rafmacalaba/data-use-mentions-extended huggingface dataset:
the least productive exporter from _i_ to _j_ are equal to _σFij_ . In the data we observe many firms with very small export sales (sometimes as low as $1) which implies unrealistic fixed trade costs. Second, as shown by Eaton et al. (2011), the model implies a perfect hierarchy of destination markets (i.e., destinations can be ranked according to profitability, with all firms that sell to a destination also selling to more profitable destinations) and perfect correlation of sales across firms that sell to multiple markets from one origin. None of these predictions holds in the data.

In this section we consider a richer model with firm-specific fixed trade costs and demand shocks that vary by destination. This is similar to the setup in Eaton et al. (2011). We assume that firm productivity, demand shocks (denoted by _αj_ ) and fixed trade costs (denoted by _fj_ ) are

> 26Using census data as well as numerous surveys and registry data, BR compiled a dataset with the number of manufacturing firms for a set of countries. Unfortunately, the sample in BR has missing observations for a number of countries in the EDD. We impute missing values projecting the log number of firms on log population. There is a tight positive relationship between log number of firms in the BR dataset and log population with an elasticity of 0.945, as reported in Table 10 and in Figure 9.
experience; reforms in the Indian service sectors in the 1990s are another example of such ‘packaging’.<sup>8</sup> This suggests that the evolution of the indicator may well be indicative of more wide-ranging reforms, of which a changed FDI policy stance is an important part.

The evolution of Indonesia’s service sector ratings in the OECD index since 1997 reflects broad based liberalization in the aftermath of the East Asian crisis, as evidenced by the fall in Indonesia’s score across all service sectors between the 1997 and 2003 iterations of the Index. There was little real change from 2003 to 2006 (Figure 5).

Between 2007 and 2010 – taking into account the impact of the 2007 Investment Law and the creation of, and amendments to, the Negative Investment List – there was moderate liberalization across most service sectors, significant liberalization in the hotel & restaurant sector, and a significant increase in restrictiveness in the communications sector. No changes in Indonesia’s service sector index rating have been registered since 2010.

> 6 For a detailed description of the index see Kalinova et al, 2010.

> 7 See OECD Reviews of Regulatory Reform, paragraph 70.

> 8 The Indian telecommunications sector offers an example. Arnold et al (2011) report that the National Telecom Policy, announced in 1994 improved the environment for private investment in general. Subsequently, the government opened the long distance sector of the telecom industry to private competition, largely eliminated restrictions on the number of service providers, and relaxed foreign ownership limitations.

7
it to the government, but less than 25% for at least one of the ownership layers that link it to the government.

Currently, the BOS database includes 53,675 state-owned enterprises, as defined by the definition in section 2.2.1, across 80 countries. However, the coverage is varied across countries, even after implementing the different data validation efforts explained in section 4.4.3. Given this, to guarantee robustness in our results, we focus on 36 countries for which the coverage on employment and revenues is at least 70%. Tables A.5.1 to A.5.10 (in the Annex) present aggregate statistics and descriptive statistics of SOEs by region, type of contestability, type of ownership, age, and size. In our sample of 36 countries, we have a total of 40,596 SOEs with employment and revenues totaling at around 9 million workers and 1.2 trillion USD, respectively. Of these SOEs, 76% have a majority state participation, 14% are SOEs with blocking minority participation, and 10% are SOEs with minority state participation.

Europe & Central Asia (ECA) is the region with the best coverage, so that 15 countries of the countries in our reduced sample are in this region (Table A.5.1). The median employment of SOEs for countries in this region is 28 workers. In comparison to the other regions the median employment is much lower as the median SOE in each of the remaining regions has more than 100 workers (Table A.5.2). The median Latin America (LAC) SOE is more than ten times as large as the median SOE in ECA. These employment patterns are also maintained for operating revenues. That is, the median firm in ECA has the lowest level of revenue while median LAC SOE has the highest level of revenue. Although most SOEs are majority owned across all regions, firms in LAC are associated with higher government control as more than
0.000797)|(0.000711)|(0.00125)|
|VIX Index|-0.0186|0.0126|0.00461|-0.0280**|-0.0103|0.00617|0.00413|-0.0178|
||(0.0189)|(0.00926)|(0.00815)|(0.0140)|(0.0249)|(0.0122)|(0.0107)|(0.0184)|
|US Economic Policy|-0.000297*|-0.000224***|-8.92e-05|-7.44e-05|-0.000327*|-0.000168*|-7.76e-05|-0.000129|
|Uncertainty|(0.000160)|(7.59e-05)|(6.75e-05)|(0.000118)|(0.000182)|(8.66e-05)|(7.67e-05)|(0.000135)|
|Commodity Terms of|-0.0324|0.00766|-0.00392|-0.0313**|-0.0295|0.00891|-0.00240|-0.0303*|
|Trade|(0.0215)|(0.0102)|(0.00912)|(0.0159)|(0.0215)|(0.0102)|(0.00909)|(0.0159)|
|Oil Price|-0.0428**|-0.00859|0.00820|-0.0406***|-0.0414**|-0.0160*|0.00906|-0.0371***|
|_(in logs)_|(0.0169)|(0.00801)|(0.
heterogeneity ( Hit ). This will allow us to ascertain the sensitivity of drought impacts across recent wet and dry periods, arid versus humid climatic zones and income classes.

gi,t = f(Dit) × Hit + βXit + γi + φt + μc×t + εi,t (4) To estimate how antecedent wet or dry conditions affect the relationship, the analysis calculates how much rainfall has deviated from long-run averages in the three years prior to the shock by summing z-scores over the three-year period. If there were significant rainfall deficits, these deficits would stack, and the cumulative z-score calculation would record a very deep three-year water deficit. This cumulative z-score is interacted with the main dry shock variables in the regression to assess whether wetter or drier conditions prior to a dry shock determines its impact on growth. For example, a higher and positive cumulative z-score captures rainfall increases in previous years that can raise green water or soil moisture in the root zone of crops.

To investigate the role of forests and landscapes in influencing conditions through which green water is maintained, past local and upstream share of forest cover is measured using satellite data from the European Space Agency. High forested areas denote places where the forest area is more than the 50th percentile of the forest distribution among all grid cells in each country. The interaction of high forested areas with the main dry shock variables in the regression allows us to assess whether higher local and upstream forest cover can help buffer the growth impacts of dry shocks.

Geography can also matter and different regions with different climatological baseline climates may respond differently to droughts. This also allows us to test for heterogenous responses to droughts. On the one hand, if regions where dry shocks are common are able to adapt to drier conditions, droughts should be less harmful to economic growth. In other words, economic growth would be less sensitive to dry shocks in arid areas. Conversely, recurring dry shocks may have cumulative impacts that weaken adaptive capacity. We test for such effects using a binary
MONGOLIA'S INFORMAL SECTOR - Contents v

# Tables

|1.|Degrees of Informality ....................................................|3|
|---|---|---|
|B1.|Monthly Income of Taxicabs .............................................|38|
|B2.|Kiosk Sampling .......................................................|41|
|B3.|Monthly Income of Kiosks .<br>.............................................|42|
|B4.|Generalized Informal Sector Surveys-<br>Activities Represented in the Sample ....|.. 45|
|B5.|Characteristics of Informals .<br>.............................................|48|
|B6.|Infonnal Ownership .<br>........................
# 1. Introduction

Sudan has been experiencing multiple macroeconomic shocks, such as low growth, a wide trade deficit, and the emergence of multiple exchange rates since its separation from South Sudan in 2011. The loss of three-quarters of its oil reserves, which were located in South Sudan territory, led to a shortage of fuels and food staples, and the country became a net oil importer. Given the high fuel prices, the government implemented subsidies to protect the poor and vulnerable. However, the subsidies contributed to a further deterioration of the fiscal accounts and an increase in inflation, which has sustained macroeconomic imbalances over the last decade. The social, economic, and political discontent led to the removal of President Omar al-Bashir in April 2019 and to a transitional civilian government in September of 2019. They latter implemented an ambitious agenda of reforms, including the removal of various subsidies and unification of the exchange rate, in an effort to reverse years of structural imbalances.

In this context, the civilian government deemed social safety net programs crucial to protect the poor and vulnerable. Indeed, the transitional government implemented a quasi-universal cash transfer program – the Sudan's Family Support Program (SFSP), parallel to its agenda of economic reforms. The SFSP program was specifically designed to mitigate the expected short-term adverse effects of the economic reforms on the population and to help generate political and economic space for the reforms. The SFSP –a quasiUniversal Basic Income (UBI) cash transfer program, was expected to reach 80% of the population. It consisted of delivering cash transfers to the Sudanese families in the recipient's territory expected to be affected by economic reforms and other short-term shocks. The SFSP sought to exclude the top 20% using administrative data and other sets of exclusion criteria.<sup>3</sup> The SFSP social safety net program was paused after a military takeover in October 2021. With the onset of the COVID-19 pandemic, and the adverse economic and social effects that it brought to the country, political discontent
designed to collect the main variables of interest that a close relative would know about (such as educational attainment, employment status, marital status, and the number of children). Table A1 summarizes the number of respondents for each survey type and the corresponding type of information we have.

# **Appendix B. Attrition and Sample Selection**

To account for attrition, we apply inverse-probability weights (IPW) following Wooldridge (2010). We predict the probability that the respondent has skills measures rather than the probability of having any data (see Appendix A and Table A1 for more details). We follow this approach as our main results relate skills to labor market outcomes, effectively treating individuals with missing skills measures as attritors in these regressions.

We implement two alternative approaches to modeling this probability. We start with a basic model that uses only correlates of attrition from the first round of data collection of the LEAPS project in 2003. We then add variables indicating if the respondent had already attrited in 2011 and 2016 (when we started the tracking process). We describe these two approaches in turn.

<u>Model 1. Probability of having full data (including skills measures) as a function of variables</u> collected in 2003 We start with a list of 35 variables from 2003 and select variables that are predictive of having skills measures using Lasso. This procedure leads us to keep 31 variables, including the respondent’s sex, age, housing characteristics (toilet on-premises, electricity access, etc.), parental education and occupation, whether the household head could read and write, and the language of the interview. We then use a probit model to predict the probability that we have full data for the respondent.

<u>Model 2. Probability of having full data (including skills measures) as a function of variables</u> collected in 2003 and indicators of attrition in 2011 and 2016 We use the same specification as in Model 1 but add two variables: a dummy
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, each of _±_ 3 year event windows enter into the analysis dataset separately and are “stacked” or pooled.<sup>12</sup> We report summary statistics from this analysis dataset in Table 1.

Our data on RTAs and agreement content have been collected as part of the broader World Bank

> 11Here we condition on strictly positive wages. Numerous respondents report a wage of zero despite reporting employment. The majority of these respondents are employed in the agriculture sector or the “commerce” sector; we speculate these individuals are employed by family-owned businesses in which household members do not draw individual wage compensation. Including such zero wages will severely bias aggregated wages to zero in our pseudopanel.

> 12Given our focus on child employment, we filter our data on country-year observations for which aggregated child employment rates (both under 14 and 14–17) are not missing.

16
<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Rwanda Nigeria Malawi Bhutan Cambodia Afghanistan Kenya Lao PDR El Salvador West Bank and Gaza Guatemala Timor-Leste Turkey Ghana Paraguay Bolivia Philippines Panama Bangladesh Nicaragua Peru Yemen, Rep. Vietnam Thailand Nepal Sri Lanka Ecuador Dominican Republic Brazil Mexico Chile Argentina Georgia Pakistan Kyrgyz Republic Malaysia Mauritius Mongolia Azerbaijan Egypt, Arab Rep. Armenia Macedonia, FYR Uruguay Kazakhstan Bosnia and Herzegovina Latvia Poland Ukraine Serbia Romania<br><!-- End of picture text --> _Source: Authors based on ASPIRE, see Annex_ The reduction of inequality as measured by the Gini index is also quite dramatic (Annex 4), even though properties of this measure of inequality mean that impacts will tend to be smaller: on average the Gini index is reduced by 10 percent by social protection programs, led by ECA averaging over 30 percent, while the other regions all experience a reduction of less than 5 percent in the Gini. Figure 5 shows a close relationship between the scale of inequality reduction and the poverty gap reduction by social protection programs, even though the relationship is not one-to one (in some countries inequality is even increasing as a result of social protection transfers- something impossible with our definition of poverty measures). Note that for several countries the standard of $1.25 / day as a poverty line means the complete elimination of poverty, while they achieve very different results in terms of inequality reduction (top of the right panel). Using relative cut-off (left panel) gives a closer relationship, and again shows much stronger effects on poverty than on inequality. In what follows we will focus on the poverty impact at the absolute $1.25/day line as the most direct and visible
not trust enrollment data as indicators of school participation. As described in section 3, even when a child is enrolled in school, the teacher has a lot of discretion in deciding whether to keep a child in class when the parents have not paid “enough.”This makes enrollment a very noisy measure of school participation.

To circumvent these problems, this evaluation uses direct observations of school participation rather than measures of o¢ cial enrollment. The NGO ...eld sta¤ visited the schools at least once per school term and observed and recorded pupil and teacher attendance, and conducted interviews with pupils and teachers. Attendance checks were done through name calls rather than headcounts, which allowed the ...eld sta¤ to match school participants to the baseline of the school. When the ...eld sta¤ found a child in class who was not yet on their list of existing participants for that school, they asked the child, the teacher and/or one of the child’s older siblings for the name of her parent, and tried to ...nd the parent in the baseline.

> 25 The 1999 Kenya Census data will, when available, enable us to estimate the probability that a child does not have an older sibling in school and is not enrolled in school herself. This will allow us to assess how representative the baseline is of the general population.

14
22 assumption of input profitability) or fine‐tuning (the missingness of factor markets and the extent of input use, land market operations and income diversification). They also call attention to neglected topics (seasonality) and open new lines of inquiry and policy attention (the role of agro‐chemicals, the lack of agronomic knowledge, the reasons behind agricultural underemployment). And some simply stand up to the data, as one would hope (the survival orientation of most nonagricultural rural household enterprises, and the virtual absence of credit use for input acquisition).

Methodologically, the findings underscore the power of nationally representative data and cross‐country standardization and comparison. They also highlight the power of data innovation and disaggregation at scale, as well as the power of descriptive statistics in shaping research and policy narratives. The hope is that the findings presented here catalyze further endeavors—to revisit our stylized facts in other areas and to update and deepen them as new data come along. Evidence‐based policy making requires sound facts as well as sound inference. With either one of them missing, researchers and policy makers alike risk flying blind.
the same dataset. Estimated program impacts appear to be sensitive to different econometric approaches used. Also, different studies tend to focus on different treatment effect parameters, with some studies estimating average effects of treatment on the treated and other studies focusing on local treatment effects for specific subpopulations. Whether and to what extent microcredit programs help poor individuals is still a matter of intense debate, but we will summarize here some of the accumulated evidence from randomized field studies and nonexperimental studies.

Pitt and Khandker (1998) is an influential and often cited nonexperimental study that estimates the impact of participation in one of three Bangladesh group-based lending programs (the Grameen Bank, Bangladesh Rural Advancement Committee (BRAC), and Bangladesh Rural Development Board (BRDB)) on women‟s and men‟s labor supply, on girls‟ and boys‟ schooling, on expenditures and on assets. It explores how the program impacts vary by gender of the borrower. Very few women work in the regular labor market in Bangladesh, because it is a Islamic society that excludes women from many activities. Self-employment is a viable alternative for these women, because the productive activities can be carried out in their home and while taking care of children.

Pitt and Khandker (1998) estimate the effect of the lending program using survey data collected in 87 rural Bangladeshi villages in 1991-92. In recognition of the fact that the program was nonrandomly placed, their analysis does not simply compare individuals that received and did not receive the program. Rather, they use a feature of the program eligibility rule as a way of generating a comparable comparison group. Only landless households, defined as households with less than one-half acre of land, were eligible to participate in the program; households with more land were ineligible. Their identification strategy estimates conditional household demand equations for households that were and were not eligible to participate in the program by virtue of the land ownership restriction but that lived within the same village (the effects of regional location are controlled using village-level fixed effects). An assumption
rely on periodic sample-based employer and workforce surveys. However, traditional surveys are conducted too infrequently and their analytical lag time is too long to capture emerging trends in labor market dynamics and demand for workforce skills. While surveys will remain useful for analytical and policy purposes, big data analytics can support more informed decision making by providing real-time data that encompass variables which cannot be observed through any other statistical technique. Moreover, online job-portal data reflect real business activities, not abstract survey questions. These data can provide a richer understanding of employers’ needs, constraints and transaction costs and uncover potential opportunities to address frictional unemployment and other labor market inefficiencies. Job-portal data can also help determine whether apparent skills gaps reflect a real deficit in the necessary 27
|2.26|1.84|-0.42|
|Seniority in teaching Science (in|1.23|0.94|-0.29|
|years)||||
|<br>Percent of teachers that participated|0.05|0.08|-0.14*|
|in trainings||||
|Percent of teachers that used a|0.15|0.07|0.09|
|teaching sequence||||
|<br>_School-level variables_||||
|Students per school|-26.98|-19.72|7.26|
|<br>Students of 7th grade|-5.38|-3.42|1.96|
|<br>School promotion rate (%)|-0.01|0.00|0.01|
|<br>School drop-out rate (%)|0.00|0.00|0.00|
|<br>School over-aged rate (%)|0.01|0.00|-0.01|
|<br>7th student’s repetition rate (%)|0.02|0.00|-0.02|
|FEPBA score in Language|0.83|2.63|1.80|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ Each entry indicates the mean difference between the two experimental groups in the column for the corresponding variable in each line. * indicates that the difference of means test is significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

1
are available only for 1991 and 2000, and the overall quality of income data is better than the wage information. Overall, we do not believe that the use of income data significantly affects our analysis, since the correlation of income and wage data, both in terms of levels and growth rates, is very high for the two years in which both are available (Table 1).

**Table 16: Correlation coefficients – wages versus income**

||Wage<br>1991|Wage<br>2000|Income<br>1991|Income<br>2000|Wage<br>growth|Income<br>growth|
|---|---|---|---|---|---|---|
|Wage 1991|1.000||||||
|Wage 2000|0.953|1.000|||||
|Income 1991|0.993|0.948|1.000||||
|Income 2000|0.948|0.989|0.958|1.000|||
|Wage growth|-0.238|0.051|-0.227|0.032|1.000||
|Income growth|-0.419|-0.147|-0.412|-0.148|0.937|1.000|

38