rafmacalaba/datause-encoder-data huggingface dataset:
|**Appendix Table A.6(Continued)**|
|---|

||**All**|||Ye|ar|||
|---|---|---|---|---|---|---|---|
||**Years**|1987|1990|1993|1996|1999|2002|
|Village Total Land (mu)|5090|4820|5080|4870|5200|5100|5760|
||(5710)|(5110)|(5190)|(5500)|(5310)|(6240)|(6460)|
|Village Cropped Land Per Capita Gini|0.21<br>(0.08)|0.18<br>(0.06)|0.18<br>(0.06)|0.20<br>(0.06)|0.21<br>(0.08)|0.23<br>(0.08)|0.26<br>(0.11)|
|Village Labor Force|861<br>(486)|780<br>(352)|851<br>(419)|867<br>(433)|899<br>(470)|820<br>(487)|948<br>(559)|
|Years Since IDs Issued|7.37<br>(5.11)|0.33<br>(1.02)|2.12<br>(1.65)|4.86<br>(2.07)|7.87<br>(2.20)|11.01<br>(2.23)|13.86<br>(2.28)|
|Cultivable Share of Village Land|0.58|0.60|0.57|0.62|0.55|0.57|0.54|
||(0.28)|(0.26)|(0.28)|(0.28)|(0.28)|(0.29)|(0.31)|
|Forest Share of Village Land|0.15<br>(0.27)|0.16<br>(0.27)|0.16<br>(0.25)|0.15<br>(0.27)|0.14<br>(0.25)|0.16<br>(0.28)|0.19<br>(0.30)|
|Orchards Share of Village Land|0.04|0.02|0.03|0.04|0.07|0.06|0.06|
||(0.07)|(0.03)|(0.05)|(0.06)|(0.10)|(0.10)|(0.09)|
|Aquaculture Share of Village Land|0.04|0.05|0.05|0.04|0.04|0.04|0.05|
||(0.06)|(0.06)|(0.05)|(0.05)|(0.05)|(0.05)|(0.08)|
|Share of Households with Non-|0.56|0.66|0.66|0.55|0.57|0.49|0.51|
|Agricultural Self-Employment Income|(0.28)|(0.27)|(0.27)|(0.27)|(0.25)|(0.28)|(0.26)|
|Quota Share of Grain Produced|0.09|0.12|0.12|0.08|0.10|0.07|0.03|
||(0.08)|(0.10)|(0.09)|(0.08)|(0.08)|(0.07)|(0.05)|
|Scaled Lagged July-November|0.15|0.02|0.02|0.02|0.04|0.02|0.02|
|Rainfall Shock, Squared|(2.12)|(0.04)|(0.07)|(0.02)|(0.05)|(0.05)|(0.05)|
|Number of Observations|3167|158|162|237|262|238|187|

Notes: The first column includes descriptive statistics for all years; the second through seventh columns include descriptive statistics for selected years.

_Sources:_ RCRE Supplemental Survey (2004), Annual RCRE Household and Village Surveys (1986-1991, 1993, 19952003).

70
# **Appendix**

## **A.1 Summary statistics: Regression data**

This section presents summary statistics on the data used in the regression analysis.

## **A.2 Matching communities between 1996 and 2011 census**

As explained in Section 4, the geographical definitions for communities changed between the 1996 and 2011 census. In this section we explore how different strategies of matching communities over time affects our estimates. In our main analysis, we match 2011 communities to 1996 by nearest centroid. It is possible that many communities from 2011 match to the same community in 1996. When this is the case, the groups of 2011 communities are aggregated into one unit. Additionally, it is possible that a number of communities from 1996 find no matches, in which case they are dropped from the sample.

A second matching strategy employs the use of cartographic data for both censuses. We calculate the areas of community polygons from 2011 inside 1996 community polygons. If over 75% of the area of a 2011 community is contained in a single 1996 community, then it is matched to that community. As before, it is possible that several 2011 communities match to a single 1996 community. Additionally, it is possible that communities from both years find no match or do not reach the threshold and are dropped.

For robustness, in Table A.2 below we present the two most common specifications presented in the paper: including 1996 controls and municipality fixed effects, and also including 2011 controls for each community matching strategy. The strategies described above are denoted Centroid and Area _>_ 75%, respectively. The first two columns correspond to the initial estimates presented in columns 3 and 4 of Table 2. Noticeably, the point estimates do not change significantly across the various community matching strategies. In addition, they remain quite stable even when restricting the sample to unique matches between censuses (columns 5 and 6).

## **A.3 Additional robustness checks**

In constructing the data set used in the main analysis, we count all missing values as zero when calculating the community’s per capita income. In Table A3, we show that our main results are robust to imputing missing values by the community average instead. Another potential concern is that our main variable of interest for capturing exposure to resource rents—the share of employment in mining in 1996—does not account for unemployment. An alternative measure of exposure would be based on the unconditional (on being employed) share of employment in mining for a community. Table A4 presents 22
Supplement Figure VI summarizes learning for selected countries for the last decade. We observe a few interesting case studies. Russia outperforms the United States. Chile outperforms Eastern European countries such as Georgia. Saudi Arabia places near the bottom outperforming only African countries. The gap between Morocco and Singapore is substantial. Singapore and Finland have low variation due to a potential plateau on the upper end of performance. Rwanda has low variation due to limited data. Russia has high variation due to improving learning, whereas South Africa has high variation due to declining learning.

<!-- Start of picture text --> Rwanda<br>Morocco<br>Egypt<br>South Africa<br>Saudi Arabia<br>Georgia<br>Chile<br>Israel<br>France<br>United States of America<br>Germany<br>Russian Federation<br>Finland<br>Hong Kong, SAR China<br>Singapore<br>0 200 400 600<br>HLO<br><!-- End of picture text --> <!-- Start of picture text --> SUPPLEMENT FIGURE VI<br><!-- End of picture text --> Average Learning (2007-2017) – Selected Countries _Notes:_ Average learning is calculated across subjects and schooling levels over the given period of time. _Source:_ Our learning outcomes database.

Supplement Figure VII plots average learning for each country by the log of their GDP per capita. This graph illuminates cases where countries have managed to improve learning despite a lack of resources, as well as cases where countries have resources to invest in to date unrealized learning potential. Former or current centrally planned economies display better learning outcomes than their income would suggest, such as Singapore, Poland, Bulgaria, Cuba and Vietnam. Countries in the Middle East and Africa reach lower learning levels than predicted by income, such as Qatar, Kuwait, United Arab Emirates, South Africa, Nigeria and Ghana. We also highlight large developing countries: India, China, Mexico, and Brazil. China outperforms its counterparts, Mexico, India and Brazil perform slightly below where their income would predict, and South Africa trails far behind.

34
Following the institutional economics literature, institutions are needed to reduce the transactions costs of participating in markets.<sup>8</sup> They do this by performing three important functions: (a) regulating information asymmetries (who knows what); (b) defining property rights; property rights is used in a broad sense here. It means the “right” to use or own assets, rights to income, (or other gains from a transaction) or the right to take an action that will constrain another’s gain from a transaction. In this sense, if the law allows the factory owner to pollute a river, it gives him a property right from which he gains, but constrains the value of the water to those who fish in or otherwise use the river; (c) determining the degree of competition (or its converse‐cooperation) in any activity. An institution may simultaneously do more than one function. For example, by directing information flows, they can affect the degree of competition.<sup>9</sup> A corollary of the above, is that these rules provide structure to societal interactions, for example, by influencing peoples’ _incentives_ and _ability_ to behave a certain way.

<u>Information Asymmetries</u> Whenever a party to a transaction knows something relevant to the transaction that another party does not, there is an information asymmetry. For example, when you take out a loan, you have the most information on your assets and your willingness to repay the loan. The bank or moneylender uses the information at his/her disposal (credit registries and bureaus, your income level, title to your assets etc.,) to make an informed decision about your creditworthiness and whether to lend. There is asymmetric information in this transaction. At the same time, when you deposit your savings in the bank, you do not know whether the bank is truly safe or likely to fail. When you buy stock in a company, you do not have all the information on the company’s financial health. You make an informed judgement based on available information. Institutions determine how much information is available to each party: accounting standards, regulations on auditing, publication of accounts, credit bureaus and

> 8 As distinct from actual production costs.

> 9 Thus, it is not always possible to categorize institutions according to a particular function.

6
53% after the scheme. To the extent that the two groups of users (new and continuous) are comparable, there is a notable shift in their travel expenditure patterns, which complements the household-level results presented above.

In interpreting the treatment effect, we note that if paid bus ridership was widespread among women prior to the policy, the eliminated fare would directly reduce their expenditures. The Delhi Survey, however, documents the presence of a large segment of new users following the introduction of the policy. For these women, free bus ridership may imply new travel demand or substitution from other modes of transport. Either of these changes are likely to impact time use, which we turn to next.

# **6 Impact on Time Use**

Our first set of main results pertains to time use. To reiterate, the CPHS reports daily time spent on work for employed women, household chores, and travel time, without breaking down the latter by motive for travel. Due to various substitution possibilities between travel modes, it is not clear whether total travel time will increase, decrease, or remain unchanged. It is possible that women switch from paid to free buses and there are no changes in travel time. However, if women who were previously budgetconstrained switch from walking to taking free buses, travel time will be reduced. Yet a third possibility is that the price effect helps women who would have otherwise not gone out to take more buses, increasing their travel time. While our ability to shed direct light on mode substitution is limited by the CPHS data, empirical results could help discern the overall impact. Finally, reallocation from travel time may affect time spent on household chores, and the possibility of cheaper commutes may affect labor supply. In what follows, we first focus on employed women and document time use changes that vary by marriage status and skill level. Subsequently, we offer a simple model of intra-household time allocation, corroborating its implications by showing results for men’s time use. Our final analysis documents results for unemployed women.

11
WPS4272

# **Migration, Remittances, Poverty, and Human Capital: Conceptual and empirical challenges**

## **David McKenzie**<sup>*****</sup> **and    Marcin J. Sasin**<sup>******</sup>

#### **Abstract**<sup>*******</sup>

This paper reviews common challenges faced by researchers interested in measuring the impact of migration and remittances on income poverty, inequality and human capital (or, in general, “welfare”) as well as difficulties confronting development practitioners in converting this research into policy advice.

On the analytical side, the paper discusses the proper formulation of a research question, the choice of the analytical tools as well as the interpretation of the results, in the presence of pervasive endogeneity in all decisions surrounding migration. Particular attention is given to the use of instrumental variables in migration research.

On the policy side, the paper argues that the private nature of migration and remittances implies a need to carefully spell out the rationale for interventions. It also notices the lack of good migration data and proper evaluations of migration-related government policies.

The paper focuses mainly on microeconomic evidence about international migration, but much of the discussion extends to other settings as well.

### World Bank Policy Research Working Paper 4272, July 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * David McKenzie is currently a Senior Economist in DECRG department of the World Bank.

> ** Marcin Sasin is currently an Economist in PREM Poverty group of the World Bank.

> *** This paper has benefited from comments provided by participants of the World Bank Migration and Development Thematic Group Seminar on September 21, 2006. Pierella Paci, Catalina Gutierrez, Katy Hull and Edmundo Murrugarra contributed further useful comments. The work has been guided and supported by Louise Cord, Manger, and Luca Barbone, Director of PREM Poverty group. For more information see: www.worldbank.org/migration

1
are not satisfactory for this purpose. For example, unobserved factors that influenced industry location decisions 30 years ago could still matter for industry location decisions today.

In our quest for appropriate instruments we examine if exogenously established historical institutions set into place a process of path dependence that has implications for present day industrial development. For this, we draw on Banerjee and Iyer’s [2005] research on colonial land tenure and revenue systems in India, where they study the different land revenue systems instituted through the British colonial rule of India during the early nineteenth century, and examine its impact on a variety of present day economic and social indicators. Land revenue was the most important source of government revenue, and the British instituted three systems defining who was responsible for paying the land taxes. These were (a) landlord based systems ( _zamindari_ or _malguzari_ ), individual cultivator-based systems ( _raiyatwari_ ) or village-based systems ( _mahalwari_ ).<sup>13</sup> Banerjee and Iyer (2005) find that post independence agricultural investments and productivity were lower in districts where land rights were given to landlords compared to districts were rights were given to cultivators. They show the effects of ‘institutional overhang’ on economic performance because while these land revenue institutions established during the British colonial rule ended with Indian independence, their effects are observed much later in history.

> 13 In landlord-based systems, the landlord was responsible for collecting revenues from a number of villages after retaining part of the revenue he collected; in individual-based systems British government officers collected revenue directly from cultivators; and in village-based systems, a village community body bore the responsibility for revenue collection.

22
whether this econometric specification adequately captures the way heterogeneity impacts on subjective welfare; I will return to this point after considering some examples.

In what appears to have been the first panel data study of subjective welfare, van de Stadt et al., (1985) modeled a money metric of subjective welfare in the Netherlands. They did not allow for latent individual effects correlated with the covariates, but they did allow for dynamics, by including the lagged subjective welfare measure as a regressor, which picks up at least some of the heterogeneity. More recently, the papers by Winkelmann and Winkelmann (1998), Ravallion and Lokshin (2001) and Senik (2004) allowed for a time-invariant latent individual effect when modeling subjective welfare data. I will discuss the example of the Ravallion and Lokshin paper in more detail as this provides insights into the implications of allowing for latent heterogeneity for standard regression results on the determinants of subjective welfare.

Using panel data for Russia on responses to the ELQ, Ravallion and Lokshin confirmed some of the results in past studies using single cross-sections, but not others. Household income was found to be a highly significant predictor of self-rated economic welfare. Controlling for household income, individual income was a far weaker predictor, suggesting that there is some degree of income pooling. Health shocks lowered subjective economic welfare, at given values of other variables in their model, including incomes. The demographic effects found in crosssectional studies (notably of household size, at given income per capita) were not found to be robust. The extent of the scale economy of household size in individual subjective welfare suggested by a number of papers in the literature may well reflect latent personality effects on the demographic characteristics of the respondent’s household.

In keeping with past work, Ravallion and Lokshin found evidence of an incomecompensated welfare cost of unemployment. They argue that most plausible interpretation is that the current welfare loss arises from some combination of lower expected future incomes and more uncertain incomes, the latter arising from greater exposure to uninsured risk outside the formal employment sector. They found that it would take a large gain in current income to compensate for becoming unemployed.

However, their results point to a number of qualifications on past findings about the welfare effects of unemployment. While becoming unemployed entails a large welfare loss, that loss is not fully restored when an unemployed person gets a job, except via the income gain. This 24
Table 6: Mechanism: Sanitation-cognition <u>gradient</u> is not found for older children

||(1)<br>(2)<br>efect of 2001-2003 lat|(3)<br>rines on:|
|---|---|---|
||6 year olds<br>7 year olds|8 year olds|
|Panel A: Readi|ng||
|TSC latrines|0.752*<br>-0.0460|-0.402*|
|per capita|(0.339)<br>(0.298)|(0.196)|
|_n_ (children)|47,684<br>44,056|59,476|
|Panel B: Math|(numbers 1-9)||
|TSC latrines|0.754*<br>-0.356|-0.121|
|per capita|(0.359)<br>(0.270)|(0.186)|
|_n_ (children)|47,133<br>43,688|59,019|
|Panel C: Math|(numbers 10-99)||
|TSC latrines|0.897*<br>0.387|-0.188|
|per capita|(0.407)<br>(0.420)|(0.406)|
|_n_ (children)|47,133<br>43,688|59,019|

Standard errors clustered by districts in parentheses. Two-sided _p_ -values: _† p <_ 0 _._ 10; * _p <_ 0 _._ 05. The ASER survey rounds are the same from 2007 to 2009 as used in table 3, so in column 1, the sample is the same set of six-year-olds used there. Within a panel, each column reports a separate regression where the TSC administrative data from 2001 to 2003 is held constant, while the age cohorts from whose 2007 to 2009 ASER test score is taken (for the dependent variable) change; thus column 1 uses the same sample as tables 3 and 5, but columns 2 and 3 do not. Each regression includes district and year fixed effects.

47
industries that experienced high subsequent import growth earn lower cumulative earnings and face elevated risk of obtaining public disability benefits. The difference between a manufacturing worker at the 75th percentile of industry trade exposure and one at the 25th percentile of exposure amounts to cumulative earnings reductions of 46% of initial yearly income, and to one-half of an additional month where payments from SSDI are the main source of income. Trade exposure increases job churning across firms, industries and sectors. Workers initially employed in industries more exposed to import competition spend less time working for their initial employers, less time in their initial two-digit manufacturing industries, and more time working elsewhere in manufacturing and outside of manufacturing. Earnings losses are also found to be heterogeneous across workers. They are larger for individuals with low initial wages, low initial tenure, and low attachment to the labor force. Low-wage workers churn primarily among manufacturing sectors, where they are repeatedly exposed to subsequent trade shocks. High-wage workers are better able to move across employers with minimal earnings losses and are more likely to move out of manufacturing conditional on separation. These findings reveal that import shocks impose substantial labor adjustment costs that are highly unevenly distributed across workers according to their skill levels and conditions of employment in the pre-shock period.

_US-Mexico_ : Hakobyan and McLaren (2017) estimate effects of NAFTA on wages using US data for 1990 and 2000. They estimate the effects of the agreement by industry and by location, measuring each industry’s exposure to Mexican imports and each locality’s dependence on exposed industries. The results indicate that tariff reductions reduced wage growth for blue-collar workers in the most affected industries and localities. These effects apply also to service-sector workers in affected localities, whose jobs do not compete with imports.

Other earlier papers examine effects of NAFTA on local labor markets in Mexico. Hanson (2007) finds that, in the aftermath of NAFTA, Mexican regions more exposed to globalization (as measured by exports, imports and FDI) experienced a decline in inequality and poverty relative to the rest of the country. Prina (2013, 2015) finds that Mexican small farmers tended to benefit from the agreement, while rural landless workers appear not to have been affected. Robertson (2004) provides evidence that the prices of unskilled-intensive goods fell in Mexico following NAFTA, which led to a decline in the wage premiums of skilled workers. Finally, Chiquiar (2008) shows 33
The goal of this study is to establish the link between laws that discriminate against women and women’s participation in the formal labor market, as well as their ability to achieve top managerial positions and become owners of firms. This study adopts a holistic approach. Our concern is not one or two laws, but a system of laws that entangle different aspects of the lives of women. We also do not want to restrict the analysis to simple labor market engagement, but expand it to include empowering outcomes such as top managerial positions. For this study, we use two prominent World Bank data sets, the Women, Business and the Law database and the Enterprise Surveys. The Women, Business and the Law database explores the laws that serve as impediments for women in the economic sphere. The overall number of discriminatory laws by gender has been quantified as a measure of legal gender disparities (Iqbal et al., 2016). We use this indicator and combine it with rich firm level data from the World Bank Enterprise Surveys spanning over 60,000 firms across 104 economies. Information regarding employment, ownership, and managerial positions by gender are available by firm.

We find that legal gender disparities do hinder women’s engagement in the economic sector, both as workers and as top managers or business owners. We also attempt to uncover pathways that explain the relationship between legal gender disparities and the likelihood of women to be business owners and managers, and some suggestive evidence points to discrimination in access to finance and corruption. Women managers and business owners are less engaged with the financial sector - more dependent on internal funds and less likely to have an overdraft facility - in economies that have a higher number of legal gender disparities. Women business owners and managers are also more likely to face bribe requests when obtaining an operating license in economies where a greater number of laws discriminate against women. One possibility is that if the legal system does not provide recourse for women, other actors may take advantage of it and 3