rafmacalaba/data-use-mentions-extended huggingface dataset:
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find that daily dietary supplementation for pregnant women in rural Gambia increased birth weight, and Kanade et al. (2008) document in observational data that Indian mothers who ate more fat and micronutrients during pregnancy had larger babies.

Is it possible that historical conditions that restricted Indian mothers’ size, but have now improved, are importantly restricting the fetal growth of their children? This question is difficult to answer in part because there are at least five reasons mothers’ and children’s height would be correlated: (1) mother’s genetics, (2) assortative mating and father’s genetics, (3) correlation of the child’s early-life environment with the mother’s early life environment, (4) endogenous effects of mothers’ early life environments on their adult ability to care for their children (including _in utero_ and through marriage markets), and finally (5) intrauterine growth restriction directly caused by the historically determined aspects of a mother’s size. Although all five would be reflected in a simple regression of child size on mother’s size, only the last mechanism would allow mothers’ stunting to itself cause present-day children’s stunting.

Arguing that “variability in stature among young children is often ascribed to to health and nutrition differences in malnourished populations and to genetic differences in wellnourished populations,” Martorell et al. (1977) hypothesized that parent-child height correlation would be greater in the U.S. than in Guatemala. This is consistent with a model in which height _h_ is the sum of two uncorrelated factors, genetics _g_ and environment _e_ , so _h_ = _g_ + _e._ Then, assuming that child genetics are a weighted sum _ω_ of mother’s and father’s genetics, so _gc_ = _ωgm_ + (1 _− ω_ ) _gf_ , and that environmental shocks are uncorrelated across generations, then the mother-child correlation of height is where _σ_<sup>2</sup> is variance. In rich countries, where there is little relevant variance
ha per farm. This is in line with evidence from remote sensing that suggests that (i) cultivated area decreased less than implied by aggregate estimates; and (ii) in areas such as the North, farmers were able to plant summer crops on fields where 2022 winter crops were damaged or destroyed during the early phases of the war. The magnitude of the increase in area planted was similar across all farm size groups, though there are regional differences with area cultivated dropping by 9% in the East and increasing by 12% in the Center.

Data for crops cultivated in the 2022 season point towards a shift from grains to oilseeds such as soybean and rapeseed (+42% and +22%, respectively, both from a modest base) or sunflower (+18%) compared to 2021. These crops either require less fertilizer or can sustain short-term lack of fertilization better due to a deep root system and often also are processed locally, lowering transport cost for output marketing. Maize and barley area decreased by 10% and 3%, respectively while wheat area increased by 4%. Contrary to what is reported from household surveys (FAO 2022b), area under ‘other’ crops (buckwheat, rye, beets, potatoes, etc.) decreased by 3% overall, mainly in the largest farm size group (-15%), a decrease balanced by increases of 17% and 30% for the 50-120 and the 120-500 ha class and in the South (+36%). Regional disaggregation also suggests the shift out of maize is driven by the North which, together with the Center, also saw a large increase in area cultivated with sunflower, soybean, and rapeseed.<sup>23</sup> While changes in crop mix across regions could possibly be observed using remote sensing (and linked to farm size categories if there is a complete cadastral map linked to the registry), only survey data can provide economic information on costs, prices received, input and output market
Policy Research Working Paper 7615

# **Abstract**

This paper develops a novel methodology to measure the quantity of jobs and value of wages embodied in exports for a large number of countries and sectors for intermittent years between 1995 and 2011. The resulting Labor Content of Exports database allows the examination of the direct contribution of labor to exports as well as the indirect contribution via other sectors of the economy for skilled and unskilled labor. The analysis of the new data sets documents several new findings. First, the global share of labor value added in exports has been declining globally since 1995, but it has increased in low-income countries. Second, in line with the standard Hecksher-Ohlin trade model, the composition of labor directly contained in exports is skewed toward skilled labor in high-income countries relative to developing countries. However, that is not the case for the indirect labor content of exports. Third, manufacturing exports are a key source of labor demand in other sectors, especially in middle- and low-income countries. And the majority of the indirect demand for labor spurred by exports is in services sectors, whose workers are the largest beneficiaries of exporting activities globally. Fourth, differences in the labor value added in exports share across developing countries appears to be driven more by differences in the composition of exports rather than in sector labor intensities. Finally, average wages typically increase rapidly enough with the process of economic development to more than compensate the loss in jobs per unit of exports. The paper also includes the necessary information to build the Labor Content of Exports database from the original raw data, including stata do-files and matlab files, as well as descriptions of the variables in the data set.

This paper is a product of the Trade and Competitiveness 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
excluding these nodes from the analysis, attention is restricted to the areas along the corridor that got connected by chance. This allows the analysis to correct for the endogenous placement of the corridor.

The second sensitivity test involves excluding partially treated locations surrounding the corridor. To accomplish this, the treatment group is considered to comprise hexagons for which the center of the hexagon is within 20 kilometers of the corridor alignment, and the control group is considered to comprise hexagons for which the center of the hexagon is 80-100 kilometers from the corridor. In this case, the treatment variable, _Tijk_ , is a dummy variable rather than the continuous variable used in the baseline specification of the estimated regression.

Despite these sensitivities, it is not possible to randomize project characteristics. Therefore, the findings should be interpreted as associations. Understanding the relationship between project characteristics and the creation of WEBs is important for policy makers and practitioners who decide about and design corridor projects. Thus, performing a robust analysis of these associations is of great interest.

# **5. Results**

## **5.1. Main Results**

The results of the analysis shed light on three questions. First, what country characteristics may help produce WEBs? Second, what design and implementation characteristics of large transport projects may help generate WEBs? Third, which complementary policies and institutions (such as reforms to improve development policy) may help ensure or amplify WEBs?

Two types of results are reported. Table 2 (columns 1 and 2) presents the results for the baseline estimation strategy that uses nightlights data for all hexagons within 100 kilometers of the transport corridor projects. In this baseline specification, the treatment variable is continuous, with locations further from the corridor 13
government spending drove the large impact on the economy. This is in sharp contrast with the procyclicality of subnational government spending in many advanced economies, such as the United States and the European Union. It is important to note that from a political economy point of view, these “federal” systems, which function within widely different political systems, are not strictly comparable. While the United States is formally a federal country, the European Union, which has a monetary union, does not yet have a fiscal union as decisions about taxes and spending remain at the national level, though some members, such as Germany, have a federal system. China, on the other hand, is a unitary state, even though in several important ways it behaves as a fiscal federalism and its fiscal system shares some of the key features of fiscal systems in federal countries (Bahl and Martinez-Vazquez 2003). In particular, fiscal decentralization has been a fundamental aspect of China’s transition to a market economy, and the country has made substantial progress in moving away from its highly centralized fiscal management system and tax sharing system, though the current fiscal system requires further reforms, particularly in the areas of sub-provincial expenditure assignment, dealing with widening fiscal disparities and intergovernmental transfer system (Shen, Jin and Zou 2012).

Quasi-conventional wisdom holds that at the central government level, fiscal policies are acyclical in industrial countries and procyclical in developing countries (Arreaza, Sorensen, and Yosha 1999; Seitz 2000; Talvi and Vegh 2000). In general, government spending in developing countries closely follows—and hence reinforces—the business cycle, because of imperfections in international credit markets that prevent developing countries from borrowing in bad times and the political economy of the strong incentive to pursue expansionary fiscal policy in good times (Gavin and Perotti 1997; Caballero and Krishnamurthy 2004; Talvi and Vegh 2005). Using a quarterly data set on 49 countries for the period 1960–2006, Ilzetzki and Vegh (2008) document the procyclicality of government spending, establishing
<sup>**19**</sup>

|**Township**|**Conflict/Non-**<br>**conflict**|**Non-Farming**<br>**Agriculture**|**Light**<br>**Manufacturing**|**Hospitality &**<br>**Tourism**|**Trade &**<br>**Logistics**|
|---|---|---|---|---|---|
|**Khi**<br>Moegaung|Conflict|10|10|10|10|
|**acn**<br>Myitkyina|Non-conflict|10|10|10|13|
|**Ki**<br>Myawaddy|Conflict|10|10|10|10|
|**ayn**<br>Hpa-an|Non-conflict|10|10|10|10|
|**Sh**<br>Lashio|Conflict|10|10|10|11|
|**an**<br>Taunggyi|Non-conflict|11|11|10|11|
|**Mandalay**<br>Chanmyatharzen|Non-conflict|11|12|10|11|
|<br>Maharaungmyay||9|11|11|14|
|Tamwe||10|14|9|14|
|**Yangon**<br>Dagon Myothit|Non-conflict|10|12|11|17|
|South Okkalapa||10|10|10|10|

19 Myanmar Gender Entrepreneurship Survey Report, Myanmar Survey Research, 2019.

11
an exogenous negative shock to local public debt, along with detailed loan-level data, to show a decrease in public spending and increase in taxes (fiscal consolidation) but with an overall positive effect on local bank lending to private firms, particularly to the more capital-intensive secondary sector, and especially when pre-law public spending was directed more towards social services

> 11 Likewise, Braga, Guillen, and Thompson (2017) find that negative shocks to Brazilian federal transfers have a particularly negative impact on low-skill employment.

> 12 Becker and Ivashina (2018) study the impact of financial repression and find that the lending of banks to their respective governments increased during the European sovereign debt crisis, leading to a reduction in corporate credit. 13 Similarly, Broner, Erce, Martin, and Ventura (2014) along with Gennaioli, Martin and Rossi (2014) show that under credit rationing and financial frictions, government debt is especially damaging for credit constrained firms. Greenwood, Hanson, and Stein (2010), Graham, Leary, and Roberts (2015), and Demirci, Huang, and Sialm (2019) describe the relationship between the structure and level of government debt and firms’ leverage, focusing on their capital structure. Finally, Chakraborty, Goldstein, and MacKinley (2018) show that banks operating in strong housing markets increase mortgage lending at the expense of commercial lending, suggesting that monetary policy accommodation has some negative spillovers to the real economy.

7
the following were your household’s sources of livelihood?” Options included: 1) Family farming, livestock or fishing; 2) Non-farm family business, including family business; 3) Wage employment of household members; 4) Remittances from abroad; 5) Assistance from family within the country; 6) Assistance from other non-family individuals; 7) Income from properties, investments or savings; 8) Pension; 9) Assistance from the Government; 10) Assistance from NGOs / charitable organization/religious bodies; 11) Other income source. A household is assigned a one if it reports “yes” to the question and zero if it reports “no.” Our variable of interest in our analysis consists of a series of indices measuring income diversification. Following methodology from Michler and Josephson (2017), we use two measures to evaluate household income diversification: (1) a simple fractional index (FI) and (2) a HerfindahlHirschman Index (HHI).<sup>4</sup> HHI scores are negatively related to diversification. That is, they are larger for less diversified households and smaller for more diversified households. For consistency, we adjust the fractional index to maintain this negative relationship. Both indices can be interpreted as specialization indices that are inversely related to diversification. Table 2 summarizes the characteristics of each index.

The simple fractional index is calculated using the count of the income sources each household is engaged in ( _m_ ) at time _t_ given the total number of income-generating opportunities ( _n_ ) in their region ( _j_ ) over the entire time series: The fraction is subtracted from one so that a higher score is associated with fewer income-generating activities, while a lower score indicates a more diversified income portfolio. The fractional index is calculated for both pre- and post-COVID-19 data. To generate the index, we collapse multiple income categories into seven categories standardized across the three countries: (1) farm, (2) wage, (3) pension, (4) remittances,
60<br>1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> Source: Authors’ regression estimates based on NBS’s Industrial Enterprise Survey. Note: The figure plots the coefficient on the ownership dummy by year.

Finally, the estimated differences in return on assets (measured using operating profits) reveal that manufacturing SOEs were less profitable than their private competitors throughout the period. In 2013, SOE profits were 9.0 percentage points lower than those of private firms (Figure 11). The deterioration in relative profitability of the state-owned firms coincided with an increase in their relative indebtedness since 2008. While the leverage ratio (total liabilities over total assets) of SOEs relative to private firms in manufacturing was on a declining trend before the global financial crisis, our estimates suggest that relative leverage rose again. The latest available data—aggregate numbers based on China’s Statistical Yearbook—suggest that the financial performance of SOEs weakened further between 2013 and 2016 but the negative trend may have started to reverse in 2017 (Figure 12).

> 15 The regressions were estimated with standard errors clustered by firm. The results are available from the authors upon request.

> 16 Cong and Ponticelli (2017) find that new credit under the 2009–10 economic stimulus package was allocated relatively more towards state-owned, low-productivity firms than to privately-owned, high-productivity firms. However, their results rely to a significant extent on the 2009–10 NBS Industrial Enterprise Survey data which we have found to be less reliable than in other years.

12
Figure 4. Changes in Prevalence of Depression and Anxiety Symptoms <!-- Start of picture text --> a. Prevalence<br>30<br>20<br>10<br>0<br>Baseline 5 weeks 3 months<br>Control Treatment<br><!-- End of picture text --> Source: Saraf, 2019; World Bank Group, 2018 Looking at the movement of individuals across the four categories of severity measured by the PHQ‐ADS (none, mild, moderate, and severe) provides a clearer picture of how these changes affect individual entrepreneurs at various points along the depression‐anxiety spectrum. Table 6 (Columns 1‐2) shows the changes across the severity categories between the treatment and the control group participants. This analysis suggests that the _treatment was most effective at improving outcomes for those at the margins or onset of depression and anxiety (that is, those in the “mild” category)_ , as well as those _experiencing moderate levels of depression and anxiety_ , the latter especially at the 5‐week mark. This becomes clearer when data is restricted to only those entrepreneurs who were interviewed at all 3 rounds (columns 3 and 4). The proportion of treatment group entrepreneurs in the mild and moderate categories go from 19.3% at baseline to 8.2% at the 3 months mark, which is a substantial reduction. While reduction is also observed in the control group (from 24.2% to 17.6%), the percentage reduction is much smaller.

Table 6. Changes in Depression and Anxiety Category across 3 Rounds

|||**A**|**ll**|**Restricted**|**sample***|
|---|---|---|---|---|---|
|**Time‐**<br>**points**|*
The World Bank Economic Review 21 Card, D., and L. Giuliano. 2013. “Peer Effects and Multiple Equilibria in the Risky Behavior of Friends.” _Review of Economics and Statistics_ 95 (4): 1130–49.

Chin, H. B., T. A. Sipe, R. Elder, S. L. Mercer, S. K. Chattopadhyay, V. Jacob, and H. R. Wethington et al. 2012. “The Effectiveness of Group-Based Comprehensive Risk-Reduction and Abstinence Education Interventions to Prevent or Reduce the Risk of Adolescent Pregnancy, Human Immunodeficiency Virus, and Sexually Transmitted Infections.” _American Journal of Preventive Medicine_ 42 (3): 272–94.

Collins, J., L. Robin, S. Wooley, D. Fenley, P. Hunt, J. Taylor, D. Harber, and L. Kolbe. 2002. “Programs-That-Word: CDC’s Guide to Effective Programs That Reduce Health-Risk Behavior of Youth.” _Journal of School Health_ 72 (3): 93–9.

Demographic and Health Survey (DHS). 2005. _Encuesta Nacional de Demografía y Salud - ENDS Colombia 2005_ . Washington, DC: World Bank.

Dhaliwal, I., E. Duflo, R. Glennerster, and C. Tulloch. 2013. “Comparative Cost-Effectiveness to Inform Policy in Developing Countries: A General Framework with Applications for Education.” In _Education Policy in Developing Countries_ , edited by P. Glewwe, 285–338. Chicago: University of Chicago Press.

Duflo, E., R. Glennerster, and M. Kremer. 2008. “Using Randomization in Development Economics Research: A Toolkit.” _Handbook of Development Economics_ 4 (5): 3895–3962.

Ebrahim, S. H., M. T. McKenna, and J. S. Marks. 2005. “Sexual Behavior: Related Adverse Health Burden in the United States.” _Sexually Transmitted Infections_ 81 (1): 38–40.

Fairlie,
the beginning of the crisis.<sup>10</sup> These controls net out any effects that may arise from the unequal distribution of men- and women-led firms across regions and income-groups, or from differences across countries in the timing of the survey and severity of the COVID-19 shock. We still denote these estimates unconditional because–unlike the conditional estimates further below–they do not control for any firm-level traits that may drive gender gaps.

We estimate (1) using ordinary least squares (OLS) when the dependent variable is continuous, and using a Probit model when the dependent variable is binary. All our computations use the inverse of the number of observations in each country as weights (to give each country the same weight regardless of the sample size).

We use Google mobility trends around transit stations to proxy for both the peak and the severity of the first wave of the COVID-19 shock (Google, 2020; Apedo-Amah et al., 2020). The peak of the crisis corresponds to the peak in the mobility drop in each country during the first phase of the pandemic, which occurred at varying dates in the spring of 2020. The fixed effects for the timing of the interview measure the number of months before or after this peak.<sup>11</sup> To proxy for the severity of the shock we use the magnitude of the drop in mobility at the peak. More precisely, we sort countries into quartiles of the cross-country distribution of mobility drops at the peak. In our sample countries in the top 25% (for example Greece, Italy, South Africa) faced more severe shocks with drops of 73 to 88% relative to the February baseline than countries in the first quartile (such as Kenya, Mongolia, and Tanzania), which exhibited drops at the peak of 24 to 45%.

Our conditional estimates test whether the gender gap is larger among specific groups of businesses, for example, in particular sectors (e.g. hospitality) or among businesses
following the rise in the Brazilian minimum wage.

**The impact of the minimum wage on non-wage living standards (prices, consumption, non-wage compensation, etc.):** So far, we have reviewed the evidence referring to wages and employment (broadly defined). However, the availability of new data sources allow researchers to observe many other effects of minimum wages on economies, and uncover previously unknown margins of adjustment for both firms and households. Examples of these datasets are credit card information, consumption expenditure surveys, surveys on firms that report prices, etc. A summary of non-wage adjustment margins of firms is provided by Clemens (2021). Even when an increase in the minimum wage has no employment effects, it might have negative welfare consequences on those who intends to help, since there are many other margins of adjustment available to firms (including output prices, non-wage compensation, job attributes like effort requirements, safety measures, and the overall quality of the working environment).

Price adjustments are an important margin as reviewed by Dube (2019), and direct evidence of this is provided by other work. In Hungary, Harasztosi and Lindner (2019) show that minimum wage increases lead to increases in prices in sectors with no international competition (services), while employment declines take place in sectors with international competition and where pass-through of higher labor costs to prices is not available (manufacturing). Overall, 75% of the minimum wage increase is paid by consumers and 25% by firm owners. This paper provides evidence that the job and industry structure of a country matters. In countries where low-wage jobs are concentrated in the local service sector (such as Germany or the US) raising the minimum wage is likely to cause limited negative employment effects, but important price increases. In cities where mainly high-income consumers enjoy services provided by low-wage workers, this redistribution will be from rich to poor. The heterogeneous responses across industries also highlight the advantages of sector-specific minimum wage policies used in some European countries such