rafmacalaba/datause-encoder-data huggingface dataset:
- Pollak, Robert A.. 1991. "Welfare Comparisons and Situation Comparisons." Journal of Econometrics 50:31-48.

- Ravallion, Martin. 1991. "Reaching the Rural Poor through Public Employment: Arguments, Experience, and Lessons from South Asia." The World Bank Research Observer 6:153-75.

- Ravallion, Martin. 1993. "Poverty Alleviation Through Regional Targeting: A Case Study for Indonesia." In Hoff, Karla, Avishay Braverman and Joseph Stiglitz (eds.) The Economics of Rural Organization: Theory, Practice and Policy. New York: Oxford University Press for the World Bank.

- Ravallion, Martin. 1994. Poverty Comparisons. Fundamentals in Applied Economics Volume 56, New York: Harwood Academic Press.

- Ravallion, Martin and Binayek Sen. 1994. "Impacts of Land-Based Targeting on Rural Poverty." World Development 22(6):823-38.

- Rosenzweig, Mark and Kenneth Wolpin. 1986. "Evaluating the Effects of Optimally Distributed Public Programs: Child Health and Family Planning Interventions." American Economic Review 76:47082.

- Selden, Thomas and Michael Wasylenko. 1992. "Benefit Incidence Analysis in Developing Countries." WPS 1015, The World Bank, Country Economics Department, Washington, D.C.

- Selowsky, Marcello. 1979. Who Benefits from Government Expenditures? A Case Study of Colombia. New York: Oxford University Press.

- Sen, Amartya. 1979. "Personal Utilities and Public Judgements: Or What's Wrong with Welfare Economics?" The Economic Journal 89:537-58.

Sen, Amartya. 1985. Commodities and Capabilities. Amsterdam: North-Holland.

- Stocpol, Theda. 1991. Protecting Soldiers and Mothers: The Politics of Social Provision in the United States, 1870-1920. Cambridge: Harvard University Press.

- van de Walle, Dominique, Martin Ravallion and Madhur Gautam. 1994. "How Well Does the Social Safety Net Work? The Incidence of Cash Benefits in Hungary 1987-89". Living Standards Measurement Study Working Paper No. 102, Washington, D.C.

- van de Walle, Dominique and Kimberly Nead. 1995. Public Spending and the Poor: Theory and Evidence. Baltimore and London: The Johns Hopkins University Press for the World Bank, forthcoming.

World Bank. 1990. World Development Report 1990. Poverty. Oxford: Oxford University Press.

49
20 estimate (Table 2) of the cofficent on IMR is .41 while that on TFR is about .90. These alternative estimates illustrate that the coefficients, while confirming interaction, are dependent on specification and should be taken as indicative and not quantitatively precise. The policy conclusions are examined in Section ; for sensitivity to changes in the interaction coefficients. The main part of the discussion, however, centers on alternative #2 which corrects for simultaneity and omits REXHP.

23. Based on the estimate of the infant mortality equation given under alternative #2, the elasticity of the infant mortality rate to an increase in non hospital expenditures is -.22. This indicates that a doubling of nonhospital health expenditures from 1300 to 2600 Rp. per capita (a 100% increase) would reduce infant mortality by about 22% even if there were no other changes in, say, food availability, literacy, or sanitation. Similarly, the coefficient on REXFP in the fertility equation indicates that a doubling of government expenditures on family planning activities would directly decrease the fertility rate by 22%.

24. The magnitude of these responses may be affected by variable definitions and data specification. For example, REXFPP includes only government expenditures on family planning and does not include private expenditures. REXFPP also does not account for differences in effectiveness of program implementation over provinces. It is difficult to state the direction of bias these omissions give to the coefficient, but if, as may be plausible, private expenditures are positively related to motivational efforts in government programs and implementation effectiveness increases
According to this adjusted ranking, we obtain a new list of top performers over the last fifteen years. The top improvements come from Jordan, Kenya, Madagascar, Trinidad and Tobago, Tanzania, Chad, Namibia, Iceland, Portugal, Botswana, Canada, and New Zealand. All top performers except Portugal, Canada, and New Zealand are new to the list. In particular, many recent top performers appear to be developing countries.

Our adjusted test score database can better inform policy on a standardized and global scale, thus enabling policymakers to determine and target meaningful education reforms for countries that need it most.

# **6. Application of Data Set**

Our database consists of internationally comparable test scores from 1965-2010 and provides a useful measure of education quality. This outcome measure can be used for an empirical analysis of the determinants of educational performance. We provide a first example of how to use our extended and updated data set to determine causal inputs in successful education systems.

One major motivation for this analysis stems from the concentration of specific types of countries on both ends of the achievement spectrum. Indeed, most of the countries that perform worse than the world adjusted test score average are concentrated in Africa, Latin America, and the Middle East, and are considered developing countries. This large discrepancy begs the question: why do some countries achieve better learning outcomes when other countries do not?

To this end, we use our panel data set to demonstrate one possible causal analysis to explain differences in qualitative achievement on international assessments. In particular we focus on governance variables.

# _A) Approach_

In this section, we briefly explain our econometric strategy, aimed at reducing omitted variable bias and other biases. We also detail the set of explanatory variables included in our dataset. The estimation strategies we want to apply are the following: First, we use a fixed effects approach, capitalizing on the variation in systemic elements over countries and time in order to establish a causal link between such elements and resulting cognitive skills. Second, we control for certain confounding factors such as macroeconomic indicators. Third, we include lagged variables as 25"
Ghani et al. (2013c) find evidence that growing women’s entrepreneurship can support the persistence of the informal sector in which many of these business start. We hope that future work considers other economic outcomes and evidence from outside of India on these important issues.

# **References**

Amin, Mohammad, “Gender and Informality”, World Bank Enterprise Note 16 (2010).

- Beaman, Lori, Raghabendra Chattopadhyay, Esther Duflo, Rohini Pande, and Petia Topalova, “Powerful Women: Does Exposure Reduce Bias?”, Quarterly Journal of Economics 124:4 (2009), 14971540.

- Beaman, Lori, Raghabendra Chattopadhyay, Esther Duflo, Rohini Pande, and Petia Topalova, “Female Leadership Raises Aspirations and Educational Attainment for Girls: A Policy Experiment in India”, Science 335 (2012), 582-586.

- Beaman, Lori, Esther Duflo, Rohini Pande, and Petia Topalova, “Political Reservation and Substantive Representation: Evidence from Indian Village Councils”, India Policy Forum (2010), 159-201.

- Besley, Timothy, Rohini Pande, Lupin Rahman, and Vijayendra Rao, “The Politics of Public Goods Provision: Evidence from Indian Local Government”, Journal of the European Economic Association 2:2-3 (2004), 416-426.

- Bruhn, Miriam, “Female-Owned Firms in Latin America: Characteristics, Performance, and Obstacles to Growth”, Working Paper (2009).

- Chattopadhyay, Raghabendra, and Esther Duflo, “The Impact of Reservation in the Panchayati Raj: Evidence from a Nationwide Randomized Experiment”, Economic and Political Weekly 39:9 (2004a), 979-986.

- Chattopadhyay, Raghabendra, and Esther Duflo, “Women as Policy Makers: Evidence from a Randomized Policy Experiment in India”, Econometrica 72:5 (2004b), 1409-1443.

- Chen, Martha Alter, and G. Raveendran, “Urban Employment In India: Recent Trends And Patterns”, The Journal of Applied Economic Research 6:2 (2012), 159-179.

- Clots-Figueras, Irma, “Women in Politics: Evidence from the Indian States”, Journal of Public Economics 95:7-8 (2011), 664-690.

- Deininger, Klaus, Songqing Jin, Hari Nagarajan, and Xia Fang, “Does Female Reservation Affect Longterm Political Outcomes? Evidence from Rural India”, World Bank Working Paper (2011).

- Dhaliwal, Spinder, “Asian Female Entrepreneurs and Women in Business—an Exploratory Study”, Enterprise and Innovation Management Studies 1:2 (2000), 207-216.

30
# **Table 1. Egypt Government Expenditures, FY15**

||Egyptian<br>pounds<br>(billions)|Expenditures<br>Share of<br>expenditures<br>(%)|GDP<br>(%)|Included<br>in<br>analysis?|
|---|---|---|---|---|
|**Total expenditure**|733.4|100.0|30.0||
|Social spending|236|32.2|9.7||
|Social protection|86.1|11.7|3.5||
|Pension fund contributions|33.2|4.5|1.4|Yes*|
|Direct cash transfers|13.5|1.8|0.6||
|Takaful<sup>a</sup>|0.5|0.1|0.02|Yes|
|Karama<sup>a</sup>|0.01|0.0|<0.01|Yes|
|Grants to the general government|6.2|0.8|0.25|No|
|Child assistance and monthly allowances<sup>b</sup>|0.096|0.0|<0.01|No|
|Social solidarity transfers<sup>c</sup>|6.7|0.9|0.27|No|
|Tamween program<sup>d</sup>|39.4|5.4|1.61|Yes|
|Education|92.3|12.6|3.8||
|Primary, preparatory, and secondary|65.7|9.0|2.7|Yes|
|Tertiary|20.9|2.8|0.9|Yes|
|Other educational levels<sup>e</sup>|5.7|0.8|0.2|No|
|Health|37.2|5.1|1.5|Yes*|
|Housing and public utilities<sup>f</sup>|20.4|2.8|0.8|No|
|Subsidies to commodities<sup>g</sup>|99.9|13.6|4.1||
|Energy|97.5|13.3|4.0||
|Electricity|23.6|3.2|1.0|Yes|
|Fuel|73.9|10.1|3.0|Yes|
|Farmers|1.3|0.2|0.05|No|
|Water|0.9|0.1|0.04|No|
|Medicine and infant dairy subsidies|0.2|0.0|0.01|No|
|Economic affairs<sup>h</sup>|39.9|5.4|1.6|No|
|Defense,<sup>i</sup>public order, safety spending|87.8|12.0|3.6|No|
|Other<sup>j</sup>|269.8|36.8|11.0|No|

_Source_ : Ministry of Finance.

* Expenditures included may not be fully allocated within HIECS 2015 for various reasons. See the methodology section on allocative methods and assumptions.

a Takaful and Karama were pilot programs in FY15 and were off budget for that fiscal year, but by the end of calendar year 2015 the programs had reached 165,000 poor households in seven governorates. Today the programs have reached just over 2 million poor households in the 27 governorates of the country.

b These transfers are given to families of soldiers, Hala’ib and Shalateen families (border dispute area with Sudan), and other vulnerable groups.

c Typically referred to as “social pensions” in GoE documentation, this program is a noncontributory, nonconditional cash transfer that targets certain populations. It is not means-tested, but the beneficiaries are broadly defined to be poor or vulnerable populations (for example, orphans, widows, children of divorced parents, and disabled children).

d The program’s expenditures are related to three categories: purchases of wheat (small share); spending on loaves of bread that are made available (five loaves per person per day) to all card holders; and the ration card allotment to purchase from a list of basic staples from family stores (allotment was LE 15 per person per month). Each component may be treated differently as part of the CEQ methodology.

6
<!-- Start of picture text --> 100 16<br>= 80 14<br>e 3<br>5 °<br>g &<br>= 60 12 9<br>fe)+S) £Cc<br>g ic<br>8 2<br>2 40 10 0<br>a (0)<br>ie)o£o<br>g oO<br>H 20 8<br>0 6<br>1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Year<br>ly Salaries ly Self-employment<br>lh S-corporations ha Capital income<br>—*—-_ Share of total income<br>Excluding capital gains and negative observations. Income categories are defined below in Section 1.<br><!-- End of picture text -->
(values of more than 100 imply that food is relatively expensive, whereas values of less than 100 imply that food is relatively cheap). However, because of Balassa-Samuelson effects, this indicator is likely to be higher in richer countries than in poorer countries. Hence, we use multivariate regressions to control for GDP per capita. However, even after controlling for GDP per capita, there are still substantial variations in food prices across developing countries (as the continent dummies in regression 1 suggest), which could be explained by transport costs, variations in agricultural productivity, the limited tradability of food (partly due to tastes), or even exchange rate distortions. Indeed, regression 2 suggests that variation in “relative food prices” across countries significantly explains variations in self-assessed food security after controlling for GDP per capita. However, the relationship is nonlinear: at low levels of food prices, the marginal effects of higher prices are quite large, but at the highest observed levels of relative food prices, the marginal effects are insignificantly different from zero. A caveat is that the result of regression 2 in table 3 is not very robust to the inclusion of continental dummies (introduced in regression 3), particularly the dummy for sub-Saharan Africa. This lack of robustness appears to be because relative food prices and self-assessed food insecurity are both very high in Africa.<sup>11</sup> Specifically, the inclusion of continent dummies results in the food price coefficients no longer being significant at the 10 percent level, although this insignificance also applies to the continent dummy coefficients, suggesting that multicollinearity is an issue.

> 11 An issue here is that food prices may be higher in Africa because of the way in which the 2005 round of the International Comparison Program was conducted on a continental basis. Specifically, it is possible that food prices in Africa are biased upward by methodological issues, although it is difficult to substantiate such a claim. A more general problem with purchasing power parities is the challenge of finding common items to compare across countries. Exchange rate distortions may be

> problematic for this index, although data on black market premia on exchange rates suggest that most exchange rate distortions have declined markedly over time.

13
Policy Research Working Paper 9736

# **Abstract**

This paper analyzes the impact of COVID-19 lockdown measures on children’s school resilience. Using an individual fixed-effect linear probability model on Nigeria data, it exploits the quasi-randomness of these measures to estimate their effect on school attendance after the lockdown was lifted. The results show that COVID-19 lockdown measures reduced children’s probability of attending school after the school system reopened. This negative impact increased with children’s age, reaching a peak among those whose education was no longer compulsory. For schoolchildren in that age group, the negative effect of COVID-19 lockdown measures is likely to be permanent, which, if not reversed, will undermine the quality of the economy-wide future labor force. The paper also finds evidence that in the child marriage-prone North-West part of Nigeria that these measures increased gender inequality in education among children aged 12 to 18. This result suggests that COVID19 lockdown measures may exacerbate harmful traditional practices such as child marriage.

This paper is a product of the Development Data 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 mtiberti@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
# **IV. Impact of overall taxes and social spending on inequality and poverty**

## **Impacts on inequality**

Government intervention through taxes, social benefits, and in-kind transfers reduces inequality by almost 10 percent, or 0.03 Gini points. Without fiscal intervention, the Gini coefficient of market income inequality (when pensions are treated as deferred income) in Serbia stands at 0.385. After government intervention in the form of direct and indirect taxes, social contributions, social benefits and in-kind transfers (healthcare and education), inequality falls to 0.351 (Figure 2). The largest contribution to the reduction of inequality comes from in-kind transfers (in health and education), which is reflected in the fall of the Gini from 0.385 for consumable income to 0.351 for final income. The effects of direct taxes (personal income tax and social security contributions) and social transfers (contributory and non-contributory benefits) are slightly smaller, bringing the Gini down from 0.385 for market income to 0.361 for disposable income. Indirect taxes (VAT and excise duties) tend to increase inequality with respect to current income, which is reflected in the increase of the Gini from disposable to consumable income `.` Over the life cycle, the incidence of indirect taxes with respect to household consumption as a proxy for permanent income is much less regressive, as discussed in Appendix 1.

**Figure 2. Serbia: Change in inequality (Gini coefficient) from market to final income**

<!-- Start of picture text --> 0.60<br>Old-age pensions as<br>0.55 transfers<br>Old-age pensions as<br>0.482<br>0.50 deferred income<br>0.45<br>0.385<br>0.40<br>0.361<br>0.351<br>0.385<br>0.35<br>0.30<br>0.25<br>Market Income Disposable Income Consumable Final Income<br>Income<br><!-- End of picture text --> Source: Own estimations using HBS and SILC data Note: The poverty and inequality estimates differ slightly from official estimates based on the SILC because imputed rent is included in the income aggregate, and the direction of the results is robust to whether imputed rent is included.

The reduction of inequality in Serbia due to fiscal intervention is smaller than that in several countries in Central and Eastern Europe, some neighboring countries, and Latin American countries of similar income level where the CEQ analyses have been conducted. When 14
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Benchmarking water and sanitation indicators**

||Unit|Low-<br>income<br>countries|**Sen**|**egal**|Middle-<br>income<br>countries|
|---|---|---|---|---|---|
|||Mid-2000s|**Late 1990s**|**Mid-2000s**|Mid-2000s|
|Access to piped water|% pop|10.5|30.1|40.9|52.1|
|Access to stand posts|% pop|16.2|18.3|19.7|18.9|
|Access to protected wells/boreholes|% pop|38.3|10.5|7.0|6.0|
|Access to nonprotected wells/boreholes|% pop||33.5|28.8||
|Access to surface water|% pop|37.4|2.3|3.3|13.0|
|Access to flush toilets|% pop|4.9|11.2|34.7|40.8|
|Access to improved latrines|% pop|9.9|34.6|24.6|1.4|
|Access to traditional latrines|% pop|50.1|18.6|14.9|30.4|
|Open defecation|% pop|40.3|41.0|25.2|14.3|
|Urban water assets in need of rehabilitation|%|35.5|—|25.0|25.0|
|Domestic water consumption|Liter/capita/day|72.4|49.1|54.5(*)|165.9|
|Revenue collection|% sales|92.7|88.8|96.73|100.0|
|Distribution losses|% production|34.3|25.5|20.94|26.8|
|Cost recovery|% total costs|56.0|59|98|80.6|
|Operating cost recovery|% operating costs|65|95|144|145|
|Labor costs|Connections per employee|158.6|225.6|284.0(*)|368.7|
|Total hidden costs as % of revenue|%|109|94.27|13.08|167|

_Source:_ Demographic and Health Surveys (DHS) and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ DHS figures on access are as of 1997 and 2005 and utility numbers are as of 2000 and 2008, except when indicated. (*) Figures as of 2005.

— = Not available.

Senegal’s urban water utility, SDE, has set an example among Sub-Saharan African countries with its good bill-collection record and reduced water losses. Together SONES and SDE provide a model of a strong public-private partnership. Water has been made available 24/7 in large urban areas and several smaller towns. Distribution losses have been kept around 21 percent, compared with 34 percent in other African low-income countries, and even well below of what is observed in African middle-income countries (table 9). SDE captures 95 percent of the revenue stream that it needs to operate effectively, a comparatively good performance by regional standards. Also, the utility’s collection ratio is 97 percent of its sales (table 10). Labor productivity doubled between 1996 and 2006, going from 5.8 employees per 1,000 connections to 2.8.

SDE’s continuous improvements in most efficiency indicators translate into relatively small hidden costs associated with operational inefficiencies (box 1). In the early 2000s hidden costs represented the equivalent of 94 percent of the utility’s revenues, but since then there has been a systematic improvement; as of 2008 hidden costs represented a mere 13 percent of revenues (figure 12).

22
Table 5: Revealed comparative advantage, disaggregated categories, 2001–2007 (extreme values subsample)<sup>_†_</sup>

|HS code|Product|RCA (2001)|RCA (2007)|Change (%)|
|---|---|---|---|---|
||**Upper **|**bound**|||
|14|Vegetable materials|41.09|30.36|-33|
|1404|Other veg products|64.89<br>|43.49<br>|-26|
|1401|Veg plaiting materials|_‡_|_‡_||
|01|Live animals|4.53|18.78|314|
|0104|Live sheep|61.12|282.78|363|
|0105|Live poultry|0.24|0.22|-9|
|07|Edible vegetables and roots|6.02|13.81|129|
|0707<sup>_∗_</sup>|Cucumbers and gherkins|0.59|3.02|410|
|0704|Cabbages and caulifowers|1.27|7.40|482|
|54<sup>_∗_</sup>|Man-made flaments|0.40|13.01|3,117|
|5407|Woven synthetics|0.62<br>|25.40|3,973|
|5402|Synthetic yarn|_‡_|1.24||
|09|Cofee, tea, and spices|6.04|10.23|69|
|0909|Seeds of anise|302.97<br>|450.83|49|
|0901|Cofee|_‡_|0.24||
||**Lower **|**bound**<br>|||
|97|Works of art|_‡_|0.03||
|9701|Handmade decorative|_‡_|0.01||
|9702|Original engravings|_‡_<br>|_‡_||
|71|Pearls and precious stones|_‡_|0.00||
|7113|Jewels|_‡_|_‡_||
|26|Ores, slag, and ash|41.09|30.36|-33|
|03|Fish and crustaceans|_‡_|0.00||
|0307|Molluscs|_‡_|0.01||
|0301|Live fsh|_‡_|_‡_||
|90|Optical and photo equipment|_‡_<br>|0.00<br>||
|9015|Surveying equipment|_‡_|_‡_||
|9032|Auto reg instruments|_‡_|_‡_||

> _†_ Calculations applied at the 2-digit and 4-digit HS level. At 2-digit level, lines exhibiting highest and lowest values for 2007 were reported (excluding products that did not exist in 2007). At 4-digit level, lines with highest two _R_<sup>2</sup> values in bivariate regression were reported, except where the scarcity of observations made this impossible.

> _‡_ No recorded exports of product in given year.

> _∗_ Indicates (2-digit) product line for which RCA switched from _>_ 1 to _<_ 1 (if change was negative) or _<_ 1 to _>_ 1 (if change was positive).

(heading 1509), as well as fruit and nut preparations (chapter 20), especially preserved nuts (heading 2006) such as pistachios and cashews.

Products which Syria has little (revealed) comparative advantage in include pearls and precious stones (chapter 71), seafood (chapter 3), and optical and photographic equipment (chapter 90). These are unsurprising: the former two depend largely on natural endowments, while the last is typically associated with high-skill, capital-intensive production, neither of which Syria is relatively more abundantly endowed.

Note that Table 5 also alludes to the possibility of production and export complementarity, especially with regard to downstream and upstream products. In particular, the production and export of vegetable materials is mostly due to cotton linters (subheading 140420); this is the upstream component that complements the well-diversified man-made 15