rafmacalaba/data-use-mentions-extended huggingface dataset:
application is not surprising. However, we do not seem to find differential recall bias between staple and cash crop harvests, although this might be partially explained by the fact that many households also sell staples, which would make information on staples equally salient as the data on cash crops.

Overall, the results seem to suggest that the majority of agricultural data collected through single visits over several months do not suffer from the large recall decay initially hypothesized, at least for the types of events and in the contexts studied here. Given the lack of studies on the topic, further research is warranted since this work is limited to specific countries and crops. Moreover, data with community or household specific harvest dates would offer a more refined measure of time elapsed from the harvest to the interview.

18
interference is spatial in nature and there is homophily in treatment effects on the space (i.e. points that generate largerthan-average effects reside close to each other), then clustering the standard errors at the region level will suffice if interference is contained within the region. Clustering the standard errors at the region level does not alter the main findings here. Appendix Table 8 replicates Table 6 (with our main impact estimates) using cluster robust inference.

> 26 This variable is constructed using data collected by the program administrators and is not self-reported by the household survey respondents.

22
2021, allowing activity to recover. Although business is not as usual yet and we are still in the midst of the crisis with more transmissible and virulent strains of the virus spreading (World Bank, 2021), this paper attempts to uncover the possible patterns of recovery by building on two waves of BPS data from 38 countries, covering 65,000 observations comprising 25,000 panel firms. Economists have long argued on the possible shapes of the recovery from the past crisis and including the current one. For instance, Sheiner and Yilla (2020) describe the possible shapes of the recovery from the current pandemic: ’Z’, ’V’, ’U’,’W’, ’L’ -shaped, and the Nike Swoosh.<sup>1</sup> While it is too early to confirm the shape of the overall economic recovery, our analysis suggests that the business recovery between May-August 2020 and October 2020 - March 2021 may take the form of either a ’U’-shaped or a Nike swoosh. Once more rounds of BPS data become available, we could possibly see a ’W’-shaped recovery.

Beyond the shape of the recovery, our analysis provides the most comprehensive assessment of several aspects of the recovery trajectory for firms in developing countries. Our cross-country analysis suggests a positive recovery with some catch up across countries in the second wave of BPS data (see Appendix Figure B1). Despite this positive recovery, heterogeneity across countries is still very large; with enormous differences between some countries in Europe and Latin America, and some countries in Africa and South Asia (See Figure 1, panel a). Sales in wave 2 range between

> 1On March 23, 2020 Angel Gurria, the OECD’s secretary-general, warned advanced economies that “... Right now we know it’s not going to be a ‘V’. It’s going to be more in the best of cases like a ‘U’ with a long trench in
Hausmann R., J. Hwang, and D. Rodrik, 2007, “What You Export Matters,” Journal of Economic Growth 12(1), 1-25.

Henry, P.B., 2000a, “Do Stock Market Liberalizations Cause Investment Booms?” Journal of Financial Economics 58, 301-334.

Henry, P.B., 2000b, “Stock Market Liberalization, Economic Reform, and Emerging Market Equity Prices,” Journal of Finance 55(2), 529-564.

Henry, P.B., 2007, “Capital Account Liberalization: Theory, Evidence, and Speculation,”Journal of Economic Literature 45(4), 887-935.

Jaud, M., M. Kukenova, and M. Strieborny, 2015, “Financial Development and Sustainable Exports: Evidence from Firm-product Data,” World Economy 38(7), 1090-1114.

- Jaud, M., M. Kukenova, and M. Strieborny, 2018, “Finance, Comparative Advantage, and Resource Allocation,” Review of Finance 22(3), 1011-1061.

- Jaud, M., M. Kukenova, and M. Strieborny, 2021,“Financial Dependence and Intensive Margin of Trade,” Working Paper.

Javorcik, B.S. 2004, “Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers Through Backward Linkages,”American Economic Review 94(3), 605-627.

- Jones, R.W., 1956-1957, “Factor Proportions and the Heckscher-Ohlin Theorem,” Review of Economic Studies 24(1), 1-10.

- Kaminsky, G.L., and S.L. Schmukler, 2008, “Short-Run Pain, Long-Run Gain: Financial Liberalization and Stock Market Cycles,” Review of Finance 12(2), 253-292.

- La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R.W. Vishny, 1998, “Law and Finance,” Journal of Political Economy 106(6), 1113-1155
across countries will not be appropriate. If education quality differs dramatically between countries, appropriate matching would require a consistent measure of education quality across all of our sample countries. We have not been able to find such a measure for all of the countries in our sample and therefore offer the following results with the caveat that they may not be robust to differences in education quality across countries.

The results in column (1) of Table 8 are consistent with those presented in Tables 1-3. The effects of shocks are strong and positive and the lagged difference is negative and statistically significant. The coefficient magnitudes are not necessarily intuitive, however. Boyer and Hatton (1994) suggest that the speed of convergence can be estimated as (1-b)/b. This implies that the speed of convergence would imply that convergence would be very slow (taking more than 53 years). For comparison, columns (2) and (3) present the results from the same exercise for shocks within the United States (2) and Mexico (3). For the United States we use monthly data from the Monthly Outgoing Rotation Groups (MORG) of the Current Population Surveys. For Mexico we use the quarterly household surveys from the ENOE surveys.

The results in columns (2) and (3) suggest that within-country convergence is much more rapid than convergence across countries. For the United States, convergence back to an equilibrium differential would take about 4 months.<sup>6</sup> Convergence to the equilibrium differential within Mexico takes considerably longer – about 7.6 years. This result is consistent with Chiquiar (2001) who suggests that differentials between Mexico’s northern border and southern regions grow over time. Note, however, that the responsiveness of shocks is similar across the three columns, which may suggest that the three are subject to common external shocks.

> 6 For another comparison, Robertson (2000) finds that the speed of convergence to the equilibrium differential between the United States and the Mexican border city
(when assessed using countries with data on both, see Wollburg et al 2023) and for the rest of this paper we refer to this measure as income regardless of whether the data used to estimate this comes from an income or consumption aggregate.

• **Education** to switch livelihoods or to access information and resources: this is proxied by a variable reflecting whether the household has an adult that has completed primary education. This is a very low level of education that is likely mostly relevant in lower income countries. The GMD is used because this allows us to have information on education and income for the same household which allows us to know whether an individual is deprived on one or both dimensions. Unlike poverty rates which have been extrapolated or interpolated to a common reference year, the estimates for education come from the year of the survey. In order to avoid using data that is too old, when the survey is outside of the range of +/- 5 years from the reference year we would ideally not use the survey. In practice, for 2019, this means we are using the range 2015-present. When we do this, data is only available for 125 of the 168 countries in the GMD and it is not available for either India or China, two very large countries which are important for global coverage. We present results including and excluding data that falls outside of this year range. In future work we will explore mechanisms to update educational attainment using demographic trends, or other surveys that provide information on education within the country and can be used to impute educational attainment in the GMD.

• **Water** : we use whether a household has access to at least limited standard water, as in the SDG indicator. This is available for 139 countries of 168 for all years and 120 countries for the period 2015present. There are some countries that are missing data on water access but that have achieved universal access to at least a limited standard of water as per the latest data available
is based on data that is nationally representative and caveat the data appropriately. For EGRA, only a sub-set of countries have nationally representation data. Of 72 countryyear observations, 42 are nationally representative. We produce the linking function based on countries that took part in nationally representative tests to guarantee population overlap. We apply the linking function to all countries with nationally representative EGRA results producing internationally comparable results. We also apply this linking function to non-national EGRA assessments. While we have confidence in the former estimates, we caveat the latter estimates. We exclude TIMSS results for Yemen which TIMSS highlighted as having a flooring effect and has an unrealistically low score. Our HLO Yemen score is thus derived from EGRA data. For Sri Lanka, we use a score derived from the National Assessment which was linked to TIMSS (Dundar et al., 2017). In the case of China and India we validate or extrapolate nationally representative data, described in Annex 3 and 4.

Second, tests should measure similar proficiencies. We link across precise subjects (math, reading and science) to ensure proficiency overlap. For EGRA, since it often has multiple languages of instruction, we only include scores for tests where the language of instruction is the same as the language of the test. This ensures we capture underlying cognitive skills rather than language effects.

Third, test differences should capture difficulty differences rather than country-fixed effects. We address this assumption in a few ways. As noted above, we link tests across doubloon countries that participate in tests that are nationally representative. The reliability of this linking exercise increases the larger the set Y ∩ X, the number of countries that take both tests being linked. Each update increases this overlap, making the inclusion of new countries in this update significant.

A novel contribution of this update which increases the reliability of this last assumption is the fixing of our conversion rate across a 15-20-year interval for recent years from 20002017. This enables us to increase the sample size in the set Y ∩ X for each doubloon
# **3.2 Survey implementation and sampling**

To collect the outcome variables of interest for this paper, I implemented surveys with women and one of their household members at the respondents’ homes. For funding reasons, these took place after the conclusion of the promotion program discussed in the previous subsection. For the analysis of the effects on the promoted women, which I call the “promotion analysis”, the field team targeted all 243 women who were nominated and eligible for the promotion program by the 27 factories.<sup>21</sup> This sample was interviewed twice in person at their homes. The first wave took place on average four months after the beginning of the promotion program in the factories, and the second wave on average ten months after the beginning of the program.<sup>22</sup> For power reasons, I also conducted biweekly phone surveys to collect additional measurements for a subset of outcomes of interest.

The sample for the analysis of role model effects on the women working with female supervisors, which I call the “role model analysis”, was determined during the follow-up surveys for the promotion program in the factories. On each trial line to which a participant of the promotion program had been assigned to trial as supervisor, female operators were sampled for the household surveys.<sup>23</sup> These are the so-called “exposed” operators. The trial lines had been chosen by the factories as needing a new supervisor before the start of the promotion program. In addition, in each factory, the same number of comparison lines was sampled from all other lines not involved in the promotion program, and from these lines, female operators were again sampled to be interviewed in the household. These latter operators are the “non-exposed” group, who are mostly supervised by men. Overall, 715 operators were sampled. The interviews for the exposure analysis took place on average nine months after the start of the promotion program.<sup>
to pull workers of both genders while scaling small enterprises, while men have a greater capacity to continue scaling with a focus on male employees.

We have also investigated a comparison point to the United States using the Longitudinal Employer-Household Dynamics (LEHD) database. The LEHD is a confidential database maintained by the U.S. Census Bureau. Sourced from required state unemployment insurance reporting, the LEHD provides linked employer-employee records for all private sector firms in 29 covered states. Among the information included for each employee is the worker's quarterly salary, age, gender, citizenship status, and place of birth. This wealth of information allows us to observe directly the establishment level workforce composition by gender in a manner similar to the Indian data. (By comparison, the LEHD does not record the gender of the business owner.) Kerr et al. (2015) provide additional details.

Our LEHD estimates are not disclosed, and we only provide an approximate benchmark in this first report for our India data. In the United States, both men and women tend to match with a workforce that is approximately 60% their own gender during the 2001-2010 period. This shows quite limited variation over states, and the time trend is overall very weak. The United States has roughly equal employment of both genders, so the expected shares are about 50%. Thus, the United 27
Policy Research Working Paper 6313

# **Abstract**

Utilizing four-dimensional (firm-product-destinationyear) Brazilian firm-level export data, the paper shows that antidumping (AD) duties result in a significant and dramatic increase in the unit values of the products that firms export to duty-imposing countries. Furthermore, it examines the effect of potential (retaliatory) AD duties on the unit price of the firms’ shipments. The findings suggest that AD activities in Brazil lead Brazilian exporting firms to increase their unit export prices for the named industries’ products to decrease the dumping margin and avoid the threat of retaliation by the target countries.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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 veysel.avsar@antalya.edu.tr.

_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. Background and Introduction_**

Integrated water resources management (IWRM) and organizing it primarily at the river basin level are two of the most common and widely repeated recommendations in the water resources literature of the last decade if not longer (Allee 1988; Galloway 1997; McDonald and Kay 1988; World Bank 1993). Basin management, which was previously assumed to be best undertaken through centralized arrangements, has come to be associated with the concept of decentralization, of managing water resources at the “lowest appropriate level.” (See, e.g., International Conference on Water and the Environment 1992; Mody 2001.) Several conceptual arguments have been presented in favor of decentralization in water resources management, and basin-level management in particular, including that the entire array of resources and use patterns in the basin will be taken into account, management decisions will be based on better knowledge of local conditions, and public participation will be greater and broader.

Empirical studies of river basin management systems provide opportunities to examine the claims made for basin-level water resources management, and to explore factors that influence its implementation and outcomes. In this research project those factors and their relationships to river basin management have been explored in two ways: with a survey of river basin organizations throughout the world, and with case studies of eight river basins analyzed in greater detail. Some of those eight cases have long histories of basin-scale institutions for water resource management, such as the Guadalquivir river basin in Spain and the Murray-Darling river basin in Australia. Others were established more recently, as in the Brantas basin of Indonesia, where a river basin corporation, the Brantas River Basin Public Corporation, the subject of this study, was established in 1990.

Indonesia shares the problems and issues of many developing countries pursuing a path of rapid economic growth. While Indonesia is rich in resources and has plentiful water supply at the outset, on a number of islands of the archipelago water resources have come under increasing pressure in recent years and are facing
household income and a higher bargaining power of female household members as female earnings increase. In all these cases, by affecting relative factor prices, trade alters the costs and benefits of restricting female participation in the labor force; countries that exhibit a larger comparative advantage in the brain sector will tend to discriminate less against women, since such practice comes at a larger cost.

We take the two main theoretical predictions of the model to the data. First, all else equal, countries where gender discrimination is less severe have a comparative advantage in exporting goods that are more intensive in the use of female labor. Second, under trade, countries with comparative advantage in female intensive sectors will reduce the severity of gender discrimination. To test these predictions, we use industry-level export data for 61 manufacturing sectors in 146 developed and developing countries over 45 years. We measure a sector’s female-labor intensity by its average ratio of female to total employment among countries with full data coverage, and test whether, consistent with the first prediction, countries where women are more empowered tend to export relatively more of the female-labor intensive goods. Next we follow the methodology of Almeida and Wolfenzon (2005), and use a country’s export shares in different sectors to aggregate the industrylevel measures of female labor intensity into a country-level measure of _female-labor needs of exports_ that captures a country’s comparative advantage in female-labor intensive sectors _._ We use this measure to test the second prediction: that gender discrimination is less severe in countries with a comparative advantage in female-labor intensive sectors. In testing both predictions, we recognize the possibility of endogeneity of our explanatory variable that comes from the model itself and address it using instrumentation strategies. In the first case, since the share of a country’s exports in a sector that is female-labor intensive may affect its attitude towards gender, we instrument the attitude towards gender by a measure of the distribution of religions in the population. In the second case, since the comparative advantage of a country in female-labor intensive
_Services PTAs_

# **Contents**

|**1**|**Introduction**|**3**|
|---|---|---|
|**2**|**Related literature**|**9**|
|**3**|**Data**|**13**|
||3.1<br>Services Policy and PTA Data . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>13|
||3.2<br>Services Trade Data . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>17|
||3.3<br>Sourcing of Value-Added . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>18|
|**4**|**Methodology**|**20**|
||4.1<br>Estimation strategy . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . .<br>20|
||4.2<br>Aggregation of services provisions . . . . . . . . . . . . . . .|. . . . . . . . . .<br>23|
|**5**|**Results for Cross-Border Services Trade**|**28**|
||5.1<br>Benchmark Overall PTA Efect . . . . . . . . . . . . . . . .
2022-30 2022-30<br>E. Potential growth with more frequent<br>natural disasters<br>Percent<br>6<br>Baseline Natural disasters damage<br>4<br>2<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>World Advanced EMDEs<br>economies<br><!-- End of picture text --> E. Potential growth with more frequent natural disasters F. Potential growth after a global recession in 2023 <!-- Start of picture text --> Percent<br>6 Baseline Recession damage<br>4<br>2<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>World Advanced EMDEs<br>economies<br><!-- End of picture text --> Sources: Consensus Economics, Haver Analytics, World Bank.

Note: EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A. B. Data for 34 countries, of which 13 EMDEs (3 in EAP [Indonesia, Malaysia, Thailand], 3 in ECA [Hungary, Poland, Romania], 6 in LAC [Argentina, Brazil, Chile, Colombia, Mexico, Peru], India in SAR) since 2000. GDP-weighted averages (at 2010-19 exchange rates and prices). Forecast error is the difference between actual and forecast investment growth; a negative error indicates overoptimism.

C. D. GDP-weighted arithmetic averages. Baseline scenario assumes that investment growth will match consensus forecasts for one- to nine-year-ahead investment growth for 2022-30. Correction for forecast errors assumes that investment growth will fall by the country-specific average historical forecast error over 1-9-