rafmacalaba/datause-encoder-data huggingface dataset:
we also hypothesize that the joint training with partners is fostering gender dialogue and empowering women participants (H1.3) which, if this latter hypothesis is true, should in turn stimulate their IGAs (H1.4).

The second series of hypotheses recognizes that relaxing financial and human capital constraints may be insufficient because women face gender-specific constraints, including high risk of expropriation by other household members (De Mel et al. 2009; Jakiela and Ozier 2016; Bernhardt et al. 2019; Riley 2020). In line with the findings of Bernhardt et al. (2019), we acknowledge that focusing on women’s employment and income is insufficient because it ignores possible indirect effects on the household as a whole. For example, the cash grants to women could actually be invested in the IGAs of other household members. The interventions could therefore have positive effects beyond recipient women and directly impact household IGAs and household well-being (H2.1). If the joint training with partners increases women’s agency, the effect of the cash grant and training program on the IGAs of other household members is expected to be lower in households that participated in the joint training (H2.2). The program could also encourage migration if cash grants relax liquidity, credit, and risk constraints to migration (Gazeaud et al. 2021a) (H2.3). Jendouba—the governorate where this experiment is taking place—is one of the primary migrant-sending regions in Tunisia, with an out-migration rate of 13.2% in 2014 (Zuccotti et al. 2018).

# **4 Empirical strategy**

The empirical strategy is based on a pre-analysis plan, which can be accessed <u>here</u> (Gazeaud et al. 2021b). Supplementary analyses are described in Appendix G.

## **4.1 Data**

Baseline quantitative data on the 2,000 female participants were collected between April 2016 and January 2017, which is 12 to 18 months before the distribution of cash grants. The endline survey was conducted between December 2020 and March 2021, which is 9
population. It is therefore “standardizing” the extent of group-based inequality, allowing for realistic comparisons to be made across populations with different group divisions. A corollary of this approach is that between-group inequality in the ELMO measure does not necessarily increase with an increase in the number of groups. This is helpful for another analytic reason. Instrumentalist and constructivist approaches to ethnicity require a flexible approach to group categorization (Varshney 2007). There may be a variety of ways in which, in principle, a society can be categorized into different groups. It is not always obvious, to the analyst, which particular categorization captures the ‘salient’ group differences. Conventional inequality decomposition techniques would inevitably point the analyst towards more groups. The ELMO measure, on the other hand, prompts the researcher to compare a much wider range of categorizations.. For example, in the Indian case castes can be categorized either by using official categories of “Scheduled Caste,” “Scheduled Tribe”, “Backward Caste” and “Forward Caste”, or by locally defined _jati_ categories. As we will see in this in this paper, these different systems of categorization can produce very different measures of the extent of group-based inequality, and it is not necessarily the case that the most disaggregated categorization – based on _jati –_ is unambiguously most relevant..

In this paper we compare the ELMO statistic with standard inequality decomposition measures to study dynamics of caste-inequality in two Indian villages over several decades. Caste-based inequality is considered notoriously durable and has been the subject of a lot of work across the social sciences (e.g. Dumont 1966, Deshpande 2000). The data examined in this paper offer a unique insight into the question for two reasons: first - they provide a longterm view of the evolution of caste inequality, and – second - they are based on repeated censuses of the villages and are thus not subject to the usual biases caused by sampling error which can be quite high for inequality measurement. The data are from detailed census surveys of the village of Palanpur in Moradabad district in Uttar Pradesh state in northern 5
rates produced by the dispute were consistent with one objective of the reform, which was to achieve lower rates of overall sampling.

Our first set of estimates are indicative of the causal effects of all policy changes related to the products overseen by FVA that occurred in FYR Macedonia in late 2014 and 2015. These changes include the portions of the reform not related to sampling as well as the changes in sampling that occurred but were not planned in the reform. The difference-in-differences specifications suggest a range of estimates, depending on the specification. Our preferred estimates lie in the 3-5% increase in imports, which implies tariff equivalent impacts of approximately 1 percentage point arising from all the policy changes. Our cleanest estimate of the reform alone employs only data from late 2014 data and thus estimates a very shortterm effect. This estimate is not statistically different from zero.

We also conduct an exercise that exploits within-broad product group-country of origin variation in sampling rates as an attempt to estimate a causal effect of reduced sampling on imports. We find no statistically significant link between sampling activity and imports. We might have estimated a stronger relationship between reduced sampling and imports if complete sampling data on food of non-animal origin and food contact materials had been collected, because these products were less risky and larger reductions in sampling were planned for these products.

Overall our estimates of the effect of the reform on trade are inconclusive. Our exercises highlight the challenges for evaluation that arise when the reforms are conducted by agencies that lack detailed and comprehensive data in a format consistent with the Harmonized System used to classify international trade flows. The reform we study took place in a challenging period for FYR Macedonia and its bureaucracy. Many if not most reforms will lack a clean experiment amenable to evaluation. Our study’s outcome may be typical in that regard.

43
36

**Table 1: Infant and under-5 mortality trends in Sub-Saharan African countries with a Demographic and Health Survey since 2005**

|||Infant mortalityrate|Under-5 m|ortalityrate|
|---|---|---|---|---|
|Country|DHS survey|per 1000<br>% decline|per 1000|% decline|
|||li bith<br>|li bith||
|||ve rs<br>peryear|ve rs|peryear|
|Benin|2001|89|160||
||2006|67<br>5.5%|125|4.8%|
|Ethiopia|2005|77|123||
||2011|59<br>4.3%|88|5.4%|
|Ghana|2003|64|111||
||2008|50<br>4.8%|80|6.3%|
|Guinea|1999|98|177||
||2005|91<br>1.2%|163|1.4%|
|Kenya|2003|77|115||
||2008|52<br>7.6%|74|8.4%|
|Lesotho|2004|91|113||
||2009|91<br>0.0%|117|-0.7%|
|Liberia|2007|71|110||
||2009|73<br>-1.4%|114|-1.8%|
|Madagascar|2003|58|94||
||2008|48<br>3.7%|72|5.2%|
|Malawi|2004|76|133||
||2010|66<br>2.3%|112|2.8%|
|Mali|2001|113|229||
||2006|96<br>3.2%|191|3.6%|
|Mozambique|2003|101|152||
||2011|64<br>5.5%|97|5.5%|
|Namibia|2000|38|62||
||2006|46<br>-3.2%|69|-1.8%|
|Niger|1998|123|274||
||2006|81<br>5.1%|198|4.0%|
|Nigeria|2003|100|201||
||2008|75<br>5.6%|157|4.8%|
|Rwanda|2007|62|103||
||2010|50<br>6.9%|76|9.6%|
|Senegal|2005|61|121||
||2010|47<br>5.1%|72|9.9%|
|Tanzania|2004|68|112||
||2010|51<br>4.7%|81|5.3%|
|Uganda|2006|71|128||
||2011|54<br>5.3%|90|6.8%|
|Zambia|2001|95|168||
||2007|70<br>5.0%|119|5.6%|
|Zimbabwe|2005|60|82||
||2010|57<br>1.0%|84|-0.5%|
|Average||3.6%||4.2%|

Source: Demographic and Health Survey website‘s STATcompiler tool, supplemented by figures from DHS reports for the most recent surveys, which are not yet accessible via STATcompiler. See References section for details. All figures correspond to the 5-year period prior to the survey. For surveys covering multiple years, the first year is recorded as the survey year.
# **1 Introduction**

The frequency and extent of macroeconomic fluctuations vary enormously across countries. During the period 1980-1997, the most volatile country was twenty times more volatile than the least volatile one when volatility is measured by the standard deviation of real GDP per capita growth. Moreover, countries in the highest quartile of volatility experienced a fall in real GDP per capita every two years on average, while the frequency for countries in the lowest quartile was once every five years. These marked differences provide good reasons for trying to understand the determinants of macroeconomic volatility.

A wide range of theoretical models and informal arguments suggest that the quality of financial institutions may be a key determinant of volatility: with good financial institutions, potentially profitable companies survive through rough times, while under bad financial institutions these same companies have their production severely reduced or may even go bankrupt. In fact, aggregate data seems to indicate that financial development might be a first order determinant of volatility. Figure 1, which plots the standard deviation of real per capita GDP growth versus private credit as a fraction of GDP, shows a clear negative correlation between a measure of financial development and macroeconomic volatility.

<!-- Start of picture text --> 12.7213 RWA<br>MDA<br>LTUTCD<br>LVA<br>GNB<br>HRV<br>ETH LCA<br>BLR MOZ<br>UKR COG IRN<br>PEREST TG ONIC SUR<br>SLE JOR<br>1.19502 ZARCPVUGAGHAKHMBTNLAOZMBPOLRUSSYRCAFROMSDNBGRBENSYCMNGBWAHTINPLMLIBDINERKAZNGABFAMDVMWIGABTURARGGT MMDGGMBLSOWSMPRYM EXBGDCRISWZLKAECUCMRZWESVNBOLPAK PNG TONDOMSLVBRASLBMARINDLUXCOLJAMHNDSVKEGYSENGUYKENIDNMUSHUNURYPHLFJIMRTCIVVENNAMVUTBHRBLZVCTDMAGRCDZADNKBELISLBRBGRDBHSTTODJIITAKNAPANCHLISRCZETUNNZLSAUIRLPRTMLTFINESPTHACANGBRAUSCHNMYSZAFKORNORAUTFRADEUSGPTWNSWE NLDUSAHKG JPNCHE<br>.000282 1.79459<br>Private credit by private banks<br>S td . d e v o f re a l G D P g ro w th<br><!-- End of picture text --> Figure 1: Financial development and volatility Of course this correlation does not imply that financial development has a causal effect on volatility because the observed correlation may be explained either by reverse causality or omitted variable bias. These problems make difficult to find evidence of a causal relation using cross country data because of the large number of possible omitted variables and the scarcity of exogenous instruments.

This paper provides evidence of a causal and economically important effect of financial development on volatility using industry level data. The strategy exploits 1
**Table S7: Results from missing data analysis for spelling.** Effect of the IST intervention at 9 and 24 months follow-up on spelling outcomes for younger (class 1) and older (class 5) children combined using a longitudinal, random effects regression modeling approach. Results presented (i) for all children with either 9 or 24 months follow-up measurements of the outcome (unadjusted), (ii) for those with baseline measurements of the outcome and accounting for age, sex and stratification effects as the primary pre-specified analysis, and (iii) for those additionally with baseline measures of parental education, SES and baseline educational level (measured by baseline spelling) as further predictors of missingness.

|**Spelling score**|||||||||
|---|---|---|---|---|---|---|---|---|
|||**Control**||**Intervention**|**Mean difference**<sup>**d**</sup>|**p-value**<sup>e</sup>|**ICC (9**|**5% CI)**|
|||**(50 schools)**||**(51 schools)**|**(95% CI)**||||
||||||||School|Child|
|**CLASS 1**<sup>**~~b~~**</sup>||**Mean (SD)**<sup>**~~a~~**</sup>||**Mean (SD)**<sup>**~~a~~**</sup>|||||
|**Unadjusted**|||||||||
|9-months|1068|11.70 (4.59)|1162|10.47 (4.57)|-1.24 (-2.00,-0.48)||||
|24-months|961|12.03 (3.05)|1062|11.04 (3.49)|-0.98 (-1.74,-0.21)|0.094|0.20 (0.15,0.25)|0.61 (0.58,0.65)|
|**Adjusted**|||||||||
|9-months|1060|11.69 (4.59)|1133|10.49 (4.58)|-0.79 (-1.28,-0.30)||||
|24-months|954|12.02 (3.05)|1036|11.04 (3.50)|-0.54 (-1.04,-0.05)|0.116|0.10 (0.07,0.14)|0.43 (0.39,0.47)|
|**Adjusted for predict**<br>|**ors of missin**<br>|**gness**<br>|||||||
|9-months|1049|11.70 (4.59)|1121|10.49 (4.58)|-0.75 (-1.23,-0.27)||||
|24-months|944|12.05 (3.03)|1025|11.03 (3.50)|-0.54 (-1.02,-0.05)|0.178|0.09 (0.07,0.14)|0.42 (0.38,0.46)|
|**CLASS 5**<sup>**c**</sup>||**Mean (SD)**<sup>**a**</sup>||**Mean (SD)**<sup>**a**</sup>|||||
|**Unadjusted**|||||||||
|9-months|1169|31.34 (12.61)|1223|28.73 (12.36)|-2.69 (-5.10,-0.27)||||
|24-months|1010|35.28 (12.91)|1060|33.97 (12.79)|-1.70 (-4.13,0.73)|0.001|0.21 (0.16,0.26)|0.85 (0.84,0.87)|

44
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randomly selected households surveyed in the Phtzcuaro region of the state of Michoacan, Mexico, during the winter of 1983. From these households we obtained data on 423 adults who were 13 years of age or older. Data were collected for a set of characteristics - of both the individuals themselves and their households - that are likely to influence the returns to households from migration versus nonmigration work by household members. Data were also collected on the allocation of each individual's labor to migration and nonmigration &ctivities and on each individual's income contribution to the household during 1982. The latter include, for nonmigrants, contributions of income from household farm production (farming, handicrafts, fishing, livestock, commerce, and the like), village wage work, and rental income.<sup>4</sup> Income contributions in the form of remittances from household members who migrated, either within Mexico or to the United States, are all net of reverse (household-to-migrant) flows and of direct migration costs. A "migrant" is defined as an individual who left the village at any time during 1982 for the purpose of working. The shortest term of migration in the sample was approximately three weeks. Nonmigrants include individuals who remained in the village throughout the year, as well as a small group of secondary and post-secondary students who studied outside the village but did not participate in migrant labor activities. (The empirical results are not significantly altered if students are excluded entirely from the sample.) Selected characteristics of the households of nonmigrants, internal migrants, and Mexico-to-U.S. migrants, together with individual characteristics of the migrant and nonmigrant subsamples, are summarized in Table 1. (In no case in the sample was a person both an internal and a
fluctuations in weather conditions captured by the SPEI.

# **3.3 Identification Strategy**

Our baseline model is estimated using ordinary least squares, and is specified as follows.

Where _Labor_ outcomesijt_ denotes the employment outcome of individual _i_ in administrative unit _j_ in year _t_ . In our main results, this is a dummy representing an individual’s employment status at the time of the survey. The specification also includes _Xi_ a vector of individual-specific control variables for age, education and type of region (urban or rural). _γjandδt_ are the grid-cell and year fixed effects. _γj_ controls for time-invariant location-specific characteristics that may be correlated with SPEI deviations, while _δt_ allows us to control for region-wide year-specific shocks such as a continent-wide drought. Additionally, the main equation includes _ηm_ as a month-of-year fixed effect to account for the seasonality of employment outcomes. The error term, _εijt_ , represents unobserved idiosyncratic shocks. Standard errors at the administrative subdivision level, to account for serial correlation in the error term as well as to account for correlation between individuals live in the same administrative subdivision.

Our main coefficients of interest are _β_ 1 and _β_ 2, which can be interpreted as the change in the likelihood of having a job in response to a wet or dry shock. In line with related literature, we define positive and negative deviations of the SPEI above and below a set threshold to estimate the impact of wet and dry shocks respectively. Specifically, a dry (wet) shock is defined as a 1.5 standard deviation decrease (increase) in the aggregate water balance over a given month compared to a long-term mean for that month. We use monthly SPEI measure to measure overall water availability, for instance, as stored in surface reservoirs and soil moisture.

The analysis uses alternative functional forms to test the robustness of our results.It directly employs the continuous measure of the SPEI, and also tests the sensitivity to varying thresholds. Additionally, to differentiate impacts of more extreme events from less extreme ones, we estimate 10
16

# **5.2 Basic data analysis**

Average electricity prices for both households and industry increased between 1990 and 2002 (Table 6). The increase probably was driven by significant improvements in cost-recovery efforts introduced in a wide range of countries during the 1990s, often as a component of reforms aimed at reducing direct public subsidies. In contrast, the average price of a local telephone call, as well as average monthly residential and business subscription fees, decreased thanks to technology-driven cost reductions and tariff rebalancing, which was a common features of reforms in the 1990s. The ratios of local call costs in 2002 to the same costs in 1990, compared with the ratios of subscription fees, illustrate the effect of tariff rebalancing. Indeed, average tariffs decreased more than connection fees **.** The basic picture is that electricity seems to have become less affordable, while telecomunications have become more affordable.

**<u>Table 6 Evolution of average prices, 1990–2002</u>**

||**Average**|**Standard**<br>**deviation**|**Number of**<br>**observations**|
|---|---|---|---|
|**Electricity price, households**<br>**(2000 U.S. cents per kWh/10,000 GDP per capita)**<br>**[[what is 10,000 GDP?]]**||||
|1990|43|50|30|
|2002|59|67|56|
|**Electricity price, industry**<br>**(2000 U.S. cents per kWh/10,000 GDPper capita)**||||
|1990|45|49|30|
|2002|57|73|56|
|**Price of local phone call**<br>**(2000 U.S. cents/10,000 GDPper capita)**||||
|1990|339|1335|83|
|2002|114|201|121|
|**Monthly residential subscription fee**<br>**(2000 U.S. dollars /10,000 GDPper capita)**||||
|1990|128|235|80|
|2002|51|61|125|
|**Monthly business subscription fee**<br>**(2000 U.S. dollars /10,000 GDPper capita)**||||
|1990|189|326|76|
|2002|71|69|123|

Source: Authors’ calculations using data from EIA, ERRA, OLADE, and SAD-ELEC.

In order to relate affordability to out two reforms, Table 7 presents “affordability” indicators for different country groups according to the extent to which they have adopted reforms (samples for some groups are too small to permit any conclusions to be drawn). This is the typical way in which anecdotal assessments of the impact of reforms on affordability are done. It does not really tell a statistically robust story, but it is widely popular among the partisans of anecdotal evidence on the impact of “privatization”.
only marginally smaller among exporting firms. These results can also be seen in terms of the ratio of engineers to blue-collar works. In all four specifications, the ratio increases with exports. These findings are consistent with our theory.

# **4 Conclusions**

Chilean exporters demand more engineers relative to blue-collar workers. Foreign consumers value product quality and quality is intensive in engineering tasks. A simple partial equilibrium model formalized these mechanisms and the evidence from a panel of Chilean firms supports the model predictions. The findings have implications for empirical research and policy design. The notion that trade, and exports in particular, affects the wage premium and thus wage-inequality needs to be carefully assessed. Exporting from developing countries may raise the demand for specific sets of skills, thus creating potential inequality even within skilled labor categories. In Chile, our results show that export opportunities boost the demand for technical skills such as engineering skills. These conclusions should contribute to our understanding of the skilled tasks needed for exports, the role of potential education policies consistent with a successful long-run export performance, and the design of social policies to reduce wage inequality and help the losers from trade.

# **References**

Acemoglu, D. and F. Zilibotti (2001) “Productivity Differences,” Quarterly Journal of Economics 116(2), pp. 563–606.

Acemoglu, D. and D. Autor (2011) “Skills, Tasks and Technologies: Implications for Employment Earnings,” in Ashenfelter, O., Card, D. (Eds.), The Handbook of Labor Economics, vol. 4b. Elsevier, Amsterdam, pp. 1043–1171.

Bastos, P., J. Silva, and E. Verhoogen (2014). “Export Destinations and Input Prices: Evidence from Portugal,” mimeo.

Bernard, A.B. and J.B. Jensen (1997). “Exporters, Skill Upgrading, and the Wage Gap,” Journal of International Economics 42, pp. 3–31.

Bernard, A., and B. Jensen (1999). “Exceptional Exporter Performance: Cause, Effect, or Both?,” Journal of International Economics 47 (1), pp. 1–25.

10
The dependent variable is a dummy equal to 1 if the top manager of the firm is a woman and 0 otherwise ( _Woman top manager_ ). The data source is WBES. The mean value of the variable is about 0.18 or 18 percent and the standard deviation is 0.38.

# 3.4 _Main explanatory variable_

Our main explanatory variable is total domestic credit provided by banks to the private sector as a ratio of GDP ( _Bank credit_ ). The variable is lagged by 1 year from the date of WBES in the countryyear.<sup>2</sup> We focus on bank credit because it is one of the most important sources of credit for private SMEs in the developing world (see Berger and Udell 1998, Muravyev et al. 2009). In our sample, the mean value of _Bank credit_ equals 0.41 and the standard deviation is 0.18. The data source is WDI, World Bank.

For robustness, we show that our main result holds for alternative measures of credit availability and financial development (all lagged by 1 year). These include total credit by banks and non-bank financial intermediaries provided to the private sector as a ratio of GDP ( _Total credit to private sector_ ), log of number of ATMs per 100,000 adults, and log of number of bank branches per 100,000 adults. The data source for all these variables is WDI, World Bank. We also use total bank credit to the private sector as a ratio of total bank deposits ( _Bank credit to bank deposit ratio_ ) taken from GFDD, World Bank. This variable captures the quality of financial intermediation and therefore the availability of credit (see Beck et al. 2007, Dutta and Sobel 2018).

# 3.5 _Controls_

> 2 Average values of the variable are used where the average is computed over one year lagged values of the variable for the years the WBES round under consideration was administered in the country and for which data are available. Unless specified otherwise, the same holds for all the other country-year level variables.

11