rafmacalaba/datause-encoder-data huggingface dataset:
education, secondary education, post-secondary education, and college), occupation (i.e., paid employee, employer, self-employed, and others—informal work—including unpaid family workers and other categories), and residence areas (i.e., rural or urban).<sup>25</sup> A couple of remarks are in order for Figure 5. First, having no education achievement, being employed as informal worker, and living in rural residence are all characteristics that are positively associated with lower-than-average chances for upward mobility during the pre-Arab Spring period (i.e., the orange dots represent the transition percentages lying below the dashed line that represents the national average in panel A). These are also the same characteristics that are associated with having higher-than-average chances of downward mobility (panel B); while the opposite holds for the remaining characteristics.<sup>26</sup> Second, the two definitions of the welfare groups offer qualitatively similar results for the different population groups. This is consistent with the findings we have at the country level, where upward mobility is generally higher than downward mobility.

The variables in the Gallup Poll surveys are defined somewhat differently from those in the harmonized household survey data and we add another variable indicating whether an individual is a migrant (from another country) or a native resident. Figure 6 then plots the same type of graphs for the subjective well-being dynamics of. The results are qualitatively similar but perhaps more muted than those for the monetary measures in Figure 5. In this case too downward mobility is stronger than upward mobility but there is somewhat more variation by population group in the

> 25 We show the conditional, rather than the joint, probabilities in Figures 5, 6, and 7 since this helps us bring out more clearly the transition patterns for the different population groups. For example, a small percentage of the population with secondary or higher education are usually found in poverty or vulnerability in the first period to start with, consequently their transitions to higher income categories are smaller. An additional assumption required for producing these graphs is that the mobility for each population group should generally follow that for the whole population.

> 26 Factors that are positively correlated with upward mobility are in general related to those associated with escaping downward mobility, but this may not always hold. See, e.g., Dang and Lanjouw (2015) for an analysis of mobility in India.

22
rounds of COV-ES and include individual firm fixed effects to eliminate selection bias stemming from time-invariant characteristics of firms. We also add a set of COV-ES variables to capture time-varying aspects of firms’ operations, reducing endogeneity concerns. To alleviate concerns about selection bias, we re-estimate panel regressions on a restricted sample. For El Salvador, this means that we drop firms that state the following reasons for not receiving government support: not aware, not eligible, no need, or applied but did not receive it. In the context of the pandemic, these firms likely faced fewer hardships than others, so we expect to reduce negative selection bias by excluding them from the control group. This same information is not available in the Georgia surveys, so to restrict the sample, we include only the firms that have not yet received support but are expecting in the next three months.

We think that some concerns about selection bias – which can be positive as well as negative, with positive selection bias likely reduced in the context of panel regressions – and endogeneity likely remain, so we refrain from commenting on the estimated size of the effectiveness of government support and only focus on whether it is estimated to be above zero.

We find nearly no statistically significant positive effect of government support on any of our four outcome variables in either country, with only one exception. Our panel regressions suggest that government support helped firms avoid reducing their total hours of operations per week in El Salvador (Table 11). COV-ES data includes questions asking firms to specify whether they received the following types of support: cash transfers, deferral of payments, access to new credit, fiscal relief, and wage subsidies (with the additional general “other” category also asked), with multiple types possible at the same time. Among these questions, we added to the respective panel regression the ones with at least 50 Yes responses. This exercise suggests that the link between government support and total hours of operations in El Salvador is through cash transfers (Table 13). We also find that cash transfers had a positive effect on firms’ permanent workforce in El Salvador, but this effect was fully counteracted by wage subsidies (Table 13).

Breakdown of the effectiveness of government support by size reveals that smaller firms, namely those employing roughly 25 or fewer workers pre-pandemic (in the baseline ES), benefited more in Georgia (Table 16) in terms of changes to the permanent workforce. This link seems to be through fiscal relief (Table 18), which appears to have benefited smaller firms more. This positive effect of fiscal relief was partially counteracted with effects of wage subsidies that appear to have 29
during and after natural disasters, with a substantial increase in remittances after the disaster in about half of those countries.

Furthermore, there is an emerging consensus in the literature that migration and remittances are part of an overall livelihood strategy by which households try to insure against shocks in disaster prone regions. Migration flows increased in the aftermath of disasters as in Jamaica in 1989 after hurricane Gilbert and in Central America in 1998 after hurricane Mitch (Wisner, 2003). In El Salvador, an agricultural shock increases the probability of migration of a household member to the United States by 24.3 percent (Halliday 2006).<sup>3</sup> Increased migration can lead to an increase in remittance transfers to the households after disaster events, but with a lag (Attzs, 2008), although figures 1 and 2 suggest that it is not necessary that there would be an unambiguous increase in remittances in all countries after natural disasters.<sup>4</sup>

**Figure 1: Increase in remittances after large natural disasters (disaster costs in constant 2000 US dollars)**

<!-- Start of picture text --> Year before<br>Disaster year<br>Year after<br><!-- End of picture text -->

* These represent the years in which developing countries experienced the highest damages from natural disasters in constant 2000 US$. Estimated damages due to natural disasters were $9.4 billion in India in 1992, $4.5 billion in Bangladesh in 1998, $10.4 billion in China in 1999, $6.9 billion in Mexico in 2005. Damages are in constant 2000 US dollars.

Source: Authors’ calculations using International Emergency Disasters Database (EM-DAT) and World Development Indicators (WDI), World Bank.

> 3 However, Yang (2007) shows for El Salvador that idiosyncratic shocks to the household such as death of a household member increase the likelihood of emigration, while covariate shocks such as earthquakes, where the entire population is affected, can even reduce emigration.

4 Furthermore, if migration and remittance decisions are undertaken as a part of the overall coping strategy by households in disaster prone regions, we may not necessarily observe a marked increase in remittances in the wake of slow onset disaster event such as drought since remittances are factored into the intertemporal consumption decisions and will not change much unless there is an idiosyncratic shock.

4
- Ginn, Thomas D. (2022) “Prison or Sanctuary? An Evaluation of Camps for Syrian Refugees,” working paper.

- Heckman, James J. and James R. Walker (1990) “The Relationship Between Wages and Income and the Timing and Spacing of Births: Evidence from Swedish Longitudinal Data,” _Econometrica_ , 58 (6), 1411–1441.

- Hotz, V. Joseph and Robert A Miller (1988) “An Empirical Analysis of Life Cycle Fertility and Female Labor Supply,” _Econometrica_ , 56 (1), 91–118.

- Hotz, VJ, S McElroy, and S Sanders (1997) “The impacts of teenage childbearing on the mothers and the consequences of those impacts for government,” in _Kids having kids: Economic costs and social consequences of teen pregnancy_ , 55–143: Urban Institute Press.

- Hussam, Reshmaan, Erin M Kelley, Gregory Lane, and Fatima Zahra (2022) “The Psychosocial Value of Employment: Evidence from a Refugee Camp,” _American Economic Review_ , 112 (11), 3694–3724.

- Ibanez, Ana Maria, Andres Moya, Mar ́ıa Adelaida Ortega, Sandra V. Rozo, and Maria Jos ́e Urbina (2022) “Life Out of the Shadows: Impacts of Amnesties in the Lives of Migrants,” iza discussion papers, Institute of Labor Economics (IZA).

- Kaushal, Neeraj (2006) “Amnesty Programs and the Labor Market Outcomes of Undocumented Workers,” _The Journal of Human Resources_ , 41 (3), 631–647.

- Lalive, Rafael and Josef Zweim ̈uller (2009) “How Does Parental Leave Affect Fertility and Return to Work? Evidence from Two Natural Experiments*,” _The Quarterly Journal of Economics_ , 124 (3), 1363–1402.

- Lanari, Donatella, Luca Pieroni, and Luca Salmasi (2020) “Regularization of Immigrants and Fertility in Italy,” MPRA Paper 98241, University Library of Munich, Germany.

- Lombardo, Carlo, Juli ́an Mart ́ınez Correa, Leonardo Jos ́e Penaloza Pacheco, and

19
government worker (positive and significant in five of ten regressions), and household with higher expenditures per person (positive and significant only two of ten) reported higher levels of the governance proxies. Agricultural households had mixed results (e.g. more likely to report government responded to environmental problems but less likely to report the government responded to social problems).

Consistent with qualitative evidence about the tendency of existing mechanisms to excluded women (DFID, 2000) female headed households reported statistically significantly less participation (on both proxies), less voice (on two of three proxies) and less responsiveness of government to economic problems. Older households seem to fare somewhat better than female-headed ones. 'The older the head of the household the less likely the household is to report engagement in protest; however, the household is also more likely to report effective voice (perhaps precluding the need for protest).

_Regional controls._ There were some patterns across the districts. Households in Ngada were more likely to report government responsiveness (two of three proxies) and more voice (two of three proxies). Wonogiri respondents report less information (one of two proxies), less participation (on both proxies) and less responsiveness to social problems. For present purposes these cross district differences are a "control" and we leave the interpretation of these cross district differences to the qualitative work as part of the larger LLI investigation.

_Sociability._ For the number of visits each household made or received, we did not attempt to distinguish between private and community effects and record private impacts only. We find that in nine of the ten cases greater sociability was associated with higher levels of the governance proxies-but the magnitude and significance of the 24
Figure 13. Employed women with children vs. informality rate by sector, all ages <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>25 30 35 40 45 50<br>Employed women with children (% total employed women)<br>2019 2020 2021<br>Sector informality rate (%)<br><!-- End of picture text --> Note: mspline regression to estimate the relationship betweeen variables. Children <12 years at home.

Figure 15. Unemployment rate by socioeconomic characteristics, 2021 <!-- Start of picture text --> Ages 18-<br>28<br>28.2<br>16.9<br>Urban Children<br>18.7 20.5<br>6.5<br>12.3<br>10.3<br>11.2<br>Ethnic<br>19.1 self-<br>23.0<br>No higher recognitio<br>education n (black<br>and<br>similar)<br>Men Women<br><!-- End of picture text --> Figure 14. Unemployment rate by socioeconomic characteristics, 2019 <!-- Start of picture text --> Ages 18-28<br>22.9<br>13.8<br>Urban Children<br>14.0 15.7<br>4.5<br>9.5<br>8.1<br>13.9 9.8<br>Ethnic self-<br>No highereducation 22.2 recognition (black<br>and similar)<br>Men<br>Women<br><!-- End of picture text --> Note: ethnicity information is for 2020 due to availability of information. children <12 years at home.

Figure 16. Average real monthly labor income by socioeconomic characteristics <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text --> <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text --> <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text --> <!-- Start of picture text --> Men 2019 Women 2019<br>Men 2021 Women 2021<br>Real labor income (thousands)<br><!-- End of picture text -->
US than in Brazil or China where activity in top locations is concentrated in just one-half of the country (Figure 6). However, after adjusting for distance, it appears that there are many more pairs of locations with relatively high trade costs in Brazil compared to the US and China.

# **5. Effects of reduced mobility frictions on spatial development in Latin America**

This section investigates how changes in mobility frictions could affect the evolution of Latin America’s spatial development over the next century. We rely again on the spatial dynamic growth model of Desmet et al. (2018) who evaluated the model by running a simulation backwards in time from 2000 to 1870 and comparing the model’s predictions on the distribution of population levels in the data. The correlations between the simulated population levels and the actual country-level population data were high: 0.96 until 1950 and around 0.70 before 1900. They also found highly significant correlations between the calculated amenities and commonly used measures of amenities such as temperature, proximity to water, and precipitation. Finally, one of the main predictions of their model – namely that the correlation between density and productivity is higher in areas with higher per capita incomes – was confirmed in the data.

We first establish a baseline in which the model predicts the spatial evolution of Latin America over the next century, assuming no change in mobility frictions. Starting from the calibrated model with data for 2000 and adjusted migration frictions, the equilibrium of the model is obtained by simultaneously solving equations (1) and (2) for a period _t_ as in lemma 3 of Desmet et al. (2018). Within this procedure, given the trade and adjusted migration frictions in 2000, the distribution of local amenities, net of congestion disutility, and productivities reflecting innovation choices and spatial diffusion are updated and used to solve for the equilibrium of the subsequent period, _t+1_ . This procedure for solving the dynamic problem as a sequence of static problems is repeated as many periods as needed (in our case 100 times). The baseline captures the evolution of the world economy towards its balanced growth path from 2000 to 2100. The baseline results suggest that LAC’s share in global output is projected to mostly stagnate during the next century while its share in global welfare is expected to rise only modestly (Figure 7).

# _5.1 Reducing trade costs by making optimal investments in domestic road networks_

Section 4 provides evidence of relatively high bilateral trade costs in LAC. For this reason, our first counterfactual explores the implications of cuts in these bilateral costs. A reduction in transportation 20
|||_Table_<br>All|_3. Event-Study_|_Analysis us_<br>Lowe|_ing Diff-in-Di_<br>r-income co|_ff Setup_<br>untries|High|-Income cou|ntries|
|---|---|---|---|---|---|---|---|---|---|
||(1)<br>ln_SOL|(2)<br>ln_SOL|(3)<br>ln_SOL|(4)<br>ln_SOL|(5)<br>ln_SOL|(6)<br>ln_SOL|(7)<br>ln_SOL|(8)<br>ln_SOL|(9)<br>ln_SOL|
|Post x High Density|0.0206*|0.0213*|0.0199*|0.0218*|0.0229*|0.0203|0.0180|0.0120|0.0169|
||(0.0116)|(0.0116)|(0.0114)|(0.0129)|(0.0130)|(0.0127)|(0.0149)|(0.0149)|(0.0149)|
|Post-Covid Dummy|-0.0895***|-0.0187*||-0.10***|0.00113||-0.0161*|-0.0615***||
||(0.00517)|(0.0103)||(0.00574)|(0.0118)||(0.00899)|(0.0154)||
|Ln(death)||-0.00756***|-0.0152***||-0.0113***|-0.0175***||0.0183***|0.0167***|
|||(0.00116)|(0.00117)||(0.00129)|(0.00128)||(0.00241)|(0.00395)|
|NPI Stringency||-0.00035***|0.000103||-0.000198*|0.000156||-0.00081**|-0.00070**|
|||(0.000101)|(0.0000992)||(0.000106)|(0.000105)||(0.000317)|(0.000278)|
|Econ support||0.000289**|0.000291**||0.0000706|0.000216||-0.00106***|-0.0012***|
|||(0.000137)|(0.000138)||(0.000153)|(0.000156)||(0.000363)|(0.000412)|
|N|198823|198582|198582|174302|174061|174061|24521|24521|24521|
|Cities|2841|2841|2841|2435|2435|2435|406|406|406|
|r2|0.963|0.962|0.963|0.956|0.956|0.956|0.937|0.937|0.937|
|r2_within|0.00326|0.00377|0.000757|0.00383|0.00462|0.000917|0.000200|0.00240|0.000762|
|City FE|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Country x<br>Month-of-year FE|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Country-specific<br>linear time trend|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Country--specific<br>Covid dummy|N|N|Y|N|N|Y|N|N|Y|

Note: All columns control for city FE, country-specific time trend and country-month-of-year FE. Columns 3, 6 and 9 include a country-specific dummy indicating the months after the first Covid case was detected. High-density cities are those that have population densities above the 75<sup>th</sup> percentile within a country. Standard error in parentheses clustered at the city-level. * p<0.1, ** p<0.05, *** p<0.01 20
This brief review of the experience with commodity booms of some Sub-Saharan African countries suggests that in many cases countries may even find themselves worse off after the boom a,;_ that their responses have not been optimal<sup>2</sup> (in the sense of not using periodic surge in export commodity prices to restore external balance and promote economic growth).

The purpose of this paper is to analyze fiscal policy in primary commodities exporter developing countries. The paper characterizes the environment in which fiscal policy decisions are made by introducing three effects (pressure to spend, limited disinvestment or policy reversal ard limited indebtedness) and suggests that these three effects may explain the lack of consumption smoothing by the fiscal authorities<sup>3</sup> . It uses an optimizing model of government spending to look for evidence, using data for the Frarc Zone countries in Africa, of the existence of

> 2There are naturally some exceptions to this. Botswana and Cameroon limited the Increase in government spending during the commodity booms. The experience of Cameroon (with a comparison with that of Cote d'Ivoire and Senegal) in reviewed in Devarajan and De Melo (1987).

3To test the permanent income hypothesis for government expenditures, one can regress (see, Cuddington anC Urzaa, 1987 for a similar approach) the logarithm of government expenditure (with separate regressions for consumption and investment expenditures) on a permanent and a cyclical component of the logarithm of jovernment revenues (a decomposition of the logarithm of government revenues Into a permanent and a cyclical component can, for example, be obtained bj the Beveridge and Nelson (1981) method). However, the decomposltion itself is meaningless unless a sufficiently large (and, if possible, not restricted to years covering a commodity boom and a commodity bust) number of observations is available. Furthermore, this testing approach is valid if government revenues can be assumed to be exogenous (i.e., no tax instrument available). This may be a reasonable approximation for lesser developed and highly primary commodities dependent African economies. It is also possible to test for an asymmetric fiscal response to commodities booms and busts by allowing for different coefficiente whether the cyclical component is positive or negative.

**4**
Intervention 2 is therefore more likely to have an impact but is also more complex to implement as intended, not because of additional requirements of man-power to implement, but because it requires more capacity and inclination from facilitators to get the village community engaged in the task of testing children and in preparing and presenting hamlet-level “report cards.” Our experiments in designing interventions 1 and 2 all faced the issue of people turning around and asking of the “outsiders” what they should do to improve education outcomes. In its own programs Pratham has been experimenting with a specific tool that might be one response to this question ultimately raised in the village—a pedagogical method that any literate adult can be trained to employ in a short span of time to teach children to read. The third, and most complex intervention, therefore includes the introduction of this tool to the village, and training of local volunteers. It involves much more engagement by both the outside facilitators and the local people.

Table 6 interprets and summarizes these interventions as “information campaigns.” Each addresses specific information gaps in the village, and is simultaneously an advocacy campaign that urges people to come together as a group to discuss education and ways to improve it through collective action.

To summarize: the baseline survey identified one area where local participation might be constrained by specific lack of information, and that is, the existence of VECs and the roles they might play in improving education services. Actual learning outcomes might be another area of information constraints, but here the evidence is less straightforward to interpret. The overwhelming issue turned out to be people’s apathy to learning outcomes and education as an area for greater public action. The basic interventions therefore address both of these information constraints, and use a particular transmission design to facilitate collective action by bringing people together in meetings and share information amongst themselves. Because of the odds against which the interventions are stacked—public apathy to begin with—a third intervention which requires immediate and dedicated engagement by outside facilitators is also being experimented with. Taken together, evaluating the relative impact of these interventions 26