rafmacalaba/datause-encoder-data huggingface dataset:
expectancy at birth was only 68.6% of that in the developed economies, but this rose to 90.5% by the 1990s. Sub-Saharan Africa has had a more modest success in narrowing the gap in life expectancy. The gap narrowed by only 24% due to reversals in life expectancy attributable to the AIDS epidemic.

In the survey specific analysis, we utilize all 919 surveys. That means that the same cohort may show up multiple times across surveys. Since cohort-specific schooling does not change within an economy, the repeated cohort observations are redundant and would overweight repeated cohorts. To correct this, we include only one observation for each economy-birth cohort in our cross-economy analysis. We use information from the oldest survey from each economy. If multiple surveys are available in an economy, we use the most recent survey to add in the birth cohorts that are not included in the earlier economy survey(s).<sup>13</sup>

# **6 Results**

In this section, first we report the survey specific estimates. In a following subsection we analyze the results from pooling the surveys in a cross-economy analysis.

## **6.1 Survey by survey estimates**

Table 1 summarizes the results of the regressions of specification (1) performed on each of the 919 surveys across 147 economies. The identification comes from within survey, across cohort variation in life expectancy at birth.<sup>14</sup> The impact of life expectancy at birth on completed years in school is quite consistent across surveys and regions. Life expectancy at birth had a positive and statistically significant

> 13 For example, Germany has two surveys in our survey-pool, one in 2005 and the other in 2012.  The youngest cohort in the former survey was born in 1980 while in the later survey the youngest was born in 1987. Since the cohorts who were born between 1980 and 1987 were under 25 during the survey of 2005, we only include these new cohorts from the second survey for Germany in our sample.

> 14 Not all regressions included the controls for urban/rural differences either because only urban data were collected or because regional information was not collected.

15
29 Table A.5: Regressions for fixed investment with exogenous instruments (economic controls only), unbalanced 5-year average panel, 1980–2009<sup>_†_</sup>

||**A.E1**|**A.E2**|**A.E3**|**A.E4**|
|---|---|---|---|---|
|Lagged investment|0.647|0.537|0.606|0.382|
||(0.13)<sup>_∗∗_</sup>*|(0.19)<sup>_∗∗∗_</sup>|(0.15)<sup>_∗∗∗_</sup>|(0.23)<sup>_∗_</sup>|
|Output|0.371|0.458|0.420|0.688|
||(0.13)<sup>_∗∗∗_</sup>|(0.18)<sup>_∗∗_</sup>|(0.16)<sup>_∗∗∗_</sup>|(0.22)<sup>_∗∗∗_</sup>|
|Output growth|1.233|1.134|1.495|1.253|
||(0.32)<sup>_∗∗∗_</sup>|(0.28)<sup>_∗∗∗_</sup>|(0.35)<sup>_∗∗∗_</sup>|(0.61)<sup>_∗∗_</sup>|
|Cost of capital|0.711|1.119|-0.588|-0.052|
||(1.35)|(1.34)|(0.89)|(0.76)|
|Trade openness|-0.214|-0.091|-0.286|-0.020|
||(0.26)|(0.23)|(0.22)|(0.38)|
|Financial openness|-0.194|-0.193|-0.140|-0.280|
||(0.07)<sup>_∗∗∗_</sup>|(0.07)<sup>_∗∗∗_</sup>|(0.07)<sup>_∗∗_</sup>|(0.08)<sup>_∗∗∗_</sup>|
|Financial|-0.297|-0.042|-0.202|-0.452|
|development|(0.26)|(0.27)|(0.23)|(0.38)|
|Institutional|0.461|0.368|0.266|0.660|
|quality|(0.19)<sup>_∗∗_</sup>|(0.20)<sup>_∗_</sup>|(0.13)<sup>_∗∗_</sup>|(0.31)<sup>_∗∗_</sup>|
|Wald _χ_<sup>2</sup>|17,411<sup>_∗∗∗_</sup>|18,052<sup>_∗∗∗_</sup>|8,422<sup>_∗∗∗_</sup>|2,299<sup>_∗∗∗_</sup>|
|Hansen _J_|38.280|35.482|36.237|22.966|
|AR(2) _z_|-1.446|-1.286|-1.536|-1.332|
|Instruments|42|41|41|36|
|N (countries) 408 (106)|403 (106)|337 (105)|408 (106)||

> _†_ All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. Period fixed effects and a constant term were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.
**Table 6: Sensitivity Analysis for Measles, Upper Middle Income Countries, IV with country fixed effects**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|Measles rate<br>(log)|
|Convention on the Rights of the Child-yes/no (CRC)|0.078<br>(3.68)**|0.091<br>(4.18)**|0.096<br>(4.42)**|0.082<br>(1.88)|0.105<br>(4.25)**|0.06<br>(2.92)**|
|GDP per capita (in constant 2000 US dollars, log)|-0.098<br>(2.11)*|-0.082<br>(1.78)|-0.006<br>(0.23)||-0.053<br>(1.15)|-0.04<br>(0.91)|
|ICRG-Bureaucratic Quality|0.03<br>(3.97)**|0.035<br>(4.68)**|0.034<br>(4.61)**||0.035<br>(4.43)**|0.022<br>(3.09)**|
|Polity2x|-0.007<br>(1.89)||||||
|Population Density (log)|0.49<br>(0.16)|0.688<br>(0.22)|0.036<br>(2.85)**||1.733<br>(0.55)|0.3<br>(0.1)|
|Total Population (log)|-0.209<br>(0.07)|-0.412<br>(0.13)|0.008<br>(0.73)||-1.36<br>(0.43)|-0.134<br>(0.05)|
|YEAR|0.005<br>(2.57)*|0.005<br>(2.51)*|0.006<br>(5.39)**|0.005<br>(2.68)**||0.003<br>(1.51)|
|Democracy||-0.007<br>(3.11)**|-0.005<br>(3.04)**||-0.006<br>(2.88)**|-0.005<br>(2.24)*|
|GDP per capita (in constant 2000 US dollars, log) (Lag)||||-0.046<br>(0.95)|||
|Log Measles Immunization Rate (Lag)||||||0.318<br>(8.26)**|
|B-spline on [1989,1995)|||||-0.035<br>(1.32)||
|B-spline on [1995,2000)|||||-0.021<br>(0.95)||
|B-spline on [2000,2006)|||||-0.013<br>(0.73)||
|ICRG-Bureaucratic Quality (Lag)||||0.02<br>(2.44)*|||
|Democracy (lag)||||-0.004<br>-1.55|||
|Log Population Density (Lag)||||(0.372)<br>-0.1|||
|Log Total Population (Lag)||||(0.189)<br>-0.05|||
||-3.024|-0.411|-8.067|-3.78|20.629|-1.202|
|Constant|(0.07)|(0.01)|(3.71)**|(0.08)|(0.52)|(0.03)|
|Observations|610|614|614|580|614|611|
|Number of group(countryc)|52|52|52|52|52|52|

22
Policy Research Working Paper 6003

# **Abstract**

In 2005 India introduced an ambitious national antipoverty program, now called the Mahatma Gandhi National Rural Employment Guarantee Scheme. The program offers up to 100 days of unskilled manual labor per year on public works projects for any rural household member who wants such work at the stipulated minimum wage rate. The aim is to dramatically reduce poverty by providing extra earnings for poor families, as well as empowerment and insurance. If the program worked in practice the way it is designed, then anyone who wanted work on the scheme would get it. However, analysis of data from India’s National Sample Survey for 2009/10 reveals considerable un-met demand for work in all states. The authors confirm expectations that poorer families tend to have more demand for work on the scheme, and that (despite the un-met demand) the self-targeting mechanism allows it to reach relatively poor families and backward castes. The extent of the un-met demand is greater in the poorest states—ironically where the scheme is needed most. Labor-market responses to the scheme are likely to be weak. The scheme is attracting poor women into the workforce, although the local-level rationing processes favor men.

This paper is a joint product of the Director’s Office, Development Research Group; South Asia Social Protection Unit; South Asia Economic Policy and Poverty Unit; Social Protection and Labor Unit, Human Development Network. 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://econ.worldbank. org. The authors may be contacted at mravallion@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
<!-- Start of picture text --> 1<br>HE Atarky<br>HE In-Kind Transfer<br>(HG vsP (Asset Replacement)<br>0.8 +) [7] VSP (Insurance Subsidy)<br>0.6<br>0.4<br>0.20 | | I 1 |<br>5 yrs 10 yrs 25 yrs 50 yrs 75 yrs = 100 yrs<br><!-- End of picture text -->

<!-- Start of picture text --> 0.25<br>HR Atarky<br>HE In-Kind Transfer<br>(MG vsP (Asset Replacement)<br>0.2 +) [7] VSP (Insurance Subsidy)<br>0.15<br>01<br>0.050 | | A o o Oo<br>5 yrs 10 yrs 25 yrs 50 yrs 75 yrs =:100 yrs<br><!-- End of picture text -->

<!-- Start of picture text --> 1<br>HE Atarky<br>HE In-Kind Transfer<br>(HG vsP (Asset Replacement)<br>0.8 +) [7] VSP (Insurance Subsidy)<br>0.6<br>0.4<br>0.2; | | | | | |<br>5 yrs 10 yrs 25 yrs 50 yrs 75 yrs = 100 yrs<br><!-- End of picture text -->

<!-- Start of picture text --> 0.25<br>HR Atarky<br>HE In-Kind Transfer<br>(MG vsP (Asset Replacement)<br>0.2 +) [7] VSP (Insurance Subsidy)<br>0.15<br>01<br>0.05; o Wo A i i<br>5 yrs 10 yrs 25 yrs 50 yrs 75 yrs =:100 yrs<br><!-- End of picture text -->
survey data. It is evident that irrigation plays a prominent role in Egypt and South Africa and also in places such as Cameroon, Kenya and Zimbabwe.

Figure 2 depicts the mean net revenue for dryland and irrigated farms in each country in the sample. Net revenue is gross revenue minus the costs of transport, packaging and marketing, storage, post-harvest losses, hired labor (valued at the median market wage rate), light farm tools (such as files, axes, machetes), heavy machinery (tractors, ploughs, threshers and others), fertilizer and pesticide. The median prices per district were used to value both crops and inputs whenever possible. In some circumstances, it was necessary to rely on median provincial or national prices. We excluded household labor in the definition of net revenue because including it led to many households having negative net revenues. This was the case whether we used the payments each household alleged it gave household workers or whether we assigned market wage rates to household labor. The inclusion of household labor in net revenues is problematic, as reported in the agricultural development literature (Bardhan & Udry 1999). We therefore defined net revenues without household labor costs and controlled for the effect of household labor by including adult and child man-days as an independent variable.

Table 4 presents the median net revenue in each country for irrigated and dryland farms. It is evident from Figure 2 and Table 4 that Egypt is a unique case in Africa. Farming in Egypt is predominantly irrigated and technology intensive, leading to significantly higher earnings. A large proportion of Egyptian farmers are also able to cultivate for two seasons, which gives them another advantage over dryland farmers in the rest of our sample.

Following the theoretical model described in Section 2, we estimated multiple regression models of net revenue across three samples (see Table 6). This initial set of regressions does not control for regional differences across Africa. The set examines three models: the entire sample (all farms), just irrigated farms, and just dryland farms. The coefficients for irrigated and dryland farms are not the same, suggesting they have different relationships with the independent variables. While we do not present the results here, a number of farmer specific variables, such as gender, education and whether or not the farmer was a full time farmer or not, were not significant and so were dropped. Overall the three regressions explain 35%, 17% and 29% of the variation in net revenues from farm to farm. The coefficients of the models are significantly different from zero. The variables identify many reasons why farm net revenue varies from place to place. However, a great deal of the variation remains 11
_5_ can be withdrawn prior to retirement. A substantially different tax regime applies to voluntary savings.

Minimum **Return Guarantee from the AFP.** Each month, each AFP must ensure a minimum return equal to the lower of average return of all AFPs for the last 12 months minus 2%, or 50% of the average industry-wide return. Returns in excess of 150% of industry average or the industry average plus 2% can, be placed in a Yield Fluctuation Reserve. In addition, the AFP must create a Cash Reserve equal to 1% of assets managed. These reserves, in that order -- and if they are insufficient, private resources of the AFP -- must make good any shortfalls below the minimum.

Investments. Investments in pension funds are tightly controlled (see Vittas and Iglesias for an extensive discussion). In particular, equity investments were not permnitted until 1985, were limited to 30% of the fund's assets over 1985-95 and since 1995 are limited to 37%. The law and regulations prescribe maximum limits on different classes of instrument, rating requirements for most interest-bearing securities, limits on maximum holding of securities of one issuer, limits on individual issue of any instrument, limits in terms of the share of the capital of the issuer in the sector and limits in terms of share of the pension fund in total pension fund assets. In addition, investment in individual securities such as equities must be weighted by liquidity factors and concentration factors. While the limits have been gradually revised upwards, the structure still contains many more rules than necessary for simply achieving diversification. In addition, new classes of assets cannot be added by pension funds as they become available, and must await approval by the Superintendent.

The requirement of ensuring minimum returns relative to the industry average strongly induce the AFPs to invest in identical portfolios, as can be seen in Table 2. As the contributions are mandated, the affiliates cannot influence portfolio composition by voting with their feet.

Table 2: **Absence of Diversification of Investments Across**

||**Chil**|**ean Pension F**|**unds**|||
|---|---|---|---|---|---|
||Government<br>Bonds|Mortgage<br>Credit Bills|Bank<br>Instruments'|Equity|Corporate<br>Bonds|
|Average of All Funds|39.41%|16.79%|5.32%|29.37%|5.11%|
|Standard Deviation|4.33%|3.87%|2.91%|1.61%|1.70%|
|Variability Coefficient|0.110|0.230|0.547|0.055|0.333|
|Notes: 'Term Deposits an<br>Source:_The Chilean Pensio_|d Promissory<br>Not<br>_n System._|es issued by fina|ncial institutions|.||

**Commissions.** There are no limits on the level of commissions. Initially, the AFPs were free to charge fixed and variable commissions based on salaries and account balances, as well on commissions for each deposit, withdrawal, etc.. Fixed and variable commissions based on balances have been abolished since 1987. Aggregate commissions, including insurance costs, increased from 5.1% of average taxable salary in
social benefits, child allowance and financial social assistance have the biggest inequalityreducing and poverty-reducing effects. These programs are well targeted to the poor. Almost 93 percent of the financial social assistance and 90 percent of the child allowance is concentrated at the bottom 40 percent of the distribution of market income (including pensions as deferred income). Half of all the spending on parental allowance goes to bottom 40 percent of the population in terms of market income (Figure 5).<sup>17</sup> However, the marginal effects of social benefits on poverty and inequality-reducing are constrained by the relatively small coverage of the most targeted transfers. The marginal contributions of all direct transfers (excluding contributory pensions) are to reduce the Gini by 0.0139 points and the poverty headcount by 0.0216.

It should be noted that the different benefit programs may have different intentions. Not all programs necessarily have a redistributive or poverty-reducing objective, for example, sick leave and wage compensation during maternity leave. Almost 40 percent of the wage compensation during maternity leave goes to top 40 percent of the market income distribution.

**Figure 5. Concentration of social benefits (by market income plus pensions quintiles)**

|0%|20%<br>40%|60%|80%<br>100%|
|---|---|---|---|
|Birth grant<br>Child allowance<br>Monetary social assistance|||Poorest<br>2|
|Sick leave|||3|
|Unemployment benefit<br>Care allowances|||4<br>Richest|
|Wage compensation pregnancy/maternity leave||||
|All direct transfers||||

Source: Own estimations using HBS and SILC data Education and health in-kind benefits have inequality-reducing effects. As noted earlier, we assume that the benefit received by the individual is equal to the amount spent per capita. Figure 6 shows that overall education spending is progressive, with primary education being the most redistributive (Table 4). The concentration coefficient for all levels of education except tertiary is negative, suggesting that a larger share of expenditures goes to the lower deciles. However, these results do not capture heterogeneity in the quality of services. For example, the PISA 2018 results<sup>18</sup> suggest that in reading, socio-economically advantaged students outperformed those from more disadvantaged background by 73 score points. Though this is still smaller than the average difference between the two groups across OECD countries (89 score points), the performance gap in Serbia had increased since 2009 when it was 66 points (87 points on average across OECD countries). Health spending is equally distributed across the income quintiles, given the way health benefits are assigned, but is progressive in relative terms since it makes up a larger share of income at the bottom of the distribution.

> 17 This result is based on quintiles of <mark>market income plus pensions per capita that excludes pregnancy and maternity allowance, which may differ from other studies looking at targeting based on quintiles of disposable income.</mark>

> 18 <u>https://www.oecd.org/pisa/publications/PISA2018_CN_SRB.pdf</u>

21
<!-- Start of picture text --> Figure 5: Number of exporting firms per capita Figure 6: Distribution of Export Value among<br>versus GDP per capita (average 2006-10) Exporting firms (percent)<br>ln GDP per capita<br>-4 70 59<br>-5 60<br>-6 BGRMUS EST ESP SWE 50<br>LBN MKD KWT<br>-7 BWA 40<br>-8 JORDOMSLVCRI TURCHLMEX 30 25<br>-9 KEN GTMMARECUCOL IRN 20 14<br>-10 MWI BFAKHMLAO CMR BRA 10 2<br>-11 NERMLI BGD YEM 0<br>-12 top 1% top 1% to top 5% to Bottom<br>6 7 8 9 10 11 5% 25% 75%<br>Figure 7: Entry rates versus total number of Figure 8: Entry rates versus survival rate of new<br>exporters (average 2006-10) exporters (average 2006-10)<br>0.6 0.6<br>KHM<br>BRA TUR<br>MWI CMR KWT EGY<br>0.5 NER LAO TZA 0.5 CRI JOR LAO<br>MLI BFA BWADOM ESTIRN ESP MUSMARALB MKDMLI BFA<br>0.4 KHM ALBECUJORLBN KEN MKD CHL BGR MEX 0.4 COLGTMLBN MEXKENCHLECUBWADOMIRN<br>MUS MAR ESP<br>0.3 CRI GTM COL SWE TUR 0.3 NEREST TZAMWICMR<br>BGD BRA<br>0.2<br>0.2<br>0.2 0.3 0.4 0.5 0.6<br>4 6 8 10 12<br>Ln total number of exporters Entry rate<br>Entry rate<br>ln number of exporters per capita<br>Survival rate<br><!-- End of picture text --> Data Source: World Bank Exporter dynamics database According to the World Bank’s Exporter Dynamics Database,<sup>2</sup> of all Brazilian exporters, a much smaller number of firms make up the overwhelming share of exports: the top 1 percent of exporting firms generates 59 percent of total exports, while the top 25 percent of firms account for 98 percent of exports (Figure 6).

> 2 <mark>The Exporter Dynamics Database provides measures of exporter characteristics and dynamics across 45 countries across all geographic regions and income levels. The Exporter Dynamics Database contains close to 100 measures covering the basic characteristics of exporters, their distribution by size, the diversification in their products and markets, their dynamics in terms of entry, exit and survival, and the average unit prices of the goods they trade. It can be accessed at:  h</mark> <u>ttp://data.worldbank.org/data-catalog/exporter-dynamics-database</u>

8
**Table 4**

**Estimated Transfer Derivatives for Households with Elderly** Residents Estimated in the Neighborhood of Multiples of the Poverty Line

|_Panel A: 1995 to 1998_<br>Multiple|Income/Capita<br>Range(in RMB)|No Migrant<br>Child(ren)|Has<br>Migrant<br>Child(ren)|
|---|---|---|---|
|1⁄4 Poverty Line|119 to 319|-0.84|-1.73|
|1⁄2 Poverty Line|338 to 538|-0.73|-1.03|
|Poverty Line|775 to 975|-0.52|-0.49|
|Twice Poverty Line|1650 to 1850|-0.28|-0.4|
|Four Times PovertyLine|3400 to 3600|-0.11|-0.31|
|_Panel B: 2000 to 2003_|||Has|
||Income/Capita|No Migrant|Migrant|
|Multiple|Range (in RMB)|Child(ren)|Child(ren)|
|1⁄4 Poverty Line|119 to 319|-0.67|-0.63|
|1⁄2 Poverty Line|338 to 538|-0.57|-0.35|
|Poverty Line|775 to 975|-0.46|-0.25|
|Twice Poverty Line|1650 to 1850|-0.35|-0.21|
|Four Times PovertyLine|3400 to 3600|-0.24|-0.20|

Note: All values are in 2000 RMB Yuan per capita. The nutrition based poverty line is equal to 875 RMB Yuan per capita. We use the RCRE Rural Household Surveys for 1995 to 2003 from Anhui, Henan, Jiangsu and Shanxi and the matched RCRE 2004 Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey.

36
<!-- Start of picture text --> SEPT SEPT SEPT SEPT SEPT<br>2019 2020 2021 2022 2023<br>IMPLEMENTATION<br>80% 76%<br>PHONE MIDLINE FOLLOW UP<br>SURVEY ROUND 1 ROUND 3<br>76%<br>IN PERSON ENDLINE<br>SURVEY ROUND 2<br>72%<br>NEIGHBOR GD<br>SURVEY<br>FORECASTS RECIPIENTS NEIGHBORS<br>COLLECTED<br>% INDICATES RESPONSE RATE<br><!-- End of picture text --> 86% OF THE SAMPLE WAS SURVEYED AT LEAST ONCE
Table N4: Effect of SNAP-benefit changes on prices, no housing controls and contiguous counties

|VARIABLES|(1)|(2)|(3)<br>Log pr|(4)<br>ice index|(5)|(6)|
|---|---|---|---|---|---|---|
|Log benefts per population|0.112**|0.109***|0.111*|0.0810***|0.0886**|0.0671*|
||(0.0459)|(0.0383)|(0.0553)|(0.0253)|(0.0413)|(0.0385)|
|Log benefts p.p., other states in same chain||||||0.0200<br>(0.0291)|
|Observations|382524|555072|555072|210240|210240|345468|
|R-squared|0.881|0.933|0.931|0.926|0.926|0.904|
|Prob _>_ F|0.000|0.000|0.000|0.000|0.000|0.000|
|Number of units|7970|2706|2706|1372|1372|7198|
|Number of clusters|48|47|47|42|42|48|
|First stage F-stat|9.832|13.952|4.411|7.188|2.441|4.073|
|Housing controls||X||X|||
|Contiguous counties||X|X|X|X||
|Same CZ||||X|X||

Notes: Robust standard errors are in parentheses, clustered by state. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Control variables from our preferred specification as well as store and period fixed effects are included. Contiguous counties refers to a specification with county pair fixed effects, where county pairs are pairs of adjacent counties that straddle state borders. Same CZ are county pairs in the same commuting zone as constructed in Autor and Dorn (2013). Log benefits p.p., other states in same chain, refers to average log benefits per population for each store at the retail chain level, weighted by store revenue in the pre-period of 2006, excluding stores in the same state.

75
<!-- Start of picture text --> 50<br>44<br>40<br>34<br>-_<br>5 30<br>&<br>25<br>20<br>10<br>1971 1976 1981 1986 1991 1996 2001<br>China = India<br>=m Russia = Latin America & Caribbean<br>Sub-Saharan Africa = Growth 13<br><!-- End of picture text -->