rafmacalaba/data-use-mentions-extended huggingface dataset:
-|
|Cuba|18,374|Fiji|1,080|
|The Bahamas|8,478|Vanuatu|557|
|Dominican Republic|4,586|Tonga|253|
|Haiti|3,241|Samoa|170|
|Dominica|2,709|||
|Jamaica|2,273|||
|Grenada|1,387|||
|St Kitts and Nevis|1,193|||
|Antigua and Barbuda|1,167|||
|Belize|985|||
|All Caribbean SIDS|44,606|All Oceania SIDS|2,076|

_<u>Source: EM-DAT</u>_ Total recorded damages from tropical cyclones suffered by SIDS in the Caribbean during 19952022 was USD 44 billion, while SIDS in Oceania suffered damages of USD 2 billion. in 2022

> 5 Some tropical cyclones hit multiple SIDS.

> 6 For most of the tropical cyclone disasters in Cuba for which deaths were reported or where there were large numbers of people affected, total damages are also recorded. But Haiti was struck by several tropical cyclone disasters which killed many people but for which no figure for damages were recorded; e.g. Hurricane Noel in 2007 which killed 90 and affected more than 100,000 people, Hurricane Gustav in 2008 which killed 85 and affected 73,000 people, Hurricane Hanna in 2008 which killed 529 and affected 48,000 people and Hurricane Laura in 2020 which killed 39 and affected 44,000 people. Jones et al (2023) discuss the problem of missing data in disaster databases and how it is handled in empirical research.

5
7.72|8.70|9.89|11.56|14.35|25.60|
|2002|81.84|3.64|4.77|5.57|6.35|7.20|8.20|9.53|11.45|14.78|28.51|

Source: World Bank Global Poverty Monitoring Database.

Despite the remarkable progress achieved in the 1980s, poverty reduction and balanced regional development were identified as key challenges facing Indonesia in the 1990s (World Bank 1990). We now review achievements over that decade with reference to the analytical framework discussed above. Table 3.1 contains the data upon which we base our analysis. Each row of the table gives the mean of monthly household expenditures in 1993 Purchasing Power Parity<sup>11</sup> (PPP) dollars, along with expenditure share of each decile. The year 1993 (our base year) is also the year when Indonesia became a middle-income country according to World Bank’s classification.

> 11 The World Bank 1993 PPP conversion factor is equal to 635.655 rupiahs to a dollar.  This is what one would use to convert dollars in local currency in 1993.  The local Consumer Price Index (CPI) can then be used to translate these values into values for a year other than 1993.

15
.

In terms of accessibility to education, constraints are high for high schools with only 55 percent of the population<sup>14</sup> able to reach high schools in 30 minutes, making it suitable for an intervention (Table 3 and Figure 3). Accessibility constraints are high for referral and emergency medical facilitates with only 32 percent of the population able to reach them in 30 minutes and about 37 percent at more than 60 minutes, therefore making it suitable for an intervention (Figure 4). Finally, in terms of accessibility to markets, about 28 percent of the value of agricultural production can reach a market within 60 minutes (Table 3).

> 14 The access to high-schools considers the whole population disregarding school-age. This assumption is made due to lack of undelaying georeferenced age population data.

8
# **Summary and conclusions**

Between 1992 and 1997, Vietnam was one of the fastest growing economy in the world. This growth was led by the industrial sector which expanded at 13% per annum. However, Vietnam's strong manufacturing growth has only had a moderate impact on industrial employment, which grew by 4% per annum. This represents less than 30% of the rate of industrial growth. In stark contrast, Asian countries which followed export oriented growth strategies during the last three decades were able to raise industrial employment at annual rates close to 80% of their industrial growth.

One explanation of this difference lies in the capital intensive and inward looking nature of much of Vietnam's industrial sector, which is still dominated by State owned enterprises concentrated in heavy industry. The surge of foreign direct investment into Vietnam in the mid to late 1990s has not altered this situation to any significant degree. Although the share of foreign capital flowing into light industry has increased in recent years, close to 90% of the stock of foreign investment is still in capital-intensive industries. This pattern of growth in large part reflects Vietnam's high barriers to heavy industrial imports, which not only protect State enterprises but also attracts foreign investors into importsubstituted and capital-intensive activities, often in joint ventures with State enterprises.

In the future, however, growth promises to be much more labor-intensive. Between 1992 and 1997, real export earnings increased by an impressive 19% per year and the share of light manufactures in total exports rose from 13.5% to 36.7%. Had manufacturing exports earnings continued to grow at their pre-crisis rates Vietnam might have exported an additional $1.2 billion of light industrial goods and created up to 300,000 manufacturing jobs in 1998.

In addition, Vietnam's high human capital and its large supply of labor relative to land suggest that there is scope for significantly raising manufacturing exports in the near future through a better allocation of resources. Cross-country statistical evidence shows that Vietnam
the hurricane. The scales are as follow: 1 for hurricanes between 119 and 153 km/h, 2 for hurricanes between 154 and 177 km/h, 3 for hurricanes between 178 and 209 km/h, 4 for hurricanes between 210 and 249 km/h, and 5 for hurricanes above 250 km/h.<sup>12</sup> As Strobl (2012) suggests, it is generally agreed that considerable damages due to windstorm only occur once a hurricane reaches a strength of 3, which is intrinsically how the scale has been created. In the case of Central America, however, it is unclear whether this relationship holds given the high levels of poverty and the fragile residence features in the region.<sup>13</sup> Therefore, this is an empirical question that we address

> 8 We use the GHSL – Global Human Settlement Layer Population dataset for available years (1975, 1990, 2000, and 2015).

> 9 Strobl (2012) suggests that this parameter relates local wind speed to the local level of damages.

> 10 Each additional unit of wind speed does not cause a constant level of damage.

> 11 The National Hurricane Center provides further explanation and a conceptual animation of the SS categories: <u>http://www.nhc.noaa.gov/aboutsshws.php.</u>

> 12 Related categories are tropical storm (63 – 118 km/h) and tropical depression (≤ 62 km/h).

> 13 For example, according to SEDLAC, the share of dwellings of low‐quality materials in Central America could be high as in Guatemala (46 percent), El Salvador (31 percent), Nicaragua (15 percent), and Honduras (14 percent).

6
*|4.00|-0.44|
|Mill's Ratio|0.59 *|1.30|0.77|0.41 *|2.45|1.01|0.22 *|3.68|0.81|
|Constant|-5.35 *||-5.35|-2.28 *||-2.28|1.00 *||1.00|
|Dependent Variable||||||||||
|<br>Log Nat Expenditure||4.01|1.42||5.79|6.01||6.98|8.08|
|Evaluated in pesos||54.91|4.13||326.44|<br>409.05||1075.65|3224.26|

Source: Own calculations based on ENIGH 96 and DGPyP, SEP

* Significant at 5%, ** Significant at 10%

**Table 9. Quantile Analysis of Household Expenditure on Educational Private Service**

||**Quan**|**tile regr**|**ession 0.01**|**Quant**|**ile regre**|**ssion 0.50**|**Quan**|**tile regre**|**ssion 0.90**|
|---|---|---|---|---|---|---|---|---|---|
|**Explanatories variables**|**Coef.**|Q 0.10|**M.W. to P.**|**Coef.**|Q**0.50**|**M.W. to P.**
# The Intergenerational Effects of Economic Sanctions<sup>∗</sup>

Safoura Moeeni<sup>†</sup>

## **1 Introduction**

Economic sanctions have become the defining foreign policy tool of the 21<sup>st</sup> century, sometimes as a prelude to warfare, and sometimes as an alternative to it.<sup>1</sup> While humanitarian impacts often feature prominently in the debate about economic sanctions, traditional estimates of the effects of sanctions have mainly focused on the effectiveness of sanctions in achieving political objectives (Ahn and Ludema (2020); Draca et al. (2019); Hufbauer et al. (2010)). More recent literature investigates the adverse consequences of sanctions on the civilian population while sanctions are in place (Petrescu (2016)). However, as the effects of sanctions may last in the subsequent period, effects on the current generation may not fully capture the negative impacts of sanctions. In particular, if sanctions reduce the educational attainment of young people, the effects of sanctions may last long after they are lifted. As early human capital investment is hard to substitute with the investment in later life (Heckman (2011)), sanctions could put children at a disadvantage for the rest of their lives. Moreover, human capital is an important factor in productivity growth and economic development. These negative externalities caused by disinvestment in human capital are not documented in the current literature of adverse consequences of sanctions. This paper studies these negative externalities of economic sanctions, in particular, it evaluates how targeted sanctions affect investment in children’s education by using Iranian data.

The theoretical effect of sanctions on children’s education is ambiguous. Sanctions significantly reduce household income, which is the major source of education funding in Iran.<sup>2</sup> How household income matters for children’s

> ∗This paper is a revised and expanded version of a chapter of my PhD dissertation, submitted to University
3 that have some correlation across groups. Rural India provides a similar context, with an organization of social interactions within castes. Although social mobility and social mixing have increased in urban India, life in villages is still organized along caste lines (Damodaran 2008; Munshi 2011; Munshi, Myaux, and Rosenzweig 2016). Education spillovers are therefore expected to occur within caste, while cross-caste effects should be absent.

This prediction is tested using data from the 2006 round of the Additional Rural Incomes Survey and Rural & Demographic Survey (ARIS-REDS), a household survey conducted in rural India. This dataset has several advantages over the datasets used in previous literature. First, the round of 2006 has a _listing_ of every household in the surveyed villages with basic demographic characteristics information on households and heads of household, such as their caste, gender, age, education level, size of the household and main occupation. This is very valuable information as it makes it possible to compute the average level of education of members from the same caste using all the heads of households from the same caste in the village and not only the heads of household that have also been sampled as is usually done in the literature. The education of neighbors is therefore very precisely measured. The other advantage of the village listing is that each sampled household is surveyed twice, once in the survey, and once in the listing, which is useful when dealing with potential measurement error as explained in section 4. Second, households report their subcaste name, which makes it possible to define the caste group at a very thin level. Finally, this round is part of a panel with earlier rounds that are exploited for robustness checks.

The findings show that the education level of households from the same caste is positively correlated with farm productivity, but there are no cross-caste effects, and no effects from members from the same caste that do not have agriculture as their main activity. These results are robust to changes in specifications, and in particular to a specification with
economic activities in places or areas that are more likely to be negatively affected by climate shocks.

The data used in the analysis are from the nationally representative Rural Agricultural Livelihood Surveys (RALS) conducted in 2012, 2015, and 2019 in Zambia. The poverty metric used in this paper is based on total household income captured in RALS. For our poverty estimates to be comparable to those based on consumption expenditure,<sup>5</sup> we used the RALS 2015 data to determine an income threshold or “poverty line” that would make the incomebased poverty rate equal to the expenditure-based rural poverty rate estimated in the 2015 Living Conditions Monitoring Survey (LCMS) in Zambia.<sup>6</sup> Besides our focus on resilience and vulnerability, the use of an income-based poverty line that equates the 2015 poverty rate in RALS to the expenditure-based poverty rate for that year is another major difference between this paper and others that use RALS data to study poverty dynamics in Zambia, see Chapoto et al., (2011); Ngoma et al., (2019) and Diwakar et al., (2020).

The paper proceeds as follows. Section 2 briefly reviews the links between climate shocks, vulnerability, and resilience. Section 3 presents a conceptual framework on the linkages between climate change, vulnerability, and livelihood outcomes. Section 4 presents the data and methods. Section 5 presents our results, which we discuss further in section 6. Section 7 concludes the paper and offers some reflections.

2. Climate shocks, vulnerability, resilience, and rural livelihoods: A brief review The linkages between climate shocks, vulnerability, resilience, and livelihoods are complex. Climate change can increase poverty directly by reducing agricultural productivity and production, and by hindering asset accumulation and return on assets. Indirectly, climate change affects poverty through output prices, labor productivity and the availability of offfarm employment opportunities. This paper focuses primarily on the direct livelihood effects. A <mark>s in other mainly agrarian SSA countries
managerial, and business environment measures. Within the WBES, the Green Economy module is rolled out by the World Bank Group, jointly with the EBRD-EIB.

_Generic and Energy-centric Management Scores_ . General management scores are calculated using 11 questions from the management practice module. These questions map to eight questions from the U.S. Management and Organizational Practices Survey (U.S. Census Bureau, 2015). They cover four categories of practices: target-setting, monitoring, incentives, and operations (see Table A1). Firms’ responses to questions on management practices are aggregated into a single management score following Bloom et al. (2019a) in two steps. _First_ , the responses to each of the management practice questions are normalized on a 0-1 scale: the response associated with the most structure is normalized to one, and the one associated with the least structured is normalized to zero. _Second_ , overall management score is calculated as the unweighted average of the individual question scores as in Bloom et al. (2012c) in computing the average based on the z-scores of individual questions. We use these scores to compute descriptive statistics. About 2% of the firms with non-missing entries report a zero average score, while the average for all firms is 0.48 (standard deviation of 0.21). For regressions, we take the z-scores of individual questions, and compute the unweighted average. We then once again take the z-score to obtain a composite management score. General management score also separates the overall management index into four subindices to separately assess the importance of monitoring, incentives, targets and operations practices.

Energy management scores use questions from the Green Economy Module of the WBES that relate to monitoring, target setting, and relative emphasis on energy efficiency. Questions and scoring scales are listed in Table 1. All scores are normalized to a 0-1 scale. Six out of nine questions in the Green Economy Module require binary responses. More structured practices are those that are more specific, formal, frequent, or comprehensive and
(top) of _Y_ 0 _L_ , meaning they are worse-off (better-off) than households in the corresponding reference population.

Finally, the variable _CLASS_ is an indicator variable identifying which economic class the households belong to. We defined four thresholds, using different lines calculated by the World Bank to define different social statuses: 3.2 $ PPP; 5.5, $ PPP; 10 $ PPP; and 20 $ PPP per capita per day. Using them, we create five distinct groups based on their consumption per capita per year: these are the “poor” (annual consumption per capita ≤ 1,170 $ PPP), “vulnerable” (1,170 < annual consumption per capita ≤ 2,000 $ PPP), “lower-middle class” (2,000 < annual consumption per capita ≤ 3,650 $ PPP), “middle class” (3,650 < annual consumption per capita ≤ 7,300 $ PPP) and “upper-middle class” (annual consumption per capita > 7,300 $PPP). In model (6), this variable is interacted with _M R P_ to investigate how the correlation of the latter with households’ welfare perceptions varies across classes with different economic status.

# 4. Results

As pointed out in the previous sections, one of the main goals of this paper consists in showing how absolute measures evolve in line with households’ welfare perception. Therefore, after describing the theoretical differences between the two sets of indices and the data, we proceed by reporting the results from perception indicators obtained from the three rounds of Moroccan household surveys. Before analyzing the perception outcomes that, as we will see, are strongly correlated with the class composition of Moroccan society, we outline a brief overview on Morocco’s economic performance and welfare distribution.

## 4.a Poverty, (relative) inequality and class composition in Morocco: 2000-2013

While real GDP per capita increased by about 1.5 percent per annum and showed substantial volatility throughout the 1990s
# Introduction

Larger firms pay higher wages. Moore (1911) first observed this regularity among Italian textile mills, and a subsequent literature has established the large‐firm wage premium (LFWP)‐‐‐or the positive difference between wages paid by large firms and those paid by small ones‐‐‐as a stylized fact of the labor market. Here, we offer the first systematic comparison of the LFWP across developing economies, using a common regression specification and harmonized controls from 69 surveys of individual income across 25 developing economies compiled by the World Bank.

A worker’s wage summarizes their productivity as well as any rents associated with employment in a particular firm. Prior LFWP literature (e.g., Brown and Medoff, 1989; Abowd, et. al., 1999, <mark>Troske, 1999</mark> ) has been primarily concerned with identifying whether the observed LFWP is due to the sorting of higher ability workers into larger firms (worker effects), or due to large firms paying higher wages to otherwise similar workers (firm effects). Overall, sorting is understood to explain at least half of the effect, though its contribution may vary depending on national context, as we confirm here.<sup>2</sup> Identifying this sorting effect requires a panel of workers with firm identifiers, which is not commonly available, especially in smaller low‐income economies or in a format that is comparable across many countries at once. We address this issue in the cross‐section by employing a heuristic benchmarking technique that reports the magnitude of potential sorting on unobserved worker quality.

The analysis reveals five stylized facts about the LFWP in developing economies. First, conditional on occupation, education and experience, the LFWP is comparable to the average gap between male and female wages, or two‐thirds of the gap between urban and rural wages. Second, the LFWP has been declining over time, consistent with what is observed in advanced economies (Bloom, et. al.
# **Appendix A: Data Samples**

We use several samples of the merged data described in Section 3 in different parts of the paper, each with a different size and coverage (as shown in Appendix Table 1). To construct the samples of firms, we start from all legal and formally registered firms in the economy (602,882 firms) with a tax identification, which we call _formal firms_ .<sup>75</sup> The first sample, which we call _active firms_ , is constrained to include only firms with positive sales in 2019, which amounts to 449,632 firms. We use this sample to conduct the aggregate analysis of the paper and the mapping between micro and macro patterns. This sample represents 75% of firms, 92% of private sector employment, 82% of the debt outstanding, and 100% of positive value added in the economy. Among active firms, 97% are SMEs and contribute 43% and 27% of total employment and credit in the economy, respectively. The remaining 3% of active firms are large firms (2%) and mega firms (1%).

The second sample is used to estimate the default probability models that measure firm-level default risk. Starting from the active firms sample, this second sample includes only firms with available data on default during 2019, plus sales, number of workers, value added, firm age, municipality, and industry in December 2018. Firms in default are those with loans past due 90 days. We consider only firms with a previous loan to estimate the model, i.e., firms with outstanding debt as of December 2019 or receive a loan over the period 2012–2019. Firms with previous loans constitute 36% of active firms (capturing 79% and 87% of employment and value added, respectively).

The third sample adds further restrictions to the active firms sample. We restrict the sample to all firms with the relevant observables to perform the main regression analysis
immigration status, parental education, occupation and income and rural versus urban location, among several others. The school features include class size, instruction time, proxies for the material endowment of the classroom, teacher characteristics including gender, education, recent training participation and experience, testing policies, curriculum determination, class formation rules and length of students’ staying with the same teacher, among others.

> 2 The only countries that performed lower were Iran, Kuwait, Morocco and Belize.

> 3 At the secondary level, Colombia participated in the 1995 TIMSS study, where it ranked 40<sup>th</sup> of 41 participating countries both in math and in science, with only South Africa performing lower. Similarly, Argentina participated in the 2002 PISA study, ranking 35<sup>th</sup> of 41 in reading, 34<sup>th</sup> in math and 37<sup>th</sup> in science, with in each case at least half of the few lower-performing countries also being Latin American.

2