rafmacalaba/data-use-mentions-extended huggingface dataset:
India-Africa height gap can be explained by heterogeneity in village-level open defecation rates, using individual-level data on child heights and decomposition analysis in the spirit of Oaxaca-Blinder. Reweighting Indian data to match the sanitation of an African sample counterfactually increases the height of Indian children by more than the India-Africa gap. All three approaches find a similar and quantitatively important association between height and sanitation. Finally, a concluding section 5 considers whether estimates of of the association between height and sanitation in this paper and from the literature are sufficient to account for the India-Africa gap.

# **1.1 Open defecation causes stunting**

A growing literature in economics documents that physical height has its origins in early life health ( _e.g._ Case and Paxson, 2008), especially in poor countries where environmental threats to health are more important than they are in rich countries, relative to genetics (Martorell et al., 1977; Spears, 2012b). Two existing literatures indicate that early-life exposure to fecal germs in the environment reduces children’s subsequent height. First, medical and epidemiological literatures have documented the mechanisms linking open defecation to poor health and early life human capital accumulation.<sup>1</sup> Humphrey (2009) documents that – chronic but subclinical “environmental enteropathy” a disorder caused by repeated fecal contamination which increases the small intestine’s permeability to pathogens while reducing nutrient absorption – could cause malnutrition, stunting, and cognitive deficits, even without necessarily manifesting as diarrhea ( _see also_ Petri et al., 2008; Mondal et al., 2011). Relatedly, Checkley et al. (2008) use detailed longitudinal data to study an association between childhood diarrhea and subsequent height.

> Second, recent econometric studies find an effect of a government sanitation program in

> 1Perhaps the recent paper most complementary to this one is Fink et al.’s (2011) regression of an indicator for child stunting on variables including sanitation in 172 pooled DHS surveys. However, one key difference is that they focus
# _5.1.4  ICT (Information and Communication Technology) constraints_

Encouraging progress has been made in expanding coverage in the area of information and communication technology due to revolutionary technical change as well as the increased role of the private sector. However, Africa is still far behind other regions of the world in terms of access to ICT. A key inhibiting factor to the development of broadband connectivity in the region has been the lack of sufficiently-developed ICT backbone networks. While the mobile infrastructure has witnessed considerable expansion in recent years, there are still very significant gaps in the regional ICT backbone infrastructure. Several recent studies suggest that the economic impacts of broadband are substantial and robust. Broadband can play a significant role in transforming a country’s economy and improving its regional or global integration and competitiveness. In the context of an increasingly information-based economy, weakly developed broadband inhibits connectivity and integration in a very fundamental way (Gillwald and Stork, 2009)..

# _5.1.5  Transit and border procedures_

Intra-regional trade in Eastern Africa is inhibited not only by the poor quality of physical infrastructure, but also by regulatory and administrative hurdles that inflate the costs of, and cause long delays for, freight movements along the region’s transport corridors. Indeed, a key reason for East Africa’s poor competitiveness and trade performance is the high cost of trade incurred in transporting and moving goods across borders. Customs inefficiency, corruption, roadblocks and weighbridges are often being cited as key barriers to business development in the region (Mwijagye, 2009).

Trucks are forced to go through weighbridges, either mobile or grounded, to ensure their compliance with regional axle load and gross vehicle weight standards. The lack of proper equipment and design problems in weighbridges causes congestion and delays. Most often, trucks are stuck in long lines. Weighbridges should normally require 3 minutes for transit. According to the East African Business Council survey, however, trucks on average spend 92 minutes and some weighbridges actually require up to five hours. There are
establishments (DEST) and the absolute difference of PCMs (DPCM), implying that higher PCMs are associated with fewer establishments.

Summary statistics for the datasets used in the regressions are in Table III. They can be found in Panel A and Panel B, respectively.

Note that the price-cost margin increases and the number of establishments decreases on average during a crisis.

We collected industry-level data over the period 1963-2002 from the ISIC threedigit level Industrial Statistics (IndStat) database compiled by the United Nations Industrial Development Organization (UNIDO) as it is dissipated via the World Bank’s Statistical Information Management and Analysis system (SIMA). The database covers, among others, information on industry-level total value added, total value of output and total cost of wages and salaries, and the EST for 28 industries in 175 countries, although there are many missing observations<sup>2</sup> . We construct the industry-level PCM for each year for which data are available, by calculating Note that although the establishments reported in the UNIDO database do not coincide with the legal boundaries of a firm, it can be interpreted as the number of competitors in the industry since it is defined as a _“unit which engages, under a single ownership or control, in one, or predominantly in one, kind of activity at a single location”_ .

To identify system banking crisis periods, we used Caprio and Klingebiel (2003). This database describes 113 banking crises in 93 countries since the 1970s. Like Laeven, 15
This discussion of absolute versus relative gains links directly to the choice between absolute and relative measures of inequality (Kolm, 1976).<sup>41</sup> All the standard measures of inequality considered so far are relative measures of inequality (Table 2, Panel A). In Panel D of Table 2, we report the absolute Gini index which increased by almost 25% over this period, consistent with Figure 7.<sup>42</sup> In sum, whether one thinks that inequality increased moderately (e.g. Gini increased by 8.6%) or very strongly (e.g. absolute Gini increased by 25%), depends on whether one cares about relative or absolute gains.<sup>43</sup>

# **VII. Using GDP per Capita Instead of Survey Consumption**

Splitting countries according to their GDP per capita reflects these patterns in the African distribution only to some extent.<sup>44</sup> There are of course very good reasons for this, such as definitional differences, or the fact that GDP per capita does not reflect distributional characteristics.<sup>45</sup> There exists an extensive literature on the gap between national accounts and household survey consumption or income, which we do not intend to review here.<sup>46</sup> For example, Figure 8 plots the national accounts gap across countries in 2008.<sup>47</sup> It is clear that the gap is larger in upper middle countries compared with low income countries. Furthermore, six of the ten countries with the largest gap are natural resource rich. It is important to recognize that the World Bank’s Twin Goals are direct measures of individual welfare (based on household surveys), and constitute a move away from focusing exclusively on low-middle-high income country status or GDP growth.<sup>48</sup> 41 Standard inequality measures, such as the Gini index, are relative
3 TRADE, TRANSPORT, AND TERRITORIAL DEVELOPMENT These patterns suggest that while a decline in _external_ trade costs itself was associated with a redistribution of activity from primary to secondary regions, a decline in _internal_ trade costs was correlated with greater concentration. To explain this pattern of territorial development in India, we develop a model featuring two countries, a home country and the rest of the world (ROW), where the former has multiple regions. In particular, the home country is characterized by a region designated as a port, the primary region, and all international trade must pass through this region.

Our model shows that the impact of a decline in external trade cost (of importing) on regional activity depends on the internal trade cost (distance from the port). A decline in import cost potentially raises competition for domestic manufacturers. Nonetheless, regions farther from the ports, the non-primary regions, remain protected by high internal trade costs. This could stimulate a reallocation of manufacturing activity from the primary region to nonprimary regions. Similarly, a decline in internal trade costs has two opposing effects on the activity of the non-primary region: while it increases the demand for its products by other regions due to lower domestic cost of trade, it also reduces protection from imports, thereby depressing activity. The overall effect depends on the initial level of internal trade costs.

Using a difference-in-difference strategy on Indian organized manufacturing data, we find that the average growth rate of activity (output, employment and plant count) in the secondary region was higher relative to the primary region in the period immediately following the trade liberalization reforms that sharply reduced import tariffs. We also show that the relatively higher growth rate in the secondary region during this period was observed for industries experiencing larger tariff cuts. This result on spatial reallocation is consistent with our model that reallocation of activity away from primary regions is driven by high internal trade costs that insulates manufacturers in non-primary regions from trade competition. The pattern of spatial reallocation reversed during the period 2000-09, coinciding with the
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's untapped export potential represents roughly 25% of its GDP and that up to 1.6 million jobs could be created in the next few years through labor-intensive exports. With high export growth rates, these jobs could be created in the next 3 to 6 years.

The paper also shows that current regulations regarding minimum wages and non-wage benefits are unlikely to harm private sector development. Their impact on foreign invested companies are, however, more ambiguous. For these companies, the minimum wage is above market wages and non-wage benefits further increase labor costs. Whether this deters foreign investment or induces a relocation of production to more competitive countries is unclear. The presumption is that at less than $2 a day, labor costs in Vietnam remain low and attractive. Finally, in theory, Vietnam's new Labor Code ensures labor mobility. Restrictive interpretations of the Labor Code may, however, deter foreign investment in joint ventures and slow Vietnam's adjustment process from an inward looking State-led economy to a more market based economy specialized along the lines of its comparative advantage.

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- Cao, Y., Chen, J., & Zhang, Q. (2018). Housing investment in urban China. Journal of Comparative Economics, 46(1), 212-247.

- Cardak, B. A., & Wilkins, R. (2009). The determinants of household risky asset holdings: Australian evidence on background risk and other factors. Journal of banking & Finance, 33(5), 850-860.

- Chen, T., Liu, L., Xiong, W., & Zhou, L. A. (2017). Real estate boom and misallocation of capital in China. Working Paper, Princeton Univ., Princeton, NJ.

- Chen, X., & Gao, X. (1993). Urban economic reform and public-housing investment in China. Urban Affairs Quarterly, 29(1), 117-145.

- Coeurdacier, N., Guibaud, S., & Jin, K. (2015). Credit constraints and growth in a global economy. American Economic Review, 105(9), 2838-81.

- Cortés, D., Santamaría, J., & Vargas, J. F. (2016). Economic shocks and crime: Evidence from the crash of Ponzi schemes. Journal of Economic Behavior & Organization, 131, 263-275.

- Courtemanche, C., Heutel, G., & McAlvanah, P. (2015). Impatience, incentives and obesity. The Economic Journal, 125(582), 1-31.

- Cull, R., Gan, L., Gao, N., & Xu, L. C. (2019). Dual Credit Markets and Household Usage to Finance: Evidence from a Representative Chinese Household Survey. Oxford Bulletin of Economics and Statistics, 81(6), 1280-1317.

- Dean, M., & Sautmann, A. (2020). Credit Constraints and the Measurement of Time Preferences. The Review of Economics and Statistics, forthcoming.

- DellaVigna
(weighted) EU-SILC data, the Gini index equals only 0.2510. Regarding the imputed tax income in the EU-SILC data set, the Gini index is nearly 50% higher and equals 0.3699. The underestimation of inequality is exceptionally high at the top. The top 10% income share equals 30.3% in the imputed EUSILC and only 20.67% in the EU-SILC. The top 1% income share increases from 2.9% to 7.2%.<sup>14</sup> The income inequality using the imputed tax income in the EU-SILC is comparable but slightly lower than in the tax data. The slightly higher inequality in tax data may reflect a better representation of top-income earners (having different characteristics than EU-SILC respondents) in the tax dataset. These outcomes show that survey data severely underestimate Romania's true income inequality level.

**Table 4. Income inequality in Romania (gross labor income, only employees).**

|**Indicator**|**Survey data**<br>**(EU – SILC)**|**Tax admin data**<br>**(July 2020 * 12)**|**Imputed tax income**<br>**(imputed EU-SILC)**|
|---|---|---|---|
|**Gini index**|0.2510|0.4321|0.3699|
|**Theil index**|0.1010|0.3679|0.2728|
|**Botom 50% income share**|32.79%|21.87%|25.76%|
|**Middle 40% income share**|46.55%|45.38%|43.94%|
|**Top 10% income share**|20.66%|32.75%|
Policy Research Working Paper 6033

# **Abstract**

In recent years, world food prices have increased and fluctuated widely. This paper explores the impact of international food prices and domestic policies on Millennium Development Goal (MDG) and macro indicators for two archetype low-income countries, a net food exporter and a net food importer, using Maquette for MDG Simulations (MAMS), a Computable General Equilibrium model. The simulations, which cover the period 2011–2025, indicate that the size of positive (negative) effects on macro and MDG indicators of a food export (import) price increase depend on the initial gross domestic product share for food exports (imports), leaving countries that are heavily involved in international food trade more exposed to international shocks. Given relatively low elasticity estimates, the impact of changes in food prices on undernourishment are relatively marginal. Flexible responses (in terms of production shares, whether output is exported or sold at home, and whether domestic demanders buy imports or domestic output) enable countries to benefit from or be less hurt by price changes. The case for policy responses to higher import prices is stronger for the net food importer. An untargeted food subsidy, financed by taxes or spending cuts, reduces undernourishment at the cost of a slight deterioration for most other indicators. By contrast, aid-financed food subsidies neutralize the negative impact of higher import prices whereas financing via domestic borrowing is counterproductive, leading to a deterioration across all indicators. If administered at moderate costs, tax-financed targeted transfers more effectively reduce headcount poverty and inequality with macroeconomic repercussions similar to those of taxfinanced subsidies.

This paper is a product of the Prospects Group, Development Economics. Prospects Group, prepared as background to the forthcoming Global Monitoring Report 2012 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 author may be contacted
, in turn dampening their aspirations. Women construct their identities through the lens of these gender stereotypes and willingly accept their disadvantaged place in the workforce (Ridgeway, 1997). In a review undertaken for the World Bank to unpack gender constraints and opportunities in India’s Micro, Small and Medium Enterprise (MSME) sector, the International Centre for Research on Women (ICRW) finds that vendors who deal with self-employment ventures prefer to interact with men. Women on their part are aware of such biases and themselves hesitate to engage in market spaces traditionally associated with men (ICRW 2014). With few opportunities to gain successful entry into well-paying jobs or gain skills for upward mobility, women end up internalizing stereotypes about themselves, leading to a ‘discouraged worker effect’ (Das 2006).

# **4. Data and methods**

This paper seeks to answer the question – what is the role of firm characteristics and attitudes of hiring managers in gender outcomes in hiring? It uses data from a unique survey of firms in three cities of MP – Bhopal, Indore and Gwalior. The survey contains questions on the characteristics of firms, gender and skill profiles of workers and attitudes of hiring managers (who are often the owners) about female employees and what they expect of them.

**Sample selection:** The sample of enterprises was selected from a manual listing of all enterprises registered with the Directorate of Industries (DIC) in the three cities. Since the data set was large enough, only enterprises registered in the five years prior to the survey i.e. between 2010 and 2015 – were considered. Separate lists were created for

6
the business was closed, hours per day spent on paid work, hours per day spent on care and domestic responsibilities by the respondent, and hours spent on care by the respondent’s spouse. We conduct this analysis among the relevant sample of entrepreneurs i.e. those who are married or living with a partner and have child dependents in their household.

Data on norms towards gender equality in opportunities were collected from both male and female entrepreneurs in 2019 prior to the onset of the COVID-19 pandemic and these measures are described and analyzed in the Data Section and Appendix C of the paper. In Table 7a Men’s Individual Beliefs is a country-level continuous variable between 0 and 1 indicating the attitudes of men in the country towards gender equality in opportunities i.e. the proportion of men that agreed with the statement “men and women should have equal opportunities.” Higher values of Men’s Individual Beliefs indicate that a higher proportion of men in that country agree with gender equality i.e. the gender attitudes of men are more liberal. In the sample the mean for Men’s Individual Beliefs is 89.69% and the observed scores range between 74.49% in Egypt and 99.22% in Canada.

# [Table 7a about here]

Table 7a examines whether the effects of school closure differ by Men’s Individual Beliefs separately for female and male firms. Overall, these beliefs do seem to be correlated with behavior – in more liberal societies both men and women report that men do more care work (columns 6 and 7). In column 1 we can see that with a coronavirus-induced school closure, female firms are significantly more likely to close than men. As societies become more liberal (in terms of men’s beliefs on gender equality) this effect declines. Indeed, moving from Egypt to Canada would diminish the effect by around 12 percentage points. The care mechanism tracks the closure effect as women in more liberal societies show a significantly lower increase in the amount
**Understanding the Climate Change-Migration Nexus through the Lens of Household Surveys: An Empirical Review to Assess Data Gaps**<sup>*****</sup>

Marco Letta _Sapienza University of Rome and Global Labor Organization (GLO)_ <u>marco.letta@uniroma1.it</u> Pierluigi Montalbano<sup>1</sup> _Sapienza University of Rome and University of Sussex_ <u>pierluigi.montalbano@uniroma1.it</u> Adriana Paolantonio _Development Data Group, The World Bank_ <u>apaolantonio@worldbank.org</u>

**Keywords** _:_ migration, climate change, household surveys, microdata, data gaps **JEL classification:** C80; O12; O15; Q54

*We are grateful to Calogero Carletto and Alberto Zezza from the LSMS team for their continued support and invaluable advice throughout the development of this work. We thank Mariapia Mendola for helpful comments and Giulia Altomare and Carlotta Vicario for excellent research assistance in collecting background materials.

> 1 _Corresponding author_ . E-mail: pierluigi.montalbano@uniroma1.it
We used two different measures of civil<sup>disturbance. One, which is used in Easterly and</sup> Levine (1997), is from Sivard (1996), who collects<sup>annual information on the numbers of</sup> people killed because of civil conflict or war fought<sup>on the national territory. The other,</sup> food aid, more generally proxies for overall<sup>disturbances-</sup> economic or other.

The information collected in Sivard allowed<sup>us to test not only whether civil strife and</sup> urbanization were related, but also whether the<sup>impact on urbanization varied with the</sup> severity of the domestic disturbances. We<sup>constructed three measures based on the</sup> information collected in Sivard: SIVARD, which<sup>indicates that the country was</sup> mentioned in Sivard as having some form<sup>of civil strife; SIV1000, indicating that the</sup> country had more than 1000 deaths; and<sup>SIV5000, which were countries with more than</sup> 5000 war-related deaths in the 5 preceding<sup>years.</sup> Regression 9 shows that countries that experienced<sup>civil strife tend to be more urbanized,</sup> holding all other variables equal. The severity of<sup>the conflict does not appear to matter,</sup> as SIVARD yielded a better fit than SIV1000 or SIV5000.

The positive correlation between urbanization<sup>and civil strife is very sensitive to sample</sup> changes, and is mostly driven by East Asian countries.<sup>Contrary to common perception,</sup> over the 1960-95 period, it is not in Africa<sup>that most civil strife occurred. Mentioned in</sup> Sivard for some form of civil strife are 34% of<sup>the observations for East