rafmacalaba/data-use-mentions-extended huggingface dataset:
wage premiums. Similarly, changes in Brazil’s labor market institutions, such as the level and coverage of minimum wages, and the degree of enforcement of formal employment contracts, may have played a role. Indeed, in a recent paper using administrative matched employer-employee data from Brazil’s formal sector (the RAIS dataset), Engbom and Moser (2016) claim that the rise in the real value of the minimum wage, including indirect spillover effects along the distribution, may account for as much as 70 percent of the reduction in the variance of log earnings in essentially the same period we study here (1996-2012).

The commodity boom that benefited Brazil during the 2000s may have triggered sectoral reallocation of employment, with additional effects on skill premiums and inequality, to the extent that different sectors have different intensities of demand for skills. Finally, there were also changes in employment and wage gaps across spatial areas: both rural versus urban, and across the country’s five main geographical regions.

We have therefore grouped the factors that may have affected earnings inequality in Brazil into five major categories: human capital, demographics, institutions, geography and sectoral distribution. Before describing our methodological approach in more detail in Section 4, and presenting results in Section 5, the remainder of this section looks descriptively at each of the five groups of candidate explanatory factors in turn. We first describe changes in the distribution of the relevant variables, and then report—again, descriptively – on changes in the partial correlation between labor earnings and each variable.

# **3.1 Changes in the Distribution of Candidate-Proximate Factors**

We begin with human capital. Figure 2 shows the cumulative distribution functions for years of schooling in the working age population (18-65), at three points in time: 1995, 2003 and 2012. These distribution functions illustrate an impressive expansion in the supply of years of schooling in the labor force. The proportion of the working age population with at least 10 years of schooling, for example, doubled
**Table 7. Correlation Between Lending IRC Bindingness and Other Financial Repression Policies**

||Full sampl<br>ρ|e<br>N|Excl. relative<br>ceilings<br>ρ|(mkt)<br> <br>N|
|---|---|---|---|---|
|State ownership of banks|-0.284*|42|-0.393**|31|
|Capital account restrictions|0.305**|45|0.377**|33|
|Entry into banking requirements|0.002|42|0.032|30|
|Excessive reserve requirements|-0.082|19|-0.403|12|
|Significance at ***1%, **5% and *10%.|||||

# **IV. Concluding Remarks**

In this paper we present a new database on IRCs in 108 countries, which account for 88 percent of global GDP. There are signs of a resurgence in financial repression policies around the world, which may be exacerbated by the Covid-19 crisis. While some measures may turn out to be temporary, others may become permanent. Understanding their unfolding is therefore important for policy makers concerned with minimizing the associated costs. This paper represents a step towards that goal. Building on a survey of financial sector authorities and bankers’ associations conducted during 2019 in World Bank Group member countries, this paper documents a key form of financial repression: IRCs, including their origins, the legal framework and their intended objectives. The data collected through the survey are then used to provide a preliminary estimation of the degree of bindingness of IRCs and to calculate pairwise correlations with other financial repression policies.

17
# **VI – References**

Ahrens, A., Hansen, C.B., Schaffer, M.E. 2018. lasso2: Program for lasso, square-root lasso, elastic net, ridge, adaptive lasso and post-estimation OLS.

- Bedi, T., A. Coudouel and K. Simler, (2007) _More than a pretty picture: using poverty maps to design better policies and interventions._ Washington DC: The World Bank Group.

- Elbers C., J. O. Lanjouw and P. Lanjouw (2003) “Micro-Level Estimation of Poverty and Inequality”. _Econometrica_ 71(1): 355–364.

- Fay, R. and R. Herriot. (1979) “Estimates of income for small places: an application of James-Stein procedures to census data.” _Journal of the American Statistical Association_ 74 (1979): 269–277.

- Goodman, S., Ben Yishay, A., Runfola, D., 2016. Overview of the geo Framework. AidData. Available online at geo.aiddata.org. DOI: 10.13140/RG.2.2.28363.59686

- Heeringa, Steven G., Brady T. West, and Patricia A. Berglund. _Applied survey data analysis_ . Chapman and Hall/CRC, 2017.

- Molina I. and Rao J. (2010) “Small area estimation of poverty indicators.” _Canadian Journal of Statistics_ , 38(3), 369-385.

- National Research Council. _National Patterns of R&D Resources: Future Directions for Content and Methods: Summary of a Workshop_ . National Academies Press, 2013.

- Office for Official Publications of the European Communities, Luxembourg.  Eurostat (2013). Handbook on precision requirements and variance estimation for ESS household surveys

Rao, J. N. K. (2003) _Small Area Estimation._ 1st ed. Wiley-Interscience.

- Statistical Office of the Republic of Kazakhstan (2015) “Income and Living conditions in the
consumption, leading to underestimation of true impacts. A reduction in electricity consumption may occur, but it may not be detected (or muted) due to contamination of the comparison group.

3. _Aggregate effect:_ Energy efficient technologies could have an effect on aggregate electricity consumption within the local distribution system. The sign and magnitude of this aggregate effect will depend on the proportion of households that use CFLs, as well as on the sign and magnitude of the impacts they individually experience. If a large enough proportion of households within a transformer adopt CFLs and their individual impacts combine into aggregate electricity savings, the probability of an outage occurring due to a transformer overload would be reduced, thus increasing reliability of electricity services.

4. _Technological externality:_ The distribution of CFLs could generate a positive (negative) technological externality if the returns to a household from adopting such technology increase (decrease) in the fraction of the population utilizing the energy efficient technology. In the scenario of a positive technological externality, when consumers install energy efficient devices at a high saturation and aggregate demand decreases such that reliability improves, households are able to consume more hours of electricity services. This effect is experienced by all households within a transformer, regardless of the individual household’s own adoption. The returns from CFL adoption increase because the potential for electricity consumption increases and therefore the potential for energy savings through CFL use does as well.<sup>26</sup> This field experiment employs a randomized saturation design to address these potential impacts of energy efficiency.

# **3 Randomized experiment with energy efficiency**

## **3.1 Sampling process**

In order to identify transformers and households within the transformers, we used data from electricity utility records on over 40,000 residential customers in a district adjacent to

> 26If electricity is limited, the benefits from CFL use are positive but small–zero if there is no electricity. As electricity becomes more available the benefits increase, possibly in a non-linear way, at
7 per country = 6; mean = 7.5; min = 1; max = 44). We then include surveys from the 1990s to make sure we increase the number of countries in our sample. In particular, data is available for 176 survey/census samples in 63 countries in 1990-1999, 630 samples in 129 countries in 2000-2009 and 278 samples in 107 countries in 2010-2016. Note that several survey samples may be available for a same country-year.<sup>8</sup> Finally, the data is highly representative of the world economy. The 145 countries comprise about 95% of the world’s population today. The data is representative for both developed countries (76% of their population) and developing countries (98%).

**Main Variables.** For the 24,437,020 individuals, we know their monthly wage, age, and number of years of education. For a substantial subsample, we know the hourly wage and the number of hours worked. Lastly, we know their employment status (e.g., self-employed or paid employee) their sector (ISIC-1: agriculture, industry, services), their occupation (ISCO-08: managers, professionals, technicians, clerks, crafts, sales, machinists, elementary occupations, skilled agriculture, other), and whether they work in the formal sector or live in urban areas. Finally, note that I2D2 mostly calculated the wage of self-employed individuals as the amount of salary taken from the business.

**Data Limitations.** While I2D2 is particularly useful in terms of external validity, the fact that all these surveys/censuses had to be harmonized implies that few variables are available (about 70). Also, the I2D2 team does not provide details on how the harmonized variables were created for each survey, and thus the level of consistency across surveys. However, given the wide use of the data across flagship World Bank reports, we feel compelled to trust that the I2D2 team did a
# **Transactional Governance Structures: New Cross-Country Data and an Application to the Effect of Uncertainty**<sup>*****</sup>

David C. Francis<sup>†</sup> Nona Karalashvili<sup>‡</sup> Peter Murrell<sup>††</sup>

**Keywords** : governance, transactions, law, bilateralism, transaction-costs, uncertainty

JEL Classifications: L14, C81, D23, P5, M21

* This is a thoroughly revised version, with much new content added, of the paper "Mapping the Landscape of Transactions: The Governance of Business Relations in Latin America". For helpful comments and suggestions, we would like to thank Mohammad Amin, Daniela Behr, Adrian Gonzalez, Peter Grajzl, Gillian Hadfield, Mary Hallward-Driemeier, Kathryn Hendley, Asif Islam, Ethan Kaplan, Aart Kraay, Stephanie Lanza, Norman Loayza, Jorge Luis Rodriguez Meza, Károly Mike, Helena Schweiger, and Jeroen Vermunt. Financial support from the Knowledge for Change Program (KCP) of the World Bank is gratefully acknowledged. The views expressed here are those of the authors, and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.

† The World Bank, dfrancis@worldbank.org ‡ The World Bank, nkaralashvili@worldbank.org †† University of Maryland, pmurrell@umd.edu
impacts by gender dimensions, the household and labor accounts in the SAM are disaggregated using the Harmonized Household Living Standards Survey 2018/2019 (ECOSIT 4),<sup>7</sup> which was conducted by the National Institute of Statistics, Economic and Demographic Studies (INSEED).

The updated 2017 Chad SAM includes 33 sectors, and the agricultural sectors (food crops, cash crops, livestock, fishing, and forestry) are further split by gender of farm/enterprise owners to represent the different cost structures of female and male-owned farms. Labor accounts for each of these sectors are disaggregated into eight categories by three dimensions: gender, salary and non-salaried payment, and farm and non-farm work. Households are disaggregated by rural and urban deciles.

The version of the model used for this study is based on Burns et al. (2020), which incorporates several developments, among which the most relevant for this study is the demographics module. The standard version of the MANAGE model tracks population in three age cohorts: 0-14, 15-64, and 65+. The new module tracks population at each age between 0 and 100 (people 100+ are assumed to be in age group 100). In every period of the model solution, people in each age move to the next age by taking into account the death rate at each age. Then, in every period, newborns join age 0 based on the birth rates for that period. Age,period-specific death rates, and the period-specific birth rates are calibrated to the UN population projections such that the model generates the same projections over time. The labor supply at time t is then the sum of all ages between age 15 and 64 multiplied by the labor force participation rate. The total labor force is then distributed across different labor types based on base

> 5 The SAM is available upon request.

> 6 The SUT, TOFE, TCEI and the household survey were sourced from INSEED. The latest available TCEI is for 2010.

> 7 In French the
<mark>market. In addition, del Rio-</mark> Chanona et al. (2020) estimate supply and demand shocks in the U.S. in the earlier stage of the pandemic using pre- and postCOVID datasets. In Australia, the impact on unemployment and reduced working hours was in the low single digits. However, workers with up to high-school education experienced larger reductions in their labor force participation and working hours than others. Moreover, immigrants and individuals with shorter job tenure or occupations unsuitable for remote work were hit the hardest in terms of unemployment (Guvet et al., 2020). Garrote Sanchez et al. (2020) use information on essential sectors in Italy and the U.S. and the recent 2018 European Labour Force Survey (EU LFS) to identify workers in non-essential industries. They find that jobs most at risk account for 30 percent of all EU employment, and a larger share of jobs in economically disadvantaged regions tend to be vulnerable ones.

After lockdown orders had eased, some studies consider the potential shocks to labor supply arising from workers’ reluctance to work due to infection risk on the job. For example, Aum et al. (2020) show that fear of infection contributed to reductions in employment in the Republic of Korea, where the government used intensive testing and contact tracing rather than enforcement of lockdown measures. They find that workers in high-contact industries can be affected the hardest and experienced the greatest reductions in local employment.

Empirical studies on developing countries were much fewer given the data limitations. Some authors focused on anticipating both the magnitude of impacts and how they would likely be distributed by taking lessons from the developed world. Some posited that certain job characteristics raised the risk of job loss. For example, Zheng et al. (2020) and Adams-Prassl et al. (2020) estimated the degree to which people can work from home during the pandemic to mitigate workplace closures or capacity limits. Other studies relied on occupation-level data from O*NET
)|(10.841)|(10.831)|(11.098)|(11.036)|
|Urbanization (%)|1.630***|1.633***|1.611***|1.616***|1.378***|1.418***|
||(0.406)|(0.411)|(0.407)|(0.409)|(0.456)|(0.457)|
|WBL Index|15.209*|14.790*|15.746*|15.280*|28.689***|28.240***|
||(8.451)|(8.393)|(8.524)|(8.382)|(8.905)|(8.822)|
|Women can work the same|2.882|2.999|2.756|2.816|1.375|1.508|
|night hours as men Y:1 N:0|||||||
||(2.879)|(2.861)|(2.818)|(2.785)|(3.206)|(3.190)|
|Women can work in the same|-1.485|-0.858|-1.410|-0.815|-2.446|-1.892|
|industries as men Y:1 N:0|||||||
||(2.114)|(2.094)|(2.087)|(2.066)|(2.184)|(2.189)|
|Ratio of Female to Male|2.474***|2.
If a country’s distortion is larger than that of the frontier country, then _τ >_ 0, since by design it produces less. Figure 7 summarizes the estimated distortions by industry in Peru, Colombia, Chile, Costa Rica, Mexico and Brazil. Except for Mexico, all the countries have significant distortions in ICT relative to Korea - in the case of Costa Rica up to 40%.

Two aspects should be noted from these estimates: (1) Output distortions have been rising over time - our hypothesis is that they limit entry, make markets more concentrated and have slowed productivity growth. (2) Input distortions in the ICT sector are high relative to frontier economies, but remained constant over the sample period.

These higher distortions are correlated with low spending by ICT firms on research and development (R&D) relative to middle-and high-income countries (Figure 8) and with low patent registrations (Figure 9).

# **3 ICT Misallocation and Productivity**

This section presents a framework to examine the role of ICT production and competition in aggregate productivity. It builds on Edmond et al. (2015) who analyzed the impact of competitive gains from international trade with varying markups and that where there is extensive misallocation, greater competition reduces markup distortions by creating competition and hence reduces productivity losses. In an extension, Edmond et al. (2018) decompose markup costs into three distinct channels: (1) Aggregate markups act as a tax on output. Competition within a market is low when markup dispersion is higher among firms in that market. (2) The result is a misallocation of factors of production. This is often associated with reallocation of production from firms with high labor shares to ones with low shares and (3) it leads to inefficient entry.

An interesting result of the Edmond et al. (2018) framework calibrated using U.S. census data is that while competition reduces the markup of incumbents, it also reallocates market share toward the largest incumbent firms, leaving
tween annual temperature and precipitation (see Model III in Table 2). Figure 3 displays the resulting distribution of temperature effects on land values, based on a 1<sup>_◦_</sup> C increase, evaluated at the sample proportion of plots irrigated in each district. The wide range of impacts mirrors, of course, the huge spatial variation in rainfall. At the left tail of precipitation distribution are districts in the arid northwest of India (in the states of Rajastan, Haryana, and Punjab), with temperature-productivity effects exceeding _−_ 18 percent in magnitude. At the right tail, a mere seven very wet districts are predicted to slightly benefit from higher temperatures, although extrapolations this far out along the rainfall distribution should probably be taken with a pinch of salt.

# **4.3 Agricultural output and weather shocks**

Panel data covering the production of the 20 most important crops in terms of cultivated area are available for nearly 500 districts from 1999 to 2005 (though not always continuously).<sup>21</sup> We aggregate crop-wise output using state-level prices averaged over the period 2001-03 and divide by area cultivated with these crops to get gross revenue per hectare. Annual cultivation cost data are not available at the district level, so we cannot calculate net revenue, the empirical counterpart of _π_ . However, since the sensitivity of gross revenue to bad weather shocks, in general, _overestimates_ the corresponding weather sensitivity of net revenue,<sup>22</sup> our results should still provide an upper bound on long-run damages. Table 3 reports descriptive statistics for the panel data.

How much intertemporal weather variation is available to identify productivity impacts of climate change? Consider the residuals from regressions of annual average temperature and total annual rainfall on district dummies and either (1) a linear year trend or (2) year dummies. Figure 4 plots temperature against precipitation residuals for both specifications of year effects and shows a strong negative correlation; wetter than average years
between baseline and endline surveys was 7.7 months, and 86% of households had their follow-up interview between seven and nine months after the baseline interview. During the endline survey, surveyors were instructed to return to the wells identified during the test sales and record if the household owing the well was still using it as a primary source of water for cooking and drinking. In case of a negative answer, the surveyor asked the respondent to accompany him/her to the actual source and would record the new GPS location and the presence of any visible indicators of arsenic status (for instance one of the metal placards distributed during our intervention). The surveyor would also ask the respondent the perceived safety of the new source as well as about the primary reason for switching to a different source. Switching behavior was thus self-reported, but earlier work in the neighboring Araihazar sub-district found that switching behavior recorded in a way similar to this study was actually consistent with urinary arsenic concentrations, an objective biomarker of exposure (Chen et al. 2007). Unfortunately, the records on the location of the new source of drinking water do not allow to measure precisely to what extent switching was associated to a reduction in arsenic risk. The smartphone’s GPS sensor—with a precision of 10 m at best—cannot uniquely identify a specific well among those surveyed due to the density of wells within a typical village in Bangladesh. The GPS data was still used to estimate the distance from the old to the new well used for drinking.

# **3.1 Summary Statistics at Baseline**

Table 1 shows selected summary statistics measured at baseline. Throughout this paper, unless otherwise noted, the analysis is restricted to the large majority of households (91%) that used their own well at baseline, as this is the sample for which baseline water source and post-intervention switching can be determined. The baseline source was not recorded for non-users, and during the endline survey they were only asked again if they used their own well
)) = f�β0 + β1Xmit + β2Zmi(t−1) + β3Tmi(t−1) � (1) mm mm mm mm cc logit (Pr(YY = 1|XX , ZZ , TT + δδ

The statistical analysis proceeds in several steps. We first start with educational aspirations (m=1) and estimate the probability of university or post-degree aspirations (Y1i) by country, and within countries in a set of logit regressions for exploring its correlation with (1) youth’s characteristics (X1it), i.e., sex, age group, highest level of education, age at which they started working, type of dream job; (2) household characteristics Z1i(t−1), i.e., age of the oldest child of the household, highest level of education and current occupation of the adult head of the household, and income quintile; and finally (3) community characteristics T1i(t−1), i.e., place of residence (urban and rural) and role models in the community. We observe the independent associations of each set of personal, family and community variables with educational aspirations. are fixed effects at the country level. cc δδ We extend this step analysis to explore career and migration aspirations. Regarding career aspirations (m=2), we estimate the probability of reporting a STEM-related occupation as dream job<sup>6</sup> (Y2i) by country, and within countries in a set of logit regressions using individual-level weights for exploring its correlations with (1) youth’s characteristics X2i(t−1), i.e., sex, age group, educational level, age at which started working, interest in the job and youth likelihood to reach the dream job (self-efficacy); (2) household characteristics Z2i(t−1), i.e., household size, adult head of the household engaged in STEM-related jobs, and