rafmacalaba/data-use-mentions-extended huggingface dataset:
). Doubling graduation rates: Three-year effects of CUNY’s Accelerated Study in Associate Programs (ASAP) for developmental education students. New York: MDRC.

- [59] Solis, A. (2017). Credit access and college enrollment. _Journal of Political Economy, 125(2),_ pp. 562-622.

- [60] Sommo, C., Cullinan, D., Manno, M., Blake, S., & Alonzo, E. (2018). Doubling graduation rates in a new state: Two-year findings from the ASAP Ohio demonstration. New York: MDRC, Policy Brief.

- [61] Stange, K.M. (2012). An empirical investigation of the option value of college enrollment. _American Economic Journal: Applied Economics 4 (1),_ pp. 49-84

- [62] Stinebrickner, R. & T. R. Stinebrickner (2004). Time-Use and college outcomes. _Journal of Econometrics, 121(1-2),_ pp 243-269.

- [63] Stinebrickner, R. & T. R. Stinebrickner (2014). Academic performance and college dropout: Using longitudinal expectations data to estimate a learning model. _Journal of Labor Economics, 32(3),_ pp. 601 - 644.

- [64] Trachter, N. (2015). Stepping stone and option value in a model of post-secondary education. _Quantitative Economics, 6(1),_ pp. 223-256.

- [65] World Bank (2016). Poverty and shared prosperity 2016: Taking on inequality. World Bank Publications.

- [66] Zamarro, G., C. Hitt, & I. Mendez (2019). When students don’t care: Reexamining international differences in achievement and student Effort. _Journal of Human Capital, 13(2),_ pp. 519-552.

50
where we pseudo-randomly generate the value of parameters within a range based on historical data. For instance, we look at the value and change in the share of employment in agriculture in the past 20 years to determine a range of possible change in agriculture employment in a country in 2030 (see Rozenberg and Hallegatte (2015) for further details).

We generate 500 scenarios per country using the Latin Hypercube Sampling method (Kwakkel, 2017) where we fluctuate independently values for the various parameters characterizing each scenario. Combining this with the two SSP scenarios, we generate 1,000 baseline scenarios of future socioeconomic and demographic change for each country. Latin Hypercube Sampling ensures that each parameter is represented evenly across the entire scenarios, thus resulting in a wide and extensive coverage of the uncertainty space (McKay, Beckman, & Conover, 1979; Kasprzyk, Nataraj, Reed, & Lempert, 2013). Each 2
training investments, inadequacies in management style, or other productivity-enhancing investments that were not undertaken, or that firm managers may not even have been aware of. Another concern regarding subjective responses is that firms' perceptions may reflect differences in firm performance -- successful firms may view their business environment as less limiting.

Given these and related shortcomings of subjective indicators, this paper aims to exploit _objective_ business environment indicators to examine and quantify the intensity with which different business environment variables are correlated with productivity and innovation outcomes, rather than rely on subjective perceptions reported by managers. Objective business environment indicators reflect the actual incidence of various factors which potentially constrain business operations. These include metrics such as the share of electricity used that came from generator(s) that the establishment owns or shares, the losses from crime as a share of sales, the share of investment financed externally, the share of sales financed on credit, and internet access.

# **Hypotheses**

Our core hypothesis is that business environment factors are important determinants of variation in productivity and innovation across firms. Productivity and innovation are likely also to be explained by additional firm-specific factors such as managerial education and practices, workforce skills, and local spillovers. We test hypotheses related to each of the following major areas of the business environment in the context of productivity and innovation<sup>5</sup> : (1) incentives for productive entrepreneurship, including the extent of competition faced by the firm, enterprise regulation and corruption, and crime and security; (2) access to skills; (3) connectivity to global knowledge through trade and other channels; (4) access to finance; and (5) access to quality physical infrastructure. This section reviews findings from the empirical literature relevant to these five major areas to motivate the main hypotheses regarding the association between business environment indicators and productivity and innovation in Sri Lanka.

**_<mark>(1) Incentives for productive entrepreneurship.</mark>_** <mark>Whether
in climate-related industries,<sup>8</sup> they do not tell us about the nature of green jobs today, wages earned, or who works in those jobs.

A recent international literature uses a more systematic approach to identify and characterize green jobs. These studies use an occupational task approach, where they label an occupation – and the jobs within – as green based on how green the tasks within an occupation are, rather than the industry that houses a job (Granata and Posadas 2024; Vona et al. 2019; Consoli et al. 2016; Vona et al. 2018). The occupational tasks approach has been widely applied in labor economics to analyze how technological change has affected skills, wages and job polarization over time (Autor et al. 2003; Acemoglu and Autor 2011; Goos et al. 2014; Cunningham et al. 2022).<sup>9</sup> We can use the same logic to understand how a shift to a green economy may affect jobs. Using the occupational task approach allows for greenness of an occupation to be defined according to the number of tasks relating to environmental sustainability that are typically carried out in that occupation (Vona et al. 2019). The advantage of the occupational task approach is that one can identify green jobs in any industry, even those not directly engaged in environmentally sensitive industries.

This paper aims to fill the gap in the South African literature by using South African household survey data and applying an occupational task-based approach to characterize green jobs and workers in South Africa. Inspired by Granata and Posadas (2024), we define two sets of green jobs: strict and broad. Recognizing that occupations are groupings of jobs with similar tasks, we first identify “strictly green jobs,” as those in occupations that require at least one task that is directly linked to the environment. We then broaden our definition to include occupations with job tasks that could reduce human-induced environmental risks, if they used green technology or processes<sup>
The results presented here suggest a number of implications for the current IDP crisis in Mali. It appears overall that the UN, NGOs, and the Malian government working with IDPs in Mali have succeeded in providing resources to IDPs and IDP hosting villages in a way that modestly enhances average village level consumption as well as not exacerbating inequality or poverty within the hosting communes. The results generally do not suggest that IDPs would have a detrimental effect on social cohesion due to the economic effects from their presence. But one should be cautious since we are observing the effects of IDP presence when there is a large humanitarian operation to help them, and acknowledge that results might differ where humanitarian operations are of a smaller scale or unable to reach IDP populations due to the conflict.

There are some limitations that are worth highlighting before we turn to policy and program implications. First, our measure of IDP presence comes from the UN and NGOs that work with IDPs and may miss IDPs who are not known of or registered with the UN or NGOs in the area. Our EMOP data do suggest that there are small numbers of people who might fit this category, but not of a scale likely to significantly bias our results. Nonetheless, the effects reported here should be considered effects of officially registered IDPs. Second, this work only measures the effects of IDP presence on non-IDP households, such that our measures of poverty inequality are only within the host community estimate and do not measure inequality between host community and IDP wealth. It is along this dimension that some of the potential effects of inequality on social cohesion might be most felt. Third, the results presented here are specific to the Malian cultural context, a country that has a long-standing cultural history of accepting and housing outsiders. Called _“Diatiguiya”_ in the local language, Bambara, which translates as the keeper of hospitality, this feature of Malian society means that all members of a community are expected to provide hospitality to strangers. Other places without a tradition of _Diatiguiya_ may not have
employment opportunities for them, outside the garment sector. Column 1 of table 3 suggests that the same is true for low-skilled men; men with below median education increase their wages by 5.4% more than those with above-median education, and their working conditions also particularly increase (column 5). We also find that wages of men who are older, married, and have a child particularly increase. It is either plausible that they also have more inelastic labor supply if their demographics make it harder to move to find other jobs<sup>15</sup> , or that they have an stronger inward shift in labor supply (which also leads to particular increases in wages if workers are not perfect substitutes) if safety concerns are particularly salient given that they have families to support.

# **7 Conclusion**

The Rana Plaza collapse of April 2013 killed over 1000 Bangladesh garment workers and injured many more. In response, international scrutiny on the garment sector increased, leading to a push by retailers to improve working conditions, an increased hesitancy for export factories to subcontract to smaller factories, and a minimum wage increase. This paper studies the net effects of these post-Rana Plaza responses on labor market outcomes of garment workers, using six rounds of the Bangladeshi Labor Force Survey and a triple difference identification strategy that compares garment workers to non-garment workers, in garment-exporting districts versus non-exporting districts, pre versus post Rana Plaza.

We find that increased international scrutiny achieved its goal of improving working conditions, while also leading to average wage increases for garment workers in treated factories. We did not find average impacts on hours of work or the receipt of written contracts. These results suggest that garment manufacturers had scope to increase total compensation, compared to a model of compensating differentials in a perfectly competitive labor market in which improvements to working conditions come at the cost of reduced wages. While a commonly voiced concern is that, in a highly competitive global garment sector market, pushes to improve compensation will lead to firms to leave one
Policy Research Working Paper 7627

# **Abstract**

Unanticipated spikes in food prices can increase malnutrition among the poor, with lasting consequences; however, livelihood strategies that include producing food for home consumption are expected to offer a measure of protection. Using anthropometric and consumption data from Indonesia collected before and after the 2007/08 food price crisis, this paper finds evidence of both effects. Based on standardized height and weight measures, the results indicate that soaring food prices had a significant and negative impact on child growth among non-farming households. A corresponding effect was undetectable for food-producing households. The results remain robust when income effects from increased commercial sales and possible attritions through migration and fostering are considered. Further, local food price changes were uncorrelated with the share of non-farming village households and the initial average child nutrition status in the village, suggesting that the observed outcomes are directly attributable to market events and livelihood strategies. Interestingly, gender differences were not detected. The findings imply that the food price crises can have negative impacts on children, potentially leading to lifelong income inequality among those affected at a vulnerable stage of life.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fyamauchi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and
# **5.2 Offshore firm-level responses**

A possible explanation for the tax reform’s significant negative effects on entry along with the zero effects on economic activity is that the intensive margin accounts for the bulk of the impact on economic activity. To test for this explanation, we move to the analysis at the firm level. As previously discussed, we focus on the balanced panel of incumbent firms that were active throughout the 2009 - 2018 period. This panel comprises the bulk of aggregate economic activity, lending credibility to the hypothesis that this is the key margin of impact. It accounts for between 76% and 97% of overall annual onshore revenues during the period and between 58% and 89% for offshores. Similarly for the overall wage bill, incumbent firms account for 80%-96% for onshores and 66%-84% for offshores.

Our first set of results relates to employment in firms in the offshore sector, measured using social security data. In Figure 6, we present results from estimating equation (3) for a set of outcomes of interest related to employment. In panel (a) we use the log number of employees as the outcome of interest. Since, as documented in section 3, a large share of firms have very few employees, we also present results using other measures of employment level categories - panel (b) uses a dummy for 2 or more employees; panel (c) for 5 or more; and panel (d) for 10 or more.

Throughout our results, we first note that we observe a strong convergence between firms in the offshore and onshore sectors in 2009 - 2010. We attribute that convergence to – the rebound of the Great Financial Crisis (GFC) offshore firms in Tunisia are heavily exposed to the European market and were strongly affected by the economic impacts of the GFC in 2007-2008. So our coefficients for 2009 and 2010 are likely documenting the recovery of those firms in relative terms to the onshore sector. In 2011 and
contrast to the ESI, we incorporate LAYS in the ASI.

In developing the ASI we followed a few guiding principles. First, we chose data series all of which were derived from a specific source such as WDI, ED Stats or Penn World Tables. The intent was to preserve data comparability across countries as much as possible. Second, comparable series for which data for all countries were available were few. Therefore, we ignored some series we deemed useful in preference to others with fewer missing observations. Third, the data used in constructing the index represent average values of the available data over the period from 2016 to 2020. In some cases, a single observation over this period served as representative of the average. Fourth, while there are many methods to handle missing values, we replaced missing values with mean values so that the overall behavior of the index reflected the average values of the available data.

The ASI, like the ESI, is based on 3 broad pillars representing different aspects of a skills system. However, the ASI allows for modifications based on the structural and institutional features of labor markets in developing countries. The 3 pillars are: i) **SKILLS DEVELOPMENT** : This pillar represents the quality of education and training available in a country and educational attainment of the population. In addition to variables such as pupilto-teacher ratios and population with at least a secondary education (which are part of the ESI), the ASI includes other variables such as the share of population with advanced education and the recently developed LAYS which provides a composite metric of learning adjusted years of schooling.

13
. The drought threatened the livelihoods of many Somalis. Lack of water and pasture led to high livestock deaths and low birth rates, and induced distress selling caused the 26 percent of Somalis relying on livestock for their livelihoods to lose between 25 and 75 percent of their herds in the first half of 2017 (FEWSNET, 2018). Households depleted productive assets and food stocks to cope with the rising food and water prices, while weak demand for labor in the agricultural sector led to lower wage levels (FEWSNET, 2017a). As a result, the drought displaced close to one million people between 2016 and 2017. Large‐scale humanitarian interventions provided critical relief to up to 3 million people to reduce the risk of famine (FEWSNET, 2017b).

Using data from two waves of the Somali High Frequency Survey (SHFS), this analysis employs a regression framework to measure the micro‐level impact of the 2016/17 drought on poverty. It exploits spatial variation in the intensity of drought that different households experienced and compares consumption before and after the drought. Households’ level of drought exposure is measured by using the Normalized Difference Vegetation Index (NDVI). The temporal difference is provided by the timing of the first two waves of the SHFS. The first wave took place before the onset of the drought in early 2016, while the second wave surveyed households in late 2017, when the drought had surpassed its peak.

The remainder of this paper is structured as follows. Section 2 describes the data used to measure the impact of drought on poverty. Section 3 outlines the identification and estimation strategy. Section 4 presents the results, and section **Error! Reference source not found.** tests the robustness of these results. Section 6 concludes with a discussion of the results and discusses policy recommendations.

# 2. Data

This analysis uses cross‐sectional household‐level data from two waves of the SHFS. Wave 1 interviewed 4,117 urban, rural, and IDP households in February and March of
Latin America. The surveys were conducted between 2009 and 2014 in the various countries and include manufacturing and services sector firms. Table A1 provides the distribution of the sample by country along with the survey year. A somewhat similar survey instrument and methodology was used across countries.<sup>5</sup> Given the cross-sectional nature of the data, each firm and country are surveyed once at a particular point in time. Thus, it cannot be guaranteed that responses from firms would be similar at other points in time.<sup>6</sup> It is important to note that there is much debate about the definition of informality. The IES data we use defines informal firms as those that are not registered with the relevant authorities. This is consistent with many other studies in the related literature (Amin and Okou, 2020; Loayza and Roginlini 2011; Gindling and Terrell 2005; Rand and Torm 2012; Mead and Morrisson 1996). Another important issue relates to the representativeness of the sample. A nationally representative sample of informal firms would require a complete list of all informal firms in the country. However, such a list does not exist for most countries at the national or even the regional and city level. Hence, most studies using data on informal firms tend to rely on random sample of informal firms taken from one or two main urban centers of the country (see for example, Harris 2014, Rand and Torm 2012). These random samples are not necessarily representative of the underlying informal economy, although it is not clear if the sample bias systematically affects the results. Likewise, the sample we use is also random, restricted to 55 regions/cities across 14 countries, and not necessarily representative of the informal economy in the city or the country. This point notwithstanding, the informal surveys we use have been used extensively in the literature to study the sector (see Amin and Islam 2015, Islam 2019, La Porta and Shleifer 2014).

> 5 Details of the methodology can
randomly generated replacement list from the original frame. Forty percent of the original sample was replaced.<sup>12</sup> The study sample was randomly divided into a control group (176 students) and three treatment groups (consisting of 107 students each). Treatment group 1 (T1) received a digital library of freely available (non-curriculum) fiction/non-fiction reading material. Treatment group 2 (T2) received the digital library _and_ core curriculum textbooks in Mathematics and English. Treatment group 3 (T3) received the digital library, the core curriculum textbooks _and_ supplementary instructional material. Supplementary materials included open source materials curated by curriculum experts to provide both remedial and current instructional content. To benchmark the impact of eReaders on school participation against a non-technology, a random subsample of 70 students from the treatment groups– identified via a cross design orthogonal to the treatment and stratified by treatment group – were read a short script by a trained enumerator on the expected returns to education in Lagos.<sup>13</sup> The experimental design is presented in Figure 1.

A baseline school survey covering 214 schools in Lagos was conducted in November 2010 before the intervention began with a series of workshops in August 2011. During these workshops, students completed a self-administered, 45-minute survey and test to capture data on student characteristics and reading comprehension and ability. The test included publicly released test items used in the Grade 5 English–Language Arts Standards Test (one of the California Standards Tests administered as part of the Standardized Testing and Reporting (STAR) Program).

After the survey and test, students in the treatment groups were provided eReaders and given a short orientation on the device. For control students, a placebo session was organized wherein students participated in a short talk about the potential of technology use in education. Students in t <mark>he cross-designed information treatment group received the returns to schooling information at the close of the workshop.</mark> <mark>Endline student testing was conducted during workshops