rafmacalaba/data-use-mentions-extended huggingface dataset:
representative sample of undocumented migrants in Colombia’s major cities as of 2020.<sup>10</sup> This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colombian government in 2018.<sup>11</sup> This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head.

We the household characteristics and the labor market outcomes of the house- compare hold heads in our sample with those in VenRePS and RAMV surveys in Table A.3.<sup>12</sup> We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census.

Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a framework of self-selection among migrants into Medellin is challenging. Moreover, it is crucial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response

> 10See Ib ́a ̃nez et al. (2022) for specific survey and sampling details.

> 11Refer to Ib ́a ̃nez
We find average income gains from import tariff liberalization in 45 countries and average income losses in the remainder 9 countries. On average, the developing countries in our study enjoy gains from trade equivalent to 1.9 percent of real household expenditure. This is mostly because the consumption gains from lower prices dominate the income losses from reduced protection.

The distributional impacts of import tariff liberalization are highly heterogeneous, across both countries and households. We find that the equality gains, the change in social welfare associated with these distributional impacts, are negatively correlated with the average income gains. Inequality costs arise primarily because trade exacerbates nominal income inequality, while the consumption gains tend to be more evenly spread. This creates trade-offs between the income gains and the equality gains in 45 of the 54 countries in our sample. The income gains typically more than offset the increase in inequality. In 39 countries, liberalization of import tariffs would result in inequality-adjusted welfare gains for a wide range of empirically plausible values of inequality aversion (between 1 and 2). In 9 countries that face trade-offs, protectionism would instead be welfare enhancing for plausible values of inequality aversion. Finally, there are 6 countries where the trade-offs are acute, in which the presence of welfare gains or losses depends crucially on the presumed level of inequality aversion and policy prescriptions are consequently more equivocal. These results imply that in the majority of developing countries in our study, the prevailing pattern of protection induces sizeable welfare losses.

The rest of the paper is organized as follows. Section 2 sets up the model and derives the formulas for the welfare effects of trade policy. Section 3 uses the tariff data and the survey data to estimate those welfare effects in 54 countries. Section 4 discusses the gains from trade and their distribution. Section 5 evaluates and quantifies the trade-off between income gains and inequality costs of trade. It also decomposes equality gains into consumption equality gains and income equality gains, presents robustness tests, and assesses the trade-offs that would arise if countries were to undertake protectionist trade reforms
countries with a lower capacity of digital technology. The dashed vertical line marks the threshold level of technological capabilities between the high-income and middle-income countries.

Figure 6: Digital services trade, data models, and technology capability (2015) <!-- Start of picture text --> Digital services trade and data model<br>By technology capability<br>100<br>10<br>1<br>.1<br>3 4 5 6<br>Index of digital technology capabilities<br>Open Conditional Limited<br>Per capita exports in 1000 Mln. USD (log)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data, World Bank WDI and WEF. Countries are categorized on the basis of the data model regarding the cross-border data transfers.

# **3.3.4 Data realms and gravity determinants**

Gravity forces such as geographical distance, market size and trade policies such as Free Trade Agreements (FTA) are all likely to play a strong role in predicting digital services trade, as they do with trade flows generally. Figure 7 confirms this assumption. The three panels of Figure 7 plot on the horizontal axis the distance between the US, EU (for which Brussels is used as a geographic reference) and China respectively, and all trading partners, whilst plotting on the vertical axis the digital services exports of these three entities to each trading partner, divided over their market size. These three trade entities are considered the main representatives of each data model. Figure 7 also highlights for each panel when trading partners share the same data model of the US, EU or China. Moreover, the country dot is marked by an additional coloured circle when it shares an FTA with each of the three trading powers.

18
# Estimating the Long-term Impacts of Rural Roads: A Dynamic Panel Approach

## **1. Introduction**

A rationale for public investment in rural roads is that poor households can better exploit agricultural and non-agricultural opportunities to employ labor and capital more efficiently. However, roads are long term investments and may take several years to complete. As a result, the immediate and longer-term policy implications of rural road improvements may differ substantially. For example, time may be required for markets to develop around new roads, and thus the benefits to employment and consumption may not appear until several years after the project. On the other hand, initial spikes in earnings opportunities may occur in areas with better access to markets and other public facilities, but may fall back over time as increased migration and labor supply catch up with demand. Price fluctuations and changes in transport costs that emerge in the short run may also revert after a few years.

Few studies, however, have examined how impacts of these types of public infrastructure programs evolve over several years. Even with well-designed, long-term panel data, unobserved community characteristics such as political influence and local norms can influence project placement and evolution in a locality. One may assume these unobserved factors are fixed only over a short period of time, but as different outcomes evolve and interact with one another over the lifetime of the project this assumption is less likely to hold. Infrastructure investments like roads in particular are also typically widespread and subject to spillovers and migration; even where project and control areas are separated in the short run, it may be difficult to maintain this separation over the longer term.

2
The relative distribution approach also includes a median relative polarization index, which is a measurement of the degree to which the comparison distribution is more polarized than the location-adjusted reference:

# (4)

The values of the MRP index range between -1 and 1: positive values represent more polarization and negative values represent less polarization; a value of 0 indicates no differences in distributional shape. The MRP index can be additively decomposed into the lower relative polarization index (LRP) and the upper relative polarization index (URP) as follows:

# (5)

As the MRP, LRP and URP range from -1 to 1, and equal 0 when there is no change.

We conclude our analysis by explicitly linking polarization to perception data. Specifically, we use a multinomial logit (MNL) model based on 2013 household data, as well as the following question which is used as perception indicator: “How is your current living standard compared to the last ten years” Possible answer categories are: 1 = Improved; 2 = Not changed; 3 = Worsened; 4 = Do not know.<sup>11</sup> In the MNL model we assume that the dependent variable is the logarithm of the ratio between the probability of selecting a response option and the probability of choosing the reference response category. In our case with _J_  4 categories, we contrast categories 1, 3 and 4 against 2, i.e. we pick the response option “Not changed” as the reference category as it merits particular attention in interpreting the recent trend of subjective welfare perceptions of households—see section 4(b). We thus estimate the following linear regression model for 2013 as:

> 11 Data on responses to this question come from the same survey that we used for the analysis of polarization, i.e. the Moroccan ‘ _Enquête Nationale sur les Niveaux de Vie des Ménages_ ’. Due to their very limited number, missing observations were treated as ‘Do not know’.

8
electricity grid is 0.38 in the LSMS sample and 0.34 in the DHS sample.

> 5This interpretation stems from the fact that each variable that measures proximity to a given infrastructure takes only “negative” values, with greater values implying greater access; the highest access is therefore achieved when the value of the variable is zero.

> 6We follow a research design similar to that of Hjort and Poulsen (2019), in which temporal variation in connectivity to the electricity grid comes from the fact that survey samples vary across years in the DHS and the LSMS for each country.

14
# **2.2 Two Field Experiments**

In this section we describe the field experiments that we use to evaluate the impact of two different modalities of the API program. A full description of the different data sources used throughout the empirical analysis is provided in Appendix A. Because both experiments were run at scale within the infrastructure of the existing program, the recruitment process and the assignment mechanism of the mentors are the same between experimental and nonexperimental schools.

**First Experiment.** As part of an effort to evaluate a broader set of interventions targeted to families and schools in disadvantaged communities (Mart ́ınez, 2012), in 2010, the government undertook the first impact evaluation of the API program. Eighty primary schools are selected among those that met the eligibility criteria for the program across four Mexican states (72 in Chiapas). Assignment to the API program was randomized at the school level using a block design, with the strata represented by the Mexican states where schools are located. Forty schools were assigned to receive the API program starting from the 2011– 2012 school year while the remaining half of the schools were assigned to the control group without mentors.

A mid-line survey collected after one year of the API assignment recorded parental behaviors and investments for 208 parents in 73 schools (the enumerators were not able to reach the parents in seven schools). Student outcomes were measured two years after treatment assignment through the results in the national standardized test for students in grades three through six. Due to the incomplete take-up of the test—mainly due to the opposition from the teachers’ unions in some states—we were able to match 70 schools with 599 test score records out of the subsample of 73 schools with parental outcomes. Both sources of sample attrition are orthogonal to treatment assignment. Five of the unmatched schools were in the treatment group and five were in the control group. Table B-1 shows the balance for the original sample as well as the nested samples with parental outcomes and student outcomes between
# **E.3**

**Figure E.2:** Confirmed Cases by Treatment

<!-- Start of picture text --> C CM NFA G C CM NFA<br>C 150<br>20<br>10<br>0<br>100<br>CM<br>20<br>10<br>0 50<br>NFA<br>20 G G G G G G G G G G G G G G G G G G G G G G<br>10 0 G G G G G G G G G G G G<br>0<br>Sep Oct Nov Dec Jan Feb Mar Apr May Sep Oct Nov Dec Jan Feb Mar Apr May<br>2014 2014 2014 2014 2015 2015 2015 2015 2015 2014 2014 2014 2014 2015 2015 2015 2015 2015<br>(a) Weekly Counts (b) Cumulative Counts<br>Confirmed Cases<br>Cumulative Confirmed Cases<br><!-- End of picture text --> Figure E.2(a) plots the time series of confirmed cases by week; bars represent the raw counts. We use the date that the case was first saved in the VHF, which is available for 96 percent of cases in our sample. Figure E.2(b) graphs the cumulative count of confirmed cases by treatment group.

A34
|6. Washing machine|YES...1<br>NO....2|
|---|---|
|7. Blender|YES...1<br>NO....2|
|8. Fan|YES...1<br>NO....2|
|9. Computer|YES...1<br>NO....2|
|10. Sewing machine|YES...1<br>NO....2|
|11. Vehicle, car, or boat (for household use)|YES...1<br>NO....2|
|12. Iron|YES...1<br>NO....2|
|13. Microwave oven|YES...1<br>NO....2|
|14. Video games (Nintendo)|YES...1<br>NO....2|
|15. Air conditioning|YES...1<br>NO....2|
|16. Well or cistern|YES...1<br>NO....2|
|17. Motorcycle|YES...1<br>NO....2|
|18. Tablet|YES...1<br>NO....2|

_Households that experienced moderate to severe food insecurity:_ According to the Food Insecurity Experience Scale (FIES),<sup>22</sup> moderate to severe food insecurity prevalence refers to a range of food security conditions experienced by the households that have difficulty accessing enough safe and nutritious food for their members’ normal growth and development and that fail to enjoy an active and healthy life due to a lack of money or other resources. This variable takes the value of 1 if the household falls
taking abilities of participants.<sup>6</sup> To this end, we applied (post-treatment) the empathy and perspective-taking subscales from the Interpersonal Reactivity Index questionnaire (Davis, 1983). These scales measure the extent of empathic concern and perspective taking in general, not just towards migrants. Both treatments had positive impacts on the perspective-taking scale, but only the game was statistically significant. The results align with the objectives of the game activity and with the fact that the game requires more involvement, since participants not only watch and listen but also make decisions and express ideas from a migrant’s perspective. Moreover, only the video had a positive impact on empathy. This accords with previous results suggesting that perspective taking through personal narratives is one potential way to increase empathy toward an outgroup (Busselle and Bilandzic, 2009; Nabi and Green, 2015).

A potential concern with our research design is the threat of social desirability bias (SDB), an individual’s tendency to answer according to how they believe others will view their Several comments are due here. responses. First, the survey was both online and anonymous, which can reduce the tendency to please others. Second, a large fraction of individuals in the control group expressed prejudice, so social desirability did not seem pervasive.<sup>7</sup> Third, we find changes in donations (a high-stakes behavior) in response to the treatments but significant changes in only a subset of attitudes, which seems inconsistent with individuals trying to act according to the study’s hypothesis. Fourth, we present formal evidence that rejects the hypothesis that social desirability—measured through the Marlowe-Crowne Social Desirability Scale—was more prevalent in the treated groups and disproportionately affected our results.

Within the broad field of prejudice reduction (Paluck and Green, 2009; Paluck et al., 2021), our contributes to a recent literature on interventions to attitudes and be- paper change

> 6An empathetic response has been documented to occur after interventions that
The choice of control variables in our model is informed by past research, e.g., Balassa (1978), Calderón, Cantú, and Zeufack (2020), Cavallo and Frankel 2008, Chang, Kaltani, and Loayza (2009), Dollar (1992), Dollar and Kraay (2003), Easterly and Levine (2001), Eaton and Kortum (1996), Fosu (2011), Frankel and Romer (1999), Greenaway, Morgan, and Wright (1998), Levine and Renelt (1992), Rodrik, Subramanian, and Trebbi (2004), and Sachs and Warner (1995). The literature has mentioned additional determinants of growth; multiple control variables were considered and tested. For instance, simultaneous inclusion of both labor quality and availability proved unnecessary econometrically.

# **3.2. Model estimation techniques**

As mentioned earlier, the estimation of equation (1) using OLS presents some empirical challenges, mainly the problem of endogeneity. This study addresses this problem by using an IV approach with innovative instruments to test the hypothesis that trade intensity has a significant and positive causal effect on growth. The estimation follows a two-step process to assess causal relationship between trade and growth. First, we examine the correlation between trade intensity and growth using a bivariate model and then we add control variables. We also deploy a Generalized Method of Moments (GMM) approach as a robustness check specifically to test the sensitivity of the results to a different estimation method.

# 3.2.1. Instrumental Variable Approach

To address endogeneity, our study estimates equation (1) using an IV approach. The strength of an IV approach is heavily reliant on the identification of suitable instruments for endogenous regressors (Acemoglu, Johnson, and Robinson, 2001; Frankel and Romer 1999). Our study relies on two separate IV strategies, obtained through the combination of several different trade instruments: “lagged air transport freight capacity,” “the mean GDP of top five trade partners,
specific woredas will necessarily result in many vulnerable Ethiopians being left without protection. The analysis finds that 37 percent of households living in non-PSNP woredas are vulnerable to absolute poverty.

# **6 Conclusions and policy implications**

We use a nationally representative dataset merged with climate and price data to estimate the impact of climate and price shocks on household consumption. We find that many Ethiopians are unable to protect their consumption against lack of rainfall and sudden increases in food prices. There is considerable heterogeneity in the impact of all types of shocks. Rural households in drought prone areas that are not currently recipients of the safety net, and households with little education in urban areas are most at risk to suffer drops in their consumption. We estimate that a moderate drought causes a 9 percent reduction in consumption for households that are not covered by the PSNP in drought-prone areas. This implies a substantial increase in poverty when rains are poor. As an example, were the drought of 2002 to be repeated, we estimate that the headcount poverty rate would increase by 21 percentage points from 30 to 51 percent. Sudden food price increases can be similarly devastating for urban households. We estimate that the high inflation experienced in the first half of 2011 resulted in a reduction of 14 percent in the consumption of uneducated households in urban areas. We take actual distributions of climate and price shocks in each district to simulate the probable ex-ante distribution of consumption and use this to quantify vulnerability to poverty in Ethiopia. Our numbers can be compared directly to official poverty numbers, to show how sensitive current poverty estimates are to shocks that are prevalent in the context. We find that 42 percent of rural households are vulnerable to poverty, but only 21 percent of urban households. Vulnerability to poverty is much higher than poverty in rural areas but this is not the case in urban areas, reflecting the fact that the household survey was conducted during a year of good conditions for rural households and very difficult conditions for urban households. Within rural areas, vulnerability is higher in
commuting.

## **3.3 Auxiliary Data**

Since our study period overlaps with the COVID-19 pandemic period, we obtain COVID19 case data from Google Health. We also use the Indian Population Census 2011 to obtain district-level characteristics like population. We combine the two data sets and construct average daily new confirmed cases as a share of the population in a district. We include this variable in our regressions to account for the potential effects of COVID-19 on outcomes like labor market participation. To address further concerns that different states experienced different levels of mobility restrictions, we use data from the Oxford Covid Tracker on the share of days each month that state governments imposed a shutdown on public transportation.

# **4 Empirical Strategy**

States that implemented the _Pink Slip program_ may differ from other states. Apart from including only the neighboring states of the treated state as controls, we employ a synthetic difference-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) to allay this concern further. The SDID re-weights the control units to make their time trend parallel to the treated units and then applies a DID analysis to the re-weighted panel. This method constructs a synthetic counterfactual for causal estimation. Since, in our setting, the treatment is applied at the state level, we construct a state-level panel using the CPHS data. With this panel, we can derive each control state’s SDID unit and A.1. time weights. A detailed explanation is provided in Appendix To estimate the impact of the _Pink Slip_ scheme on women (or households with women), we employ two regression specifications for individual (or household) _i_ in state _j_ and time _t_ weighted by the individual (or household) sampling weight, along with the SDID time and unit weights. Starting with a standard stacked event-study design where _Y_ are outcome variables such as household on _ijt_ monthly expenditure transport and individual time on travel. The variable _Treated_ takes the value 1 usage dummy _j_ if a state implemented the _Pink Slip program_ and 0