rafmacalaba/data-use-mentions-extended huggingface dataset:
poverty almost identical to module 2 – 55.1 percent – while the other recall modules report poverty headcounts all over 60 percent, with module 4, the collapsed list, yielding a poverty estimate of 66.8 percent. Poverty numbers derived from household diary figures also record substantially higher poverty levels than the benchmark – the infrequent household diary records the poverty rate at 55.6 percent and the frequent diary at 59.5 percent.

The poverty headcount is attractive for its ease of understanding, but is also notorious for its implied welfare discontinuity at the poverty line as well as the inability to measure the extent or depth of poverty (Ravallion, 1996). Figure 1b presents two additional poverty measures, the poverty gap and the squared gap measures, that give greater weight to the poorest households among those below the poverty line. The overall message in Figure 1b is the same as Figure 1a. The lowest poverty figures are for the benchmark module 8 while the highest poverty measures are observed in recall modules, especially modules 1, 4, and 5. The ranking of poverty scores varies somewhat by poverty measure. While the 7-day full recall module 2 had the second lowest headcount estimate (54.9%) followed by module 3 (55.1%) and then module 7 (55.6%), module 7 has the lowest poverty gap measure (18.9%) followed by module 2 (19.1%) and then module 3 (21.4%). The largest proportional differences across module are apparent in the squared gap measure where the highest estimated squared poverty gaps, those for modules 4 and 5, are more than twice as large as the benchmark squared gap measure. The changes in module rankings across the different poverty measures, as well as the greater proportional differences in the squared gap measures compared with the poverty headcount, reflect the differing degrees of inequality below the poverty line measured by the various modules.

Clearly poverty measures are highly sensitive to the mode of consumption
15 concerned with the distribution of health care payments _per se,_ but rather with their effect on the distribution of income. The decomposition proposed by Aronson et al. provides a useful framework for assessing the merits of WHO's index. The decomposition emphasizes that progressive and regressive payments have different effects on income inequality, the former reducing income inequality, the latter increasing it. The WHO index's inability to distinguish between progressive and regressive payments thus renders it incapable of distinguishing between a health care financing system that reduces income inequality and one that increases it. If it is true that the concern of policymakers lies with the impact of health care payments on income inequality, this is a serious limitation of the index. The decomposition also emphasizes that if we wish to examine the influence of health care payments on household's ability to purchase food and other key goods and services, we need to assess the progressivity of health payments on prepayment income _before food spending._ Assessing the impact of health care payments on income _after_ food has already been netted out, as the WHR does, makes little sense. In addition, the decomposition provides a framework for empirically disentangling (a) the vertical redistribution associated with progressivity (K) from vertical redistribution associated with health care absorbing a larger share of income (g), and (b) redistribution attributable to vertical differences _(V)_ from redistribution attributable to horizontal differences (H). Since the policy issues in each case are different, this seems to be a major advantage of the Aronson et al. decomposition and a major limitation of the WHO index.

# **VI. An Empirical Illustration: Out-of-Pocket Payments in Vietnam, 1993-98**

In this final section, we compare empirically the usefulness of WHO's FFC index and the alternative Aronson et al. approach, using data on out-of-pocket payments in Vietnam as an illustration. As indicated in the Introduction, this is not an uninteresting case study, since around 80% of health spending in Vietnam
Policy Research Working Paper 9715

# **Abstract**

The rural land use fee and agricultural income tax are major payments for rural landholders in Ethiopia. This paper examines the gender implications of these taxes using tax - payment and individual land ownership data from the Ethi opian Socioeconomic Survey 2018/2019. It finds that the rural land use fee and agricultural income tax, which are assessed on the area of landholdings, are regressive. Femaleheaded- and female adult-only households bear a larger tax burden than male-headed and dual-adult households.

Norms limiting women’s role in agriculture and gender agricultural productivity gaps are likely to result in lower consumption and accordingly, a higher tax burden for female-headed households than for male-headed households. Reducing the tax rates for smallholders can diminish the gender difference in tax burdens, but the tax continues to be regressive. This highlights the difficulty of area-based land taxes to be vertically equitable.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hkomatsu@worldbank.org, _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
; Dinç, 2005). Within the context of capital regulations, state ownership affects transparency, risk-taking and market discipline.

**Figure 4: State Ownership (% of Banking Sector Assets, equally weighted)**

<!-- Start of picture text --> 66.0<br>39.7<br>37.5 37.5<br>33.9 32.9<br>30.5<br>14.2 17.9 16.414.3<br>11.2 9.0 9.4<br>5.5 6.6 3.6 3.1<br>EU CE & BC WB SC CA Russia Turkey EE RoW<br>2008 2018<br>80<br>60<br>40<br>20<br>Banking system's assets government-controlled (%) 0<br><!-- End of picture text --> Source: Bank Regulation and Supervision Survey conducted by the World Bank and national sources. Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus – data not available for 2008; **CA** for Central Asia; **EE** for Eastern Europe; **ROW** for the rest of the world **.** Data for ECA sub-regions is as per year end 2018 and is obtained from national sources with the help of colleagues in Finance, Competitiveness and Innovation global practice of the World Bank. Data from the ROW is per year-end 2016.

4
the tax/transfer. Theoretically, the Kakwani index can vary between -1 to 1; the larger the index is, the more progressive is the expenditure or tax.

The data for comparative analysis comes from the CEQ analyses for separate countries produced by country teams at the World Bank that were available at the time of writing: Armenia (World_Bank 2016), Belarus (Bornukova, Shymanovich et al. 2017), Georgia (Cancho and Bondarenko 2017), Kyrgyzstan (Ismailakhunova, Shymanovich et al. 2019), Moldova (Cojocaru, Matytsin et al. 2019), Russian Federation

> a Wherever possible, the analysis relies on the results from the CEQ based on household surveys from respective countries. When estimates are unavailable from the CEQ results, the analysis depends on other sources, such as national accounts reported in publicly available sources.

> b Size of taxes and social spending is derived either from CEQ results or from national accounts available from publicly available sources.

7
/sup>|145.7<sup>_∗_</sup>|-1330.5<sup>_∗∗∗_</sup>|
|N|110|123|23|53|42|122|
|||||Mopti|||
|CC|639.7|780.8|2923.3|786.7|460.1|1469.8|
|ML - CC|195.1<sup>_∗_</sup>|77.4|-235.0|289.2|-90.6|93.9|
|SR - CC|227.7<sup>_∗_</sup>|317.8|955.7|459.4|70.8|-525.3<sup>_∗_</sup>|
|N|70|21|40|2|19|21|

_Notes:_ The training set is the EACI 2017. The mean CC yield and differences with ML and SR yields are computed using survey design weights. Statistical significance are assessed using standard errors clustered at the enumeration area level. In the case of the ML, the standard errors used for statistical significance tests account for the between imputations variance computed with 100 simulations. Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 25
.

- Mulwa, C. K. and M. Visser (2020). Farm diversification as an adaptation strategy to climatic shocks and implications for food security in northern Namibia. _World Development 129_ , 104906.

- National Bureau of Statistics (NBS) (2019). Nigeria - General Household Survey, Panel 2018-2019, Wave 4. Public Use Dataset. Ref: NGA_2018_GHSP-W4_v03_M. Dataset downloaded from `https://doi.org/10.48529/1hgw-dq47` .

- National Bureau of Statistics (NBS) (2022). Nigeria - National Longitudinal Phone Survey (NLPS) 2021-2022. Public Use Dataset. Ref: NGA_2020_NLPS_v12_M. Dataset downloaded from `https://doi.org/10.48529/xeym-xv94` .

- Neumark, D. (2001). The employment effects of minimum wages: Evidence from a prespecified research design. _Industrial Relations 40_ (1), 121–44.

- Olken, B. A. (2015). Promises and perils of pre-analysis plans. _Journal of Economic Perspectives 29_ (3), 61–80.

- Oskorouchi, H. R. and A. Sousa-Poza (2021). Floods, food security, and coping strategies: Evidence from Afghanistan. _Agricultural Economics 52_ (1), 123–140.

- Rahut, D. B., J. P. Aryal, and P. Marenya (2021, May). Understanding climate-risk coping strategies among farm households: Evidence from five countries in Eastern and Southern Africa. _Science of The Total Environment 769_ , 145236.

22
# **Figure 10: Probability of being married for the first time, by year and birth cohort, Cambodia Demographic and Health Survey 2000**

<!-- Start of picture text --> 0.2<br>0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>19651966196719681969197019711972197319741975197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999<br><!-- End of picture text -->

**Year**

Women born 50-54 Women born 55-59 Women born 60-64 30
<!-- Start of picture text --> Demographics<br>Ethiopia Malawi<br>Rural<br>—_———])<br>Household size<br>Female headed<br>-_+<br>Head age | am |<br>Head is literate —e—4 J<br>Head educ level - none oe | ee|<br>-.2 -.1 0 1 2 -.2 -.1 0 | 2<br>Nigeria Uganda<br>Rural<br>Household size<br>——=])<br>Female headed<br>Head age<br>Head is literate<br>| a | to<br>Head educ level - none<br>ee|<br>-.2 -.1 0 1 2 -.2 -.1 0) 1 2<br>@ LSMS-ISA @ HFPS - Adjusted<br>@ HFPS - Unadjusted<br><!-- End of picture text -->
**Figure 9. Seasonal variation in staple crop prices in Sahelian countries according to Food and Agriculture Organization data**

Panel A: Sorghum <!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Panel B: Millet<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Burkina Faso Mali Niger Senegal Chad<br>Price (USD/kilogram)<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20<br>Price (USD/kilogram)<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20<br><!-- End of picture text --> Note: Prices taken from the capital city market for each country. Sorghum prices are wholesale for Burkina Faso, Mali, and Niger and retail for Senegal and Chad. Millet prices are wholesale for Burkina Faso and Mali and retail for Niger, Senegal, and Chad. Source: Food and Agriculture Organization Food Price Monitoring and Analysis Tool and World Bank estimates.

31
and the extent to which they were more or less likely to adopt reforms in the fully specified model of Henisz, Zelner and Guillén (2004) that includes the role of multilateral lenders.

_Crises_ . We combine the coding schemes of (Frankel and Rose, 1996; Detragiache and Spillimbergo, 2001; Hamann and Prati, 2002; Beers, 2003; Kaminsky, 2003), who collectively identify 10 different types of crisis including currency crises, banking crises, stabilization episodes and various forms of default, to construct a dichotomous measure of the existence of a financial or macroeconomic crisis in a given country in a given year. Table 2 provides a listing of the country-years in which a crisis has occurred according to the most inclusive definition.

_Vintage._ We create a variable measuring reform vintage that is initially zero for each country and increases by one for each additional year following the initiation of electricity sector reforms. The initial year of reform is that in which (1) the regulator becomes independent from the ministry, or (2) the regulator becomes independent from the state-owned electricity generating company, or (3) entry by new private generators for resale to final customers is permitted. Table 3 provides a list of the initial reform year for the countries in our sample.

_Checks and balances in established political institutions._ Our measure of checks and balances is the “Political Constraints Index”, ( _POLCON_ ) developed by Henisz (2000).<sup>14</sup> The first step in constructing this measure is the identification of the number of independent branches of government (executive, lower and upper legislative chambers, judiciary and sub-federal institutions) with veto power over policy change in each country. Countries lacking any formal veto points are assigned a score of “0.” For all other countries, the majority preference of each of these branches and the _status quo_ policy are then assumed to be independently and identically

> 14 Data and codebook are available from http://www-management.wharton.upenn.edu
# **Appendix B:** Detailed description of construction of outcome variables

This appendix provides additional detail on the full set of outcomes presented in the tables in the main text, as well as those in this supplementary online appendix. Additional detail can also be found in the pre-analysis plan described in Appendix A and found at <u>https://www.socialscienceregistry.org/trials/36</u> or in the survey instruments which can be found at https://sites.google.com/site/decrgberkozler/datasets.

Our outcomes cover six domains for the core respondent – education and competencies, marriage and fertility, health and sexual behavior, empowerment and aspirations, employment and wages, and consumption – as well as outcomes in these domains for their husbands and children. We briefly summarize these in the text below, and then provide full detail of each in Appendix Table B1.

_Education and Competencies._ The primary outcomes we examine for education are highest grade completed (self-reported) and the overall competencies score. Secondary outcomes include the highest qualification obtained which are separated into the Primary School Leaving Certificate (PSLC), Junior Certificate of Education (JCE) and the Malawi Secondary Certificate of Education (MSCE). We also present the components of the competencies index and the total time taken to complete these tests.

_Marriage and Fertility._ Our primary outcomes include self-reported data on whether or not the core respondent was ever married or ever pregnant. We also examine age at first marriage and at first birth,<sup>36</sup> as well as total live births. Desired fertility is a secondary outcome.

_Health._ Our primary health outcomes are HIV and anemia prevalence, both measured using biological data. Additional secondary outcomes include psychological wellbeing measured with the General Health Questionnaire 12 (GHQ-12), and the number of meals eaten in the last week that contained, meat, fish, or eggs.

_Sexual behavior._ All outcomes in the sexual behavior domain
suspicion remains that electricity is unlikely to have a causal impact on choice of cooking fuel, yet empirical analysis has not been able to rule out this possibility.

_4.2.3.4 Maternal health and fertility_ **In addition to reducing indoor air pollution from lighting and possibly cooking and heating, electricity has been hypothesized to affect health through refrigeration (improving nutrition and decreasing food poisoning) and access to information. IEG (2008) finds evidence for both of these pathways in multivariate regressions for multiple countries, but does not distinguish by gender. There is also evidence of reduced fertility, possibly via television, in that census data from across Latin America suggests a systematic negative association between birth propensities and household electrification status in rural areas.** Using secondary data on all of Argentina except Buenos Aires from 1990 and 2000, Gonzalez-Eiras and Rossi (2007) investigate the determinants of birth weight, which is partly a function of mothers‘ well-being during pregnancy (Agénor et al. 2010 summarizes evidence on this link).<sup>44</sup> They find a 2024% decrease in the proportion of children born with very low weights (and a 5-6% decrease in low birth weight) in provinces where electricity had been privatized. They argue that privatization increases both the number of households with access to electricity and the reliability of the electricity supply, which in turn mean that more households acquire refrigerators and that food stays consistently cold, which in the final step in the causal chain allows for a more varied diet and a reduction in food-borne illnesses.

Turning to fertility, Grogan (2011) documents that birth propensities are significantly higher among women in unelectrified rural households in Argentina, Bolivia, Brazil, Mexico, Panama, Peru and Venezuela. Grogan (2011) and Grogan and Sadanand (2009) provide careful analyses of the impacts of electrification on fertility in Colombia and Guatemala, arguing in both cases that it does reduce the number of children born. In Colombia, electrification reduced birth propensity (in the
<mark>WPS3565</mark>

# Trade Preferences to Small Developing Countries and the Welfare Costs of Lost Multilateral Liberalization<sup>∗</sup>

Nuno Limão<sup>†</sup> Marcelo Olarreaga<sup>‡</sup> University of Maryland and CEPR World Bank and CEPR

## Abstract

The proliferation of preferential trade liberalization over the last 20 years has raised the question of whether it slows down multilateral trade liberalization. Recent theoretical and empirical evidence indicates this is the case even for unilateral preferences that developed countries provide to small and poor countries but there is no estimate of the resulting welfare costs. To avoid this stumbling block effect we suggest replacing unilateral preferences by a fi xed import subsidy. We argue that this scheme would reduce the drag of preferences on multilateral liberalization and generate a Pareto improvement. More importantly, we provide the fi rst estimates of the welfare cost of preferential liberalization as a stumbling block to multilateral liberalization. By combining recent estimates of the stumbling block effect of preferences with data for 170 countries and over 5,000 products we calculate the welfare effects of the United States, European Union and Japan switching from unilateral preferences to Least Developed Countries to the import subsidy scheme. Even in a model with no dynamic gains to trade we fi nd that the switch produces an annual net welfare gain for the 170 countries ($4,354 million) and for each group: the United States, European Union and Japan ($2,934 million), Least Developed Countries ($520 million) and the rest of the world ($900 million).

JEL classi fi cation: D78; F13; F14; F15 Keywords: Preferential trade agreements; multilateral trade negotiations; MFN tariff concessions, preference erosion.

> ∗We thank Stephanie Aaronson, Bernard Hoekman, Aaditya Mattoo, Petros Mavroidis, Stefano Inama, Chris Stevens, Alan Winters and participants of the seminar on the WTO and developing countries (Columbia Law School) for helpful comments and discussions. The usual
shows difference-in-difference results: PCRs in lower primary decreased significantly more rapidly in MESIP districts, by 14.8 pupils per classroom; overall PTRs decreased by 12.3 pupils per teacher; and PTRs in lower primary experienced a large reduction of 21.4 pupils per teacher.

# **Di** ff **erence-in-di** ff **erence analysis: Outcomes**

## **Repetition**

**Table 5** shows difference-in-difference analysis of the impact of MESIP on repetition rates. We find that repetition rates increased slightly between 2016 and 2020 in both MESIP and non-MESIP districts. However, we find that the increase in MESIP districts was significantly smaller in MESIP districts in lower primary by 0.7 percentage points.

In order to confirm the relationship between class size reduction and repetition, we explore the dynamics of change in PTR and lower primary repetition rates, exploiting the fact that both PTR and repetition rate are measured using EMIS data which is available on an annual basis. **Table 6** shows the year-by-year change in PTR in both MESIP and comparison districts. A particularly rapid reduction in PTRs in MESIP districts occurs between the 2016/17 and 2017/18 school years, reflecting the commencement of MESIP, the allocation of auxiliary teachers to schools, and the targeting of regular deployment of teachers to the eight disadvantaged districts.

To more clearly assess the relationship between PTR reduction and repetition rates, **Table 7** presents year-on-year, difference-in-difference analyses of repetition rates for a particular subset of treatment and control schools for three time periods: 2017-18 to 2018/19, 2018/19 to 2019/20, and 2019/20 to 2020/21. For treatment schools, for each year-on-year comparison, we limit the analysis to schools in the MESIP districts which had an overall PTR of above 90 in the first year of the comparison, received additional teachers, either auxiliary teachers or new regular government teachers, and as a result had a PTR below 90 in the second