rafmacalaba/datause-encoder-data huggingface dataset:
||(0.133)|(0.119)|(0.339)|(0.169)|(0.222)|(0.329)|
|---|---|---|---|---|---|---|
|Observations|9,370|4,328|5,042|3,733|2,981|2,656|
|Adjusted R square|0.233|0.237|0.238|0.229|0.234|0.248|
|Other vehicles|0.179|0.123|0.994**|0.101|0.352|0.097|
||(0.137)|(0.125)|(0.387)|(0.130)|(0.348)|(0.677)|
|Observations|9,448|4,371|5,077|3,773|3,008|2,667|
|Adjusted R square|0.393|0.394|0.396|0.391|0.397|0.392|
|Durable goods|0.348***|0.201*|1.039**|0.315**|0.314|0.298|
||(0.130)|(0.120)|(0.400)|(0.145)|(0.224)|(0.871)|
|Observations|9,476|4,381|5,095|3,787|3,015|2,674|
|Adjusted R square|0.429|0.422|0.444|0.420|0.446|0.422|
|Medical|0.146*|0.095|0.459***|0.069|0.281*|0.458|
||(0.082)|(0.096)|(0.171)|(0.110)|(0.143)|(0.410)|
|Observations|9,395|4,350|5,045|3,748|2,989|2,658|
|Adjusted R square|0.362|0.350|0.385|0.375|0.370|0.339|
|Health and fitness|0.222**|0.095|0.514**|0.100|0.322*|0.610*|
||(0.107)|(0.099)|(0.215)|(0.132)|(0.165)|(0.353)|
|Observations|9,478|4,384|5,094|3,798|3,014|2,666|
|Adjusted R square|0.299|0.278|0.371|0.294|0.313|0.301|
|Education|-0.142|-0.127|0.167|-0.232***|0.087|0.082|
||(0.116)|(0.130)|(0.269)|(0.083)|(0.201)|(0.396)|
|Observations|9,417|4,359|5,058|3,767|2,997|2,653|
|Adjusted R square|0.217|0.228|0.219|0.221|0.210|0.245|
|Home repair|-0.065|-0.153*|0.393|-0.082|-0.186|0.088|
||(0.082)|(0.084)|(0.349)|(0.103)|(0.134)|(0.648)|
|Observations|9,470|4,386|5,084|3,785|3,017|2,668|
|Adjusted R square|0.452|0.437|0.469|0.483|0.445|0.420|
|Gifts|-0.013|-0.031|0.545**|-0.050|0.115|0.170|
||(0.101)|(0.098)|(0.243)|(0.137)|(0.113)|(0.495)|
|Observations|7,802|3,626|4,176|3,039|2,525|2,238|
|Adjusted R square|0.111|0.113|0.116|0.079|0.120|0.167|

Note: Extended model with additional variables of control, including the lagged household per capita income growth, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling household gender ratio, average age/10, age squared/100, dependency ration, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 36
# **Common Transport Infrastructure: A Quantitative Model and Estimates from the Belt and Road Initiative**<sup>**1**</sup>

## **François de Soyres, Alen Mulabdic, Michele Ruta**

World Bank _Keywords_ : Transportation Infrastructure, Trade, Structural General Equilibrium, Belt and Road _JEL Codes_ : F10, F11, F14 1 This paper should not be reported as representing the views of the World Bank. The views expressed in this paper are those of the authors and do not necessarily represent those of the World Bank or World Bank’s policy. We thank Lorenzo Caliendo and Fernando Parro for their help and for sharing their code to simulate the counterfactuals and Treb Allen, Erhan Artuc, Alan Deardorff, Caroline Freund and seminar participants at the GTAP conference in Bogota and the World Bank for comments. Errors are our responsibility only.
We also make a methodological contribution by introducing the idea of a priming or salience experiment to the study of migration intentions. This technique has been used in social psychology and economics to make more salient a particular idea or concept and bring it to the top of mind when answering subsequent questions or carrying out as subsequent task. We randomize the ordering of modules in our survey, so that some respondents get asked about migration intentions before getting asked about COVID-19, and others get asked about COVID-19 first. A further group gets both shown an information video about COVID-19 and asking questions about the virus before getting asked migration intentions. We find that making COVID-19 more salient does not affect migration intentions, despite the strong impact of the pandemic on intentions to migrate discussed above. The reason appears to be that the virus was already very salient in people’s minds, and knowledge of the virus, while imperfect, was perhaps enough to already well inform migration decisions. Nevertheless, we believe that illustrating such a method may open avenues for further research on how issues such as climate change, xenophobia, and the closeness of family connections affect migration intentions.

This paper contributes to a broader literature on how COVID-19 has affected migration. Several studies have examined how the occupations that immigrants typically work in have been affected differentially by the crisis, and the implications of this for immigrant unemployment (Borjas and Cassidy, 2020; Fasani and Mazza, 2020) and the exposure of natives to COVID-19 health risks (Bossavie et al, 2020). Efforts to examine impacts in migrant-sending countries have been hampered by a shortage of data. One approach has been to use convenience-based internet samples to provide some basic summary information on migration intentions, as was done by IOM (2020), who report Central Americans and Mexicans saying they had postponed migration plans or changed their minds about migrating. One of the few studies to collect more representative survey data is Barker et al. (2020) who use phone surveys in Bangladesh and Nepal to show that migrant households experienced a greater fall in income and more food insecurity than non-migrant households. We complement this literature by directly looking at how COVID-19 has affected intentions for new migration, using an in-person survey that also provides the opportunity to conduct our salience experiment.

The remainder of the paper is organized as follows: Section 2 provides a simple theoretical model of how COVID-19 might affect migration intentions, which we then use to guide our empirical 4
# **Table 2. Differences in Firm Characteristics**

This table shows average firm characteristics during 2010-12 and reports tests for differences in means across firms. Panel A compares the full sample of domestic and foreign listed firms. Panel B compares the full sample of connected and unconnected domestic listed firms. Panel C compares the propensity score matched (PSM) sample of connected and unconnected domestic listed firms. ∗, ∗∗, and ∗∗∗ indicate statistical significance for the mean difference tests at the 10%, 5%, and 1% levels, respectively.

## **A. Foreign Listed versus Domestic Listed**

|Variables|Foreign<br>(1)|Domestic<br>(2)|Difference<br>(2) - (1)|
|---|---|---|---|
|Equity Raised over Assets|0.07|0.03|-0.04 ***|
|Assets (Logs)|20.32|19.70|-0.62 ***|
|<br>Market Capitalization (Logs)|20.02|20.21|0.18 ***|
|<br>Total Debt (Logs)|18.46|17.76|-0.71 ***|
|<br>Leverage|0.20|0.21|0.01|
|Cash Flow|0.07|0.06|-0.01 ***|
|Cash Flow Volatility|0.10|0.06|-0.03 ***|
|Capex over Assets|0.05|0.06|0.01 ***|
|<br>Cash over Assets|0.21|0.22|0.01|
|Acquisitions over Assets|0.005|0.004|-0.001|
|<br>Number of Firms|438|2,017||

## **B. Unconnected versus Connected Domestic Listed**

|Variables|Unconnected<br>(1)|Connected<br>(2)|Difference<br>(2) - (1)|
|---|---|---|---|
|Equity Raised over Assets|0.02|0.03|0.01 _***_|
|Assets (Logs)|19.23|19.97|0.73 _***_|
|Market Capitalization (Logs)|19.70|20.49|0.79 _***_|
|Total Debt (Logs)|17.31|18.01|0.70 _***_|
|Leverage|0.22|0.20|-0.02 _**_|
|Cash Flow|0.04|0.07|0.02 _***_|
|Cash Flow Volatility|0.07|0.06|-0.02 _***_|
|Capex over Assets|0.06|0.06|0.00|
|Cash over Assets|0.21|0.22|0.01|
|Acquisitions over Assets|0.003|0.005|0.002_***_|
|Number of Firms|728|1,289||

## **C. Unconnected versus Connected Domestic Listed, PSM Sample**

|Variables|Unconnected<br>(1)|Connected<br>(2)|Difference<br>(2) - (1)|
|---|---|---|---|
|Equity Raised over Assets|0.02|0.03|0.01|
|Assets (Logs)|19.28|19.38|0.09|
|Market Capitalization (Logs)|19.74|19.87|0.13 _***_|
|Total Debt (Logs)|17.33|17.48|0.16|
|Leverage|0.22|0.21|-0.01|
|Cash Flow|0.05|0.06|0.01 _**_|
|Cash Flow Volatility|0.07|0.06|-0.01|
|Capex over Assets|0.06|0.06|0.00|
|Cash over Assets|0.22|0.22|0.01|
|Acquisitions over Assets|0.003|0.003|0.000|
|Number of Firms|534|534||
> observations on income distribution separated by at least five years within countries. We are also able to construct 285 observations on non-overlapping changes in income distribution within countries over a

> period of at least five years.

In that paper we were interested in how incomes of the poor vary with average incomes, and in what explains deviations from this general relationship. We defined "the poor" as those in the bottom 20 percent of the income distribution, and used the availab' income distribution data to construct average incomes in the poorest quintile. 1<sup>4</sup> We then examined the relationship between growth in average incomes and growth in incomes of the poorest fifth. Figure 4 summarizes our first finding that there is a strong relationship between growth in average incomes and growth in incomes of the poorest. This is equivalent to the observation that the share of income going to the poorest quintile does not vary systematically with .average incomes.

However, there are also significant deviations from this general relationship. In that paper, we considered a large number of possible explanations for these deviations, in other words, what makes growth especially "pro-poor" or not. In the context of this paper, one class of possible explanations relates to trade. The question of whether increases in trade systematically lead to higher income inequality within countries is the same as the question of whether trade systematically explains deviations between growth in average incomes and growth in incomes of the poor. To answer this question, we estimate variants of the following regression of the logarithm of per capita income of the poor on the logarithm of average per capita income:

> (3) Yct= xO + Oc1 Yct + c2'X ct + **jc** + Cct

where c and t index countries and years, respectively; X, is a vector of other determinants of mean income of the poor; and 11c + ct is a composite error term including unobserved country effects.'<sup>5</sup> We are interested in two key parameters from Equation (3). The first is _a,_ which measures the elasticity of income of the poor with respect to mean income. A value of **_a,=l_** indicates that growth in mean income is translated one-for-one into growth in income of the poor -- that is, that changes in income distribution are not systematically associated with changes in average incomes. Estimates greater or less than one indicate that growth more than or less than proportionately benefits the poor, i.e. that growth systematically leads to decreases or increases in the income share of the poorest quintile. The second

> **14** For the vast majority of our observations, we did this directly by using the first point on the Lorenz curve. For about 15% of our sample, we only have Gini coefficients and so we calculated average income in the bottom quintile under the assumption that the distribution of income is lognormal.

23
7 imposed minimum wage (the median-minimum differential is .43 for unskilled workers and _.50_ for skilled workers). 8,9

# **IL** Evidence on Wage Inequality

One consequence of the Heckman and Sedlacek (1981) model, which evaluates the effects of the minimum wage on workers with heterogeneous skill, is the prediction that wages of all workers able to retain covered sector employment folloNwing areal increase in the value of the minimum wage will rise in equal proportion to the minimum hike. This view contrasts with the experience of several countries including the U.S., where evidence suggests that minimum wages have disproportionately small effects on wages above the minimum (Grossman, 1983). If minimum wages have a larger impact on the wages of low paid workers, then an increase in the minimum wage (or an expansion in coverage) should compress wages among high and low paid workers, and therefore decrease measured wage inequality. Conversely, a fall in the real value of the minimum wage (or a narrowing of coverage) should result in a rise in wage inequality.

Abstracting from the effect of minimum on individual wages to consider their effect on firm average wages seems a reasonable extension of the theory. Reworked, the idea would be that firms that pay wages at the minimum would be more affected by real changes in the minimum wage than would firms paying wages far in excess of the minimum wage. One might logically reason that higher real minimum wages would cause a narrowing in differences between firms or industries because of this effect. Indeed, if this were the case, we should expect to see some evidence of rising inequality in the Mexican firm data and falling inequality in the Colombian firm data. All else equal,<sup>1 0</sup> 8 Of course the precise shares of affected workers will depend on the proximity of the firm's average wage to the minimum, the relationship between mean and median wages (the distribution), and the level of inequality in earnings (the standard error of wages within firm).

9 Histograms produced for apprentice and technician workers showed a spike that occurred considerably far to the left of the rninimum wage line. Although both distributions spike to the left of the legally imposed minimum-- and might therefore warn of high levels of noncompliance among certain groups-the excessively low average wages are most likely due to the relatively high use of part-time and part-year work among apprentice and technician labor, and idiosyncrasies in the data that do not permit for an adjustment in light of this fact.<sup>9</sup> 1 0Altematively, rising inequality may be the result of poor economic growth and the differential impact of recession on high and low paying firms.
methods to estimate the vulnerability lines. The fifth section shows preliminary results and discuss briefly welfare dynamics across multiple potential states (i.e. poor, vulnerable and middle class). The last section presents some final remarks.

# **2. Methodology**

A proper study of welfare dynamics typically entails a demanding minimum set of data requirements. It is necessary to follow the same observation (household or individual) for at least two—or preferably, multiple—periods. However, panel data or longitudinal data sets are hard to come by, especially in developing countries, while “snap-shots” of welfare captured in cross-section surveys are far more common (Dang and Lanjouw, 2013). This paper proposes to rely on a synthetic panel approach to provide point estimates of the income mobility in Colombia using as few as two rounds of cross section surveys. Moreover, this study extends the typical analysis of transition in and out of poverty to analyze a more general setup of household movements across different income groups (poor, vulnerable and middle class).

The approach is intended to overcome the lack of available panel data by constructing a “synthetic panel” using only time-invariant individual and household characteristics from multiple rounds of the _Gran Encuesta Integrada de Hogares_ (GEIH) of Colombia and exploiting this information to estimate the vulnerability lines necessary for the analysis of welfare dynamics. First, the following section explains the estimation of the vulnerability line and then presents an overview of the methodology used to study the transitions across poverty, vulnerability and middle class.

# **2.1. Vulnerability lines**

On occasions researchers or policy makers are interested in studying more than the transitions in and out of poverty. In Colombia, where a large share of the population has escaped poverty during the last decade—despite the country was recently exposed to downside risks from volatile commodity prices— there is an increasing interest in identifying the dynamics into the condition of vulnerability (i.e. population out of poverty but at risk of falling back into poverty, hence being _vulnerable_ ) and the middle class. In this context, the discussion on the estimation of a vulnerable group in Colombia is relevant from both a technical and a public policy perspective.

Since true panel data are not available in Colombia, this paper proposes to rely on the approach by Dang and Lanjouw (2017) to estimate vulnerability lines, using as few as two rounds of cross sections and moderate assumptions. Dang and Lanjouw (2017) define the vulnerability line V1 such that a specified proportion of the population with a consumption level above this line in period 1 will fall below the poverty line Z in period 2. This proportion is referred to as the “insecurity index”, Ρ<sup>�</sup> , since the population with 5
|**Cont**|**ents**|||
|---|---|---|---|
|**1**<br>**Intr**|**oductio**|**n**|**5**|
|**2**<br>**Posi**|**tive Th**|**eory**|**10**|
|2.1|The M|odel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|10|
||2.1.1|The Household’s Behavior . . . . . . . . . . . . . . . . . . . . . .|14|
||2.1.2|Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|17|
|2.2|Diseas|e, Increasing Inequality and Economic Collapse<br>. . . . . . . . . . .|19|
||2.2.1|Short-Run Dynamics . . . . . . . . . . . . . . . . . . . . . . . . .|21|
||2.2.2|Long-Run Dynamics . . . . . . . . . . . . . . . . . . . . . . . . .|22|
|2.3|Poolin|g as a Social Response . . . . . . . . . . . . . . . . . . . . . . . . .|23|
||2.3.1|The household’s behavior under complete pooling<br>. . . . . . . . .|24|
||2.3.2|The virtues and drawbacks of pooling . . . . . . . . . . . . . . . .|26|
|**3**<br>**The**|**Policy**|**Problem**|**27**|
|3.1|Ration|ale and Instruments<br>. . . . . . . . . . . . . . . . . . . . . . . . . .|27|
||3.1.1|Education Policy . . . . . . . . . . . . . . . . . . . . . . . . . . .|28|
||3.1.2|Health Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|30|
||3.1.3|The Government’s Budget Constraint . . . . . . . . . . . . . . . .|32|
|3.2|The E|ffcacy of a Single Policy Instrument . . . . . . . . . . . . . . . . . .|34|

2
families spend more time unemployed than those whose families are not poor, but the difference is not more than a month in any country.

# **V. Source of Unemployment and Destination of the Unemployed** _Where Do the Unemployed Come From?_

Most unemployed youth were in school or out of the labor force in the period prior to their unemployment spell. Table 7 presents the probability that the unemployed transition into sector j for each country and age group, disaggregated by gender and income quintile. In Mexico and Argentina, about one-quarter of male youth who are unemployed were in school three and six months ago, respectively. Female youth in all three countries and male Brazilian youth, on the other hand, primarily exited the no school-no work status to become unemployed (25-35%). The difference in transition patterns between countries may be due to the fact that we observe Brazilians in the previous month, whereas the lag time in the observation for Argentines and Mexicans is longer. Thus, over a one month period, young Brazilians may be ready to end their post-school break and begin their job search whereas we do not observe the “break” period for Argentines and Mexicans, instead only seeing their school to unemployment transition.<sup>8</sup> Young adults also enter unemployment from out-of-the-labor force or school states, but school-leavers are rarer than among the younger cohort. Only young adult males in Argentina and wealthy young adults in Mexican enter unemployment from school more than from being out-of-the-labor force. The only young adults who enter unemployment from a job are non-poor Argentines, who primarily have left the formal sector.

If we limit our transition only to exits from a job, we find that most unemployed youth had been working in the informal salaried sector. This is particularly the case for youth and young adults, while prime aged adults who left a job come from all three incomeearning sectors. This patterns cuts across genders, but not across income levels, where most of the non-poor young adult workers who left their jobs had been in the formal sector in all three countries.

> 8 Three-month transition patterns of Brazilian unemployed are similar to those of one-month transitions, suggesting that Brazilians take a long break between leaving school and entering the labor market.  The threemonth transition matrices available upon request.

14
trust on tax morale, potential endogeneity issues must be addressed.

I tackle potential endogeneity issues using an instrumental variable approach. An appropriate instrument for trust is one that is correlated with trust but uncorrelated with tax morale. I employ a set of three variables that meet these requirements based on previous research on the slave trade and social capital. The first instrument relies on the pioneering work of Murdock (1959) on slavery at the tribe level. Murdock (1959) mapped the spatial distribution of ethnic groups and the number of slaves exported during the transatlantic and the Indian Ocean slave trade. The identification strategy consists first in matching the number of slaves exported and the initial land area to the corresponding ethnic group information collected in the WVS. I then generate a variable capturing the intensity of the slave trade at the tribe level as the ratio of the number of slaves exported during the transatlantic and the Indian Ocean slave trade over the initial land area of the ethnic group. I argue that this variable is a suitable instrument for several reasons. The trade of slaves caused a culture of mistrust, which may have persisted to this day and alter taxpayers’ trust both in public institutions and in the neighborhood. The literature supports the argument on the long-term legacy of slavery in Africa and the receiving countries.<sup>12</sup> The trust shocks caused by slavery, an event lasting for more than 400 years, may remain persistent and affect the actual level of trust (Nunn and Wantchecon, 2011). The slaves were captured through state-organized raids and warfare, and a ubiquitous environment of insecurity caused individuals to turn on each other (Piot, 1996). The slave trade results, therefore, in a general environment of mistrust in the ethnic groups affected (Nunn and Wantchecon, 2011). The historic nature of the data on slavery provides a solid basis of the exogeneity of the instrument as there is no apparent reason for which the slavery origin of the ethnic group could affect the actual tax morale directly, except through the trust channel.

As both vertical and horizontal trust are potentially endogenous, I use two additional instruments for the identification of the equation. The latter relies on the literature on the

> 12See for instance Nunn and Wantchecon (2011), Engerman and Sokoloff (2008), Nunn (2008a), and Nunn (2008b), and Mitchener and McLean (2003).

19
15 FIGURE **1** a,b,c,d: The prallel **premium** and auction exchange rate relationship (source: Aron and Elbadawi, 1993a).

<!-- Start of picture text --> ZAMBIA: monthly black market premiurm<br>16.0_ 2.00<br>-1.00<br>1 4.0<br>1.180<br>12.0 1. 7<br>6.0 ~~~~~~~~~~1.5 20<br>8.0 1140<br>6.0~~~~~~~~~~~~~~~12<br>1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 b1 65<br>3 7 11 15 19 23 27 31 35 39 43 47 51 55 59 63 67<br>auction mxtrw<br>la cucdtan rate bka/oucltn reta<br><!-- End of picture text --> <!-- Start of picture text --> UGANDA: weekly bureaux premium.<br>1050 . -1.32<br>1040 - Vo -1.30<br>1030 - , ,.1.28<br>1020 - 1.26<br>fIo 1010 - -1.24E<br>1000 .1.22<br>990 -1.20<br>980- ,1.18<br>970 -1.16<br>15 9 13 17 21 25 29 33 37 41 45 49 53 57 61<br>3 7 11 15 19 23 27 31 35 39 43 47 51 55 59<br>aulion n±rter<br>acticn u- rate bersa TC/aucllan<br><!-- End of picture text -->