Place-based economic policies (pt. 2 of 3): How effective are tax incentives for investments in low-income communities

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. This 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.

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."

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).

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."

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".

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, wrote: "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."

It is therefore unsurprising that these tax incentives in the Trump tax bill have received significant backlash (see here, here, and here 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 9.2 percent of taxpayers that report realizing any long-term capital gains at all - while their social benefits are reduced to these big "ifs". The California Budget and Policy Center 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.

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.

Freedman (2012)

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 this presentation.

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 this article on gentrification in NYC.

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.

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).

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.

Freedman (2014)

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.

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.

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.

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."

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 this 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."

Policy implications

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."

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 limited reporting requirements and guidelines to ensure that investments are socially impactful. A bill on these requirements has been introduced in the House and should be followed closely.

References

1. Freedman, M. (2012). Teaching new markets old tricks: The effects of subsidized investment on low-income neighborhoods. Journal of Public Economics. 
2. Freedman, M. (2014). Place-based programs and the geographic dispersion of employment. Regional Science and Urban Economics. 
3. 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. 
4. Hirasuna, D., Michael, J. (2005). Enterprise Zones: A Review of the Economic Theory and Empirical Evidence. Policy Brief: Minnesota House of Representatives Research Department. 

In-depth look at income and wealth data (pt. 3 of 3): Minimum wage policy and the income distribution

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 this 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.

In this post, I focus on two papers - Autor, Manning, and Smith (2016) and Dube (2018) both in American Economic Journal: Applied Economics - 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 earlier post 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.

Context

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). This 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):

Source: UC Davis Center for Poverty Research (2018)

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.

Estimation strategy

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 Allegretto et al. (2013). 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.

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.

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.

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.

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).

Findings

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 this post 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).

Source: Bureau of Labor Statistics (2013)

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.

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.

Source: Washington Center for Equitable Growth (2017)

Implications

• 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). 
• 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. 
• 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."
• 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. 
• 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 this article in the New York Times. 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. 
• 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. 

Sources

1. Autor, D., Manning, A., Smith, C.L. (2016). The Contribution of the Minimum Wage to US Wage Inequality over Three Decades: A Reassessment. American Economic Journal: Applied Economics. 
2. Dube, A. (2018). Minimum wages and the distribution of family incomes in the United States. Forthcoming in American Economic Journal: Applied Economics. 
3. Allegretto, S., Dube, A., Reich, M., Zipperer, B. (2013). Credible Research Designs for Minimum Wage Studies. IRLE Working Paper #148-13. 
4. Card, D., Krueger, A. (1993). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. American Economic Review. 

In-depth look at income and wealth data (pt. 2 of 3): Wealth

First, to preface with why wealth as distinct from income is relevant to economists and to policymakers at large. Kopczuk (2014) 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.

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 Alvaredo, Atkinson, and Morelli (2018):

1. Household surveys including the U.K. Wealth and Assets Survey and the U.S. Survey of Consumer Finances;
2. Administrative data on individual estates at death; 
3. Administrative data on wealth of living from annual wealth taxes; 
4. Administrative data on investment income that are capitalized; and 
5. Lists of large wealth-holders (e.g. Forbes).

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.

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 Saez and Zucman (2016) and Chetty et al. (2016).

Kopczuk presents a few interesting stylized facts about wealth that provide a good introduction to the wealth distribution and methods of estimating it:

• 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); 
• 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; 
• 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). 

In a recent issue of the Journal of Public Economics 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.



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%:

"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."

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). This New York Times 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.

Sources

1. Alvaredo, F., Atkinson, A., Morelli, S. (2018). Top wealth shares in the UK over more than a century. Journal of Public Economics.
2. Kopczuk, W. (2014). What do we know about the evolution of top wealth shares in the United States? NBER Working Paper 20734.
3. 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. Journal of American Medical Association.
4. Saez, E., Zucman, G. (2016) The distribution of US wealth, capital income, and returns since 1913. Quarterly Journal of Economics. 

In-depth look at income and wealth data (pt. 1.5 of 3): Non-traditional data and machine learning approaches

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 previous publication 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.

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 Science, "Fighting Poverty with Data", 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.

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.

Mobile phone data - which was discussed in part in the above article and in greater detail in this other Science piece 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.

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.

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.

In-depth look at income and wealth data (pt. 1 of 3): Background

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.

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 Journal of Public Economics honoring the late Tony Atkinson. For an introduction to that series of papers see here. My previous post on individual and household level inequality is based on a paper within this special issue. Additionally, a recent issue of the Quarterly Journal of Economics features an article that combines national accounts data with micro data to produce estimates of inequality in the U.S. that are consistent at the macro level.

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.

To begin, the World Inequality Database - a database of global wealth and income inequality data co-founded by Tony Atkinson - provides a concise description of data and research in this field over the past twenty years. Two important trends:

1. 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 Capital in the Twenty-First Century was not the first but perhaps the most prominent description of the growing role of capital in widening divisions between haves and have-nots.
2. 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 QJE, 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.

Discussion of the main data types and their roles in inequality studies

Administrative micro data

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.

Specifically as noted in this 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."

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 this presentation 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 article published in the Economist last month on the topic.

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.

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.

National accounts macro data

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.

For a valuable introduction to balance sheets and the national accounts data see here 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.

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.

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.

Sources

1. Piketty, T., Saez, E., Zucman, G. (2018). Distributional National Accounts: Methods and Estimates for the United States. Quarterly Journal of Economics.
2. Kleven, H., Luttmer, E. (2018). A Special Issue of the Journal of Public Economics: Honoring the Work of Sir Anthony B. Atkinson (1944-2017). Journal of Public Economics. 
3. Blundell, R., Joyce, R., Keiller, A.N., Ziliak, J.P. (2017). Income inequality and the labour market in Britain and the US. Journal of Public Economics. 

Trends in household income inequality: comparing the U.S. and Britain on labor, marriage, and government transfers

A recent paper highlighted by the Institute for Fiscal Studies and forthcoming in Journal of Public Economics 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.

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.

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 Tony Atkinson's life's work and his numerous contributions in the field built the foundation for modern studies of inequality.

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."

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.

Inequality in male labor market outcomes has increased significantly in both countries

• 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. 
• I suspect there are a couple reasons for the disparity:

1. 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).
2. 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.
3. Differences in the composition of employment 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.

• 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.
• 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.

• Finally, the authors provide evidence of the wage stagnation in the U.S. continues to make headlines. 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). 

Lower marriage rates among the bottom half of the income distribution indicate inequalities in household composition and spousal income

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 discussed 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.

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

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. 

The authors identify the following when discussing the disparity in tax and transfer outcomes between the U.S. and Britain:

• Much more generous social welfare programs in Britain vs. the U.S. in particular due to successive Labour governments from 1997-2010. 
• 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. 
• 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.

Follow-up questions 

The study raises several further points of research/questions to be answered by existing research - 

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.

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.

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. 

Sources

1. Blundell, R., Joyce, R., Keiller, A.N., Ziliak, J.P. (2017). Income inequality and the labour market in Britain and the US. Journal of Public Economics. 
2. Atkinson, A. (2000). The welfare state, budgetary pressure and labour market shifts. Scandinavian Journal of Economics.
3. Atkinson, A. (1992). Towards a European social safety net. Fiscal Studies.

Unintended consequences of tax reform in developing countries

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.

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 Devereux and Loretz, 2012, 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.

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 Journal of Public Economics ("Taxes, Informality, and Income Shifting: Evidence from a Recent Pakistani Tax Reform") analyzing firm responses to an increase in the tax rate for noncorporate firms in Pakistan that looks at this issue.

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.

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.

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.

Estimation strategy

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.

The model estimated for the intensive margin elasticity is:

Δlog zit =α+β Partnershipi +ε Δlog(1−τit)+Xi δ+λt +uit

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.

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.