rafmacalaba/datause-encoder-data huggingface dataset:
As noted in the introduction, the literature on developing countries mainly focuses on intergenerational educational mobility because of data limitations. Although there is a growing literature studying the persistence of educational attainment across generations at the country level, the studies that attempt to provide comparable estimates across a sample of developing countries remain limited.<sup>19</sup> The most widely known cross-country analysis of intergenerational educational mobility is Hertz et al. (2008) that provides estimates of relative mobility using intergenerational regression coecient (IGRC) and intergenerational correlation (IGC) between father and children for 42 countries. Neidhofer et al. (2018) report estimates of a variety of measures of absolute and relative educational mobility for 18 Latin American countries. A number of recent papers focus on Sub-Saharan African countries, see, for example, Alesina et al. (2021), Azomahou and Yitbarek (2021), and Razzu and Wambile (2022).<sup>20</sup> The most extensive analysis of intergenerational educational mobility around the world is oered in a recent book by Narayan et al. (2018) covering 153 countries. They provide estimates of a number of absolute and relative educational mobility measures, but their main analysis is based on the IGRC estimates. Perhaps, more important, all the cross-country studies noted above focus on the intergenerational link between parents' and children's educational attainment, and none report estimates of sibling correlation.

In more than two decades following the publication of the handbook of labor economics chapter by Solon (1999), there have been only a few studies on developing countries that use sibling correlation as a measure of educational mobility. This is puzzling because Solon (1999) and the subsequent surveys of the eld (e.g., Bjorklund and Salvanes (2011)) provide substantial discussions on the advantages of sibling correlation as a measure, especially in the data scarce environment common in developing countries. The most widely cited is a study by Dahan and Gaviria (2001) who report estimates of sibling correlation in schooling for 16 Latin American countries. But as noted earlier, they do not follow the methodology developed in Solon et al. (1991), and Solon (1999). They use a measure of educational failure (lack of grade

> 19At the individual country level, recent contributions on intergenerational educational mobility in developing countries include Kundu and Sen (2022), Azam and Bhatt (2015), Azam (2016), Emran and Shilpi (2015) on India, Fan et al. (2021), Emran and Sun (2015) on China, Torche (2015b) on Mexico, Assaad and Saleh (2018) on Jordan; Ahsan et al. (2023), Ahsan et al. (2021) on Indonesia. For surveys of this literature, please see Iversen et al. (2019), Torche (2019), and Emran and Shilpi (2021).

> 20These studies rely on census data from IPUMS.

8
- 42 -

# Referencea

Adam, J. (1979), Wage control and inflation in the Soviet bloc countries; Praeger Publishers.

Adam, J. (ed.), (1982), Employment policies in the Soviet Union and Eastern Europe; MacMillan Press.

Bauer, T., (1990), The Microeconomics of Inflation uxder Economic Reforms: Enterprises and Their Environment; mimeo.

Calvo, G. and Coricelli, F. (1990), Stagflationary effects of stabilization programs in reforming socialist countries: supply side vs. demand side factors; The World Bank.

Central Statistical Office, Monthly Bulletin of Statistics; Budapest, Hungary.

Charemza, W. and Gronicki, M., (1983), Rational expectations, wage illusion and consumption excess demand: an empirical investigation for Poland; Birkbeck College Discussion Paper, no. 143.

Commander, S. and Coricelli, F., (1990), Levels, rates and sources of inflation in socialist economies: a dynamic framework; The World Bank, mimeo.

Commander, S. and Coricelli, F., (1990) The macroeconomics of price reform in socialist countries: A dynamic framework; The World Bank, mimeo.

Ellis, C.J. and Fender, J., (1985), Wage bargaining in a macroeconomic model with rationing; Quarterly Journal of Economics.

Friedman, J.W., (1986), Game theory with applications to economics; Oxford University Press.

Granick, D., (1987), Job rights in the Soviet Union: their consequences; Cambridge University Press.

Gomulka, S. and Rostowski, J., (1984), The Reformed Polish Economic System, 198283; Soviet Studies, 36,3.

Horvat, B., (1986), The theory of the Worker-Managed Firm revisited; Journal of Comparative Econtales, 10.

International Monetary Fund, (1989a) Economic Reform in Hungary since 1968; Washington, mimeo.

International Monetary Fund, (1989b) Economic Reform in Poland since 1981; Washington, mimeo.

Jones, D., and Svejnar, J.,(eds),(1988), Advances in the Economic Analysis of Participatory and Labour Managed Firms; JAI Press.
|Stimulation|NNS Form<br>3.1|Cognitive stimulation|Caretaker reads & plays with<br>child|Yes|No|
|---|---|---|---|---|---|
|||Child is currently<br>breastfed||Yes|No|
|Feeding|NNS Form<br>3.1|Child is currently eating<br>complementary foods<br>**Age appropriate**<br>**breastfeeding (WHO)**|**Children up to 6 months are**<br>**exclusively breastfed, at or**<br>**above 6 months receive**<br>**complementary foods along**<br>**with breastmilk**|Yes<br>**Yes**|No<br>**Yes**|
||NNS Form<br>4.3|Early initiation of<br>breastfeeding||Yes|No|
|||Minimum dietary<br>diversity (6m & older)||No|No|
||NNS 24<br>hour-recall|Minimum meal<br>frequency (6m & older)||No|No|
|||Minimum acceptable<br>diet (6m & older)||No|No|

Good hygiene habits are quite common, 4 in 5 children under-2 have mothers who report always handwashing before and after food handling and toilet use, with only minor differences associated with family income or education. In contrast, only 39 percent of children are read to and played with by the caretaker, with moderate differences associated with family income and the mother’s education. Less than 1 in 2 children under-2 are appropriately fed for their age. Age appropriate feeding is the only nutrition intervention indicator that is negatively associated with family income and mother’s education. For example, children under-2 from the poorest quintile are twice as likely to be breastfed according to WHO standards for their age than children from the richest quintile. The reason is that women in higher earning households are less likely to breastfeed and more likely to use breast-milk substitutes. Overall, 16 percent of children under-2 are in a home environment which provides good hygiene, cognitive stimulation and WHO recommended breast- and complementary- feeding practices.

A closer examination of the age-profile of different infant and young child feeding practices reveals that women with elementary educational attainment or less (EE) are increasingly more likely to be breastfeeding their child compared to women with college education (CE) as the child gets older (Figure 19). For example, in the first two months of life, children of EE women are 16 percent more likely to be breastfed compared to children of CE women. At just over a year old, children of EE women are 66 percent more likely to be breastfed compared to children of CE women. EE women are both more likely to breastfeed and breastfeed for longer than CE women. The same is true for exclusive breastfeeding, though by 8 months of age, very few children are still exclusively breastfed. EE women are also more likely to introduce complementary foods earlier than CE women. For example, between the age of 4 and 6 completed months, 29 percent of children of EE women are being fed solid or semisolid foods, compared to 18 percent of CE women. As CE women transition away from breastfeeding, not only do they introduce complementary foods, they also introduce breastmilk substitutes, such that by the age of 2, almost all children of CE mothers are drinking milk of any kind, whereas the proportion of children of EE women near the age of 2 drinking milk of any kind is only 69 percent.

30
environment.

We begin with the fiscal analysis, asking the question: if we could reduce the infant mortality rate by 1% point starting today, or if we could eliminate open defecation in India today, what would be the impact on the tax revenues collected by the Indian government as a result of higher wages? We then ask a similar question about the improvements in welfare for individuals resulting from higher consumption.

# **4.1 Fiscal Gains from Improved Sanitation & IMR**

In the previous section, we demonstrated that improvements in sanitation and the disease environment are associated with higher wages and consumption. An immediate corollary of these results is that investments in sanitation improvements will raise the tax revenues collected by the Indian government, through increased payments of both income and consumption-based taxes.<sup>13</sup> Given our estimates of the impact of sanitation and IMR on wages, and an approximation to the tax system in India, we can estimate the future gains in tax revenues above and beyond the “business-as-usual” value. Since these revenue gains occur in the future, starting when the year-of-birth cohort born today enters the labor market, and gradually phasing in after that as more treated cohorts enter, we can use an appropriate discount rate to add up these future gains and express the revenue gains as a present-value equivalent.

For our main analysis, we use the linear regression results from Tables 2 and 3; we also use the quantile estimates found in appendix B to perform a robustness check in appendix C.1. As our baseline estimates, we use the values from column 1, where a 1% point increase in the IMR (or 10 points of IMR per 1000) is associated with 1.74% lower wages, while a 10% point increase in sanitation coverage is associated with 2.96% higher wages. These values are in the middle of the range of estimates, but we will also present results later with additional controls included.

We assume that the impact of sanitation and IMR on tax revenues is the same size: 1.74%

> 13Only about 3% of the population of India pays income taxes, as the income threshold for paying tax is set at 200000 rupees, several multiples of per-capita GDP. However, the quantile regressions in the previous section indicate that the effect of sanitation and IMR on wages is larger at the upper end of the distribution, meaning that it is reasonable to expect an impact on the wages of people paying income tax, as well as an increase in the number of people earning enough to qualify.

15
TABLE 8.

# D1STRIBUTDN OF MARGINAL REVENUE PRODUCTS<sup>OF</sup> LABOR (), EXPENSES E) AND CAPIrAL (C)

|_TYPE_**OF**FiM||TOTA|L||FEM A|LE|M|ALE<br>1D|--<br>-||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|DECLE|L|E|K|L|E|K|L|E|K|L|E|K|
|1|.054|490|.021|.057|.468|.019|.057|.492|.019|.049|.601|.096|
|2|.087|.638|.038|.088|.584|.037|.093|.629|.038|.079|.763|.037|
|3|.115|.751|.057|.116|445|.062|.138|.713|.066|.101|.884|.050|
|4|.147|.860|.078|.150|.814|.132|.110|.817|.094|.125|.985|.060|
|5|.190|.992|.116|200|.925|.232|375|.957|.178|.147|1.091|.069|
|6|.245|1.127|.253|.263|1.067|.315|.321|1.067|.266|.185|1 .204|.082|
|7|322|1.300|.391|.360|1.266|.438|.415|1.225|.404|.226|1.352|.100|
|8|.456|1.536|.590|.498|1.494|.603|.645|1470|.848|.306|1.614|.398|
|9|.916|1.945|1.069|1.188|1.882|1.010|1.601|1.765|1.367|.431|1h418|1.183|
|10|67.053|7.247|3550|67.053|4.542|3202|7.835|7247|3.550|6.980|6.396|3.363|
|M EAN|.535|1.170|.385|.646|1.096|.385<br>|.632<br>|1.154<br>|.467<br>|.249<br>|1.327<br>|.322<br>|
|ST DEV|2253|.801|568|3.000|.642|.488|1.152|.927|.676|.497|.944|.614|
|OBS|1098|1098|1098|576|576|576|230|230|230|292|292|292|

A feeling for the results can be<sup>obtained by considering the row of means.</sup> For the total sample of firms,<sup>the mean marginal revenue product for expenses</sup> is 1.170 intis and is slightly<sup>in excess of the value of one inti suggested</sup> above.<sup>23</sup> The mean rate of return to<sup>capital is38.5percent and the shadow wage</sup> for one hour of labor is .535 inti.<sup>Before considering comparative returns in</sup> different types of enterprises,<sup>it is useful to determine the extent to which</sup> the mean values are informative. Table 8 also shows the cumulative<sup>distribution</sup> (by decile) of the marginal revenue<sup>products for each factor and by each type</sup> 23 As discussed earlier, these estimates<sup>are unbiased with respect to the</sup> expected revenue component.

44
# **Appraising the Thailand Village Fund**

## **Jirawan Boonperm**

Ministry of Information and Communication Technology, Bangkok, Thailand

## <u>jirawan@nso.go.th</u>

## **Jonathan Haughton**

Suffolk University, Boston <u>jonathan.haughton@suffolk.edu</u>

## **Shahidur R. Khandker**

World Bank, Washington DC <u>skhandker@worldbank.org</u>

## **Pungpond Rukumnuaykit**

National Institute of Development Administration, Bangkok <u>Pungpond_r@yahoo.com</u>

### **Keywords**

Village Fund; Thailand; microcredit; endogenous switching regression; cost-benefit analysis; fund performance.

### **Keywords**

JEL codes: 016 G21 I38 Note: The authors listed above are in alphabetical order. They would like to thank Sanonoi Buracharoen for her excellent help with collecting and understanding the data, and Rikke Nørding Christensen, Samad Husain, Saowaluck Imbumrung, Budsara Sangaroon, and Sorn for their help and comments. They are also indebted to Will Martin for very helpful suggestions. The Thai National Statistics Office was very helpful in supporting this work, and we also wish to thank the National Office of the Thailand Village Fund for discussing the issues with us. The views expressed in this paper are those of the authors, and not necessarily of the institutions with which they are affiliated.
necessary re-ranking. Some individuals who started in the bottom 40% will move into the top 60%, while others will fall into the bottom 40. The difference in the annualized growth rates of the bottom 40% and the mean is 2pp, with the bottom 40% growing at 8.1% and the mean growing at 6.1%, the 10 year historical growth rate of national accounts. Interestingly, the reduction in the annualized growth rate of the top 60% necessary to ensure that the bottom 40% grow 2pp above the mean is relatively small (see Figure 2). The top 60% grows at an annualized rate of 5.7%, just 0.4pp below the growth in the mean, or what would have been the case in a distribution-neutral scenario ( ). mm= 0

**Figure 6: 2011-2030 final growth incidence curve for China**

<!-- Start of picture text --> Bottom 40 Top 60<br>g40 final=8.1%<br>m=2<br>g (mean) final=6.1%<br>g60 final=5.7%<br>0 20 40 60 80 100<br>Percentile group<br>.09<br>.08<br>.07<br>annualized growth<br>.06<br>.05<br><!-- End of picture text --> The re-ranking process also leads to a strong concentration of the distribution around the 40<sup>th</sup> percentile. The simulations, and particularly the re-ranking process, produce a density function with incomes bunched around the 40<sup>th</sup> percentile. Such a distribution shape seems quite unrealistic. However, it stems from the imposition of a certain pattern of growth based on a step function over almost 20 years, which is obviously an artificial assumption.

13
that find greater trade openness to be associated with higher female employment include, for example, Ozler (2000), Wood (1991), Cagatay and Ozler (1995), Cagatay and Berik (1990), Kasnakoglu and Dikbayir (1997), Chen et al. (2013), Juhn et al. (2014), Ederington et al. (2010), Aguayo-Tellez et al. (2010), Spieldoch (2004), Gaddis and Pieters (2012), Pradhan (2005), Bussman (2009), Rocha and Winkler (2019), World Bank (2020) and United Nations (2009). In contrast, studies showing no effect or a negative effect or contradictory effects of trade liberalization on female employment in developing countries include, for example, Meyer (2006), Wamboye and Seguino (2014), Joekes (1995), Gray et al. (2006) and Cooray et al. (2012).

Our study provides several contributions to the literature. First, we use firm-level survey data for 44,539 manufacturing firms across 91 (mostly) developing economies covering multiple waves of surveys between 2006 and 2017. Second, regressing female employment in a firm on exporting activity of the same firm is fraught with endogeneity problems. Thus, we follow the broader literature and proxy firms’ exporting activity by the average exporting activity of all other firms in the same country-year-industry cell (“cell average”). As discussed below, use of cell averages has been made in the literature and it helps to considerably reduce the possible spurious correlation problem. We go beyond and exploit the repeated cross-section nature of the data. That is, the identification of our main results comes from changes in exporting activity of firms within a country-industry pair over time and the associated change in female employment. This helps limit several avenues of omitted variable bias that plague pure cross-sectional regressions.

Third, we test for several predictions or implications of the theoretical models in the related literature. These tests not only serve to provide a better understanding of the relationship, but also raise our confidence against endogeneity concerns. For instance, it is argued that greater competition that exporters face relative to non-exporters raises the cost of discriminating against 3
Appendix B

**Table B3: per capita household income and inequality – DHB tables**

|**ESTADO**|**REND**<br>**PER C**<br>**(Sal. M**|**A FAMIL**<br>**APITA M**<br>**in. de set **|**IAR**<br>**ÉDIA**<br> **/ 91)**|**GRAU DE**<br>**(T**|**DESIGUA**<br>**HEIL - L)**|**LDADE**|
|---|---|---|---|---|---|---|
||_1970_|_1980_|_1991 _|_1970_|_1980_|_1991 _|
|Alagoas|0.30|0.62|0.62|0.52|0.58|0.71|
|Bahia|0.36|0.84|0.68|0.58|0.65|0.80|
|Ceará|0.26|0.64|0.65|0.60|0.69|0.81|
|Maranhão|0.27|0.49|0.46|0.33|0.51|0.64|
|Paraíba|0.23|0.56|0.59|0.58|0.65|0.73|
|Pernambuco|0.38|0.83|0.81|0.63|0.65|0.80|
|Piauí|0.19|0.43|0.50|0.46|0.61|0.76|
|**Rio Grande do**<br>**Norte**|**0.26**|**0.68**|**0.72**|**0.57**|**0.62**|**0.74**|
|Sergipe|0.31|0.76|0.73|0.53|0.61|0.71|
|**Brasil**|**0.63**|**1.43**|**1.31**|**0.68**|**0.70**|**0.78**|
|Nordeste|0.31|0.69|0.65|0.57|0.65|0.78|

53
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# **S8. Selection of staff, drugs, and equipment for surveying**

During the first survey visit, information on all staff working at the facility, by professional cadre, and whether or not they were conducting patient consultations, was collected. Then at most 10 health workers were randomly sampled from the list of health workers present for the vignettes. Thus, the proportion of doctors, nurses, and midwives was not stratified and would roughly reflect the proportion of those professionals present at the clinic.

The World Health Organization Service Availability and Readiness Assessment (SARA) formed the basis for the list of drugs and equipment, intended to capture a list of essential or minimal items that should be available at every health facility.<sup>11</sup> Within each country, the SDI team collaborated with the government to adjust items on the list according to local guidelines and priorities. Availability of drugs and equipment was assessed during the first visit by visual inspections of storage facilities and consulting rooms at the health facilities.

A few days after the first visit, enumerators made a second unannounced visit to the facility and assessed the presence of a randomly pre-selected list of providers (or all providers, if less than 10 providers were available). Only staff on duty that day were included in the absenteeism measure. Staff who were away from the facility due to outreach or fieldwork were counted as present.

52
7 grew at a rate of 3.5 percent, of which almost 50 percent is explained by capital accumulation, 45 percent by population growth and only 5 percent by the contribution of productivity growth.<sup>7</sup> In fact, six countries show negative contributions of productivity to GDP growth - Bolivia, El Salvador, Guyana, Nicaragua, Paraguay and Venezuela. It is worth noting that, even though the worst performer in TFP growth also has the lowest rate of GDP growth (Guyana), there is no clear correlation between the countries' rate of GDP growth and the contribution of TFP to this rate. However, evidence in favor of such a correlation is provided by the average growth decompositions for the periods 19501979, 1980-1989 and 1990-1995, reported in table 1. The data do show a positive relationship between output and productivity growth. During the 1980s, the rate of GDP growth fell to less than 20 percent of the figure for the previous three decades - from 4.4 percent to 0.8 percent - and the contribution of TFP to GDP growth went from 16 percent to minus 204 percent. During the first half of the 1 990s, the rate of GDP growth came recovered, almost reaching its previous historical levels. However, the region's rate of capital accumulation remained at relatively low levels, and thus the contribution of TFP to overall growth increased to 31 percent, which is almost twice the average for the period that preceded the "lost decade" - as the eighties have been called by analysts of the LAC region.

Table 2 reports growth decompositions, both for the periods when the economies were reformed and for those in which they were not reformed. Only 6 out of the 18 countries experienced slower GDP growth in the periods of reform, during which the effect of short-run fluctuations - see, for example, Loayza (1994), Islam (1995) and Caselli, et al. (1996).