rafmacalaba/data-use-mentions-extended huggingface dataset:
of the pandemic and women's endowments. The study uses probabilistic models and standard decomposition methodologies to assess gender gaps and their drivers. It also offers a non-comprehensive view of national policies aimed at closing gender gaps in the labor market.

These are the five key conclusions drawn from the research. First, the economic recovery in 2021 did not close the pre-existing gender gaps, which were further exacerbated by the pandemic. This suggests that there are underlying structural factors that need to be addressed. Second, while the gender gap in labor income and hours worked narrowed during the pandemic, this was due to a sharp decline in the levels for men rather than significant progress for women. Third, the formal sector showed more recovery in labor demand for women than the informal sector, but women are still largely concentrated in the informal sector. Fourth, women with children and lower levels of education had worse indicators and wider gaps compared to men and women without children or with higher levels of education, and this situation worsened during the pandemic. Finally, although there are programs in Colombia aimed at reducing gender gaps, such as those promoting economic opportunities for women, there is a need to increase the focus on addressing barriers related to 1 Based on information by the World Bank from 80% of the economies in 2020.
Table 2: Percent of firms with FTM by sector

||Percent of frms<br>with FTMs (%)|Observations|
|---|---|---|
|Leather and footwear|54.01|188|
|Food processing|33.48|1,501|
|Health services|32.61|653|
|Financial services|31.59|452|
|Accommodation|26.17|605|
|Wearing apparel|20.97|934|
|Wholesale or retail|20.53|2,243|
|Agriculture - Crops|18.19|912|
|Pharmaceuticals|17.20|285|
|Other services|17.13|3,160|
|Other manufacturing|17.10|2,071|
|Land transportation|14.99|758|
|Livestock|13.35|385|
|Motor vehicles|2.40|288|
|Total|19.44|14,435|

Note: This table shows the percents of firms with FTMs across industries.Shares are weighted by sampling weights. Number of observations are unweighted. Data from the FAT surveys in 11 countries including Brazil, Burkina Faso, Chile, Ethiopia, Georgia, Ghana, Kenya, Korea, Rep., Poland, Senegal, and Viet Nam. FTM = Female Top Manager.

# **2.3 Productivity and other covariates**

To measure firm performance, we primarily focus on labor productivity, measured as the log of value added per worker. We take the log of the ratio of the difference between sales and the cost of intermediate goods and the number of employees. We convert values to US dollars using the Purchasing Power Parity (PPP) conversion from the World Bank.<sup>6</sup> In addition to labor productivity, the survey provides information on several firm and manager characteristics, including exporting and multinational status, management practices, firms’ sector and
in the 1960s, but by 1992 their combined imports had dropped to less than 1% of total world imports.

Table 4: **Principal importers of refined sugar, 1990-93**

|Amount|Countries|
|---|---|
|More than 2.5 mln MT|USSR / FSU.|
|0.4-0.8 mln MT<br>0.2-0.39 mln MT|Algeria, Egypt, Iran, Mexico, Nigeria, Saudi Arabia<br>Indonesia, Iraq, Israel, Jordan, Pakistan, Persian Gulf, Sri<br>Lanka, Yemen|
|0.1 -0.19 mln MT|Canada, Chile, EU, Ghana, Hong Kong, Korea, Libya,<br>Norway, Peru, Romania, Switzerland, Turkey,|

Source: See Data S7 for details Data S5 shows India's exports and imports of raw and refined sugar for the period 196493. India has been a net exporter of raw sugar since 1964, except for the years 1983-87. Exports have always been quite small, both in relation to Indian production and world trade . India has also consistently exported some of its plantation white sugar in every year since 1964. In most years these quantities have also been small. The share of Indian exports of raw and refined sugar as a percent of world exports are presented in Fig 3.

Figure 3: **Share of Indian exports of raw and refined sugar as a percent of world exports** Source: See Data S5 for details Data S5 also shows India's sugar imports. Except for a few years, there have been no imports of raw sugar. There have been sporadic small imports of refined sugar mostly in very 10
# 2. Measuring AI exposure and labor market impacts

<mark>Artificial intelligence (AI) may transform labor markets and the nature of work in a manner analogous to the Industrial Revolution (Cazzaniga et al. 2024). With its capacity to automate tasks, optimize processes, and augment human capabilities, AI is poised to reshape job roles, the skills required, and the composition of the economy. In contrast to previous episodes of technological change, this process is not characterized only by the automation of routine, repetitive tasks. Generative AI models can learn from data and create new things on their own, affecting diverse sectors of the economy. As a result, AI could fundamentally alter the nature of work. To g</mark> auge <mark>the potential e</mark> ffects <mark>of AI, the literature has used a “task model” approach following Autor et al. (2003).</mark> These <mark>models consider each</mark> occupation as a set of tasks. Linking this to AI, tasks are classified by their level of “exposure” to AI, and then these measures are aggregated into a measure of exposure at the occupation level. Definitions of exposure and its interpretation vary across studies.

It is important to note that exposure does not necessarily mean that a task or job will be replaced by AI. It could also mean that AI will augment productivity of the task or result in a reconfiguration of the composition of occupations or jobs.

The following section describes approaches to measure the exposure of occupations to AI before reviewing some of the current empirical literature on AI.

## 2.1. Measuring AI exposure

A task model approach has been employed by numerous research efforts to assess the impact of digital technologies and AI on occupations, utilizing the US-based O*NET database (e.g., Autor and Handel 2013). The O*NET database has most often been used for analysis of the labor market of
nels. First, they may distort entry and technology adoption decisions and thus reduce the productivity of individual rms. Second, nancial frictions may generate dierences in the returns to capital across individual rms, thus generating eciency losses through misallocation (Banerjee and Duo (2005); Restuccia and Rogerson (2008); Hsieh and Klenow (2009)).

One potential source of nancial frictions are policy-induced interventions. During several decades the Brazilian government intervened heavily in credit markets. As will be discussed later, several credit reforms contributed to a substantial increase in private credit in the 2000s. However, after the 2008-2009 nancial crisis there was a signicant increase in the expansion of subsidized credit provided by public banks. The main source of long-term nancing to rms in Brazil is the Brazilian Development Bank (Banco Nacional de Desenvolvimento Econômico e Social, BNDES), a government-owned development bank.

Several papers do not nd positive eects of BNDES nancing on productivity, especially for large rms.<sup>15</sup> Even when there is evidence of a positive impact on productivity, they do not seem to be strong enough to be cost-eective when the sizable subsidy costs are considered. We will next briey discuss a few papers that illustrate the main issues.

Bonomo et al. (2015) use a large repository of loan contracts between banks and rms to investigate the eect of the expansion of BNDES credit expansion between 2004 and 2012. Their results show that larger, older and less risky rms beneted most from the government sponsored credit expansion. Additionally, the eect on investment appears to be insignicant for publicly traded rms. These results are consistent with the hypothesis that those established public rms in part substituted more expensive credit with subsidized loans.

Calice et al. (2018) study the impact of BNDES credit on resource misallocation in Brazil, using manufacturing rm-level data from 2003 until 2014. The paper rst estimates measures of resource misallocation based on Hsieh and Klenow (
5951|3.70|8.35|30.33|
|#16|October 2018|1112|6.47|4.68|–|
|#17|December 2018|5918|5.86|11.20|–|
|#19|July-August 2019|5961|5.12|10.45|–|
|#20|December 2019|5752|4.71|8.14|–|
|#21|November 2020|2627|4.19|5.14|–|
|#22|February-March 2021|5847|6.86|9.30|–|
|**Overall**|**July 2017-March 2021**|**49831**|**4.64**|**8.19**|**30.58**|

Table 2: Details on Surveys and Displacement Trends Second, the paper conducts an individual-level analysis of two cross-sectional surveys of 1,933 and 5,951 individuals conducted in March-April 2018 and June - July 2018, respectively, to probe the relationship between hosting displacees and social cohesion in more detail. This survey wave 22
insurance, crop yield insurance and commodities futures and options tend to be complementary in efficient risk financing strategies.

# **7.  Conclusions**

A comprehensive review of the data that are available and credible is a fundamental step in the design and rating of any insurance scheme. Individual coverage is typically preferred by farmers. However, the data needed to derive accurate insurance rates for individual farmers are almost never available. In the event that consistent data are lacking at the individual level, two options are available. One may wish to use aggregate data in an attempt to measure individual contract parameters. An alternative approach is to consider coverage based upon observable indices that reflect as close as possible the loss incurred by the farmer. Examples include area-yield insurance and weather-based insurance.

The availability and credibility of data are also a central issue in production risk modeling. In cases where a significant amount of reliable data is available, one may choose to adopt nonparametric approaches to modeling yield risks. More common, however, is the case where data are limited. In such cases, parametric methods may be preferable. The choice essentially involves a tradeoff between bias and efficiency.

Ratemaking procedures are also based on data availability. When historical losses are available, rating procedures are based on the historical loss cost ratios. In contrast, the rating of a new insurance scheme in the absence of historical losses usually relies on simulated losses derived from crop yield models.

This paper has laid out recommendations on the design and rating of viable agricultural insurance policies. They rely on recent statistical and actuarial developments as well as empirical work based on the U.S. experience. They provide Bank staff and policymakers with technical guiding principles for the development of viable agricultural insurance programs in developing countries.

Finally, prior to any considerations regarding the contract design, one must investigate the demand for the insurance plan, i.e., willingness and ability to pay on the part of those targeted by the insurance plan. This issue has not been addressed in this paper
# **3 Benchmarking Exporter Dynamics in Argentina**

This section describes and benchmarks aggregate export patterns in Argentina. We rely on estimates from cross-country regressions whose dependent variables are different indicators of export performance to benchmark exporters in Argentina against those in comparator countries. The regression controls for the size (GDP) and the level of development (GDP per capita) of each country and for time trends, which are critical to consider given that the sample period encompasses commodity price shocks that have affected many of the countries in the sample. The cross-country regressions are estimated on a panel of country-year exporter competitiveness indicators covering the 2000–17 period and encompassing all developing and developed countries included in the Exporter Dynamics Database. Each regression includes a dummy variable identifying the observations for Argentina, whose estimate will determine how Argentina performs relative to the benchmark countries. Table 1 displays the results from the cross-country regressions that provide the foundation for the analysis described in this section.

We begin by characterizing the export sector in Argentina in terms of the number of firms and their size. The results indicate that after controlling for country size and level of development, Argentina has significantly fewer exporters than comparator countries (column 1, table 1), as shown in the previous section. The average size of exporters is statistically higher (columns 2) than in similar countries, whereas the median is not statistically different, indicating a higher concentration of export value in Argentina than in other countries. This is confirmed when using the Herfindahl index of exporter shares (column 4).<sup>4</sup> Indeed, Argentina is one of the most concentrated countries in the region, with a Herfindahl index similar to that in Peru but higher than those in all other comparators. The same result is obtained when considering the share of exports accounted for by the top 1 percent or top 5 percent of exporters as the measure of concentration (columns 5–6).

Next, we examine export diversification. The average number of exported products

> 4The Herfindahl
Finally, the paper demonstrates how a utility can leverage its administrative billing database to recover estimates of household willingness to pay for power and use this information to set fixed prices. Based on the social marginal cost estimate and the historical consumption data from the administrative data set, the paper propose a two-part tariff which allows the utility to fully break-even on its costs. As variable costs are set to the social marginal cost for all households, the recommended tariff does not distort consumption by charging some households more than others, achieving the goal of economic efficiency. The recommended tariff design leads to progressive prices: it generates an average gain of Rs. 4.5 to Rs. 10 for households at the lowest quintile of the income distribution and an average loss of Rs. 20 per month for households in the top quintile.

The proposed schedule aims to stem losses from the residential sector and is likely to benefit both consumers and utilities. Other sources of technical losses, either related to transmission or distribution, are not studied in this paper.

The remainder of the paper proceeds as follows. Section II sets the context by describing the current challenges facing the Indian power sector and summarizes previous topically or methodologically related research. Section III describes the data sets used in our analysis and presents descriptive statistics to provide background for our analysis. Section IV describes the two-step econometric model of energy services demand. Section V describes the estimation procedure and results. Section VI estimates the social marginal cost for electricity in Rajasthan and describes counterfactual tariff experiments using own-price elasticity estimates from the demand model. Section VII concludes the paper.

# **SECTION II:  Residential Electricity Prices and Policies in India and Rajasthan**

Electricity for household consumption accounts for approximately a quarter of all electricity sales in India. As shown in Figure 1, the share of residential end-use of electricity was approximately 27 percent of all sales in 2014-15 and varied between 50 to 17 percent in the larger states. A sizable portion of these sales made by publicly-owned electricity distribution companies (“discoms
## **Figure 4 Average MSE of Poverty Gap at 6.85 USD 2017 PPP, for LAC Countries**

<!-- Start of picture text --> 3.00<br>2.00<br>1.00<br>0.00<br>2017 2018 2019 2020<br>Old Model New - Rescaling New - No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

# **Inequality**

This section presents results for the three model variations when estimating the most standard inequality measure – the Gini coefficient.<sup>22</sup> Results indicate that, like the case of the international upper poverty line, the prediction is similar for the three model variations in the short run, and the fit is generally reasonable (Figure 5). Still, errors increase with the distance between the baseline and the estimated year. In addition, results show that the assessed variations tend to constantly overestimate inequality for some countries (i.e., Bolivia, Costa Rica, El Salvador, Honduras, Mexico, Panama, Peru, and Paraguay) and underestimate it in others (like the Dominican Republic). Thus, the bias is not always in the same direction or magnitude. For instance, the _new model with rescaling_ has the lowest gap in Bolivia in 2017 but the largest from 2018 through 2020.

Figure 6 presents the MSEs for LAC countries by year and its trend to compare all the model variations over time. In this case, errors seem to increase in a linear pattern instead of the exponential-shaped trend observed in the poverty and vulnerability indicators. Overall, results suggest that the _old model_ is the best variation of the micro-simulation model to estimate inequality since its MSE is constantly lower than the other tested variations, except for atypical years such as 2020.

In summary, results suggest that no variation of the model should be considered a best-all-countries/allmeasures fit since
Table 4 also shows that matching services were made available almost exclusively to jobseekers in the treatment group, in line with the experimental design. More than 80% of jobseekers in treatment benefited from general matching. The corresponding number for the control group was 7.5 percent (column 4). The reason for this spillover was that during the design it was agreed that while matching services would be restricted to the treatment group, if any jobseekers from the control group became aware about and requested these services, they would be provided to them as well. Specific matching (which involved pre-selecting candidates and matching them with employers in response to specific needs of the firm) was provided to 23 percent of jobseekers from the treatment group and only a single jobseeker from the control group (column 4).

# **5.3. Main Treatment Effects and Heterogeneity**

The overall intention-to-treat effects of assignment to matching services on short- and medium-run employment outcomes are presented in Table 5. The first two columns include controls from administrative data, whereas the last two include additional control variables collected using the baseline survey (and therefore only include individuals who responded to the survey). Matching services increased the likelihood of formal employment by 5.8-8.8 percentage points (pp) in the short run. The effects are smaller over the medium run but remain positive and significant at 4.1-6.9pp. Given that 22.9 percent and 17.9 percent of jobseekers in the control group were employed in the short and medium run respectively, these effects translate to an approximately 25 percent increase in employment probability in the shortrun, and 22.6 percent over the medium-run.

Next, we examine heterogeneity of the treatment effects along five dimensions – gender, age, level of education, length of unemployment and _“employability”_ level (as determined by the job counselor). Table 6 shows that the treatment effects in the short and medium run are large, positive, and significant for men at around 12
<!-- Start of picture text --> Figure 1.8 Costa Rica: Per Capita GDP, Open Unemployment, Underemployment and<br>Percentage of Households Below the Poverty Line - 1982-2003<br>4500<br>33<br>4000<br>28<br>3500<br>23<br>3000<br>18<br>2500<br>13<br>2000<br>1500 8<br>1000 3<br>1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Per Capita GDP Open Unemployment (%)<br>Underemployment (%) Poor Households (%)<br>Export Promotion Import Liberalization<br>FTZ Law Reform<br>US Dollars Percentage<br>Capital Account Liberalization Intel’s Arrival<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

Costa Rica’s production behavior, in per capita terms, indicates that the new development model has provided greater robustness to the country’s economy, since it was not seriously affected by significant external events such as the Tequila crisis (1995), the Asian crisis (1999), the Argentine crisis (2001) and the most recent recession in the developed economies, notably the United States (2002).

Summarizing, Costa Rica has managed to attain a positive economic performance over the last two decades, as a result of changing the development model from one based on import substitution to another of economic liberalization. Indeed, and as cited by numerous scholars and international organizations, Costa Rica is presented as an example of successful reform, albeit incomplete, in which no crisis episodes have occurred vis-àvis the greater degree of integration into world market (Figure 1.9), while there have been significant improvements in the fight against poverty.

11
average of 1.5 beans reported by women who received a negative test result. Most of the women who received a positive test result assign at least some likelihood to not being HIV infected.

I obtain information about births from family rosters that were collected in 2006 and 2008. Respondents were asked to list all of the children ever born to them, but year of birth is missing for children who died more than two years before the interview. I therefore use only birth outcomes reported for the years 2004 to 2007. Depending on the year of marriage, year of birth, and the rounds in which the woman participated, I observe between 19
children of age 10-15 years. This result is in the ballpark of the actual 3 percent increase in enrolment for the same cohort of boys between 1999 and 2003 estimated from PNAD 2003, which is _not_ a result from an impact evaluation. The forecast from the BFL model is also close to the results from a quasi-experimental evaluation of the Bolsa Escola program by Cardoso and Souza (2004), in which a matching estimator suggests a 3.1 percent increase in the enrollment rate in boys and 3.0 percent increase for girls aged 10-15 years associated with the Bolsa Escola. De Janvry and Sadoulet (2006) also use _PROGRESA_ data to develop a predictive model. They analyze the potential for increase in school attendance in order to help increase the efficiency of such transfers, concentrating on secondary school attendance and the extent to which it can be affected by a conditional transfer.

Lise et al (2003) work with data from the Canadian Self-Sufficiency Project (SSP) and construct a dynamic, partial equilibrium model to simulate the effects of the program in terms of labor market behavior and compare these to observed treatment effects on individuals. The SSP is a social program providing time-limited earnings supplements to Income Assistance recipients who are able to obtain full time employment within a 12 month period. Recipients (i.e. the treatment group) of SSP were picked at random from among those eligible for these benefits. Lise et al restrict their sample to single mothers only, with around 2,300 women in the control and treatment groups alike.<sup>5</sup> Their _ex ante_ model is calibrated to control group data and the experiment is simulated within this model to imitate the “welfare-to-work transition of the treatment group.”<sup>6</sup> The results are then compared with those of an _ex post_ evaluation of the SSP, which produces similar results.

An earlier body of literature, including LaLonde (1986), Heckman and Hotz (1989) and others, use experimental results as benchmarks for