rafmacalaba/data-use-mentions-extended huggingface dataset:
of social protection is positively correlated with national income. Government effectiveness is likely to be correlated with GDP, but it also has its own independent variation, in that there are poor countries with relatively effective governments, and some rich countries for which one could not reasonably say that. This is illustrated by Figure 2, which plots the “government effectiveness (GE) index” from the World Bank’s _<u>Worldwide Governance Indicators</u>_ (WGI) against GDP per capita. The GE index is highly correlated with GDP per capita. However, there are low-income and middle-income countries with relatively high GE values and high-income countries with low values. (For example, Saudi Arabia’s GE index is about the same as Rwanda’s.)

> 7 Thus, this strand of the literature gives much attention to the likely endogeneity of institutions in explaining differences in rates of economic growth; see, for example, Acemoglu et al. (2005).

7
with those reported in income surveys, assuming that tax evaders have no incentives to conceal their true income when responding to an income survey. It is also possible to investigate tax evasion based on data on food spending and estimates of the link between income and food spending (Engels curve). For example, Kukk & Staeher (2014) show that in Estonia, the reported total income of households with business income above 20% of total income must be multiplied by 2.6 to attain the same propensity to food consumption as households of wage earners. In this sense, households with business income underreport 62% of their ‘true’ total income. The comparison of consumption patterns of the public sector employees (where envelope wages are not an issue due to legal constraints) and the private sector is another method. Kiełczewska et al. (2021) apply this method to Poland. They find that 12% of the employees receive envelope wages and that the most important problem is near the minimum wage threshold. Lopez-Luzuriaga et al. (2023) apply this method in Ecuador using income tax and online billing data. They find that the underreporting of income among private sector workers is 7%-9%, corresponding to 3% of the GDP. Envelope wages are most common in small companies. Alternatively, discrepancies in economic statistics can also reveal information about tax evasion. This approach involves comparing actual tax revenue with the National Accounts, which can help identify discrepancies indicative of tax evasion. Recently, a novel strand of the literature on tax evasion developed based on leaked lists of tax evaders (e.g., Alstadsæter et al., 2019).

**Studies based on Eurobarometer data show that in 2007, the share of employees receiving envelope wages in Romania stood at 22%, the highest among transition economies.** However, the study based on Eurobarometer data for 2019 shows that approximately 5% of the employees receive envelope wages in Romania, and this share is medium compared to other CEE economies
the control households. One should bear in mind that PROSPERA by design targets children and therefore it is plausible that the main long-run positive effects are found among those who were the primary target of the policy in terms of health, nutrition, and schooling. While the original households might have had an initial bump in their incomes, that was fairly limited in the original experiment, so that eventually the original control household might have caught up. The same does not need to be true for the treated children as some of the control children, given their age and school grades, might not be affected at all by the late roll out of the program in control localities. For example, those children at the margin of dropping out of school who were in the control villages would have dropped our while their counterparts in treatment villages would have stayed in school.

# **VI.A.2 New Households**

In this section we analyze the effects for durable goods purchase, and imputed consumption expenditures, by the new households as previously defined: these are mostly the offspring of the original households, potentially those children in 1997 that were the target of the policy. As these new households are composed by the direct beneficiaries of the program transfers, they 18With certain regularity PROSPERA gathers a similar survey called ENCASEH, whose purpose is to determine if the households are still in poverty and should stay in the program or not.

22
quality, specific nutrients of FAFH and individual excess body weight at the population level. This allows us to study the differences in nutritional quality across the food environments of various socioeconomic groups and explore whether these differences contribute to the commonly found socioeconomic gradient in excess body weight.<sup>3</sup> In contrast to other studies that use self-reported body weight measures, this study relies on data of the Demographic and Health survey (DHS), which contain anthropometrics measured by health professionals and are therefore less prone to measurement error.

More specifically, the representative restaurant survey that measures the nutritional composition of the most frequently consumed lunch menus in Metropolitan Lima is combined with the household survey that contains anthropometric measures of women ages 15 to 49 living in Lima.<sup>4</sup> This unique data set allows an assessment of whether there is a link between the nutritional quality of FAFH in the food environment and excess body weight among women in 40 neighborhoods of urban Peru.<sup>5</sup> In addition, the stratification of the restaurant data allows us to examine the differences in nutritional quality between neighborhoods of different socioeconomic groups and whether this explains the socioeconomic gradient in excess body weight.<sup>6</sup> Based on an ordered logit model, the findings indicate that a lower diet quality of FAFH in restaurants located in the food environment of the household is significantly associated with individual excess body weight. A 10 percent increase in the PDRNI of restricted nutrients is associated with a 7.4 percent increase in the probability of obesity and a 1.7 percent increase in the probability of overweight. This relationship holds after accounting for a number of factors that correlate with body weight, including socioeconomic fixed effects that control for food quality at home and other unobserved factors that correlate with body weight and the nutritional quality of FAFH. In exploiting the importance of specific nutrients, we find that _sodium_ in most restaurant menus seems to be responsible for the association between the diet quality of
, and career development) in 24 European countries.<sup>7</sup> Brisbois (2003) uses the EWCS dataset, together with the Ekos

> 5 See Measuring Decent Work (dashboard), International Labour Organization, Geneva, https://www.ilo.org/integration/themes/mdw/lang--en/index.htm.

> 6 Multidimensional poverty indexes often include good quality employment as one of the dimensions considered (Dirksen 2020). Employment is not part of the World Bank’s multidimensional poverty measure because, in the surveys considered for that aim, the relevant indicators of employment are not sufficiently harmonized (World Bank 2018).

> 7 EU-SILC (European Union Statistics on Income and Living Conditions) (dashboard), Eurostat, European Commission, Luxembourg, http://ec.europa.eu/eurostat/web/microdata/european-union-statistics-on-income-and-living-conditions.

5
, NBER Working Papers: 29589. Couttenier, M., N. Monnet and L. Piemontese. 2022. "The Economic Costs of Conflict: A Production Network Approach." Centre for Economic Policy Research, Discussion Paper DP16984, London.

d'Agostino, A. L. and W. Schlenker. 2016. "Recent Weather Fluctuations and Agricultural Yields: Implications for Climate Change." _Agricultural Economics_ 47: 159-171.

Dara, A., et al. 2020. "Annual Landsat Time Series Reveal Post-Soviet Changes in Grazing Pressure." _Remote Sensing of Environment_ 239: 111667.

de Roux, N. and L. R. Martinez. 2021. "Forgone Investment: Civil Conflict and Agricultural Credit in Colombia." Johns Hopkins University, Washington DC.

Deines, J. M., et al. 2021. "A Million Kernels of Truth: Insights into Scalable Satellite Maize Yield Mapping and Yield Gap Analysis from an Extensive Ground Dataset in the Us Corn Belt." _Remote Sensing of Environment_ 253: 112174. Deininger, K., D. Nizalov and S. K. Singh. 2018. "Determinants of Productivity and Structural Change in a Large Commercial Farm Environment: Evidence from Ukraine." _The World Bank Economic Review_ 32 (2): 287-306.

Deininger, K., et al. 2023a. "Quantifying War-Induced Crop Losses in Ukraine in near Real Time to Strengthen Local and Global Food Security." _Food Policy_ 115: 102418.

Deininger, K. W., D. A. Ali and R. Neyter. 2023b. "Impacts of a Mandatory Shift to Decentralized Online Auctions on Revenue from Public Land Leases in Ukraine." _Journal of Economic Behavior & Organization_ 213: 432-450. Devadoss, S. and W. Ridley. 2024. "Impacts of the Russian Invasion of Ukraine on the Global Wheat Market." _World Development_ 173: 106396.

Duncan, E. C., S. Skakun, A. Kariryaa and A. V. Prishchepov. 2023
tertiary education, where the vast majority of beneficiaries (over 85 percent) are concentrated in the top quintile. See Figure 3, below. The methodology for assigning beneficiaries in scenarios 1 and 2 (see Table 8 above) does not alter these findings.

Figure 3: Distribution of beneficiaries for education <!-- Start of picture text --> Distribution of enrollment (a) Distribution of enrollment (b)<br>800 400 800 400<br>686 718 699 694 684 676<br>700 350 700 653 651 641 350<br>606<br>684<br>600 661 644 300 600 634 648 300<br>603 610<br>500 250 500 573 250<br>400 483 200 400 462 200<br>136<br>300 150 300 150<br>98<br>200 84 100 200 100<br>100 24 33 53 64 50 100 18 28 41 62 46 50<br>1 1 2 7 1 0 0 6<br>- - - -<br>Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5<br>(Poorest 20%) (Richest 20%) (Poorest 20%) (Richest 20%)<br>Scenario 1 Total Basic High Tertiary Scenario 2 Total Basic High Tertiary<br>Welfare composition of enrollment (c) Welfare composition of enrollment (d)<br>100% 100%<br>90% 16% 90% 16%<br>80% 41% 80% 40%<br>70% 70%<br>60% Q5 (Richest 20%) 60% Q5 (Richest 20%)<br>85% 86%<br>50% Q4 50% Q4<br>Q3 Q3<br>40% 40%<br>Q2 Q2<br>30% 30%<br>Q1 (Poorest 20%) Q1 (Poorest 20%)<br
Using 1960-200 panel data, they find that an increase in agricultural GDP raises non-agricultural GDP in developing countries, whereas a reverse relation exists for developed countries. The authors suggest that there exist regional differences of the positive relation in developing countries, where the effect is higher in non-Latin American and Caribbean countries.

An additional problem that arises with cross-country studies is that differences in country conditions do not allow for a general relationship between agricultural and aggregate economic growth. Factors such as openness to trade could alter the relation between agriculture and nonagriculture. Global markets can be a substitute for what Timmer (2002) calls first-order effects of agricultural growth (because they provide international capital flows and food imports). This might explain the different magnitudes of the positive relation found by Bravo-Ortega and Lederman (2005) for Latin America compared with other regions. Hence, the importance of linkages between the agricultural sector and the rest of the economy differs across countries. Some authors have tried to shed light on the importance of such linkages in different developing countries. As shown by de Janvry and Sadoulet (2009b) for China over 1980 – 2001, a 1 percent agricultural growth had an effect on aggregate growth of 0.45 percent, whereas the indirect effect through the non-agricultural sector represents half this effect. In line with Mellor‘s findings for non-agricultural employment, multipliers from agriculture to the rest of the economy have been mainly driven by consumption linkages (Thirtle et al. 2003, Tiffin and Irz 2006).

# _Agriculture and the Urban Bias_

Probably the most important contribution to development made by the agricultural sector in poor countries in the past has been to provide the savings, i.e., the surplus—extracted through various means—over and above what is required for the reproduction of agricultural producers, which allowed industrialization to take place. The tax and price policies that are necessary to bring about surplus extraction have been extensively discussed in the literature (see Sah and Stiglitz 1984, Carter 1986). They were made famous by the policy discussions
related school closures. These data are available for 93% of the sample in ECA+ countries and 98% of the sample in EU countries. We also merge PIRLS data with information from UNESCO on the total number of weeks schools were closed fully or partially during the pandemic. These data are available for all EU countries, although they do not distinguish between primary and secondary levels. Thus, the UNESCO information might be misleading, as primary schools remained open in some countries while secondary schools were closed.

# **Empirical Strategy**

We model the effect of school closures on student achievement by predicting the deviation of the most recent 2021 results from an estimated linear trend in reading achievement using data from all PIRLS rounds. We estimated each country’s linear trend separately and included country-level fixed effects to control for unobserved time-invariant country characteristics. More formally, we estimate a linear regression model on pooled PIRLS data from different years, given by the following equation: In general, PIRLS surveys 4<sup>th</sup> -grade students who are around ten years old at the assessment time. However, students might take the test earlier or later. Some are repeating, and some are advancing a grade. Also, some countries decided to test students in 3<sup>rd</sup> , 5<sup>th,</sup> or even 6<sup>th</sup> grade. Moreover, while most countries sampled the same population over the years, some changed the target population. Finally, due to the pandemic, some countries postponed the assessment to the beginning of the fifth grade. To consider these differences, we took two approaches. In one, we additionally control for student age (10 dummies, one for each age decile, to allow for a non-linear age effect), student grade, and for the average age effect calculated as a mean age for each round and country. In the second approach, we re-estimated all regressions for countries from
datacatalog.world_<br>=1 if share of manufacturing in GDP less than 10<br>percent|_bank.org/home)_<br>132|3,911|0.692|0.462|0.000|1.000|
||=1 if share of manufacturing in GDP equal to or|132|3911|||||
|High share|greater than 10 percent||,|0.308|0.462|0.000|1.000|
|**_Share of trade in GD_**|**_P_**_(Source: The World Bank - https://datacatalog.worldbank.org/_|_home)_||||||
|Low share|=1 if share of trade in GDP less than 10 percent|132|3,924|0.632|0.482|0.000|1.000|
|High share|=1 if share of trade in GDP equal to or greater than<br>10 percent|132|3,924|0.368|0.482|0.000|1.000|
|**_Broadband internet_**(|_Source_: https://www.collinsbartholomew.com/)|||||||
|ICT|ICT Development Index|118|3,828|5.045|1.833|1.040|8.980|
|2G|Internet coverage at subnational level|130|3,955|0.913|0.161|0.000|1.000|
|3G|Internet coverage at subnational level|123|3,337|0.809|0.264|0.000|1.000|
|4G|Internet coverage at subnational level|94|1,861|0.781|0.331|0.000|1.000|
in Georgia. However, as compared to decreasing the tobacco-related medical expenses, reducing the productive YLL yields much lower welfare gains for all households.

Figure 5. Effect of reducing the years of productive life lost

# Panel A. 25% Price increase Panel B. 50% Price increase

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Decile Decile<br>Medium-bound Elasticity Lower-bound Elasticity Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity Upper-bound Elasticity<br>Source: Estimation using a price shock of 25% Source: Estimation using a price shock of 50%<br>Panel C. 100% Price increase<br>1 2 3 4 5 6 7 8 9 10<br>Decile<br>Medium-bound Elasticity Lower-bound Elasticity<br>Upper-bound Elasticity<br>Source: Estimation using a price shock of 100%<br>.15 .25<br>.2<br>.1<br>.15<br>.1<br>.05<br>Inconme Gains (%) Inconme Gains (%)<br>.05<br>0 0<br>.5<br>.4<br>.3<br>.2<br>Inconme Gains (%)<br>.1<br>0<br><!-- End of picture text --> _Source:_ Author estimations, based on the IHS 2012-2016 and HIES 2017. Data on smoking-attributable death events is taken from the GBD (2017). _Notes:_ Deciles are created based on per capita household total consumption.

## Net income effect

The net income effect of increasing taxes on cigarettes in Georgia (Figure 6) is calculated by adding the individual cost and benefit components (A) to (C), according to Equation 1. Across all price simulations, the net distributional effect
points in Appendix 2, Table 2.3, assuming that there were no missing data.<sup>20</sup> The latter statistics is simply the ratio of the figures in Table 4, column 3 and the total numbers of country-indicatoryear data points that may be available (Appendix 2, Table 2.3, column 3).

Table 4 points to the data challenge with tracking the SDGs, which varies widely by each goal. First, we never have full data for all the 249 countries formally recognized by the UN, with the number of countries with missing data ranging from four (SDG 15, Life on land) to as many as 96 (SDG 13, Climate action). Furthermore, for two countries during the period considered, we do not even have any single data point.<sup>21</sup> Second, we also never have full data for all the indicators in each goal. Again, the number of indicators varies widely from goal to goal, and ranges from one (SDG 4, Quality education or SDG 9, Industry, Innovation, and Infrastructure) to 17 (SDG 17, Partnership for global development). In fact, the last row of Table 4, column 2 indicates that the total number of indicators that we have some data accounts for just over half of all the SDG indicators (i.e., 134 out of 244 indicators). While this empirical finding is consistent with our earlier discussion in Section II (i.e., we can only have some data coverage for at most 80 percent of all the indicators in theory), it also points to a seemingly worse picture on data availability in practice.

But note that the goal-by-goal “tests” for missing data offered by columns 1 and 2 in Table 4 may not uncover the full severity of the missing data challenge, since the availability of just one country data point for one indicator satisfies these tests. What is most relevant to analysis is the absolute number
||4.77|4.15|
|Savannah|||||
|Export crop farm|||6.06|5.75|
|Food crop farmer|||5.50|6.10|
|C6te d'Ivoire|||||
|Export crop farm|6.92|6.70|6.70|5.86|
|Food crop farmer|7.06|6.38|5.47|5.66|

Source: 1985 and 1988: Demery and Grootaert (1993); 1993 and 1995: Priority Surveys.

**Table 21 Mean Size of Cocoa- Growing Export Farmer Households**

||_House_|_hold size_|
|---|---|---|
|_Region_|_1993_|_1995_|
|East Forest|7.49|6.10|
|West Forest|6.65|5.38|
|Savannah|7.16|6.25|
|C6te d'Ivoire|7.09|5.92|

23
4.1 _Rates of Returns to Education_ . The earnings inequality evolution is not the result of changes in the distribution of education, whereas the income profile, which is related to the returns to schooling, is the leading force in the explanation of inequality in Mexico. In light of this evidence, this section analyzes the structure and evolution of the rates of returns to education. Returns for upper secondary rose sharply in the late 1980s and early 1990s and then fell after 1993. Returns to tertiary education continued to rise until 1996, before falling to levels that remained much higher than in the early 1990s (Figure 4.1). These results took into account the change in the ENEU 94 questionnaire and are consistent with rate of return patterns found by de Ferranti et al. (2003) and World Bank (2000a), as well as with estimates using the National Survey of Household Incomes and Expenditures, or ENIGH.

26
<!-- Start of picture text --> Least Squares Leader Estimates and their Precision<br>fa]<br>< Insignificant Region ® Autocracy/Transition<br>_ #10 Mobutu (COD) — #151 Deng (CHN) * Established Democracy<br>lw<br>Ww)<br>=<br>e<br>®, ) : e<br>S @ +41 Cedras haem .<br>$5 e AK. y 7 of @#1 Khama (BWA)<br>a : aes os Soe " e ®<br>Oo “ -,. | x x<br>Fra} -#3 Amin; (UGA) \ Hex X®%eK<br>#11 Lee (SGP)<br>o | Significantly Negative Significantly Positive<br>-04 -03 -.02 -.01 0 01 .02 .03 .04 .05 .06<br>Least Squares Leader Estimate<br>Notes: Orange lines mark the border of statistical significance, where 1.96*5D=abs(leadereffect)<br>Each LS leader effect is a dot/cross. Open circles are named leaders. Sample with tenure>=3yrs<br>and complete growth data. Leaders in SE(SW) comer are significantly positive (negative).<br><!-- End of picture text -->
At this point, the first four steps encompass the calculations to calculate the panel weights in the absence of attrition. The round 2 household weight would be:

# _6) Attrition correction factor_

Household panel survey weights need to address the problem of attrition, i.e. where survey observations (household or individual members) are selected for re-interview but cannot be located or refuse to be interviewed. The methodology used to adjust weights for attrition in the LSMS-ISA surveys follows Rosenbaum & Rubin (1984), which has also been adopted in the PSID; see for example, Gouskova et al (2008). Predicted response probabilities from a logistic regression model based on the covariates are used to form the weighting classes or cells.

Due to the somewhat unusual design of the panel, the attrition correction in the case of the LSMS-ISA surveys needs to take into account two distinct sources of attrition: entire households that are not found, and split-off individuals that are selected for tracking but not found. The two potential options for the calculations given these two conditions are (1) to treat the split-off households as household heads and perform the calculations at the level of the household, or (2) to treat the households that are not found as individuals and perform the calculations at the individual level. The first option is problematic if the characteristics of household heads are dissimilar to the characteristics of split-offs. The LSMS-ISA surveys have generally found that the groups most likely to attrite from the sample are young people, generally but not always male, often residing in urban areas. Therefore for the LSMS-ISA surveys, the second methodology is employed.

To obtain the attrition adjustment factor, the probability that a sample household was successfully reinterviewed in round 2 is modeled with the linear logistic model at the level of the individual. A binary response variable is created by coding the response disposition for eligible household members that are not interviewed in round 2 as zero, and household members that are interviewed as one. The eligibility requirements vary between surveys (for example,