputed rents of homeowners and taxes on top of unreported and untaxed labor income in the form of tax-exempt fringe benefit. Piketty, Saez, and Zucman (2018) estimate that the fraction of national income reported on tax returns in the U.S. has declined from 70 percent in the late 1970s to roughly 60 percent today which indicates that micro data alone may underestimate the level and growth of income in this country and perhaps more so for certain parts of the income distribution than others depending on what exactly is being excluded from the tax data that is present in the national income.<br />
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For a more in-depth description of the methods and the process by which these two data are being combined, I would look to the article. The authors effectively illustrate both the motivation and the methods for incorporating national income macro data into inequality studies. In the next part of this three-part series, I will discuss the data and research on wealth inequality specifically to provide greater detail on wealth estimates using estate tax data compared to those using capital income.<br />
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<b>Sources</b><br />
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<ol style="text-align: left;">
<li style="margin: 0px 0px 0.25em; padding: 0px;">Piketty, T., Saez, E., Zucman, G. (2018). Distributional National Accounts: Methods and Estimates for the United States. <i>Quarterly Journal of Economics.</i></li>
<li style="margin: 0px 0px 0.25em; padding: 0px;">Kleven, H., Luttmer, E. (2018). A Special Issue of the Journal of Public Economics: Honoring the Work of Sir Anthony B. Atkinson (1944-2017). <i>Journal of Public Economics. </i></li>
<li style="margin: 0px 0px 0.25em; padding: 0px;">Blundell, R., Joyce, R., Keiller, A.N., Ziliak, J.P. (2017). Income inequality and the labour market in Britain and the US. <i>Journal of Public Economics. </i></li>
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com2tag:blogger.com,1999:blog-3364384706420369338.post-75932026338874171352018-06-01T10:35:00.000-07:002018-06-07T00:56:21.297-07:00Selected articles on AI and job displacement<div dir="ltr" style="text-align: left;" trbidi="on">
Given my hiatus from posting and recent time constraints (multidimensional vector spaces occupy most of my time now that summer session has started), I'm discussing here a few interesting papers rather than providing an in-depth review of a single topic or piece of literature as I usually do. But worry not, I will be back to discuss the econometric details in another post soon enough.<br />
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And, a thank you to Intelligent Economist for mentioning this blog in his <a href="https://www.intelligenteconomist.com/economics-blogs/" target="_blank">"Top 100 Economics Blogs of 2018"</a> and a thank you to everyone who is visiting as a result of that post. I've tried to add some value with this blog (both for myself and for my readers) and I hope that you've found it valuable. Thanks very much for reading.<br />
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<b>Automation and labor</b><br />
In an earlier <a href="http://chandniraja.blogspot.com/2018/01/automation-and-labor-insights-from.html" target="_blank">post</a> from January, I discussed Acemoglu and Restrepo (2017)'s paper that models the relationship between automation and labor force displacement.<br />
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In the earlier post I identified key policy items to focus on in a future defined by automation:<br />
<ol style="text-align: left;">
<li>Identify market failures that contribute to "excessive" automation or the adoption of technologies that are only marginally more cost effective than labor and lead to little productivity gain or job creation</li>
<li>Determine whether and to what degree jobs will be created at all in the process of automation if the adoption of new technologies leads to marginal but limited productivity gains</li>
<li>Address the inequality implications inherent in the displacement of jobs that require particular skill sets and the creation of jobs that require another</li>
<li>Identify the type of jobs that are created and the quality of those jobs</li>
<li>Prepare the labor market for "new skills" and a culture of lifelong learning</li>
</ol>
<div>
A recent <a href="https://poseidon01.ssrn.com/delivery.php?ID=089119105064004122105016107076095124022046039021042055064086124096098075117109108028063033000008109016026092119023096020115094006041062046036000082029112119121122005009028067098086120016012115071111120023095026126028127096100001020023077107087069123&EXT=pdf" target="_blank">paper</a> from Jason Furman and Robert Seamans discusses a lot of these key items and more tangible policy proposals, including universal basic income and guaranteed employment, that would address the labor market implications of a future that will come to depend heavily on artificial intelligence. One challenge associated with automation that they mention in the paper is the decline in the male labor force participation rate, which I discussed in my previous <a href="http://chandniraja.blogspot.com/2018/05/trends-in-household-income-inequality.html" target="_blank">post</a> on rising inequality in male labor market outcomes.<br />
<br />
The decline in male labor force participation is a signal that, at least in part, existing policies have had limited positive impact on (3) addressing the inequality implications inherent in job displacement and creation - the disproportionate impact on low-skilled labor - and (5) preparing the labor market for "new skills" and a culture of lifelong learning - failure to re-integrate workers that have been displaced by the system into new jobs requiring new skill sets. The paper highlights that addressing labor market transitions for individuals who have been displaced from their jobs is more challenging than it appears. For a discussion on skills in the context of automation see <a href="https://www.mckinsey.com/featured-insights/future-of-organizations-and-work/skill-shift-automation-and-the-future-of-the-workforce" target="_blank">this</a> new report from McKinsey Global Institute.<br />
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The paper also discusses non-labor related policy issues with rising automation including a need for new approaches to antitrust regulation. In particular, they draw attention to the fact that large datasets can serve as a barrier to entry in the AI field. I mentioned the role of big data in competition in an earlier post in the context of Amazon's edge in entering the grocery market: Amazon's access to high quality data on consumer preferences through its dominance of e-commerce retail is non-negligible given that its competitors in the grocery store market that it entered will have much less of that type of data. Even more so in the case of AI, data could serve as a crucial factor for entrants meaning there need to be novel ways of thinking about competition (or lack thereof) in these markets due to this new barrier to entry. It is also interesting to think about how institutions and laws such as the recent European Union General Data Protection Regulation can play a role in this area by limiting data retention.<br />
<br /></div>
<div>
Another point of further reading is the <a href="http://publica.fraunhofer.de/documents/N-432348.html" target="_blank">European Commission's "Analysis of the impact of robotic systems on employment in the EU"</a>. It adds value to existing literature because it is one of the first studies to use firm-level data to assess the impact of robotics on productivity (finding a significant and positive effect on labor productivity but not identifying an effect on employment levels which is an interesting finding that will have to delve into further in another post). The common alternative - using macro level data on productivity - has a more limited scope in terms of understanding what happens at a granular level. This is perhaps not as relevant for isolating a causal impact as for using descriptive statistics to explore the topic in greater detail and to ask more refined questions about automation's effects on firm behavior. </div>
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A third <a href="https://www.sciencedirect.com/science/article/abs/pii/S0927537118300228" target="_blank">paper</a> in the current issue of <i>Labour Economics</i> also merits a mention given that it adds another layer of complexity to the relationship between automation and job displacement. Lordan and Neumark (2018) find that increases in the minimum wage lead to significant decreases in automatable employment held by low-skilled workers and that, while there is significant heterogeneity across industries and demographics, well-intentioned minimum wage laws interact with rising automation to have adverse impacts on a vulnerable population. One important question for this and economic research on AI more broadly: to what extent can new technologies be grouped together to analyze the impact of their adoption on the labor force? This paper relies on the U.S. Consumer Population Survey from 1980-2015, during which time a range of new technologies were adopted with potentially different implications and benefits of adoption for firms.<br />
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<div>
One of the key takeaways from Acemoglu and Restrepo (2017) is that new technologies have varying effects on firm productivity and the creation of new jobs. They discuss the "so-so technologies" that are only marginally more cost-effective and lead to little job creation. This implies that nuances in the type of technology and to what extent they increase firm productivity are extremely important. The nuances are, however, more important in determining job creation rather than job displacement based on their model.<br />
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<b>A new report debunking myths on agriculture in Africa</b></div>
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<br />
The World Bank came out with a notable <a href="http://documents.worldbank.org/curated/en/323081508746081610/pdf/120599-PUB-PUBLIC-PUBDATE-10-20-17.pdf" target="_blank">publication</a> discussing common myths and truths on agriculture in Africa for policymakers and practitioners. The publication discusses the common myths in the table below and identifies whether or not they are true based on detailed data from the World Bank's Living Standards Measurement Survey.<br />
<br class="Apple-interchange-newline" />
It is notable both as a primer for researchers to get a more accurate, big picture sense of small holder agriculture and because its format effectively marries the technical details with concise policy takeaways without eliminating the relevant nuances across countries and settings.<br />
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com1tag:blogger.com,1999:blog-3364384706420369338.post-6902760589493697352018-05-05T16:09:00.000-07:002018-05-05T22:30:10.200-07:00Trends in household income inequality: comparing the U.S. and Britain on labor, marriage, and government transfers<div dir="ltr" style="text-align: left;" trbidi="on">
A <a href="https://www.sciencedirect.com/science/article/pii/S0047272718300562?via%3Dihub" target="_blank">recent paper</a> highlighted by the Institute for Fiscal Studies and forthcoming in <i>Journal of Public Economics</i> presents an intriguing look at the relationships between individual labor market outcomes, household composition/spousal labor market outcomes, government tax and transfer systems, and household income inequality in the U.S. vs. Britain over the past four decades.<br />
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Blundell et al. (2017)'s descriptive analysis compares individual labor market outcomes in the two countries' by education level, income level, and gender and compares spousal labor market outcomes and government tax and transfer systems to provide a comprehensive look at the components of household income inequality. Their analysis enables us to connect each country's experience of/response to key shared events - including the rise in female labor force participation, the decline in low-skilled labor, and the 2007-08 financial crisis and recession - to household income inequality.<br />
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Many of the findings presented confirm existing ideas about the interaction between the tax and transfer system and labor market outcomes. The paper adds value in its use of micro data through 2015 and its use of data that has been standardized to facilitate comparison between the two countries. It adds to a literature on the role of the welfare state in exacerbating or alleviating individual-level labor market inequalities. This literature, and the inequality literature more broadly, formed the crux of the late economist <a href="https://www.tony-atkinson.com/" target="_blank">Tony Atkinson</a>'s life's work and his numerous contributions in the field built the foundation for modern studies of inequality.<br />
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In fact, in his work on redistributive preferences and the welfare state, Atkinson (2000) discusses the responses of various countries to the universal shift in labor markets in industrialized countries away from low-skilled labor: "We are concerned not only with policy before and after a shift in the external circumstances, but also with how different societies respond to the same shift. It is striking that a number of OECD countries have in common a rise in the inequality of market incomes (incomes from earnings and investments) between 1980 and the mid-1990s, but that the outcomes in terms of disposable outcomes (after direct taxes and social transfers) differed."<br />
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So as not to diminish the rich nuances in Blundell et al. (2017), I only mention that one of the paper's findings is exactly this: inequalities in market incomes between the two countries are very similar but this is not the case for the disposable incomes at the household level. I discuss the findings below.<br />
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<b>Inequality in male labor market outcomes has increased significantly in both countries</b><br />
<ul style="text-align: left;">
<li>The two countries' experiences in the Great Recession were different: in the U.S., real incomes for the most part kept pace with inflation whereas Britain experienced a sharp drop in real incomes, particularly for the top income percentiles and the educated. Adjustment in the U.S. came in the form of declines in employment rather than in real wages whereas in Britain employment on both intensive and extensive margins was relatively robust. </li>
<li>I suspect there are a couple reasons for the disparity:</li>
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<ol style="text-align: left;">
<li>High subsidization of capital vs. labor may bias the U.S. economy towards lower but more productive employment levels (due to higher investments in capital).</li>
<li>Stricter labor market regulation in Britain may imply that adjustments to shocks take the form of real wage declines rather than layoffs and declines in employment.</li>
<li>Differences in the <a href="https://www.ft.com/content/83e7e87e-fe64-11e6-96f8-3700c5664d30" target="_blank">composition of employment</a> between the two countries post-recession (numbers of full-time, part-time, and self-employed workers) may impact the aggregate figures on wage growth and hours worked.</li>
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<li>This discussion is connected to the authors' second finding: while both countries have experienced a significant rise in male income inequality in the past four decades, in the U.S. this increase is largely driven by male hourly wage inequality whereas in Britain it is driven by fewer hours worked by men at the bottom of the distribution. Therefore while employment was relatively robust in the Britain it is because any response to the recession was part of a longer term trend in decreasing hours for male low-skilled workers on the intensive margin.</li>
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<li>This is clearly illustrated by the green dotted lines in the two graphs below on hours worked for G.B. Men who left education at or below 16 years of age and U.S. Men with less than high school education. The line on the left for G.B. indicates a steep downward trend beginning 1995.</li>
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<li style="text-align: left;">Finally, the authors provide evidence of the wage stagnation in the U.S. continues to make <a href="https://www.nytimes.com/2018/05/04/business/economy/unemployment-jobs.html?rref=collection%2Fsectioncollection%2Fbusiness&action=click&contentCollection=business&region=rank&module=package&version=highlights&contentPlacement=1&pgtype=sectionfront" target="_blank">headlines</a>. They state that the only group of male workers that has a higher median real wage today compared to 1979 is those with a college education (compared to those without high school, those with high school, and those with some college who have not seen any improvement to their real wages in the past four decades). </li>
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<b>Lower marriage rates among the bottom half of the income distribution indicate inequalities in household composition and spousal income</b></div>
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Assortativeness of marriage - the tendency for people to marry others who are found in roughly the same area of the wage distribution - is a trend that has only increased in the U.S. in the past twenty years (in Britain it has remained constant). A second, commonly <a href="https://www.brookings.edu/wp-content/uploads/2016/06/56-Shortage-of-Marriageable-Men.pdf" target="_blank">discussed</a> finding is the decline in marriage rates - among the entire wage distribution but more sharply among the lower half of the income distribution including low-skilled and unemployed men. Together the findings indicate that, rather than alleviate male earnings inequalities, the marriage market has likely amplified those inequalities.</div>
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<b>The tax and transfer system in Britain has done a much better job of ensuring that the inequality in male labor market outcomes has not translated into large household income inequalities</b></div>
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The following image presented by the authors is the most striking: while male earnings inequality (red dotted line) has increased steadily in both countries and perhaps even more sharply in Britain, household net income inequality (labor earnings plus government transfers minus taxes) in Britain has not grown in the past twenty years. This is not the case in the U.S. indicating that tax and transfer systems in Britain have done a much better job at ensuring that disparities in male labor market outcomes have not translated into as large disparities in net household income. </div>
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The authors identify the following when discussing the disparity in tax and transfer outcomes between the U.S. and Britain:</div>
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<li style="text-align: left;">Much more generous social welfare programs in Britain vs. the U.S. in particular due to successive Labour governments from 1997-2010. </li>
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<li>The one and very important exception being the recessionary period: in the U.S., average transfer generosity increased greatly in response to the recession and were in place through the six-year recessionary period whereas in Britain, fiscal consolidation policies beginning in 2011 indicated a reduction in social programs. </li>
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<li>Welfare policy in Britain that does not link transfers to work status indicating net income growth of non-workers whereas this is not the case in the U.S. where the generosity of welfare for non-working families declined greatly in the past two decades.</li>
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<b>Follow-up questions </b></div>
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The study raises several further points of research/questions to be answered by existing research - </div>
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What are the differences in structural factors that led the decline in low-skilled labor to manifest itself as a decline in hours worked in Britain vs. a stagnation of real wages in the U.S.? In Britain, real wages experienced a sharp decline only during the recession and prior to the recession were even on the uptick for most men. Yet their hours of work had been declining for decades. To research this further we would need to look at the labor force participation rates of low-skilled men to determine whether the U.S. experienced similar decreases in employment (though on the extensive rather than intensive margin) that are masked by lower labor force participation rates among low-skilled men.<br />
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Through what channels did monetary and fiscal policy in the U.S. in the recessionary period contribute to the stabilization of real wages? The decline in real wages in the recession and post-recession Britain has been attributed to a number of factors - high inflation due to high energy prices, expansion of lower-paid, self-employment or part-time jobs rather than full-time jobs, limited investment in capital and as a result low levels of productivity - a number of which should also be issues in the U.S.<br />
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Though the study doesn't delve deeply into female labor market outcomes it paints an interesting picture of stability in women's employment and wages over the past forty years and particularly during the recession. This is likely due to higher relative attrition of women from the labor force at times when jobs are hard to be found given that men remain the main earners in most households, but again we would need to see the labor force participation rates to be sure. </div>
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<b>Sources</b><br />
<ol style="text-align: left;">
<li>Blundell, R., Joyce, R., Keiller, A.N., Ziliak, J.P. (2017). Income inequality and the labour market in Britain and the US. <i>Journal of Public Economics. </i></li>
<li>Atkinson, A. (2000). The welfare state, budgetary pressure and labour market shifts. <i>Scandinavian Journal of Economics.</i></li>
<li>Atkinson, A. (1992). Towards a European social safety net. <i>Fiscal Studies.</i></li>
</ol>
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0tag:blogger.com,1999:blog-3364384706420369338.post-10141311296694053792018-04-07T14:01:00.004-07:002018-04-07T20:32:25.202-07:00"Intelligent evolution of humanity"<div dir="ltr" style="text-align: left;" trbidi="on">
First of all, I'm sorry for the long hiatus in posts: midterms, a bout of the flu, and starting a new work project have all taken away from the time that I normally spend on this blog.<br />
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Given that the project that I've started working on focuses on the gender gap in agricultural productivity levels in sub-Saharan Africa, I visited a number of seminal papers in agricultural economics and in the study of firm and individual-level productivity and efficiency. In this survey of the literature, I stumbled on Theodore Schultz's Nobel Prize lecture, <a href="https://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1979/schultz-lecture.html" target="_blank">"The Economics of Being Poor"</a>, after having read his work on smallholder farmer efficiency ("poor-but-efficient" hypothesis which states that farmers are calculating economic agents who are highly efficient in a traditional agricultural environment (Schultz, 1964)) and his consequent focus on human capital and the gains to labor productivity and entrepreneurial ability as the key to improving the well-being of the agricultural population.<br />
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In his lecture he discussed the entrepreneurship inherent in agriculture and criticized the government price distortions in developing countries: "experts fail to recognize how efficient they [small farmers] are... This allocative ability is supplied by millions of men and women on smallscale producing units; agriculture is in general a highly decentralized sector of the economy.... The allocative roles of farmers and of farm women are important and their economic opportunities really matter (Schultz, 1978b)." Yet he maintained that despite their efficiency in a traditional agricultural setting, small farmers needed further investments in human capital and skills to be just as entrepreneurial and efficient in a dynamic setting (i.e. one with constant technological and economic change made more dynamic by increased globalization in past decades).<br />
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He also gave the following hopeful exposition on humanity addressing natural resource constraints (economic growth models to come such as Nordhaus (1992) viewed natural resources including land and energy as lags on economic growth): "It is ironic that economics, long labelled the dismal science, is capable of showing that the bleak natural earth view for food is not compatible with economic history; that history demonstrates that we can augment resources by advances in knowledge. I agree with Margaret Mead: 'The future of mankind is open ended.' Mankind's future is not foreordained by space, energy, and cropland. <b>It will be determined by the intelligent evolution of humanity</b>."<br />
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In other words, that land is a fixed resource and that traditional sources of energy are a depleting resource do not preclude the fact that investments in human capital can modify the production function and the relationship between the traditional inputs and output.<br />
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Look no further than the growth and takeoff of alternative, renewable energy sources and the expansive yields from <a href="https://www.nationalgeographic.com/magazine/2017/09/holland-agriculture-sustainable-farming/" target="_blank">sustainable farming</a> as examples. As much as Schultz critiqued government for distorting agricultural systems that he viewed as otherwise efficient, he may not have appropriately appreciated government's unique role in incentivizing the resource allocation decisions of firms towards that "intelligent evolution of humanity" that he spoke of. For example, we've seen in the last decade the role of government in incentivizing alternative, renewable energy sources through large-scale investments and subsidies for the research and development and market viability of those energy sources.<br />
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The interesting and challenging piece of the lecture is connecting those two goals: improving the human capital and skills of much of the population (key to improving well-being) and moving towards a more sustainable future that is not tied to traditional, fixed or depleting natural resources. While positing these ideas, he also provided advice to economists that is still relevant today:<br />
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"We all know that most of the world's people are poor, that they earn a pittance for their labor, that half and more of their meager income is spent on food, that they reside predominantly in low income countries and that most of them are earning their livelihood in agriculture. What many economists fail to understand is that poor people are no less concerned about improving their lot and that of their children than rich people are."<br />
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If you want a more humorous speech, try <a href="https://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1979/schultz-speech.html" target="_blank">this</a> one that he gave at the Nobel banquet. </div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com1tag:blogger.com,1999:blog-3364384706420369338.post-20743412499964539442018-02-10T10:22:00.003-08:002018-02-14T10:09:23.173-08:00China and the future of development aid<div dir="ltr" style="text-align: left;" trbidi="on">
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At the end of last year, research organization Aid Data <a href="http://aiddata.org/blog/aiddata-releases-first-ever-global-dataset-on-chinas-development-spending-spree" target="_blank">published</a> the first extensive data set documenting Chinese aid flows around the world. The Chinese government is notoriously secretive about its development aid outflows: it does not publish any project-level or country-specific data on its own nor does it work with international organizations that attempt to quantify and release this information. To create the data set, researchers at Aid Data scoured publicly available news reports, official embassy documents, and aid/debt information from other countries for the past five years. The Tracking Underreported Financial Flows methodology that they rely on is detailed <a href="http://aiddata.org/methods/tracking-underreported-financial-flows" target="_blank">here</a>.</div>
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China's lack of transparency has been cited as a growing issue given its increasing role on the international stage. In the past few years, China surpassed the U.S. in terms of annual spend on development aid and has established itself as one of the key development players in Africa. Critics - such as Moises Naim in <a href="http://www.nytimes.com/2007/02/15/opinion/15naim.html?_r=0" target="_blank">this</a> opinion post years ago in the <i>New York Times</i> - have raised concerns that in competing with Western donors and international organizations such as the World Bank, China is seen as the "no strings attached" donor likely to give to undemocratic regimes and countries with poor institutions that would be subject to higher scrutiny under traditional Western giving and lending practices. </div>
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It is unsurprising, then, that researchers have already taken to the new Aid Data data set to answer a myriad of questions about the impact of Chinese aid on local economies. In this post I look specifically at the paper, <a href="https://ac.els-cdn.com/S0047272718300021/1-s2.0-S0047272718300021-main.pdf?_tid=50ce9bac-0c5c-11e8-968a-00000aab0f02&acdnat=1518045307_5683229af756c52ce30cfc727090bf2b" target="_blank">"Chinese aid and local corruption"</a> published last month in the <i>Journal of Public Economics</i>. Authors Isakkson and Kotsadam (2018) employed Aid Data's <a href="http://aiddata.org/data/chinese-global-official-finance-dataset" target="_blank">Chinese Official Finance to Africa data set</a> to identify locations with: (1) ongoing Chinese aid projects, and (2) those selected for future Chinese aid projects. They then connected this data with Afrobarometer survey data eliciting survey respondents' experiences with corruption (whether they "had to pay a bribe, give a gift, or do a favor to government officials") in order to estimate the effect of an ongoing Chinese aid project in a given location on the level of local corruption. Because they geocode both data sets and restrict their sample to only those aid projects for which they can identify a granular location, the authors are able to identify respondents within 50 or 25 km of aid project locations to analyze their experiences with localized corruption. </div>
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The authors find that Chinese aid projects have a statistically significant effect on local corruption with point estimates of a 3.5% (bribes given to "avoid a problem with the police") or 2.7% (bribes given to "get a document or permit") increase in bribery in locations with ongoing Chinese projects relative to locations selected for future Chinese projects. They speculate that Chinese aid increases local corruption through two potential mechanisms: first, that presence of the donor changes the cost-benefit structure of engaging in corruption (i.e. if the donor is indifferent as to the "means" by which a project is completed and is willing to reward for the "ends" of completing it then this raises the benefits associated with corruption). Second, they posit that the donor is in a position of power to influence social norms and create institutional change. A donor's acceptance or propagation of corrupt activity could worsen norms (noting that norms are easier to change for the worse than the better). Finally, they employ the same strategy around World Bank aid project locations and do not find any effect of these projects on local corruption. </div>
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<b>Estimation strategy</b></div>
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The paper employs a model similar to a difference-in-differences model wherein the responses of individuals who live near a site that is currently developed by the Chinese are compared to the responses of individuals who live near a site that will be developed by the Chinese in the future. In the following regression model, the authors employ the difference between the coefficients on "active" and "inactive" as the key parameter of interest. Individuals located within the radius of an ongoing Chinese aid project are "active" ("active" = 1). Those located within the radius of a future Chinese aid project are "inactive" ("inactive" = 1). And those that are outside the radii of any current or future Chinese aid projects are neither active nor inactive ("active" = 0; "inactive" = 0). </div>
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<span style="font-family: "stixtwotext"; vertical-align: -1pt;">(1) Y</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;">it </span><span style="font-family: "stixtwomath";">= </span><span style="font-family: "stixtwotext"; font-style: italic;">β</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">1 </span><span style="font-family: "stixtwotext"; font-style: italic;">active</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">i</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">t </span><span style="font-family: "stixtwomath";">+ </span><span style="font-family: "stixtwomath";"><span style="font-family: "stixtwotext"; font-style: italic;">β</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">2 </span><i>in</i></span><span style="font-family: "stixtwotext"; font-style: italic;">active</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">i</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">t</span><span style="font-family: "stixtwotext"; font-style: italic;"> + </span><span style="font-family: "stixtwotext"; font-style: italic;">α</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">s</span><span style="font-family: "stixtwotext"; font-style: italic;"> + </span><span style="font-family: "stixtwotext"; font-style: italic;">δ</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">t</span><span style="font-family: "stixtwotext"; font-style: italic;"> +y</span><span style="font-family: "stixtwotext"; font-style: italic;"> </span><span style="font-family: "stixtwotext"; font-style: italic;">X</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;">it</span><span style="font-family: "stixtwotext"; font-style: italic;"> +</span><span style="font-family: "stixtwotext"; font-style: italic;">ε</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">ivt </span></div>
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Isakkson and Kotsadam employ this method rather than interpreting the coefficient on the "active" dummy to avoid the ex-ante assumption that "there is no relationship between project localization and the pre-existing institutional characteristics of project sites." In other words, if the locations for Chinese aid projects were selected based on certain institutional characteristics it is very possible that those institutional characteristics are correlated with corruption levels and that, as a result, interpreting the coefficient on "active" alone erroneously captures pre-existing differences in corruption levels between locations with Chinese aid projects and those without. </div>
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To control for the geographic and time-based variation in the data set - which includes data from across the African continent and spanning 2000-2013 - the authors include spatial fixed effects, year fixed effects, and a set of individual controls. While the baseline results indicate that Chinese aid projects led to an increase in local corruption, the various iterations do lead to questions:</div>
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<li>The authors point to two statistics from the regression output to determine whether the parameter of interest is significant: coefficient on the "active" dummy variable (if there is an effect this should be positive and statistically significant) and the statistic for an F-test testing the hypothesis "active - inactive = 0" (if the effect on corruption is a result of a Chinese aid project this hypothesis should be rejected). The baseline results indicate that both with respect to police bribes and permit bribes the coefficient on "active" is positive and highly statistically significant and the F-test hypothesis can be rejected at the 5 percent level. See Table 1 for details.</li>
<li>However, the sensitivities indicate that the coefficient on "active" is statistically significant across most but not all iterations and the F-test cannot be rejected at the 5 percent level in at least one of the iterations. The results indicate that the effects are stronger for police bribes than they are for permit bribes which leads to questions about the mechanism that leads to increased corruption and why it impacts police bribes more so than permit bribes. See Table 2 for details.</li>
<li>Furthermore, the authors conclude that World Bank aid projects do not similarly lead to an increase in local corruption not because the coefficient on "active" in that sample set is not positive or statistically significant (in fact it is significant in several iterations) but because the F-test results indicate that it cannot be rejected that the "active" and "inactive" coefficients are equal. But why is it that both "active" and "inactive" locations with World Bank projects see a higher level of local corruption than those without World Bank projects (where Chinese aid projects don't, because the "inactive" coefficient is not significant in most Chinese projects)? It's not a question that this paper seeks to answer but there should be a reasonable hypothesis for why locations selected for World Bank vs. Chinese aid projects differ in this way.</li>
<li>My main question reading this paper was whether the implementation of an aid project leads to a change in the demographic population of a locality. Given that the Afrobarometer survey is not a panel data set, the same individuals are not necessarily interviewed pre- and post-implementation of an aid project. </li>
<ol>
<li>While it is possible that the implementation of a project leads to the corruption of existing actors it also seemed possible that it led to inflows of new actors into the locality due to a possible increase in local economic growth and activity. If the increase in local corruption is due to the influx and changing composition of the locality this is distinct from an increase due to corruption of the existing population. </li>
<li>The authors attempt to address this question by analyzing whether there are more police stations in active aid areas vs. inactive aid areas (to address the claim that more bribery is a result of more police stations rather than more corruption), stating: "Neither do we find any evidence that the results are driven by increased resource flows making the project areas into 'honey pots' attracting corrupt actors." However, the empirical investigation does not seem to answer the original question of whether aid projects lead to an influx of corrupt actors. </li>
<li>It boils down to how the effect is interpreted: in the case of this paper, the parameter of interest does not distinguish or isolate the two effects presumably because both lead to an increase in local corruption whether by migration or by impacting existing populations. </li>
</ol>
</ol>
<br />
<div class="separator" style="clear: both;">
<b>Implications for the future of development aid</b></div>
<div class="separator" style="clear: both;">
<br /></div>
<div class="separator" style="clear: both;">
Overall, the implications of the paper that Chinese aid projects lead to local corruption are the first step in understanding how different forms of aid (and specifically "no strings attached" aid) can create institutional change and impact social norms. While the quantification of this impact is important, the paper does not explicate the mechanism by which these projects increase corruption and without that linkage it is difficult to prescribe appropriate policy solutions to improve local governance and reduce corruption. But the paper reaffirms questions about China's development strategy to work within existing entrenched systems to create economic growth vs. the traditional Western approach to attempt to improve governance and create institutional change at the same time. The broader takeaways for the field of development aid:</div>
<div class="separator" style="clear: both;">
</div>
<ul style="text-align: left;">
<li>It is clear that conceptualizing Chinese vs. Western aid as a competition is not the most effective way to improve growth and development in these economies, rather, assuming Chinese aid will continue at its current rate how can each set of aid practices complement and supplement one another? Transparency and data availability make it easier to answer these questions. </li>
<li>Given the importance of international coordination in aid, this new availability of data on Chinese aid offers a novel opportunity for other donors to provide a value-add to these economies in sectors and projects that the Chinese are not investing in and to advocate more strongly for better governance and effective democratic institutions given the apparent worsening of certain aspects of local governance as a result of Chinese aid projects. </li>
<li>China's increasing role in development and aid places places a need for further introspection on the part of international organizations such as the World Bank, specifically: how should the organization continue to advocate for good governance and effective democratic institutions while simultaneously recognizing the need to work with one of the largest unilateral donors that may not be interested in propagating those norms? Will the World Bank's role and priorities change as funds from China and from <a href="https://www.nytimes.com/2018/01/25/business/world-bank-jim-yong-kim.html" target="_blank">private investors</a> play an increasing role in the growth of developing economies? How can it position itself most effectively in this rapidly changing space and provide a distinct value-add?</li>
</ul>
<b>Sources</b><br />
<div>
<ol style="text-align: left;">
<li>Isaksson, A. and Kostadam, A. (2018). Chinese aid and local corruption. <i>Journal of Public Economics</i>. </li>
</ol>
</div>
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Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0tag:blogger.com,1999:blog-3364384706420369338.post-48152736914694557462018-01-17T09:53:00.000-08:002018-01-18T13:25:32.208-08:00Automation and labor: insights from the American Economic Association meetings last week<div dir="ltr" style="text-align: left;" trbidi="on">
As briefly mentioned in my previous post, the American Economic Association held its annual meetings last week in Philadelphia. While the panel on gender bias was not webcast, several other lectures and discussions are available <a href="https://www.aeaweb.org/conference/webcasts/2018/" target="_blank">online</a>. Another <a href="https://www.aeaweb.org/webcasts/2018/economic-consequences-of-artificial-intelligence-and-robotics" target="_blank">session</a> that caught my eye was on automation and the future of labor, seeking to answer: what are the projected effects of automation, artificial intelligence, and robotics on labor share, wages, and the nature of work?<br />
<br />
Daron Acemoglu presented a theoretical <a href="https://economics.mit.edu/files/14641" target="_blank">paper</a>, co-authored with Pascual Restrepo, that provided a framework for understanding the relationship between artificial intelligence and labor share. He breaks down the impact of artificial intelligence into two countervailing effects: a displacement effect and a productivity effect. The displacement effect is the inevitable displacement of the labor force that takes place when firms substitute machines to complete specific tasks previously done by labor. The reason there is a displacement effect at all is that the capital is cost-saving for firms. One result of this cost-saving displacement is that there may be an increase in productivity associated with the firm's output. This productivity effect will lead to a demand for new skills and new job creation. But the question posed by Acemoglu and Restrepo is how large is that increase in productivity associated with employing machines instead of labor and will it lead to large enough job creation that it will balance out the displacement effect?<br />
<br />
An example provided in the paper of these two effects comes from Bessen's (2016) analysis of the introduction of ATM machines. The paper found that the introduction and wide dispersal of ATM machines, a technology that took over many of the existing tasks of bank tellers (notably many existing tasks that were performed more expensively by bank tellers), allowed banks to cut costs. This cost saving allowed them to open more branches, which in turn led to an increased demand for bank tellers who could then focus on more specialized skills that the ATMs did not have. I don't review that paper here, but I note that it is <a href="https://www.forbes.com/sites/eriksherman/2016/12/17/automation-has-created-more-jobs-in-the-past-but-will-it-now/" target="_blank">contentious</a> in its isolation of the causal effect of ATM machines on the banks' decisions to open new branches. The example, however, illustrates the mechanism by which the displacement and productivity effects work according to the paper (some bank tellers in existing branches displaced and bank tellers in new branches added).<br />
<br />
The model in Acemoglu and Restrepo indicates that the effect of automation on labor share is unambiguous (labor share will decrease with the displacement effect holding productivity constant) but if the productivity effect leads to new job creation (demand for new skills leads to new job creation) then it has the ability to lessen the inevitable job displacement associated with AI. The productivity effect from employing ATM machines arguably led to an increase in the number of bank branches employed and the number of bank tellers employed who then needed to have new skills in the tasks that the ATM could not complete. I don't think that the "new skills" required in the bank teller positions are necessarily a good example of the demand for new skills modeled by Acemoglu and Restrepo since the new jobs created are effectively the same as the old jobs being displaced at existing branches but it's possible they may require improving on some existing skills in order to better advise the client on the different transaction opportunities available to them or bringing in new clients.<br />
<br />
There are a couple of takeaways I think are important here:<br />
<ul style="text-align: left;">
<li><b>Will jobs be created at all?: </b></li>
<ul>
<li>The paper highlights the case in which firms adopt technologies that are only marginally more efficient than labor at performing the same task ("so-so technologies"). The adoption of these technologies leads to few productivity gains and as a result lesser job growth through new skills. But the displacement effect will still be resounding and Acemoglu and Restrepo argue that it is these marginally more efficient technologies that will be the most harmful to the labor force since they don't lead to productivity gains. </li>
<li>In the case of the bank tellers, what if instead of investing in new physical branches (requiring employment of bank tellers), banks invested in improving their mobile and online infrastructure to better serve clientele online? Firms' productivity may be growing but the productivity gains do not necessarily translate into job creation at the same rate (creates jobs for those tasked with updating and maintaining the online infrastructure but would this be comparable to creating jobs for a new set of tellers at new locations?).</li>
</ul>
<li><b>Address inequality implications: </b>This leads to the next point. It is clear that the jobs that are created through the demand for new skills will not employ the same skills as the jobs that are displaced (see the example of investing in new physical branches versus investing in a better online infrastructure and the skills needed to maintain each of those). Which raises the question of whether income and wealth inequality will be exacerbated by rising automation if the jobs that are displaced disproportionately impact those at the lower quintiles and the jobs that are created disproportionately require skills that those at the lower quintiles do not possess or cannot reasonably acquire. Perhaps anticipating the impacts on those at the lowest quintiles of the income ladder several prominent tech executives, including Elon Musk, have <a href="http://fortune.com/2017/06/29/universal-basic-income-free-money-silicon-valley/" target="_blank">advocated</a> for a universal basic income that they claim will be the only way to address the widespread job loss associated with automation. </li>
<ul>
</ul>
<li><b>Identify type of jobs created</b>: The response from Ben Jones in the discussion directly following Acemoglu's presentation raised an important point: the model appears to assume that all of the new tasks that are created based on the productivity effect are essential, i.e. there would be no output if the task were not completed. How likely is it that the new jobs created in the aftermath of technology adoption would be essential jobs (essential to production)?</li>
<ul>
<li>If, as Jones hints at, the new jobs are non-essential, it is also likely that they may be of lower quality. Quality of employment is particularly important given the rise of the gig economy and trend towards temporary and part-time employment that offers fewer benefits and protections to workers. </li>
</ul>
<li><b>Prepare for "new skills":</b> In order to preempt the potentially negative implications on labor share and inequality the key would be to identify the kinds of new skills that will be most valuable in a future with automation and how governments, policymakers, and educators can effectively plan for such a future by preparing students for those skills. Furthermore, they would want to be able to prepare those outside of formal education (those who are not in schools, universities, or training programs) for retraining and <a href="https://medium.com/rsa-journal/lifelong-learning-6f4c9c088571" target="_blank">lifelong learning</a> so that they can better adapt to changing conditions in the labor market. </li>
<li><b>Identify market failures contributing to "excessive" automation: </b>In their paper, Acemoglu and Restrepo outlined the phenomenon of "excessive" automation that is only marginally more cost effective than labor and that leads to few productivity gains and little job creation. They provided a few reasons for the "excessive" automation, one being that capital is potentially over-subsidized through the tax system which in turn encourages firms to automate.</li>
</ul>
</div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com2tag:blogger.com,1999:blog-3364384706420369338.post-81334611004530410802018-01-10T17:01:00.000-08:002018-02-12T11:55:51.272-08:00Women in economics: Elinor Ostrom's work and its lasting impact on fishing communities in the Gulf of California<div dir="ltr" style="text-align: left;" trbidi="on">
Researcher Erin Hengel's recent paper <a href="http://www.erinhengel.com/research/publishing_female.pdf" target="_blank">"Publishing while female"</a> (2017) was <a href="https://www.economist.com/blogs/graphicdetail/2018/01/daily-chart-4" target="_blank">chronicled</a> in a brief article in the Economist last week profiling the differences in readability standards posed to publications authored by male versus female academics in economics. I don't do a thorough review of that piece here, but Hengel's results indicate that: (i) female-authored articles are better written in terms of readability than similar papers by men, controlling for year, journal, editor, topic, institution, and English language ability; (ii) the gap widens during peer review; (iii) female economists' readability improves over the course of their careers whereas male economists' does not presumably due to the higher standards they face. These findings, if true, add to an existing body of evidence that suggests that female academics are held to higher standards than their male counterparts and often receive less credit than their male counterparts for their accomplishments.<br />
<br />
Yet, despite the biases that persist, progress is being made in that we have the data to identify and analyze them now more than ever. The New York Times <a href="https://www.nytimes.com/2018/01/10/us/politics/women-economics.html" target="_blank">reported</a> on a panel at the American Economic Association's annual meeting of the minds held this week which presented the research of several academics on systemic gender bias within the field.<br />
<br />
My reading on the gender bias in economics led me to write on the first and to-date only woman to win the Nobel Prize in Economic Sciences, <a href="https://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2009/ostrom-facts.html" target="_blank">Dr. Elinor Ostrom</a>, and on the contributions that she made to the fields of economics and governance through her writings on collective action. While the lack of female prize winners reflects a gender bias in economics in the 1960s, 1970s, and 1980s more so than biases that exist today (the prize rewards contributions that were made more than several decades ago) it is still striking that economics only has one winner where <a href="https://www.nobelprize.org/nobel_prizes/lists/women.html" target="_blank">other fields do better on this dimension</a>.<br />
<div>
<br /></div>
<div>
<b>Existence of collective action outside of the public and private sectors</b></div>
<div>
<br /></div>
<div>
The prevailing notion of collective action during Ostrom's time was posited by Mancur Olson (1965) in the <i><a href="https://moodle.drew.edu/2/pluginfile.php/225050/mod_resource/content/2/Olson%20%281967%29%20Logic%20of%20Collective%20Action%20%28book%29.pdf" target="_blank">Logic of Collective Action</a>. </i>To provide a brief summary of Olson's thesis: </div>
<div>
<br />
<ul style="text-align: left;">
<li>Olson posited that individuals in groups would choose to free-ride to reap the communal benefits from public goods without incurring any individual costs to procure or maintain said goods. After all, public goods are non-excludable and individuals would obtain the benefits whether or not they incurred the costs (so long as others paid the price). </li>
<li>He argued that individuals would not act collectively in their common interest unless selective incentives were provided or force used to induce them to participate. </li>
<li>Furthermore, large groups faced greater costs of collective action than smaller ones: not only larger selective incentive costs but larger monitoring costs, and the total benefits from participation would be spread more thinly across the members of the group. Which leads to, as he put it, "surprising tendency for the exploitation of the great by the small" (smaller groups with more concentrated incentives are more effective at organizing than larger ones). </li>
</ul>
</div>
<div>
Ostrom's work was designed to bridge the gap between the predictions made in the theory of collective action and the empirical evidence that often evidenced widespread, voluntary cooperative behavior. She theorized the existence of norm-using players in addition to the rational egoist actors traditionally employed in game theory: the norm-using players value social norms including reciprocity, fairness, and being trustworthy. Certain norm-using players are willing to initiate cooperate action when they believe others will reciprocate and will continue to do so as long as a significant number of others reciprocate; others are willing to punish free-riders either verbally or through sanctions. The existence of these actors, and equally or even more importantly, the existence of strong social norms within a community, makes voluntary collective action feasible in a way that Olson did not theorize. </div>
<div>
<br /></div>
<div>
She posited that cooperative behavior especially where communication is involved "can work as well or nearly as well as externally imposed set of rules and monitoring and sanctioning in order to generate cooperative behavior" and furthermore claimed it is more effective in settings where external authorities impose rules but can only achieve weak monitoring or sanctioning (Ostrom, 2000). She proposed <a href="http://wiki.p2pfoundation.net/Common_Property_Regime" target="_blank">eight design principles</a> critical to the survival of voluntary cooperative behavior, including local rules that restrict the amount, timing, and technology of harvesting the resource in question and access to rapid and low-cost methods to resolve conflict among users.<br />
<br /></div>
<div>
<b>Local resource governance</b></div>
<div>
<b><br /></b></div>
<div>
Ostrom's work was directly relevant to the governance of common-pool resources, or "natural or humanly created systems that generate a finite flow of benefits where it is costly to exclude beneficiaries and one person's consumption detracts from the amount of benefits available to others" (Ostrom, 2000). Common-pool resources are distinct from public goods: in the case of public goods one person's consumption does not subtract from the pool of resources available to others but in the case of common-pool resources it does. Examples of common-pool resources are fisheries, irrigation systems, and water. </div>
<div>
<br /></div>
<div>
Indeed the implications of her work are even more relevant today, as these and other environmental resources continue to be depleted at high rates and long-term benefits of sustainable use of said resources are foregone in favor of short-term gains. Not to mention her theoretical and empirical evidence for the viable existence of self-organized resource regimes, distinct from any government or private entities, is hopeful especially in environments where there is a lack of political will, public sector leadership, or public sector capability in the realm of environmental conservation. </div>
<div>
<br />
<a name='more'></a><br /></div>
<div>
<div>
As stated by Tim Forsyth (2012) in a piece on Ostrom's impact: "[F]or many environmental analysts, the findings of Ostrom's research offered the prospect of a solution to long-standing fears of Malthusian collapse or ecological ruin resulting from unregulated economic exploitation... And secondly, in a period when individualistic economic thinking was popular, Ostrom offered a vision of cooperative behaviour that did not rely upon reverting to a centralized state. Indeed, for donors and NGOs, Ostrom's design principles offered a model of decentralization and local resource governance that could be replicated in multiple field settings, and which used empowering local and incentive-based governance mechanisms."</div>
</div>
<div>
<br /></div>
<div>
<b>Fishing cooperatives in Baja California (2017)</b></div>
<div>
<br /></div>
<div>
An interesting and hopeful feature article was published in the September 2017 issue of <i>National Geographic</i> magazine detailing <a href="https://www.nationalgeographic.com/magazine/2017/09/baja-mexico-marine-conservation-tourism-fish-sharks-whales/" target="_blank">successful cooperatives operating in Baja California's fishing villages</a> and strongly reminding me of Ostrom's design principles. Several communities featured in the article decided to organize collectively to maintain their resources and engage in sustainable fishing at a time when overfishing threatened the collapse of fisheries in the region. In Punta Abreojos, where the primary resources are lobster and abalone, the cooperative is financially stable enough to offer pensions to retired fishermen and scholarships to students within the community.<br />
<br />
The author points to five rules that form the basis of successful and "sustainable community-supported ocean management" that resound strongly of Ostrom's design principles. In addition to having: (i) a fairly isolated site (indicating clear boundaries and understanding of who is using the site and who is not), (ii) strong visionary community leaders (Ostrom's "conditional cooperators" who were needed to initiate and maintain the cooperative), and (iii) trust-building exercises within the community, e.g. soccer games set up by local NGOs (to strengthen the social norms and the number of people who abide by them), he also pointed to (iv) having resource of high-value and (v) a way that fisherman could support themselves while the resources recover.<br />
<br />
In each of the successful cooperatives featured, fishermen forewent fishing during part of the season in order to allow the resources to replenish: in Punta Abreojos, rather than beginning the fishing season in January (month in which fishing season commences according to government regulations), the community imposes a four-month ban and only starts the season in April; in Laguna San Ignacio, the community both restricts the number of tourist boats in the ocean and bans fishing in the lagoon during whale-watching season to sustain the area as a peaceful location for gray whales to spend time with their young. In this way these communities avoid the rational actor equilibrium of free-for-all fishing or tourism that is not optimal in the long-run.<br />
<br /></div>
<div>
<b>Ostrom as the first and to-date only female Nobel Prize winner in Economics</b></div>
<div>
<br />
Today, novel sources of evidence are increasingly able to document the existing biases in the field of economics. Those biases were all the more evident and widespread when Ostrom first took up an academic position at the University of Indiana in the mid-1960s. </div>
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<br /></div>
<div>
Forsyth (2012) wrote of Ostrom's experience: "According to one later interview, 'there was no encouragement to think about anything other than teaching in high school or being pregnant and barefoot in the kitchen' (Zehr and Carson, 2009 in Ostrom, 2012:26). She often remarked that she was hired partly because she was willing to teach a class on American government at the unpopular hour of 7:30 am (Woo, 2012). The department did not even have female bathrooms at the time, requiring women to use the men' room and to put a sign on the door when they were inside (<i>Solutions</i>, 2010)."</div>
<div>
<br /></div>
<div>
These experiences certainly remind female economists today of the challenges faced by those that came before us, the progress that has been made in the past six decades, and that progress which is still yet to come. In part due to the recognition that she received by the Nobel committee, Ostrom's work has had lasting effects on environmental conservation and development policy. </div>
<div>
<br /></div>
<div>
<b>Sources</b></div>
<div>
<ol style="text-align: left;">
<li>Ostrom, E. (2000). <i>Collective action and the evolution of social norms</i>. Journal of Economic Perspectives. </li>
<li>Olson, M. (1965). <u>The Logic of Collective Action: Public Goods and the Theory of Groups</u>. Cambridge: Harvard University Press. </li>
<li>Forsyth, T., & Johnson, C. (2014). <i>Elinor Ostrom's legacy: governing the commons, and the rational choice controversy</i>. Development and Change. </li>
<li>Hengel, E. (2017). <i>Publishing while female. </i></li>
</ol>
</div>
<div>
<br /></div>
</div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com3tag:blogger.com,1999:blog-3364384706420369338.post-34675083868105704592018-01-02T21:07:00.000-08:002018-01-11T10:08:39.637-08:00"Your margin is my opportunity" 2.0<div dir="ltr" style="text-align: left;" trbidi="on">
An <a href="https://www.wsj.com/articles/the-limits-of-amazon-1514808002" target="_blank">article</a> this week in the Wall Street Journal makes a bold statement on Amazon's growth prospects: it argues that Jeff Bezos' well known mantra ("<a href="https://www.inc.com/jessica-stillman/7-jeff-bezos-quotes-that-will-make-you-rethink-success.html" target="_blank">Your margin is my opportunity</a>") is a double-edged sword that will limit Amazon from achieving the world domination that everyone fears. A section entitled "What Amazon Can't Do" discusses Amazon's inability to compete in high-margin industries, citing Amazon's Fire Phone and Amazon Studios as products that are far behind the premium iPhone and HBO of their respective markets. The article claims that these high-margin industries are where the profits lie (in some form evoking Benjamin Graham's cautionary advice, "Obvious prospects for physical growth in a business do not translate into obvious profits for investors").<br />
<br />
But I wouldn't be too quick to write-off Amazon's ability to compete in these high-margin markets. In my opinion the article misses a few key points:<br />
<br />
<ul style="text-align: left;">
<li>Amazon is and has been constructing an infrastructure that allows it to enter new markets much more easily than the average firm. This infrastructure includes (1) its seemingly infinite access to capital: <a href="http://www.slate.com/articles/business/moneybox/2014/01/amazon_earnings_how_jeff_bezos_gets_investors_to_believe_in_him.html" target="_blank">investors that are willing to endure Amazon's losses quarter on quarter in pursuit of long term results</a> and its own <a href="https://www.wsj.com/articles/how-amazon-has-diversified-1464829457" target="_blank">diversified set of businesses</a>, as well as (2) its access to high quality data on consumer preferences through its dominance of the e-commerce retail market.</li>
<li>It already does have at least one high(er)-margin business: Whole Foods. <a href="https://www.investopedia.com/articles/investing/070115/whole-foods-price-gouging-and-skyhigh-margins.asp" target="_blank">With average grocery store margins hovering around 1 percent</a>, Whole Foods profit margins at 4 percent are high even if the market is not a glamorous one. More telling in my opinion is the way that Amazon obtained this business: through M&A. I wouldn't underestimate Amazon's ability to use M&A strategically in the future to merge other high-margin businesses to capitalize on its strong fundamentals in physical logistics and e-commerce to compete and compete effectively.</li>
</ul>
<br />
Though "your margin is my opportunity" may originally have applied to Amazon's strategy for the basics (your Amazon Basics, Amazon tablets, and AWS) and not high-margin businesses, having a foothold in those low-margin businesses will allow Amazon to obtain an advantage with high-margin businesses in the future if it so chooses. </div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0tag:blogger.com,1999:blog-3364384706420369338.post-25698895104050607712017-12-31T18:06:00.000-08:002018-02-12T11:56:21.688-08:00Unintended consequences of tax reform in developing countries<div dir="ltr" style="text-align: left;" trbidi="on">
Corporate tax reform has been a big topic of conversation among economists here in the U.S. with the passage of the recent tax bill on Capitol Hill. Proponents argued that lowering the corporate tax rate from 35 to 20 percent would increase U.S. competitiveness and increase after-tax profits for corporations and in turn increase investments and returns to labor.<br />
<br />
A large component of the discussion surrounding the corporate tax rate in developed, wealthy nations revolves around international tax competition and large multinational firms with the capacity to move their operations across borders to take advantage of lower tax structures (see <a href="http://eureka.sbs.ox.ac.uk/4386/1/WP1229.pdf" target="_blank">Devereux and Loretz, 2012</a>, for a review of the relevant literature on tax competition). Much of the study of corporate taxes has been based in these high enforcement settings and has been a study of corporate firms.<br />
<br />
Setting appropriate tax rates is arguably more challenging in developing countries with low enforcement and large informal sectors. In these settings firms (noncorporate firms in particular) have two additional methods of responding to changes in the tax rate: they can move into the large existing informal sector, thereby reducing the overall tax base, or they can engage in tax evasion, given the low enforcement capabilities of the government. Evidence from these settings can be informative as to the elasticity of noncorporate firms to tax rates in a setting that is distinct from that of developed countries where there are fewer tax evasion opportunities. Waseem (2017) recently published a paper in the <i>Journal of Public Economics </i>(<a href="https://ac.els-cdn.com/S0047272717301871/1-s2.0-S0047272717301871-main.pdf?_tid=2a34fc50-ea76-11e7-b4c1-00000aab0f6c&acdnat=1514318069_3d03f29d56d4243e56c76f43ea436ef5" target="_blank">"Taxes, Informality, and Income Shifting: Evidence from a Recent Pakistani Tax Reform"</a>) analyzing firm responses to an increase in the tax rate for noncorporate firms in Pakistan that looks at this issue.<br />
<br />
He finds that the responses by firms to the tax reform were so large that the Pakistani government was collecting less in in revenue three years after the reform than it was prior to the reform. On the intensive margin, firms impacted by the increase in tax rate were reporting significantly smaller earnings. On the extensive margin, fewer firms were registered with the government and reporting positive profits, indicating a shrinking of the formal tax base.<br />
<br />
The policy in question was enacted by the Pakistani government in 2009 and raised the tax rate on partnerships from a bracketed system with average tax rates that varied progressively from 0% to 25% (a tax rate that continued to be applied to sole proprietorships) to a flat tax rate of 25%. This change was enacted in order to reduce the variation in tax rate between partnerships and corporate firms, the latter of which was taxed at and remained taxed at 35% after the tax reform, and to reduce the disincentives for incorporation of new firms.<br />
<br />
The implications of this study for developing countries are stark. Policies such as this one need to account for the "unintended" effects of tax reform on formalization and tax evasion as well as the intended effects on incentives to incorporate. High variation in the tax rates across noncorporate firms in this particular case (specifically between partnerships and sole proprietorships) led to significant levels of tax evasion, income shifting, and movement into informality that counteracted higher tax revenues collected from the partnership firms still remaining in the formal tax base and from incorporated firms.<br />
<br />
<b>Estimation strategy</b><br />
<b><br /></b>
The author employs a difference-in-differences method to take advantage of the tax reform in Pakistan in 2009. He uses administrative data of income tax returns filed from 2006-2011 to compare the outcomes of partnership and non-partnership firms (corporations and sole proprietorships): log change in reported earnings and log change in the number of filers. The former represents the intensive margin response of firms that remained in the tax base and the latter the extensive margin response of firms that exited the tax base.<br />
<br />
The model estimated for the intensive margin elasticity is:<br />
<br />
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<span style="font-family: "stixtwotext";">Δlog </span><span style="font-family: "stixtwotext"; font-style: italic;">z</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;">it </span><span style="font-family: "stixtwomath";">=</span><span style="font-family: "stixtwotext"; font-style: italic;">α</span><span style="font-family: "stixtwomath";">+</span><span style="font-family: "stixtwotext"; font-style: italic;">β Partnership</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -2pt;">i </span><span style="font-family: "stixtwomath";">+</span><span style="font-family: "stixtwotext"; font-style: italic;">ε </span><span style="font-family: "stixtwotext";">Δlog(1</span><span style="font-family: "stixtwomath";">−</span><span style="font-family: "stixtwotext"; font-style: italic;">τ</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;">it</span><span style="font-family: "stixtwotext";">)</span><span style="font-family: "stixtwomath";">+</span><span style="font-family: "stixtwotext"; font-style: italic; font-weight: 700;">X</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;">i </span><span style="font-family: "stixtwotext"; font-style: italic; font-weight: 700;">δ</span><span style="font-family: "stixtwomath";">+</span><span style="font-family: "stixtwotext"; font-style: italic;">λ</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;">t </span><span style="font-family: "stixtwomath";">+</span><span style="font-family: "stixtwotext"; font-style: italic;">u</span><span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;">it</span><br />
<span style="font-family: "stixtwotext"; font-style: italic; vertical-align: -1pt;"><br /></span></div>
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This regresses the log change in the reported earnings of a firm i at time t on its status as a partnership, the log change in its net-of-tax rate, a set of control variables, and year fixed effects.<br />
<br />
To account for potential endogeneity between the change in the tax rate and change in reported earnings (i.e. not only does the change in the tax rate impact the change in reported earnings but reported earnings also impacts the tax rate), the author uses an instrumental variables strategy regressing the change in the tax rate on a dummy variable for whether the observation is a partnership firm in the post-reform period in a first-stage regression that allows him to isolate the variation in the tax rate change that is due to the tax reform for partnerships.<br />
<br />
<a name='more'></a><br /><br />
Key identification issue here is whether partnerships would in fact grow at the same rate as corporations and sole proprietorships in the absence of the reform. Based solely on intuition it would appear to me that larger, better financed, and more regulated firms (corporations and to an extent partnerships) would have different responses to shocks than smaller, less regulated firms with fewer financing options (sole proprietorships and to an extent partnerships). It may also be the case that corporations make up a disproportionate amount of certain industries (e.g. those with higher fixed costs and entry barriers or those that are more concentrated) than others, leading there to be differential responses to shocks in which certain industries are hit harder than others. This is especially relevant given that the reform took place in the immediate aftermath of the 2007-2008 financial crisis.<br />
<br />
To address the parallel trends assumption the author provides visual evidence of the trends in reported earnings and firm entry over time. It certainly appears that there is a parallel trend in reported earnings (though with only three data points it is challenging to confirm these similarities as a trend).<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhY0CcydUZKpzQaVAXZ-_VKmQUfJEEFkzdmZ8j6jXTaa_XnlkAtySI8N9AYq5al_ahxnyIosDSBRUn8NoMujBUO8K3FwIySsuacTT0zhoo8tLvfOmJa_RedhE3hFAs7-FBzEIAaM6yzIqWf/s1600/Screen+Shot+2017-12-27+at+6.35.09+PM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="612" data-original-width="1600" height="244" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhY0CcydUZKpzQaVAXZ-_VKmQUfJEEFkzdmZ8j6jXTaa_XnlkAtySI8N9AYq5al_ahxnyIosDSBRUn8NoMujBUO8K3FwIySsuacTT0zhoo8tLvfOmJa_RedhE3hFAs7-FBzEIAaM6yzIqWf/s640/Screen+Shot+2017-12-27+at+6.35.09+PM.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="font-size: 12.8px;">Waseem (2017) - Fig 4</td></tr>
</tbody></table>
He also shows the composition of the various industries in terms of the three firm types in the following charts. It appears that the composition is roughly balanced across partnerships and sole proprietorships but not as much so for partnerships and corporations (as I hypothesized in a previous paragraph). Helpfully, the author breaks out the results into two sets: one set using corporations as the control group and the other using sole proprietorships so we can investigate this further.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEggnzaHpJUpfvzykGB9NMA-yn1-EdjdxD_DfDqKSAsZWvuNhvzQDcli9J22G0rkcZX6XXNaas7S1c0JY9hZz_OO-e_YW0vsEYCwW9urTv6yqPabZkqFqL2gTFy0b7-QFyDY2lgElmZjTkC2/s1600/Screen+Shot+2017-12-27+at+7.11.55+PM.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="588" data-original-width="1600" height="234" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEggnzaHpJUpfvzykGB9NMA-yn1-EdjdxD_DfDqKSAsZWvuNhvzQDcli9J22G0rkcZX6XXNaas7S1c0JY9hZz_OO-e_YW0vsEYCwW9urTv6yqPabZkqFqL2gTFy0b7-QFyDY2lgElmZjTkC2/s640/Screen+Shot+2017-12-27+at+7.11.55+PM.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="font-size: 12.8px;">Waseem (2017) - Fig A1</td></tr>
</tbody></table>
The results reported in the paper are highly significant and robust to several sensitivities. This is the case for both regression iterations that rely on sole proprietorships as controls as well as those that rely on corporates as controls. The results indicate that the reported earnings and firm participation in the tax base are highly elastic to the partnership tax rate to a degree of 2:1 (for every percent decrease in the net-of-tax rate there is a close to two percent decrease in reported earnings).<br />
<br />
<b>Interesting implications</b><br />
<ol style="text-align: left;">
<li>First, because this natural experiment exploits a change in the partnership tax rate rather than the corporate tax rate, the author finds the elasticity of reported earnings of likely domestic small to medium sized firms rather than large multinationals. The options for these firms in response to the tax rate (if at all) would be to report lower earnings, exit into the informal sector, or go out of business. This elasticity is distinct from one associated with corporate firms which more than likely have the option to invest and produce across borders (assuming mobility of capital) if faced with relatively high effective tax rates. </li>
<li>Estimating the costs of shifting between business forms (sole proprietorship-partnership and partnership-corporation) would allow policymakers to set more appropriate tax rates. While the costs of moving from partnership to sole proprietorships are clearly low, "how low?" is a yet unanswered question that if answered could allow for more effective tax rate setting. If the variation between business forms is too high with respect to income shifting costs then you'll see greater income shifting with implications on overall revenue collection. </li>
<li>In Pakistan, firms engaged in income shifting from former partnerships to sole proprietorships to take advantage of differential tax rates. Income shifting is not unique to developing countries - the recently passed tax bill in the U.S. will have its own income shifting implications to watch over the next few years. </li>
<ol>
<li>For example, the bill will allow sole proprietors and partnerships and other pass-through entities to deduct up to 20 percent of their revenue from their taxable income. It incentivizes individuals to classify their income as business rather than individual income and to move towards contract-based labor arrangements to reap larger tax benefits. <a href="https://www.nytimes.com/interactive/2017/12/20/us/politics/small-business-tax-cut-pass-throughs.html" target="_blank">This</a> article provides a good summary on how this would work with various examples. Such a shift towards sole proprietorship and partnership income will have implications for revenue collection. It will also, as pointed out by journalists <a href="https://www.nytimes.com/2017/12/31/business/economy/tax-work.html" target="_blank">here</a>, steepen the rise of the gig economy and the good and bad associated with it including fewer benefits and protections for workers.</li>
</ol>
</ol>
<b>Sources</b><br />
<ol style="text-align: left;">
<li>Waseem, M. (2017). <i>Taxes, informality, and income shifting: Evidence from a recent Pakistani tax reform</i>. Journal of Public Economics. </li>
<li>Devereux, M.P., & Loretz, S. (2012). <i>What do we know about corporate tax competition? </i>(Working Paper 12/29). Oxford University Centre for Business Taxation. </li>
</ol>
</div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0tag:blogger.com,1999:blog-3364384706420369338.post-21484686390798114342017-12-13T08:19:00.000-08:002018-02-12T14:46:16.659-08:00Insurance products for increasingly risky livelihoods<div dir="ltr" style="text-align: left;" trbidi="on">
As climate change increases the frequency and severity of extreme weather in developing countries, a key question is whether agricultural insurance can play a larger role in mitigating the household level losses associated with catastrophic droughts.<br />
<br />
Insurance products are particularly relevant in regions where agriculture or pastoralism constitute the primary source of income. In these regions indemnity payouts from insurance products can provide significant consumption smoothing benefits to insurance policyholders in the event of drought and improve material well-being.<br />
<br />
A paper published last week by the World Bank's Development Research Group (<a href="http://documents.worldbank.org/curated/en/747921511792941207/pdf/WPS8256.pdf" target="_blank">"Insuring Well-being? Buyer's Remorse and Peace of Mind Effects from Insurance"</a>) finds that index-based livestock insurance (IBLI) also improves <i>non</i>-material well being for a sample of pastoralists in southern Ethiopia. Specifically, Tafere et al. (2017) find that a "peace of mind" effect associated with purchasing insurance that is positive and statistically significant. They find this effect outweighs a negative "buyer's remorse" effect on well-being, which arises when households purchase insurance and realize after the uncertainty period ends that they did not need the insurance after all.<br />
<br />
They note:<br />
<br />
<i>"The implication is that, despite premiums set above actuarially fair rates, IBLI improves buyers' SWB [subjective well-being] even over a period when pastoralists in southern Ethiopia lose money on the policy. The ex ante peace of mind effect dominates any ex post buyer's remorse. In other words, even an insurance policy that does not pay out still improves people's perceptions of their well-being."</i><br />
<br />
<b>Estimation strategy</b><br />
<br />
To estimate the "peace of mind" effect, the authors randomize the provision of 10-80 percent discount coupons and comic book or audio tape information interventions to households in the communities in southern Ethiopia. These incentives act as instruments which increase uptake of IBLI among households in an instrumental variables model. In the reduced form stage, the measure of well-being is regressed on the probability of IBLI uptake predicted in the first stage.<br />
<br />
The authors measure SWB using the question: "On which step do you place your present economic conditions?" with possible responses ranging in the Likert scale from very bad (1) to very good (5). They employ a vignette-based adjustment of the SWB scores by asking respondents to rank their own circumstances with respect to a set of individuals described in short vignettes to improve the comparability of the subjective welfare measurements to one another.<br />
<br />
In both of the years in which insurance policies were active there were no indemnity payouts. This allowed the authors to disentangle the <i>ex ante</i> peace of mind effect from the <i>ex post</i> buyer's remorse effect on SWB. It also ensured that the effects measured were not material or payout based.<br />
<br />
There are two issues to consider with respect to the empirical strategy:<br />
<ol style="text-align: left;">
<li>Depending on how well-being is assessed it is possible that well-being could be positively affected by discounts for the sole reason that it is a discount and is saving people money. If true this would invalidate the instrumental variables assumption that the instrument be exogenous from the reduced form model. Though I considered this possibility, I dismissed it since SWB is assessed in the period after the period in which the discount is applied and insurance purchased, it seems unlikely that said discount would have such a lasting impact on SWB. <a name='more'></a></li>
<li>The effect that is measured here (local average treatment effect) is the effect of IBLI on SWB for households whose probability of buying insurance was impacted by the incentives. It does not capture the effect for households who would have purchased insurance even without the incentives (who may better represent the buyers in an actual market). The question then is whether the latter households would face similar improvements in well-being? It is possible those households would have greater improvement in well-being (if they are even more risk-averse than households that would only purchase with discounts) but it is difficult to tell without using a different empirical strategy. </li>
</ol>
<b>Implications</b><br />
<br />
The implications of these finds are interesting from a market equilibrium perspective: if people experience an improvement in their SWB even in cases where the market rate is not actuarially fair, could it lead people to purchase insurance in cases where the market rate is not actuarially fair? This could potentially increase social welfare if it induces insurers who could not viably enter the market at lower rates to supply insurance.<br />
<br />
However, the key question is whether households would purchase the insurance at non-actuarially fair rates <i>without discounts</i>. Not only would they need to pay a higher rate but they would need to incorporate the impact on their own SWB into their financial decisions. It is not clear that this is the case judging by the number of households that lapsed on their insurance policies between the first and second years of the survey: of the 130 households that purchased insurance in the first sales period only 77/130 renewed it again the next year. Similarly of the 94 households that purchased insurance in the third sales period only 21 renewed it the next year.<br />
<br />
One reason for this may be that the same households do not necessarily receive a discount in each sales period ("The randomized assignment of respondents into information treatments and discount coupons with varying discount levels was implemented independently for each sales period.") So the relatively low rate of renewal captures either one or likely both: the price differential between different years and a lack of internalization of the increase in SWB felt from insurance.<br />
<br />
<b>Broader context and market viability</b><br />
<br />
There is a real space for agricultural insurance in developing countries. There have traditionally been limited formal insurance products in these spaces due to moral hazard and adverse selection problems. It is difficult on the lender or insurer side to model the risk profiles of unbanked clientele with limited financial records, including limited or no proof of income or credit history, who have had limited experience with formal financing (McKinsey, 2012). There are furthermore high transaction costs for insurers in rural and remote areas (Besley, 1995). Informal insurance systems (agreements that arise between individuals and communities as opposed to in formal markets) are increasingly overburdened as climate variability increases and there are more frequent payouts.<br />
<br />
These issues with formal and informal insurance providers are also the case for provision of other financial services for the poor in developing countries. I discuss the disconnect between financial product offerings and the financial needs of the unbanked in a paper that I co-wrote entitled <a href="http://finclusion.org/blog/india-livelihoods-based-study-wins-fii-data-challenge-grand-prize.html" target="_blank">"Tailored Finance and Organic Growth: A Livelihoods Approach to Financial Inclusion in India."</a><br />
<br />
There are a few main areas I think are relevant going forward:<br />
<ol style="text-align: left;">
<li>Though informal community-based insurance systems find themselves in an increasingly challenging environment, the survey in this paper indicates that membership of an informal community insurance system (<i>iqub</i>) was negatively correlated with IBLI uptake meaning where it is available, households view it as a viable substitute for formal insurance. </li>
<ol>
<li>It would be interesting to see how the rates/coverage offered by the <i>iqub</i> compare to the scheme offered here and whether there are other reasons people prefer informal systems (e.g. they are seen as more trustworthy given limited experience with formal providers). </li>
</ol>
<li>It would also be relevant to study the insurance rates with respect to household income patterns to assess pricing and timing of collection. The authors hinted at the fact that seasonal liquidity changes could be a factor in the fewer purchases at the second sales period in a given year than the first. It may make sense to develop insurance schemes that are better tailored towards the income patterns of households in the community being served. </li>
<li>A final note that this paper specifically looks at index-based insurance products where indemnity payouts are not linked to actual losses but to a weather index. A pre-determined threshold on weather indicators triggers the payout. Interest is growing around these products in developing countries because they reduce insurer costs (e.g. monitoring costs) and control for adverse selection and moral hazard more so than traditional insurance products which may make the market more appealing to insurers. For more details see <a href="https://indexinsuranceforum.org/faq-page#n75" target="_blank">here</a>. It will be interesting to keep an eye on the supply side for index products and whether it is a more active market than the one for traditional insurance products in this space. </li>
</ol>
<b>Sources</b><br />
<ol style="text-align: left;">
<li>Tafere, K., Barrett, C.B., Lentz, E., & Ayana, B.T. (2017). <i>Insuring well-being? Buyer's remorse and peace of mind effects from insurance</i> (Policy Research Working Paper No. 8256). The World Bank. </li>
<li>Baer, T., Goland, T., & Schiff, R. (2012). <i>New credit risk models for the unbanked</i> (McKinsey Working Papers on Risk No. 30). McKinsey & Company. </li>
<li>Besley, T. (1995). Chapter 36 savings, credit and insurance. In T. N. Srinivasan &
J. Behrman (Eds.), Handbook of development economics (Vol. 3, p. 2123-2207). Amsterdam:
Elsevier.</li>
</ol>
<br /></div>
Chandnihttp://www.blogger.com/profile/02821478285983487021noreply@blogger.com0