rafmacalaba/data-use-mentions-extended huggingface dataset:
. This is particularly true for developing countries, where the ‘relevance’ of a poverty threshold tends to erode with increasing access to markets, new product varieties and overall economic growth. This was also true in the case of Rwanda, where significant changes in the socio-economic structure of the country as measured by EICV4, necessitated an update of the poverty line.

In Rwanda, changes in consumption patterns between 2000-2014 were primarily reflected through a reduction in the share of food consumed by the poorest households (Engel’s law), decreases in household size (reflected in reduced fertility rates and falling dependency ratios), increases in asset ownership (ownership of more expensive assets like televisions and mobile phones increased in the bottom quintile), and improvements in child and maternal health (reduced stunting, wasting, infant and maternal mortality).<sup>6</sup> In addition, per capita GDP growth remained positive, and education and labor market outcomes also improved over time. All these changes that were reflected in the consumption patterns captured by EICV4 needed to be incorporated in a poverty measure intended for evidence-based policy making going forward.

> 6 See Rwanda Poverty Profile Report (2015).

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# _Ignoring SGRY_

Finally, I estimate the effect of ignoring SGRY when measuring treatment based on program expenditure. Column 5 of table 1 reports the impact of program expenditure on wages but after replacing expenditure values under SGRY by zero. As can be seen, keeping the same set of controls as in column 4, column 5 underestimates the effect of public workfares by approximately 43% when I ignore expenditure under SGRY. In column 5, _β_<sup>�</sup> is equal to 0 _._ 013 which is insignificantly different from zero. Consistent with the result in the baseline model with binary treatment, ignoring SGRY underestimates the impact on wages even with continuous treatment.

In the online appendix, I check for the stability of the elasticity estimate over the two program regimes in table S4. Thereafter, I test whether there is any sub-sample heterogeneity based on how well the employment programs were implemented (table S5). The results are consistent with previous studies like Imbert and Papp (2015) that report a more significant impact for states that implemented NREGA well. Table S6 reports wage elasticities based on gender and over different program regimes. Finally, table S7 reports table 1 but for wages for relatively skilled labor such as carpenter, cobbler, and blacksmith.<sup>15</sup> We do see a positive impact on wages for skilled labor as well but consistent with the expectation, there is a greater impact on wages for unskilled laborers. Not controlling for SGRY underestimates the impact on wages for skilled labor by 47%.

# Impact on Casual Earnings

I now estimate a baseline model on casual earnings with NSS data. Here, I estimate the impact of NREGA on daily casual earnings by comparing the growth in the outcome variable over time between districts that received NREGA early (before 2008) to those that received it late (from 2008). Similar to Imbert and Papp (2015), I estimate equation 3 where _log_ ( _wi,d,t_ ) refers to the log average
8 so that our measure of length of maturity equals long term debt divided by the sum of long term debt, short term debt and trade debt.

The second sample, that unfortunately cannot be linked to the first one, is also derived from the data of the Superintendencia de Companias, includes 850 companies and covers the period 1984-1992 (SC2 from now on). The period covered by this sample is longer and more recent so that it allows us to investigate more convincingly changes in the allocation mechanism of long term credit, before and after financial liberalization. This data set, however, contains fewer and much more aggregate variables. In this case we only have available data on total long term liabilities (that includes also debt of shareholders -quite important in smaller companies- and other deferred liabilities unrelated to fmancial or trade debt). In this case our measure of length of maturity is total long term liabilities divided by total liabilities.

# **111.1. DESCRIPTIVE STATISTICS FOR THE TWO PANELS**

For the firms in the **SC1** sample, long term financial debt represented in 1984 _11.5%_ of total debt (see Table 5). This figure is of the same order of magnitude as the one obtained from aggregate financial data. The share of long term debt in total debt increased until 1987, reflecting the faster decline in short term debt during that period. In the SC2 sample, we observe after 1987 a decrease in the length of maturity, as a result of the real decline in long term debt and of the real expansion in short term debt we have already observed at the aggregate level.
|60.9|57.2|47.9|71.2|71.1|48.8|
|Part-time (<25 hours/week)|44.6|30.4|26.6|27.4|32.4|36.9|23.2|
|Full-time (>25 hours/week)|55.4|69.6|73.4|72.6|67.6|63.1|76.8|
|_Types of occupation_||||||||
|Managers|2.3|9.6|12.4|14.5|6.0|6.0|15.9|
|Skilled employee|9.9|18.0|17.1|23.2|9.0|13.1|22.5|
|Unskilled employee|16.9|10.1|9.1|14.2|5.5|9.9|9.4|
|Family worker|26.8|6.9|9.1|12.1|8.3|14.1|4.9|
|Own-account worker|44.1|55.3|52.2|35.9|71.1|56.9|47.2|

_Source_ : Kinshasa Survey 2018.

Workers’ education levels are clearly associated with their employment conditions. More specifically, workers with higher education are more likely to have wage/salaried jobs (76 percent), and work in the formal (68 percent) and/or public sectors (36 percent) compared to those with lower levels of education (Figure 6). In contrast, workers who have only preschool/primary education usually end up in the informal sector (83 percent) and are mostly self-employed (64 percent) in the private sector.

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*Honduras**|5.1|4|5|4.6|0.49|5.4|6|5|4.4|0.46|
|**Nicaragua**|5.0|4|8|4.7|0.51|5.9|6|7|5.0|0.48|

Note: Dif 75 25 refers to the difference in mean years of education between the 75th and 25th percentile. Source: Authors calculations based on Costa Rica EHPM 2001 and 2009, Guatemala ENCOVI 2000 and 2006, Honduras EPHPM 2001 and 2009, Nicaragua EMNV 2001 and 2009, Panama ECH 2000 and 2009, El Salvador EHPM 2001 and 2009

# **Inter and Intra Generation Evolution of Inequality**

The average gain in years of education is mainly achieved by marginal gains as new generations accumulate more years of education, which raises two important questions. First, is the education achievement of more educated new generations more equal? And second, what is the current level of intergenerational inequality?

Clearly, new generations are more educated, and currently the education achievement reaches its peak for the age group 25-29 years old in Panama and for the age group 20-24 years old in other countries in the region (Figure 5). Surprisingly, no country in the region has a cohort that has finished on average upper secondary; Panama is the closest one to achieving that level (approximately 11 years of education); in Costa Rica, the highest level achieved is lower secondary education (9 years) whilst in Guatemala the youngest cohort finishes only primary education (approximately 6 years of education). In comparison, in the OECD countries, an average of 81% of 25-34 years olds have achieved upper secondary education<sup>9</sup> and the average number of years of schooling for this cohort is just over 12.5 years.<sup>10</sup> Analyzing the education attainment across cohorts enables us
4 as they consider specific policy measures to try to improve the distribution of rural incomes. Third, the paper uses regression analyses to identify the role of different factors - including land ownership - in "determining" the level of different sources of rural income. This analysis finds that while land ownership is not significantly related to the determination of nonfarm income, it is positively and significantly related to the determination of agricultural and rental income.

The study proceeds in four further sections. Section I presents the decomposition of the Gini coefficient. Section II presents the household data set from rural Egypt. Section III analyzes the contribution of the various sources of income - including nonfarm income - to overall rural inequality. Section IV analyzes how exogenous changes in various sources of income affect overall inequality and welfare, and Section V discusses how land and other factors determine different types of rural income. Section VI concludes.

# I. Decomposition of Income Inequality

At the start of any decomposition exercise, the question arises: what measure of inequality should be used? Several different inequality measures have been proposed in the literature. Following Foster (1985) and others, the chosen measure should have five basic properties. They are: (1) Pigou-Dalton transfer sensitivity; (2) symmetry; (3) mean independence; (4) population homogeneity; and (5) decomposability.

Pigou-Dalton transfer sensitivity holds if the measure of inequality increases whenever income is transferred from one person to someone richer. Symmetry holds if the measure of inequality remains unchanged when individuals switch places in the
over subnational jurisdictions, provides a unique opportunity to identify the impact of procompetition reforms on establishments’ productivity and markups in a quasi-experimental setting within the controlled institutional environment of a single country.

We match the information on eliminated subnational regulatory barriers to competition across municipalities in 2013 and 2014 with establishment census data to estimate the impact of the enforcement of the elimination of regulatory barriers to market entry and competition on establishment productivity. We find that establishments located in municipalities that eliminated regulatory barriers to entry experienced a significant increase in subsequent (revenue) TFP growth relative to establishments not located in reform-municipalities. The effect is largest if municipalities eliminated operating licenses requirements not complying with national law which constitute local barriers to market entry. At the same time, these regulatory reforms did not impact establishments’ markups, or, if anything, tended to reduce them, suggesting that establishments’ physical productivity has indeed improved. We provide a wide range of evidence supporting a causal interpretation of these findings.

The results highlight the importance of subnational regulatory barriers in attenuating competition and reducing firm productivity. Such barriers can undermine the impact of seemingly best practice national regulatory frameworks and trade liberalization at the macroeconomic level. Our findings further suggest that strengthening the mandate of institutions enforcing competition is critical to raise productivity. In Peru, they help explain why (firm) productivity growth has been lagging for a long time despite the relatively competitive business regulations at the national level and the introduction of one of the world’s most liberal trade regimes. The reforms strengthening Peru’s competition authority in 2013/14 partly correct this shortcoming. Legal reforms in 2016/17 further empowered Peru’s competition authority by strengthening its judicial mandate and establishing that its ruling on regulatory barriers declared illegal is universally binding for all government bodies throughout the country. Similar analysis based on subnational microeconomic data might shed light on explaining the laggard productivity growth also in other countries with relatively liberal trade regimes and macro-regulatory environments, such as Mexico.

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empirical support for the model provided in this paper suggests that scarce attention may be part of explanations for phenomena in economic development, including beyond the specific application studied here.

The remainder of the paper is organized as follows. Section 2 discusses related literature and provides further background on agricultural extension services. Section 3 presents the model and derives predictions about farmers’ demand for agricultural advice. Section 4 provides empirical evidence on the predictions of the model and addresses alternative explanations. Section 5 concludes.

# **2 Related literature and background on extension services**

Our paper contributes to several different literatures. First, we add to the recent literature in development economics that tries to explain low levels of technology adoption and low input use in agriculture, in particular in Sub-Saharan Africa. Various channels have

> 5 The empirical work is based on survey data from the Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) database (World Bank, 2018).

4
should be considered in the broader context of socio-economic outcomes.

Further examples with detailed information on how such diagnostics could be implemented is provided by Cruz and Zhu (2022). The first part of the diagnostic toolkit describes in more details the implementation of each stage we propose, including references to data set and examples of implementation. A pilot of these diagnostics, following the conceptual framework and stages of assessment proposed in this paper, have been implemented in several countries, including Senegal (Cruz, Dutz, and Rodríguez-Castelán (2022) and Cruz, Torres, and Tran (2022)), Kenya (Cruz and Zenaida, 2022), and Romania (Cruz et al., 2022). In each of these countries, the implementation of the assessment relied on the most comprehensive and representative establishment-level data, used to identify the potential of key local entrepreneurial ecosystems, along primary data collection of enablers to providing support in the ecosystem.

In Romania, the cross-country context analysis conducted in the first phase suggested that despite the relatively high entry, there was a lack of high-growth and innovative entrepreneurship, with many necessity driven firms. A deeper dive into the entrepreneurial ecosystem pillars suggested significant room for improving allocation of existent human capital. The local analysis, supported by representative primary data focusing on digital startups demonstrated significant differences on resource endowments and allocation barriers between firms that received venture capital investment and the average young digital businesses. The mapping of public policies have identified significant needs to improve the policy mix for innovation, entrepreneurship, and digitalization and its institutional capabilities to implement these programs, by reallocating resources that were already being targeting private sector development. The complementary analysis, following the conceptual framework we propose in this paper, has facilitated the identification of key priority actions for government policy, as well as other stakeholders in the ecosystem. The description of the diagnostic following this conceptual framework is provided by (Cruz et al., 2022), while the detailed guidance for implementation of the recommended policies is described by (Kapil et al.
unique data set allows to circumvent. We contribute to the broader debate on the link between globalization and gender inequality in developing countries.

This paper is organized as follows. In Section 2, we describe the data and provide summary statistics. Our empirical approach is described in Section 3, while regression results are shown in Section 4. Section 5 concludes.

# **2. Data**

Our analysis uses data from a firm-level survey conducted by the World Bank in Bangladesh during November 2004–September 2005 covering the apparel and textiles sectors.<sup>4</sup> For each sector the sample was drawn resorting to a different data source. For the apparel sector, a stratified random sample was drawn based on the rich Bangladesh Garment Manufacturers and Exporters Association directory covering 350 firms, which corresponded to about 10 percent of the total population of the domestic firms and 100 percent of FDI firms operating in the sector as of 2004 were covered. The sample of apparel firms was stratified to reflect the population distribution of firms by size, by industry (woven garments versus non-woven garments), and by location (Chittagong, Chittagong-Export Processing Zone, Dhaka, and Dhaka-Export Processing Zone). For the textiles sector, the random sample was drawn based on the most recent list of firms from the corresponding business association also stratified by size. Strict quality control criteria were applied during the data collection and data processing phases. The survey collected a wealth

> 4  See Fernandes (2009) and Kee (2015) for studies on firm productivity based on the Bangladesh survey data.

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no studies in the economic literature that are specifically focused on the distinction between bonding and bridging. Bartolini et al. (2008) consider, besides generalized trust, people’s participation in social organizations, whether of the Olson or Putnam variety, thus following Knack and Keefer (1997). The impact on life satisfaction of the former type of organizations emerges as negative and of little significance, while the impact of the latter emerges as positive and highly significant.

Other studies focus on the _impact of specific indicators of social capital on life satisfaction_ , but their findings are not necessarily in line with the results discussed above. _Family ties_ are found to be positively, significantly, and sizeably correlated with life satisfaction (Alesina and Giuliano 2007), which is consistent with the recurrent result of the positive association between marriage and life satisfaction. However, this same result is not easily reconciled with the negative correlation found between family ties and generalized trust, which is positively associated with life satisfaction.

A similar but less clear issue emerges from _religiosity_ . This variable appears to be positively correlated with life satisfaction in several studies (Dolan et al. 2008; Clark and Lelkes 2009), but it also depends on the indicators of religiosity used (Helliwell 2008), and (according to psychologists) on the indicators of self-reported well-being used (Lewis 2005). In particular, if the life satisfaction scale is of the ‘Cantril ladder’ type, as in the Gallup Poll dataset, then the importance of religion does not show a significant correlation with life satisfaction (Helliwell 2009).

The conclusions from this brief review of the literature (summarized in Table 1) are thus the following. _First_ , social capital cannot be taken as a homogeneous concept. In fact, some exploratory and partial analyses show that social capital can be captured by specific measurable indicators, which fit nicely with the definitions of bonding and bridging as two dimensions of social capital, and which seem to relate negatively to each other in some important instances. Therefore,
outflow from the fund. Then our aim is to answer the following questions for a sizeable oil discovery in a small economy that adds to the fund:

- What are the desired scale and pace of public investments over time?

- How should public investments be allocated over physical and human capital?

In order to answer these questions, we develop a multi-sector, small open economy Dynamic Stochastic General Equilibrium (DSGE) model that is based on Melina _et al._ (2016). We add to the latter, on the one hand, the accumulation of human capital, built via public expenditures in education and health expenditures; and, on the other, a richer array of fiscal options as far as the usage of natural resource revenues is concerned. These include Permanent Income (PIH) and Bird-in-Hand (BIH) based rules.

The model characterizes multiple types of public sector debt, multiple tax and spending variables, and a resource fund. The country produces a composite of traded goods and a nontraded goods using capital, labor, and its productivity is affected by governmentsupplied infrastructure, health and education. It is also endowed with natural resources, the production and prices of which are assumed to be exogenous. Since the time horizon is 20+ years, the model abstracts from money and all nominal rigidities.<sup>4</sup> The model has a number of important features specific to LIDCs. These are financially constrained households who do not have access to capital and financial markets and consume all of their disposable income each period; remittances received by households; a productivity effect of health and education; investment adjustment costs; international grants received by the government; public investment inefficiencies and absorptive capacity constraints, and finally a resource fund. It includes also standard distortionary taxes and investment adjustment costs.

We calibrate our model by using the available data from Kenya, which discovered an estimated 600 million barrels of oil in 2012 and is expected to start commercial production in the early 2020s. Although the proven reserves are relatively small
exclude the observations from the lower half of the schooling distribution to capture the idea that a higher achieving adult is more likely to be the role model a child looks up to and aspires to emulate.

The estimates for the investment equation in Table 7A show that the impact of role models on the educational expenditure is different from that of peers in the urban areas. The coefficient _θm_ is _negative_ and significant at the 1 percent level _; the role model effect is thus a substitute for educational expenditure made by parents in urban Indonesia, implying that δ_ 6<sup>_m<_0</sup><sup>_._In contrast, the evidence in the rural sample cannot reject the null hypothesis</sup> of separability in the education production function, similar to the evidence on peer effect discussed earlier.

The results on intergenerational mobility in Table 7B show that the interaction of role models with father’s education is negative and statistically significant both in rural (at the 5 percent level) and urban (at the 1 percent level) areas. The inclusion of the role model effects in the mobility equation, however, does not reduce the convexity with respect to father’s education.

# **(8) Conclusions**

We incorporate the rural-urban differences in returns to education and school quality in a Becker-Tomes model to study the rural-urban divide in intergenerational educational mobility in Indonesia. Following Becker et al. (2018), the model includes an interaction effect between parent’s education and financial investment in children’s schooling production function which can give rise to a convex CEF. Using household data free of the truncation bias due to coresidency restrictions in surveys, we find that the linear CEF widely used in the current literature is rejected in favor of a convex CEF, both in the rural and urban areas. The rural-urban relative mobility curves cross; while the children from less educated households enjoy higher relative mobility in rural areas, the pattern reverses for the higher educated households. The rural children face