rafmacalaba/data-use-mentions-extended huggingface dataset:
industries (using the ISI classification), indicating that it was largely **_not_** due to shifts from low to high skill industries as would be the case if trade patterns or preferences for goods had changed. Furthermore, estimated capital-skill complementarity increased significantly between the 1980s and 1990s from about zero, to a level similar to that for U.S. manufacturing in the 1960s or 1970s. This could well be due to skill-biased change in technologies embodied in capital.

On the other hand, the combination of increased capital-skill complementarity and increased investment account for very little of measured skill-upgrading, assuming constant returns to scale. The only variable that consistently predicts skill upgrading in Indian industries is increased output. Why growing output predicts skill upgrading is a mystery for future research. We can speculate, though. One possibility is that restrictions on layoffs reduce the ability of firms to adjust their skill mix to make it appropriate for new technologies. Rapidly growing firms can raise the proportion of skilled workers by hiring them disproportionately while firms with stable employment may not do so. That constrained labor demand explanation is consistent with the pattern shown by U.S. manufacturing in the recession of the early 1980s, when firms freed of the usual firing constraints by the crisis laid off less-skilled workers disproportionately while new hires in the recovery were on average more skilled.

Another reason to doubt that SBTC is a major influence is that the industries undergoing skill-upgrading are not the typical suspects revealed by cross country correlations in the literature. Data on investments in technology would help us understand why demand for skills has increased in some industries and not in others. This would be a particularly interesting investigation in India as tariffs on computers were reduced sharply in the early 1990s.

An important topic for future research is the high cross-state variation in increased demand for skills in registered manufacturing. Among the states with large manufacturing 30
mitigate this, we also code incidents involving the term “Fulani” (e.g., the tribe most identified with the nomadic herding) as herder-related. Thus, these data include violent events that occur between herders and civilians, clashing militias, and/or reprisals or actions taken against herdsmen or Fulani tribes. Due to this, we prefer to use the general term herder-related violence. In total, we code 1,564 incidents between 2009 and 2019 as both violent and herder-related.

We combine the GHS and ACLED datasets using GPS coordinates, which are available in both datasets. To construct our main treatment variable of exposure to a herder-related violent event, we create a series of binary variables that take a value of one if a household was within a given distance from any herder-related violence in the month prior to the start of the GHS interview. The exposure measures are calculated for distance windows of 10, 20, and 30 kilometers (km.). We are able to calculate exposure measures that are as precise as 10 km. because we were able to use the restricted-use unmasked GPS coordinates from the GHS data.<sup>26</sup> Our preferred specifications use the 10 km. exposure radius as it is the most precise available in our measures, while allowing for potential mis-measurement of the exact location of violent events in the ACLED data.

# **3.2 Descriptive Statistics**

Table 1 shows basic descriptive statistics for our set of key, labor-related outcomes at the individual level. We focus on seven labor outcomes. The first three are binary values that take a value of one if an individual reports to have worked in own-account or household-enterprise work, agricultural work, or work outside the home, respectively, in the last week. These variables are available in all four rounds of the GHS and provide information on the extensive margin of changes in labor supply. Starting in the 2015-2016 round, the GHS began recording the number of hours individuals worked in each of these activities as
developing countries, particularly lower-income countries. In 1950, 33 percent of the global working age population lived in high-income countries. Developing East Asia and the Pacific—the region with some of the most rapid fertility declines and life expectancy improvements in recent years—accounted for 28.5 percent of the working-age population, while Sub-Saharan Africa—the region with the most modest improvements—accounted for only 6.7 percent. By 2015, this distribution had shifted substantially: high-income countries accounted for just 19 percent of the global working age population and Sub-Saharan Africa for 11.2 percent. If we take into consideration the global population between 20 and 40 years of age, the share of highincome countries dropped from 32 percent in 1950 to 16.7 percent in 2015.

The working-age population share increased across all groups of countries between 1950 and 2010. Thus, despite the evidence of positive association between an increase in the share of working age population and GDP per capita growth, there are very few cases of countries with a shrinking working age population share over this period. High-income countries have on average a larger share of working age population, peaking at around 67 percent for OECD countries between 2000 and 2010 (Table 2). At the same time the working-age population share in low-income economies is still below the levels observed even in upper middle-income countries before the 1980s. Table 3 shows that not only has the share of working age population been larger in higher income countries, but also the human capital has been higher.

> 16 Tables A1, A2, and A3 in the annex provide additional descriptive statistics.

12
administrative targeting variables (Kaboski and Townsend, 2012) or the error structure of a predictive model of entry (Mallick, 2012; Berg, Emran and Shilpi, 2015). Two of these studies, both based on data from Bangladesh, find a positive impact of MFI entry on informal lending rates (Mallick; Berg, Emran and Shilpi), though in the latter the effect is only significant when MFI coverage rates are high. Kaboski and Townsend, using data from Thailand, find no statistically significant impact on lending rates, but a small positive effect on the probability of default on other loans. A fourth study uses panel data from the Indian state of Jharkhand, and finds an inverse U- shaped relationship between SHG coverage and the rates charged by moneylenders, consistent with a model in which the SHG lender has superior information on borrowers’ creditworthiness and serves those with lower risk of default (Demont, 2016).

The identification of causal impacts in these studies relies on the assumption that community characteristics associated with the entry of new lenders do not affect informal credit rates directly. Given the multiple objectives of MFIs, which may include profit (or at least cost-recovery) as well as a social mission to assist the poor, it is impossible to sign the direction of potential bias in estimates from observational studies. The use of random assignment to a credit market intervention permits causal inference based on a much weaker set of assumptions. However, previous randomized evaluations of microcredit programs have not reported impacts on interest rates, presumably due to a lack of power on this outcome. The present study, which is based on the randomized roll-out of a government-led SHG program that offered microcredit and credit linkages to formal banks to the poor across 179 panchayats<sup>2</sup> in rural Bihar, overcomes this limitation.

Critical to the identification strategy, the SHG intervention had a strong direct effect on household use of informal credit. Just over two years after program initiation, households in panchayats selected for early roll-out were 51
44 mortality are probably the main source of the differences in excess infant mortality of the index child by survival status of earlier children" (Hobcraft et al pg 375).

The remaining problem of interpretation is the possibility that the factors determining the risk status of the family (e.g. income)are in fact endogenous, affected by the demographic structure of the family. In particular, this might arise if a pattern of closely spaced births were related to low income. There is much evidence that while children in Latin America, Africa and Asia pertorm many valuable labor services (see above pg 26), large family size is associated with lower money income per capita (Visaria (1980), Lipton (1983), although caution must be exercised in the interpretation of such income data, both in what it leaves out and the deflators used (Deaton and Muellbauer (1980), Sen (1983)). On the other hand, there is little evidence relating child spacing and resources, although one study in the Philippines shows constraints on the mother increase with the number of children aged under six years (Ho (1979)).

As noted above, some of the effects of birth spacing on child mortality are considered to work through a deleterious influence on maternal health, the 'maternal depletion syndrome' (Jelliffe (1966)). There is certainly evidence that pregnancy and lactation place additional strain on maternal health and nutrition, especially iron stores (LLewelly-Jones (1965), Prema (1980), Royston (1982), Aebi and Whitehead (1980)). However, evidence that these pressures are converted into a worsening of women's nutritional status during pregnancy and lactation is very
UNHS 2016/17._ _Note for d: A positive redistributive effect means inequality declines, while a negative redistributive effect means inequality increases (in Gini points). A positive poverty reducing effects means poverty is declining while a negative poverty reducing effect means poverty is increasing (in percentage points)._

# 5.2.3. In-kind Transfers

Except for the tertiary education transfer and, to a lesser extent, the technical education transfer, all inkind transfers are concentrated relatively evenly (Figure 11a). Taken together, all in-kind transfers are progressively distributed with a Kakwani index of 0.16 (Figure 11b). Primary education and primary health are the most progressively distributed. Given that the primary education transfer is a large proportion of households’ income, its marginal contribution to equality is sizeable at around 1.24 Gini points. This effect on redistribution is partially offset by the tertiary and technical transfers and, overall, the marginal contribution of the net education transfers to inequality reduction is 0.95 Gini points. Health transfers are small in size and their marginal contribution to reducing inequality is small, around 0.09 Gini points. We lack data on the distribution of _quality_ in the public education and health systems. However, studies in the Africa region and elsewhere have demonstrated a marked variability in the quality of such public services as well as a tendency for lower-quality public services to be produced in areas with higher concentrations of poor individuals.<sup>24</sup> That is to say, including a variable quality dimension to our analysis of in-kind transfers might significantly alter the estimated impact of these services on inequality.<sup>25</sup>

> 24 World Bank (2016) shows that in the case of education and health, more and better inputs seem to be available in better-off locations (e.g. lower number of pupils per teacher or lower number of pupils per classroom). Similarly, World Bank (2019c) points to an overall low quality of education in
# _CHAPTER 7. SEMI-PARAMETRIC AND MACHINE-LEARNING METHODS_ 57

# man, 2001).

In the context of SAE for poverty mapping, there are numerous recent applications of machine-learning techniques, and we will discuss just a sample of them. For example, Blumenstock et al. (2015) employed the gradient boosting techniques by Friedman (2001) to predict poverty and wealth in small areas of Rwanda, using covariates obtained from mobile phone metadata. Pokhriyal and Jacques (2017) applied Bayesian Gaussian Process (GP) regression, which assumes a linear regression with a non-parametric additive term, where a GP prior is assumed for the nonparametric part, and utilized elastic net regularization for model selection. They predicted the Global Multidimensional Poverty Index (MPI) at the commune level in Senegal, by combining the predictions obtained from two separate Bayesian GP regression models, each using a different data source. Steele et al. (2017) obtained highly granular poverty maps for Bangladesh using Bayesian Geostatistical Models with auxiliary information obtained from mobile phone and satellite data. Hersh et al. (2021) employed four different machine learning techniques (ridge and elastic net linear regression, random forests and extreme gradient boosted trees) to map poverty by enumeration districts of Belize, using open satellite derived features. Chi et al. (2022) utilized gradient boosting techniques to obtain estimates of average asset-based relative wealth indexes (RWIs) for 2.4-km populated microregions in the 135 low and middle income countries, using aggregated satellite imagery and big data sources, namely mobile phone data, topographic maps, and aggregated and deidentified connectivity data from Facebook. The latter procedure is specific for the asset-based RWIs, as these indexes are obtained from household assets and amenities, recorded by household surveys from the Demographic and Health Survey (DHS) Program, which include subregional geomarkers. Hence, the procedure may not be easily extendable to other types of poverty indicators.

The aforementioned machine-learning SAE procedures may be applied
observation holds up across the different sub-groups and measures of income considered. The results obtained in chapter 11 (by Mitnik, Helso, and Bryant) show that the United States also ranks unfavorably in terms of inequality of opportunity (in addition to inequality of outcomes).

The fifth and last section of MDMIW consists of five chapters that combine macro data (national accounts) with micro data (household surveys). Household survey data are well known to undercover households from the very top of the income distribution for a variety of reasons, while they are the ideal source of data for incomes below the 95<sup>th</sup> percentile. Adequate coverage of top incomes however is crucial for the estimation of income inequality. National accounts on the other hand are an ideal source of data for capturing top incomes but may be a less reliable source of data below the top-income level. That highlights the potential for combining these two sources of data. The concluding chapter (chapter 23 by Dominic Webber, Richard Tonkin, and Martin Shine), for example, develops a novel methodology employed by the UK’s Office for National Statistics (ONS) that utilizes that potential to produce a new top income adjusted series. The UK's statistical agency is the first among its peers to do this. (See also the recent study by Jenkins 2022.) Chapters not highlighted here are no less impressive and we encourage the interested reader to explore these chapters. One chapter that is missing in the book, however, is a concluding 6
nevertheless, extremely likely to be selective processes, in the sense that they are correlated with family and student characteristics that also affect test scores. If one is interested in comparing the educational achievement of the _population_ of fifteen year-olds across countries, therefore, the PISA samples suffer from selection bias.

Correcting for such biases is never simple, and even less so when non-participants are not observed at all in the sample (unlike, say, when seeking to correct for labor force participation on the basis of surveys that contain information on both earners and non-participants). While we do not offer a sample selection bias correction procedure for all countries in the PISA sample in this paper, we propose a simple two-sample non-parametric mechanism for assessing the sensitivity of our inequality measures to alternative assumptions about the sample selection process.

Denote the (density of the) distribution of test scores _y_ in a particular country _j_ by . Consider a vector of covariates _X_ that is observed both in the PISA sample and in an ancillary household survey, which is representative of the full population of 15 year-olds. Note that the density of test scores in the PISA sample can be written as:

> 11 One presumes that coverage rates in excess of 1.00 must be due either to statistical discrepancies in the estimates of 15 year-olds in the total population, or to errors of inclusion in the sample of test-takers.

12
Direct taxes and most contributions are progressive and equalizing, as indicated by the positive value of the Kakwani index (figure 6). The personal income tax is the most progressive of all, while at the same time equalizing and very slightly poverty-increasing, as assessed by its marginal contribution to the poverty rate and the Gini index (figure 7). Contributory social security schemes such as the Provident Fund and NPPF are also progressive, though the magnitude is smaller than direct taxes. Among social insurance contributions, the rural insurance scheme has a large regressive impact (figure 6). Most social contributions are equalizing yet slightly poverty-increasing, with the NPPF having the largest effect on increasing poverty.

Figure 6. Progressivity of Direct Taxes and Figure 7. Marginal Contribution of Taxes and Contributions (Kakwani Index) Contributions to Change in Poverty and Gini <!-- Start of picture text --> 0.60<br>0.40<br>0.20<br>0.00<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br><!-- End of picture text --> _Source_ : World Bank staff estimation using BLSS 2017.

<!-- Start of picture text --> GIS<br>Rural Insurance<br>GISL<br>Provident fund<br>NPPF<br>Personal Income Tax<br>-0.5 0 0.5 1<br>Change in Poverty Headcount Change in Gini<br><!-- End of picture text --> <!-- Start of picture text --> Source : World Bank staff estimation using BLSS 2017.<br>Note: Positive marginal contribution to Gini implies that<br>the tax/contribution is equalizing.<br><!-- End of picture text --> Indirect taxes are uniformly progressive which could be attributed to at least two reasons
methods for interpolating and interpreting the data from those sensors. And so, while each EO product endeavors to measure the objective truth of weather conditions, each produces its own “truth.” In theory, these “truths” should all be the same. In practice, they are not. Figures 1 and 2 show these different “truths” across six remotely sensed EO precipitation products and three temperature products. One precipitation product reports rainfall of less than five mm while a different product reports rainfall of more than 47 mm for the same location on the same day. Temperature also varies by EO product. One product reports a maximum temperature of 23<sup>_◦_</sup> C while another reports the maximum temperature that day as 27<sup>_◦_</sup> C.

Where does this disparity leave researchers who want to incorporate EO weather data into economic contexts? If using ground-based weather station data, the gold standard for weather measurement, is not possible, then we are left to use EO sources. But, their mismeasurement of weather can introduce two issues. First, if economists are unaware of these measurement issues, we 2
is an oligopoly market (OLI-NASH) and that the corn market behaves like a PCO. An additional feature of WELCOM is that it enables users to study multiple sectors simultaneously under different market structures.

WELCOM also allows the user to choose the type of welfare function applied to assess the impacts of the competition reforms. There are three alternative money metric measures available to assess changes in welfare: (a) Laspeyres, (b) equivalent variation, or (c) compensated variation. The latter two options also require the selection of the functional forms to define the utility function of a household. The options available for the simulation method are (a) Cobb-Douglas and (b) constant elasticity of substitution (CES).<sup>18</sup> The user then needs to define the original and final conditions or the characteristics of the market under analysis. The default scenario assumes that the policy reform enhances competition in the market (that is, the movement from concentration to competition); however, the tool also allows an analysis of the opposite scenario (from a competitive market to a market with higher concentration). WELCOM also enables the user to carry out the simulations in gradual steps, thereby providing users with the option to see the gradual liberalization or concentration of sectors of interest, while also focusing on providing the extreme case of perfect (imperfect) competition to determine upper-bound estimates of such an outcome.

In sum, the empirical analysis produced by the WELCOM tool will be based on microeconomic models in which the household is the unit of reference. The data requirements of the module include a representative sample of the population, information on total household expenditures, and household expenditures on the product of interest where the market concentration is observed, or the competition policy reform is expected.

# **4. The Mexico case study**

Mexico is well known for its history of monopolies, including TELMEX on fixed lines (privatized in 1990) and PEMEX in fuel (a state-owned enterprise opened to competition only after 2013
A university degree has a rate of return of 15 percent. These are truly high returns and they have remained high over time. They are high during periods of economic growth as wages rise. They are high during periods of financial distress when foregone earnings are lower.

Despite the revolution in access to school, there is still an enrollment problem. According to UNESCO <u>(http://uis.unesco.org/en/topic/out-school-children-and-youth), there were more than</u> 258 million children and youth out of school for the school year ending in 2018. The total includes 59 million children of primary school age, 62 million of lower secondary school age and 138 million of upper secondary age.

Even more importantly, there are another 260 million children in school but who cannot read (UNESCO). This is particularly worrying. Also, according to UNESCO, 1 in 4 young people in developing countries are unable to read. The World Bank recently released its Learning Poverty estimates <u>(https://www.worldbank.org/en/topic/education/brief/learning-poverty).</u> Learning poverty means being unable to read and understand a simple text by age 10. It is estimated that more than half of the world’s 10-year-olds are in learning poverty. The World Bank has also estimated the gaps in learning outcomes between countries and regions of the world as part of its Human Capital Project. The harmonized learning outcomes show the gap between highest performers in the world (in East Asia, Europe and North America) and the lowest performers (mostly low-income countries) to be 2 to 3 standard deviations (Patrinos and Angrist 2018). While these are technical terms, they mean that almost the entirety of low-income countries’ education outcomes are eliminated once the quality of learning is considered. In other words, there is not much learning taking place in low-income developing countries (Angrist et al 2019). The situation is only slightly better
Figure C.4. Women’s Marriage Before Age 18 Post the Gujarat Riots of 2002: Borusyak et al. (2021) Estimation Using Neighboring States as Control <!-- Start of picture text --> Marriage Year<br>Data Source: NFHS-4<br>.2<br>.1<br>0<br>Diff-in-diff Coefficients<br>-.1<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV. The outcome variable is an indicator of women’s marriage before age 18. The control states only include the bordering states of Gujarat, which are Maharashtra, Rajasthan and Madhya Pradesh. Standard errors were clustered at the state-year level 38