rafmacalaba/data-use-mentions-extended huggingface dataset:
theoretical and empirical implications: since trade shocks impact the size of the choice set, this new mechanism matters for workers’ mobility and welfare; and also fundamentally changes how we should conceptualize the impact of trade shocks on workers.

The new channel we emphasize matters to workers for two main reasons. First, if a worker can choose her job out of more options, it is more likely that the best one delivers higher welfare. Second, even when she is hit by a negative labor demand shock in the future, it is more likely that she will be able to find another job without having to move to a different region or sector. Thus, a regionsector pair (henceforth referred to as a _labor market_ ) receiving a positive trade shock will attract more workers not just because it provides a higher wage, but also because of the larger number of job options that are created there.

In addition, a labor market with a positive trade shock will experience larger internal churning, i.e., more job switching within the labor market, which has been largely overlooked in the literature. Our model clearly shows how trade shocks affect workers’ welfare through labor mobility between and within labor markets. The model delivers a structural equation of trade-induced changes in workers’ welfare, which can be conveniently estimated in a reduced-form way. We then quantify the magnitude of the welfare effect of a trade shock through the full simulation of the model.

Our framework is motivated by reduced-form evidence on the effects of export shocks on labor market outcomes, which draws on rich employer-employee panel data combined with customs records on export transactions from Brazil during 2003-2015. To account for the endogeneity of exports, we construct an instrument at the labor market level, exploiting exogenous variation in sectoral import demand directed to the labor market. The IV estimates reveal a positive causal effect of exports on residual wages, employment, worker inflows, and job turnover rates within the corresponding labor market. The effect on the internal job turnover rates is the key motivation for
household i at follow-up, Ti is a dummy variable equal to 1 if household i is assigned to receive treatment and 0 otherwise. β is the estimate of the average effect of the TUP intervention at follow-up. Since randomization is stratified by community, we follow Bruhn & McKenzie (2008) and include Vj , which is a dummy variable equal to one if household i comes from community/PRA group j (of a total of 133 PRA groups). For household-level variables, standard errors are not clustered. Where data on multiple

> 15 We define response rates based on households with a complete LHH survey. We experienced an 88% response rate for the MHHH survey, however all main outcomes can still be estimated without this survey. 16 https://www.socialscienceregistry.org/trials/2665

18
Table 8: 2SLS Estimates of Labor Supply by Household Head Status

||Wage Em|ployment|Self Emp|loyment|
|---|---|---|---|---|
||(1)<br>Household Head|(2)<br>Other Members|(3)<br>Household Head|(4)<br>Other Members|
|Panel A: Labor market par|ticipation||||
|Household with migrant|-0.414***|-0.231**|0.125|0.298***|
||(0.147)|(0.100)|(0.098)|(0.091)|
|Observations|5791|11088|5791|11088|
|Wald _χ_<sup>2</sup>|906.861|1882.340|6353.722|8786.879|
|_F_-test 1stage|53.309|62.508|53.309|62.508|
|Coecient 1stage|1.633***|2.030***|1.633***|2.030***|
|Panel B: Hours supply|||||
|Household with migrant|-17.642**|-11.559*|-11.369*|-10.493**|
||(7.434)|(6.610)|(6.662)|(4.285)|
|Observations|2772|3073|4450|7068|
|Wald _χ_<sup>2</sup>|2009.570|2551.071|943.402|1107.469|
|_F_-test 1stage|24.419|30.862|50.477|57.718|
|Coecient 1stage|1.515***|
incidence and behavior at the individual or household level, as opposed to using a _representative_ household to capture the behavior of an aggregate grouping of households.

A great many papers fit into the partial-equilibrium/cost-of-living analysis category.<sup>3</sup> One example is Levinsohn, Berry and Friedman (2000), who examine how the 1997-1998 Indonesian economic crisis affected poor households in that country. Although their study does not involve trade liberalization, the approach can be used for such a purpose, and it serves to highlight the general methodology of many studies within this category. The authors combine 1993 consumption data for 58,100 households from the Susenas survey, along with price changes contemporary with the crisis, to compute household-specific cost-of-living changes. The salient finding is that very-low-income households were not insulated from the international shocks, and in fact tended to be hurt the most. Regardless of urban or rural orientation, households at lower expenditure levels experienced larger cost-of-living increases. Additionally, the consumer price impacts of the crisis were greater for urban than rural areas, and greatest overall for the urban poor.<sup>4</sup> The beauty of the Levinsohn, Berry and Friedman analysis is its simplicity and high level of household disaggregation. However, from a methodological perspective, this work has several drawbacks. Firstly, the analysis does not isolate the financial crisis from other phenomena, including the El Nino drought and widespread forest fires occurring in the same period. To do so would require a more complete model of macro-economic activity, such as that offered by Robilliard, Bourguignon, and Robinson (discussed in the micro-macro synthesis section) who provide an alternative assessment of the Indonesian crisis that provides such a decomposition.

A second methodological limitation of the Levinsohn, Berry and Friedman (2000) study is that it abstracts from changes in the households’ pattern of consumption in the wake of the financial crisis. In terms of a household’s demand schedule for a given good, there are no movements along or shifts 3 A
Concerns about the effects of child labor on schooling have often been raised in developmentpolicy debates, including in recent discussions of the welfare effects of labor-intensive growth fuelled by trade liberalization.

The poverty trap argument depends critically on the substitution possibilities between children's leisure and schooling. On a priori grounds it would not seem difficult for parents to assure that a child in Bangladesh working for (say) 20 hours per week can still attend all primary school classes. Nor does it seem that the majority of children who have not finished primary school, but are not at school, are normally working. Casual observations and the descriptive statistics available from surveys do not seem to offer much support for the poverty trap idea.

To explore the question more deeply, we have used a targeted school stipend to identify how much child labor substitutes for schooling. We find strong effects on school attendance of the incentive provided by Bangladesh's Food-for-Education program. A stipend with a value considerably less than the mean child wage was enough to assure nearly full school attendance amongst participants. This impact on schooling is likely to be socially beneficial from a number of points of view.

Our results suggest that the enrollment subsidy also reduced the incidence of child labor. However, this effect only accounts for a small proportion of the increase in school enrolment; the reduction in the incidence of child labor by boys (girls) represents about one quarter (eighth) of the increase in their school enrollment rate. Parents are clearly substituting other uses of their children's time, so as to secure the current income gain from access to the program with modest impact on earnings from their children's work.

Our tests are limited in a number of respects. Work may well displace time for doing homework or attending after-school tutorials; we have not been able to identify such effects with 16
Figure S3: Prevalence of moderate and/or severe food insecurity among adults, by whether household children are engaged in learning activities Prevalence of food insecurity among adults is calculated using the FIES and is presented by whether the household has school-age children that are engaged in any learning activities following the school closures.

<!-- Start of picture text --> Children engaged in learning activities (yes/no)<br>100<br>80<br>60<br>40<br>20<br>0<br>No Yes No Yes No Yes No Yes<br>Ethiopia Malawi Nigeria Uganda<br>Moderate or severe Severe<br>Prevalence of food insecurity<br><!-- End of picture text -->
SPEI < −2) (1 = yes, 0 = no)|47,796|0.059|0.235|0.000|1.000|
|SPEI: Normal (1 = yes, 0 = no)|47,796|0.907|0.290|0.000|1.000|
|SPEI: Rainy (SPEI > 2) (1 = yes, 0 = no)|47,796|0.034|0.182|0.000|1.000|
|High flood risk (1=yes, 0=no)|47,796|0.251|0.433|0.000|1.000|

_Source:_ Based on IFLS 1993, 1997/98, 2000, 2007/8, and 2014/15. _Note:_ Poverty is measured with the national poverty line; vulnerability is measured with the vulnerability line, which is set at 1.5 times the poverty line. We classify the areas with top 25 percent flood depth in each country as high flood risk areas (see Section 3.1.3).

9
areas are more likely than average to escape poverty, while people living in urban areas are more likely to fall into poverty. Such dynamics suggest that the poverty trend in Bhutan during the years considered is somehow linked to productivity or price shocks in the agriculture sector, which have benefited rural families compared to urban ones, independently from other household characteristics. This hypothesis is also supported by the analysis of available macroeconomic indicators and other extra-survey sources, which also show a sectoral shift away from agriculture: the share of employment in the primary sector decreased steadily, with marginal producers shifting to the (mainly urban) tertiary sector. Rural migration outflows, combined with a favorable increase in food prices, might explain why living in rural areas can increase the likelihood of escaping poverty (Amendola et al., 2023).

The rest of the paper is organized as follows. Section 2 presents the data used throughout the paper, which are drawn from the Bhutan Living Standards Survey (BLSS). Section 3 discusses methodological aspects in detail: it describes a solution to the problem of estimating utility-consistent poverty lines in the presence of partially reliable CPI (section 3.1) and illustrates the estimation methodology based on synthetic panels (section 3.2). Section 4 contains: estimates of transition probabilities, both conditional and unconditional (section 4.1); a profile of households exhibiting characteristics of chronic poverty, as well as those that have managed to escape poverty (section 4.2); and a general analysis of the statistical robustness of the results (Section 4.3). Section 5 presents a summary of the main findings.

# **2 Data**

The Bhutan Living Standards Survey (BLSS) is a nation-wide cross-sectional household survey conducted by the National Statistical Bureau, which has been carried out every five years since 2003. It is the main source of information on household living conditions in Bhutan, and focuses on household expenditures, within the framework of the World Bank’s Living Standard Measurement Surveys (LSMS
further lags in levels. The literature has paid considerable attention to the relevance of instruments in dynamic panel estimations (Bazzi and Clemens, 2013; Kraay, 2015).

Alternatively, we employ a cross-sectional estimation that regresses the level of income per capita in 2018 on the 2005 levels of the SCI score as well as other covariates as shown in equation 4.

=∝0 1 2S (4) ii,2018 ii,2005 ii,2005 XX ii,2005 ii YY + (1 + ββ )YY + ββ SS + ββ XX + εε We also employ an Instrumental Variables (IV) approach where we instrument the SCI score with legal origins and perceptions of government effectiveness. Details are provided in the Instrumental Variables subsection of the results.

The sample of the study largely consists of developing economies between 2005 and 2018. The panel data set consists of 124 economies while the cross-sectional data set consists of 91 economies.<sup>4</sup> The beginning of the sample is close to the onset of the financial crisis in 2007. Thus, the sample largely covers a period of considerable volatility in growth across the world. While the year effects included in our panel estimations does capture global shocks, there is considerable heterogeneity in how the financial crisis unfolder across economies. Tables A2 and A3 provide a list of economies in the panel and cross-sectional sample, respectively.

The outcome variable - income per capita - is measured using the log of Real GDP per Capita (constant 2010 USD) obtained from the World Bank’s World Development Indicators. Tables 3 and 4 provide summary statistics for Real GDP per capita for both the panel and cross-section data sets, respectively. Tables 5 and 6 present the correlation between the log of real GDP per capita with SCI and other covariates

> 4 Several economies have gaps in the SCI data, with some of them missing data for 2005 or 2018. Thus, the sample of economies covered for the cross-sectional data is less than the panel data.

14
own ideas to a wider audience (Nash, 2008). Yet it is difficult to quantify or measure many of these effects, although several bloggers attest that

> 11 Blog traffic statistics from http://www.gongol.com/lists/bizeconsites/ [accessed March 2011 rankings on July 28, 2011]; Marginal Revolution and Freakonomics traffic data from SiteMeter; and <u>www.websitevalue.us. Chris Blattman traffic statistics based on a blog posting in which he</u> said he had 800,000 page views in 2010.

11
they consumed each food. Food was categorized into seven groups, following the dietary diversity guidelines from the Food and Agriculture Organization of the United Nations (FAO): (1) grains, (2) vitamin A-rich plant food, (3) other fruits and vegetables, (4) meats, fish, and seafood, (5) eggs, (6) dairy, and (7) oils and fats. This information was then combined into two measures of dietary diversity. The first measure is whether children consumed foods from a minimum number of dietary groups (four or more groups) and the second measure is whether children consumed foods from the maximum number of dietary groups (seven) in the past week.

Finally, we also asked parents to provide enrollment histories for each child from the 2015/2016 school year going back to the 2012/2013 school year. These were used to construct whether or not a child enrolled in a particular time of school service as well as a measure of their cumulative months of enrollment at the time of the survey. Descriptive statistics on all these variables are presented in Table A7.

We see that immunization rates and minimum dietary diversity rates are quite high in this population, yet the majority is not close to getting maximum dietary diversity. Mothers are well aware of pre-school opportunities around them but much fewer of them act on that knowledge on average. Differences between literate and illiterate mothers are sizeable, around 9 p.p. for immunization rates, 5 to 6 p.p. in dietary diversity and about 8.5 p.p. in attendance of pre-schooling institutions (or about 1.1 less months).

# **3 Empirical design and causal identification strategy**

Motivated by the correlation between child outcomes and maternal education, as well as by the opportunity to trace changes in maternal access to education with different parenting styles and parental investments, we focus on quasi-experimental variation generated by the INPRES schooling expansion to investigate impacts across generations. Following Duflo (2001
The impacts of endemic framing are marginally larger in interventions conducted later than earlier ( _β_ = 0.046, _p_ = 0.057), but only when controlling for Wellcome indicators. Specifically, on average, for each month increase of the intervention start date, there is a marginally corresponding increase of 0.046 units in the impact of endemic messages on vaccination intentions.

# **_Country-Level Trust in Healthcare Workers_**

Trust in healthcare workers (e.g., doctors and nurses) was not a significant moderator for any of the framings.

# **_Country-Level Perceived General Vaccine Safety_**

General perceived vaccine safety (i.e., not COVID-specific) was not a significant moderator for any of the framings.

# **_Country-Level Perceived General Vaccine Effectiveness_**

The impacts of variants framing ( _β_ = 1.21, _p_ = 0.096) are marginally larger in countries with higher general perceived vaccine effectiveness. Specifically, on average, for each unit increase in level of agreement with the statement “Vaccines are effective”, there is a marginally corresponding increase of 1.21 units in the impact of variants framing on vaccination intentions.

_Table 3. Study-level moderation meta-regression results._

|Moderators|Exp|erts<sup>a</sup>|Safety|Efficacy<br>and Pro-<br>Social|End|emic<sup>a</sup>|Variants|
|---|---|---|---|---|---|---|---|
||(1)|(2)|||(1)|(2)||
|COVID-19 vacc. rate in sample||||0.003|-0.000|-0.004||
|||||(0.002)|(0.005)|(0.004)||
|Start month of
# **_C3.Wage skill premiums and terms of trade: evolution and scatter plots by country_**

## **Figure C5**

**Wage skill premiums and terms of trade over time Deviations from each country’s mean**

<!-- Start of picture text --> ARG BOL BRA CHL<br>COL CRI ECU HND<br>MEX NIC PAN PER<br>PRY SLV URY VEN<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Wage Premium S-U Wage Premium H-D Log Terms of Trade Index<br>.4 .4 .2 .4<br>.2 .2 0 .2<br>0<br>-.2 0 -.2 0<br>-.4 -.2 -.4 -.2<br>.4 .2 .4<br>.2<br>.2 .2 .1<br>0 0<br>0 0 -.1<br>-.2 -.2 -.2 -.2<br>.2 .4 .2 .4<br>.1 .2 0 .2<br>0<br>0 -.2 0<br>-.1<br>-.2<br>-.2 -.4 -.2<br>.2 .2 .2 1<br>.1 .5<br>0 0 0<br>0<br>-.1<br>-.2 -.2 -.2 -.5<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank) and World Development Indicators.

50
# **C2 The Impact of political connections on competition during the Suharto era**

One of the assumptions of the paper is that a higher degree of political connectedness during the Suharto era led to lower competition. To assess whether this assumption finds support in the data we regress competition indicators on the aggregate market share held by politically connected firms during the Suharto era, controlling for industry and year fixed effects, as well as time-varying industry controls including government and foreign ownership shares, imports and exports. The timespan of our data is fairly short, and changes in the market share of politically connected firms are partially driven by entry (recall that we only observe political connectedness in 1997). The results, which are presented in Table C2.1 below, do point towards higher market shares of connected firms being associated with less competition. According to our estimates a 10% increase in the market share of Suharto family firms, which is roughly one standard deviation, is associated with an increase in the Herfindahl index of 0.025 points (see column 5) and a decrease in the competition index of -0.48 (e.g. approximately 0.12 standard deviations, see column 9). Similarly a 10% increase in the market share of firms with broad connections is associated with an increase in the Herfindahl index of 0.039 points (column 4), a 2.7 percentage points increase in the market share of the 4 largest firms (column 15), and a 0.43 point decrease in the competition index (column 18).

**Table C2.1: Impact of political connectedness on competition during the Suharto era (1993-1996)**

||(1)<br>Entry|(2)<br>Exit|(3)<br>PCM|(4)<br>PE|(5)<br>HHI|(6)<br>MS4|(7)<br>lnN|(8)
# 6. Comparison of 2012/13 and 2016/17 Fiscal Incidence Analyses

Below we compare the results from the previous CEQ Assessment in Uganda, presented in Jellema et al. (2017), which is based on 2012/13 fiscal and household survey data, and the CEQ Assessment summarized here, which is based on 2016/17 data.

Overall, the redistributive impact of fiscal policy has not changed drastically between 2012/13 and 2016/17. While across all CEQ Income Concepts, estimated inequality was _lower_ in 2012/13 than in 2016/17, in both years all taxes and transfers accounted for a decline in inequality of approximately 3 Gini points from Market Income plus Pensions to Final Income (Figure 12a). While the marginal impact of indirect taxes on inequality appears larger in 2016, the marginal impact of subsidies and in-kind transfers on inequality has fallen (Figure 12c).

As discussed above, the increased impact of _net_ indirect taxes – that is, indirect taxes paid minus any subsidy benefits received – on inequality in 2016/17 relative to 2012/13 is due to the increase in the relative magnitude of indirect taxes (or, put differently, the _decrease_ in the relative magnitude of subsidies allocated). As it was primarily richer households who were benefitting from the electricity subsidy, the decision to not allocate an electricity subsidy in the current incidence exercise impacts those richer households the most. So, although rates of _net_ indirect tax payments have gone up for everyone, they have gone up by more for richer households, which in turn creates a larger impact on inequality.

The reduction in the impact of in-kind services on inequality is due mostly to a reduction in the impact of in-kind health care services. Whereas in 2012/13 in-kind health care service delivery contributed approximately 0.6 Gini points to a reduction in inequality, in 2016/17 the same in-kind services contributed approximately 0.1 Gini points to a reduction in inequality. This reduction in impact is likely due in part to a
per capita in the most arid countries, the relationship between rainfall and GDP is flat. This initial decrease is in fact driven by a specific set of countries – rich oil and gas exporting countries from the gulf. The downward relationship between GDP per capita and temperature is preserved with country level data, with the notable exception again of the Gulf countries (Figure 1-D). In the following section we provide more robust evidence of these patterns.

## **2.B Empirical Strateg** y

We start the analysis at the grid level using data from Kummu, Taka and Guillaume (2018). For each 0.5-degree grid cell and year between 1991 and 2014, we calculate the annual growth rate of the cell’s GDP per capita adjusted for inflation ( _g_ ). We use a panel regression to determine if _g_ is impacted by rainfall and temperature. We estimate the following model: g�,� �β�Rainfall�,� �β� Rainfall�,�� �β�Temperature�,� �β�Temperature�,�� �γ� � φ� �μ��� �μ���� �ε�,� �1� Where cells are indexed by _i_ and year by _t_ . We allow a quadratic relationship between the two weather variables, total annual rainfall in liters and average annual temperature in Celsius degrees, and GDP per capita. Time invariant factors that affect cells’ growth rates are controlled by grid cell fixed effects (γ�). Global events are captured by year fixed effects (φ�). Unobserved country changes are accounted for by quadratic country � specific time trends μ��� �μ��� . Deviations in rainfall and temperature are likely to be randomly distributed and unexpected and therefore orthogonal to any possible confounders. The rich set of fixed effects in this specification isolates these localized and unexpected fluctuations in rainfall and temperature, facilitating causal inference. Standard errors are clustered at the Administrative 1 level (