rafmacalaba/datause-encoder-data huggingface dataset:
school if households reported experiencing the food price shock; there is little difference across households with different asset levels. A negative association between schooling and food shock is slightly larger for the older age group (children 10–14 years old). For this age group, the selfreported food price shock led to a 5.8 percentage point decrease of school attendance for households in the first quartile; the negative effect is significantly lower for households in the highest quartile. An examination of the impact of the self-reported food shock on whether children were working or idle shows that, for households in the first quartile, the probability of children working increased by 8.2 percentage points; for households in the fourth quartile, the self-reported food shock is associated with an increase in the probability of being idle. However, self-reported food shock is endogenous and, in order to remove the self-reporting bias, the district-level wheat price was used to directly measure the impact of the wheat price increase on child activity status.

# **5.2 Impact of Wheat Price Changes on School and Labor Participation**

First, household-level analysis using the household fixed-effect model was conducted in order to understand how wheat price changes affected child activity at the household level. The dependent variables are the number of children in each activity within the household. The samples were constructed based on the presence of children in specific age group (age 5–14, age 5–9, and age 10–14) in the household. The results are presented in Table 5. It shows the average number of the children enrolled in school dropped as a result of the wheat price increase. The impact does not vary significantly by welfare status measured by the asset index. Further, for the lowest quartile, the drop in the school enrollment rate is mirrored by an increase in the child labor participation rate.

Next, the child-level regression analysis was conducted. The analysis controls for child fixed effects, and the standard errors are clustered at the household level. In order to investigate the heterogeneous impacts of wheat price increases, the impact of the wheat price changes on school participation probability, by age group, gender, and locality (Table 6) was examined. When children ages 5 to 14 were examined as a group, the wheat price increase corresponds to lower school enrollment. A 100 percent increase in the wheat price decreased the probability of school enrollment by 12 percentage points for children in the first quartile based on the asset index.

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in a regional war complex).

2. If the coding of civil war is expanded to include “internationalized internal armed conflict” (type 4, denoted by I when there is no overlap with type 3 denoted by O) and the intensity continues to be “war,” some countries that were not coded as having a civil war now are Congo-Brazzaville, Guinea, Guinea-Bissau, and Senegal. In addition, the conflicts of some countries are denoted by F; these countries intervene in violent conflicts in other countries but do not experience associated violence within their own boundaries, which is hardly anyone’s idea of a civil war. Mauritania, Namibia, and Tanzania would not even appear in table 1 except for these types of conflicts. Such a conflict seems to be the only reason that Namibia appears in table C1 of Miguel et al. (2004), and such coding is sometimes material for their econometric inferences (Jensen and Gleditsch 2009). Tanzania seems to appear as a result of its invasion of Uganda after Amin attempted to annex part of Tanzania, so these dates for both countries seem closer to an interstate war than either type of internalized conflict. Ugandan exiles in Tanzania were also involved but probably could not have undertaken hostile action against Amin alone. Libya appears almost only for its interventions, except that in 1987, it was burned in its intervention in Chad, leading to the recording of an I.<sup>1</sup> 3. Even those countries that would be coded by battle death–based rules as having had civil wars under the “war” intensity (1,000 battle-related deaths) in the PRIO/Uppsala data often show relatively few years of warfare (row 1 of table 1). Similarly, there are often oddly isolated years in what would seem, from narrative accounts, to have been sustained periods of conflict over the monopoly of force. Liberia is such an example, which experienced violent contestations of the monopoly of force following the Doe coup in 1980, beginning with the Dokie raid of 1983, intensifying with the reentry of Taylor in 1989, and continuing at least until the 2003 13
# **3 Bounding the biases of selection and natural barriers**

We wish to know what portion of the ratios _Ro_ is the result of policies that prevent the movement of labor. That is, we wish to know _Re_ , the wage ratio for equally productive workers who wish to move. _Re_ is the relevant measure because (a) it represents wage discrimination as workers would be willing to work at those wages but are prevented from doing so by government policy, (b) it is analogous to price gaps across identical goods and services induced by borders, and (c) it represents the potential welfare gain to the marginal mover if the policy induced costs were removed.

Going back to equation (3), we want to know what portion of _Ro_ represents (i) unobservable differences in productivity between selected migrants and non-migrants ( 1 + δ _s_ ) , (ii) a compensating differential for “natural” barriers such as credit constraints, search costs, distance, language, and unfamiliarity ( 1 + δ _n_ ) to make movers willing to move, and (iii) what portion represents the pure wedge introduced by borders ( 1 + δ _p_ ) .

No one would claim that δ _p_ = 0 , which would imply that the entire apparatus of control of the US border—from visas at airports and border crossings to fences and agents—was ineffectual with respect to all countries and workers from all countries are just indifferent between moving and staying. This is obviously counter-factual as both high-skill and low-skill visas to work in the United States are vastly oversubscribed: the entire stock of high-skill temporary “H-1” work visas for all of fiscal year 2008 was famously exhausted in a few hours on the first day in 2007 that they became available. The same year, the US “diversity visa” lottery drew 6.4 million applications (representing 10 million individuals) for 50,000 visas—oversubscribed by a factor of 200 to 1.

Drawing upon economic theory and a range of empirical results in the literature, we discuss five separate ways to place useful bounds on the relative contributions of selection, natural barriers, and policy:

- Simulation exercises with a calibrated structural model of selection

- A version of our regressions that eliminates selection on unobservables, but must be adjusted for international differences in wage-returns to observables

- Discussion of the sole extant experimental analysis allowing precise evaluation of the wage effect of an exogenous change to δ _p_ in isolation

- Several migration flows in which policy barriers δ _p_ are roughly zero, allowing measurement of ( 1 + δ _s_ )( 1 + δ _n_ )

- Comparison with predictions based macroeconomic data, where δ _s_ ≈ 0

Surprisingly, all of these methods give very similar results. They suggest that the wage ratios of observably equivalent workers overestimate wage discrimination and by at most a factor of ( 1 + δ _s_ )( 1 + δ _n_ ) ≈ 5.1 , resulting in roughly equal measure from selection δ _s_ and natural barriers δ _n_ .

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# Part I: The Migration Experience

## 1. Overview of Migration Patterns

Reliance on migration is deeply entrenched in Lesotho and has historically been dominated by male employment in South African gold and diamond mines. For most of the 20<sup>th</sup> century, moving across the border to work in the mining industry was considered a rite of passage, with almost all able‐bodied men being offered a job in the mines. Up to 40 percent of Lesotho’s male labor force was employed in South Africa in the 1980s, when remittances accounted for a third of GNP.<sup>8</sup> The situation changed dramatically in the 1990s, however, as the shift to more capital‐intensive production, stagnant gold prices, and pressure from the post‐Apartheid government to employ more locals led to a drop in demand for Basotho miners. By 2002, only 12 percent of households had someone working in the South African mines, down from 50 percent 20 years earlier.<sup>9</sup> According to the National Union of Mineworkers, no new workers (‘novices’) have been recruited from Lesotho since 2002,<sup>10</sup> thus undermining what used to be the economic backbone of Basotho villages and rural communities for generations.

The retrenchment of miners from South Africa has led households to develop new livelihood strategies, which, in the absence of local economic opportunities, continue to involve migration. Notably, the decline in mining jobs for men has coincided with a growing stream of female migrants seeking employment in export garment factories in urban Lesotho (see Figure 1). Fostered by a preferential trade agreement with the United States under the African Growth and Opportunity Act (AGOA), the textile sector has become the single largest formal employer in Lesotho, providing 39,000 jobs in 2011 and up to 50,000 jobs at its peak in the mid‐2000s.<sup>11</sup> Despite dominating this area of employment – 90 percent of textile workers are women ‐ the majority of female migrants still end up working informally in domestic service or as street vendors, and even those employed in textile factories face a high level of insecurity and often bounce between the formal and informal sectors. These figures highlight an unforeseen consequence of male migrants’ retrenchment from the mines: Basotho women are increasingly compelled to migrate in order to support their families, but they are limited to less stable and lower‐paying income opportunities than the mining jobs formerly held by the male migrants.

> 8 World Bank (1995), citing 1986 Lesotho census.

> 9 Boehm (2003).

> 10 Interview with Head of Lesotho Office, National Union of Mineworkers (NUM), Maseru, January 2009 (Crush et al., 2010). 11 UNCTAD (2012).

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<!-- Start of picture text --> —Baseline<br>—Govt revenue spent on public consumption<br>5%) Govt revenue spent on public investment<br>—Govt revenue spent on tax reduction<br>0% SS<br>5%<br>10%<br>=15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Govt revenue spent on earedconsumption<br>5%—GovGov t revenue enespent on asnesjuctiormivestmens<br>0% | ' ' ' ' 1<br>5%<br>-10%<br>15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> —Baseline ; .<br>—Govt revenue spent on Public consumption<br>Govtrevenuesperon em patent<br>10% Govt spe ul<br>5% _—<br>0% a 1<br>$%<br>“10%<br>15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —Baseline: revenues spent on public consumption<br>—Baseline: revenues spent on publictax cut investment<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>2012 2015 «892018 2021 2024<br><!-- End of picture text -->
**Figure 3.a Product market regulation – Romania’s ranking in 2002**

<!-- Start of picture text --> 3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>United KingdomIrelandAustraliaUnited StatesNew ZealandSwedenNetherlandsAustriaDenmarkGermanyCanadaJapanHungarySpainFinlandPortugalSwitzerlandMexicoBelgiumFranceNorwayGreece ItalyKoreaRomaniaTurkeyCzech. Rep.Poland<br><!-- End of picture text --> _Source_ : Nicoletti et al. (1999) and OECD (2002a). _Note_ : Data refer to 2002 for Romania and 1998 for other countries. Methodology changed somewhat in 2003 so the ratings are not strictly comparable between Figures 3.a and 3.b. For details on the change see Conway et al (2005).

**Figure 3.b Product market regulation – Romania’s ranking in 2006**

<!-- Start of picture text --> Relatively Liberal<br><!-- End of picture text --> <!-- Start of picture text --> Relatively Restrictive<br><!-- End of picture text --> <!-- Start of picture text --> BelgiumNorwayRomaniaPortugalSpainKoreaSwitzerlandBulgariaFranceCzech. Rep.<br><!-- End of picture text --> _Source:_ Conway, Janod, Nicoletti (2005) and, for Bulgaria and Romania, World Bank estimates based on information provided by Bulgarian and Romanian authorities and Doing Business in 2005. Note: the values refer to 2006 for Romania and Bulgaria, 2004 for Brazil, and 2003 for all other countries. _Note_ : Values refer to 2006 for Bulgaria and Romania and 2003 for all other countries. The confidence intervals are calculated using stochastic weights on the low-level indicators to generate a distribution of overall PMR indicators for each country. The 90 per cent confidence intervals are calculated from that distribution. Indicator values for the 'relatively liberal' and 'relatively restrictive' countries are significantly different at the 90 percent level of confidence (Conway, Janod, Nicoletti, 2005).

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6 growth of government especially when the use of tax expenditures changes. A telling example of .'his pro'-- from an OECD setting follows: Another case in point 'L government child support; in some countries (e.g. Canada and the United States) the number of children in a family determines the size of tax free income allowances, thus co-determining total tax revenue and its share in GDP. In other countries, (e.g. Germany and the Netherlands) the Government pays cash allowances to families with children, while a family's tax bill (and thus total tax revenue) is largely unaffected by the number of children. Thus, an item which enters the accounts as direct expenditure in the latter countries is treated as a tax expenditure in the former, reducing revenue rather than adding to expenditure. Even in the hypothetical case where the net effects on family disposable income and tax revenue are identical, the gross figures for government expenditure and revenue differ under the two 5 approaches ... and are thus not directly comparable.

While the extent of off-budget activity is not well quantified, the trend in OECD nations has been toward increasing use of these alternatives to direct expenditures. The same may be true for developing economies, although it is also possible that on balance fiscal reforms have led to direct payments replacing implicit subsidies.

# 3. Should one deflate?

In making intertemporal comparisons of government expenditures one runs into familiar index number problems. One method of avoiding the. problem of deflating to constant dollars is to compare the ratio of government expenditures to GDP. An argument against this procedure is that the price of public goods may tend to rise more quickly relative to the prices of all other goods, i.e., private consumption and investment goods. This will be the case
# **4 Empirical Strategies**

## **4.1 Regression Discontinuity**

We first rely on a Regression Discontinuity (RD) framework to identify a causal effect of FA on decisions regarding registering to vote. We exploit the discontinuity in FA eligibility for people around the threshold, since people just below the eligibility threshold, the first bracket of the SISBEN score, were able to enroll in FA, while people just above were not. Moreover, the cutoff for the first bracket of the SISBEN score does not determine eligibility to other major social programs except for a child feeding program given to children between 6 months and 5 years old, which is an in-kind daily transfer that does not coincide specifically with the payment of FA, therefore this is not expected to invalidate our empirical strategy. In section 5.3 we discuss the robustness of the results when potential beneficiaries of the child feeding program are excluded from the sample of analysis.

We need to validate several assumptions for the RD strategy to work in this context. Specifically, if the eligibility indicator affects registration to vote only through enrollment in FA, then two necessary conditions should hold. First, there should be no bunching in the distribution of the _SISBEN_ score around the eligibility threshold. Figure 2 shows the histograms for all the individuals in the SISBEN data base (top graph), and in our matched sample for men and women (bottom left) and for women only (bottom right). By design of the proxy-means test algorithm, the distribution is bimodal and the overall distribution is very similar to the one in our matched sample. Furthermore, the density is higher above the eligibility threshold<sup>12</sup> than below it, which is contrary to the idea of manipulation of the score in order to become eligible for FA. Neither distribution shows bunching at the eligibility threshold. Second, as Figure 3 shows, other variables which are not affected by the program are continuous at the eligibility

> 12Normalized to a value of zero and depicted with a vertical line.

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Policy Research Working Paper 7122

# **Abstract**

This paper explores the impact of structural reforms on a comprehensive set of macro-level labor-market outcomes, including the unemployment rate, the average wage index, and overall and female employment levels and labor force - participation rates. Together these outcome variables cap ture the overall health of the labor market and the aggregate welfare of workers. Yet, there seems to be no other comprehensive empirical investigation in the existing literature of the impact of structural reforms at the cross-country macro level on labor-market outcomes other than the unemployment rate. Data were collected from a variety of sources, including the World Bank World Development Indicators, the International Monetary Fund International Financial Statistics, and the International Labor Organization Key Indicators of the Labor Market. The resulting dataset covers up to 88 countries, the majority being developing, for 10 years on either side of structural reforms that took place between 1960 and 2001. After documenting the average trends across countries in the labor-market outcomes up to 10 years on either side of each country’s structural reform year, the authors run fixed-effects ordinary least squares as well as instrumental variables regressions to account for the likely endogeneity of structural reforms to labor-market outcomes. Overall the results suggest that structural reforms lead to positive outcomes for labor. Unlike related literature, the paper does not find conclusive evidence on unemployment. Redistributive effects in favor of workers, along the lines of the Stolper-Samuelson effect, may be at work.

This paper is a product of the Trade and Competitiveness Global Practice Group; and the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at chollweg@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Figure 4: Impact of increase in petty corruption on employment growth for firms that face low vs. high levels of financial constraints**

<!-- Start of picture text --> Figure 2A: Petty Corruption Incidence<br>0.6 0.54<br>0.5<br>0.4<br>0.3<br>0.2 0.15<br>0.1<br>0<br>Low Corruption High Corruption Low Corruption High Corruption<br>-0.1<br>Low financial constraint High financial constraint<br>-0.2<br>-0.21<br>-0.3<br>-0.26<br>Figure 2B: Petty Corruption Depth<br>0.5<br>0.41<br>0.4<br>0.3<br>0.2<br>0.13<br>0.1<br>-0.002<br>0<br>Low Corruption High Corruption Low Corruption High Corruption<br>-0.1<br>Low financial constraint High financial constraint<br>-0.2<br>-0.3<br>-0.27<br>-0.4<br>residuals)<br>Employment growth rate (%,<br>Employment growth rate (%, residuals)<br><!-- End of picture text --> Source: Enterprise Surveys. Based on authors’ own calculations.

Notes: The figure is a partial scatter plot of the residuals of growth rate of employment plotted against residuals of petty corruption (Petty Corruption Incidence in Figure 4A and Petty Corruption Depth in Figure 4B) and residuals of financial constraints variable. The residuals of employment growth are obtained by regression employment growth on country fixed effects, industry fixed effects and initial employment at the firm. Residuals of petty corruption are obtained by regressing petty corruption on country fixed effects, industry fixed effects, initial employment at the firm and financial constraints variable. Residuals of financial constraints variable are obtained like for petty corruption with the control for financial constraints replaced by petty corruption variable. The cut-off used for low vs. high financial constraints groups in the graph is the 75<sup>th</sup> percentile value of the residuals of financial constraints variable. For petty corruption, the cut-off used is its median value within each of the two financial constraints groups shown. All variables are as defined in the main text.

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