rafmacalaba/data-use-mentions-extended huggingface dataset:
the case of the EU this includes ACP and EBA eligible countries; in the case of the US better than GSP treatment is accorded under AGOA, ANDEAN and the Caribbean Basin Economic Recovery Act (CBERA). Thus, a country that trades under the GSP is actually suffering discrimination, as it is excluded from the better preference schemes available to (a subset of) its competitors. Any analysis that looks only at the GSP will therefore provide a rather distorted picture of the reality. The same is true if account is not taken of other trade policy instruments. In the case of textiles and clothing, the Multi-fiber Arrangement (MFA) for many decades established a

> 13 See Brown (1988) for a survey of the literature up to the mid 1980s.

12
|(0.0315)|(0.0289)|
|Under 35 years|-0.00586|-0.0175|-0.0260|-0.0247|-0.0308|-0.00270|
||(0.0221)|(0.0226)|(0.0240)|(0.0239)|(0.0241)|(0.0221)|
|Rural|-0.00892|0.00578|-0.00602|-0.0100|-0.00703|-0.00331|
||(0.0358)|(0.0368)|(0.0391)|(0.0388)|(0.0389)|(0.0357)|
|Complete Secondary School|0.00731|0.0182|0.0264|0.0278|0.0237|0.00906|
||(0.0230)|(0.0235)|(0.0249)|(0.0249)|(0.0250)|(0.0229)|
|Richest 20%|0.0171|-<br>0.0000312|-0.00815|-0.00658|-0.0139|0.0182|
||(0.0284)|(0.0290)|(0.0308)|(0.0307)|(0.0309)|(0.0284)|
|Constant|0.621***|0.626***|0.560***|0.610***|0.569***|0.656***|
||(0.0454)|(0.0466)|(0.0548)|(0.0507)|(0.0491)|(0.0510)|
|Observations|1843
diverting talent and resources away from production and innovation activities, rent-seeking activities can have sizable negative effects on aggregate efficiency.

For the quantitative analysis, I calibrate an undistorted version of the model to match key features related to the size distribution of firms in the U.S. economy, as it is usual in the literature. Next, to evaluate the quantitative implications of the model, I use data from the WBES, which is one of the few surveys that provide establishment-level evidence related to bribing. I use this micro-level evidence of bribing in Argentina to calibrate the distorted version of the model. In particular, I use the frequency of bribing, the average cost of bribing in terms of sales, bribing expenditures across the size distribution of firms, and statistics from the firm-level productivity

> 4 In the model, productivity captures physical productivity as well as management practices and intangible capital. 5 In another background paper, Castro et al. (2019) find evidence supporting the fact that firms that pay bribes tend to have worse management outcomes, which would be reflected in productivity. This evidence supports the modeling strategy of this paper and the paper’s general interpretation of productivity.

4
the subsistence level is affordable for the large majority of those already connected. However, for those not already connected consumption at the subsistence level would be affordable at existing tariffs for only one‐quarter of such households. Were tariffs to be raised to cost recovery levels, far fewer of the unconnected would be able to afford to purchase even consumption at the subsistence level. An even larger barrier to access is the existence of a connection charge. Golumbeanu and Barnes (2013) described the way in which connection charges can be so large that they are unaffordable for many low‐income households. Evidence from a number of countries indicated that countries with higher connection charges, controlling for the level of gross domestic product (GDP) per capita, tended to have lower access.

Surveys of household access to electricity and its use have highlighted the issue that some households pay nothing or only part of the appropriate charge for the use of electricity. Smith (2004) pointed out that electricity theft could be in the form of fraud (meter tampering), stealing (illegal connections), billing irregularities, and unpaid bills. Calculations based on data from 102 countries showed that average transmission and distribution losses, which include losses from theft, had increased between 1980 and 2000 for all regions except Western Europe and North America. Antmann (2009) provided an analysis of methods of reducing technical and non‐technical losses in the power sector drawn from experience in several countries. The use of pre‐payment meters has been identified as one way of dealing with delinquent bill payment by households. Tewari and Shah (2003) describe the South African experience, starting in 1989, of the introduction of pre‐paid meters and their benefits, costs, and problems. A study by Jack and Smith (2015) of purchasing patterns of electricity in South Africa through pre‐payment meters found that the bottom‐ quintile households tended to buy electricity three times as often but in amounts that are only one‐fifth of those households in the top quintile
3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km) Bandwidth (Km)<br>Mean of Outcome: .14 Mean of Outcome: .01 Mean of Outcome: .71<br>(d) Aggregate Yield Index (1961–79) (e) Bean Agro-climatic Yield (f) Coffee Agro-climatic Yield<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3
**[Belief Correction - Only T2:]** According to your previous answers, overall you expect about -<sup>_a_</sup> out of 10 Tunisian men and women to support a legal reform making gender equality in inheritance the legal standard in Tunisia. Is this correct? _[Record_

_respondent’s answer, allow for correction]_ _Answer:_ You indeed overestimated / underestimated this percentage / guessed correctly. A recent survey found that about 35% of Tunisian men and women support legal reform.<sup>_b_</sup>

> _a_ Average of items 3. and 4, rounded to the next integer.

> _b_ The stated percentage is based on a nationally representative study conducted by the researcher in Tunisia in March 2022. The question is specific and the respondents were not aware of the correct answer as the study results had not been published.

Depending on their priors, this information may lead treated respondents to update their perceptions of public support for inheritance law reform upwards or downwards. The perceptions of respondents in T1 were not corrected.

Respondents in both treatment groups received the following information on gifting.

**[Gifting Info - T1 & T2:]** Did you however know that there are alternative ways of arranging a more gender equitable bequest without legal reform?

Gifting, as defined in art. 200 XII of the Code of Personal Status is a way of transferring assets to the person of one’s choice. By “gifting” their daughter, parents can arrange a more gender equitable inheritance split, and Law 69 of October 28, 2006, even creates a fiscal benefit for donations to one’s children by significantly reducing property registration fees.

# **3.2.3 Endline Surveys and Outcomes**

Two endline surveys evaluated the causal effect of these treatments. The first directly followed the information treatment. The first main outcome asks respondents to choose their ideal bequest allocation in a hypothetical scenario. They were asked to allocate a bequest
parameter values are selected as follows for Latin American countries: discount rate � = 0:988, sticky wage elasticity "w = 10:50 (matching 10% unemployment rate), steady-state government purchases Gss = 0:19 (from World Development Indicators), and capital share � = 0:35 (in order to match K/Y from KLEMS). Since macroeconomic and in‡ation volatility is larger in emerging markets, the price-setting behavior in those countries is less rigid (see Barros et al., 2009, for Brazil). For this reason, we calibrate a higher frequency of price adjustments in developing countries (1.5 times a year relative to once a year in the developed world). This implies Calvo probabilities �p = �w = 0:625. Based on this alternative calibration exercise, the quantitative STM delivered by the model on impact is 0.84 in the Latin American sample, only 0.08 smaller than the benchmark calibration. Moreover, the prevalence of HtM individuals, �, and the ...scal targeting e¤ort aimed to reach HtM individuals beyond their population representation, STT , help explains about 80 and 8 percent, respectively, of the overall di¤erence in the e¤ect of social transfers on output depicted by the model (i.e., 0.71). This implies that about 12 percent of the overall di¤erence in the e¤ect of social transfers on output depicted by the model (i.e., 0.71) is explained by other parameter di¤erences between developed and Latin American countries.<sup>37</sup> To sum up, a standard calibrated TANK model is able to match key STM empirical regularities shown in Section 2 in terms of the crucial role of the higher share of ...nancially constrained individuals who live HtM in the Latin American sample relative to the developed countries has in explaining the large di¤erence in the size of
checks. The first set of checks allows for additional factors that may be associated with cross-border M&A, or alternative measures of existing variables. In the interest of space, and given the relatively good performance of the parsimonious model ( _B2_ ), we rely on this specification as the basis for the robustness tests to follow.<sup>16</sup> The results of this first set of checks are reported in Table 2.

In the benchmark models, per capita GDP was used as a proxy for factor prices. While doing so considerably expands the data coverage, there may be concern that this measure may be capturing other relevant factors beyond factor prices _per se_ . For example, GDP per capita—

> 14One argument against this is that patents may reflect the extent to which the legal structure of the country supports patent filings, rather than innovation. While this may be true, the legal environment is controlled, in part, by our inclusion of the political risk variable. Furthermore, we regard the patent data as the best proxy that we have available for measuring innovative capacity. As an additional robustness check, however, we substitute the total patents measure with data that includes only cross-border patents granted (which arguably better controls for differences in domestic patent law). While the results are somewhat weaker, the qualitative message remains unchanged; these additional results are available on request.

> 15M&A activity does not, however, appear to respond to political risk measures in South-South acquisitions. This suggests that the hypothesis that emerging market firms may exploit their comparative advantage in more challenging institutional environments is not supported by the evidence presented here, a result that has also been corroborated by others (Arita forthcoming).

> 16Results obtained with the fully specified model ( _B3_ ) were qualitatively similar, and are available from the authors on request.

13
Our findings also point to the mechanisms through which this effect operates. We find that contractual institutions have effects through both extensive and intensive margins. In particular, the effects of institutional quality on the probability of exporting (extensive) and the volume of export conditional on exporting (intensive) matter most for products for which contracting is relatively difficult. These results are robust to using alternative definitions of the extensive and intensive margins. We corroborate this finding using more granular export data based on firm-level aggregates. We also find that, regardless of the definitions of margins that are used, the effect of institutional quality on the intensive margin is greater (in magnitude) than that on the extensive margin. We also find that institutional quality enhances the chance of exporting countries to survive in the market, and particularly for more contract-intensive products.

Moreover, we have examined if the influence of institutional quality on trade differs between country groups. The return to better institutions is higher in developing countries than in their developed (OECD) counterparts. This holds true for the intensive margin (value exported conditional on exporting or value of export per variety). In terms of the probability of exporting in more contract-intensive products or the number of product varieties exported, OECD countries benefit more than non-OECD countries. This is in contrast to our expectation that the extensive margin is likely to be more important in developing than in developed countries. However, we have analyzed the effect of institutions on trade over and above the effect on domestic production, which is an important channel behind the hypothesis.

The effects of institutions on the trade pattern established for the full set of countries are also affirmed for within-region and across-region trade patterns. The quality of contracting institutions has a statistically significant differential (positive) effect on the export of contract intensive products. This conclusion generally holds for both extensive and intensive margins and for within OECD, within non-OECD, within region as well as across region trade.

In a nutshell, institutional quality is a robust source of comparative advantage. Its effect
loadings in waterways (Zheng et al. 2016). These are obtained from the Indian Meteorological Department. In some specifications, we also control for fecal coliforms from the CWC data set. They are an oft‐used measure of domestic pollution, and are a major focus of water supply, sanitation, and hygiene (WASH) operations. It is measured as the “most probable number” of coliform organisms per 100 mL of water (MPN/100 ml, reported in thousands).

> 5 Indeed, even in the United States, the percentage of public water systems that have violated safety limits for nitrates in drinking water has increased in the 15 year period between 1994 and 2009, due to the difficulty of coping with the rising nitrate pollution and the concomitant rise in the costs of water treatment (Ward et al. 2018). 6 In Senegal, studies have recorded nitrate-nitrogen levels going beyond 40 mg/l, more than 4 times the safety limit for NO3-N. Extremely high levels of nitrate have also been reported in the Gaza Strip, where nitrate reached concentrations of 500 mg/L NO3 in some areas (10 times the safety limit for NO3), and more than 50% of publicsupply wells had nitrate concentrations above 45 mg/L NO3. Other site-specific studies in India have found nitrates in drinking water supplies to be particularly high in rural areas, where average levels are reported to be between 46 mg/L NO3 and 66.6 mg/L NO3 with maximum levels exceeding 100 mg/L NO3 in several regions.

6
consider their informal activities, such as preparing food to sell or helping in a family-owned business, as work. Second, standard data collection protocols for household surveys allow for proxy respondents to provide responses on behalf of other household members. This practice may result in biased reports of labor indicators, particularly when the absent household member works in the informal sector and the proxy respondent is not aware of the household member’s labor activities. This would be the case, for example, for a male proxy

> 7 For example, in 2022, only 43.8% of women compared to 67.9% of men were employed (ILO, 2022). Similarly, only 43.7% of youth between the ages of 15 to 24 years in low-income countries are recorded as employed compared to 71.3% of adults (ILO, 2022).

2
Figure C2: Average nighlights in treatment vs. comparison wards <!-- Start of picture text --> 1st assessment Baseline Endline<br>ULGSP (Adjusted)<br>ULGSP<br>Control (Adjusted)<br>Control<br>Year<br>15<br>1.50<br>10<br>1.00<br>5<br>Mean NTL harmonized (2000-2019)<br>Mean NTL non-harmonized (2012-2018)<br>0.50<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> _Note:_ This figure presents average NTL for ULGSP and comparison wards using different NTL measures. Adjusted series use harmonized data excluding pixels with NTL < 7, following the recommendation in Li, Zhou, Zhao and Zhao (2020), while the unadjusted series use all pixels. The dashed lines show data from the raw VIIRS dataset, covering only the 2012-2018 period. The sample is restricted to wards included in the household and government officials surveys.

64
.4 **|

Source: for Serbia, LFS, 2009; for other countries Eurostat LFS Database <u>http://epp.eurostat.ec.europa.eu/portal/page/portal/employment_unemployment_lfs/data/database</u> While the overall labor market situation is difficult, women appear to be disproportionately affected (Figure 1). Despite similar education levels, women’s employment rates are 26% lower than men’s (with no changes between 2008 and 2009). Women also tend to be overrepresented in the category of inactive population and the unemployed<sup>1</sup> . Furthermore, a large number of the unemployed women – 12.5% have been looking for a job for over ten years, with negative consequences for their skill levels and welfare.

**Figure 1. Labor market indicators for working** **<u>age men and women in Serbia, 2008 and 2009</u>**

<!-- Start of picture text --> 80 68.4<br>60 51.7 60 43.9 54.2 62.446.1 60.5 52.8 50 57.442.7<br>% 40<br>20 14.1 12.316.3 17.4 16.119.1<br>0<br>Activity Rate Employment Rate Unemployment Activity Rate Employment Rate Unemployment<br>Rate Rate<br>2008 2009<br>Total Male Female<br>Source: Serbia LFS 2008 and 2009<br><!-- End of picture text -->

> 1 Restricting the analysis of labor market indicators to the 25-64 age group, to account for the relatively large share of population obtaining secondary and university education, improves labor market indicators to some extent. Activity rate in this age category is 67.2%, employment rate – 57.1% and unemployment rate – 15.1
how to utilize the methodology for a country-specific analysis.

The rest of this paper is organized as follows. Section 2 describes the data and methodology while Section 3 shares a descriptive analysis. In Section 4 we present and discuss regression results. Section 5 concludes and offers five policy messages for a more inclusive and successful green labor force and economy.

# 2 Data

We use the Post-Apartheid Labour Market Series (PALMS) dataset (Kerr et al. 2019). PALMS is constructed from several South African Labour Force Surveys for the years 1993-2019,<sup>13</sup> curated and harmonized by DataFirst at the University of Cape Town. We extract the Quarterly Labour Force Survey (QLFS) 2012-2019 data from the PALMS dataset for our analysis. The QLFS is a nationally representative household survey collected by the national statistical agency Statistics South Africa (Stats SA) since 2008. It includes data on the labor market activities of individuals aged 15 years and older who live in South Africa. Each survey samples approximately 33,000 dwelling units based on about 3,324 Primary Sampling Units (PSUs). For each quarter of the QLFS, a quarter of the sampled dwellings are rotated out of

> 11 Examples of studies that make projections about employment due to the transition to a greener South African economy include Rutovitz (2010), Maia et al. (2011) and Merven et al. (2019). 12 As of 2019, there were approximately 16.4 million jobs in South Africa.

> 13 The PALMS dataset includes the 1993 Project for Statistics on Living Standards and Development (PSLSD), the annual October Household Surveys (1994-1999), the biannual Labour Force Surveys (20002007) and the Quarterly Labour Force Surveys (2008-2019). We only use the QLFS 2012-2019 in this analysis.

4
data ranging from 2000 to 2017. Column (1) shows that carbon emissions per capita from transport increase by about 35% when the average number of cars per household doubles.<sup>9</sup> Column (2) reports the results for motorcycle ownership. Carbon emissions per capita from transport increase by about 15% if the number of motorcycles per household doubles. This result is in line with the literature reporting lower impact of motorcycle ownership on CO2 emissions (see e.g., Vasic and Weilenmann, 2006).

9The coefficient might be slightly overestimated for our next step as the regression uses average vehicle number per households while our estimates report results for owning at least one vehicle. In richer countries, many households own more than one vehicle. However this concern is valid only for a few countries while our sample covers most countries in the world. Household survey data do not consistently report the total number of vehicles in the household.

36
- 14 -

Finally, the subnational poverty rates are regressed on the RAI and other relevant indicators. There is no doubt that poverty is relevant to the RAI (table 5). The coefficient is estimated at -0.103.

Among the RAI determinants, road density is found to be significant, but road quality is not correlated significantly with poverty. These results seem to be consistent with the abovementioned twin challenges in Africa. African countries need not only road rehabilitation, but also network expansion. The evidence reconfirms the particular importance of road network expansion to reduce poverty in rural areas. The poverty regression is only focused on three countries in Africa: Kenya, Mozambique, and Uganda. Therefore, the results can be interpreted to mean that many of the rural poor in these countries live beyond the current road networks. Thus, significant efforts would likely be required to provide them better access to the road network.

**<u>Table 5. OLS Regression of the Poverty Rate</u>**

|Variable|Coef.|Std.Err.||Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|ln_RAI_|-0.103|(0.020)|***||||
|ln_RdDen_||||-0.133|(0.033)|***|
|ln_Quality_||||-0.011|(0.015)||
|ln_PopDen_||||0.020|(0.021)||
|ln_PopConc_||||-0.075|(0.037)|**|
|ln_Urban_||||-0.014|(0.002)|***|
|_v_(_Mozambique_)|-0.008|(0.035)||-0.354
--|
|Self-employed/Family agricultural business|21|2,812|11|
|Self-employed/Family non-agricultural business|58|2,812|11|
|Wage employee|19|2,812|11|
|Seasonal or Temporaryworkers|2|2,812|11|

Source: Own elaboration based on HFPS-Wave 1. Notes: Urban observations only. Countries include Burkina Faso, Ethiopia, Madagascar, Malawi, Nigeria, Sudan, Senegal, Sierra Leone, South Sudan, Sao Tome and Principe, Zimbabwe.

# **Income declines were most prevalent among households that owned a non-farm business,**

**which are also the vast majority of households** . Among households that own a business, those with non-farm businesses were most likely to experience an income decline (88 percent) as compared to 78 percent of those with only a farm business (Table 4). If we consider a slightly larger sample of countries that asked about farm and non-farm income declines (9 versus 3 countries) or a decline in business revenue, we see the same trends: greater incidence of income loss among nonfarm enterprises (83 percent and 82 percent, respectively) than farm enterprises (46 percent and 55 percent, respectively).

> 27 The list of countries is similar to that when using the household business definition with five exceptions: data from Burkina Faso, São Tomé & Príncipe, and Senegal are added while data from Chad and Somalia are dropped.

> 28 The share of self-employed is much higher in the HFPS sample than in comparable samples before the pandemic, when more than half the sampled household heads reported being wage workers. This may reflect that job loss was higher among wage workers than among the self-employed at the onset of the pandemic or other characteristics of the survey that were not corrected by the weighting.

13