rafmacalaba/data-use-mentions-extended huggingface dataset:
complete higher education seem to be experiencing increasing returns, especially in recent years, reaching 23 percent in 1999.

Others also document an increasing trend over the long term going back to 1975 (Cossa 2000). Andres (2003) shows that workers educated in private schools have higher returns and the quality of schooling significantly affects returns. The returns to schooling are relatively high and increased over time.

There has been research on the determinants of growing wage inequality in Argentina. Galiani and Sanguinetti (2003) test whether trade liberalization played any role in shaping the wage structure during the 1990s. By looking at sectors of the economy where import penetration deepened and observing whether wage inequality increased, they argue that despite some evidence of a trade role, it explains only a small proportion of the observed rise in inequality.

We estimate rates of returns to education in Argentina for a ten year period using official household surveys covering urban areas. The next section describes the methodology used to estimate these returns and the empirical model applied. In section III, data and variables included in the analysis are summarized and variables are compared across sample years. In addition to comparable Mincerian earnings for the period using OLS, we also estimate the returns using quantile regression analysis to detect differences in the returns across the whole distribution. Thus, we focus on within-education-levels wage inequality. Finally, the results for 4
**Table 8: School Construction and Child Activities Inventory: Difference-in-differences estimates.**

||1966-1970 Materna|l Cohort|Differen|ce in Diff|erences|
|---|---|---|---|---|---|
||High INPRES<br>High-L|ow INPRES||||
||Mean|Gap|[1]|[2]|[3]|
|Child read books/magazines last week (Yes=1)|0.84|0.00|-0.018|-0.014|-0.014|
||||(0.035)|(0.035)|(0.035)|
||||[0.616]|[0.716]|[0.708]|
|Child told tale/story last week (Yes=1)|0.40|0.17|-0.05|-0.045|-0.044|
||||(0.032)|(0.031)|(0.031)|
||||[0.128]|[0.164]|[0.192]|
|Child drew last week (Yes=1)|0.91|0.05|-0.034|-0.031|-0.029|
||||(0.016)|(0.016)|(0.017)|
||||[0.096]|[0.108]|[0.124]|
|Child played music last week (Yes=1)|0.68|-0.09|-0.091|-0.082|-0.081|
||||(0.041)|(0.039)|(
skilled and unskilled labor has been used in several GTAP IO database version (GTAP 1-7) which are used in this analysis. Liu et al. (1998) provide a detailed description of the methodology used in GTAP to disaggregate labor payments by skills level.

The IO tables in the GTAP data set allow one to exploit a form of social-accounting matrix (SAM) where incomes are shown in the rows of the SAM while expenditures are shown in the columns (Hertel, 2013 and McDougall, 2001 cited in Cali et al. 2016). The structure of the data is based on a general equilibrium principle – every income has a corresponding expenditure. The advantage of using this framework is that it provides a comprehensive record of the interrelationships of an economy, including intermediate and final demand linkages (Cali et al. 2016). This database 11
summarizes the vulnerable population estimates for each category of malaria intensity by presenting the top-10 malaria cases based on total exposed population.

# **[Insert Figure 1]**

> 15 Due to the spatial reference of the data and the number of observations in the combined data, the use of Geographic Information Systems functions from ESRI ArcGIS (v 9.3.1) were used and automated in the python (v 2.5) language.

> 16 This product was made utilizing the LandScan (2005)TM High Resolution global Population Data Set copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DEAC05-00OR22725 with the United States Department of Energy.

9
# **Migration, Trade, and Foreign Direct Investment in Mexico**<sup>*****</sup>

# Patricio Aroca Gonzalez

_Universidad Católica del Norte, Antofagasta, Chile_ William F. Maloney _Office of the Chief Economist for Latin America The World Bank_

**Abstract:** Part of the rationale for NAFTA was that it would increase trade and foreign direct investment (FDI) flows, creating jobs and reducing migration to the U.S.  Since poor data on illegal flows to the U.S. make direct measurement difficult, this paper instead evaluates the mechanism behind these predictions using data on migration _within_ Mexico where the census data permit careful analysis.  We offer the first specifications for migration within Mexico incorporating measures of cost of living, amenities and networks.  Contrary to much of the literature, labor market variables enter very significantly and as predicted once we control for possible credit constraint effects.  Greater exposure to FDI and trade deters out-migration with the effects working partly through the labor market.  Finally, we generate some tentative inferences about the impact on increased FDI on Mexico-U.S. migration. On average, a doubling of FDI inflows leads to a 1.5-2% fall in migration.

# World Bank Policy Research Working Paper 3601, May 2005

_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._

> * This paper was financed by the regional studies program of the Office of the Chief Economist for Latin
, indirect taxes are neutral, and the rest of the policies are progressive (except for spending in tertiary education). We revisit the 2009 data with the updated CEQ methodology in an effort to produce estimates for income concepts that are as comparable as possible and to the extent allowed by the data available. A notable difference with respect to the methodology used in previous analyses is that our approach scales the in-kind transfers _down_ instead of scaling-up income and monetary interventions (as was suggested in previous versions of the methodology) (CEQ 2018). We present results comparing the two years, but raise caution about potential limitations to the comparability with earlier results.

Official data report that inequality and poverty decreased between 2009 and 2017. As we have access to estimations for all defined income concepts for 2009, in this section we explore whether there is a role of on fiscal interventions in explaining the observed evolution of well-being indicators.

23
partially offset the potential benefits of the centralization reforms (Decarolis, 2018).

Another study using a unique dataset on tender prices of selected drugs for hospital usage provided by a sample of 52 Italian local health service providers between 2009 and 2012 tests which procurement system (centralized, decentralized or hybrid) performs better. Controlling for several covariates, including measures of institutional quality and corruption, it finds that centralized and hybrid procurers pay lower 21
Arab Rep.|0.542|0.005|27042|Senegal|0.598|0.012|3850|
|Jordan|0.481|0.008|17023|Sierra Leone|0.517|0.019|2209|
|||||South Africa|0.490|0.020|2407|
|South Asia||||Tanzania|0.528|0.012|5619|
|Afghanistan|0.563|0.009|7585|Togo|0.521|0.020|1615|
|Bangladesh|0.641|0.007|12031|Uganda|0.620|0.012|4176|
|India|0.629|0.002|151142|Zambia|0.643|0.010|5595|
|Nepal|0.623|0.010|5574|Zimbabwe|0.589|0.015|3369|
|Pakistan|0.633|0.003|40964|||||

_Notes_ : This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the full sample.

35
# **1 Introduction**

Economic development is associated with higher transport mobility. Households get richer and transition from walking to using public transport or buying a private vehicle. Consumption habits also change, and cars are increasingly used to drive around in sprawling cities. However, transport currently accounts for a quarter of energy-related carbon emissions and ranks among the fastest growing sources of such emissions (Sims, 2014; IEA, 2012). In addition, carbon emissions are unequally distributed not only between countries but also within countries (Chancel, 2021). This paper first quantifies how transport consumption, and its composition, changes as incomes rise across and within countries. It then simulates changes in carbon footprint and carbon inequalities for 18 low- and middle-income countries as income reach their 2035 levels. Such estimates can be used to assess the future distributional impacts of climate and energy policies.

The literature has shown that household demand for transport varies significantly across income levels. In high-income countries, studies using survey micro-data provide scattered evidence that direct transport-related energy consumption is less than proportional to income (Caron and Fally, 2022). Evidence from low- and middle-income countries is more limited. Studies focusing on the purchase of energy-intensive durable goods however, tend to find clear income effects towards the middle of the income distribution, suggesting significant future increases in energy demand, and carbon emissions, as large populations are just beginning to purchase energy-intensive goods (Gertler et al., 2016).

In this paper, we show how standard household surveys can be used to understand how rising incomes will affect transport consumption and the resulting transport carbon footprints and inequalities. Similar works have mostly relied on cross-country data or focused on one country, often an OECD country. However, carbon emission are expected to grow the most in nonOECD countries, for which consistent cross-country data or transportation household surveys are difficult to find. In addition, recent works have shown the benefits of using household-level data. Gertler et al. (2016) use household surveys to document motivating evidence
2 institutions, suggesting that as the number of veto players increases, independent of their preferences, their incentives to offer favors to special interests diminishes. While simple, the model has testable implications that are examined in the last half of the paper. The second contribution of the paper is to take advantage of cross-country data on banking crises to test empirically the political determinants of special interest influence. The empirical analysis tests the propositions that incomplete information, the prospect of elections and the number of veto players are significant determinants of special interests influence over government oversight of the financial sector. These tests offer some support for both informational and institutional explanations of special interest influence, and have significant implications for the design of policy interventions in the financial sector.

Information and special interests Substantial theoretical research has found that if the electorate is uninformed about veto player actions or characteristics, veto players are more likely to pursue the preferences of narrow interests. Key contributors to this literature argue that when voters are less informed, or more susceptible to campaign persuasion, candidates have a greater incentive to promise policy concessions to special interests in exchange for campaign contributions (Baron, 1994 and Grossman and Helpman, 1996). There is, however, limited empirical work exploring the effect of voter information on policy outcomes.

In Brunetti and Weder (1998) the presence of a free press is inversely associated with corruption, which can be viewed as an extreme form of special interest favoritism in which the special interests are government officials themselves. Besley and Burgess (2000) have shown that Indian states in which there is greater penetration of print media in vernacular languages exhibit faster responses to food calamities.2 Although they are not concerned with the 2 In a revealing case study, Johnson and Libecap (1999) have also extensively documented the efforts to
# **B Sample-selection Criteria**

In this appendix, we describe our REDS sample selection criteria and the reasons why we need to impose them. First note that we do not have panel data on household savings. We only know how much a household saved during the year before the survey. To test if households are saving to finance future dowry for their children, we therefore need to focus on households that have unmarried individuals at the time of survey. Out of 8,659 households in our REDS data, we drop 2,089 households that either have no unmarried member or the unmarried members are all older than 18.<sup>44</sup> We drop households where the unmarried individuals are older than 18 as these individuals are likely to be off the marriage market in our context, i.e., rural India. This leaves us with 6,570 households that have at least one unmarried member who is 18 years of age or younger.

Next, to implement our identification strategy, we need to identify firstborn children. REDS does not report the birth order of household members, so we impute it by first linking individuals with their mothers and then assigning birth order by ranking siblings by age. We can only identify an individual’s mother (and hence his/her siblings) if the mother resides in the same household. Therefore, we drop 406 households where the mother of a household member in our sample is not co-resident (e.g., because she might have died). This leaves us with 6,164 households.

Among this sample, there are 727 households where multiple mothers and their children are present. Essentially, these are non-nuclear families (e.g., two brothers living together with their spouses and children) and, as such, they comprise multiple firstborn children from different mothers. We drop these from our sample because our outcome variable (savings) is only available at the household level, whereas one of the key explanatory variables (
%<br>|12,794,095M|62.90%|
|Provincial|||||
|treasuries|198,470|91.30%|7,530,580M|37.10%|
|Total|217,474|100%<br>|20,324,676M|100%|

_Source:_ Authors.

# Analysis of the Cambodia Expenditure Transaction Profile

It is observed that only 15% central expenditure transactions in 2017 are below $2,500 or 10 million Riel, while 71.6% of the Provincial transactions are below 10 m Riel or $2500. From this appears that at the central level the system may be used mainly for drawing out advances from Treasury which are then processed off-line. This can be tested to determine the granularity of the transactions. The transaction profiles for Cambodia are given at Annex II.

The granularity of the transactions would then show whether there is a wider control problem with the payment approval process in the country. In this case the line ministries and SUs would resort to taking advances, since it takes an inordinate time to process payment requests through the Treasury.

20
# **1. Introduction**

The 2009-2010 economic crisis hit households in multiple ways, as workers lost their jobs, wage earnings were reduced, and remittances fell. The extent and severity of the impact on individual and household welfare has varied, depending on the nature of the shocks experienced, the policy response and the coping mechanisms available to households. Because such effects go beyond changes in consumption or incomes, subjective data can potentially yield important insights on both the impact of the crisis and its consequences. For example, the trauma associated with jobloss can go beyond the associated drop in household income, there may be increased insecurity about the future, and so on. The subjective impact of the crisis may therefore depend on the coping strategies adopted, the intrinsic value of employment, or the extent to which households value leisure, uncertainty or different types of expenditures.

Although subjective measures can capture impacts that are not fully reflected in objective (i.e. consumption- or income-based) measures, they also have important shortcomings, notably with regards to the comparability of responses across individuals and countries. There could be, for example, systematic reporting biases if different groups have systematically different perceptions of and attitudes to objective shocks (Bertrand and Mullainathan, 2001). This paper thus has two main objectives: (i) characterize variations in subjective perceptions of crisis impact across countries and, within countries, across individuals; and (ii) examine the extent to which subjective measures of impact are comparable, and whether they can be explained by the objective shocks experienced or the coping strategies available. The paper also analyzes the relevance of subjective measures of crisis impact, in particular their relation to other measures of well-being and socio-political outcomes, such as relative income status and perceptions of social mobility, satisfaction with life and perceptions of government performance. We use the second Life in Transition Survey (LiTS II), which allows such an exploration of the effects of the crisis, based not only on outcomes but also subjective perceptions, beliefs
and fishers’ profit-motivated search can induce an increase in the catch. The strength of our results suggests that technically determined saline tolerance parameters have limited utility as predictors of the magnitude and spatial distribution of fish catch quantities as salinity changes. Since fish catch records of the type we have surveyed are commonly available, we believe that future analyses could profit from the evidence they provide.

To assess the implications for households at different income levels, we have performed separate econometric analysis for low-, medium, and high-price fish. We find great responsiveness to salinity change in all groups, but the response of the fish catch to salinity change is greatest in the high-price group. In a parallel exercise, we have performed an econometric analysis of the fish price response as varying salinity changes the typical catch size. We find a statistically significant but relatively modest price response for high-price fish, perhaps reflecting a greater quantity response for luxury commodities. However, for low- and medium-price fish we find no significant price response to salinity change. This somewhat surprising result may warrant further research, since it suggests that fish sellers in southwest coastal Bangladesh choose to keep prices relatively stable for fish that are marketed to households of more modest means. By implication, reduced stocks in high-salinity periods are rationed by non-price methods.

protein consumption in low-income households, with associated impacts on maternal/child nutrition and health. The magnitude of the impact will depend on whether poor consumers’ resources enable them to substitute other protein sources. Poverty has fallen significantly in Bangladesh during the past two decades, although the southwest coastal region has remained relatively poor by national standards. To test whether recent income growth has increased household resilience in this context, we analyze over 16
. This is thus a model in which urbanization does not lead to agglomeration economies, although the absence of agglomeration economies is not tested directly.

Collier et al (2018) test for agglomeration economies in cities using a firm‐level dataset constructed from the World Bank Enterprise Surveys from 111 cities in 51 low‐ and middle‐income countries. They do this by testing whether city size and population density affect productivity, wages, firm size and city employment. City size and density, proxies for agglomeration economies, are found to increase productivity, wages, firm size and employment, but these benefits are not as great in African cities as they are for cities in Latin America and Asia. This is some evidence that African cities are not generating agglomeration economies as they grow rapidly.

As far as we are aware there are no other studies examining both urbanization and localization economies in African economies. Duranton (2016) examines urbanization economies in Colombia but notes that examining localization economies in developing countries is hard since much employment is informal and often not measured, which is why his paper only examines urbanization economies. Collier et al (2018) note that whilst a strength of their study is the range of countries, the study uses the World Bank Enterprises Surveys (WBES), which are limited to formal sector firms with more than 4 employees.

The Ghanaian firm censuses we use in this paper covered all non‐household‐based firms with a permanent location, as well as household‐based firms with a sign advertising the firm, whether formal or informal. Since the informal sector is such a large part of the economy in Ghana and almost all African economies our data are arguably a more useful source of data to test for agglomeration economies than the data of Collier et al (2018), which covered the formal sector only. The coverage of formal and informal firms means that we can examine both urbanization economies and also localization, which Duranton (2016) notes is usually impossible because of the lack of detailed sectoral information due
**Table 1. Stylized comparison of different price data sources**

||Market price survey|CPI price data|Unit values|
|---|---|---|---|
|Nationally<br>representative|(+)<br>(but a potentially large<br>number of missing values)|(−)<br>(often only urban,<br>purposive sampling)|(+)<br>(but potentially biased toward<br>purchasing households)|
|Representative<br>for survey<br>strata|(+)<br>(but a potentially large<br>number of missing values)|(−)<br>(data collection delinked<br>from household survey)|(+)|
|Sample<br>size/number<br>of<br>observations|(−)<br>(only few price<br>observations per item and<br>cluster, missing values)|(−)<br>(limited number of price<br>observations per item)|(+)<br>(large number of price<br>observations, especially for<br>diary surveys)|
|Food and non-<br>food coverage|(+)<br>(often only few non-food<br>items)|(+)|(−)|
|Precisely<br>defined items|(+)<br>(if enumerators are well<br>instructed)|(+)<br>(typically well-trained<br>enumerators, every month)|(−)<br>(generally not, especially<br>recall surveys with aggregated<br>item list)|
|Direct<br>measurement|(+)|(+)|(−)<br>(computed as value divided by<br>quantity)