rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. INTRODUCTION**

The performance of immigrants in the destination labor markets is among the key issues in the migration literature. Successful integration and career performance increase the benefits of migration to both the migrants and the destination countries. On the other hand, migrants’ failures to receive wages or to obtain jobs that are commensurate with their qualifications might lead to social costs and negative public opinions on migration. The stereotypes of immigrant taxi drivers with physics doctorates exist because there is, at least, some anecdotal evidence to support it. The critical questions are how widespread and long lasting these labor market “under-placements” of skilled immigrants are. These issues are the focus of this paper.

There is significant variation among the findings in the literature on immigrants’ performance in the destination labor markets. Some papers find that migrants perform quite well and catch up rapidly with the native workers<sup>1</sup> while others cast doubt whether convergence occurs at all.<sup>2</sup> This paper contributes to this debate by investigating the long-term convergence of labor market performance among male immigrants to the US who come from different countries. We track the same male cohorts in terms of age and arrival dates using data from 1980, 1990 and 2000 Censuses. While controlling for individual attributes and focusing on occupational quality indices as opposed to wages as a measure of assimilation, we answer three closely related questions. First, do the initial occupational placement levels of skilled immigrants differ by country of origin when other factors are taken into account? Second, how does occupational performance by country of origin change over time and, more specifically, do we observe convergence among migrants from vastly different origin countries? Finally, to what extent do country of origin attributes explain these temporal patterns of the occupational placement?

The first finding is that predicted entry-level occupational placement of the tertiary educated immigrants varies widely by country of origin. We observe significant variation even among neighboring countries that have similar economic and social characteristics. Thus narrow
**We base our vulnerability rate on a threshold equal to a 50 percent probability of falling below the poverty line over the next two years** .<sup>10</sup> This measure is equivalent to a 29 percent chance or higher of falling into poverty in a given year. The vulnerability rate is the share of the population having a higher probability of falling into poverty. If the probability of falling above the probability threshold is driven by a mean household consumption/income below the mean, vulnerability is poverty-induced. If the probability is driven by high consumption/income volatility, vulnerability is risk-induced. The combination of both is the overall vulnerability rate.

**Due to a lack of panel data in the country, we rely on repeated cross-sections.** The El Salvador Multipurpose Household Survey (EHPM) is a cross-sectional survey collected by Dirección General de Estadística y Censos (DIGESTYC) since 1975. There is no longitudinal survey of individuals or households in the country. Therefore, we rely on repeated cross-sections, which provides less information than panel data. Ideally, for the methodology, we would also draw from a community-level survey, which is not available in El Salvador. Instead, we rely on household surveys from 2016 to 2019 and average our variables of interest at the municipality level. We focus on the period 2016 to 2019, as data on self-reported hazards was only collected for the years 2016 onwards. We do not consider the post-COVID period (2020 onwards) due to data limitations. Due to the pandemic, the survey round collected in 2020 is not fully comparable with previous survey rounds.

**In contrast to previous studies applying the vulnerability tool, we employ an income-based and not consumption-based approach, given that poverty is officially measured via income-based measures in El Salvador.** Our primary outcome is overall household per capita income and not per capita consumption, as poverty is measured in El Salvador using income. The tool draws from three sets of parameters: i) A set of
ultimately this is an empirical question.<sup>5</sup> But the empirical literature has produced very mixed results.<sup>6</sup> It has been suggested that these mixed results reflect a gap between the intricacy of the relationship, as expressed in the theoretical literature, and the simple relations that are commonly estimated (Voitchovsky, 2009). In support of a more complex relationship, several papers provide empirical evidence of a non-linear, 4. Using a different model to predict headcount poverty, Lakner et al (2020) also find that reducing inequality by 1% per year leads to a greater reduction in 2030 headcount poverty than 1% increases in mean incomes.

5. Theoretically, the effect of inequality on growth is ambiguous: the conventional view is that higher inequality promotes stronger incentives, generates greater savings and investment, and endows the rich with the minimum capital needed to start economic activity, thereby stimulating growth (Aghion et al, 1999; Okun, 2015; Kaldor, 1957; Barro, 2000). Conversely, it has been argued that inequality hurts growth as it leads to redistributive pressure through taxes, social conflict, expropriation, or rent-seeking behavior (Alesina and Rodrik, 1994; Alesina, 1994; Benhabib and Rustichini, 1996; Benabou, 1996; Perotti, 1996; Persson and Tabellini, 1994; Glaeser et al, 2003); or in the presence of market failures, inequality prevents the talented poor from undertaking profitable investments, thereby limiting growth (Galor and Zeira, 1993; Banerjee and Newman, 1993).

6. Early papers, using a cross-section of countries, typically found negative effects of inequality on growth (Alesina and Rodrik, 1994; Persson and Tabellini, 1994). Then, with the introduction of the Deininger and Squire (1996) panel dataset, studies started to find positive effects (Li and Zou, 1998; Forbes, 2000). More recently, however, several papers find again a negative effect (Castell ́o-Climent,
- McGuire, Joel, Caspar Kaiser, and Anders Bach-Mortensen. 2020. “The impact of cash transfers on subjective well-being and mental health in low- and middle- income countries: A systematic review and meta-analysis.” Tech. rep., SocArXiv. URL `https://osf.io/ preprints/socarxiv/ydr54/` . Type: article.

- McKelway, Madeline. 2020. “Women’s Employment in India: Intra-Household and IntraPersonal Constraints.” _Working Paper_ .

- McKenzie, David. 2012. “Beyond baseline and follow-up: The case for more T in experiments.” _Journal of Development Economics_ 99 (2):210–221. URL `https://ideas.repec. org/a/eee/deveco/v99y2012i2p210-221.html` . Publisher: Elsevier.

- Morse, Nancy C. and Robert S. Weiss. 1955. “The Function and Meaning of Work and the Job.” _American Sociological Review_ 20 (2):191–198. Publisher: [American Sociological Association, Sage Publications, Inc.].

- Mree, Himaloy Joseph and Karen Homer. 2019. “Cash-for-work programme empowers Rohingya refugee women.” URL `https://www. wvi.org/myanmar-%E2%80%93-bangladesh-refugee-crisis/article/ cash-work-programme-empowers-rohingya-refugee-women` .

- Olesen, Sarah C., Peter Butterworth, Liana S. Leach, Margaret Kelaher, and Jane Pirkis. 2013. “Mental health affects future employment as job loss affects mental health: findings from a longitudinal population study.” _BMC Psychiatry_ 13 (1):144. URL `https://doi. org/10.1186/1471-244X-13-144` .

- Patel, Vikram, Benedict Weobong, Helen A. Weiss, Arpita Anand, Bhargav Bhat, Basavraj Katti, Sona Dimidjian, Ricardo Araya, Steve D. Hollon, Michael King, Lakshmi Vijayakumar, A.-La Park, David McDaid, Terry Wilson, Richard Velleman, Betty R. Kirkwood
Policy Research Working Paper 7969

# **Abstract**

The dramatic expansion of mobile phone use in developing countries has given rise to a rich and largely untapped source of information about the characteristics of communities and regions. Call Detail Records (CDRs) obtained from cellular phones provide highly granular real-time data that can be used to assess socio-economic behavior including consumption, mobility, and social patterns. This paper examines the results of a CDR analysis focused on five administrative departments in the south west region of Guatemala, which used mobile phone data to predict observed poverty rates. Its findings indicate that CDR-based research methods have the potential to replicate the poverty estimates obtained from traditional forms of data collection, like household surveys or censuses, at a fraction of the cost. In particular, CDRs were more helpful in predicting urban and total poverty in Guatemala more accurately than rural poverty. Moreover, although the poverty estimates produced by CDR analysis do not perfectly match those generated by surveys and censuses, the results show that more comprehensive data could greatly enhance their predictive power. CDR analysis has especially promising applications in Guatemala and other developing countries, which suffer from high rates of poverty and inequality, and where limited fiscal and budgetary resources complicate the task of data collection and underscore the importance of precisely targeting public expenditures to achieve their maximum antipoverty impact.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at marcohernandez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should
8|9.2|23.0|
|Transport|2,777.60|7.0|1.1|2.0|10.2|
|Business|3,475.03|9.9|1.3|1.5|12.7|
|Other|3,819.22|10.2|-0.7|4.4|14.0|
|_Total_|_23,178.11_|_75.9_|_3.0_|_5.9_|_84.9_|

Source: Authors’ calculations based on data from DOSM Note: Total contribution does not add up to 100 percent due to the omission of the contribution of changes in the demographic structure to the growth in output per capita.

Figure 17 repeats the sectoral decomposition of growth in output per capita of Table 11 for the three subperiods from 1987 to 1997, 1997 to 2007, and 2007 to 2018. It shows that the manufacturing sector was the largest contributor to the growth of output per capita between 1987 and 1997 and between 1997 and 2007 while the trade services sector had the largest contribution between 2007 and 2017. In fact, growth in output per worker in the manufacturing sector was highest in the period from 1987 to 1997 and fell in the subsequent periods. With regard to the components of sectoral contributions to output growth, in the period from 1987 to 1997, the contributions of employment in the manufacturing sector as a share of the working age population and between-sector shifts were both positive. However, after 1997 the contribution of the manufacturing sector to growth in output per capita could be fully be attributed to improvements in within-sector productivity, with between-sector shifts and changes in employment contributing negatively to output per capita growth.

**Figure 17: Decomposition of growth in output per capita by sector (2015 RM), 1987-2018**

<!-- Start of picture text --> 14,000<br>12,000<br>
WPS3784 particularly strong impacts on small farms which support the poorest households. For example, facilitating the shift out of subsistence into coffee production for those farmers that have a propensity to produce coffee would reduce the national poverty headcount index by more than a quarter. However, substantial barriers to rural development will constrain movements out of subsistence into commercial production. The key barriers are the lack of availability of credit in rural areas to finance the transition and lack of knowledge of relevant techniques for cultivating and preparing new crops. While these constraints remain and transport costs are high the poverty reducing potential of trade in Rwanda cannot be realized. Rwanda is severely constrained in addressing these structural barriers, which should be a focus of assistance from the international community.

The rest of the paper is organized as follows. The next section analyzes the main determinants of poverty in Rwanda, emphasizing the role of trade costs. Sections 3 and 4 examine the impacts of lower transport costs and higher quality coffee on poverty, respectively. Section 5 looks at the structural implications of those two simulations on the rural economy. It highlights the poverty impact of switching from subsistence to more commercial agriculture and open trade. The main policy implications and conclusion are drawn in section 6.

# **2. Trade Costs as Key Determinants of Poverty in Rwanda**

The first step in putting in place policies and programs to reduce poverty is to gain an understanding of its key determinants. We investigate here the role of trade costs, among other key determinants, in allowing individuals to escape poverty in Rwanda. To do this, we estimate the determinants of the probability of not being poor in Rwanda through a probit regression, using the Rwanda Household Living Conditions Survey (EICV). The EICV was carried out between October 1999 and July 2001 and it contains community characteristics indicators together with information from 6,420 households distributed among Rwanda’s 12 provinces.<sup>1</sup> The overwhelming majority of the households interviewed live in rural areas, where the incidence
Table 2: Conflict Incidence and Country Economic Performance: Ethnic-level data

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||light per capita growth|light per capita growth|light per capita growth|light growth|population growth|
|Confict Incidence|-0.074**|-0.069**|-0.063**|-0.077***|-0.003**|
||(0.029)|(0.034)|(0.031)|(0.029)|(0.001)|
|N. of groups in confict in the country|||-0.0078*<br>(0.00424)|||
|Group FE<br>15|YES|YES|YES|YES|YES|
|Year FE<br>|YES|YES|YES|YES|YES|
|Lagged growth control|NO|YES|NO|NO|NO|
|Observations|7,949|7,425|7,949|7,949|7,949|
|R-squared|0.268|0.473|0.268|0.267|0.045|
|Number of groups|502|499|502|502|502|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Errors in column (3) are clustered at the country level. We use data over the period 1992-2013. Unit of observation are ethnic groups. “Light per capita growth” and “Population growth” are the log difference of the variable to the previous year. “Conflict incidence
. Estimates of joint transition probabilities based on synthetic panels are available for a number of countries: a selection of the most relevant to the context at hand is shown in Table 9. Bhutan’s recent experience emerges almost unequivocally as the most favorable in the bunch. A natural comparison is with earlier mobility estimates for Bhutan itself (World Bank 2014): the picture that refers to 20072012 is remarkably similar to the one obtained for 2017-2022 – if anything, the country’s performance improved, as the probability of chronic poverty (poor to poor) decreased, and upward mobility (poor to non-poor) increased. Compared to other countries, Bhutan’s overall mobility (the sum of the two cross-state transitions, poor to non-poor and non-poor to poor) appears low: Viet Nam 2006-2008 is the only example where aggregate mobility is lower (though the shorter time period considered should be factored into the comparison). However, mobility in Bhutan is overwhelmingly positive, that is, it is mostly 15
, and hence influence the smoothed distribution. In a setting where some variables are treated as observed efforts (as in Bourguignon et al. 2007), Equations (6) and (7) capture the _reduced-form_ influence of circumstances on advantages, both directly and indirectly through efforts. By construction, therefore, the only omitted variables that matter for IEO are omitted circumstances.<sup>12</sup> Table 1 presents the estimates of IEO-L and IEO-R for each of the forty-one countries studied by the eight aforementioned papers. The table also lists their gross national income (GNI) per capita; overall inequality and, when available, a measure of intergenerational earnings elasticity (IGE) reported in the literature; a measure of the intergenerational correlation of education from Hertz at al. (2007); and the Human Opportunity Index. Overall inequality is measured by whatever index was used in the construction of the IEO indices for each country. Except where indicated, this measure was the mean logarithmic deviation, also known as the Theil-L index, and a member of the generalized entropy class of inequality measures. Whereas overall inequality, IEO-L and IEO-R come from the eight studies mentioned above, the other variables come from other sources. GNI per capita comes from the World Bank’s World Development Indicators database. Our measure of intergenerational correlation of

> 12 Of course, this does not hold for the estimates of the individual coefficients β� . First, these coefficients are reduced-form, rather than structural, estimates. In addition, they are likely to be biased (upwards or downwards) even as reduced-form estimates, by the omission of unobserved circumstances. The lower-bound result applies only to the overall measures of inequality of opportunity, IEO-L and IEO-R.

9
in the early 2000s that followed a slight increase during the early 1990s. The increase of labor income inequality during the 1990s was associated with a reduction in real wages, most severe for the workers on less well paid jobs, particularly for those in the left half of the earnings distribution. On the other hand, we find evidence that this decline in labor income inequality in the 2000s was associated with more rapid growth rates in the earnings of less well paid jobs relative to labor incomes among the more well-paid earners. This expansion at the bottom of the wage distribution that narrowed the inequality in the lower tail of the earnings distribution was accompanied by a smaller, but still significant expansion in the middle part of the wage distribution that also helped narrow the inequality at the upper tail of the labor income distribution. In contrast to the increase in labor income inequality in the 1990s, the trend reversal in labor income inequality observed during the 2000s is robust to the selection of inequality measure, the aggregation method of country indicators (population weighted or unweighted), the countries selected to define the regional trend, and the definition of the time interval.

We also find that the trend reversal in labor income inequality is correlated with a steady compositional change in labor force attributes along the two decades of analysis: workers are becoming generally more educated, more highly experienced, and more urbanized, and more women are participating in the workforce. Because this compositional change has been fairly constant during the two decades, it does not explain alone the trend reversal in earnings inequality, suggesting that the influence of demand and institutional factors are among the forces behind the regional trend.

23
# Other data

Data on human capital are based on Barro and Lee (2001). Data on GDP per capita and population data are taken from the PWT and from the World Development Indicators. Data on legal origins are taken from La Porta et al. (1999).

# 2.2.3 Regression results

Tables 1, 2, 3, 4 present our main general results from estimating equation 1 and using the Freedom House PR and CL indicators, the Polity2 measure from the Polity IV data set, and the Economic Freedom Indicator (EFW). We start by considering as variables of interest the lagged dependent, the total emigration rate, the share of tertiary educated workers over the total resident labor force, and the log of population size.

Column 1 of each table shows the pooled OLS relationship between the total emigration rate and democracy by estimating equation 1. The results show a positive correlation between openness to migration and democracy, statistically signi...cant, however, only when considering the Polity2 and EFW indexes (all standard errors are robust and clustered by country group). In column 2, when we control for ...xed e¤ect, the coe¢ cient related to the total emigration rate becomes negative (except for EFW), and statistically not signi...cant. We know that in a dynamic panel data model, the standard ...xed e¤ect estimator is biased and inconsistent in panels with a short time dimension (the so called Nickell bias (Nickell, 1981)). Moreover, both in our ...xed e¤ect and pooled OLS estimations, explanatory variables are considered as exogenous. To deal with these problems we use the system GMM estimator that is consistent in dynamic panel estimations and rely on "internal instruments" to control for a weak form of exogeneity of all explanatory variables. We consider the explanatory variables of interest as predetermined, i.e. instrumented using their own one-period and further lags, in order to
minimum value of 1.5 and maximum of 5.0.

The Freedom House civil liberties indicator is used as a supplementary indicator measuring other aspects of the quality of recipients’ aid management systems. Civil liberties scores are higher in countries with a free and independent media, freedom of assembly, freedom for NGOs, and greater personal autonomy and individual freedoms.<sup>6</sup> The use of donor and other public funds is likely to be more transparent in a more open society. Moreover, the principle of “country ownership” may have a stronger appeal to donors operating in countries with stronger mechanisms for public voice and government accountability to broader constituencies. In short, donors will tend to trust country systems more when aiding recipients that look more like most donor countries, i.e. those that are relatively open and democratic. Where there is reason to think local elites are not committed to development objectives and will divert aid to maintain their political and economic power (Bjornskov, 2010; Angeles and Neandis, 2009), donors are less likely to provide budget support and to trust country PFM and procurement systems. Civil Liberties can measure the potential for elite capture of aid flows.

Other recipient characteristics included as control variables are log of per capita income, recent growth in per capita income (average annual increase in the two preceding years), log of population, and log of per capita aid. These variables can capture the effects of real or perceived quality of country systems, separate from what is measured by PFM Quality and Civil Liberties.<sup>7</sup> Donors may sometimes infer that wealthier or more rapidly growing countries have stronger capacity for managing aid, in the absence of more direct evidence. If there are economies of scale in aid management systems, their quality may tend to be higher in larger countries. For a

> 6 An index scored from 1 to 7 is available going back to 1972, but we use a more finely-grained version scored from 0-100 that is