rafmacalaba/data-use-mentions-extended huggingface dataset:
recent evidence suggests that gender equality is correlated with good governance and institutions. For instance, Kaufmann et al. (2008) find a positive correlation between a control-of-corruption index and the World Bank’s (2004) measure of gender equality. Branisa et al. (2009) find that social institutions that reduce gender inequality are correlated with institutions that reduce corruption, strengthen the rule of law, and offer good protection for property rights.

4 Other examples include Dollar and Gatti (1999), who find that gender inequality is negatively correlated with growth only in the sample of countries with high female secondary attainment (more than 10.35 percent of the population). In contrast, Klasen (2002) finds that the effects are stronger in sub-Saharan Africa, that is, among the poorest countries in the world. Heterogeneity across time appears to be equally problematic. For instance, Klasen (2002) shows that the correlation between gender inequality and growth disappears when using data from the 1980–90 period instead of the 1960–90 period.

5 We note that this is not because gender equality in educational attainment is a recent phenomenon and that newly educated women need time to gain economic and political parity. In their historical account of gender inequality using microlevel census data, Ganguli et al. (2010) show that for the two-thirds of their sample countries in which the gender gap (defined as the difference in years of education) was reversed, this reversal occurred for the cohort born in 1975 at the very latest. These women are past the age when occupational choices are made.

> 6 Data from the OECD family database ( _<u>www.oecd.org/els/social/family/database</u>_ <u>),</u> accessed October 6, 2011. The only countries that offer full-pay paternal leave for more than five weeks are Iceland (10 weeks), Sweden (nine weeks), and Norway (six weeks).

7 Duflo (2012
# **1 Introduction**

In the quest for explaining the drivers of the observed wide gaps in total factor productivity across countries, a large and growing literature has emerged focusing on cross-country differences in the size distribution of manufacturing firms. A conjecture in part of this literature is that the size distribution of firms in developing countries exhibits a "missing middle" that reflects market frictions and policy distortions that discourage production at an intermediate scale; thereby reducing aggregate productivity. However, the literature evaluating this hypothesis has not yet been conclusive. Tybout (2000) and Tybout (2014) document a lower employment share accounted for by middle-sized firms in low-income countries, while Hsieh and Olken (2014) argue against the missing middle providing an adequate characterization of the firm-level data in these countries.<sup>1</sup> In this paper, we revisit the missing middle hypothesis by exploiting a set of comprehensive firm-level databases from four Sub-Saharan African economies, a region where some of the underlying frictions and distortions are arguably most likely at play (see for example Cirera et al., 2019). One challenge when characterizing the "true" distribution of firms in developing countries is lack of firm-level data covering the informal sector since businesses operating informally often do not appear in official registrar records (Aga et al., 2022). To this end, we compile detailed and comprehensive censuses of establishments including informal producers in four Sub-Saharan African countries, namely Burkina Faso, Cameroon, Ghana, and Rwanda.<sup>2</sup> A unique advantage of these datasets is that they cover all businesses in the non-agriculture sector with a physical location, including both formal and informal (unregistered) firms. Each dataset provides information on the establishment’s registration status with various government agencies and its bookkeeping practices. This information is used to construct comparable definitions of informality across countries and assess the robustness of the results to alternative definitions. A limitation of this data, on the other hand, is the lack of financial information about
Policy Research Working Paper 9521

# **Abstract**

Can repatriation inflows impact firm behavior in origin countries? This paper examines this question in the context of repatriation inflows from the United States and Mexico to El Salvador. The paper combines a rich longitudinal data set covering all formal firms in El Salvador with individual-level data on all registered repatriations from 2010 to 2017. The empirical strategy combines variation in the municipality of birth of individuals repatriated over 1995–2002—before a significant change in deportation policies—with annual variation in aggregate inflows of repatriations to El Salvador. The findings show that repatriations have large negative effects on the average wages of formal workers. This is mainly driven by formal firms in sectors that face more intense competition from the informal sector, which deportees are more likely to join. Repatriation inflows also reduce total employment among formal firms in those sectors. Given that most deportees spend less than a month abroad, these findings suggest that the experience of being detained and deported can have strong negative effects not only on the deportees, but also on their receiving communities.

This paper is a product of the Poverty and Equity Global Practice and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at ldinartediaz@worldbank.org and hwinkler@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
they are on average 33.6 years old, have an average monthly income of $3,087 and monthly credit card expenditures of $1,106.

As one might expect, the summary statistics shown in column (1) of Table 2 indicate that clients of the partner bank are not representative of the entire Malaysian population. On the one hand, the age and gender composition of our sample is not substantially different from the country average: data from the Malaysian Department of Statistics for 2020 indicate that 51.4% of the Malaysian population is male (compared to 62% in our sample of bank customers) with a mean age of 31.4 years (compared to 33.6 years in our sample). On the other hand, we find substantial income differences. According to data from the Salaries and Wages Survey Report,<sup>10</sup> the average Malaysian household earned $1,767 (compared to $3,087 in our sample).

However, Table 2 also shows that there is no indication of selection into treatment. Customers who participated in our experiment are similar to non-respondents. Columns (2) and (3) compare the characteristics of the 2,872 clients who answered our survey (reported in column (2)) to the sample of 3,126 clients who were invited to the survey but did not respond (column (3)). Comparing columns (2) to (3) indicates that although there are some statistically significant differences in average characteristics, none of these differences are meaningful in magnitude. For example, the average age is 33.28 years among survey respondents as compared to 33.88 among non-respondents. The average monthly income is $3,129 among survey respondents as compared to $3,050 among non-respondents. The average credit card expenditures are $1,135 among survey respondents and $1,080 among non-respondents. The one possible exception is gender, where we find that men are more represented
of the firm-specific conflict exposure on economic performance.<sup>14</sup> In what follows, we present our core results and examine in detail potential threats to the validity of our identification strategy.

> 13We also employ alternative measures of conflict exposure constructed using: i) different buffer distances from the firm’s location or weighting by inverse distance; ii) a different time period; iii) different types of conflict events; and iv) the ACLED dataset as an alternative source for conflict events (see Section 2.2).

> 14In our sample, the onset of conflict does not necessarily occur at _t_ 0. As a result, we cannot provide dynamic difference-in-differences estimates of the effect of conflict between _t−_ 1 and _t_ 0.

9
>**and**Steel|41.22|34.21|31.81|35.09|29.49|28.76|
|Radio and TV Receivers|52.85|39.11|35.81|40.39|37.04|36.47|
|MotorVehicles|36.88|27.94|25.65|24.94|21.75|21.41|
|Weighted<br>average17industries|42.33|32.04|29.74|34.05|29.70|29.02|

Source: US census concept ratios derived from table 3.10; present national accounts ratios derived from tables 1 for industries **Al** to A17 of the Statistical Appendix; former national accounts ratios derived from tables 4.1 and 4.5
# **4. CH4 Concentration Anomalies over Irrigated Rice Production Areas**

The current version of our database provides monthly mean CH4 concentration anomalies in 266,884 terrestrial grid cells at 25 km resolution for 2020, 2021, and the first half of 2022. The means are calculated from daily observations that are pre-filtered to remove global background concentrations, trends, and latitudinal clustering. We overlay the IFPRI shapefile on the global grid to extract cells with irrigated rice production. For this exercise, CH4 observations in each cell are limited to the months in which fields are actually flooded. We identify those months using RiceAtlas, a spatial database of global rice calendars developed by Laborte et al. (2017). RiceAtlas distinguishes up to three rice cultivation seasons and identifies the first flooding and harvest days for each season by day-of-year.

For each pixel in RiceAtlas, we use this information to identify months that lie fully within periods lasting from first field flooding to ten days before harvest (to account for pre-harvest drainage (IRRI, 2022)). Then we intersect the RiceAtlas shapefile with our SP5/IFPRI spatial grid and assign each grid cell the cultivation calendar with the greatest representation in that cell.<sup>9</sup> For grid cells with IFPRI irrigated rice areas but no RiceAtlas information, we assign the cultivation calendar in the nearest RiceAtlas cell. We compute mean grid cell CH4 anomalies for all within-calendar monthly observations during the period 2020-2022.

# **5.  Sources of Variation in Concentration Anomalies**

# **5.1 The Effect of Field Scale**

We posit a monotone increasing relationship between CH4 anomalies and the scale of IFPRI flooded fields in a grid cell and perform a linear regression using percentile values.<sup>10</sup> To test for robustness, we use alternative estimators that incorporate different assumptions about the structure of the stochastic error term (εit) in the model. These techniques produce the same point estimates for model parameters, but their differing estimates of standard errors
_of Development Economics_ 64.2 (2001): 371-388.

Skoufias, Emmanuel, and Susan W. Parker. "Job loss and family adjustments in work and schooling during the Mexican peso crisis." _Journal of Population Economics_ 19.1 (2006): 163181.

Sparrow, Robert, et al. "Coping with the economic consequences of ill health in Indonesia." _Health Economics_ 23.6 (2014): 719-728.

Strauss, John, and Duncan Thomas. “Health, nutrition, and economic development _.” Journal of Economic Literature_ (1998): 766-817.

Thomas, Duncan, and John Strauss. “Health and wages: Evidence on men and women in urban Brazil.” _Journal of Econometrics_ 77.1 (1997): 159-185.

Thomas, Duncan, et al. “Causal effects of health on labor market outcomes: Experimental evidence.” California Center for Population Research (2006).

Wagstaff, Adam. “The economic consequences of health shocks: evidence from Vietnam.” _Journal of Health Economics_ 26.1 (2007): 82-100.

Wagstaff, Adam, and Magnus Lindelow. "Are health shocks different? Evidence from a multishock survey in Laos." _Health Economics_ 23.6 (2014): 706-718.

37
Therefore, instead of relying on summary measures of poverty, one could use full distributions of income or consumption expenditures from representative household surveys to better understand distributional changes. Instead of focusing on economic growth, which can also be thought of as the proportional change in the mean of the income distribution, it is best to analyze how the entire distribution changes over time. Moreover, given the richness in household income and expenditure surveys, one can further disaggregate the observed distributional changes by decomposing the factors that underlie these distributions. The rest of the paper focuses precisely on this.

# **The forces behind poverty reduction**

We begin with a household consumption identity where household consumption per capita is defined by where Yh is total household income, _n_ is the number of household members, and θh is the consumption-to-income ratio. Because poverty depends on the distribution of consumption, changes in any of the factors on the right-hand side of equation (1) will lead to changes in poverty: demographic changes ( _n_ ), growth in labor and nonlabor income (which make up Yh), and changes in consumption patterns (θh).

First, demographics could play a role by affecting the dependency ratio, or the number of earners relative to the number of consumers in a household. Among the countries considered here, the population of Bangladesh grew by 25 percent between 2000 and 2010, adding 19 million people to its total, while Brazil has added 18 million (a 16 percent increase) during the same time period. Despite these increases in population, the rate of population growth has decelerated enough so that the age-dependency ratio has begun to decline across countries in our sample (figure 3) as the number of adults per household has increased, on average (figure 4 and table 3). Note, however, that the share of adults per household among the poor decreased in some cases, notably in Panama and Colombia.

Second, growth in labor income could be the main driver in the observed changes in poverty. This could
per cluster, four rounds of surveys in one pair of village clusters (0, 5, 10, and 15 years from start) would cost US$240,000. This is the cost per treated village. Even this conservatively high estimate is only 16% of the $1.5 million total intervention cost for each treated village (MVP 2010e). This direct cost is small compared to the indirect cost of the large waste that would occur if the project were to be ineffective but were massively expanded without discovering the ineffectiveness early.

> 28 Standard power calculations imply that it would require 23 pairs of village clusters to detect a change in the proportion of people in each household meeting some criterion (such as measles vaccination or school enrollment) from 0.5 to 0.6, with 80% probability, at the 5% level of statistical significance. This figure assumes 300 households sampled per cluster (MVP 2009: 21), _without_ baseline data, and assumes a plausible value for the coefficient of variation of the proportion within matched cluster pairs of 0.3 (Hayes and Bennett 1999). This could be diminished with better matching, thus lowering the required number of clusters (Bruhn and McKenzie 2009). Even this is a conservative upper bound on the number of pairs required, because the collection of baseline data would allow it to be substantially smaller; exactly how much smaller would depend on assumptions about the correlation between baseline and follow-up measurements.

27
the total income, schooling and ISEI, with one exception. The LMM stratum clearly has higher income than the LMS, but their average schooling and ISEI are lower. This may be due to the fact that, as shown in table 5, these strata show differentiated composition of sexes, with the LMM stratum predominantly made up of males and the LMS of females. Despite having more average schooling, the more feminized stratum registers lower income, which is an example of gender inequalities in the labor market. Furthermore, in relation to the ISEI, the difference in order may be due to some occupations in Mexico not providing an income at a comparable level to that in other countries captured in the index (Solís, 2010).

While the order of strata may be considered ordinal, as observed, the differences in income are not equidistant. That is, the movement between the different strata may mean changes of different magnitude in the average access to income and other life opportunities. Furthermore, it must be considered that a movement to an adjacent stratum (or nearby) represents a change of lesser magnitude than one that is of greater distance.

# D.2 _Methods for the analysis of absolute mobility_

Absolute mobility analysis was based on calculations derived from tables of intergenerational occupational mobility, which are contingency tables in which each row corresponds to a different occupational origin (the stratum of the providers) and each column to an occupational destination

> 46 The order remains when using labor income, except for the stratum of commerce, which has an average labor income that would place it in the same level as the HM stratum, due to fact that its total income has a higher composition of non-labor income than the other strata. We consider it is correct to use the total income because belonging to an occupational stratum is related not only to labor income, but also to access to other sources of income and life opportunities in general.

26
RBF mechanism in _Ceará_ (instead, they use the state assessment called SPAECE) and for this reason is more appropriate to minimize potential gaming. All regressions restrict the sample to the first three immediate municipalities on each side of the border between _Ceará_ and adjacent states as shown in Figure 1. The municipality controls are: number of births in the municipality _−_ year ( _Sistema de Nascidos Vivos_ (SINASC)); a dummy for rural schools (from SAEB); and a dummy for schools offering grades 1 _−_ 9 (School Census). Student controls come from SAEB and include: a dummy if the student is male, if the student is white, age in years, number of bedrooms, if the mother has higher education and their household size. The estimates also include time trends and city-specific time trends. Border fixed-effects introduces dummies for each border between _Ceará_ and its adjacent states (see the different colors in Figure 1). By including these dummies, the regressions compare students at the same border instead of simply averaging test scores on both sides of the border. Panel A presents _DD_ estimates of _RBF_ impacts. Panel B refers to the effects after introducing the TA component. Panel C shows _DD_ estimates after the RBF formula included weights for the percentage of students below minimum thresholds of performance in Mathematics and Portuguese. The baseline difference at the border, _β_<sup>_pre_</sup> , and a parallel trend test estimating changes in the trends during pre-RBF periods is shown at the bottom of each column. Standard errors are clustered by municipality and shown in parenthesis. P _−_ values are shown in brackets.

21
poverty and village funds to families experiencing severe financial difficulties and hardship. While we acknowledge that some income from this source might not properly be considered local _wage_ income, we show later that village aid and subsidies are negligible compared to household total local wage income. We take comfort that village aid and subsidies could not be obscuring our findings.

Our regression results show that the income gap between cadre and non-cadre households shown in the paper could not be simply driven by the fact that cadre households earn compensation and subsidies for being a cadre while non-cadre households do not (Table 12). Specifically, when looking at wage income from businesses and economic activities managed by villages, which do not contain compensation and subsidies for being a cadre, cadre households appear to earn about 25 Yuan more per capita than non-cadre households (Panel A). In contrast, and interestingly, cadre households do not earn more wage income from the private sector than non-cadre households. These results suggest that village businesses and economic activities contribute about one-third of the local wage income returns earned by cadre households. The remaining two-thirds of the local wage income returns come from compensation and subsidies for being a cadre, which are included in village subsidies, aid and funds. Thus two thirds of the wage increase is driven by direct compensation for cadre status, and not additional income related to work off-farm. Fixed effects estimates, which control for time-invariant unobservable heterogeneity, show qualitatively similar results (Panel B).

For the period of 1993 onwards, a change of the household survey questionnaire makes it impossible to separate cadre wage income from local wage income. Nevertheless, we are still only able to disaggregate local wage income into two income 26
# **Figure A.2. Predicted consumption distribution, Model 9**

<!-- Start of picture text --> Malawi 2019/20 Nigeria 2018/19<br>5 10 25 50 75 9095 5 10 25 50 75 9095<br>Percentile Percentile<br>Tanzania 2020/21 Vietnam 2016<br>5 10 25 50 75 9095 5 10 25 50 75 9095<br>Percentile Percentile<br>95% CIs of true value True percentile value<br>95% CIs of imputed value Imputed percentile value<br>13.5 12.5<br>13 12<br>12.5 11.5<br>12 11<br>Log of consumption Log of consumption<br>11.5 10.5<br>11 10<br>15 11<br>14.5 10.5<br>14 10<br>13.5 9.5<br>Log of consumption 13 9 Log of consumption<br>12.5 8.5<br><!-- End of picture text --> 59
# Insert Figure 1 Here

Furthermore, existing migration rates increased to some extent as a consequence of the civil war (MHP/NE/MI 2007). This migration caused numerous married couples to live apart for extended periods. Our calculations based on the Nepal DHS data indicate that during the conflict period, the proportion of women whose husbands had migrated grew over time, as did the proportion of women who reported themselves as their household‟s head. As shown in Panel A of Figure 2, 16 percent of all ever-married women reported that their husbands had migrated in 1996; this proportion had risen by about ten percentage points by the end of the conflict period. Another 5 percent of all ever-married women reported the loss of their husbands due to death, divorce, or separation; this proportion did not change much during the conflict period. Closely related, Panel B of Figure 2 shows that the proportion of women who reported themselves as the household head more than doubled during the conflict period from seven percent to 15 percent. While most of this increase occurred due to the migration of husbands, some growth also occurred due to the death, divorce, separation, and incapacitation of husbands.

# Insert Figure 2 Here

Hence the data show that over time, family separation rates increased, and conflictinduced mortality of men combined with substantial male migration left a growing proportion of women to manage their households. Nepal is certainly not alone in experiencing this dynamic - conflict in other countries has also spurred the disintegration of families through recruitment from armed groups, forced migration, abductions, and death of family members.<sup>1</sup> Moreover, conflict also imposes economic shocks and curtails development at the macroeconomic level. On average, civil wars cause a reduction in GDP per capita growth of 2.2 percent per year (Collier 1999). Evidence specific to Nepal indicates that declining development expenditures stemming 6