rafmacalaba/data-use-mentions-extended huggingface dataset:
# V. Conclusion

This study relies on a Tropical Cyclone Integrated Assessment Model to predict the damages that climate change may cause to tropical cyclones. The A1B emission scenario is combined with four climate models. A tropical cyclone generator is then used to create 3000 tropical cyclones in each of five ocean basins for both the current climate and the future climate. The path and magnitude of each of these storms is followed for each scenario. The results of this analysis suggest that climate change will cause tropical cyclone intensity to increase in both the North Atlantic and North West Pacific ocean basins. The reliance on modeling individual storms in this study leads to far more accurate measures than using blanket assumptions about increased intensity. The analysis is the first to be able to detect different outcomes in different locations.

The study also estimates a new damage function for tropical cyclones. Data from the United States is used to estimate the relationship between damage per storm and storm intensity. The analysis suggests that minimum pressure provides a more accurate measure of storm intensity than maximum wind speed. The results of this analysis suggest that damages are highly sensitive to storm intensity. Damages are expected to double with a 1.2 percent decrease in minimum pressure.

International data is then used to estimate the relationship between national damages and income and population density. The results of this analysis suggest that damages increase with income but fall slightly with population density. A doubling of income increases damages by 40 percent. Doubling population density reduces damages by 20 percent. The results using the international data indicate that the vulnerability of countries does not vary in proportion to income and population as previously thought. The income and population elasticity of damages is less than unitary. As incomes rise in the future, damages from tropical cyclones will increase, but not as fast as previously thought. Future income and population is predicted to increase tropical cyclone damages from $26 billion to $55.5 billion USD/yr with the current climate. Future damages will double even without climate change. However, damages
since women participate in far lower numbers than men to the labor market and, among those who do participate in the labor force, unemployment levels among women tend to be higher. However, focus group participants held the view that for men employment was an economic and social imperative, while for women it was more of a choice. Hence, the self-esteem of males was tied more closely to their employment status than that of females. The World Bank (2011) study summarizes focus group participants’ viewpoints as follows: “ _Young men are thus seen as suffering most from adverse labor market conditions, not because the objective situation is more adverse for them, but rather, because it is much more socially detrimental for men to be excluded from the labor market. Although it may be highly desirable for women to work, it was not a social requirement: a woman without work would still be respectable and able to find satisfaction in life primarily in marriage and children._ ” (p.41) Quantitative data are also indicative of strong gender norms. For example, the time use module in the MHYS 2009-10 suggests that young women on average do far more of the housework than do men. In fact, employed women spend much more time in household chores than non-employed men as shown in Table 1. Akerlof and Kranton (2000) argue that such patterns, seen in the wealthier developed countries as well, results not from women’s comparative advantage in home production but rather from the importance of gender identity.

**Table 1: Share of Time (%) spent on household chores and child care for 15-29 year olds (as a fraction on non-sleep / non-personal time)**

|**Characteristics**|**Female**|**Male**|**Characteristics**|**Female**|**Male**|
|---|---|---|---|---|---|
We find significant differential effects of bribery by firm size and firm age. Paying bribes decreases sales growth of low-sales-generating firms more severely than high-sales-generating firms. Findings of a negative effect of bribery are robust to different sub-samples and controlling for unobservable firm fixed effects with a panel data set. By applying an instrumental variable method, we correct for reverse causality.

It is important to discern which types of firms are the most sensitive to business environment distortions. From a policy perspective, our results reinforce the importance of micro-level studies to reach conclusions to help policy makers make specific recommendations.

23
# **1. Introduction**

A large empirical literature has been concerned with the sources of macroeconomic fluctuations in developing countries, seeking to disentangle the respective contributions of domestic and external disturbances to business cycles. For the most part, the literature finds that idiosyncratic shocks play a much bigger role than external shocks. The latter are typically taken to include global output shocks, terms of trade (or global commodity price) disturbances, and international interest rate disturbances plus, in some cases, other exogenous shocks such as natural disasters or shifts in investor risk perceptions; see for example Hoffmeister, Roldós and Wickham (1998) and Raddatz (2008a) on Sub-Saharan Africa, Raddatz (2007) on low-income countries, and Ahmed (2003) and Raddatz (2008b) on Latin America. These papers employ VAR (or PVAR) methods, but similar conclusions emerge from other approaches – e.g., using a factor model, Kose, Otrok and Whiteman (2003) conclude that domestic factors typically account for the lion’s share of output fluctuations in developing countries.<sup>1</sup> The literature also tries to establish the leading sources of these externally-driven fluctuations. However, except in rare cases, the global variables affecting developing economies are not mutually orthogonal, and therefore this requires a suitable approach to identification. Most of the literature employs Cholesky factorizations imposing a causal ordering among the external variables considered in the analysis. Depending on the country and time samples employed, the studies variously attribute the leading role to financial disturbances – usually in the form of

> 1 These papers use annual data. Empirical studies using higher‐frequency (i.e., monthly or quarterly) data often find a bigger (but in most cases still secondary) role of external shocks; see e.g., Canova (2005), Uribe and Yue (2006) and Mackowiak (2007).

2
This has important implications for empirical investigations of international trade. Typically, studies use trade data from specific nomenclatures, ensuring consistent comparisons for products in the years in which that nomenclature is used. However, this approach can inadvertently include years marked by major discrepancies due to the nomenclature’s introduction. This is especially significant given that many empirical applications of trade data bypass nomenclature conversions. Some studies have moved toward replication of findings across different nomenclatures to show the robustness of the obtained results.

A notable finding from Figure 5 is that, although only about half of all records are successfully mirrored, a substantial part of the trade value in cross-border transactions can be compared against mirror records, applying equally to both imports and exports. The percentage of trade values verifiable against mirror data is impressively high. This has important implications for reconciliation procedures of trade data. Reconciliation procedures supply a reconciled value for records that can be successfully mirrored, and Figure 5 demonstrates that a considerable volume of global trade is contained in such records. However, caution is still necessary as the large concentration of trade volumes in few reporting countries obscures persistent reporting problems in data quality from some reporters.

The high percentage of trade value with successful mirroring does not necessarily imply accuracy in reporting and absence of discrepancies. To investigate whether discrepancies in reporting have decreased, for each year, we calculate the weighted discrepancy index across all bilateral transactions at the highest level of product aggregation. This will allow us to explore the magnitude and direction of the discrepancy in international trade flows. In addition, we calculate the weighted discrepancy index for successful mirrored records. This will allow us to explore whether the improvements were mostly due to an increasing number of reporters or accompanied by an increase in the quality of reporting on successfully mirrored records. We observe a welcoming trend in which the divergence in recorded values, as indicated by the weighted discrepancy index, is declining. Thus, we note not only an increase in dual recording but also a reduction in the discrepancy level between recorded values.
# APPENDIX: UPDATING THE NEHRU AND DARESHWAR (1993) DATA BASE

The data base constructed by Nehru and Dareshwar (1993) for the period 1950-1990 was updated until 1995 for the 18 Latin American countries considered in this paper. To this end, we used information from the World Bank's "World Development Indicators Data Set" (WDI). A brief description of the procedures used in this updating are described in this appendix.

1) Capital Stocks This series was calculated using the perpetual inventory method, which is based on the following accumulation equation: Kt= (1- d)<sup>t</sup> K(0) + Z(1- d)' **It-i** (1) where Kt is the capital stock at time t (in 1987 prices), K(0) is the initial capital stock (in period 0), It-i is the Gross Domestic Fixed Investment in period t-i, and d is the depreciation rate. Nehru and Dareshwar (1993) estimate K(0) by a modification of a technique proposed by Harberger(1978). The procedure is based on the assumption that in steady state the rate of growth of output (g) is equal to the rate of growth of capital stock. By re-arranging (1), this rate can be written: (Kt-Kt_)/Kt_, = -d +(It/Kt-1) (2) which, by the above assumption, implies Kt-I = It/(g+d) (3) Thus, in period 0, the capital stock can be calculated as: K(0) = I/(g+d) Nehru and Dareshwar (1993) calculate I, as the fitted value of a log-linear trend of _I,,_ adjusting for trend-breaks when appropriate. The depreciation rate is assumed to be 4 percent, and g is derived from the series of real GDP at market prices. Equation (1) is then applied to calculate the rest of
concept of “semi-elasticities” has been proposed to address this, but arguably suffers from the reverse issue (a country with a baseline poverty rate of 9 percent cannot achieve a 10 percentage-point poverty reduction, while a poorer country with baseline poverty of 60 percent can).

> 18 This would also mean that inequality in SSA is higher than what is measured by household surveys, which is what is argued by Chancel et al. (2023).

9
) to split green sectors. BLS has identified 333 detailed industries where green goods and services are produced.

# **2. Review of the Literature**

In the existing literature, there are several studies that have quantitatively examined the impact of trade liberalization on labor markets for both developed and developing countries. However, a very few of these studies have focused on measuring the impact of trade liberalization on women’s employment and wages. In the empirical literature, the trade-gender nexus has been examined - using cross-country, country or sector multiple regression analysis, or computable general equilibrium (CGE) models. Moreover, in most cases this gender-differentiated impact of trade liberalization has been assessed using single-country CGE models only. Also, given the paucity of gender differentiated data, most of these studies have assessed trade impacts on gender focusing mainly on labor market impacts for the manufacturing sector. Though there are many other descriptive studies that have analyzed gender-specific effects of trade liberalization, the focus of this review is on empirical studies, especially the ones that have used CGE models.

7
Note that poverty rates estimated with welfare aggregates are _higher_ than poverty rates estimated with total expenditures. This result is a priori surprising, given that the former include food subsidies whereas the latter do not. The reason for this is that, given the official poverty measurement methodology, ignoring food subsidies not only decreases welfare aggregates, but also decreases poverty lines. Indeed, the price of a calorie, which defines a household’s food poverty line, depends on the unit values of food items. Ignoring subsidies implies smaller unit values for subsidized food items, which in turn reduces the price of a calorie and thus the household’s food poverty line. One might think that, on average, this reduction in the food poverty line could approximately compensate for the reduction in household consumption, implying that the net effect of ignoring subsidies might cancel out. This is not the case, however, as we illustrate with a simple example. Assume that ignoring subsidies decreases the value of household consumption by  . Under simplifying assumptions,<sup>14</sup> the food poverty line is also reduced by  . However, this does not mean that the poverty rate is unchanged, because the non-food poverty line is computed as a fraction of the food poverty line. If the imputed share of food is 0.5, this implies that the non-food line is reduced by 0.5  . Overall, the poverty line is reduced by 1.5  , which is a larger reduction than the decrease in the value of household consumption. This explains why poverty rates are lower when ignoring food subsidies.

This explains why, in all regions, the poverty rates estimated with welfare aggregates are higher than those estimated with total expenditures. Yet, if the difference is non-negligible, we deem that the two estimates are sufficiently close to one another for our purposes, namely assessing the poverty trend over the period 2015 to 2017/18 and contrasting the official poverty measurement methodology with more standard alternatives. Nevertheless, when interpreting the
Middle East that have a survey completed in the last three years and have a large sample size of over 600 firms in order to construct sectoral breakdowns.

The World Bank Enterprise Surveys are establishment-level surveys conducted on a stratified random sample of small (5-19 employees), medium (20-99 employees), and large establishments (over 100 employees). The questionnaire includes a wide range of topics, from infrastructure to management practices, labor, and performance. The survey is administered to businesses with at least 1 percent private ownership, that are not cooperatives, and were in full operation for the entirety of the last completed fiscal year. The sector of coverage includes all manufacturing (ISIC 3.1 Rev 15-37); Wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods (50-52); Hotels and restaurants (55); Transport, storage and communications (60-64); and Computer related activities (72). The sample contains a total of 11,759 interviews with top managers or owners; and approximately half of sampled firms (6,897) submitted income statement and balance sheet data (Table 1). Exporters account for 7 percent (Kazakhstan) to 31 percent (Morocco) of the sample.

# **<u>Table 1: Sample Details</u>**

|<br>**Country**|<br>**Survey**<br>**Year**|<br>**Last**<br>**Completed**<br>**Fiscal Year**|**Survey**<br>**Sample**<br>**Size**|**Number of firms**<br>**with full income**<br>**statement data**|**Share of**<br>**Exporters in**<br>**the Sample**|
|---
work as laborers instead (Johnson 2009). The government could therefore purchase in international markets drought insurance that would pay precisely when the costs of welfare programs are higher.

In 2006, Ethiopia participated in one of the first government-level index-based insurance products, a project spearheaded by the UN’s World Food Program (WFP) with technical assistance from the World Bank. Twenty-six weather stations throughout the country monitored rainfall daily, providing data to the French reinsurance company Axa Re. In the event of a drought, Axa Re would have paid US$7.1 million, to be disbursed in cash to as many as 300,000 farmers. The stations thus served as an early-warning system to trigger aid in the initial stages of a drought—up to four months sooner than traditional crisis aid—which would have enabled farmers to smooth their consumption by planting alternative crops and/or avoid selling off assets to survive. In 2007 the WFP and the World Bank developed software based on weather data to enhance this early-warning and monitoring system, and several donors, including the bank, the UK Department for International Development, and the U.S. Agency for International Development, are pledging resources earmarked for distribution in case these early-warning systems indicate a drought (World Bank 2010).

# 9.2 Role of the private sector

Our research findings also suggest some possible practical innovations to the way the product is delivered to households. For example, the importance of liquidity constraints suggests policies should be paid out as quickly as possible, especially during the monsoon season, when our data suggest households are particularly credit constrained. This has not always occurred in the past, at least in our study areas. (On a related matter, it is also likely to be helpful to be as explicit as possible up front with the farmer about the timing of the payment of any payouts.) A further
|
|Effective power tariff|U.S. cents per KWh|**Benin**|**Predominantly**<br>**thermal**|**Other developing**<br>**regions**|
|Residential|at 100 kWh|18|14.5||
|Commercial|at 100 KWh||18.8|5.0 – 10.0|
|Industrial|at 50,000 kWh|11|14.2||

_Source_ : Low-income country figures as of 2005 from the power AICD database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data. For Benin, national access data were taken from World Bank (2009a) and are based on estimates in 2006. Electricity consumption was taken from the CIA Factbook and is for 2007. Power outages were taken from World Bank (2009a) and are for 2009. Value lost to outages was taken from Enterprise Surveys 2009. Firms that indicate that power is a constraint and firms that own generators taken from Enterprise surveys 2004. Collection rate is taken from World Bank 2009a and is for 2009. System losses are for 2009 and taken from World Bank 2009a. Tariff information is for 2009 and is from World Bank 2009a.

* Represents information only for SBEE and does not include information for CEB; ** As of 2004

The power distribution utility in Benin, SBEE, is relatively efficient. Its system losses are modest relative to African peers, and its collection rates are relatively high. Total system losses are estimated at 21 percent, with 17 percent from distribution. While these losses are high relative to the 10 percent international benchmark, system losses are lower than in comparable low-income countries and even with the level of middle-income countries in Sub Saharan Africa (table 11). In other West African countries losses are higher: Togo has been burdened with transmission losses of around 30 percent (2004), Côte d’Ivoire around 23 percent (
PASS-THROUGH OF COMPETITORS’ EXCHANGE RATES 21 unfortunately data on sector shares for US manufacturing are unavailable below the 6 digit level, and the number of prices in each industry is small (important for calculating competitors’ prices).

The competitors’ RER variable has a correlation with the Federal Reserve’s Broad Trade Weighted Index of about 0.8 (calculated over the life of the good for either import or producer price samples), which might be considered an alternative measure of competitors’ RER (and was used in GI2011). See the online appendix for a discussion.

B.4. **Construction of competitors’ prices.** In the main specification, I construct an index of foreign competitors’ prices using import price microdata from the same NAICS 6 sector. For regressions where the dependent variable is an import price, the set of foreign competitors will change depending on the country producing the good, resulting in a different price index for each country × sector. That is, the foreign competitors’ price index for a German firm includes the prices of imports from all other countries selling in the US in the same sector (excluding all other German firms). For regressions where the dependent variable is a US producer price, the foreign price index includes the prices of all foreign producers in the same sector.

The index is constructed using a similar method as that in GI2011: I take the average growth rate of competitors’ prices in the same sector in the month, and cumulate growth rates to get an index. As the BLS samples according to size, no weighting adjustment is needed. Alternative methods produce similar results.<sup>29</sup> As an extension in Section 6, I consider pass-through of the prices of two other types of competitors in the same NAICS 6d sector: (i) other firms from the same country (compatriots), and (ii) US firms. Other compatriots’ prices are constructed from BLS microdata in a similar manner to foreign competitors’ prices
Policy Research Working Paper 5738

# **Abstract**

Shortly following its independence in 1991, Tajikistan suffered a violent civil war. This study explores the effect of this conflict on education and labor market outcomes for men and women. The results are based on the data from the 2003 and 2007 Tajik Living Standards Measurement Surveys that were separated from the 1992–1998 Tajik civil war by five and nine years, respectively. The regression analysis that controls for the cohort and regional-level exposure points toward a persistent and lasting gap in the educational attainment by women who were of school age during the war and lived in the more conflict-affected regions as compared with women the same age who lived in the lesser affected regions and also to the older generation. These empirical results support the anecdotal and observational evidence about the decline in female educational attainment in Tajikistan. Interestingly, this group of young women is more likely to hold a job as compared with the rest of the analytical sample. Conditional on being employed, men and women in the more conflict-affected areas do not receive wages that are significantly different from wages received by men and women in the lesser affected areas.

This paper is a product of the Gender and Development Unit, Poverty Reduction and Economic Management Network, with generous funding from the Government of Norway. 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 at olga.shemyakina@econ. gatech.edu.

_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