rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **III. EMPIRICAL ANALYSIS**

## **A- Sample**

Our empirical objective is to examine how the poverty reduction effect of openness may depend on a variety of country characteristics, including some that are subject to reform. For this purpose, we work with pooled cross-country and time-series data for 30 African countries averaged over five-year periods from 1981 to 2010. Summary statistics and the correlation matrix for the variables used in our estimation exercises are provided in Appendix 1. Following the same approach as Chang (2009), we start with a linear regression specification and then extend it to account for interaction terms between an openness measure and proxies for various county characteristics (financial depth, education and governance).

These three dimensions (finance, education, and governance) should characterize an economy’s ability to reallocate resources away from the less productive sectors to the more productive ones and, hence, take advantage of the opportunities offered by greater trade openness. A more developed financial sector, as measured by the private sector credit-toGDP ratio, would allow a faster identification of new and promising sectors and a redirection of credit. A more educated population, as measured by primary completion rates, would be more able to acquire the new skills sought by growing sectors and adjust more rapidly to the new conditions of the labor market. Finally, better governance, as measured by the rule of law, would allow contracts to be made and conflicts to be resolved more easily.

## **B- Model and Definition of Variables**

## _Model_

<mark>We use the basic growth–poverty model suggested by Ravallion (1997) and Ravallion and Chen (1997) as well as the frameworks posited by Dollar and Kraay (2001) and Berg and Krueger (2003). Our point of departure can be written as:</mark> <mark>Poverty</mark> i,t <mark>= β</mark> 1 <mark>TO</mark
convincingly argues that this distinction is important for examining and understanding the extent of wage rigidity over the business cycle.<sup>5</sup> Second, we exploit the role of top managers’ skills—which we show are correlated with management practices observed in data—in shaping these effects. Although a growing body of evidence reveals that manager attributes are important for management practices and firm performance (Bertrand and Mullainathan, 2003; Bertrand and Schoar, 2003; Bastos and Monteiro, 2011; Bender, Bloom, Card, Van-Reenen, and Wolter, 2018), there is little evidence on whether and how managers matter for how exogenous firm shocks are transmitted to workers. Finally, the

> 3The broader literature on firm and labor market responses to exchange rate movements includes Revenga (1992), Bertrand (2004), Verhoogen (2008), Brambilla, Lederman, and Porto (2012), Amiti, Itskhoki, and Konings (2014) and Bastos, Silva, and Verhoogen (2018), among others. Most closely related to this paper, Araujo and Paz (2014) and Macis and Schivardi (2016) use unexpected devaluations of the Italian and Brazilian currencies, respectively, to examine effects on rent sharing and workforce composition.

> 4A related strand of work examines the relationship between exports and wages, but does not exploit quasiexperimental variation in exports, including Schank, Schnabel, and Wagner (2007), Munch and Skaksen (2008), Baumgarten (2013), Irarrazabal, Moxnes, and Ulltveit-Moe (2013), Helpman, Itskhoki, Muendler, and Redding (2017).

> 5Using administrative payroll data from the largest U.S. payroll processing company, they provide descriptive evidence that firms use base wages to cyclically adjust the marginal cost of their workers, although about one third of workers receive no base wage change year over year.

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# **5.2   Attrition Analysis**

An important concern for any impact evaluation is the fact that participants sometimes drop out of the study before its completion. Our baseline and endline survey records show an overall attrition rate of 20 percent after three years. This rate is in line with the attrition rates found in other similar randomized studies of this type.<sup>40</sup> In addition to the fact that sample size is reduced, when attrition is systematically related to the outcomes under study or to the program itself, it can bias the estimates of the causal effects of the interventions studied. We followed the standard procedures in the impact evaluation literature to address this potential problem.

First, we explored whether the attrition rates differed between the treatment and comparison groups for each of the interventions studied. As Appendix 2 shows, the proportion of households that stayed in each group ranges from 0.72 to 0.84, and we cannot reject the null hypothesis of equal level of attrition in the control and treatment groups at conventional significance levels in any of the samples studied.

The fact that there was no differential attrition in the treatment and comparison groups is consistent with the hypothesis that attrition is ignorable, and it suggests that the estimate of the treatment effects will not be biased unless different types of households dropped out of the sample in the treatment and the comparison groups.<sup>41</sup> For example, if households with healthier children were more likely to drop out when they were assigned to the treatment group, this could bias the estimator of the program effect on health outcomes downward. Fortunately, this does not seem to be the case in our study. Baseline balance was achieved for the entire initial sample in most of the variables (see Appendix1), and in Appendix 2 we can see that baseline balance is also high for a set of variables for the non-attriters’ sample. This implies that those remaining in the sample and the attriters were balanced in terms of observable
percent of the women for whom an exit interview was completed, an observation of the consultation was not conducted.<sup>8</sup> A key advantage of using data collected through direct observations is the ability to overcome potentially large recall biases in maternal self‐reports. Direct observation data are also more precise, as they are collected by trained enumerators with a medical background, and thus contain more detail on the procedures implemented. In addition, the information collected is more complete, as the direct observation tools measure not only which procedures were conducted during the consultations, but also

> 7 The Performance‐Based Financing programs are the Projet de Renforcement du Système de Santé pour Améliorer les Résultats de Santé Maternelle et Infantile and Projet d’Urgence relatif à la Violence Sexuelle Basée sur le Genre et la Santé des femmes dans la Région des Grands Lacs.

> 8 Unfortunately, the data do not allow us to separate cases in which consent was not given for the observation (by the provider or user) from cases in which a member of the survey team was not available. A survey team spent a day in each facility and had to complete a set of interviews and observations.

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development committees, Sapkota (2018) found that households with longer travel times to services have higher poverty levels and lower levels of health, education, and reported happiness. Shrestha (2012) found that a 1% increase in travel times to paved roads decreased rural farmland values and implied profits by 0.25%.

# 2.2. Measures of accessibility

Measuring accessibility baselines and improvements is critical to establishing rural roads’ benefits. Simple metrics like linear distance, densities of roads and services, or reported travel times in household surveys are easy to compute or parse from existing data sources but are often difficult to scale, only tangentially related to access, influenced by idiosyncratic survey responses (Ahlstr ̈om et al., 2011), or inaccurate in mountainous areas (Huber, 2015). Nevertheless, these are commonly used in economists’ evaluations of rural roads: for instance, Banerjee et al. (2012) and Thapa and Shively (2017) measure accessibility in terms of road length density per administrative area in China and Nepal, respectively.

Planners and researchers looking for greater accuracy and scalability will typically employ geospatial models of accessibility. The foundational models employed are network and cost-time models, which may be further elaborated in gravity models or measures of geographic and potential accessibility (Rodrigue, 2016). Network analysis calculates the anticipated travel time between one or many origins and destinations over an integrated transportation network. Transportation networks can take the form of roads, paths, railways, waterways, flights, or combinations thereof. Speeds are assigned to segments of this network which are used to calculate travel times between origin-destination pairs, with adjustments for impedances like traffic or border crossings. Implementations of network analysis range from the study of P ́aez (2004) on network accessibility’s relationship to economic activity in East Asia, the study of intra-city accessibility in Colombia by Sabogal et al. (2018), World Bank studies of accessibility to jobs in Cox’s Bazar Bangladesh (World Bank, 2020), and
# Persistent Misallocation and the Returns to Education in Mexico

Santiago Levy* Luis Felipe López-Calva<sup></sup>

## **JEL classifications: J24, J23, O17, L11[O]**

**Keywords: earnings inequality, returns to schooling, misallocation, Mexico** .

*Santiago Levy is the Vice President for Sectors and Knowledge at the Inter-American Development Bank; his email address is slevy@iadb.org.<sup></sup> Luis Felipe López-Calva (corresponding author) is the Regional Director for Latin America and the Caribbean at the United Nations Development Programme in New York; his email is luis.lopezcalva@undp.org. The opinions of the authors do not necessarily coincide with those of the institutions with which they are affiliated. The authors are grateful to Matias Morales for excellent research assistance and to Luca Flabbi, Samuel Freije, Rafael de Hoyos, Julian Messina, Hugo Nopo, Norbert Schady, and Miguel Székely for useful comments on an earlier draft. Comments by three anonymous referees and the journal editor were helpful to improve the presentation and content of this version. Participants in presentations held at the World Bank, the Inter-American Development Bank, the Bank of Spain, Universidad Iberoamericana and Centro de Investigación y Docencia Económica in Mexico; and the Latin American and Caribbean Economic Association’s Conference in Medellin, Colombia, also provided useful comments. The authors also thank, without implicating, Rodrigo Negrete and Tomas Ramirez of Mexico’s Instituto Nacional de Estadística, Geografía e Informática, for their help interpreting the employment surveys.
it is unlikely to create a significant impact on the magnitude of _θ_ 2 for the following reason: the share of private expenditure remained small compared to the public expenditure during the 1980s and 1990s; for example, tuition and other fees paid by the parents amount to only 4.42 percent of total educational expenditure in 1991 and 10.72 percent in 1995 (see Table 7.2 in Hannum et al. (2008)).

# <u>Rural India</u>

For the estimates of IGRCs across farm and nonfarm households in India to be consistent with the extended Becker-Tomes model of section (2), the returns to schooling in nonfarm households need to be _higher_ than that in the farming households in the parental generation. Table 4 (panel B) reports the estimates of household-level returns to education in rural India using household expenditure data from the NSS 1993 survey (the employment and 24
at the Port of Durban cost its growers US$10.5 million per season (on approximately US$400 million of exports), based on an average delay per load of 12 hours for each of the 20,000 citrus laden trucks that enter the port during peak season (World Bank, 2011).

It is no surprise that import and export traders have very little tolerance for poor performance by a state run freight company, neither does the Department of Trade and Industry which has the responsibility of attracting investments into the country.

In what looks like the key dynamic of the private sector in SA, a Barloworld survey of businesses involved in manufacturing and logistics, the industry expresses a sense of ownership to the logistics process. While recognizing that public sector is key to this, they feel that the onus is on them to agitate and drive the process to see transformation at Transnet (Barloworld Logistics, 2010). The strategy seems to have contributed to reducing delays at some of the ports in South Africa. Facing internal pressure

> 26 Logistics Performance Index, 2010.

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projected before the disaster. The deviations in these variables were all much more pronounced than in the control group, with the exception of the changes in public debt, and both revenues and expenditures moved in the opposite direction to the control group.

**Table 3. A Hurricane Erika on Dominica (August 2015): Deviations of macro-fiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Dominica**||**Other**<br> <br>**SIDS**|**Caribbean**|
|---|---|---|---|---|
||2015/16|2016/17|2015/16|2016/17|
|GDP growth|-1.4|-6.2|-0.8|-1.0|
|Govt Revenue|26.4|23.8|-2.1|-2.3|
|Govt Expenditure|11.1|19.3|-2.7|-2.2|
|Net Lending|15.3|4.4|0.7|-0.1|
|Gross Public Debt|-5.1|0.6|-5.6|-5.0|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._

# <u>Source: WEO database</u>

**Table 3. B Hurricane Maria on Dominica (September 2017): Deviations of macrofiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Dominica**||**Other**<br>**SIDS**|**Caribbean**|
|---|---|--
combining the World Bank’s Service Delivery Indicators (SDI) on teacher absenteeism with geolocated data on schools (Bold et al., 2017). The reasons for absenteeism may vary, for example, if teachers in remote areas with fewer trained professionals are called on to perform a wider array of tasks in the community; this too could be examined by combining the SDI data with geolocated data. Understanding how to deliver technological infrastructure sustainably, however, requires not only geolocated data but also longitudinal data, which are limited in international education studies (McEwan, 2015). More research is thus needed to understand what school design and improvement can be implemented given limited access to building resources in rural areas, and how these vary as schools grow more rural, with decreasing access to urban centers.

Many teachers prefer to teach in urban areas, such that education systems offer financial or other incentives to entice teachers to rural areas (Crawfurd & Pugatch, 2020). Several studies have evaluated whether such programs boost teacher presence in rural schools (Chelwa, Pellicer, & Maboshe, 2019; Elacqua, Hincapie, Hincapié, & Montalva, 2019; Pugatch & Schroeder, 2014). However, the impacts of such a program—and thus its optimal design—may well be non-linear depending on the remoteness of the school and access to urban amenities.

Some challenges are very specific to education. For example, language ability of teachers is a distinct challenge in schools at different points along the urbanization spectrum. Increasing research shows that initial literacy instruction in a language with which students are already familiar (e.g., their native tongue) is most effective (Evans & Mendez Acosta, 2021; Piper, Zuilkowski, & Ong’ele, 2016). Another challenge is that of girls in rural areas having less access to menstrual hygiene management technology and private sanitation facilities compared to girls in urban areas, which can cause them to miss school during their menstrual periods (Adukia, 2017; Kazianga, Levy, Linden,
helps to identify and correct for these responses, whether they are generated by social or political factors.

# **3.  Research Design, Data and Analysis**

To explore the relationship between investment and corruption, we utilize the Bangladesh Business Confidence Survey from the 4<sup>th</sup> Quarter of 2009. This business confidence survey was conducted on a quarterly basis from the first quarter of 2009 to the first quarter of 2010 by the Bangladesh Investment Climate Fund involving leading Bangladeshi survey research firms<sup>4</sup> . The objective of the survey is to read the pulse of the economy and the mood of the business community on a quarterly basis. Typically, the owners or the managers of the firms were surveyed in a nationally representative sample of firms covering all the six divisions of the country. In the survey, business owners or managers report on their current business situation compared to the immediate past and anticipate business conditions for the near future in terms of investment, employment, and profitability. Jointly with one of more leading business chambers, BICF disseminate the survey findings in a high profile national workshop in the presence of key policy makers of the government such as the Commerce Minister, Prime Minister’s advisers, leading economists and development practitioners, private sector leaders, business community, and representatives from relevant government organizations, which captures the extensive print and electronic media coverage. The role of government officials in the dissemination of the survey isn’t unique Bangladesh Business Confidence Survey,

> 4 The Bangladesh Investment Climate Fund (BICF) provides advisory services aimed at improving business operating environment in Bangladesh. BICF is managed by IFC, in partnership with the U.K Department for International Development and the European Union. BICF administered the first two quarterly surveys jointly with the Bangladesh Enterprise Institute and the subsequent rounds with the OrgQuest Research Limited.

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**Figure 12  Percentage of total household expenditures spent on LPG by all those citing LPG as the primary cooking fuel and those citing LPG as the primary cooking fuel who had abandoned polluting fuels**

<!-- Start of picture text --> 4<br>3<br>2<br>1<br>0<br>LPG as primary cooking fuel LPG as primary cooking fuel and no polluting fuels<br>% fo household expenditures<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : * = urban population only.

To probe the above findings further, data from the top two quintiles in regions with enough households that had abandoned polluting fuels—who were essentially using only LPG and electricity—were analyzed. Table 1 compares the statistics related to spending on LPG and electricity by two groups of households: (1) those who were consuming LPG (whether as the primary cooking fuel or in any other capacity) and had abandoned all polluting fuels, and (2) those who cited LPG as their primary cooking fuel but continued to use polluting fuels. Mauritius and Burkina Faso are omitted in the table, the former because too few people had not abandoned polluting fuels and the latter because too few people had abandoned polluting fuels. In the remaining 10 regions, the percentage of the total population in each quintile that belonged to the first group (abandoning polluting fuels) ranged from a mere 5 percent in urban Kenya in the fourth quintile to 85 percent in Gabon. The highest LPG expenditure share of 4.6 percent in urban Kenya was below the 5 percent threshold for affordability. At the opposite end of the spectrum, the LPG expenditure share was only 0.7 percent in Gabon—leading to about 85 percent of the population in each quintile abandoning polluting fuels—and, although not included in Table 1, in Mauritius where 98 percent of the entire population had abandoned polluting fuels
are combined, the _R_<sup>2</sup> rises above 0.2, almost four times the initial 0.054 value.

Unlike ours, the framework of Cerutti et al. (2017c) includes only two factors and no factors. One to assess how this affects group global way their results is to add to their setup one more factor per group, as a crude of the contribution of the factor to the variance of way capturing global in each of their These extra factors are cominflows country groups. puted as the second principal components of the non-central advanced and emerging-country inflows, respectively. Doing this raises the _R_<sup>2</sup> to 0.274. The 63 ’non-large’ countries considered by Cerutti et al. (2017c) are mainly emerging (although Australia, Canada, Denmark, Norway, New Zealand, Sweden, Switzerland, Iceland and Hong Kong SAR, China, are also included). The _R_<sup>2</sup> we obtain for emerging countries with our data is 0.290, thus roughly similar (this is the adjusted _R_<sup>2</sup> ; the slightly larger values in Table 5 correspond to the unadjusted _R_<sup>2</sup> ). Taking all countries in our sample, but excluding US, UK, GER, CHE and JPN, we obtain 0.376; if we further exclude all EMU countries, we obtain 0.315.

If we examine the explanatory power of the estimated factors for the flows of advanced countries (those with "ad"=1 or "nonlarge"=0 in the Cerutti et al. (2017c) data), we obtain the results shown in the middle section of 67
Many studies have shown that out-migration can be selective.<sup>27</sup> Thus, the set of individuals who decide to return to their countries of origin or leave the US for other destinations may not be a random sample from the initial distribution of the migrants. If the most (least) able members of a cohort decide to out-migrate, the improvement in occupational placement across the decade is understated (overstated). A differential selective effect of return migration by country of origin may affect the results obtained in the paper. Examining the effects of return migration is a challenging task when using the US Censuses as a data source mainly because different individuals are surveyed in different years.<sup>28</sup> Duleep and Regest (1997) suggest a method for testing the sensitivity of results based on US Census data to biases caused by sampling error and migration. The approach taken here is a very simple one and consists of calculating the rate of attrition for the skilled immigrants in a particular cohort over the decade that is closest to their arrival in the US. As the next step, the countries with the highest rates of return migration are identified and excluded them from the samples. Re-estimating equation (1) and examining the relationship between the new predicted occupational placements obtained after eliminating those countries from the sample, we find that the improvement patterns do not differ from the ones obtained before trimming the sample.

Another source of bias is related to the fact that the sample of people responding to the long-form questionnaires of the US Censuses is drawn anew each decade from the entire population of the country. Hence, the validity of the inferences stated in this paper may be affected by the comparison of different sets of individuals over time. Because real panel data is typically less easily available repeated cross-section datasets, researchers have determined the conditions under which the latter can produce unbiased estimates equivalent to the ones based on estimating genuine panels. The pseudo-panel methodology in which repeated cross-sections are treated as panels was introduced by Deaton