rafmacalaba/data-use-mentions-extended huggingface dataset:
losses because of the conflict and macroeconomic crises, the consumption losses experienced by better off households were larger than those of the poorer households (World Bank, 2018). Thus, the driver of the reduction in inequality was not pro‐poor growth but rather a greater decline in welfare for wealthier households relative to poorer households. This is not entirely unexpected since the poorer households already experienced extreme deprivation, and thus could not fall much further even as the crisis worsened. Inequality remains nevertheless greater in urban areas than in rural areas though only slightly, at 0.41

> 34 FEWSNET Food Security Outlook, February to September 2018.

> 35 The Gini index is calculated from the area under the Lorenz curve, which plots the cumulative percentage of consumption expenditure against the cumulative percentage of the population, with perfect equality lying along the 45‐degree line.

15
# **5.2 Empirical Studies of the Impact of Regulatory Agencies on Electricity Outcomes in DTEs**

To date, there have been very few studies applying econometric methods to a multicountry data set, including DTEs. We have found only two such studies that focus on this topic:

- (i) Bergara, Henisz and Spiller (1998); and

- (ii) Zhang, Kirkpatrick and Parker (2002).

There are some other studies which cover this topic among others. The most relevant is Pargal (2003). This explores the determinants of private investment in electricity in 9 Latin American countries along with private investment in various other infrastructure industries (roads, railways, water and telecoms).

# **5.2.1 Bergara, Henisz and Spiller (1998)**

The stated intention of this paper (henceforth BHS) is to explain the role of different countries institutional endowments on their electricity utilities’ investment decisions<sup>41</sup> . As the title of the paper suggests, the focus is on _political_ institutions and risk, including the legal system. However, the dependent variable in the BHS regressions is _total generating capacity (MW) per thousand population_ for some year (unstated) presumably in the mid-late 1990s rather than investment.

The independent variables include a few simple economic control factors – per capita GDP, industrial output as a share of GDP, urbanisation and the share of hydrogeneration.

Following Levy and Spiller, the BHS emphasis is on _political and legal institutions_ rather than regulatory agencies. Indeed, the latter are not included in the data set. The major problem for the analysis was that the individual political and legal variables were highly correlated with one another so that it was necessary to include a set of political variables as an index variable. Hence, BHS constructed two weighted average composite variables which were used in a regression on a sample of 86-87 countries. However, for a sub-sample of 38 countries, BHS had data on judicial tenure. They claim
productivity and earnings in middle age—when children themselves become parents. To the extent that health in childhood depend on the time that mothers allocate to their brood, health dependence gives a crucial role to mothers' time allocation in shaping the future of their children.

# **2.  Women’s Labor Supply and Occupational Constraints**

# **2.1 Evidence on Women’s Participation Rates**

While participation of women in the labor force has increased in recent decades in almost all regions of the world, there are still significant gender gaps in participation rates, occupational levels and wages. As noted in the introduction, in today's low- and middleincome countries the labor force participation rate for women (the percentage of the women population aged between 15 and 65 years who are in the labor force) is only 57 percent, compared to 85 percent for men. The share of women above the working age who are employed (the employment-to-population ratio) was about 50 percent in 2009 compared to a male employment-to-population ratio of about 74 percent (International Labour Office (2010)). In the Middle East and North Africa adult labor force participation rates are below 30 percent, and in South Asia below 35 percent (see Table 1).<sup>8</sup> Moreover, paid employment for women has expanded slowly and women continue to assume the largest share of unpaid work. Close to two-thirds of all employed women in developing countries work as contributing family workers or as workers on their own account, typically in forms of employment that are highly vulnerable and lack job security and benefits. In that sense, the relatively high labor force participation rates for women in East Asia and Sub-Saharan Africa documented in Table 1 do not tell the full story. Indeed, women's share of waged non-agricultural employment has increased in the last decade but only marginally; the share of women in the agricultural labor force exceeds 45 percent in SubSaharan Africa and South East Asia and the Pacific (Figure 1
The implications for standard models of tax evasion and regulatory compliance are straightforward. Traditional approaches assume that enforcement agencies try to maximize social benefit (minimize social harm) subject to a binding budget constraint. Both these approaches and ours predict that large firms (the biggest violators on a per unit output basis) are more likely to participate. The difference between approaches lies in the determinants of compliance: traditional approaches assume that enforcement solely determines compliance; we model the (relative) net benefit to the firm. The actual importance of gross benefits versus gross (penalty) costs is an empirical matter, one that, unfortunately, we cannot test with our data. However, our approach shows that both costs and benefits to the firm should be accounted for when attempting to identif the importance of enforcement efforts.

Moreover, our approach indicates that the _duration_ of an economic activity should be considered when modeling participation. Traditional approaches to tax evasion and regulatory compliance typically ignore this issue, in part because they consider long-lived economic agents; in particular, firms are viewed as infinitely long-lived. However, we have shown that if firm dynamics play an important role in the economy -- as they appear to do -- then they should be factored into participation considerations.

# **III. Erapirical results**

1992 National Micro Enterprises Survey (ENAMIN) from Mexico, offers the first comprehensive survey to date on compliance with or participation in several distinct markers of formality including registration with the tax authorities, tax payment, labor protection, participation in guilds or trade associations, and enumeration in the census, as well as other relevanit characteristics. It thus, permits us to generate a reliable picture of the nature of informality, as well as to test the consistency of our framework with reality. The sample was generated by selecting approximately 11,000 individuals from the 1991:4 National Urban Employment Survey who declared that they were self-employed or heads of firms of five workers or fewer (fifteen or fewer in manufacturing). They were reinterviewed in the
Real wages and rents rise since the decline in nominal factor prices is more than offset by a decline in domestic consumer prices. The reform does not harm the rural poor because real wages of unskilled farm workers rise more than those of unskilled non-farm workers. Real returns to farm land rise the most suggesting an overall improvement in returns to farming.

# **5. Concluding remarks**

Several broad conclusions emerge from this analysis. An increase in the duty drawback has a positive impact on export competitiveness and employment in the export-oriented industries, but could lead to exports with low domestic value added. The welfare effects of duty drawback and trade reform are ambiguous. An increase in the drawback is more likely to be welfare enhancing if the economy is small with negative effective protection on the export processing activities and positive protection on the domestic-oriented activities, high input tariffs and intensity of imported input use in the export-oriented sectors, low initial drawback and low administrative costs and leakages in the tariff collection system. In such an economy liberalizing intermediate good markets can be welfare enhancing if the cost share of the imported input is higher in the export processing than in the import competing sector. This is more likely to be true for lower duty drawback and higher intensity of import use in the export-oriented sector. Liberalizing intermediate input markets is welfare reducing if there is a complete duty drawback in place.

In China duty drawback removal after meeting its WTO commitments will deepen domestic supply chains and improve welfare, but will hurt China’s economic efficiency, output growth, export competitiveness in key manufacturing industries and real factor should disaggregate components from vehicles. This however is a nontrivial task which requires a lot more data than currently available from the GTAP database.

41
# Trade Policy and Exporters’ Resilience: Evidence from Indonesia<sup>∗</sup>

Massimilano Cal`ı<sup>†</sup> Devaki Ghose<sup>‡</sup> Angella Faith Montfaucon<sup>§</sup> Michele Ruta<sup>¶</sup> _JEL Classification_ : F12, F13, F14, F31 _Keywords_ : Trade policy, exchange rate shocks, global value chains, resilience

> ∗We would like to thank Gianluca Orefice, Giorgio Presidente, Ariel Weinberger and participants at the World Bank Trade Seminar for helpful comments. We are also grateful to Indonesia’s Directorate General of Customs and Excise for the cooperation—particularly Rudy Rahmaddi—including also through the provision of customs data and to Agnesia Adhissa Hasmand and Bayu Agnimaruto who helped assembling NTM data. Nabil Rizky Ryandiansyah provided excellent research assistance. This paper has partially benefited from financial support from the Umbrella Facility for Trade trust fund, financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors or the countries they represent. All errors are our responsibility.

- †World Bank, `mcali@worldbank.org` .

- ‡World Bank, `dghose@worldbank.org` .

- §World Bank, `amontfaucon@worldbank.org Corresponding author` .

- ¶World Bank `mruta@worldbank.org` .
- Migration: internal migration across states is defined when respondents report a different current state (ER36004 for 2007, and ER66004 for 2017) with respect to baseline (ER17005). International migration is defined when the respondent reports living in a foreign country and was living in the US at baseline (or the opposite).

# **C Randomized and data con- experiments: Summary struction**

## **C.1 Uganda Youth Opportunities Program (YOP)**

YOP was a program implemented by the Uganda government, which invited groups of people aged 16 to 35 to apply for cash grants to start a skilled self-employed activity. The intervention had four main characteristics: (i) people had to apply in groups; (ii) each group had to submit a proposal on how they would use the grant for nonagricultural training and start-up costs; (iii) proposals were screened by government officials at the village and district level; and (iv) the grant given to successful applications was unconditional and not subject to government monitoring thereafter. The program was initially started in 2006, but the data collected by Blattman et al. (2014) and Blattman et al. (2019b) comes from 14 northern Uganda districts with funds still available as of 2008. Of the 535 groups passing initial government screening, 265 (5,460 individuals) were randomly assigned to treatment, and 270 (5,820 people) to control, stratified by district. The authors surveyed five people per group, for a total of 2,677 individuals. The baseline survey was administered in February–March 2008. The government disbursed then funds to groups in the treatment group between July and September 2008. The authors conducted three endline surveys: the first between August 2010 and March 2011 (24 to 30 months after disbursement); the second between April and June 2012 (44 to 47 months after); the third between March and May 2017 (103 to 106 months after).

<u>Variable details:
annotations, as well as how we use LLMs for data augmentation in combination with iQual. We test three different LLMs- the closed-source ChatGPT (gpt-3.5-turbo) by OpenAI, as well as two open-source LLMs by Meta, the Llama-2 (13b) and its fine-tuned "chat" variant (Touvron et al., 2023).The base Llama-2 is pretrained on publicly available online data sources. The chat variant is then fine-tuned on publicly available instruction datasets and over 1 million human annotations. This fine-tuning is designed to make the model align with human preferences using techniques such as Reinforcement Learning with Human Feedback (RLHF). ChatGPT is also fine tuned using RLHFFor all three LLMs, our approach 3
is observed. We have 3,305 father-son pairs, and children’s age is about 40 years in the survey year, 2010 (29 years in 1999, same as that for India in 1999 REDS data). Fathers are aged 68 years on average in 2010. About half of the fathers work in the agricultural sector. Children receive 6.31 years of schooling on average, significantly higher than their fathers (less than 3.81 years).

While our main empirical analysis is based on the CFPS 2010 and REDS 1999, we take advantage of a number of additional data sets for exploring the economic mechanisms identified by the theoretical analysis. To understand how the relation between father’s education and household income varies by farm and nonfarm occupation in rural China we utilize the data from the Chinese Household Income Project (CHIP) 1995 and 2002. To estimate the relation between father’s education and household income in rural India, we use the data on household total expenditure from the National Sample Survey 1993.

16
15.8|25.2|25.5|23.3|
|prediction|Sources: CFUWBES1, LMPS 2016,|and CMMHH Jordan 202|1.|||
|of 29.9||||||

and 17.4 percent, respectively. The model-predicted estimates for wave 2 were once again more in line with the CMMHH data than were the survey-measured numbers.

As with gender, the reverse pattern occurs when considering non-PFPS jobs, according to the CMMHH Less than basic survey. In non-permanent or Basic Education Secondary Education informal private sector Higher Education positions, those with higher Total levels of education Source: CMMHH, 2021.

_Table 5-22 Jordan: Percent of Non-Permanent Private Sector Workers Employed Pre-COVID No Longer Employed, by Demographic Group_

||Unemployed,|2021<br>Out of labor force,2021|
|---|---|---|
|Less than basic|10.9|0.0|
|Basic Education|16.1|1.7|
|Secondary Education|19.5|9.3|
|Higher Education|24.9|4.8|
|Total|17.8|3.4|

# experienced a higher rate of

job loss (Table 5-22). Yet the highest rates of withdrawal from the labor force occurred for those with secondary education.

# **5.6.2 Georgia**

In Georgia, where a much higher share of the working age population has tertiary education, the rate of loss of PFPS jobs held by those with less than tertiary education in wave 1 was projected to have a modest gradient from 25.6 for primary schooled (or less) workers to 14.9 percent for university-educated ones. For wave 2 the gradient was similarly flat for those with less than a university education at 11-13 percent, and jobs held by those with more than a university degree were predicted to actually increase by the winter of 2020 compared to COVID-19 levels
# **5. Measures of relative poverty**

Recall that the relative poverty lines rise with the mean above some point, as determined by the alternative calibration methods described in section 2. Table 4 gives the average poverty line by region and year for both methods.<sup>28</sup> Using Method 1, the overall mean poverty line rises from $2.00 a day in 1981 to $2.90 in 2008. The mean poverty lines are similar, though slightly higher for Method 2. In 2008, the mean lines vary from $1.60 in Sub-Saharan Africa to almost $7 a day in Eastern Europe and Central Asia (these are for Method 1, but they are similar for Method 2).

Figure 6 gives the series of implied weakly relative poverty measures for the developing world using both methods, alongside the corresponding series of absolute measures for $1.25 a day. We see that the overall measures of relative poverty are quite robust to the choice of method. Using the survey means (Method 2) gives slightly higher poverty counts in the 1990s and slightly lower at the end of the period. By either method we see an overall trend decline in the incidence of relative poverty, but a trend increase in the proportion of the population who are not absolutely poor but are relatively poor.

The slight upturn in 2008 using Method 1 is probably deceptive, as it appears to largely reflect a short-run gap in the growth rates implied by the national accounts consumption and those found in the surveys. We will rely more on Method 2 using the schedule in equation (3). (The Addendum gives detailed results using Method 1.) Table 5 gives the estimates by region using Method 2, corresponding to Table 2. We see that the proportion fell over time, from 63% in 1981 to 56% in 1990, 53% in 1999 and 47% in 2008. The speed of decline in the incidence of relative poverty has not been sufficient to
approach (Panel B), with 47 percent of the non-poor and 56 percent of the poor being reclassified. It is worth noting that spatial deflation primarily reduces poverty in rural areas due to their relatively low prices.

# **5.4 Additional analyses**

In this sub-section, we present additional analyses to demonstrate the practical applications of our approach in analyzing other key outcomes, including spatial inequality, non-monetary poverty, and employment.

## _Spatial inequality_

In addition to poverty, the urban and rural dimensions of inequality are a crucial factor in assessing welfare distributions in low- and middle-income countries. By utilizing household-level consumption expenditures from our dataset, we are able to measure inequality using various standard indicators. Our focus lies on the Theil index, which is calculated based on per capita household consumption expenditures and can be easily decomposed into within-group and between-group components.<sup>28</sup>

> 28Similar results are obtained based on another inequality indicator, such as Gini Coefficients (results available upon request).

21
|15|Sfax|4,698.13|133.87|2.85%|1.03|1.72|
|---|---|---|---|---|---|---|
|16|Kairouan|2,269.21|62.51|2.75%|1.01|1.70|
|17|Kasserine|2,542.98|92.31|3.63%|1.01|1.69|
|18|Sidi Bouzid|2,663.65|75.74|2.84%|1.04|1.74|
|19|Gabes|3,043.21|83.40|2.74%|1.01|1.69|
|20|Mednine|3,318.50|91.06|2.74%|1.05|1.76|
|21|Tataouin|3,538.70|106.89|3.02%|1.08|1.81|
|22|Gafsa|3,154.77|106.11|3.36%|1.01|1.70|
|23|Tozeur|3,191.66|106.38|3.33%|1.01|1.70|
|24|Gbeli|2,834.20|72.02|2.54%|1.03|1.74|
||**Tunisia**|**3,871.94**|**53.00**|**1.37%**|**1.30**|**2.19**|

Source: our elaborations of the EBCNV 2015.

During the first planning phases of the new EBCNV 2021, the INS initially
A downside of this harmonization exercise is that it cannot be applied to CBT, given the lack of a welfarebased ranking for all data sets except Niger 2.

The performance of PMT under a harmonized threshold tells a different story, compared to the analysis based on different thresholds (i.e. using CBT selection rates for comparison between PMT and CBT). Threshold adjustment affects both the levels of performance and the variations in performance across data sets (Table 6). The median PMT scheme delivers 38% more resources to the poorest households than a random allocation would, compared to 20% for the median PMT scheme under CBT selection rates (see Table 3). The median targeting error rates based on per-capita consumption also increases from 38.6% (see Table 3) to 51.9%. Both results are a mechanical effect of the lower threshold (35%) used in Table 6.

Figure 2, which computes targeting error rates for each selection threshold between 5% and 100% of the population, confirms the decisive role played by the selection thresholds in targeting performance. In fact, the figure illustrates that the performance of targeting varies more across selection thresholds than across targeting PMT schemes. Targeting errors decrease at a quasi-constant rate with the increase of the percentage selection in all data sets. But decreasing targeting errors do not mean an increasing contribution of targeting relative to a random allocation (as illustrated by the 45-degree line). After a selection threshold of around 30 percent, the larger the selection threshold, the smaller the contribution targeting can make relative to a random allocation.<sup>28</sup> A similar result is obtained when measuring PMT performance based on the FCS, although in most data sets targeting errors are indistinguishable from errors obtained from random selection (Figure 5).<sup>29</sup> Using a harmonized selection threshold of 35% (Table 6) confirms that the differences in performance across PMT methods exist, but these are substantially smaller than the observed
|PIabPIITRN|-0.008*|0.002*|-0.006*|Continuous|
|---|---|---|---|---|
|PIabPIIPROT|0.005|-0.001|0.004|Continuous|
|SUPPLIER|0.037||0.037|Binary|

The significant marginal effects from the perception equation include sickness, pesticide toxicity, time spent in the shop, training and the interaction effect. Health impairment has the most sizable direct impact on trader’s perception, followed by training, where traders who fell sick were 39.7% more likely to view the pesticide as hazardous (perception=1). Those receiving training were 33.7% more likely to view the pesticides as hazardous. Higher proportional use of WHO II pesticides increases risk perception by 0.014%, whereas higher use of WHO III reduces risk perception by 0.002%. In regards to the time spent in the shop, a one hour increase increases a traders’ perception of health risk by 0.051 (or 5.1%). The interaction term, although found to be negatively signed in the regression, has an overall effect of decreasing risk perception by 0.006%.

# **6.  Summary and conclusion**

Systematic studies of pesticide exposure on trader’s health and health risk perception are scarce in developing countries. Drawing on a new survey conducted in Bangladesh, this paper is an attempt to address this gap. To model trader health risk perception, we initially used a 2-equation, bivariate probit framework with health impairment effects as an endogenous dummy variable. Wald and likelihood ratio tests rejected the presence of endogeneity, and thus the two equations were estimated separately.

Even casual inspection during the survey revealed that traders work, and surround themselves, with toxic substances. When questioned, 54% of the traders reported frequent health symptoms commonly associated with acute pesticide
Table 16: IV split regression innovation score over IT use

|Dependent variable: Innovation score|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Low com**|**petition **|**with Chin**|**a**|||
|Products and processes||||||
|∆Computers-per-worker|-6.954*|-6.372*|-6.715|-5.958|-6.228|
||(4.012)|(3.854)|(4.534)|(4.518)|(4.631)|
|F-frst stage|9.7|10.2|7.1|6.5|6.6|
|Sargan|0.92|0.72|0.75|0.96|0.95|
|Marketing and Organization||||||
|∆Computers-per-worker|-4.461|-3.678|-2.779|-2.353|-2.328|
||(3.069)|(2.925)|(3.375)|(3.404)|(3.474)|
|F-frst stage|9.7|10.3|7.0|6.5|6.6|
|Sargan|0.56|0.73|0.85|0.72|0.71|
|Observations|336|336|336|336|336|
|**High com**<br>|**petition **|**with Chin**|**a**|||
|Products and processes|||||