rafmacalaba/data-use-mentions-extended huggingface dataset:
|-0.023|-0.018|
||(0.034)|(0.024)|(0.022)|
|Electricity access|-0.368*|-0.357**|-0.284*|
||(0.212)|(0.161)|(0.147)|
|Tap water access|-0.352*|-0.541***|-0.432***|
||(0.212)|(0.144)|(0.128)|
|Pseudo-R|0.0720|0.0611|0.0535|
|Log Lik.|-2065|-3182|-3846|
|No. of enumeration areas|345|||
|No. of constituencies||140||
|No. of provinces|||10|
|No. of observations|5,256|6,756|7,113|

_Notes_ : Fixed effects logit model is used to predict the probability of a sample household reporting owning or using land, excluding the households that are not reporting owning or using land and are renters or obtaining free dwelling from companies. Due to enumeration areas (EAs) containing all observations of reporting owning land or all of non-response, higher levels – of Constituencies and Provinces – fixed effects logit models are conducted to generate predicted probability of responding for each of households that are in the EAs having all reporting owing land. Based on the desirability of parametrizing the model on the association between responses on land use/own and non-responses, the point estimates should not be interpreted as causal relationship.

30
pandemic on poverty and inequality within countries (Clark et al. 2021, Lastunen et al. 2021, Lustig et al. 2021, Palomino et al. 2020).

We find that the world in 2020 witnessed the largest single-year increase in global inequality and poverty since 1990, when our study begins. When compared against a counterfactual without COVID-19, we find that the pandemic increased the global Gini index by 0.7 point and pushed an additional 90 million people into extreme poverty---measured as having a daily income or consumption less than $2.15 per capita. Put differently, we observe an increase between 2019 and 2020 by 0.5 Gini points and 71 million poor people. However, the pandemic also prevented a reduction in global inequality of about 0.2 Gini points and 19 million people from moving out of poverty. The net impact of the pandemic is the sum of the two effects. To put this in perspective, the only other marked increase in global inequality and poverty in the last three decades happened during the Asian financial crisis when the global Gini index increased cumulatively by 0.7 point between 1996 and 1999 and global extreme poverty increased by 0.2 percentage point or 37 million people. The pandemic-driven increase in global inequality and poverty reverse declines that have taken three years to accomplish. The uptick in global inequality is driven by increases in inequality between countries, reflecting that the shock to mean welfare was larger in poorer countries. Likewise, the uptick in extreme poverty was driven mostly by shocks to average incomes and not by changes to inequality within countries.

A secondary contribution is to provide annual estimates of global inequality from 1990 to today. Since not every country has a household survey in every year, we have to interpolate and extrapolate household surveys such that we cover all 218 countries in every year. This approach is similar to the methodology used for reporting global poverty by the World Bank (see Prydz et al. 2019 and Ferreira et al. 2016). Previous studies
the incident, Russia temporarily banned the import of 17 Turkish agricultural products including fruits and vegetables, poultry and salt; prohibited the sale of charter holidays for Russians to Turkey; and halted construction projects with Turkish firms in Russia, unless a special exemption was granted. Among the population of firms in the EIS registry, only 3 percent were exporters in 2015; moreover, those firms that were exporting were mostly engaged in manufacturing, a sector which was not targeted by the sanctions (Akgunduz et al., 2019). In addition, Russia is not among the highest ranking destinations for exports from Turkey (it ranks 14<sup>th</sup> among all destinations), and less than 1 percent of firms in the registry were exporting to Russia as of 2015. An adverse effect of the sanctions on the survival probability of those firms, if any, would thus have a minimal influence on our estimates. Furthermore, exporting firms are typically larger and pay higher wages, making them less exposed to variations in the minimum wage. Thus, the confounding effect of the Russian sanctions, if any, would lead us to underestimate the effect of the minimum wage on firm destruction.<sup>22</sup> A final consideration in the interpretation of our difference-in-difference estimates is the stable unit treatment value assumption (SUTVA), which states that the control group should be unaffected by the treatment intervention; that is, the control group should not react to the introduction of the new minimum wage. In our context, this assumption would be violated if there are spillovers from cells that are highly exposed to the minimum wage to those that are weakly exposed in a way that triggers firm closure. To provide comfort on the validity of the assumption, we examine whether the exit behavior of firms in low-exposure cells significantly changed after the minimum-wage increase. As shown in Figure 8, the firm exit rate in low-exposure cells (belowmedian exposure) hardly increased after the minimum-wage hike, compared to quarters in the previous years
interdependence in infrastructure spending among states are also important factors. The present study differs from Mohanty et al. (2017) in multiple ways. First, our study tries to empirically find out the drivers of infrastructure development in stock form for three categories of infrastructure – physical, social, and financial, whereas Mohanty et al. examine the determinants of per capita infrastructure (flow) expenditure on economic and social infrastructure. The outcome of per capita expenditure leading to the real stock of infrastructure differs from state to state, given the heterogeneity of states in terms of efficiency, governance, geographical location, etc. Second, we make a composite index of infrastructure development for physical, social, and financial infrastructure by multiple indicators to reflect the real level of infrastructure development. Third, we explore more control variables and carry out multiple functions for the robustness of the results.

7
# **1 Introduction**

Since the Covid-19 pandemic started in early 2020 there has been an increasing interest in understanding firms’ resilience to economic shocks. Since firms are increasingly operating within global value chains (GVCs), it is of particular interest to scholars and policymakers to understand the vulnerabilities that come along with this interconnectedness in order to better prepare for future shocks. Covid-19 disrupted many GVCs and led to supply shortages in different sectors all around the world (Cerdeiro and Komaromi, 2020). Additionally, the pandemic revealed structural impediments in the trade sector that could be addressed with the right policy changes. However, determining whether participation in GVCs is beneficial or detrimental for firms during a global crisis situation remains a difficult endeavor, as illustrated by the fact that previous studies reveal heterogeneous effects between different firms (Cirera et al., 2021), sectors (Espitia et al., 2022), countries (Bruhn et al., 2021) and outcome measures (Constantinescu et al., 2022).

In this paper, we analyze how the pandemic affected firm-level imports and exports in Indonesia as well as quantify the role of non-tariff measures on imports, using detailed monthly firm-level trade data from February 2019 to June 2021. We measure the effect of the pandemic on a variety of outcomes, including firm-level export and import values, volumes, number of transactions, and number of products. To measure the effects of NTMs on firm-level trade, we compare various firmlevel import and export outcomes before and after Covid-19 for firms exposed to NTMs compared to firms that were not, including firm-, HS-8 product-, and partner country fixed effects. We find evidence that certain types of NTMs, such as the port of entry restrictions on imports, negatively affected firm-level exports during the pandemic.

In the second step, we compare the impact of the pandemic on firms that participated in GVCs with those that did not. GVC firms are defined as firms that both imported and exported in the Covid-19 pre-period (February 2019-November 2019)
**Table D.18:** Water and Sanitation (z-scored)

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Water and sanitation index**(SUR)||0.110<br>(0.044)<sup>_∗∗_</sup><br>[0.037]<sup>_∗∗_</sup>|0.066<br>(0.051)<br>[0.210]|0.154<br>(0.051)<sup>_∗∗∗_</sup><br>[0.025]<sup>_∗∗_</sup>|5053|
|(1) Water and sanitation HH index|0.000<br>(1.000)|0.068<br>(0.058)<br>[0.089]<sup>_∗_</sup>|_−_0.022<br>(0.068)<br>[0.362]|0.159<br>(0.063)<sup>_∗∗_</sup><br>[0.040]<sup>_∗∗_</sup>|5053|
|(2) Water and sanitation village index|0.000<br>(1.000)|0.174<br>(0.086)<sup>_∗∗_</sup>|0.131<br>(0.097)|0.
to establish eligibility. Overall, the census listing contains data from 8,572 adolescents from 4,341 households. Our baseline survey sample consisted of randomly selected 2,393 households that were stratified by community cluster and adolescent gender.

The main source of data we use for the evaluation is three rounds of individual-level surveys administered to both adolescents and their parents: one baseline, and two follow-up surveys (midline and endline). The baseline survey was conducted from February to July 2018 just before program implementation started. The midline survey was administered between February 2019 and January 2020 - after approximately half of those assigned to the _Choices_ training had completed the training. The endline survey was completed between September 2020 and October 2021, after all _Choices_ training and _EMB_ sessions were completed and after a coronavirus government-mandated lockdown period had been removed in Somalia.<sup>29</sup> The follow-up surveys were timed to take place approximately 6 months and 2 years post-intervention. To participate in the surveys, written or verbal consent, depending on literacy level, was obtained from parents or guardians, as well as from the adolescents themselves. Surveys were translated into Somali language and data collection took place through face-to-face interviews with enumerators. Wherever possible, the enumerator was from the same region as the adolescent. Questions and scales were piloted extensively and enumerators were trained carefully in the wording of the questions and how to appropriately interact with young adolescents.

Each respondent was interviewed in private and adolescents and parents were asked two different questionnaires. Typically the adolescent’s mother and/or father were interviewed for the adult survey.<sup>30</sup> At baseline, adolescents were asked to provide information on basic demographic characteristics (such as age, education and health), attitudes towards gender equality and perceived community norms (particularly on the role of women in- and outside of the household), opinions on marriage, hope and aspirations for the future, as well as their answers to a strengths and difficulties questionnaire (SDQ).
based on data from the first national economic census for 2005. Ghana increased its estimate of nominal GDP for 2006 by around 60 percent in 2010, partly as a result of re-basing its volume estimates. Nigeria released re-based estimates in 2014, resulting in new estimates of its GDP for 2012 that are above that of South Africa.

To be useful for classification purposes, GNI per capita estimates must be converted into a common currency so that they can be compared on the same basis. The current analytical country classification system, and the related operational guidelines, uses the US dollar as the common currency or numeraire. Conversion factors are estimated from market exchange rates, adjusted to lessen the impact of any large transitory changes. A clear advantage of using market exchange rates compared with purchasing power parity exchange rates is that they are readily available on an annual basis for almost all countries (this issue is discussed further in section 5.3).

# **_5.2 Reducing Volatility of the GNI measure: The Atlas Method_**

The Atlas method is designed to smooth the effects of short-term transitory changes in exchange rates which introduce undesirable volatility into the classification system. The Atlas conversion factor for any year is the average of a country’s exchange rate for that year and its exchange rates for the two preceding years, adjusted for the difference between the rate of inflation in the country and international inflation.<sup>12</sup> Consistent with the threshold adjustment, international inflation is estimated using the SDR deflator, which is itself compiled as a weighted average of the inflation in the Euro Area, Japan, the UK, and the US (the SDR deflator is discussed in section 6 of this paper). The current methodology was introduced in 1984 following discussion by Executive Directors of a report prepared by the Economic Analysis and Projections Department, in consultation with a panel of experts (World Bank, 1983b).

There have been criticisms of the Atlas method and its theoretical underpinnings, but nonetheless the weighted three-year moving
- Stromquist, Nelly. 2007. _The Gender Socialization Process in Schools: A Cross-National Comparison (2008/ED/EFA/MRT/PI/71)_ . Background paper prepared for the Education for All Global Monitoring Report 2008, Education for All by 2015: Will we make it?

- Teixeira, Pedro J., Marta M. Marques, Marlene N. Silva, Jennifer Brunet, Joan L. Duda, Leen Haerens, Jennifer La Guardia, et al. 2020. “A Classification of Motivation and Behavior Change Techniques Used in Self-Determination Theory-Based Interventions in Health Contexts.” _Motivation Science_ 6 (4): 438–55. https://doi.org/10.1037/mot0000172.

- World Bank. 2012. _World Development Report 2012: Gender Equality and Development_ . Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/4391.

- ———. 2019. _Ending Learning Poverty: What Will It Take?_ Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/32553.

- ———. 2021. EdStats: Education Statistics (database). Washington, DC: World Bank. https://datatopics.worldbank.org/education/.

- Younger, Mike, and Mary Cobbett. 2014. “Gendered Perceptions of Schooling: Classroom Dynamics and Inequalities within Four Caribbean Secondary Schools.” _Educational Review_ 66 (1): 1–21. https://doi.org/10.1080/00131911.2012.749218.

38
**Box 2. Pairwise Correlations for Components of Related Lending Index**

|Single Owner Limit|Single Owner Limit<br>1<br>[n=74]|Related Party<br>Limit|Non-Financial Firm<br>Ownership of<br>Banks|Bank Ownership of<br>Non-Financial Firms|
|---|---|---|---|---|
|Related Party Limit|0.758***<br>[n=74]|1<br>[n=74]|||
|Non-Financial Firm|0.474***|0.482***|1||
|Ownership of Banks|[n=74]|[n=74]|[n=74]||
|Bank Ownership of|-0.027|0.087|0.079|1|
|Non-Financial Firms|[n=74]|[n=74]|[n=74]|[n=74]|

*** indicates p<0.01.

# <u>C.  Rule of Law</u>

In order to measure the strength of the rule of law we use the index developed by Kaufmann, Kraay, and Zoido-Lobaton (1999, hereafter KKZ). The KKZ data are available for 1996, 1998, 2000, and then annually from 2002 to 2008.<sup>5</sup> We use the index values from 2004 because it is close to coterminous with the 2003 BCL survey, and because it offers wider country coverage than the 2002 survey. We note that our results are not sensitive to the index year chosen, because the KKZ rule of law index does not vary dramatically over time.<sup>6</sup>

# <u>D.  Depositor Monitoring</u>

We use the level of deposit insurance to measure the intensity of depositor monitoring
income countries perform notably better on the Statistical Performance Indicators and Index (SPI) – the World Bank’s new official tool to measure country statistical capacity and is being added to the SDG indicators under SDG17 (Dang et al. 2023).

One explanation for this paradox on reporting SDGs and statistical strength overall is that richer countries may have been slow to report SDGs compared with low- and middle-income countries that have experience in engaging with the Millennium Development Goals (MDGs) (MacFeely 2018). A second explanation lies in the specific focus of some SDGs. This difference in the overall performance of national statistical systems and the reporting on gender SDGs might be explained by presence in the latter of indicators which relate to phenomena that are arguably infrequent or rare for high income countries (or perceived as such). For example, data on child marriage and on female genital mutilation (covered in SDG target 5.3) are rarely collected in OECD countries. OECD (2022) describes the extra lengths needed to get such data from alternate sources in order to be able to report on this SDG.

A third explanation, related to the second one above, is the presence of systematic and large-scale data collection under the Demographic and Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS) programs, which are focused on low-income countries (and often financed with non-national resources). These surveys are often the source of gender-related data, especially in the domains of female health and empowerment. To assess this, we examine the main data sources for the 50 SDG gender indicators. The DHS or MICs is the source for at least one country data point for 13 out of 50 indicators but only extensively (well over half of the data points) for 5 indicators.<sup>7</sup> We find very slight evidence that the DHS/MICs data source explains lower

> 7 For the 13 indicators where the DHS/MICs is ever the source, the share of data points which are from DHS
Generally, there could be several reasons behind to the lack of technology adoption. The literature has identified many factors that could affect a country’s technology adoption, such as human capital (Wozniak, 1987; Benhabib and Spiegel, 2005; Che and Zhang, 2018), trade and FDI (see Keller, 2004 for a review), and competition (Aghion et al, 2005; Seim and Viard, 2011).

With a cross-country regression framework, we show that the lack of competition could be one of the reasons behind MENA’s lack of technology adoption. Our measure for market competition is market concentration. The argument is that, _comparing within the same industry_ , countries or regions with higher market concentration tend to have weaker competition (see Berger and Hannan, 1989 and Bikker and Haaf, 2002 in the banking industry, and Sung, 2014 for the telecom industry). Market concentration is widely used to proxy for market competition. The calculation of market concentration indices such as <mark>Herfindahl-Hirschman Index (H</mark> HI) has been a starting point for assessing the state of market competition (see for example, U.S. Department of Justice and the Federal Trade Commission, 2010). Obviously, market c <mark>oncentration is just one indicator and does not contain all relevant information about competition. However, given our data limitation in cross-country regressions, it is our best choice.</mark> In general purpose industries (GPT) such as telecom and finance, there is a high level of market concentration in MENA. Table 4 and the associated graphical illustration in Figure 6 show that market concentration for Mobile Operators and Banking in MENA increases significantly faster as income rises than other countries with the same income (see the red lines in Figure 7)<sup>14</sup> . To account for possible nonlinearities we include a quadratic term. Figure 7 shows that for mobile operators, while market concentration is smaller in MENA when income is low,
negative.

3. In contrast, a child-labor ban that takes the form of a fine imposed on employers caught with under-age workers (a demand-side ban) will tend to _lower_ unskilled wages, lowering them more for underage workers than for other low-skill workers, and unambiguously increase the labor supply of the rest of the household, including children aged 14-17. In addition, it will not stop under-age child labor and can even increase it.

4. A labor clause that prohibits households from sending children below the age of 14 into the labor market is likely to raise low-skilled wages, lowering wage inequality. However, the direct effect of the lost labor on income of low-income households may tend to push inequality upward. For example, if high-skilled households—that never use child labor—are in the upper household income quantiles and low-skilled households—that would use child labor when it is allowed—are in the lower quantiles, income inequality will rise if the drop in<sup>_<u>w</u>_</sup> _w_<sup>_HL_dominates</sup> the direct loss of labor income to low-skill households from loss of child labor earnings.

# **3 Data and Empirical Strategy**

Our labor market data comes from the World Bank’s International Income Distribution Database (I2D2) which harmonizes income survey microdata from 170 countries covering response years (nonuniformly) between 1960 and 2019. The data include 1,777 unique nationally representative survey years covering 180,027,049 respondents. The underlying survey data comes from a variety of survey modules that cover income-related questions, including respondent age, employment status, wages, household income, etc. Because the incidence of and concerns over child labor are concentrated on developing countries, we likewise restrict our analysis to developing countries in the I2D2 data, resulting in a sample of 101 countries.

The temporal resolution varies by country and surveys are repeated cross-sections—respondent identification is unique only within survey-country-year, which precludes linking any repeatedly sampled individuals across
.7 1.8 1.9 2<br>Proportion of initial loan duration elapsed<br>d) Share of Principal Repaid<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2<br>Proportion of initial loan duration elapsed<br>Flex Contract Mean Std. Contract Mean<br>95% CI Extension Pass<br>No-Extension Pass<br><!-- End of picture text --> Notes: The graphs use FMSD's monthly administrative data to show treatment effects over the course of the loan. Graphs show the mean in the standard loan group at a given point in time (dashed yellow lines), mean of the standard loan group plus flexible contract treatment effect (solid blue lines) including 95% confidence intervals (dotted blue lines). Regressions are based on monthly data. Since loans in our sample differ in length, we show the share of loan duration elapsed on the horizontal axis rather than months. We use a loan's original length to make flexible loans and standard loans comparable. We round the share of the loan elapsed to the nearest increment of 0.025 with linear interpolation for values in bins between data points for each loan. We use a similar process for the pass-use bar graph. Pass-use bar graphs are based on bins of 0.0833 (1/12) that match the modal 12-month loan length.