rafmacalaba/data-use-mentions-extended huggingface dataset:
country than in others. We would expect larger reductions in weather-induced price volatility in areas where improvements in market access have been faster. Data on access to markets in 1994 and 2007 is available from Schmidt and Kedir (2009) allowing a comparison of markets that saw significant improvements in market access prior to 2007 with markets that saw less improvement. We test whether the price impact of shocks is lower in markets with greater improvements in market access.

The demand effect of weather on local grain prices will fall if the sensitivity of household income to weather is reduced. This can occur if households increase the share of their income that comes from non-weather dependent sources. Since 2005, transfers have been made to households in selected districts as part of the PSNP. These transfers increase slightly when weather conditions are bad as there is some provision within the program for benefits to be scaled up when the weather is bad. Some districts have received transfers in kind and some in cash. The in-kind transfers from PSNP affect both the local demand and supply of food, but cash transfers have no supply effect, only a demand effect. We examine whether the impact of weather on local prices is smaller in places where the PSNP has been introduced. We examine whether the presence of cash transfers made as part of the PSNP has been associated with a lower demand effect of weather on prices in markets of the recipient districts, causing the inflationary supply effects to dominate in these markets.

Districts are not selected to receive transfers at random. The PSNP has been targeted to more food-insecure _woredas/_ districts in Ethiopia.<sup>11</sup> Weather shocks may have had a differential impact on prices in these markets even before the PSNP transfers were introduced in 2005. After controlling for these differences, we expect that the introduction of transfers in PSNP districts would keep demand for goods high mitigating any price impact of lower demand in the wake of a shock. This, however, requires that there were no other reasons for the relationship between weather
procedures completed by the providers. Direct observation data collected in the facilities also allow us to consider other proxies for the quality of care, such as the length of the consultation and whether the procedures were explained to the patients.

We first estimate the overall differences in access to antenatal care by comparing coverage and effective coverage among women of different wealth levels. We use concentration indexes to characterize inequality in antenatal care, following previous work by Wagstaff, Paci, and Van Doorslaer (1991). we show that inequality in effective coverage is more than three times as large as inequality in coverage alone. We then estimate a wealth‐quality gradient and show that each standard deviation increase in household wealth is associated with a four‐percentage‐point increase in quality of antenatal care, as measured by protocol completeness, corresponding to eight percent of average quality in the lowest quintile. Similarly, the proportion of procedures received by women with secondary education is nine percentage points higher than that received by those without schooling.

Next, we decompose the observed wealth gradient into three main components. First, we expect health facilities in wealthier areas on average to be of higher quality, explaining at least part of the observed wealth‐quality gradient. Indeed, controlling for village of residence reduces the wealth‐quality gradient from 4 to 1.8 percentage points of completeness, suggesting that spatial differences explain about half of the overall gradient. The remaining wealth gap represents within‐village variation in the quality of care. The within‐village wealth gradient appears to operate mostly through facility choice: we show that wealthier women are more likely to visit a facility outside their catchment area or go to a hospital or other higher‐level facility. Nevertheless, we also find a within‐facility quality‐wealth relationship, suggesting that even conditional on the specific facility chosen, wealthier women receive higher quality of care.

To understand within‐facility differences in the quality of care, we use detailed exit interview data to analyze the specific services provided and prices charged
is similar in sampling design with the Ethiopian data; it covers the universe of establishments employing more than 10 workers and takes a representative sample of firms employing less than 10 workers. The census was undertaken in two phases. In the first phase, the registry covers all establishment (25,865) and includes information on persons engaged, location, age and industrial group. However, it contains no balance sheet information. In the second phase, the survey covers _all_ establishments with at-least 10 workers and a 5 % sample of manufacturing establishments engaging less than 10 workers. Data on production, sales, wages and salaries, material costs, fixed assets are reported for these firms. The raw data consist of a total of 3,302 manufacturing establishments. Applying the weights constructed by the GSS, sampled establishments represent a population of 22,544 firms in the country.<sup>10</sup>

**Kenya** The Kenyan data come from the 2010 Census of Industrial Production (CIP), conducted by the Kenyan National Bureau of Statistics (KNBS). The dataset provides detailed information needed for our analysis, including total sales, value of production, labor cost, capital, material and energy costs. The raw data contain information on about 2,089 manufacturing firms. However, a large number of firms report either missing or zero values of labor cost and capital stock, and thus were omitted from our analysis.

> 8Note that although the LSMI targets establishments with more than 10 employees, they remain in the census even if the number of workers decrease.

> 9In both LMSMI and SSMI, industries are classified according to the four-digit ISIC Rev 3.1 classification. The manufactures of food products and beverages is the largest sub-sector, measured by the number of firms.

> 10For more details about the sampling design and detailed description of the data, see Krakah et al. (2014).

13
for some of the right hand side variables.<sup>13</sup> Nguyen (2012) proposes this solution for the case when only a dated census and a recent survey are available.

8. Unit-context **two-fold nested error** Census EB estimators _τ_ ˆ _a_<sup>_UC−CEBac_</sup> based on a twofold nested error model with random location effects at the **area and cluster level** . This estimator follows the approach from Masaaki et al. (2020) and Nguyen (2012), where only area means for some of the right hand side variables are used.<sup>14</sup> Model bias and MSE are approximated empirically as in MR (2010), as the averages across the _L_ = ˆ 10 _,_ 000 simulations of the prediction errors in each simulation ( _l_ ), _τa_<sup>_j_(</sup><sup>_l_)</sup> _− τa_<sup>(</sup><sup>_l_)</sup> and of the squared prediction errors respectively, where _j_ stands for one of the methods: _DIR, CEBac, CEBa, CEBc, ELLc, UC− CEBa, UC − CEBac_ .<sup>15</sup> Model bias and root MSE for a given area’s estimate are computed at the area level as follows:

> 13The covariates used in this model are: _x_  ̄1 _ac_ , _x_  ̄3 _a_ , _x_  ̄4 _ac_ , _x_  ̄5 _a_ , and _x_  ̄7 _ac_ .

> 14The covariates used in this model are: _x_  ̄1 _ac_ , _x_  ̄3 _a_ , _x_  ̄4 _ac_ , _x_  ̄5 _a_ , and _x_  ̄7 _ac_ .

> 15 _E_<sup>�</sup> _τ_ ˆ _c_<sup>_j−τ_</sup> _c_ � for the bias and _E_<sup>�</sup> _τ_ ˆ _c_<
debt swaps (for climate/nature/loss and damage); ii) insurance-based approaches to cover debt repayments while handling shocks; and iii) disaster clauses in borrowing terms to allow SIDS to prioritize recovery and defer debt servicing (principal and interest) to a later date (also called pause clauses). There are also proposals for parametric risk insurance to cover sovereign debt service payments in the event of a natural disaster, facilitated by a Global Fund to enable SIDS to pool risks as well as financing investments to strengthen their resilience (Bharadwaj, Mitchell and Karthikeyan, 2023).

This paper evaluates the challenges SIDS face, focusing on the nexus between vulnerability to climate change, requirements for investments in adaptation and public debt, and the implications for public policy. Vulnerability to climate change and public debt are clearly linked; one of the main reasons why many SIDS are assessed as being at high risk of debt distress in their most recent DSA is their vulnerability to large climate related negative shocks. However, that does not axiomatically mean that debt relief measures are optimal policy instruments to generate the fiscal space required to strengthen the resilience of the SIDS.

The organization of the rest of this paper is as follows: Section two examines the main climaterelated vulnerabilities of SIDS, using data from the international Disasters Database (EMDAT). It also provides an event study analysis of the macro-fiscal impact of some recent large cyclone disasters which struck SIDS in the Caribbean and Oceania. Section three discusses the investment requirements of the SIDS for adaptation to climate change, drawing on existing literature. Section four examines current levels of public debt in the SIDS and their risks of public debt distress, drawing on the assessments and data in their most recent DSAs. It also briefly examines the composition of public debt, differentiating between SIDS with higher and lower debt-to-GDP ratios. Section five discusses what type of external assistance most SIDS need to help them strengthen their resilience to climate change, and whether debt relief measures offer feasible and optimal instruments to deliver this objective.
.0%|7.6%|67%|3.9%|
|GJ12|281,750|21.6%|6.9%|7.6%|67%|16.9%|
|GJ13|98,831|5.4%|0.6%|7.6%|67%|6.1%|
|GJ14|448,126|3.0%|0.2%|7.6%|67%|4.6%|
|Unwe|ighted average:|6.3%|1.12%||||
|S|ample variance:|0.338%|||||

Second, calculate the Empirical Bayes Credibility Factor for ratemaking at the crop/state level using the capped loss cost histories of Table 12. For cotton in Gujarat the credibility factor of 67% is calculated as follows: Let denote the weighted average capped loss cost for district in year . These are tabulated in columns [3] to [12] of Table 12;

- , since there are fourteen districts in the state;

- , since all products have ten years of loss cost history to be used;

- , the product base rate is given in column [3] of Table 13;

- (see the bottom of column [3] of Table 13);

- (see the bottom of column [3] of Table 13);

21
sectoral activity estimates with emissions factors that vary by region; and (3) IFPRI estimates of rice production, which we combine with our CH4 anomaly observations to index relative methane intensities (emissions per unit of output) for different areas.

Regression analysis reveals a highly significant relationship between IFPRI irrigated field extents and local SP5 CH4 anomalies. We map expected values from the regression results, as well as regression residuals that identify areas where the CH4 anomalies identify methane concentrations that are higher and lower than expectations from field extents alone. For comparison, we also perform a regression analysis of the relationship between methane emissions estimated by EDGAR and irrigated field extents. As expected, we find a closer “fit” for EDGAR emissions because their “bottom-up” computation actually incorporates field extent. In a follow-on exercise, we regress local CH4 anomalies on field extent and EDGAR emissions and use the residuals to identify outlier areas with unexpectedly high or low emissions. These residuals represent the “value added” of the S5P data, because they identify local methane emissions variations that are not captured by IFPRI field extent or EDGAR estimates. The regression residuals identify a large number of positive and negative outlier clusters in all regions. We map these results and summarize them by identifying the states and provinces within countries that have the largest positive and negative outlier values.

Finally, we compute a spatial measure of methane intensity that indexes CH4 emissions per unit of output. This information is particularly interesting from a development perspective, since it incorporates the food value of rice production as well as the associated methane emissions. We find particularly high and low methane intensities among Indian states and Indonesian provinces, respectively, along with smaller clusters of positive and negative outlier states/provinces in Asia and other global regions.

In summary, this first application of the new World Bank database suggests that CH4 atmospheric concentration measures from the ESA’s Sentinel-5P system can provide useful new information for identifying and assessing local variations in methane emissions from irrigated rice production. While more ground-truthing would undoubtedly be useful
**Table 5: Alternative Specification including total unemployment and the ratio of male to female unemployment**

||(1)<br>Physical<br>Violence|(2)<br>Sexual<br>Violence|(3)<br>Any<br>Violence|
|---|---|---|---|
|ln(U)|-0.019|-0.030|-0.049|
||(0.055)|(0.037)|(0.066)|
|ln(U<sup>ratio</sup>)|0.509***|0.157|0.554***|
||(0.082)|(0.108)|(0.155)|
|Observations|348165|268173|266517|
|X controls|Y|Y|Y|
|Year FE|Y|Y|Y|
|CountryFE|Y|Y|Y|

# _Source:_

Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. All estimates are of equation (3). The dependent variable in each regression is a binary dummy variable taking the value one if the individual female respondent was a victim of violence, defined as physical (1), sexual (2) or either (3) in the last twelve months. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. Relative to Table 1, the HP-filter detrended male and female unemployment rates are replaced with the HP-filter detrended unemployment, ln(U), and the detrended ratio of the male to
<mark>weights, and a combination of propensity score weighting and calibration, Lee and Valliant (2009) find a bias reduction in the range of 1 percent to 94 percent for health status, and 18 percent to 98 percent for health insurance coverage. However, both these variables themselves were used in the propensity score weight estimation, and health status was used in the calibration of weights. Specifically, the study finds that matching after adjustments is better in cases where the variable in question itself is used for the adjustment. In other words, the matching for model variables performs better than non-model variables, a result we also observe.</mark>

# **<mark>6. Conclusion</mark>**

<mark>Traditional surveys that track economic and social indicators use probability sampling techniques based on a complete sampling frame. While these surveys accurately represent the national population, they are expensive and difficult to conduct in national crisis situations, such as the COVID-19 pandemic or fragile or conflict affected environments. Quicker, less expensive, and more convenient methods of data collection have emerged, including phone surveys and online surveys. While Random Digit Dialing technique-based phone surveys are based on a representative sampling frame, they may be biased, since the population of phone owners who would answer the phone and respond to the survey may differ from the general population. Internet surveys are even more convenient and inexpensive to administer, but their scope in developing countries could be curtailed by lower levels of internet access and educational attainment. These surveys are based on non-probabilistic sampling frames because it is difficult to ascertain the universe of Internet users.</mark> <mark>In this study, we utilized three waves of online surveys conducted in six countries during the COVID-19 pandemic between December 2020 and August 2021. The objectives were to capture key economic variables during the pandemic that would have otherwise been harder to collect through physical door-to-door surveys. We compared economic indicators from these surveys with regular labor force surveys conducted in these countries around
<!-- Start of picture text --> Bulgaria 2020<br>Headcount poverty (AROP) by household category<br>30.0<br>30 273 28.1<br>23.5<br>22.3 22.2<br>20<br>10<br>0<br>No children With children<br>TM Market Income plus pensions TM Disposable Income mm Consumable Income<br>Source: Author estimations based on EU-SILC 2021 and HBS 2021 using CEQ methodology<br><!-- End of picture text --> <!-- Start of picture text --> Bulgaria 2020<br>Headcount poverty (AROP) by household category<br>41.6<br>40<br>34.4<br>31.2<br>30 287 29.4<br>23.2<br>20.9<br>20 18.9 19.5:<br>16.3<br>13.2 13.7<br>10<br>0<br>No children & no elderly Nochildren w/ elderly Children & no elderly With Children & elderly<br>TM Market Income plus pensions TM Disposable Income mm Consumable Income<br>Source: Author estimations based on EU-SILC 2021 and HBS 2021 using CEQ methodology<br><!-- End of picture text -->
14.76<br>|15.08<br>|
|Agricultural<br>Fertilizers<br>12.01|11.77|11.89<br>|8.40|7.61|8.00|
|Petroleum<br>Refining<br>and Products<br>14.56<br>|14.39<br>|14.47<br>|11.02<br>|10.76<br>|10.89<br>|
|Tires and Inner Tubes<br>13.17|11.64|12.38|32.68|29.29|30.94|
|Cement<br>9.90|<br>9.90|9.90|12.18<br>|12.11|12.14|
|Bricks<br>4.03|3.62|3.82|9.79|13.60|11.54|
|Iron and Steel<br>8.95|7.27|8.07|14.09|11.25|12.59|
|Radio and TV Receivers<br>7.72<br>|7.64<br>|7.68<br>|8.92<br>|11.39<br>|10.08<br>|
|Motor Vehicles<br>6.49|6.32|6.40|13.74|13.55|13.64|
|Weighted average PPP for sample||||||
|(value added -US census concept-||||||
|weights)<br>9.10|<br>6.58|7.74|14.81|11.71|13.20|
|Exchange<br>Rates<br>8.
# WPS3622

## **Trade Policy, Income Risk, and Welfare**

**Tom Krebs Brown University**

**Pravin Krishna** † **Johns Hopkins University**

**William Maloney World Bank**

### **Abstract**

This paper studies empirically the relationship between trade policy and individual income risk faced by workers, and uses the estimates of this empirical analysis to evaluate the welfare effect of trade reform. The analysis proceeds in three steps. First, longitudinal data on workers are used to estimate time-varying individual income risk parameters in various manufacturing sectors. Second, the estimated income risk parameters and data on trade barriers are used to analyze the relationship between trade policy and income risk. Finally, a simple dynamic incomplete-market model is used to assess the corresponding welfare costs. In the implementation of this methodology using Mexican data, we find that trade policy _changes_ have a significant short run effect on income risk. Further, while the tariff _level_ has an insignificant mean effect, it nevertheless changes the degree to which macroeconomic shocks affect income risk.

World Bank Policy Research Working Paper 3622, June 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ We would like to thank Alberto Alesina, Pol Antras, Robert Feenstra, Pierre Gourinchas, Kishore Gawande, Boyan Jovanovic, Mark Melitz, Marcelo Olarreaga, Arvind Panagariya, J. David Richardson and seminar participants at Brown, Columbia, Georgetown, Harvard,
Second, traditional tenure regimes often restricted women’s inheritance rights (Casari & Lisciandra 2016).<sup>2</sup> In addition to contradicting basic tenets of equality, such discrimination undermines productivity of land use as documented by studies showing that insecure inheritance rights reduced women’s input use (Dillon & Voena 2018) and investment in soil fertility (Deininger _et al._ 2021) even during spouses’ lifetime. Unclear inheritance arrangements increased women’s fear of land loss (Deininger _et al._ 2019b) and their likelihood of being involved in land disputes (Deininger & Castagnini 2006). In Ghana, stronger female inheritance rights encouraged women to invest on their plots and men, whose outside options were better, to take up non-agricultural employment, supporting structural transformation (Barker 2022).

Data from the Afrobarometer survey point to strong public support for gender equality with more than 70% of respondents in favor of equal land rights for women. Yet, women’s land rights in case of divorce or inheritance often remain fragile as in Tanzania where progressive views regarding women’s rights by members of local judicial bodies were not reflected in actual judgements (Genicot & Hernandez-de-Benito 2022). While changes in family law can be a first step towards shifting customary gender norms (Aldashev _et al._ 2012), training of officials, awareness raising, and access to mediation in specific cases is often needed (Cecchi & Melesse 2016).

Finally, co-optation of chiefs by colonial institutions to collect taxes <mark>(Frankema & van Waijenburg 2013) or help transfer l</mark> and to outsiders via concessions (Lowes & Montero 2020) <mark>has i</mark> n many settings <mark>undermined mechanisms that traditionally underpinned trust in and accountability by such leaders (</mark> Acemoglu et al. 2014).<sup>3</sup> Chiefs whose downward accountability has been compromised may ‘privatize’ land for personal benefit even if this reduces
Angrist, J., Bettinger, E., & Kremer, M. (2006). Long-Term Educational Consequences of Secondary School Vouchers: Evidence from Administrative Records in Colombia. _The American Economic Review_ , _96_ (3), 847-862.

Barrera-Osorio, F., Blakeslee, D. S., Hoover, M., Linden, L. L., & Raju, D. (2011). Expanding Educational Opportunities in Remote Parts of the World: Evidence from a RCT of a Public Private Partnership in Pakistan. In _Third Institute for the Study of Labor (IZA) Workshop,“Child Labor in Developing Countries,” Mexico City_ . Retrieved from http://www.iza.org/conference_files/childl2011/blakeslee_d6783.pdf Barrera-Osorio, F., & Raju, D. (2015). Evaluating the Impact of Public Student Subsidies on Low-Cost Private Schools in Pakistan. The Journal of Development Studies, 51(7), 808-825.

Bold, T., Kimenyi, M., Mwabu, G., & Sandefur, J. (2010). Does abolishing fees reduce school quality? Evidence from Kenya. _Centre for the Study of African Economics (CSAE) Working Paper WPS/2011-04_ .

Deininger, K. 2003. Does cost of schooling affect enrollment by the poor? Universal primary education in Uganda. Economics of Education Review 22(3):291-305 Friedman, M. (1955). _The role of government in education_ . Rutgers University Press.

Friedman, M. (1962). _Capitalism and freedom_ . University of Chicago press.

Gertler, P. J., Patrinos, H. A., & Rubio-Codina, M. (2012). Empowering parents to improve education: Evidence from rural Mexico. _Journal of Development Economics_ , _99_ (1), 68–79. Hoxby, C. M. (2003a). School choice and school competition: Evidence from the United States. _Swedish Economic Policy Review_ , _10_ (2),