rafmacalaba/data-use-mentions-extended huggingface dataset:
# **III.** The Data

The data set for this study is compiled from diverse sources for 14 major states of India over the period 1960-1994.11 The political data on elections is taken from Butler, Lahiri and Roy [1995]. The public finance data on taxes and expenditure is available from the 1960-1994 volumes of the Reserve Bank of India Bulletin, a quarterly publication of the central bank of India with annual issues on the finances of state governments<sup>12</sup> . Highways and roads data is compiled from the 1961-1995 volumes of the Basic Roads Statistics, an annual publication of the Ministry of Surface ransport of India and from various state Statistical Abstracts<sup>13</sup> . State demographic and economic characteristics, and a state-level price index to convert all variables into real terms, are available from an Indian data set put together at the Poverty and Human Resources Division, Development Research Group of the World Bank. A detailed description of these variables is available in Ozler et al. [1996].

" l The States Reorganization Act of 1956 divided the Indian federation into 14 states and 5 union territories that were administered by the Central government. In 1960, the state of Bombay was divided into Gujarat and Maharashtra. In 1966, the PEPSU (Patiala and E. Punjab States Union) was divided into its two main constituents, Haryana and Punjab. This study includes 13 states that were already established in 1960, namely Andhra Pradesh, Assam, Bihar, Gujarat, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal. The fourteenth state in 1960, Jammu and Kashmir, has been excluded because of the political uncertainties in the region that continue to this day. The state of Punjab is included after 1966, when it attained separate statehood. Haryana is not included because data for this state is not available across many explanatory variables.

Currently, India has 25 states because several union territories have attained statehood
SOML gave priority to a package of health interventions known as ‘the six pillars’.<sup>19</sup> The World Bank actively supported SOML, using its “Program for Results” instrument<sup>20</sup> to financially reward Nigerian states based on improvements from their previous best performance on six key indicators.<sup>21</sup> Improvements were to be measured through yearly household surveys providing robust estimates at the state level.

In practice, of course, these six pillars (or intervention areas) were wildly different in their drivers and complexity – improvement within them was therefore destined to move at different trajectories and different speeds for different groups in different places. State actors, keen to raise their aggregate measure of success and get paid for it, soon realized that there was some gaming to be done. Our field research documented how the emphasis on singular measures of success introduced a perverse incentive for states to focus on the easier metrics at the expense of the hard (Bridges and Woolcock 2019). Interviews with state officials revealed that front-line staff were increasingly focusing their time and energies on those constituent variables that they discerned were easiest to accomplish (e.g., dispensing vitamin supplements) over those that were harder or slower – typically those that involved a plethora of “pesky human elements” – such as lowering maternal mortality or increasing contraceptive use. Thus, in selecting certain outcomes for measurement and managing these alone, others inevitably end up being sidelined.

Likewise, a recent report on Results-based Financing (RBF) in the Education Sector (Dom et al. 2020) finds evidence of a “diversion risk” associated with the signposting effect of certain reward indicators, with important areas deprioritized because of the RBF incentive. For example, in Mozambique they find that an emphasis on simple process indicators and focus on targets appears to have led to officials diverting resources and attention away from “more fundamental and complex issues”, such as power dynamics in the school council,
18 only for those countries that, according to the country-specific notes in the Balance of Payments Statistics Yearbook, explicitly advice to do so<sup>23</sup> .

While recognizing that the balance of payments data on remittances very likely underestimate the true amount, we believe that measurement error does not significantly affect our findings for at least two reasons. First, since classical measurement error causes an attenuation bias, that is a reduction in the significance of the coefficients of the variables imprecisely measured, we conclude that our results on remittances are possibly even stronger than what found here. Secondly, we do not see any a-priori reason for the underestimation of remittances to bias the cross-country variance which our results are drawn from; along these lines, the just shown exercise with a different measure of migrants’ remittances can be seen as a partial and indirect proof of it.

A different issue concerns the validity of the results over the entire distribution of observations: unpleasantly, as it can be inferred by combining the coefficients of the linear, the quadratic and the interaction terms, the intuitive negative (positive) effect of reserves (external debt) on the probability of current account reversals may become, less intuitively, positive (negative) for high values of reserves (external debt) over GDP. We believe, though, this not to be a too serious issue for two reasons. On one hand, a linear model can not be asked to capture strong nonlinearities occurring in the tails; in particular, the apparent inversion could instead be reflecting that the probability effect of decreasing reserves and increasing external debt asymptotically converges to 1. On the other hand, these turning points occur at the very extremes of the distribution of the variables<sup>24</sup> .

As a further check, we have excluded the country-year cells falling below the 1<sup>st</sup> or above the 99<sup>th</sup> percentile of the distributions in terms of international reserves and external debt:
33

# **BIBLIOGR&PBY**

1. Barnum, Howard and Hamid Mohtadi, "A Teaching Model to Simulate the Demographic Effects of Health and Family Planning Programs in Nepal" in Robin Barlow, (ed.), Case Studies in the Demographic Impact of Asian Developaent Projects, University of Michigan, 1982.

2. Barnum, Howard and Daniel Tarantola and I. Setiady, "Cost Effectiveness of An Expanded Program of Immunization in Indonesia", Bulletin of the World Health Organization, Vol. 58, 1980.

3. Barnum, Howard and David Yaukey, "Economic Analysis of an Integrated Rural Health/Family Planning Services Project for Nepal", USAID Project Paper. Washington, D.C., U. S. Agency for International Development, 1979.

4. Birdsall, Nancy, 'Analytical Approaches to the Relationship of Population Growth and Development", Population and Develogment Review, vol. 3, pp. 63-102, 1977.

5. Bongaarts, John and John Stover, The PoRulation Council Target-Setting Model: A User's Manual, The Population Council, Center for Policy Studies Working Paper No. 130, December 1986.

6. Bulatao, Rodolfo A., "Reducing Fertility in Developing Countries: A Review of Determinants and Policy Levers", World Bank, Staff Working Paper No. 680, 1984.

7. Cleland, John, and Zeba Sathar, "The Effect of Birth Spacing on Childhood Mortality in Pakistan", World Fertility Survey, August 1983.

8. Cochrane, Susan H., Fertility and Education: What Do We Really Know, Staff Occasional Paper No. 26, World Bank, Washington, D. C., 1979.

9. Cochrane, Susan H., and K. C. Zachariah, "Infant and Child Mortality as a DetG=r5rn1t of FertilHtv -- The Policy Implications", World Bank Staff Working Papers No. 556, 1983.

10. Easterlin, Richard A., Robert A. Pollack, and Michael
often the poverty line.<sup>3</sup> Vulnerability to expected utility estimates the intensity of vulnerability by measuring the gaps between the utility attained under conditions of certainty and the expected utility (Calvo and Dercon 2013; Gallardo 2020; Günther and Maier 2014; Ligon and Schechter 2003; Magrini, Montalbano, and Winters 2018). Lastly, vulnerability as uninsured exposure to risk estimates the degree to which exogenous shocks reduce household consumption (Amin, Rai, and Topa 2003; Cafiero and Vakis 2006; Cochrane 1991; Dutta, Foster, and Mishra 2011; Jalan and Ravallion 1999; Povel 2015; Townsend 1994). Despite their methodological differences and various data requirements, all these methods aim to estimate expected household consumption and its variance.<sup>4</sup> Estimating the mean and variance of household consumption is challenging because of the intertemporal nature of vulnerability. Long panel data at the household level are necessary to obtain accurate estimates, but such data are often unavailable in developing countries. If panel data are not available, householdspecific mean consumption and its variance may be estimated from cross-sectional data under certain restrictions. Chaudhuri, Jalan, and Suryahadi (2002) propose a method for estimating the mean and variance of household consumption on cross-sectional data by assuming that the variance of consumption can be modeled as a function of observable household and community characteristics, while the error term in a consumption regression captures the unexplained variability caused by household- and community-specific income variance. Given the low availability of panel data, it is not surprising that a large number of published studies rely on cross-sectional data to estimate vulnerability (Chaudhuri 2003; Christiaensen and Subbarao 2005; Dang and Lanjouw 2014; Günther and Harttgen 2009; Kamanou and Morduch 2002; Suryahadi and Sumarto 2003; Tesliuc and Lindert 2004).<sup>5</sup>

# 3. Data and methods

Estimating a map of poverty and vulnerability requires a household survey and a population census. In the case here, the map is constructed using the Harmonized
Table A3: Food Insecurity Experience Scale (FIES) Questions in Malawi

||Pre-COVID-19 Surveys||COVID-19 Surveys||
|---|---|---|---|---|
|FIES|Question|Recall|Question|Recall|
|FS1|Did you worry that your household would not have<br>enough food?|7 day|You or any other adult in your household were<br>worried about not having enough food to eat be-<br>cause of lack of money or other resources?|30 day|
|FS2|How many days have you or someone in your<br>household had to rely on less preferred and/or less<br>expensive foods?|7 day|You, or any other adult in your household, were<br>unable to eat healthy and nutritious/preferred<br>foods because of a lack of money or other re-<br>sources?|30 day|
|FS3||7 day|You, or any other adult in your household, ate only<br>a few kinds of foods because of a lack of money or|30 day|
||||other resources?||
|FS4<br>40|How many days have you or someone in your<br>household had to reduce number of meals eaten<br>in a day?|7 day|You, or any other adult in your household, had to<br>skip a meal because there was not enough money<br>or other resources to get food?|30 day|
|FS5|How many days have you or someone in your<br>household had to limit portion size at meal-times?|7 day|You, or any other adult in your household, ate less<br>than you thought you should because of a lack of<br>money or other resources?|30 day|
risk and realized _ex post_ default (for the period after 2020).<sup>31</sup> There is a strong positive correlation, with _ex ante_ default risk explaining 75% of the variance of _ex post_ defaults. Figure 3, Panel B shows the correlation between the same variables but using a non-parametric fit with a local polynomial smoothing to address the uncertainty of the correlation. For the majority of the mass of the distribution (up until .25 of the horizontal axis), the fit is tight. The model performs even better if one considers only the center of the distribution, where the mass of the correlation lies on top of the 45-degree line. For values in the lower tail of the distribution of _ex ante_ risk, _ex post_ default is higher, whereas for values in the upper tail of the distribution, _ex post_ default is lower.<sup>32,33</sup> Our risk model appears to be a mean-preserving spread of _ex post_ default, implying that _ex post_ risk is less heterogeneous across firms than the prediction of our risk model.<sup>34</sup> That said, banks face significant uncertainty at the beginning of 2020 about the intensity and duration of the pandemic. Our risk measure does not capture this increased uncertainty because it is calculated using data from 2019. Nevertheless, we believe our measure of default probability can be useful because it is based on the information set that banks have about each firm before the start of the pandemic (including firm age, measures of size, productivity, and indebtedness).

# **4.2 Selection into the Government Programs**

We next focus on the selection into the credit and employment programs by firms with different characteristics. Based on the findings in the literature (Jim ́enez et al., 2018; Cascarino et al., 2022; Core and De Marco, 2023), we expect that riskier firms are more likely to apply to and receive program credit. The reason is
Latin American countries: Global Wage Report, International Labour Organization. Seventeen Latin American countries: SEDLAC database. See annex A.3 for details about the underlying data and Annex B for circa periods. _Note:_ The data have been multiplied by 100. For SEDLAC countries, the sample covers full-time, wage, and self-employed workers 15– 64 years of age. The values of the 1st and 100th percentiles of the earnings distribution were trimmed by each gender-education cell. The Global Wage Report data are not strictly comparable with SEDLAC data. In some countries, different types of surveys were used, and the sample and trimming criteria are different. The size of the bubbles represent the population of the country.

The most pronounced drop in earnings inequality since 2003 was observed in República Bolivariana de Venezuela, although the relevant data are not strictly comparable because they are provided through a harmonization process that differs relative to the process in the rest of Latin America (an International Labour Organization method instead of the SEDLAC method). República Bolivariana de Venezuela is followed by Uruguay (urban), Nicaragua, Peru, Ecuador, and Argentina (urban). The sharp contrast among the trends in labor income inequality in Argentina, Nicaragua, and Peru, which experienced large increases in labor income inequality over the 1990s, are of particular interest. In contrast, Costa Rica was the only country in Latin America on which data are available that experienced a widening in labor income inequality in the 2000s and, thus, across two consecutive decades.

The trend reversal in labor inequality can be illustrated through a graphic on the growth of real hourly earnings in Latin America. Thus, figure 6 shows that average earnings in Latin America rose at the bottom, the middle, and the top of the labor income distribution over 2002–13, after experiencing no change or even a slight reductions between the 1990s and early 2000s. The largest increase occurred among workers at the bottom of the earnings distribution, who experienced a rise of more 50 percent in real earnings
26 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115(9), 23202329.

Zhou, Y., Smith, S. J., Zhao, K., Imhoff, M., Thomson, A., Bond-Lamberty, B., ... & Elvidge, C. D. (2015). A global map of urban extent from nightlights. Environmental Research Letters, 10(5), 054011.

Zhou, N., Hubacek, K., & Roberts, M. (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data. Applied Geography, 63, 292-303.
countries (Albania, Bosnia and Herzegovina, the Former Yugoslav Republic Macedonia, Serbia). 3 In each country and wave, a nationally representative sample of 1,000 individuals aged 15 years or older is polled based on multistage random sampling procedures. For the purpose of this analysis, we exclude respondents who are younger than 18, as these will probably lack experience in significant saving decisions. Data weighting is used to ensure a nationally representative sample for each country. Sampling weights use population statistics on gender, age and region and where available education and socioeconomic status as well as ethnicity.

We employ data from 2 surveys conducted in fall 2012 and 2013, as these two waves include our main variable of interest as well as a comprehensive set of necessary control variables. Thus, our analysis focuses on 10 countries and around 20,000 individuals.

# **3.2. Measuring household saving behavior**

We measure household savings behavior looking at the percentage of households that hold a diverse set of saving instruments conditional on having any savings. The central variables of our analysis are based on two questions reported in Table 1.

> 3 Further details on the survey can be found at: https://www.oenb.at/en/Monetary-Policy/Surveys/OeNB-Euro-Survey.html

5
# **4.4 Differences across firm capabilities (pre-pandemic)**

An important factor associated with the digital divide in response to the pandemic is the gap in digital readiness and overall firm capabilities pre-pandemic. Figure 9 shows the distribution of the information about increasing use and investment in digital technologies based on three groups of firms classified by the levels of digitalization pre-pandemic.<sup>12</sup> The figure shows significant persistence in digitalization, with a ratio close to 3 in likelihood of using and investing in digital technologies between ex-ante highly digitalized companies compared to those that had prior low digitalization. These results are aligned to previous findings combining the BPS data with very granular information on pre-existent digital technologies, from the Firm-level Adoption of Technology for firms in Brazil, Senegal, and Vietnam, as described by (Comin et al., 2022).

<!-- Start of picture text --> 80<br>68<br>60<br>52<br>45<br>40<br>28 29<br>20 16<br>0<br>Low Medium High<br>Digital readiness score (pre-pandemic)<br>Increasing use of digital tech.<br>Investing in digital solutions<br>Predicted probability<br><!-- End of picture text --> Figure 9: Predicted probability of digital response to COVID-19 by digital readiness (pre-pandemic) A potential explanation for this persistence in digitalization might be associated with the capabilities for digital upgrading and awareness. When comparing similar firms in terms of size and sector but differences in management practices, Figure 10 shows that among firms that did not increase the use of digital technologies in response to the pandemic, those adopting better managerial practices pre-pandemic were less likely to do report that they did not need digital upgrading.<sup>13</sup> . The role

> 12 Groups are created using the number of digital technologies used by the firms before the pandemic from the following
that the relative increase of the high skill group was larger than that of the middle skill group. The evidence indicates the presence of stronger relative demand forces for high school graduate workers in 2002-2009 than in the 1990-2002 period: demand factors must have operated in the most recent years, since the relative wage of high skill workers increased moderately with simultaneous increase in its relative supply.

Data limitations in the case of Ecuador restrict the comparability of the available surveys from the 1990s with those of the 2000s. The analysis here is based on comparable statistics constructed for the 2000s, based on the ENEMDU survey. The wage skill premium fell at an annual rate of 3.2 percent between 2003 and 2010, while the relative supply increased steadily in the same period. This indicates that, conditional on the assumptions on elasticities of substitution, the relative demand for skilled labor must have fallen over that period. With respect to unskilled workers, the increase in the relative supply of the high school group resulted in a reduction of its wage premium over the 20032010 period. This pattern is similar to that of the aggregate skilled-unskilled premium and relative demand, although the changes are smaller for the high school-dropout differentials. As in the cases of Colombia and Costa Rica, Tables 3.1 and 3.2 indicate that most of the reduction in the share of employment of the low skill group was accounted for by an increase of that of the high skill group, although, as discussed in the sector decompositions section, in the case of Ecuador there were relatively large within and between sector effects for both skill groups.

Table 3.1 indicates that the relative supply of skilled labor increased strongly in El Salvador during the 1990s. The corresponding wage premium also increased significantly during that decade, and these simultaneous trends are consistent with a strong increase in the relative demand for skilled labor. The evolution of these variables in the decade of 2000, however, indicates a marked change. The relative labor supply of skilled labor stopped growing, while the corresponding wage
limited variables to capture this information, the analysis suggests that "having a checking or savings account" is also positively associated with increasing the use of digital technology and having online sales. Using similar data from WBES, Amin and Viganola (2021) show that firms with better access to finance were significantly less likely to experience a decline in sales during COVID-19.<sup>16</sup> An important element is that even after controlling for proxies that aim to capture potential drivers of adoption, results still suggest a robust gap between firm size groups in the participation in digital markets. For investment in digital solutions, size seems the most robust observable characteristic for which significant differences are observed. In terms of sectors, ICT, retail and wholesale, and food services are among those with a more robust response towards a significant increase along different dimensions of digital adoption. Retail and food services are typically characterized by having a large share of SMEs and hiring a large number of workers.

Even after controlling for several factors, there is low explanatory power about the decision of starting or increasing the use of digital technologies. To investigate this issue, figure 17 presents a Shapley decomposition for differences of the estimations described in columns (3) and (6) of Table 2. This exercise decomposes these differences by factors attributable to specific sources. The results show that differences in observable characteristics of the firm and differences across firms do not explain a large portion of the observable differences in digitalization indicators. Furthermore, differences across countries explain only 7% of the differences in digitalization and about 90% remains unexplained. This result highlights the relevance of having a more in-depth analysis of how and which technologies are adopted by firms. For example Cirera, Comin, and Cruz (2022) show that technology adoption heterogeneity is not only large across similar firms but also within firms; across business functions inside the firm. Taking into account this heterogeneity is a key step to design better support mechanisms for firms’ digitalization.

> 16The authors used as