rafmacalaba/datause-encoder-data huggingface dataset:
# _Brazil_

The Pesquisa Mensual do Emprego (PME- Monthly Employment Survey) follows monthly employment indicators. Households are interviewed four months in a row, and then re-interviewed eight months later. 25% of the sample is renewed every month. Given this panel structure we can construct four yearly employment status transitions for each individual. We have put together 9 consecutive panels starting in February 1982. Each panel consists of 12 consecutive cohorts covering approximately 2 years covering the period 1982-2001. The total number of transitions is 2,520,000: 1,190,000 for men and 1,330,000 for women.

# _Sectoral definitions_

We divide the labor force into three sectors of work: formal salaried, informal salaried and self-employed. While the term "informal" suffers from overly broad and imprecise usage, it refers here to owners (self-employed) and workers (informal salaried) who do not have social security or medical benefits and are therefore not protected. Formal salaried workers are defined as those enjoying labor protections. The remainder of the sample is divided into two groups those out of the labor force, and the unemployed.

The sample was further divided into two education groups, those with 8 or less years of education (low education) and those with more than 8 years (high education) as well as three age groups: less than 24 years of age, 24 to 40, and then above 40. We follow Kamionka and Fougere in assuming time homogeneity within each age class but not necessarily across age classes. That is, we hypothesis that if t is the calendar time, and a the age of the individual, _qii_ ( _t_ , _a_ ) = _qij_ ( _a_ ) = _qij_ , _m_ where _m_ corresponds to each of our sub-divisions of the sample. Table 1 retrieves the summary of the population distribution among different sectors split according to age and education.

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# Refugees per USD/PPP/Capita

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76 Figure A8: Dynamic Ban Effects on Firms: Deseasonalized Agricultural Exports (High vs Low Fertilizer Intensity) <!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4<br>Quarter relative to period when treatment first changes (t=0)<br>.3<br>.2<br>.1<br>0<br>−.1<br>−.2<br>−.3<br>−.4<br>−.5<br>Effect on log(exports)<br>−.6<br>−.7<br>−.8<br>−.9<br><!-- End of picture text --> _Notes_ : The chart shows De Chaisemartin and d’Haultfoeuille (2022) estimates of the effects of fertilizer import bans on the (deseasonalized) value of firms’ agricultural exports, relative to the last quarter before the ban takes effect, using a firm-level panel. The sample covers the Q1/2019-Q3/2022 period (but only coefficients in Q2/2020-Q2/2022 are reported) and includes all firms who exported at least one of 23 crops for which we have fertilizer intensity data in 2017-2019. We compute each firm’s fertilizer exposure as a weighted average of crops’ fertilizer intensities with weights given by each crop’s share in the firm’s 2017-2019 exports. The treatment group consists of firms whose fertilizer exposure is above the sample’s 75th percentile. Firms below the 75th percentile serve as the control group. The treatment period is defined as the set of all quarters when any fertilizer import bans were in place. For each quarter of the year, we compute the (log of the) firm’s historical average export value for that quarter in 2017-2019; then, for every firm-quarter, we subtract that historical average from the (log) export value, thus obtaining a deseasonalized version of the outcome variable. The y-axis shows the effects of fertilizer import bans on the log value of agricultural exports, where negative values correspond to a decrease in exports. The p-value of the joint significance test for the placebo estimators is 0.196.

# **C.B Food’s Expenditure Share**

The fraction of the consumer’s income that is spent on agricultural goods is an important variable in our analysis. To illustrate how much this variable can vary, we use data from the 2019 edition of the HIES to display the empirical distribution of food expenditure shares across Sri Lankan households in Appendix Figure A9, which shows a wide dispersion.

Moreover, as detailed in Section V.C.2, we model consumer preferences as non-homothetic. The main motivation for this assumption is the marked negative relationship observed in the data between income and the food expenditure share. This can be seen in Appendix Figure A10, which plots household income against the food share of household expenditures, showing a strong and clear negative correlation between the two variables. The two variables plotted in Appendix Figure A10 are “residualized” versions, i.e. deviations from the (district-level) average.
ratio as one gets closer to the benefits exhaustion time. Meyer (1990) finds evidence of large spikes in the hazard in the prior weeks before exhaustion, a result that is also shared by a large amount of literature<sup>15</sup> .

To summarize: age, gender, region and educational achievement all seem to be correlated with unemployment duration; while monetary incentives also play a significant role as the deadline of benefit exhaustion approaches. In the next subsection it will be asked how unemployment duration and well-being are related. As duration spent in unemployment and well-being can influence each-other, it is difficult to isolate the impact of one on the other. Individuals might suffer when they lose their job, but perhaps they also get used to be unemployed. Hence, we could observe that time spent on unemployment has a positive effect on well-being. A reverse mechanism could also exist. The well-being difference between the two states can play as an incentive to return to work. As the gap of well-being increases between being jobless and employed, the incentive to find a job increases, thus reducing duration. Understanding which of these stories is true (both can be) is important for both policy and research reasons, as it will be explained below.

# **5.1.1 Unemployment duration and well-being: how to find the causality direction ?**

Unemployment duration might affect well-being through a habituation effect. In their review of unemployment-related psychology findings, Darity and Goldsmith(1996) describe three phases of emotional response after a job loss. A first shock phase where optimism still predominates, followed by a phase of pessimism and helplessness, and finally a phase of fatalism feelings with habituation.

If individuals adapt to being jobless, we should observe a higher well-being in long-term unemployed than in those who recently lost their job. Controlling for individual effects we should observe, among the unemployed, a rise in well-being with time spent in unemployment. However, upon comparing well-being across different categories of unemployed one cannot rule out a sample selection issue - arising in pooled regressions.<sup>16</sup> That is why pooled regressions yield different estimates than panel data regressions. Using panel data (individual fixed effects) is the proper way to estimate the impact of duration on well-being. Clark (2006) does this exercise using three European panel data sets. For the pooled data, he uses an interaction term between duration and unemployment. He finds that panel data

> 15Moffit (1985), Meyer (1990), Vodopivec (1995), Dormont _et al_ (2001) all find, using data from different countries, that the exit rate from unemployment to employment rises sharply as the end of the entitlement period approaches. See Card, Chetty and Weber (2007) for a review of findings.

> 16Through a shift-share mechanism. Those who stay unemployed longer are different : those suffering the most might have left to inactivity or back to work. Those suffering less from unemployment stay jobless. A selection bias arises in cross-section

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- 21 -

# **Figure 5: Total Fees over Contributions – Hungary and Other Countries (%)**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1 3 5 7 9 Years of operation11 13 15 17 19 21 23<br>Argentina Bolivia Chile El Salvador México Perú Uruguay Hungary<br><!-- End of picture text --> Note: Contributions and costs are net of death and disability insurance for Latin American countries Source: AIOS and own calculations on HFSA raw data

**Figure 6: Total Fees over Assets – Hungary and Other Countries (%)**

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1 3 5 7 9 11 13 15 17 19 21<br>Years of operation<br>Chile Argentina Bolivia Costa Rica El Salvador Mexico Peru Uruguay Hungary<br><!-- End of picture text --> Note: Costs are net of death and disability insurance for Latin American countries Source: AIOS and own calculations on HFSA raw data 45. If operating and asset management fees are kept at their current levels of 6.5 percent of contributions and 1 percent of assets, total fees in Hungary would compare unfavorably with other reforming countries in the long-run. As shown in Figure 7, total fees over contributions would increase to more than 35 percent in 30 years, driven primarily by the fixed asset management fee of 1 percent charged on the increasing assets. Total fees over assets would converge to about 1.2 percent, as shown in Figure 8, an excessive level for a mandatory system. Total fees over assets would not be much higher than the levels of fees in Chile today, but this comparison does not provide too much
persistent over time in Section 4. Second, our 95% credible intervals exclude estimates from Fagereng et al. (2019) at both year one and year three — our year one estimate in developing countries is smaller than in Norway, while our year three estimate is larger. As discussed in Section 4.1, this larger persistence of impacts is consistent with a larger marginal return on investment in developing countries than in Norway.

Table 6: Intertemporal marginal propensities to consume

||Efect on consumption<br>per unit of transfer|Fagereng<br>et al. (2020)|
|---|---|---|
||(1)|(2)|
|_β_1: Years since last transfer _≤_1|0.358<br>(0.065)<br>{ 0.250, 0.505}|0.52|
|_β_2: 1 _<_ Years since last transfer _≤_2|0.768<br>(0.416)<br>{-0.050, 1.585}|0.17|
|_β_3: 2 _<_ Years since last transfer<br>|0.316<br>(0.103)<br>{ 0.132, 0.541}|0.10|
|_στ_: Std. dev. of efects|0.129<br>(0.074)<br>{0.014, 0.300}||
|_β_1+_β_2+_β_3|1.442<br>(0.435)<br>{0.596, 2.304}||
|# of RCTs<br># of observations|7<br>18||

_Notes:_ Estimates of the model of impacts of UCT interventions on household consumption per unit of transfer in Equation 3 are presented in Column 1 of this table. Column 1 reports Bayesian posterior means of each parameter, with posterior standard deviations in parentheses and 95% credible intervals in curly brackets. Column 2 reports comparable point estimates from Fagereng et al. (2019), as reported by Auclert et al. (2018).

Lastly, we use these results to conduct a cost-benefit analysis of UCT over the first three years. We estimate the net present value of the consumption impacts of UCT over the first three years is 1.44 per unit of transfer, statistically indistinguishable from the average cost of UCT in our sample. We interpret this result as evidence that UCT pass a cost-benefit analysis following Banerjee et al. (2015), even without any assumptions on the persistence 37
15 enterprises. Like the work in the Philippines, the paper finds that access to international remittance income helps to overcome the wealth and credit constraints that tend to restrict small and micro-business activity in the developing world.

The fourth paper uses a 1997 household survey of 14,286 people (aged 6-24) to examine the impact of international remittances on school retention rates in El Salvador (Edwards and Ureta, 2003). International remittances represent a key source of household income in El Salvador: in 1997 about 15 percent of all households received international remittances. While standard economic theory suggests that the source of income should not affect how money is spent, the paper analyzes how two different types of income – income from other sources and remittance income – affect the household choice of schooling levels for children. The results suggest that the source of income <u>does</u> matter for investment in schooling: income from remittances has a much larger impact on school retention rates than income from other sources. In urban areas in El Salvador, international remittances have 10 times the size of the effect of other income on the hazard of dropping out of school. For example, in urban areas the average level of remittances lowers the hazard that a child will drop out of elementary school (grades 1-6) by 54 percent. In rural areas in El Salvador, international remittances have a smaller effect on school retention rates, but still the average level of remittances in rural areas lowers the hazard rate that a child will drop out of elementary school by 14 percent. According to the paper, one possible reason why remittance income has a greater impact on school retention rates than income from other sources is that households may have a higher propensity to spend on education out of remittance earnings.

The fifth paper uses data from a 2000 survey of 7,276 households in urban and rural Guatemala to analyze how international remittances affects the marginal spending behavior of households on consumption and investment (Adams, 2005b). The paper compares the marginal budget shares of remittance-receiving and non-remittance receiving households on six consumption and investment goods: food, consumer goods, housing, education, health and other (household services, transport, legal). Table 6 presents the marginal budget shares on these goods. Like the study in the Philippines, the table shows that households receiving international remittances actually spend less at the margin on food than do households with no remittances. Instead, remittance-receiving
Labor force participation rates by gender differ quite widely across the nine countries. In our regression samples, women’s share of the remunerated workforce ranges from 43% in Kenya to 64% in Ukraine.<sup>42,43</sup> The distribution of workers in formal wage, informal wage and selfemployment is starkly different across the nine STEP countries,<sup>44</sup> and there are key differences in the employment distribution of men and women. Men are more likely than women to be employed in the formal wage sector, although this is not the case in the former socialist countries in Europe where the share of women in formal wage employment exceeds that of men and a larger share of men than women are self-employed.<sup>45</sup> Women are more likely to be employed in the informal wage sector in the lower-income countries. Among those in the informal wage sector, men are more likely to be self-employed, a pattern that may be due to unequal access to savings and capital between men and women.

Education levels have risen significantly over the past two generations (1950-2010) in the STEP countries, but those levels have progressed differently for men and women.<sup>46</sup> The STEP data show not only gender gaps in years of schooling but also gender differences in cognitive and

> 42 Using cross-country data, men’s average participation rates are within 20 percentage points of each other (with Colombia falling far lower with men’s participation rate at just 60 percent); women’s labor force participation rates differ more widely, ranging from 32 percent in Colombia to 80 percent in Vietnam.

> 43 Women’s labor force participation rate is influenced by the pull on women’s time of marriage and caring for young children. While the mean singular age at marriage for women in these countries is similar at 22-24 years, their total fertility rate ranges from 1.4 in Serbia to 4.4 in Kenya.

> 44 While 80-90 percent of employment is in the formal sector in Ukraine, formal wage employment accounts for less than 30 percent in Ghana and Kenya. Formal wage employment is highest in the former socialist countries in Europe; in the lower-income countries, informal wage employment and self-employment together far exceeds half of all employment.

> 45 Georgia is an exception, where more men are self-employed than women.

> 46 Data presented in Barro and Lee (2015) show that Colombia has consistently shown the smallest education gender gap over that 60-year period. In the countries with the highest completed years of schooling in 2010 (Armenia, Ukraine, and Serbia), gender differences have greatly narrowed since 1950. In the rest of the countries, the gender difference in years of schooling has tended to rise first before declining, indicating that boys’ schooling rose first and faster than girls’ schooling as school enrollment expanded in those countries.

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# PPR Working Paper Series

|Title|Author|Date|Contact|
|---|---|---|---|
|WPS1O1 Measuring the Impact of Minimum Wage||||
|Policies on the Economy|Luis A. Riveros|||
||Ricardo Paredes|October 1988|R. Luz<br>61762|
|WPS102 Effects of the Multifibre<br>Arrangement<br>(MFA) on Developing<br>Countries:||||
|A Survey|Junichi Goto|October 1988|J. Epps<br>33710|
|WPS103 lndustrial<br>Portfolio Responses<br>to Macroeconomic<br>Shocks: An||||
|Econometr7c<br>Model for LDCs|Jemes Tybout<br>Teeho Bark|October 19A8|C. Cabana<br>61539|
|WPSIC4 Economic Effects of Financial Crises|Manuel Hinds|October 1988|L. Hovsepian<br>32979|
|WPS105 Securing International<br>Market Access|Richard H. Snape|October 1988|J. Sweeney<br>31021|
|WPS1O6 Energy Issues in the Developing<br>World|Mohan Munasinghe<br>Robert J. Saunders|January 1989|M. Fernandez<br>33637|
|WPS107 A Review of World Bank Lending for||||
|Electric Power|Mohan Munasinghe<br>Joseph Giilling<br>Melody Mason|January 1989|M. Fernandez<br>33637|
|WPS108 Some Considerations<br>in Collecting<br>Data<br>on Household Energy Consumption|L. Leitmann|||
|WPS109 Improving<br>Power System Efficiency in<br>the Developing<br>Countries Through<br>Performance<br>Contracting|Philip Yates|||
|WPSIIO Impact of Lower Oil Prices on||||
|Renewable Energy Technologies|Ernesto Terrado<br>Matthew Mendis<br>Kevin Fitzgerald|February 1989|J. Littleford<br>33627|
|WPSIII Recent World Bank Activities in Energy|lndus;ry<br>and Energy<br>Department|February 1989|M. Fernandez<br>33637|
# Data and methodology

This exercise builds on a similar one conducted in 2020 to examine child poverty using the GMD (Silwal, Engilbertsdottir, Cuesta, Newhouse and Stewart, 2020). The analysis contains records from 10.4 million individuals from 147 countries, taken from the 2022 spring version of the GMD with 2019 as a base year. The GMD is a collection of globally harmonized household survey data compiled by the Data for Goals group of the World Bank’s Poverty and Equity Global Practice. Further details on the background of the GMD are given in Castañeda et al. (2016). A unique feature of the GMD is that the welfare aggregates are the same as those used to compute the poverty estimates published by PovcalNet and the World Development Indicators. These aggregates are based on household per capita income or consumption, depending on the specific concept that is used to measure national poverty in any given country.

For the effects of this exercise, the rescaling of welfare was performed using the Poverty and Inequality Platform (PIP) which contains data on poverty headcounts for each year (2013-2018). For 2020-2022, the data was provided by the Poverty and Shared Prosperity team. A rescaling of the weights was done using the UNDESA population estimates from 2013-2021. In other words, the number of children is estimated by applying the proportion of people that are children in the relevant survey to the total UNDESA national population projections for each country. This could lead to discrepancies with UNDESA estimates of the number of children in a country. For the 2022 estimates, UNDESA population prospects were used.

Poverty estimates for 2020, 2021 and 2022 are “nowcasted”—that is, gross domestic product (GDP) growth rates have been used to forecast household incomes, assuming all households experience equal consumption growth in percentage terms.

Trends from 2013 to 2018 are all based on back casted projections rather than repeated survey data. These are based on distributional neutral growth assumptions applied to one survey per country rather than changes observed in repeated cross-sections.

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Lybbert, T., C. Barrett, S. Desta, and D. Coppock (2004) “Stochastic Wealth Dynamics and Risk Management Among a Poor Population”, The Economic Journal 114: 750-777.

McKenzie, D. (2001) “Estimation of AR(1) models with unequally-spaced pseudo-panels”, Econometrics Journal, 4: 89- 108 McKenzie, D. (2004) “Asymptotic theory for heterogeneous dynamic pseudopanels”, Journal of Econometrics 120(2): 235-262.

McKenzie, D. and C. Woodruff (2003) “Do entry costs provide an empirical basis for poverty traps? Evidence from Mexican microenterprises”, BREAD Working Paper No. 020.

Mookherjee, D. and D. Ray (2002) “Contractual Structure and Wealth Accumulation”, American Economic Review 92(4): 818-849.

Moffitt, Robert (1993) “Identification and estimation of dynamic models with a time series of repeated cross-sections”, Journal of Econometrics 59(1): 99-124.

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Verbeek, M. and F. Vella (forthcoming) “Estimating dynamic models from repeated cross-sections”, Journal of Econometrics.

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