rafmacalaba/data-use-mentions-extended huggingface dataset:
coefficients that are small and not even close to statistically significant at conventional levels for second and third round estimates of a mortality rate.<sup>10</sup> The mortality estimates we generate based on this approach are derived from the sibling history reports of 850,000 respondent women ages 15-49 (473,000 of which are from SubSaharan Africa). These respondents generate almost 5 million adult mortality reports, that is,

> 7 Indeed, in these smaller sample cells, the size of the adjustment depended almost exclusively on the functional form specified for the estimation of the relationship between the number of surviving and dead siblings. 8 For this discussion, we focus on the mortality of siblings only, and do not include the respondent on the grounds that the reporting on their status is correct (that is, they are alive).

> 9 This is consistent with Obermeyer and others (2010) who find only small, and inconsistent, recall effects. 10 15,54 The regression is:  M t,t+5<sup>= α</sup> 1<sup>+ α</sup> 2<sup>* R</sup> 2<sup>+ α</sup> 3<sup>* R</sup> 3<sup>+ α</sup> 4<sup>* R</sup> 4<sup>+</sup><sup>**P + Z + ε**15,54</sup> t,t+5<sup>where R</sup> 2<sup>, R</sup> 3<sup>, and R</sup> 4<sup>are</sup> dummy variables for survey round, and **P** is a set of period dummy variables (that is, a dummy variable equal to 1 if the mortality estimate is for the period 1980-84, another dummy variable equal to 1 if
8 possible to use this decomposition to ask whether an income source is inequalityincreasing or inequality-decreasing on the basis of whether or not an enlarged share of that income source leads to an increase or decrease in overall income inequality. On the basis of equation (6): where gk is the relative concentration coefficient of income source k in overall inequality.

From equation (8) it follows that income source k is inequality-increasing or inequality-decreasing according to whether gk is greater than or less than unity.2

# II. Data Set

Data come from a single-round, nationally-representative household budget survey that was conducted in 1997 on 2,500 households in 20 different urban and rural govemorates in Egypt. This survey - the Egypt Integrated Household Survey - was quite broad, collecting data on such diverse topics as: income, expenditures, education, employment, food consumption, health and nutrition, landownings, migration and rural credit.<sup>3</sup> The sample frame used for selecting households in the survey was supplied by the Egyptian Central Agency for Public Mobilization and Statistics (CAPMAS).<sup>4</sup> The rural portion of this Egypt Integrated Household survey included 1,327 rural households drawn from 17 rural govemorates. Of this total, 26 households were excluded because of missing or incomplete data. The analysis is therefore based on data from 1,301 rural households.
# 1 Introduction

Health and human capital are important drivers of economic growth, and research from multiple disciplines has shown that health during infancy is critical in determining how children develop throughout their lives. Malnutrition is a key obstacle to achieving appropriate levels of early childhood development. While substantial progress has been made in combating malnutrition at a global level, chronic maternal and child malnutrition remains a serious problem in parts of the developing world. Even within developing countries, the rural poor disproportionately bear the burden of child malnutrition (World Development Report 2008). A key task for researchers and policy makers alike is to examine the reasons behind malnutrition in rural areas.

Two important causes of malnutrition are a lack of information and a lack of income. Unlike other childhood diseases, malnutrition is a “silent killer” - symptoms are not obvious until it is often too late to reverse stunting or its other deleterious consequences (UNICEF 2011). Therefore, providing parents and/or pregnant mothers information on best practices regarding nutrition and maternal health issues can lead to significant reductions in malnutrition. The second relevant cause is a lack of income. Even if parents know the right ways to care for a child, they might lack the resources to engage in these best practices. For example, young children need diets that are higher in nutritional content than adult diets (Dewey, 2013). However, income constraints might prevent the parents from engaging in these activities. In this paper, we evaluate a program that provided information on best practices regarding child care and cash to families in extremely poor areas with pregnant mothers and/or children below the age of 2.

The context of our study is Nepal, where maternal and child malnutrition remains a serious problem. Nepal has one of the highest malnutrition, stunting and wasting rates for children under the age of 5. According to the Nepal Living Standard Survey 2010/11, 46.7 percent of children under the age of 5 are stunted, 15.1 percent are wasted, and 36.3
# **I. Introduction**

,Crude estimates suggest that the informal production sector is large, accounting for 20 to 50 percent of employment in many developing countries (Portes, 1994). Yet progress towards consensus on the sector's origins, operations and even definition has been hampered by two problems.' First, the lack of comprehensive data has prevented accurately establishing the basic characteristics of informal production beyond conjecture and casual observation. Second, whereas, the literature on informal finance -- i.e. unregulated financial intermediation -- has a broad theoretical underpinning,2 the literature on informal production tends toward ad hoc characterizations and lacks a comparably broad foundation.<sup>3</sup> In general, these frameworks rely on an institutional distortion such as a binding minimum wage, evasion of government regulation and taxation, or differences between firms in worker monitoring ability to generate the informal sector.<sup>4</sup> This paper makes two contributions. First, it offers systematically collected data on a broad cross-section of urban firms in Mexico with details on compliance with or participation in a number of different societal institutions. The data are derived from a nationally representative sample of all such firms, a significant improvement over pre-existing case study data sets. We are thus able to move beyond anecdotal analysis and establish some definitive stylized facts about irnformal production for the first time.

Second, it offers a theoretical framework to motivate the analysis of the data. The 'A large body of literature equates informality with the low-wage, low-productivity segment of a dual labor market (for example, Lewis, 1954, and Harris and Todaro, 1970). An equally sizeable literature equates informality with unregulated self-employment (for example, Hart, 1972, and de Soto, 1989). See Thomas (1992) and Portes (1994) for excellent overviews. Our approach in this paper equates informality and noncompliance with societal norms such as tax obligations, labor protections, census enumerations, business guild participation, _etc.
**Figure 4: Cluster coordinates and 5km buffer**

# **2.2. Hydrological Data**

To identify areas upstream and downstream of DHS clusters we used the HydroBASINS product of HydroSHEDS (Lehner et al., 2008), which represents a series of vectorized polygon layers that depict sub-basin boundaries at a global scale. HydroBASINS divides a basin into two sub-basins at every location where two river branches meet which each having their respective upstream area of at least 100 square kilometers (km2). The data is further divided into 12 nested levels using a modification of the Pfafstetter level system (Verdin and Verdin, 1999), where level 1 corresponds to continental scale, and level 12 has an average area of 130km2. HydroSHEDS’ coding scheme also allows for the analysis of watershed topologies, by providing identifiers for the polygon immediately downstream of each sub-basin.

We overlay household cluster-buffer polygons with level 12 HydroSHEDS sub-basins and determine in which sub-basin(s) each cluster-buffer is contained. The index system is then used to identify sub-basins upstream. We define our upstream area as the union of all sub-basins immediately upstream of the sub-basin(s) that contain the cluster-buffer, removing the area of the cluster-buffer in case it intersects the upstream sub-basins. The average size of the upstream watersheds used in our analysis is 456km<sup>2</sup> . However, this size varies considerably depending on the number and size of the upstream watersheds, ranging from merely 2km<sup>2</sup> to nearly 2,300km<sup>2</sup> . We follow this procedure in the inverted direction to identify downstream polygons for placebo tests (Figure 5).

10
disposable income, inequality has widened in Europe and United States since the end of the 1980s. Figure 4 shows the evolution of the Gini index of per capita household income for the countries included in the ISSP survey and the evidence points to a slow but stable upward trend in income inequality.

– <u>Figure 4 Evolution of income inequality, ISSP countries</u> Source: own elaboration based on Branko Milanovic’s “All the Ginis” data set available in <u>https://www.gc.cuny.edu/Page-Elements/Academics-Research-Centers-Initiatives/Centers-andInstitutes/Stone-Center-on-Socio-Economic-Inequality/Core-Faculty,-Team,-and-Affiliated-LISScholars/Branko-Milanovic/Datasets</u> Note: the figure plots the Gini index of the per capita household income distribution of every country. Light gray lines indicate individual countries and the black solid line indicates the simple average for all countries in the sample. The average is plotted for years 1987-2012; remaining years have few data points to estimate a consistent average.

Several authors have emphasized that the increase of inequality measured from household surveys may be an underestimate of the real inequality, as a large part of that increase occurred through a concentration of incomes at the top of the distribution and very rich people are normally not sampled in these surveys. Indeed, using administrative (tax) data, Piketty and Saez (2014) show that income inequality measured as the national income in the hands of the top 10% decreased considerably from 1930 to 1970, both in Europe and the United States, but it increased strongly in the United States after 1970 and to a less extent in Europe after 1980 (see Figure 5). While inequality is similar in both regions, current differences 17
# **B Synthetic Controls**

Figure B.1. Synthetic Controls: Women’s Age at Marriage Post the Gujarat Riots of 2002 <!-- Start of picture text --> 1990 1995 2000 2005 2010 2015<br>year<br>gujarat synthetic gujarat<br>21<br>20<br>19<br>age_marriage<br>18<br>17<br><!-- End of picture text --> _Note_ : This figure plots the estimates using synthetic controls approach and NFHS-IV. The outcome variable is women’s average age at marriage.

33
inducing greater local value added, lead to greater LBD and more durable effects on production and exports.<sup>13</sup> Unfortunately, it is difficult to test this more interesting conjecture in the AGOA context since almost all African countries were allowed access under identical liberal ROO.

# **3. Data**

The analysis in this paper is based on a new highly detailed database that we constructed by combining US trade data from the US Census Bureau with tariff data published by the US International Trade Commission. The US database provides detailed information on tariffs and product-eligibility for trade preferences in the US each year, including the applied MFN tariff the country-product faces in the US, the unilateral preferences the country-product can benefit from in the US (e.g., GSP, AGOA), and the best preferential tariffs the country-product can benefit from in the US in the period 1997-2017.<sup>14</sup> Tariff measures are all expressed as ad-valorem.<sup>15</sup> The US database also includes value and quantity of US imports at country-HS 8-digit product-year level.

The US database offers important advantages relative to the widely-used trade and tariff data from the World Trade Integration Solution (WITS). First, it provides information at a more disaggregated 8-digit level. Second, it provides much better coverage in terms of years for all types of tariffs, MFN or preferential (under a large number of programs and regimes), whereas WITS has many tariffs missing. Imputation techniques used in the literature to correct those missing tariffs may yield inaccurate tariff rates. Third, preferential tariff rates are constructed based on

> 13 Cherkashin et al. (2015) show that EU’s EBA preferences that enabled firms in Bangladesh to start exporting apparel to the EU also allowed them to export to other markets as firms overcame fixed costs of production and exporting. The strict ROO requirements of EU’s EBA and its potential encouragement of greater local value added may have helped explain those benefits. 14
Policy Research Working Paper 7790

# **Abstract**

In low- and middle-income countries, scaling essential health interventions to achieve health development targets is constrained by the lack of skilled health professionals to deliver services. This paper takes a labor market approach to project future health workforce demand based on an economic model that projects economic growth, demographics, and health coverage, and using health workforce data (1990–2013) for 165 countries from the World Health Organization’s Global Health Observatory. The demand projections are compared with the projected growth in health worker supply and health worker “needs” as estimated by the World Health Organization to achieve essential health coverage. The model predicts that by 2030 global demand for health workers will rise to 80 million workers, double the current (2013) stock of health workers. The supply of health workers is expected to reach 65 million over the same period, resulting in a worldwide shortage of 15 million health workers. Growth in the demand for health workers will be highest among uppermiddle-income countries, driven by economic growth and population growth and aging, resulting in the largest predicted shortages, which may fuel global competition for skilled health workers. Middle-income countries will face workforce shortages because their demand will exceed supply. By contrast, low-income countries will face low growth in demand and supply, but they will face workforce shortages because their needs will exceed supply and demand. In many low-income countries, demand may stay below projected supply, leading to the paradoxical phenomenon of unemployed (“surplus”) health workers in those countries facing acute “needs-based” shortages.

This paper is a product of the Health Nutrition and Population Global Practice 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 amaeda@worldbank.org.

_The Policy Research Working Paper Series
# **7 Conclusion**

This paper contributes to understanding the energy price-firm competitiveness nexus in an emerging economy context. Firms use four main coping mechanisms to navigate energy price increases: innovation and competitiveness, substitution across fuel types and other inputs, and absorption or pass-through. Our results indicate that while electricity price increases are associated with reduced production and firm size, fossil fuel price hikes result in increased capital investment, manifesting in improved productivity, thereby supporting the _strong_ version of the Porter Hypothesis. However, these productivity increases do not translate into higher profitability (at least not in the short run), indicating that such price hikes are at least partly absorbed by firms.

These broader results mask heterogeneity by firm attributes. The strong version of the Porter Hypothesis can only be observed in large firms. Small firms are more negatively affected by surges in energy prices. Likewise, exporters and foreignowned firms are also less affected compared to domestically oriented firms. Moreover, energy-intensive firms are also found to experience more extensive Porter-type innovations, and their business metrics and employment are typically more affected by energy price hikes than firms in non-energy-intensive sectors.

Given the firm heterogeneity in outcomes, policy reforms affecting energy prices and accompanying measures are best targeted based on solid micro-level analyses. The most vulnerable firms, such as the smaller and domestically oriented firms, may not have the means to adjust to energy price fluctuations and may need complementary support to undertake the necessary changes and investments. A better understanding of how existing inequalities interact with the risks posed by energy price policies, taking into account firm capabilities and management skills, will inform a more efficient policy design.<sup>23</sup>

> 23A word of caution is warranted here in over-interpreting. These results remain broadly illustrative as they build on the publicly available version of the ENIA data set, which includes firm-level panel data up to 2015. Additional work is required to merge post-2015 years to the panel dimension of the data set and include the introduction of the exogenous Carbon tax, which
# **1 Introduction**

Total factor productivity (TFP)—the ability to generate greater value or output with fewer inputs—is one of the key elements of economic growth. There is now a broad consensus that TFP differences account for the bulk of observed cross-country income differences (Klenow & Rodriguez-Clare 1997, Hall & Jones 1999). As Krugman (1994) succinctly put it “productivity isn’t everything, but, in the long run, it is almost everything”.

Considerable scholarly analysis has been devoted to measuring productivity. The recent, increased availability of detailed firm-level datasets has further intensified the interest in the subject, including investigations of how productivity varies by characteristics of firms (see, for instance, Syverson (2011)). To the extent that data can be disaggregated, researchers can delve into within-economy differences; if data are comparable across economies and across time, cross-economy differences can be examined. For example, a large and growing body of work explores the link between within-industry productivity dispersion across firms and cross-country differences in aggregate productivity (Banerjee & Duflo 2005, Restuccia & Rogerson 2008, Hsieh & Klenow 2009, Bartelsman et al. 2013).

In the absence of comparable census data, researchers have attempted to estimate productivity using survey-based data, which are often the only available data in less developed economies. The Enterprise Surveys (ES), detailed firm-level data collected by the World Bank’s (WB) Enterprise Analysis unit, are well suited for such inquiry. To facilitate the study of productivity by researchers and policymakers, the data published with this paper provide estimates of both TFP and factor ratios. The latter, more straightforward estimates are provided as TFP estimation may be troublesome for multiple reasons, e.g. the endogeneity of input choice (Olley & Pakes 1996, Levinsohn & Petrin 2003, Ackerberg et al. 2015). Unlike TFP estimates, some of these ratios are also available for most non-manufacturing firms.

The rest of this paper is organized as follows. Section 2
each city, enumerators received classroom and field training from one of the coauthors over a two day period. In the intervention, enumerators first presented a small gift and an information pamphlet to each of the randomly selected sample respondents. After a short introduction, the enumerator read a brief summary of the benefits and costs of participating in pension and health insurance programs from the first page of the pamphlet.

> 10The 2015 survey was completed later than previous rounds, and the Guangzhou and Zhengzhou surveys ran through the end of 2015 with data available even later. These cities were not included in the intervention because there was not sufficient time to both distribute the pamphlets and allow respondents time to react before the 2016 RUMiC survey. In addition, within the 13 included cities, households lacking a working migrant worker, defined as someone with a rural _hukou_ and currently working and not retired (aged 16-55 for women and 16-60 for men), are excluded from the random selection for the intervention sample. Further, 8 households (from both treatment and control groups), were not included because their homes were too far from the city center. In total we excluded 304 households, which accounted for 6.9% of households in the 13-city sample.

> 11Of the 13 cities, 8 used enumerators from the survey firm and 5 used students. When students were used as enumerators, a person from the survey firm was hired to accompany students to the enumeration site to insure that the target respondent was interviewed.

10
, I did not find any significant effects on completing high school two perhaps because Iranian students follow the entry age cutoff rule set by the Iranian Ministry of Education. For high school two, the minimum and maximum entry ages are 15 and 18, respectively. Also, the maximum graduation age is 20.<sup>61</sup> Also, cohort analysis results in Panel B are consistent with earlier results in Panel A.<sup>62</sup> These results are robust to the use of different bandwidths of treated cohorts. Another important concern is that affected children may return to school after the sanctions are lifted. To check for this possibility, I use the data in 2019 (the most recent available data) and find similar results (Panel B) that indicate affected children did not return to school after the sanctions were lifted. Delay entry into college is rare among boys because non-student men older than 18 years old must go for two-year military service. While there is no such restriction for girls, I did not find any evidence of postponing going to college among affected girls. Also I did not find any significant effects on returning to high school in 2019 perhaps because of the age restriction for entering and completing high school. Panel C shows that the effects are small and not significantly different from zero for fake treatment cohorts It is also helpful to understand the heterogeneity of the sanctions effects at different ages. Age plays an important role in the school enrollment decision. The crucial ages for children’s enrollment/dropout rates are at the entrance to the first grade (6 years old), high school dropout age (16 years old), and matriculation at a university (18 years old). As Table 7 shows, the economic sanctions increased the probability of dropping out of high school. The

> 60Using HIES this analysis cannot be done for family education spending because in this data the share of total education spending on each child is not observable. Also, I cannot do this
)|(0.37)|(1.26)|(0.64)|
|Dakar|2.07<br>(0.61)|0.47***<br>(0.17)|29.14***<br>(2.02)|7.94***<br>(0.70)|
|Other urban|11.23**|2.93***|37.12***|11.61***|
||(1.95)|(0.66)|(2.53)|(1.11)|
|Rural|32.00***|9.15***|61.60***|23.31***|
||(1.87)|(0.70)|(1.82)|(0.99)|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations.

Note : First panel presents poverty estimates using per capita household consumption, second panel presents poverty estimate using per capita cell consumption. Sample sizes are 1762 households and 4293 cells. Bootstrap standard errors (250 replications) between parentheses. Equivalent scale: 0.5: children 0 to 14 years old; 1: adults. *,**,***: difference with per adult eq. household consumption poverty measure is significant at the 10%, 5%, or 1% level (see Note 12 and Appendix S2 for details).

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