rafmacalaba/data-use-mentions-extended huggingface dataset:
cases the low score reflects the ready<br>availability of domestically developed technology rather than low business sophistication.<br>Source: World Bank staff calculations based on Enterprise Survey data and the Investment Climate Assessment 2.0 toolkit<br><!-- End of picture text --> Zambian firms also face skills and capability constraints at the worker level. Zambia’s Human Capital Index (HCI)—a measure of health and education’s contribution to worker productivity—is well below most aspirational and structural peer countries (Figure 39).<sup>14</sup> Zambian firms are unable to overcome these challenges after onboarding workers. Although a high portion of formal Zambian firms report offering formal training, they offer it to a low share of workers (Figure 40).

> 14 The HCI calculates the contributions of health and education to worker productivity. The final index score ranges from zero to one and measures the productivity as a future worker of child born today relative to the benchmark of full health and complete education.

22
|--|

_Source_ : Authors’ calculation using the Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

As with any targeting methodology, we also propose performance criteria for the PPMT models. Figure 7 illustrates the ways to calculate these referral statistics or the performance criteria for PPMT. While the performance of the PMT (inclusion and exclusion errors) is evaluated with respect to the “true” poverty status, the performance of the PPMT is evaluated with respect to the PMT, i.e., how well does PPMT reproduce the eligibility decisions of the PMT? In Figure 7, pseudo-PMT score, , is represented on the vertical axis, the full PMT score, , is on the horizontal axis. The vertical dotted line represents the full MM PMT targeting threshold, , which changes only when the overall targeting (e.g. poorest 18, 25, 30 yy FF percent nationally) is changed. The horizontal dotted line is a probabilistic yy and, in contrast, changes FF with the chosen confidence level of referral. Lower the threshold, higher is the referral accuracy, but WW MM higher may also be exclusion error as many who would have been eligible under the full PMT will not be WW referred.

Cases in quadrant A in the figure are those that are eligible for the MoLSA program under the full PMT but would not be referred by the pseudo-PMT formula (e.g. this would be a failure to refer). Cases in quadrant B are correctly not referred (as they would not be eligible under the full PMT model); those in C are incorrectly referred (they are also not eligible under the full PMT model but the pseudo-PMT would indicate that they are); and those in D are successfully referred to the MoLSA program (both pseudo-PMT and full PMT indicate they are eligible). Based on these case types, one can calculate various referral statistics as shown in the figure. Statistic related to the “referral of ineligible cases” and the “non-referral of eligible cases” are
# **Output Fluctuations in Latin America: What Explains the Recent Slowdown?'**

# Santiago Herrera Guillermo Perry Neile Quintero

# Abstract

_The object of the paper is to explain Latin America's growth slowdown experienced in 1998/99. To do so we used two complementary methodologies. The first one aimed at determining how much of the slowdown could be explained by specific external factors, namely, the terms of trade, international interest rates, spreads on external debt, capital flows and climatological factors (El Nino). Using quarterly GDP data for the 8 largest countries of the region, we estimate a dynamic panel that allowed to calculate that between 50% and 60% of the slowdown was due to the role of these externalfactors. The second approach allows for the role of some endogenous variables, like domestic real interest rates and real exchange rates, to affect output. Using monthly industrial production data, we estimate country-specific Generalized Vector Autoregressions (GVAR)for the largest countries. We found that during the whole sample period (19921998) output volatility was mostly associated with shocks to domestic factors, but the slowdown in the sub-period 1998-1999 was explained in more than 60% by the external factors' shocks._ I This paper is a modified version of the presentations in the Latin America Round Table Conference, Duff and Phelps, New York, April 15, 1999 and in the Harvard Institute for International Development The authors acknowledge and thank excellent research assistance by Conrado Garcia and Bernadette Ryan. Comments by Eliana Cardoso, Daniel Lederman, Deepak Mishra and Luis Serven are gratefully acknowledged.
# **Figure 2: Total Factor Productivity, relative to US**

<!-- Start of picture text --> 0.45<br>0.4<br>0.35<br>0.3<br>China<br>0.25<br>Nigeria<br>0.2<br>Indonesia<br>0.15<br>India<br>0.1<br>0.05<br>0<br><!-- End of picture text --> _Source: UNIDO, World Productivity Database._ 9. The growth of the non-oil economy was largely driven by the agriculture sector, which contributed on average more than 50 percent (Table 3).

10. The contribution of agriculture was followed by that of the wholesale and retail sector (about 20 percent), the manufacturing and financial sectors (4-5 percent), and the telecommunications sector (about 3-4 percent).

7
# **Section III: Variation in bias by income**

It is possible that these differences in weekly versus monthly data are not a terrible problem. If the underestimation of the frequency of illness and the use of health services were orthogonal to variables that researchers are interested in using to explain these phenomena, then the overall explanatory power of models using monthly data would be lower than with weekly and there would be bias in the scale of all coefficients uniformly – all effects would be about half of their real effect. Relative impacts of different variables would still be recoverable. However, in this section, we use the observational data to show that this recall effect is related to an important determinant of health care demand – household income. In this case, mismeasurement of the dependent variable biases gradients like the income elasticity of doctor visits, and therefore, affects our understanding of how illnesses are reported and how individuals seek treatment.

Specifically, we can decompose the income elasticity of expenditures on health, commonly believed to be quite high, into three parts – the probability of being ill, the probability of seeking treatment conditional on being ill, and the price of services sought. We present four figures that explore each of these components in turn. We then turn to a further aspect of health reporting. It is possible that many of the episodes not reported in the monthly recall surveys are ―minor‖ with few impacts on households, and small expenditures. We therefore also look at the effect of the recall period on labor impacts of sickness, again across poor and non-poor households.

Figure 2 shows the number of family members who reported at least one acute illness in any one month when asked on a weekly versus a monthly basis as a function of the family‘s log per capita expenditure.<sup>11</sup> In this and other figures, the vertical line represents the income range beyond which the data is very sparse and therefore, less reliable. There is an obvious difference between the recall periods
researchers and is consistent across countries. The baseline is for a “normal” day so, for example, Monday mobility data is measured as the deviation from the “normal” Monday baseline and similarly for other weekdays. The “normal” day reflects the median value from five weeks of pre-crisis data from January 3 through February 6, 2020. The data are aggregated to monthly values at the first subnational administrative division in each country. The index is measured for six places, including workplaces, residential, grocery and pharmacy, transit stations, retail and recreation, as well as parks.

The real-time indicators at the levels of subnational administrative division by month are merged with the aggregated HFPS panel data at the same administrative level, yielding the panel data used for the analysis.

# 2.3 Multiple Imputation

Because Google mobility data are missing for several regions, we employ multiple imputation techniques to fill them. Google does not disclose at least one mobility indicator in a total of 559 out of 1,096 regions, across the 6 indicators (Residential, Retail and Recreation, Workplace, Transit Stations, Groceries/Pharmacies, and Parks). Using multiple imputation techniques allows us to include these regions in the models, which generate more precise estimates of the relationship between outcomes and other non-mobility predictors. To fill in missing Google mobility indicators, we create 100 imputations using imputation by chained equations, based on the other mobility indicators, night-time lights, NO2, and vegetation index (EVI). The chained equations approach carries out imputation iteratively for each of the Google Mobility variables, each time substituting values drawn from the conditional distribution for missing values, until convergence is achieved. Because we carry out analysis on two different samples, one for initial impacts and one for the subsequent evolution of the crisis, we perform two imputation exercises: one only for data from the first wave, and one for data for all waves. In the first case, mobility values are imputed for between 65 and 135 of the 311 total regions,
household level, the PSF data provide us with information at each survey date on the stock of savings of each household member, which allows us to focus on wives’ savings and resource allocation decisions.

We implement a two-step analysis, detailed in the Empirical Approach and Identification Strategy section. In the first step, we obtain for each union the predicted risk of polygamy with a duration model. Identification in the second step relies on the different time patterns of transitions of unions to polygamy depending on the polygamy of the husband’s father. We thus make the assumption that the polygamy of the husband’s father does not affect the variation of saving behaviors over time other than through the risk of polygamy. Potential violations of this assumption caused by differential inheritance rules across groups or polygamy of the husband’s father partly capturing household wealth are investigated and ruled out in the Empirical Approach and Identification Strategy section.

Our main results show that the risk of polygamy has a positive impact on wife savings, but only on savings entrusted to formal or informal institutions, as opposed to savings kept at home, especially for women living in the poorest households. Household food expenditures are found to decrease with the risk of polygamy, suggesting a possible reallocation of women’s resources from food consumption to savings. In addition, women facing a larger increase in their risk of polygamy spend more on the education of their children, but this increase in education expenditures is exclusively funded by a higher contribution of their husband, while their own contribution decreases. They are also found to transfer more to their social networks. These results suggest that the risk of polygamy leads monogamous wives to engage in self-protective strategies by investing in assets that are out of the reach of their husbands and in their social networks. On the other hand, the risk of polygamy is not found to affect women’s labor market participation. By contrast, the risk of polygamy is positively correlated with monogamous husbands’ labor market participation and income, consistent with an accumulation strategy to
and have been used extensively to improve national account estimates of GDP (e.g. Henderson et al. 2012, Lyu et al. 2018, Chen et al. 2019). Both proxies have also been used to assess the economic impact of major policy measures. Nighttime light intensity, for example, allowed assessing the impact of India’s demonetization in November 2016 (Beyer, Chhabra, Galdo, and Rama 2018, Chodorow-Reich, Gopinath, Mishra, and Narayanan 2020). It is also invaluable to approximate economic activity at the sub-national level, including in India (Gibson, Datt, Murgai, Ravallion 2017, Prakash, Shukla, Bhowmick, and Beyer 2019, Chanda and Kabiraj 2020). In concurrent work, electricity consumption has been shown to have closely tracked economic activity in the United States during the global financial crisis (Cicala 2020a).<sup>4</sup> And it has been employed for an assessment of the economic impact of the COVID-19 pandemic in the European Union (Cicala 2020b, Chen et al. 2020b).<sup>5</sup> In this paper, we first confirm a meaningful relationship between electricity consumption, nighttime light intensity, and economic activity in India. We then propose a new real-time measure of daily economic activity in India at the country and state levels based on daily electricity consumption. We estimate an electricity consumption model based on the day of the week, the week of the year, the temperature, and holidays that explains 90 percent of the variation in India’s electricity consumption. Comparing the actual electricity consumption in 2020 to the one predicted by the model allows us, first, to quantify the economic costs of the COVID-19 pandemic and the national lockdown implemented on March 25, 2020 and, second, to understand different impacts between states. In addition, we use night light intensity to gauge the impacts at the district and city level and to explore their local drivers.

We find a strong impact of the national lockdown on India
financial development from WDI. Finally, we use a measure of Rule of Law from The World Bank‘s World Governance Indicators.

We use dynamic panel regressions based on three-year panel data for each country. The basic regression specification is the following: where is the natural logarithm of GDP per capita, is the level of GDP per capita at the beginning of each three year period, is the dynamic service export sophistication measure, is the set of relevant control variables for growth determinants, represents time dummies, stands for country fixed effects and is the error term.

We present the growth regression results in Table 5. In column 5, we add three European groups that we interact with _Service EXPY_ , to see if there is a significantly different impact of service sophistication in EU-15, NMS or ACC compared to the rest of the world. The result is show that in the NMS case, there is a positive significant additional impact on growth. This means that service sophistication has a positive association with growth in all countries but the association – given a certain level of sophistication – is stronger in NMS than in other countries.

**Table 5: Regression results with EU-15, NMS and ACC dummy, Fixed effect 3-year panel of growth in** **<u>per capita GDP, 1990-2007.</u>**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|log lag initial GDP per capita|-0.481***|-0.644***|-0.650***|-0.690***|-0.680***|
|log lag initial Service EXPY|(0.0780)<br>0.315***|(0.0895)<br>0.221**
A number of factors make India’s flagship MGNREGA program an ideal setting to study the impact of employment guarantee schemes on schooling and child labor. First, the scale of the program is massive. By its fifth year, the program provided employment opportunities to 53 million households for 2.3 billion man-days, making it the world’s largest operating employment guarantee scheme. Second, the program was gradually rolled out in the districts of India based on pre-determined characteristics measured 10-15 years prior to the program. This variation provides an excellent opportunity to evaluate the impact of this program.

Using a longitudinal data set of 1.13 million primary and upper-primary schools in India, the study compares within school enrollment across the districts which received the program early versus late. We find that, conditional on school characteristics, school enrollment grew more slowly in districts where the program was phased in early. This result is driven by primary schools rather than upper primary schools. Using nationally representative survey data, we examine the effect on children’s employment both outside and within the house. We find that children are more likely to be employed in early phase districts post treatment.<sup>2</sup> Responses to the program are heterogeneous. Responses to the program vary by age, type of school and quality of private schools within private schools. Enrollment falls for younger primary school children whereas school outcomes deteriorate for upper primary children. Grade 7 school outcomes in early treated districts get worse for both government and private schools but effects are larger for private schools. Within private schools, the effects are driven by low quality private schools.

The timing of the roll-out was not random and was largely ascertained by three district characteristics: district Scheduled Castes and Scheduled Tribes population in the 1991 Census of India, 1996-97 agricultural wages, and the 1990-1993 output per 2In many settings, children who work a significant number of days in a year are less likely to be enrolled in school (Jensen, 2000). Our results show that safety net
(5) Management practices are worse among informal businesses than their formal counterparts (consistent with Kanbur, 2017; Benhassine et al., 2018; Campos et al., 2018), with the components on record-keeping and planning appearing least prevalent.

(6) Analysis of business relations reveals limited integration of informal businesses into the rest of the economy (consistent with Lewis 1954; La Porta and Shleifer 2014; De Paula and Scheinkman 2007).

(7) Performance (as measured by sales per worker) of informal businesses is considerably worse than their formal counterparts (consistent with De Mel et al., 2008, 2009; Bardasi et al., 2011, La Porta and Shleifer 2014; Ulyssea 2020; Aga et al., 2021).

(8) Analysis of the reasons behind operating informally suggests that informal businesses mostly perceive no benefits, only costs associated with registering, with very few ever considering formalization (consistent with Maloney, 2004; De Mel et al., 2013; Benhassine et al., 2018; Campos et al., 2018).

By presenting broadly comparable business-level data across multiple cities and countries based on representative samples at the city level and collected through a rigorous sampling method, this paper fills a large gap in data availability. The World Bank's Enterprise Analysis unit is continuing the implementation of the Informal Sector Enterprise Surveys in additional cities and countries, making both—information about ongoing projects and the resulting data—publicly available through its website.<sup>4</sup> The ultimate purpose of the work of this unit is to enable and encourage further research into the informal sector, to better understand business environment characteristics and practices of a segment of the economy that remains largely unexplored.

# **2. The Data, Coverage, and Questionnaire**

We use the combined data from the Informal Sector Enterprise Surveys conducted by the World Bank Enterprise Analysis Unit in twenty-four cities across seven countries in between 2017 and 2022. The countries and cities covered are:
The implications of land area measurement extend well beyond agricultural productivity. Disaggregated land ownership data are an input into analyses of agrarian structures and how these evolve with economic and demographic change, the related analyses of land inequality, and how this might be related to income inequality and its trends. Notable examples are the literature on the relationship between asset distribution and income inequality (Deininger and Squire, 1998; Deininger and Olinto, 1999), or the related work on agrarian structures and inequality (Carter, 2000). Unequal distribution of land has been linked to less propoor growth, participation in and occurrence of civil strife, and delayed long-run human capital development (Deininger and Squire, 1998; Macours 2011; André and Platteau, 1998; Baten and Juif, 2013). Carletto et al. (2015) find that the area measurement methodology used in calculating the land Gini coefficient has consequences on the level of inequality observed, with self-reported area estimates resulting in underestimated land inequality. Failure to adequately measure land limits the ability to analyze the agricultural economy and its relationship with land inequality.

Land registration and titling programs require high quality land area measurement for fair program implementation. Such programs are frequently met with opposition and accusations of corruption or favoritism. Ongoing land certification reform efforts in Ethiopia have recently moved from the first stage of certification, which consisted of identification of plots by land markings and neighbor recall, to the second stage certification in which GPS measurements would replace the first stage data. A recent study analyzing the demand for the second stage certification concluded that the majority of the demand for such area measurement comes from land administrators, as households exhibited a low and declining willingness to pay over the period 2007 to 2012 (Bezu and Holden, 2013). International organizations have also emphasized the importance of objective area measurement in land registration and redistribution. In its support to the Zimbabwe Ministry of Lands and Rural Resettlement 2014-2016 Action Plan (UNDP, 2014), the United Nations Development Programme explicitly mentions GPS and remote sensing as requirements for
>publicly available.|
|ASD|Asian<br>Development<br>Bank|Country<br>Performance<br>Assessment|26 Asian Development<br>Bank client countries.|Every two years;<br>used in WGI<br>since 2005.|Expert/GOV|Expert assessment along 16 dimensions, scored by ADB<br>country economists subject to centralized review.<br>Questonnaire and process are similar to the World Bank<br>Country Policy and Insttutonal Assessment (PIA), the IFAD<br>Rural Sector Performance Assessment (IFD), and the African<br>Development Bank Country Performance Assessment (ADB).<br>Link to methodologyhere.Data are publicly available only for<br>countries eligible for concessional fnance.|
|BPS|World Bank and<br>European Bank<br>for<br>Reconstructon<br>and<br>Development|World Bank<br>Business<br>Environment<br>and Enterprise<br>Performance<br>Survey (BEEPS)|30 transiton economies<br>in Eastern Europe and<br>Central Asia.|Every three<br>years, used in<br>WGI 2000-2015.|Survey/Firm|Enterprise surveys conducted by the World Bank Enterprise<br>Survey (WBES) unit and cofnanced with the European Bank for<br>Reconstructon and Development. Before 2023, this was the<br>only region for which the WBES produced surveys with<br>sufcient frequency to be included in the WGI. Beginning in<br>2023 the WBES has global coverage on a three-year rotatng<br>cycle and is included as a new data source in the WGI (see<br>source WBS below). Record-level survey data is publicly<br>available through the WBES website. Link to methodology and<br>questonnaireshere.|
|BTI|Bertelsmann<br>Foundaton|Bertelsmann<br>Transformaton
.

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Iimi, Atsushi (2011), “Effects of Improving Infrastructure Quality on Business Costs: Evidence from Firm-Level Data in Eastern Europe and Central Asia” The Developing Economies 49(2): 121-147.

Islam, Asif (forthcoming), “The Burden of Water Shortages on Informal Firms,” _Land Economics_ .

Kumar, Sanjiv, David M. Lawrence, Paul A. Dirmeyer, and Justin Sheffield (2014), “Less reliable water availability in the 21st century climate projections.” _Earth's Future_ 2, no. 3: 152160.

Levinsohn, James and Amil Patrin (2003), “Estimating Production Functions Using Inputs to Control for Unobservables” _Review of Economic Studies_ 70(3): 17-341.

Liu, Yanyan, and Robert Myers (2009), “Model selection in stochastic frontier analysis with an application to maize production in Kenya” _Journal of Productivity Analysis_ 13: 33-46 19