rafmacalaba/datause-encoder-data huggingface dataset:
_3._ **Data and sample means** Data: The main sources of data used for this paper are the 1985/86, 1991, and 1997 Peru Living Standards Measurement Surveys (LSMS). These surveys contain reliable information on household characteristics, school attendance, grade attainment and labor force participation. The questionnaires in all three surveys are generally comparable." Unfortunately, the coverage of the surveys is not comparable across years. While the 1985/86 and 1997 LSMS visited households in each one of Peru's seven "natural regions"-Lima, and the urban and rural areas of the coast, sierra (or highlands) and selva (or jungle)-the 1991 LSMS sampled households only in Lima, the urban coast, and the urban and rural areas of the sierra. There is also some reason to be concerned about the 1991 sample for the rural sierra: Mean income in the rural sierra appears to be _higher_ in 1991 than in 1997, and the mean years of education of parents in the rural sierra approximately doubles between 1985/86 and 1991, and stays constant thereafter (see Table la below). It is likely that lack of funding for the 1991 survey, or the dangerous conditions caused by widespread terrorism in the rural sierra in the early 1990's prevented enumerators from visiting outlying, poorer, less well-educated households in this region in 1991. I therefore limit the sample to Lima and the urban areas of the coast and sierra for all of the analysis. In 1997, these three regions jointly accounted for approximately 58.1 _%_ of the population of the country. (Separately, I also run all of the regressions I report in the paper in samples which include the rural sierra. In all of these results, which are available from the author upon request, the crisis impact was larger in absolute value than those I report for the urban sample.) More details on other sources of data and their reliability, as well as on the construction of the variables used for my analysis can be found in a Data Appendix.

Sample means: Tables I a and lb suggest that the 1988-1992 crisis affected all regions, and all income quintiles. Income dropped throughout, and credit became harder to come by: The fraction of households who report having had access to credit is lower in 1991 (16%) than in either 1985/86 (22%) or 1997 (40%). Tables Ia and lb also show predictable secular trends in parental education (rising), age (rising), and household size (falling).

Children (or their parents) make decisions about schooling and employmentjointly. In so doing, they sort themselves into one of four mutually exclusive categories: Those who attend school only, are employed only, are neither attending school nor employed, and are both attending school and employed. In Tables I a and lb, I show unconditional, unweighted means

> " There are some unfortunate exceptions. For example, there are differences across surveys in the recall periods and in the level of disaggregation that is asked in the questions concerning household expenditures on education.

8
rate and the reports of discrimination. In the three panels, this relationship shows small and insignificant coefficients, and further, as we include individual level controls and municipality of origin fixed effects, the coefficients become even smaller (and still not statistically significant).

As discussed above, the OLS results presented in Columns (1)-(3) cannot be interpreted as causal due to an endogeneity issue that arises from the fact that immigrants make their location decisions based on the local labor market conditions. To overcome this problem, we use an instrumental variable strategy in which we exploit exogenous variation in the impact of national level export shocks on local (informal) employment rates depending on detailed measure of the local industry composition.

Spatial variation in both the informal employment rate in 2017 and the instrument are show in Figure 3 and 4, respectively. Importantly, there is clear variation in both across and within regions. The first stage relationship is shown graphically in Figure 5. An increase in predicted local exports has a positive relationship with _formal_ employment rates. Consequently, higher predicted exports implies that a lower share of people work in the informal sector, where most Venezuelan immigrants are employed. The full results of the first stage relationship between export shocks and informal employment rates are provided in Appendix Table A.1. Using a linear and a quadratic specification for the first stage yields a very strong instrument, with an F-stat for the excluded instrument that ranges between 22 and 27 even while controlling for a number of other measures of the local economic environment.

Columns (5) and (6) in Table 2 show the second stage results from our IV strategy. The first thing to note is that the IV coefficients in all instances are much larger than the OLS coefficients. This difference indicates that Venezuelan immigrants are selecting themselves into labor markets where there is more discrimination. This is consistent with the idea that immigrants are willing to deal with more discrimination as long as the labor market offers better opportunities and higher wages. The selection seems more relevant for men than for women.

Higher employment rates in the informal sector causes a reduction in the level of discrimination reported by Venezuelan immigrants. On average, a 10 percent increase in informal employment in a centro poblado causes a 2.3-3% reduction in discrimination depending on whether we use a linear or quadratic specification of the instrument. Furthermore, the results show that men are more likely to suffer from discrimination due to changes in the informal employment rate: a 10% increase in employment reduces discrimination against men by almost 4%, while for women, the effect ranges between 1.2 and 1.9% and is not statistically 15
Policy Research Working Paper 10180

# **Abstract**

Despite the magnitude of return migration from overseas to South Asia, the labor market outcomes of return migrants to this region have been understudied. This paper aims at filling this gap by examining systematic differences between the labor market outcomes of return migrants and nonmigrants in Bangladesh, Nepal, and Pakistan using nationally-representative surveys that include information on past migration. Conditional regression analysis is used with a focus on four labor market outcomes: (i) labor force status (ii) sectoral choice (iii) employment type, and (iv) earnings. The paper finds that return migrants are somewhat less likely to be employed than nonmigrants, which is mainly driven by returnees who returned at an older age. As evidenced in other contexts, return migrants in Bangladesh and Pakistan are more likely to become entrepreneurs compared with nonmigrants. Self-employed returnees are also more likely to hire paid employees and to be engaged in non-farm activities, compared with nonmigrant entrepreneurs. Return migrants who become employees earn a small wage premium relative to nonmigrants, compared with contexts where temporary migrants are higher-skilled. The returnee wage premium, however, is larger in the construction sector where most temporary migrants were employed overseas.

This paper is a product of the Social Protection and Jobs 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 lbossavie@worldbank@worldbank.org and hwang21@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
Subsidies in Malawi and Zambia. _World Development_ , _45_ , 75–91. https://doi.org/10.1016/j.worlddev.2012.11.006

- Mason, N. M., Wineman, A., & Tembo, S. T. (2020). Reducing poverty by ‘ignoring the experts’? Evidence on input subsidies in Zambia. _Food Security_ , _12_ (5), 1157–1172. https://doi.org/10.1007/s12571-020-01032-z

- Matita, M., & Chirwa, E. W. (2014). _Targeting in Social Cash Transfer and Farm Input Subsidy programmes in Malawi: should they be harmonized? Discussion Paper. Evaluation of the 2013/14 Farm Input Subsidy Programme, Malawi_ . London.

- Matita, M., Chiwaula, L., Chirwa, E. W., Mazalale, J., & Walls, H. (2022). Subsidizing improved legume seeds for increased household dietary diversity: Evidence from Malawi’s Farm Input Subsidy Programme with implications for addressing malnutrition in all its forms. _Food Policy_ . https://doi.org/10.1016/j.foodpol.2022.102309

- Matita, M., Mazalale, J., Matthew, Q., Deborah, J., Laura, C., Kamwanja, T., ... Walls, H. (2023). Food choice responses to changes in the price of a staple crop: a discrete choice experiment of maize in rural Malawi. _Food Security_ . https://doi.org/10.1007/s12571-02301401-4

- Miller, C. M., Tsoka, M., & Reichert, K. (2011). The impact of the Social Cash Transfer Scheme on food security in Malawi. _Food Policy_ , _36_ (2), 230–238. https://doi.org/10.1016/j.foodpol.2010.11.020

- Mwale, M. L., Kamninga, T. M., & Cassim, L. (2022). The effects of the Malawi Farm Input Subsidy Program on household per-capita consumption convergence. _Developement in Practice_ , _32_ (336–348). https://doi.org/10.1080/09614524.2021.1937552

- Nandi, R., Nedumaran, S., & Ravula, P. (2021). The interplay between food market access and farm household dietary diversity in low and middle income countries: A systematic review of literature. _Global Food Security_ , _28_ , 100484. https://doi.org/10.1016/j.gfs.2020.100484

- Nisbett, N., van den Bold, M., Gillespie, S., Menon, P., Davis, P., Roopnaraine, T., ... Warren, A. (2017). Community-level perceptions of drivers of change in nutrition: Evidence from South Asia and sub-Saharan Africa. _Global Food Security_ , _13_ (September 2016), 74–82. https://doi.org/10.1016/j.gfs.2017.01.006

27
**Figure 1. Prevalence of Price Controls: EMDEs and advanced economies**

<!-- Start of picture text --> A. Price controls in EMDEs and advanced B. Extent of price controls in EMDEs and<br>advanced economies (0 least – 6 most restrictive)<br>Percent of economies Index Interquartile ranges<br>100 EMDEs Advanced economies 4 Minimum / maximum<br>Median<br>80<br>60 3<br>40<br>20 2<br>0<br>1<br>0<br>Advanced economies EMDEs<br>materials Energy Food and Beverages Others<br>Construction<br><!-- End of picture text -->

# **A. Price controls in EMDEs and advanced economies**

Source: OECD and World Bank 2018; World Bank; World Trade Organization. A. EMDEs = emerging markets and developing economies. Sample includes 100 EMDEs and 15 advanced economies. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication. A. B. Unweighted averages.

B. OECD-WBG Product Market Regulation “Price controls” sub-indicator evaluating the extent and type of price controls in 8 sectors (air transport, road freight transport, retail distribution, telecommunication, electricity, gas, water, professional services).

Among EMDEs, they are more prevalent in LICs (Figure 2.A). In EMDEs that have become middle-income countries (MICs) since 2001, price controls are somewhat less common than in the average EMDE, especially in goods other than energy, food, and construction materials.<sup>3 , 4</sup> Virtually all EMDEs, impose price controls on energy products. Price controls are also frequently applied to basic foodstuffs. This practice is more widespread in LICs than in other EMDEs: virtually all LICs impose price controls on some food items, compared with three-quarters of other EMDEs. Lastly, a minority of EMDEs impose price controls on construction materials. The prevalence of these controls is significantly higher in LICs thank in other EMDEs. Beyond LICs, controls on construction materials are most common in the Middle East and North Africa (MNA) and Sub-Saharan Africa (SSA; Figure 2.B).

> 3 The set of LICs in 2001 that are now MICs includes Angola, Armenia, Azerbaijan, Bangladesh, Bhutan, Côte d’Ivoire, Cameroon, the Republic of Congo, the Comoros, Georgia, Ghana, Indonesia, India, Kenya, Kyrgyz Republic, Cambodia, the Lao People’s Democratic Republic, Lesotho, the Republic of Moldova, Myanmar, Mongolia, Mauritania, Nigeria, Nicaragua, Pakistan, Sudan, Senegal, Solomon Islands, São Tomé and Príncipe, Turkmenistan, Ukraine, Uzbekistan, Vietnam, Zambia and Zimbabwe.

> 4 Almost all LICs, including Ethiopia, Mali, Niger, Guinea and Rwanda, impose some form of price controls on petroleum products. As for food products, LICs such as Burkina Faso and the Democratic Republic of Congo impose price controls on sugar. Chad, Haiti and Guinea-Bissau impose controls on rice, and Benin, Ethiopia and Niger impose controls on bread. Burkina Faso imposes controls on cement, reinforcing bars and metal sheets. In addition to goods, price controls are also often imposed on public transportation services such as bus, train, and ship fares.

4
<!-- Start of picture text --> 2,500<br>_. 2,000<br>2 Argentina<br>£ Chile<br>1,500 bs Si China<br>=] Ne India<br>H 4,000 = ~®- Russian Federation<br>5 S . ~~ South Africa<br>g —eBrazil<br>0<br>2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->
- 10 -

# Table (1.1)

# Average Ratios of Producers Prices to Border Prices for selected African countries (converted at nominal exchange rates), 1970-86.

||1970-81|1982-86|
|---|---|---|
|KENYA|||
|Smaliholder coffee|0.93|0.84|
|Smallholder tea|0.64|0.73|
|MAL AWl|||
|Smaliholder Dark-fired|0.23|0.23|
|Estate Burley|0.51|0.36|
|Estate Flue-cured|0.67|0.41|
|TANZANIA|||
|Smallholder tobacco|0.42|0.33|
|Smaliholder cotton|0.51|0.85|
|Smaliholder coffee|0.41|0.46|
|CAMEROON|||
|Arabica Coffee|0.52|0.37|
|Robusta Coffee|0.44|0.38|
|Cocoa|0.44|0.46|
|Cotton|0.48|0.73|
|NIGERIA|||
|Cotton|0.70|0.90|
|Palm Kernel|0.73|1.27|
|SENEGAL|||
|Groundnuts|0.32|0.52|
|Cotton|0.13|0.15|

Source: Computed from Uma Lele (1990) Tables (12) and (14).
# **I. INTRODUCTION**

Education is critical for economic growth and poverty reduction. Quality education systems produce the global economy’s workers and expand knowledge. Schooling enables students to learn the skills that propel individual labor productivity. A host of social and non-market benefits are also produced by schooling, including but not limited to increased child well-being, health status, efficiency of consumer choices, and social capital. The individual contribution of schooling has often been measured by labor market earnings. Similarly, potential experience is a proxy for skills learned in the workplace. In many poor economies, education and labor market experience are the only human assets for a vast part of the labor force.

The study of the relationship between schooling and earnings has led to a number of empirical studies on a variety of social issues. These include, for example, racial and ethnic discrimination (McNaab and Psacharopoulos (1981); Chiswick (1988)); gender discrimination (Goldin and Polacheck (1987); income distribution (Mincer (1958); the determinants of the demand for education (Freeman (1976)); the impact of technology on wage differentials (Krueger (1993)); the impact of unexpected price, productivity and technology shocks (King, Montenegro, and Orazem (2012)); the impact of information on demand for schooling (Jensen (2010)); and the returns to schooling in the context of job creation (World Bank (2012). Under certain assumptions, earnings differentials by level of education have been used to identify the sources of economic growth (see, for example, Denison (1967). But perhaps the quintessential application has been the estimation of the rate of return to investment in schooling. Earnings of workers classified by some dimension have been at the core of empirical economics and other social sciences for many decades, starting with human capital theory (Schultz (1961); Becker (1964); Mincer (1974).

For 40 years, researchers (Banerjee and Duflo (2005); Colclough, Gandhi Kingdon, and Patrinos (2010); Harmon, Oosterbeek, and Walker (2003); Psacharopoulos (1972), Psacharopoulos (1973), Psacharopoulos (1985), Psacharopoulos (1989), Psacharopoulos (1994); Psacharopoulos and Patrinos (2004), Psacharopoulos and Layard (2012)) have reported on the patterns of estimated returns to schooling across developing economies. The returns are typically in the form of the estimated proportional increase in an individual’s labor market earnings from each additional year of schooling completed. Among the consistent findings across the various surveys are:

1. Private returns to schooling are generally positive and the cross-economy average rate of return to schooling is 10 percent a year.

2. Returns to schooling seem to be higher in low or middle income economies than in high income economies.

3. Returns to schooling are highest at the primary level and become smaller (although still large) at the secondary and tertiary levels of schooling.

2
farm revenue and reduced climate sensitivity. Due to the extensive use of technology and access to markets our hypothesis is that the response of farm revenue to annual temperature should be hill-shaped (convex).

# **Data**

Most of the farmland in Israel is publicly owned by the Land Authority. The land is leased on a long term basis and its price is not determined in the free market. Thus, the prices of agricultural land in Israel cannot be used for the Ricardian approach. In order to conduct the analysis linking profits to climate conditions, we rely on annual net farm income and not land values as in MNS (1994).

Farm data were collected by conducting a face-to-face survey among a representative sample of farmers. The sampled farmers were chosen according to their location in the geo-climatic zones and type of village. Rural communities vary in their organization. There are 863 rural villages which can be subdivided to 3 types: kibbutz (collective communities, 36%), moshav (cooperative communities, 47%) and other private villages (17%). The kibbutz and moshav today account for 80 percent of the country's fresh agricultural produce. The kibbutz being collective communities are much larger farms than the moshav farms whose ownership is on a per family basis. Thus we account for the size of the farm in the analysis.

The different types of rural villages define three strata, which were represented proportionally in the sample. Three maps were created, each showing the geoclimatic zones of Israel: the first one denotes the location of each kibbutz, the second one the location of each moshav, and the third one the location of the other types of villages. The dispersion of each type of village in the different geoclimatic zones can be observed using these maps.

In the next stage we ordered the villages in each map from north to south in stratums of 4 km each. All the villages in such a stratum received a number identifying the stratum and were ordered according to their stratum number from north to south. The most northern stratum received number 1, the one south of it number 2, and so on. Sampling within each stratum was done by systematic sampling. A number between 1 and 10 was chosen randomly. Each village receiving this chosen number was included in the sample, as was every 10<sup>th</sup> village thereafter.

A total of 86 rural villages out of 863 potential villages were sampled: 41 moshav, 31 kibbutz, and 14 other villages. Five farmers were chosen randomly and interviewed 6
considered. Besides issues of data availability regarding outcomes, impacted agents, or time horizon, the choice of treatment and how it is measured deserves careful consideration. This is especially true in the case of infrastructure investment, where various measures are relevant (e.g. total expenditure, size of the network, quality of investments, etc.). Most empirical papers focus on improved accessibility associated with a measure of infrastructure investments. A second challenge is the implementation of an appropriate identification strategy given that the treatment variable of interest is likely to be endogenous. We discuss these two issues in the subsections below.

# **3.1 Choice of treatment**

Within the literature focused on evaluating the impacts of transport infrastructure, there are several treatment variables which are considered. In many instances, these variables can be thought of as indicators of access, ranging from proximity to transportation services to a variety of accessibility indicators accounting for locations of relevant markets and physical connections to those markets. A first approach is to consider the available local supply of transport infrastructure. Some authors use a dummy variable indicating the presence or absence of a road or railway within the geographic unit of observation (e.g. Atack and Margo, 2011; Atack _et al_ 2010; Burgess and Donaldson, 2012; Chandra and Thomson, 2000; Datta, 2012; Faber, 2014; Jedwab and Moradi, forthcoming; and Michaels, 2008). Others consider the distance or time to the nearest highway (e.g. Faber, 2014; Emran and Hou, 2013; Ghani _et al,_ 2012; Ahlfeldt and Wendland, 2011; Duranton _et al_ , 2014, Duranton, 2014; Gibbons and Machin, 2005; and Banerjee _et al_ , 2012). Still others take into account the road or rail density within a geographic unit of observation. For example, Duranton and Turner (2011) and Hsu and Zhang (2014) focus on lanekilometers within metropolitan statistical areas. Baum-Snow (2007a, 2010) and GarciaLopez _et al_ (2015) focus on the number of “rays” (radial highways) stemming from metropolitan areas.

A second, more elaborate approach accounts for various measures of transport costs to markets. A difficulty with this approach is identifying the relevant market to consider. Dorosh _et al_ (2012), for example, take distance to the nearest town in the context of agricultural goods. Ali _et al_ (2015a, 2015b, 2015c) attempt to more accurately measure 7
Access to Pre-Primary Education and Progression in Primary School: Evidence from Rural Guatemala<sup>_∗_</sup> Paulo Bastos<sup>_†_</sup> Nicolas L. Bottan<sup>_‡_</sup> Julian Cristia<sup>_§_</sup> _Keywords_ **:** Early childhood development, pre-primary education, rural areas.

_JEL classification_ **:** I21, I28.

> _∗_ We thank the co-editor and an anonymous referee for very helpful comments and suggestions. We are also grateful to Samuel Berlinski and seminar participants at the Research Department of the InterAmerican Development Bank, Mind the Gap: From Evidence to Policy Impact, and LACEA-Medellin for very helpful comments.

> _†_ Development Research Group, The World Bank, United States. E-mail: pbastos@worldbank.org

> _‡_ Department of Economics, University of Illinois at Urbana-Champaign, United States. E-mail: bottan2@illinois.edu

> _§_ Corresponding author. Research Department, Inter-American Development Bank, United States. E- mail: jcristia@iadb.org