rafmacalaba/datause-encoder-data huggingface dataset:
**Table 12: Number of workers in enterprises sampled for UBI and weighted estimates of industry employment‐2002 and 2009**

|||Worke|rs in enumer|ated enter|prises|We|ighted estimat|e of total nu|mber of w|orkers|
|---|---|---|---|---|---|---|---|---|---|---|
|**One Digit Industry (ISIC Rev. 3.1)**|N|umber|% share|in total|Growth<br>rate|Estimat|ed total|% share in|total|Growth<br>rate|
||2002|2009|2002|2009|(%pa)|2002|2009|2002|2009|(%pa)|
|**Traded sector:**|||||||||||
|A ‐ Agriculture, hunting and forestry|13,966|7,047|10.3|3.6|‐7.1|39,802|320,284|2.4|4.9|100.7|
|B ‐ Fishing|785|82|0.6|0.0|‐12.8|1,971|36,687|0.1|0.6|251.7|
|C ‐ Mining and quarrying|136|1,058|0.1|0.5|96.8|6,117|13,409|0.0|0.2|17.0|
|D ‐ Manufacturing|44,112|71,438|32.5|36.9|8.8|233,098|467,782|13.8|7.1|14.4|
|_Traded sector total_|58,999|79,625|43.5|41.1|5.0|280,987|838,162|16.7|12.7|28.3|
|**Non‐traded sector:**|||||||||||
|**_Other industry:_**|||||||||||
|E ‐ Electricity, gas and water supply|1,853|358|1.4|0.2|‐11.5|19,240|2,014|1.1|0.0|‐12.8|
|F ‐ Construction|4,460|9,490|3.3|4.9|16.1|10,851|64,828|0.6|1.0|71.1|
|**Services:**|||||||||0.0||
|G ‐ Wholesale/retail trade and services|13,883|17,846|10.2|9.2|4.1|781,035|3,357,276|46.4|50.9|47.1|
|H ‐ Hotels and restaurants|8,326|10,579|6.1|5.5|3.9|200,282|886,824|11.9|13.4|49.0|
|I ‐ Transport/storage/communications|7,507|8,849|5.5|4.6|2.6|54,681|168,922|3.2|2.6|29.8|
|J ‐ Financial intermediation|4,732|13,379|3.5|6.9|26.1|27,779|517,303|1.6|7.8|251.7|
|K ‐ Real estate, renting business|10,712|16,770|7.9|8.7|8.1|42,955|197,120|2.6|3.0|51.3|
|L ‐ Public administration and defense|5,389|1,520|4.0|0.8|‐10.3|64,457|1,749|3.8|0.0|‐13.9|
|M ‐ Education|8,479|22,105|6.2|11.4|23.0|87,176|95,451|5.2|1.4|1.4|
|N ‐ Health and social work|7,073|7,285|5.2|3.8|0.4|45,500|242,458|2.7|3.7|61.8|
|O ‐ Other social and personal|4,164|2,758|3.1|2.9|‐4.8|68,213|221,823|4.1|3.4|32.2|
|Q‐ Extraterritorial organizations|134||0.1|0.1||1,117|293|0.1|0.0|‐10.5|
|_Non‐traded sector_|||||||||||
|<br>_total_|76,712|110,939|56.5|58.9|6.4|1,373,197|5,689,218|81.5|86.3|44.9|
|**Grand total**|135,710|190,564|100.0|100.0|5.8|1,684,275|6,594,222|######|100.0|41.6|

46
Table 6 (cont.) The Gains From Cereals Tariff Liberalization

|Country|Aggregate<br>Gains|St.Dev.<br>Gains|Consumption<br>Efects|Income<br>Efects|Pro-Poor<br>Bias|Gains<br>Poor|Gains<br>Rich|
|---|---|---|---|---|---|---|---|
|Developing world|0.42|0.26|0.67|-0.25|0.53|0.30|0.23|
|Liberia|0.38|0.09|0.43|-0.06|0.43|0.30|0.13|
|Madagascar|0.04|0.09|0.12|-0.09|0.00|0.13|-0.13|
|Malawi|-0.01|0.07|0.07|-0.08|-0.07|0.09|-0.16|
|Mali|0.39|0.38|0.72|-0.33|0.74|0.06|0.68|
|Mauritania|1.05|0.33|1.16|-0.12|1.39|0.61|0.78|
|Moldova|0.22|0.08|0.26|-0.04|0.31|0.15|0.15|
|Mongolia|0.78|0.18|0.79|-0.00|1.04|0.56|0.49|
|Mozambique|0.07|0.15|0.20|-0.13|-0.06|0.26|-0.33|
|Nepal|0.63|0.32|0.81|-0.18|1.06|0.24|0.82|
|Nicaragua|1.03|0.52|1.33|-0.30|1.75|0.43|1.31|
|Niger|1.17|0.34|1.25|-0.08|0.72|1.44|-0.72|
|Nigeria|0.40|0.24|0.78|-0.38|0.11|0.59|-0.48|
|Pakistan|0.33|0.55|0.60|-0.28|1.03|-0.37|1.41|
|Papua New Guinea|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Rwanda|0.37|0.29|0.71|-0.33|0.66|0.19|0.46|
|Sierra Leone|0.67|0.21|0.95|-0.28|0.79|0.52|0.27|
|South Africa|0.00|0.00|0.00|-0.00|0.00|0.00|0.00|
|Sri Lanka|0.52|0.21|0.61|-0.09|0.74|0.21|0.53|
|Tajikistan|0.69|0.19|0.69|-0.01|0.66|0.65|0.00|
|Tanzania|1.29|1.03|1.73|-0.44|2.61|0.42|2.18|
|Togo|0.53|0.11|0.69|-0.16|0.55|0.46|0.10|
|Uganda|0.29|0.60|0.81|-0.52|1.19|0.01|1.17|
|Ukraine|0.46|0.10|0.47|-0.01|0.60|0.33|0.26|
|Uzbekistan|0.34|0.08|0.55|-0.21|0.34|0.33|0.01|
|Vietnam|-1.70|0.83|0.05|-1.75|-2.70|-0.59|-2.11|
|Yemen, Rep.|1.11|0.34|1.13|-0.02|1.47|0.67|0.80|
|Zambia|1.66|0.19|1.78|-0.12|1.82|1.48|0.33|

Notes: Authors’ calculations based on HIT data. The table presents the gains associated with the elimination of import tariffs on cereals.

37
# **3 Synchronization of U.S. and global cycles**

## **3.1 Synchronization of business cycles**

Business cycles in the United States, other advanced economies and EMDEs have been highly synchronous (Figure 7). This is partly a reflection of the strength of global trade and financial linkages of the U.S. economy with the rest of the world. In addition, it is also a reflection of global shocks that had a common effect on many countries at the same time. Business cycles in the United States are somewhat more correlated with those in other advanced economies than those in EMDEs (with the important exception of Mexico) because of deeper economic integration.

## **3.2 Concordance of cyclical turning points**

International business cycle synchronization tends to be particularly strong when the U.S. economy is in recession but, over the phases of the U.S. business cycle, GDP growth in the rest of the world correlates with the U.S. cycle substantially. For example, growth was on average higher in other advanced economies and EMDEs during periods of U.S. expansions than it was when the U.S. economy was in recession. More importantly, although the four recessions the global economy experienced since 1960 (1975, 1982, 1991, and 2009) were driven by a host of problems in many corners of the world, they all overlapped with severe recessions in the United States.<sup>5</sup> The global recession of 1975 coincided with the beginning of a prolonged period of stagflation, with low output growth and high inflation in the United States. During the 1982 recession, the United States and several other advanced economies experienced a sharp decline in activity along with a steep increase in unemployment in the wake of anti-inflationary monetary policies. The economy again went into recession in July 1990 following a period of depressed activity in the housing market and a credit crunch. The deep global recession of 2009 was driven by the global financial crisis, which had its origins in the U.S. mortgage market but turned into a truly global crisis after the collapse of Lehman Brothers in September 2008. These four U.S. recessions coincided with global recessions; there were, however, four other U.S. recessions post-1960 that did not.

An event study of the last two U.S. recessions, in 2001 and 2009, illustrates the concordance of the turning points of the U.S. business cycle with those of other advanced economies

> 5Global recessions are contractions in inflation-adjusted output per capita accompanied by broad, synchronized declines in various other measures, such as world industrial production, employment, trade and capital flows, and energy consumption (Kose and Terrones 2015).

11
# **Introduction**

Data on sectoral investment and capital stocks are essential for empirical research in sectoral productivity, yet cross-country panels are rare for countries outside of the OECD. Crego et al. (1998) introduced a database on the capital stock in the agricultural and manufacturing sectors for 57 developed and developing countries for the years 1967-1992. We have updated the agricultural component of this database to the year 2000 for a subset of 30 countries. We construct three capital sub-components series: treestock, livestock, and fixed capital in agriculture. We modify a commonly used methodology for integrating investment to obtain the fixed capital stock. This methodology can also be used to compute comparable fixed capital stocks for other sectors and for the economy as a whole, in order to facilitate comparative analyses.<sup>1</sup> After describing selective characteristics of the updated agricultural capital data set, including the evolution of aggregate capital stocks over time and the changing composition of agricultural capital, we revisit an earlier analysis of agricultural productivity that utilizes our data set on agricultural capital. We then show how the frequently used practice of employing farm machinery as a proxy for agricultural fixed capital leads to substantially different results.

# **Background**

Measures of agricultural capital are fundamental to two important and related fields of empirical study. The first concerns the determinants of agricultural productivity and growth; the second concerns the structural transformation of developed and developing countries. Both areas of study are important for understanding economic development, since agriculture remains the largest source of employment in many poor countries today.<sup>2</sup> In general, as economies grow, labor flows from agriculture to other sectors. As a consequence, the shares of labor and GDP in the agricultural sector decline as part of this process; however in economies that perform well, the sector continues to grow through a build-up of physical and human capital and through the adoption of more productive technologies.<sup>3</sup>

> 1 This avenue of research is beyond the scope of this paper.  Larson et al. (2000) provide an analysis of capital stocks in manufacturing and agriculture, as well as for the economy as a whole for 60 countries from 1967-1992.

> 2 For example, the World Bank’s World Development Report (2008) on agriculture estimates that 75 percent of the world’s poor live in rural areas and that most poor rural households depend on agriculture as their primary source of income. The report classifies 21 developing countries as agricultural and an additional 19 as transitional.

> 3 See Mundlak (2001), Mundlak, Larson, and Crego (1998), and Larson and Mundlak (1997).

2
to armed conicts ever since. In the case of Sri Lanka, terrorist groups such as the Liberation Tigers of Tamil Eelam (LTTE) and Janatha Vimukthi Peramuna (JVP) have remained active after being defeated in the Sri Lankan Civil War (1983 to 2009), motivated by ethnic disputes between a Tamil minority and the Sinhalese majority. As a result, terrorism in SriLanka and India has concentrated in specic areas This paper aims to estimate the relationship between violence and women's decisions to work in four South Asian countries. In doing so, we aim to extend the literature in two directions. First, while other studies use national-level data, we look at within-country dierences in violence using micro-data on employment and (i) number of attacks by administrative division; (ii) number of deaths, and (iii) number of people wounded as proxy measures for violence. Second, we pay particular attention to the distinction between the eect over the extensive and intensive margins; that is, whether women work or do notwork or to not work vis-Ã -vis the number of hours supplied, respectively. As such, our paper extends Menon and van der Meulen Rodgers [2015] and Kreibaum and Klasen [2015] by allowing for the possibility that the added worker hypothesis is at work in the context of terrorism in South Asian countries. In Section 2 we present two simple models that illustrate the relationship between violence and the extensive and intensive margins of female labor supply. Section 3 describes the data use in this paper. Section 4 explains the methods used to estimate the eect of our three violence measures on FLFP. Section 5 describes the results and their implications. Section 6 draws conclusions.

# 2 Theoretical Considerations

Labor supply decisions are distinguished between the extensive margin, the decision to work or not, and the intensive margin, the decision of how much to work. In this section, we describe the theoretic intuition for each concept in turn.

## 2.1 The extensive margin of labor supply

Women decide to work if the market wage, _w_ , is above the sum of their reservation home value, _h_ , and the costs of working (paid child care, transportation, and other factors) that we summarize in _c_ . Therefore, the decision to work is represented by a simple equation: While the presence of violent conicts may increase the cost _c_ of working in the market, we argue it is dierent from other costs because it introduces risk. As long as women are averse to risk, we can decompose the cost of violent conict into a level component that is included in _c_ and the variance (as a component of risk) that we denote by _σ_<sup>2</sup> . Thus, we can augment equation (1) by adding in the variance, or the uncertainty caused by conicts. The new equation 4
In sum, when controlling for village, household, and individual characteristics, the estimated effect of village participation in migrant labor markets remains negative. In each case, the _− −_ coefficient estimate is between 1 _._ 80 and 2 _._ 43 and significant at the 5 percent level. In all specifications, over-identification tests on the quartic in years-since-IDs offer further support for the hypothesis that the instruments are not systematically related to unobservables influencing both migrant opportunity and high school enrollment.

To provide a further robustness test, we test an alternative IV approach in Appendix A.2, which makes use of time-series information on city level GDP collected by China’s NBS to proxy for annual changes in labor demand in potential migrant destinations. The instrument is a function of GDP growth in the nearest city outside the home county and the distance from the village to that city. When this instrument is used in a basic version of equation (2), regressing high school enrollment on the village migrant share, village fixed effects and province-year effects, the coefficient on migrant share is −2.12, while it is −2.43 in the model with the quartic in years-since-IDs was distributed, and the difference in these coefficient estimates is not statistically significant.

Given that our primary interest lies in understanding how migration affects incentives to enroll in high school, we have devoted our attention to the coefficient on the migrant share of the village labor force in the IV-GMM model. To understand the performance of the instruments, the reduced form effect of introducing IDs on the probability of enrolling in high school is also of interest. We thus also estimate the reduced form: where _ID_ is the years-since-IDs were issued in the county where the village is located. The estimates α�1 _,_ α�2 _,_ α�3 _,_ and α�4 can be used to predict the effect of ID availability on the probability of enrolling in high school after controlling for province-wide trends in enrollment, time-varying household and village characteristics and village fixed effects. In the pure reduced form, the predicted effect of ID availability on the probability of enrolling in high school is pronounced and negative (as illustrated in Panel A), while the effect on migrant share is positive. This negative probability is a deviation from a positive trend toward increased enrollment. In Panel B, we illustrate a trend based on average predictions from a regression including only village and province-year effects and plot across calendar years, instead of years-since-IDs became 22
Policy Research Working Paper 7536

# **Abstract**

How do global supply chain linkages modify countries’ incentives to impose import protection? Are these linkages empirically important determinants of trade policy? To address these questions, this paper introduces supply chain linkages into a workhorse terms-of-trade model of trade policy with political economy. Theory predicts that discretionary final goods tariffs will be decreasing in the domestic content of foreign-produced final goods. Provided foreign political interests are not too strong, final goods tariffs will also be decreasing in the foreign content of domestically-produced final goods. The paper tests these predictions using newly assembled data on bilateral applied tariffs, temporary trade barriers, and value-added contents for 14 major economies over the 1995–2009 period. There is strong support for the empirical predictions of the model. The results imply that global supply chains matter for trade policy, both in principle and in practice.

This paper is a product of the Trade and International Integration Team, Development Research 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 cbown@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1 Introduction**

Electricity generation is a water-intensive process, with most power plants requiring water to spin hydroelectric turbines or cool thermoelectric generators (Sanders, 2015). In Latin America and the Caribbean (LAC), approximately half of the total power generation comes from hydropower, while the other half comes from combustion power, including coal, oil, gas, and biomass (IEA, 2021). Because hydrological droughts predominantly limit the generation capacity of hydropower plants, droughts can shift generation to combustion power plants. A plausibly important but understudied consequence of this shift in generation is the worsening of local air quality. Changes in air quality are a first-order concern, as exposure to fine particle air pollution (PM2 _._ 5 _, <_ 2 _._ 5 μm) is detrimental to human health (Dockery et al., 1993; Pope III et al., 2002, 2009; Cohen et al., 2017), with recent studies showing that even low levels of exposure lead to adverse health outcomes (Weichenthal et al., 2022). With nearly half a billion individuals in LAC residing near a combustion power plant and climate change leading to more frequent and severe hydrological droughts in the region, a key question is determining how air quality changes in response to this shift in electricity generation.

In this paper, we fill this gap by quantifying the relationship between hydrological droughts and increased PM2 _._ 5 concentrations around combustion power plants in LAC. We begin by assembling a monthly frequency power plant-level panel covering the 2000 to 2020 period. The panel provides information on both PM2 _._ 5 concentrations in the proximity of combustion power plants and marketlevel measures of the level of hydropower generation capacity in water stress. These market-level measures are derived from granular watershed-level measures of hydrological drought. Our preferred market-level measure is the fraction of hydropower generation capacity affected by drought (FHD). The panel also provides information on an extensive set of meteorological factors, emissions from wildfires, proxies of electricity demand, and characteristics of power plants. Using this dataset and fixed effect methods, we estimate the excess air pollution created by changes in the FHD. Our estimates have a causal interpretation because, conditional on meteorological factors, changes in electricity demand, and seasonal trends, hydrological droughts create a plausibly exogenous shock to hydropower generation. Because hydrological droughts can increase PM2 _._ 5 through alternative mechanisms, such as increasing the likelihood of wildfires, we exclude plant-month observations affected by wildfires.

Our results fall into four categories. The first set of results shows that hydrological 2
# **Figure 5 Translation of Monopoly Equilibrium and Profit Variation in the Scenario of Higher Dwell Time**

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Therefore, a rational monopolist that charges the profit-maximizing price will seek to reduce port dwell times to optimize profits. However, other pricing behaviors of monopolist companies seem to contradict this conclusion.

A few traders operating in monopolistic situations, especially in landlocked countries, set their prices such that their profits are not affected by adverse logistics conditions, such as delays in delivery and congestion in ports. They just calculate their total logistics costs for each operation after delivery and apply a constant markup to set the final selling price (cost-plus strategy). These traders seem to be indifferent to longer dwell times because their margins and profits are unaffected and they pass on to their customers any extra logistics costs due to longer dwell time.

However, if we try to project this situation, we reach a different conclusion: higher marginal costs would normally lead to different monopoly equilibrium, with a higher selling price, but also lost sales. If the company manages to keep its total profits unaffected by a price rise, the demand curve will be different from the one depicted in Figure 5.

In this case, we have a situation close to the one depicted in Figure 6, where demand is inelastic to price, at least for reasonable price variations. Very desirable products, such as critical production inputs or indispensable food supplies or drugs, perhaps would be purchased by customers at any price, unless their price reaches unaffordable levels or becomes so high that the customer would bear the consequences of not buying the product. We can represent demand in this context by a vertical line or a kinked curve of the kind shown in Figure 6.

13
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