rafmacalaba/datause-encoder-data huggingface dataset:
# 1 Introduction

One-fth of the 30% poverty reduction in Nepal occurring between 1995 and 2004 is attributed to work-related international migration and remittances sent home (Lokshin et al., 2010). More than two million prime-age (mostly male) Nepalese are working outside the country and the inow of remittances accounts for 30% of the country's GDP (Ministry of Finance, 2014). Decrease in labor stock and substantial income from abroad are likely to have profound eects on the labor market and, yet, the impacts of the migration on the non-income dimensions in Nepal remain relatively unexplored. The paper addresses this issue by documenting the dierential impact of international migration on labor supply of the left-behind family members.

Traditionally, the literature on the household-level impacts of migration has focused on income and consumption of the left-behind families. There is a general consensus among this literature that temporary out-migration for employment helps increase income and reduce poverty (Adams and Page, 2005, summarize the results of micro-level analysis in several countries).<sup>1</sup> However, a relatively new strand of the literature focusing on the non-consumption dimension of left-behind family members' well-being provides more mixed evidence. These new studies suggest that male migration decreases non-migrating women's labor market participation and increases their labor supply in farming and unpaid family work (Lokshin and Glinskaya, 2009, in Nepal; Binzel and Assaad, 2011, in the Arab Republic of Egypt; Mu and van de Walle, 2011, in China; Mendola and Carletto, 2012, in Albania). Similarly, migration has a negative eect on the left-behind elderly parents' health (Antman, 2010) and on children's educational attainment (Antman, 2011; McKenzie and Rapoport, 2011). So, how benecial is international migration for the left-behind members? Murard (2016) provides a more complete theoretical framework incorporating both household consumption and labor supply. Using data from Mexico, the paper nds that temporary migration for employment leads to both increase in consumption expenditure and farm labor of household members staying behind. The consumption gains, however, are too large to be purely explained by the endogenous increase in non-migrants' labor supply.

> 1Remittances help smooth consumption (Yang and Choi, 2007), provide mutual insurance (Stark and Lucas, 1988), relax credit constraints (Yang, 2008) and alleviate liquidity constraints (Taylor et al., 2003) allowing nonmigrating members to engage in higher remunerative activities. At the same time, other community members may benet from positive spillovers: Amuedo-Dorantes et al. (2010) nd that children in communities with more migrants have greater school attendance and McKenzie and Rapoport (2007) nd that migration reduces inequality in sending communities in the long run.

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**_Table 1: Characteristics and Uses of Accessibility Metrics and Models_**

|**Method /**|_Ch_<br>**Data**|_aracteristics assu_|_me a well‐executed mod_<br>**Coverage /**|_el with high‐quality, c_<br>**Ease of**|_omplete data inputs_|
|---|---|---|---|---|---|
|<br>**Model**|**gathering**<br>**requirements**|**Accuracy**|**scalability /**<br>**generalizability**|**adaptation /**<br>**interpretation**|**Use cases**|
|Cost time|High|High|High / High /<br>High|Medium|• Various, highly flexible<br>• Multi-scalar analysis<br>• Areas with significant terrain,<br>landcover and/or road surface<br>impedance<br>• Routingto multiple destinations|
|Linear<br>Distance|Light|Very low|High|High|• Areas without significant terrain<br>or road surface impedance<br>• Rapid/no-budget analysis|
|Road<br>Density|Medium|Low|Medium / Low<br>/ Low|High|• Econometric modeling<br>• Simple comparisons between<br>areas|
|Survey<br>response|Medium|Variable<br>_(idiosyncratic_<br>_to household_<br>_and survey)_|Low / Low /<br>Low|High|• Econometric modeling<br>• Correlations with household-<br>level characteristics|
|Network<br>analysis|High|High|High /<br>Medium / Low|High|• Data rich environments<br>• Scenarios without off-road<br>travel<br>• Routing to multiple destinations<br>• Multi-scalar analysis|
|Weighted<br>Index|Medium|Medium|High / Low|Low|• Comparison between areas<br>• Balancing multiple accessibility<br>criteria|
|Advanced<br>models|High|High|Variable<br>_(depends on model)_|Low|• Data rich environments<br>• Routing to multiple destinations<br>• Balancing multiple accessibility<br>criteria|

# Linear distance

Linear distance measurements are simple to compute for non‐specialists, even manually without computers, and therefore by far the most inexpensive and data‐light accessibility measure. Distance measurements can be calculated at any scale, are very simple to communicate to policy makers, the public and other non‐specialists, and are easy to incorporate in any distance‐based analysis. In flat or near‐flat environments where distance is the main impediment to physical access metric linear distance measurements are a useful tool for analyzing analysis.

However, Nepal’s rugged terrain and underdeveloped infrastructure mean linear distance measurements there are grossly inaccurate both in absolute terms and relative to our cost time model. Our model matches linear distance measurements’ advantages of providing useful detail at every conceivable scale. While the model’s construction may appear complex, we selected units of time as the output to make it approachable for specialists and non‐ specialists alike.
**Figure 3: Module for the Energy Sector**

<!-- Start of picture text --> Energy bundle<br>(XNRG)<br>Electric bundle Non-electric<br>(XELY) bundle (XNELY)<br>Oil and biofuels<br>Coal (XCOA) Gas (XGAS)<br>bundle (XOBF)<br>Other oils bundle Ethanol/gasoline Biodiesel/diesel<br>(XOIL) bundle (XETG) bundle (XBDD)<br>Ethanol (XETH) Gasoline (XGSO) Biodiesel (XBDL) Diesel (XDSL)<br><!-- End of picture text --> Land use changes are incorporated into the model via a constant elasticity of transformation (CET) representation of land supply for each country/region. Figure 4 presents structure of the land-use module incorporated in the CGE model. Total land areas are first divided into 18 agro-ecological zones (AEZ) in every country/region. Under each AEZ in a country or region, total available land area is allocated to forest land, pasture and crop land. On the second level, crops are further divided into the four different categories: rice, sugar-crops, grains and oilseeds, and fruits and vegetables. Finally, the grains and oilseeds category is partitioned into wheat, corn, other coarse grains, and oilseeds. Land use change is induced by changes in relative returns to land as each of the CET nests of our land module, agents maximize payoffs by optimally allocating the fixed land area for this nest to the various competing uses.

While modeling the household sector, we assumed that a representative household maximizes its utility, using a non-homothetic Constant Difference of Elasticities (CDE) function, subject to the budget constraint. The households’ disposable income consists of the factor incomes (net of taxes) minus the direct tax. A household savings rate determines the fraction of disposable income that is saved, and thus available for investments. Hence, total national income accrues to government expenditures, household expenditures, and investments.

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**Table 7: Cross-Country Regression Results, 1962-2007**

|||O|LS|||2S|LS||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Dependent Variable:|Fertility<br>Rate|Fertility<br>Rate|Female<br>Labor<br>Force Par-<br>ticipation|Female Ed-<br>ucational<br>Attainment|Fertility<br>Rate|Fertility<br>Rate|Female<br>Labor<br>Force Par-<br>ticipation|Female Ed-<br>ucational<br>Attainment|
|(Log) Female Labor|-0.30***|-0.31***|0.19*|0.24*|-0.49***|-0.51***|0.30*|0.25|
|Need of Exports|(0.082)|(0.080)|(0.112)|(0.138)|(0.136)|(0.126)|(0.160)|(0.190)|
|(FLNX)|||||||||
|(Log) Openness|-0.00|-0.07|-0.06|0.10|-0.01|-0.08|-0.06|0.10|
||(0.04)|(0.051)|(0.059)|(0.098)|(0.038)|(0.052)|(0.057)|(0.096)|
|(Log) GDP per capita|-0.40***|-0.39***|-0.07***|0.48***|-0.41***|-0.40***|-0.06**|0.48***|
||(0.020)|(0.021)|(0.028)|(0.045)|(0.020)|(0.020)|(0.028)|(0.046)|
|(Log) Population||-0.05**|-0.04|-0.00||-0.06**|-0.04|-0.00|
|||(0.023)|(0.027)|(0.032)||(0.022)|(0.026)|(0.031)|
|_R_<sup>2</sup>|0.636|0.649|0.116|0.592|||||
|||||||First|Stage||
|Dep. Var.: (Log)|||||||||
|FLNX|||||||||
|(Log) Predicted FLNX|||||3.14***|3.14***|3.14***|3.08***|
||||||(0.326)|(0.320)|(0.320)|(0.330)|
|F-test|||||45.38|34.70|34.70|29.73|
|_R_<sup>2</sup>|||||0.385|0.387|0.387|0.397|
|Observations|146|146|146|126|146|146|146|126|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. _Fertility Rate_ is (log) total births per woman; _Female Labor Force Participation_ is (log) female labor participation rate (% of female population ages 15+); _Female Educational Attainment_ is the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals). All of the variables are averages over the period 1962-2007, except for _Female Labor Force Participation,_ which is averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.
young enough to benefit from the _Mirt_ treatment. Similar identification strategies based on the biology of height-for-age have been used to examine the child health impacts of primary care services (Frankenberg et al., 2005), pension income (Duflo, 2003), cash transfers (Farfan et al., 2011), and supplementary child feeding programs (Giles and Satriawan, 2015), among others.

With 40 months between the baseline and follow-up waves, we are able to separate children into two groups based on their age and expected effect of exposure to the _Mirt_ stove:

|**Age at baseline**|**Age at follow-up**|**Expected**<br>**Treatment**|**Effect**<br>**Control**|
|---|---|---|---|
|**(2013)**|**(2016)**|||
|< 36 months|< 76 months|(+)|0|
|(or not born)||||
|≥ 36 months|≥ 76 months|0|0|

Figure 1: Expected child height-for-age effects by treatment status and age The fact that only very young children in treatment households should benefit from the intervention allows for within-household comparisons to estimate the treatment effect—conditional on age and gender, young children in treatment households should be closer in height to their older siblings than young children in control households are to their older siblings.

This intuition is mapped to a regression model with the following modification of (1): z��� �γ�I�Treat&BaseAge�36 mo. ��� � γ�I�Treat&BaseAge�36 mo. ��� � δX�� � μ� � ε��� (2) where a child’s height-for-age z-score is the dependent variable. Additional baseline controls, in _X_ , include the same factors as in model (1), as well as controls for mother and father’s height to capture the genetic determinants of height. The parameter of interest, γ� , measures the relative change in height for children in the treatment group who were under 36 months old or not yet born at the time of the baseline.<sup>7</sup> Compared to the treatment vs. control difference in equation (1), the model in equation (2) incorporates two key additional features. First, the inclusion of household fixed effects, μh, is now possible, as treatment effectively varies at the individual level. This allows for the fixed effects to capture any shared characteristics common to all children in a household and restricts identification to within-household comparisons. This strategy accounts for a large set of potentially confounding

> 7 The validity of this approach requires that fertility did not respond to the intervention in a way that might bias the sample of new, young children in the 2016 follow-up. Appendix Table A2 reports results from regressing the number of new children in a household against treatment status, showing the two have no relationship.

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# **1. Introduction**

There are only two possible ways of paying for infrastructure services: through fiscal transfers financed by tax payers, or through user charges financed by consumers. Moreover, the participation of the private sector does not materially alter this underlying reality, since finance raised in private capital markets must ultimately be remunerated either through fiscal transfers or user charges.

This paper focuses on user charges as a mechanism for infrastructure financing. For some ⎯ components of infrastructure with strong public good characteristics for example rural roads ⎯ user charges are not a feasible option. For other components of infrastructure ⎯ ⎯ notably telecommunications full cost recovery through user charges is not only feasible but has widely become the norm in Latin America and hence does not warrant any major comment. This chapter focuses on two major areas of infrastructure ⎯ water ⎯ and electricity where cost recovery is technically feasible and economically attractive but may be politically difficult to achieve.

During the 1990s, the majority of countries in Latin America passed sector reform legislation espousing principles of cost recovery for water and electricity services. Cost recovery was a key prerequisite for reducing the fiscal burden associated with these sectors, and opening the doors to private sector participation. However, ten years later, it is apparent that the transition to cost recovery pricing has been much more challenging than originally supposed.

Despite substantial efforts to raise tariffs in nominal local currency terms, a large part of these gains have been wiped out by price inflation and currency devaluation. Nevertheless, Latin American utility tariffs are currently the highest of any developing region and above the average for upper middle income countries. Analysis of household income distribution shows that, except in a handful of the poorest countries, only a small segment of the population in Latin America (around 20%) faces genuine affordability problems in paying cost recovery tariffs, far fewer than in other developing regions. As a result, there is considerable potential for targeted subsidies to reconcile cost recovery and social protection objectives. Nevertheless, the increasing block tariff structures, which are the region’s most prevalent form of social policy, tend to be poorly designed failing to make a significant contribution in this direction.

This paper begins by evaluating to what extent Latin American water and electricity utilities have advanced towards the cost recovery goal during the last decade, comparing them where possible to their peers in other regions of the world. It goes on to consider why cost recovery has proved to be such an elusive goal, focusing on the problem of affordability among low income households. Finally, it considers to what extent Latin American countries have succeeded in establishing effective safety nets for water and electricity services that might allow some reconciliation between the goals of cost recovery and social protection.

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Panel C of Table 4 then reports marginal effects from probit estimation of equation (2), using having a bachelor degree, and having an advanced degree as the outcomes of interest. The coefficients on the return migrant term will be discussed in the next section. We confirm the association between current migration and higher levels of undergraduate degrees among Tongans and Micronesians, although the relationship for Micronesians is not quite significant. There is also a strong and significant positive association between being a current migrant and having an advanced degree for each of the five countries. While part of this might reflect selection, the more limited set of educational choices in the home countries suggests that if many of the individuals had not migrated, they would not have obtained the education that they now have. This is confirmed by directly asking the migrants who are currently studying what they would be doing now in their home country if they hadn’t migrated: 0% of the Papua New Guineans, only 12% of the Ghanaians, 13% of the Tongans, and 18% of the Micronesians aged 22 and older who are abroad and currently studying say they would be studying now if in the home country. In contrast, 37% of the New Zealanders say they would still be studying even if they hadn’t migrated. This leads us to believe that most of the measured difference in education rates in Table 4, panel C is indeed the true impact of migration on human capital attainment for the individuals from developing countries.

Finally, the recent literature on brain gain (e.g. Mountford, 1997, Vidal, 1998, Stark et al. 1997) has emphasized that the mere prospect of migration can induce individuals to undertake additional human capital investments, even if they don’t end up actually migrating. If individuals are obtaining bachelors or advanced degrees for this purpose, then comparing migrants to nonmigrants will understate the gain in education attributable to migration. More generally, there may be other improvements in human capital aside from degrees. Our survey asked whether people had taken any additional classes, or changed the subjects they studied during high school to improve their prospects of being able to work or study overseas. The last column of Table 3 shows some evidence of people undertaking such actions, particularly in Ghana, and to a lesser extent in the other developing countries. The main actions taken were to take private lessons, study a language, and take test preparation classes to help pass tests such as the SAT. That is, among our sample, any effect is more in terms of what is studied, rather than how much schooling takes place.

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due to a lack of viable opportunities in the formal sector. For those in this camp, informality represents a wealth of untapped and under-utilized resources, due to an inefficiently segmented market.

A second view is that informality is an informed decision, undertaken for the avoidance of taxes, fees, and regulations by operating outside of official channels. From this viewpoint, informality presents a means to evade costs, and so it provides marginal benefits to employees and entrepreneurs who would be unable (or unwilling) to conduct business formally. These informal operators will be less likely to formalize if the benefits from formalizing are minimal (as in the case of a low-productive formal sector or low expected payoffs to formality), and so the choice of informality may reflect a lack of enticing opportunities in the formal sector (Maloney, 2004). This second view is consistent with informality as an operational or individual choice. This choice implies some level of fluidity, and thus informality can serve as a safety net (Loazya and Rigolini, 2011) or last resort (Günther and Launov, 2012) in times of economic duress.

More recent work, along with more available household-level data, has shown the importance of the heterogeneity of the informal sector (Cunningham and Maloney, 2001; Günther and Launov, 2012; Nguimkeu, 2014). Maloney (1999), for instance, finds that self-employed, informal business operators are likely differentiated from those working informally; they are more akin to entrepreneurs at the margin of the formal market and may represent a top “tier”. This is especially true if returns to formal employment are particularly low relative to the off-the-books income from operating informally (e.g. as shown by Falco and Haywood (2016) in Ghana). Various types of entrepreneurs may be operating in the informal sector, demanding similar heterogeneity among informal sector businesses (Amin, 2009). Businesses in the informal sector may also have varying levels of output, rely on differing levels of human capital, and use public resources differently.

This heterogeneity can exist even within closely defined areas or neighborhoods. Indeed, informality tends to be highly clustered, even within urban areas. A recent World Bank study using city-based data for Kampala from the Uganda Bureau of Statistics found that the vast majority of informal business activity was centered around three locations near the central business district, and that 84% of informal businesses sold to customers thought to be within a 30-minute walk to their business (World Bank, 2018). Informal businesses may also be differentiated by household-level factors. Unsurprisingly, members of poorer households tend toward informality. At least some (4)
# **Figure 2: Informal employment rates estimated using RLMS**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>2000 2001 2002 2003 2004 2005 2006 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>RLMS (primary only) Rosstat official<br><!-- End of picture text --> <u>Source: Authors’ calculation using RLMS. Official Rosstat from Rosstat, “Labor force, employment and unemployment in</u> Russia”, various years. Available at <u>http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1139918584312</u> Accessed on March 16, 2018.

<u>Note: Using RLMS, the authors define informal employment as workers that are employed at an enterprise without a contract in</u> their primary job and workers employed in firms with no more than one employee (i.e., self-employed). Workers with a secondary job or in no job but in irregular economic activities are not included. This RLMS-based measure is henceforth referred to as “RLMS (primary only)”. Rosstat’s official measure of informal employment is estimated using the LFS and includes workers that are employed but not at an enterprise in their primary or secondary job.

32 Informal Employment and Worker’s Wellbeing in Russia / June 2019