rafmacalaba/data-use-mentions-extended huggingface dataset:
nor their affiliates, nor shifted profits. Instead, we focus on measuring the importance of tax expenditures as a share of firms’ reported net profit, holding evasion and profit-shifting opportunities fixed. We also note that Dyreng et al. (2017) show that ETRs have decreased in the same way for domestic and multinational firms, suggesting that this distinction may not be first-order.

# **3 Effective Tax Rates and Firm Size**

## **3.1 Aggregate Tax Expenditures**

We construct firm-level effective tax rates (ETRs) with the data and methods described in section 2. The last three columns of table 1 show for each country the top statutory tax rate, the average ETR for all firms (imputing a zero ETR for loss-making firms), and the average ETR for profitable firms. We use the difference between the top statutory tax rate and the firm-specific ETRs multiplied by their net profits to compute firm-level tax expenditures. By aggregating across firms, we obtain the total tax expenditures, which we express as a share of each country’s GDP. This measures the forgone revenue due to corporate tax expenditures absent behavioral responses.

Figure A.2a displays our aggregate corporate income tax expenditure estimates, ranking countries by GDP per capita. For countries in our sample tax expenditures represent 0.87% of their GDP on average. The magnitude varies across countries: Albania and Ethiopia’s CIT expenditures are close to zero, while all other countries’ tax expenditures are over 0.5% of GDP, and Costa Rica and Honduras display expenditures in excess of 1.7% of GDP. In Figure A.2b, we include for comparison data from an additional 66 countries, available in the Global Tax Expenditure Database, which collects information from official tax expenditure reports (Redonda, von Haldenwang and Aliu, 2022).<sup>9</sup> The average CIT tax expenditures in this extended sample of 79 countries are slightly lower than in our sample but still substantial at 0
Lybbert, T. & Wydick, B., 2018. Poverty, Aspirations, and the Economics of Hope. _Economic Development and Cultural Change ,_ Volume 66, p. 709–753.

Macours, K. & Vakis, R., 2014. Changing Households’ Investment Behaviour through Social Interactions with Local Leaders: Evidence from a randomized transfer program. _Economic Journal,_ p. 124(576): 607–633.

<mark>Malawi National Statistical Office (NSO) (Government of Malawi). Malawi - High-Frequency Phone Survey 2020-2024 (HFPS-COVID-19 2020-2024). Ref: MWI_2020-2024_HFPS_v18_M. Downloaded from https://microdata.worldbank.org/index.php/catalog/3766 on January 2, 2024;</mark> Manski, C., 2004. Measuring expectations. _Econometrica,_ 72(5), pp. 1329-1376 .

Marjoribanks, K., 2003. Family background, individual and environmental influences, aspirations and young adults' educational attainment: A follow-up study. _Educational Studies,_ 29(2-3), pp. 233-242.

Mau, W., Chen, S.-j., Li, J. & Johnson, E., 2020. Gender Difference in STEM Career Aspiration and Social- Cognitive Factors in Collectivist and Individualist Cultures. _Administrative Issues Journal Education Practice and Research,_ pp. 10. 30-46.

Mau, W.-C. & Li, J., 2017. Factors Influencing STEM Career Aspirations of Underrepresented High School Students. _The Career Development Quarterly,_ pp. 66: 246-258.

Mickelson, R. A., 1990. The attitude–achievement paradox among black adolescents. Sociology of Education. 63(1), p. 44–61 .

Migali, S. & Scipioni, M., 2019. Who's About to Leave? A Global Survey of Aspirations and Intentions to Migrate. _International Migration._ Mussa, E. C., 2020. Youth Aspirations, Perceptions of Farming, and
the end of each year allows for the inclusion of more overlapping materials across years of testing, reduces the impact of measurement errors specific to the day of testing by having multiple tests around two weeks apart, and also reduces sample attrition due to student absence on the day of the test.

For the rest of this paper, Year 0 (Y0) refers to the baseline tests in June-July 2005; Year 1 (Y1) refers to the mean score across both rounds of tests conducted at the end of the first year of the program in March-April, 2006; and Year 2 (Y2) refers to the mean score across both rounds of tests conducted at the end of the second year of the program in March-April, 2007. All analysis is carried out with normalized test scores, where individual test scores are converted to

> 29 The data was collected from a short survey that was only based on the ―main‖ child who was being covered in the school assessments and not for other siblings or other components of household spending.

> 30 We obtained spending data from a total of 8,612 households for Y0 (no data was collected for retrospective spending on children in grade 1, because it was their first year in school), 13,572 households for Y1, and 10,189 households for Y2.

20
interviews, lasting on average 25 minutes. All three datasets of each country contain sampling weights. COVES data combines the information contained in the ES sampling weights with the information about firm closures obtained during the implementation of COV-ES survey and provides updated sampling weights. Since the panel estimation method requires time-invariant sampling weights,

> 5 See details of the survey methodology here: www.enterprisesurveys.org/en/methodology.

> 6 Although the unit of observation is an establishment in ES, we refer the establishments in ES as firms following the common convention in literature.

11
is followed by Bermuda (36.24) and Aruba (36.08), countries with the highest income level in the Caribbean. In some way, the correlation between nightlight and income levels could be proved for these countries. However, nightlight might not be a reliable proxy for income levels due to cultural differences in the use of lights and light saturation in large cities (Ghosh et al. 2010). Instead, nightlight data work better as a proxy of economic growth (Henderson, Storeygard and Weil 2012). For this reason, our empirical strategy (see Section 3) centers on variations in nightlight intensity across pixels and over years, after controlling for time-invariant and time-specific effects, to isolate the impact of hurricanes on local economic activity.

Mean nightlight by Caribbean country or territory conveys different shapes of trends (Annex 1). In 2000, Haiti, Dominica, Montserrat, and Cuba presented the lowest DN value in the region (< 5), while St. Martin, Sint Maarten, Bermuda, and Puerto Rico, showed a value of 38–48, with 63 being the maximum value of nightlight. Surprisingly, five years later, in 2005, the islands with the lowest DN value at the beginning of the period had decreased their luminosity by approximately 2 points, while those with the highest DN values had decreased by around 8 points. In other words, the low-income islands reduced their value of nightlights by a lower number than the high-income islands. Overall, mean nightlight in the 29 countries and territories fell from 7.73 in 2000 to 6.12 in 2015. The impact of hurricanes and tropical storms on their local economies during that period could be a reason for this decline.

By 2010, DN values in almost all Caribbean islands had increased by 2–16 points, with the exception of Aruba, where it had reduced by 1 point. Trinidad and Tobago exhibited the highest growth in luminosity, from 13.51 to 29.32 points, followed by St.
the degree of aid-dependence of the economy, in ways that are consistent with the implications of some basic theory.

# **7.  Conclusions**

In this paper, I have used a novel dataset of loan-level transactions covering lending by official creditors to developing country governments to estimate government spending multipliers. My identification strategy exploits the substantial lags that occur between loan commitment and the eventual full disbursement of the loan, a process which typically takes several years. The key identifying assumption is that loan approvals, and the decision to embark on the associated spending plans, do not anticipate future shocks to growth. Given this assumption, fluctuations in disbursements that are attributable to fluctuations in past loan approval decisions are plausibly exogenous to contemporaneous shocks, and can be used as an instrument for fluctuations in government spending. Deploying this strategy in a large sample of developing countries, I find reasonably precise estimates of the government spending multiplier that are in the vicinity of 0.4. These results survive a range of robustness checks designed to address concerns about data quality and potential violations of the exclusion restriction. I find some evidence of heterogeneity in estimated multipliers that is consistent with the implications of basic theories. However, these differences typically are not statistically significant.

To put these findings in context, it is useful to compare them with estimates of the government spending multiplier in the empirical literature, which has overwhelmingly been based on evidence from developed countries, most notably the United States. For the United States, Hall (2009) suggests that estimates of the federal government spending multiplier are between 0.5 and 1, while Ramey (2011b) suggests a somewhat higher range from 0.8 to 1.5. Moreover, many of the studies of subnational spending multipliers cited in the introduction are even higher, ranging from 1 to 2. Policy discussions in many countries are also often premised on the assumption that the spending multiplier is substantially above one. The smaller, but nevertheless reasonably-precisely estimated, multipliers estimated in this paper for developing countries stand in
Table C.2, in appendix C, presents the quality indicators that are used in the analysis of the direct observation and household data. The direct observation indicators correspond only to the first antenatal care visits. The household self‐reported indicators correspond to the entire sequence of consultations the women attended during their most recent pregnancy. The descriptive statistics show the variation in the content of antenatal care. Although some physical examinations, such as weighing the pregnant women and measuring their uterine height, are conducted relatively frequently, the rates of diagnostic testing, counseling, and preventive treatments are alarmingly low.

In addition, we create three indexes of quality of care, representing the proportion of actions performed among a set of actions recorded in the different data sources. The first index, computed from household data, summarizes the reported content of care recently pregnant women received during any antenatal care visit during the entirety of their pregnancy, among women who had a live birth in the past two years. The second quality index is based on the direct observation data. The third index is based on the procedures reported in the exit interviews. Although we prefer the more objective direct observation data, we have 177 exit interviews with women whose consultations were not observed.<sup>9</sup> In addition, during the exit interviews, we collected data on the prices paid for the consultations. Thus, this larger sample allows us to study the relationship between quality of care and price. As shown in table B.1, in appendix B, the indexes computed with the direct observation and exit interview data are strongly correlated, and the correlation between the two indexes is not significantly affected by wealth.

The indexes assign equal weights to the different components of antenatal care, although of course the clinical importance of the different actions might differ. For example, estimating a pregnant woman’s due date might not be as important as prescribing iron supplements. However, any selection of weights for the different components in the DRC context will be
- 31 -

# ENDNOTES

1. This unhappy situation, and the recommendation to shift more towards consumption taxation, is discussed in an African context by Z. Shalizi and L. Squire, "Tax Policy for Sub-Saharan Africa", World Bank, June 2986.

2. More generally, the change in a Bergson-Samuelson social welfare function, W - W (U1 ..UH) can be written as dW - M18W/8Ui . aUi/ayi . yi where aW/aU1 is the ethical weight attached to the i'th household's change in utility and the product aw/au1 . aui/ly, can be interpreted as the marginal social utility of income. In the utilitarian framework the ethical weights are all normalized around unity and the marginal social utility of income declines with higher incomes.

3. It is worth emphasizing that the value of a is unique to each particular commodity tax substitution and that it is the single part of our methodology that compels resort to some kind of modelling exercise. The purpose of our analysis is to see what welfare implications can be extracted from the limited household survey information on consumer behavior that is typically available in developing countries. If more information on consumer behavior were available it woulC also be possible to incorporate secord-order excess burden effects into the analysis.

**HaMc1ll4/Welfar, Dominance** & **the** Design of Excise Taxation in tho Cote D'Ivoire/WThirsk:ab/5/18/88
productivity growth among covered firms. As floors rise, however, cushions are compressed, leading to an average pass-through rate of about 50%. In the context of the Great Recession (2008–2016), as part of the debt relief package, there were legislative changes that would reduce the coverage of sectoral bargaining. However, this effort largely failed. Nevertheless, significant downward real wage adjustments occurred within the existing bargaining system, which allowed for downward wage adjustment via reductions in real wage floors, reductions in real wage cushions and a reallocation of workers to lower wage floors.

When it comes to differences across workers, overall, unskilled workers tend to do better under industry-wide or economy-wide bargaining, whereas skilled workers gain more from company bargaining, especially where it allows for some individual pay bargaining. Multi-employer bargaining tends to offer more inclusive labor protection for vulnerable categories of workers, such as migrant workers, those in non-standard forms of employment and workers employed in small firms (International Labor Office, 2017). In other words, decentralized wage bargaining is associated with a wider wage distribution, while centralized bargaining is associated with a more compact wage distribution.

Further evidence of this is the fact that the specific level of unionization is negatively related to the level of wage inequality and inequality across different types of capital (Fortin et al., 2021; Farber et al., 2021; Azkarate-Askasua and Zerecero, 2022). Fortin et al. (2021) show that over the period from 1979–2017 de-unionization contributed to the rise in income inequality. Farber et al. (2021) focus on a wider time span (pre-1973, year in which household surveys start capturing union affiliation information) to show that the direct effect of unionization accounts for 46% of the decline in the 90–10 gap between 1936–1968 and 16% of its increase between 1968–2014. Azkarate-Askasua and Zerecero (2022) argue that unions (and collective bargaining) have a large distributional effect across different types of capital in the context
assumptions about maize yield response to fertilizer, the authors do find that the program has generated a positive, though modest benefit-cost ratio in three of the four years since the subsidy program was initiated.

Despite the reported increase in maize production, it is not clear that the program has enhanced food security. Domestic maize prices have been high in three out of four years of the program, incurring a major hardship for poor people, including the 60 percent of farmers who are net maize buyers. There is a tendency for the program to focus on production objectives and producer welfare, but to ignore consumers, and thus the conditions necessary for overall food security. Also, based on farm panel data over a 6 year period, Ricker-Gilbert and Jayne (2011) find that the receipt of the subsidy in multiple prior years had little enduring effect on recipient households‘ incomes or asset wealth after they stopped receiving the subsidy.

Targeting has posed continuous difficulties. Household surveys suggest that the 2006/7 program was highly variable across locations in terms of targeting criteria, but that there was a tendency to reach households which were productive full-time farmers. Female-headed and poorer households were less likely to receive coupons (Holden et al. 2010b). Holden and Lunduka (2010b) also report the presence of secondary markets for coupons—not from households that initially received the coupons, but from other leakages in the distribution system. The secondary market for fertilizer coupons also favored wealthier households. The authors ask whether targeting is more effective at reaching poor and vulnerable people than would be a general subsidy.

Given the type of household reached it is not surprising that the voucher program displaces commercial sales. If the voucher for subsidized fertilizer is received by a farmer who would otherwise have bought fertilizer at a commercial price, then the voucher program may shift the composition of retailer‘s profits from commercial fertilizer to subsidized fertilizer, with uncertain effects on the total quantity of fertilizer applied to the farmer‘s field. Ricker-Gilbert, Jayne and Chirwa (2011) found
makes use of detailed data on actual survey implementation and technical assistance costs from a group of countries around the world, unlike previous attempts at costing household survey data gaps.<sup>7</sup> The budget envelope for achieving the objective laid out by WBG President Jim Yong Kim across the 20162030 period is estimated at USD 945 million, of which USD 692 million is projected to cover the costs of household survey implementation across 78 countries, and USD 253 million is projected to cover the costs of direct technical assistance to be provided to the NSOs.<sup>8</sup> These estimates imply

> 6 The press release is available at: <u>http://www.worldbank.org/en/news/press-release/2015/10/15/world-bank-new-endpoverty-tool-surveys-in-poorest-countries.</u>

> 7 This paper is also different in scope from existing studies such as Jerven (2014) and Demombynes and Sandefur (2014) in that it focuses on the resource requirements for implementing multi-topic household surveys (that collect information to construct a comprehensive welfare aggregate for monitoring poverty and shared prosperity), and does not focus on other data sources.

> 8 Previously, United Nations Sustainable Development Solutions Network (UNSDSN) (2015) have provided resource needs for several lines of survey data production, including multi-topic household surveys. The differences in resource need estimates between UNSDSN (2015) and our study can be traced back to assumptions regarding sample size per survey, the scope of technical assistance, local survey implementation cost parameters, and increases in implementation unit costs. In communication with Geoffrey Greenwell of Paris21, based on the work of UNSDSN (2015) and following minor adjustments to their estimates to take into account local overhead costs, our total cost estimate of 945 million for 390 surveys compares well with their estimate of 1,006 million for the same target. The following may be traced as the main sources for the remaining discrepancy. The authors assume a sample size of 5,000 per survey while
such as past income, expectations of future income, as well as factors such as education and current health (via their impact on expected income). Unemployment can have a negative effect on perceptions of welfare independently of the associated income loss. Marriage can offer a greater sense of security, given income. The influence of such factors on perceptions of economic welfare can be seen in Table A3.1 (Appendix).

Our findings are largely in line with the earlier findings of Ravallion and Lokshin (2002) which were based on Russian data. In particular, subjective welfare is rated higher by married individuals, and also exhibits a U-shaped relationship with age. Compared to those residing in households with main income coming from wages, those in households that rely primarily on pensions, social assistance, or remittances rate their welfare lower, holding expenditures constant. Better health as well as a higher level of education are also associated with higher perceived welfare. In terms of the attitudinal variables, we find that those who have not been affected by the recent financial crises (as per their own assessment) have a higher subjective assessment of welfare, while those who perceive that connections are necessary to get ahead in key aspects of life such as getting a government of private sector job, or university education, tend to perceive their current welfare position as lower. Perceptions of need in society as being mainly driven by injustice (laziness) are associated with lower (higher) subjective welfare (relative to bad luck being the main driver or need). Finally, it is notable that the R<sup>2</sup> also increases from 0.09 in the specification with only expenditures and household size to 0.26 in column (III) where individual characteristics and attitudinal variables are controlled for, although this improvement in the R<sup>2</sup> is much below that reported by Ravallion and Lokshin (2002) for Russia.

Other behavioral explanations, including differences in views on what constitutes poverty and wealth
|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.7636***|3.3031***|1.3617**|4.2311***|5.2798***|1.1064***|1.8050***|0.7455***|
||(0.1263)|(0.8451)|(0.5453)|(0.5251)|(0.4835)|(0.3660)|(0.4543)|(0.0861)|
|Observations|12,154|12,154|12,154|12,154|12,154|12,154|12,154|12,154|
|R-squared|0.178|0.032|0.039|0.102|0.059|0.039|0.111|0.132|

Note: This table reports district fixed-effect RDD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

55
br>Code 2:_employment_office_statistics_and include persons in employment who are seeking a change of job or extra<br>work and are therefore also registered at employment offices. This method of data gathering may result in an<br>underestimation<br>of the actual unemployment rate, since registration at the employment office requires time and<br>money and the incentives may not be sufficient to justify it. Central Govemment Employment is taken from IMF<br>Staff Country Report No. 96/5 of February 1996. It relates to 1994. Armed Forces estimates include the<br>Gendarmerie, a military corps entrusted with some policing activities. Average Civilian wage in the Public sector is<br>taken from the IMF Staff Country Report No. 96/5 Burkina Faso-Background<br>Papers and Statistical Update and<br>refers to 1994. GDP per capita is an estimate based on GDP at market prices figures stated in the IMF Staff<br>Country Report No. 96/5, and refers to 1994. Wages and Salaries as percent of GDP is based upon IMF Staff<br>Country Report No. 96/5 estimate of Civilian Wage bill and GDP at market prices and refers to 1994. Average<br>wage over per capita GDP is the result of the division of these two results.|
|Burundi|Population estimate is taken from IMF Report No. 96/43 of May 1996 and relates to 1996.<br>Labor force data are taken from the World Bank's Social Indicators of Development 1996 and refer to 1994.<br>Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers<br>to 1992. The data are from official estimates of the Burundian government.<br>Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1992 and source1II,Code<br>_2: employment officestatistics_and include persons in employment who are seeking a change of