rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 5: Median Force Values Through Time** <u>(percentage points)</u>

<!-- Start of picture text --> 6 Advanced Emerging<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>1980 1984 1988 1992 1996 2000 2004 2008 2012<br>Source: IMF GFS data and authors’ calculations<br><!-- End of picture text --> The evolution of force through time is shown in Figure 5.<sup>13</sup> For each year and country grouping we take the median force across countries for each year. The first pattern we note is that force is more volatile and of greater magnitude for emerging countries compared to advanced economies. A second interesting pattern is the large reduction in force as the financial crisis took effect and countries began to rein in spending. Emerging markets applied the brakes much harder than advanced economies with a record amount of force while advanced economies also applied the brakes but at levels similar to previous economic crises in the 1980s and 1990s.

Figure 6 shows the duration and characteristics of force increases and decreases. The left-hand side of Figure 6 shows the percentage of force increases that last from 1-3 years. As we can see, the majority of force changes are only for a single year (around 64 percent for advanced and 67 percent for emerging) while consecutive force changes occur around 24 percent of increases. The right-hand side of the figure shows the magnitude of the changes for different durations. For force increases that last 1-year, the median increase (red bars) is around 3 percentage points for advanced economies and 9.7 percent for emerging economies. For occasions when force is positive in consecutive years, the increase in the second year is around 2.5 percent for advanced economies and 6.7 percent for emerging. For all durations, the magnitude of decreases (in absolute terms) is shown by the red bars in the right-hand chart. For both advanced and emerging economies the magnitude of the decreases is slightly larger for the first and second year while slightly lower for the third year.

13 The chart shows the force measurement for changes in momentum calculated over 1-year periods.
-3-

politically difficult, as have reforms of sick pay, maternity benefits and child allowances. As a consequence, the social safety net is perceived as both too costly and ineffective in reaching the most vulnerable groups in society.

This paper attempts to shed some light on the structure of income and income inequality in the midst of Bulgaria's transition. It also looks at who the poor are and how effectively they are reached by the social safety net. This paper, like all analyses of incomes and expenditures in transitional economies, suffers from measurement problems, underreporting of "private incomes" (such as self-employment and property incomes), and methodological defects derived from measuring incomes and expenditures during bouts of high inflation. Nevertheless, important insights can be gained from the available data to guide policymakers through this difficult period.

The analysis is based on a recent household budget survey--1992 Individual Budget of Households, compiled by the National Statistical Institute (NSI) of Bulgaria. The sample was constructed as a two-tier random sample, involving 2,508 households (or less than one percent of households). It is based on a sample frame developed from the 1985 Population Census. The sample was constructed from a sample of 418 sectors or Census districts: each district contains about 90 households and 6 households were sampled from each sector.' Each household was paid a nominal amount, Leva 100 per month (about US$4.00), for participating in the survey. While the sample was "random", according to the

**1/** The sample of 418 sectors were taken from a 'control' sample of 4,000 sectors which was, in **tun,** taken from the 1985 Population Census of 40,000 sectors, including approximately, 3.2 million households.
# **References**

Antras, P., A. de Gortari, and O. Itskhoki (2017). “Globalization, Inequality, and Welfare,” Journal of International Economics, 108, pp. 387-412.

Arkolakis, C., A. Costinot, and A. Rodriguez-Clare (2012). “New Trade Models, Same Old Gains?,” The American Economic Review, vol. 102(1), pp. 94–130.

Arkolakis, C., A. Costinot, D. Donaldson, and A. Rodriguez-Clare (2019). “The Elusive Pro-Competitive Effects of Trade,” Review of Economic Studies, 86, 46–80 Artuc, E., D. Lederman, and G. Porto (2015). “A Mapping of Labor Mobility Costs in the Developing World,” Journal of International Economics, vol. 95(1), pp. 28–41.

Artuc, E., G. Porto, and B. Rjkers (2019). “Trading Off the Income Gains and the Inequality Costs of Trade Policy,” Journal of International Economics, forthcoming.

Atkin, D. and D. Donaldson (2015). “Who’s Getting Globalized? The Size and Implications of Intranational Trade Costs,” mimeo.

Atkin, D., B. Faber and M. Gonzalez-Navarro (2018). “Retail Globalization and Household Welfare: Evidence from Mexico,” Journal of Political Economy, 126(1): pp.1–73.

Benjamin, D. and A. Deaton (1993). “Household Welfare and the Pricing of Cocoa and Coffee in Cˆote d’Ivoire: Lessons from the Living Standards Surveys,” The World Bank Economic Review vol. 7, pp. 293–318.

Bergquist, L. (2018). “Pass-Through, Competition, and Entry in Agricultural Markets: Experimental Evidence from Kenya” mimeo.

Caliendo, L. and F. Parro (2015). “Estimates of the Trade and Welfare Effects of NAFTA,” The Review of Economic Studies, vol. 82(1), pp. 1–44.

Caliendo, L., M. Dvorkin, and F. Parro (2015). “Trade and Labor Market Dynamics,” NBER Working Paper No. 21149.

15
Our results also have implications for the growing literature that uses face-to-face IPV data from multiple countries to draw inferences about IPV prevalence, predictors, and causes (Devries et al., 2013; García-Moreno et al., 2013; Cools and Kotsadam, 2017; Jewkes et al., 2017; Alesina, Brioschi and La Ferrara, 2020; Heise and Kotsadam, 2015). Our finding of significant and systematic underreporting for some types of IPV in our two rural African contexts contrasts with findings from Lima, Peru, where researchers found no overall difference between face-to-face and list method prevalence (Agüero and Frisancho, 2017). These divergent results indicate that reporting bias is, unsurprisingly, strongly affected by contextual factors. Given significant, systematic face-to-face misreporting in some contexts but not others, our results imply that caution should be used when aggregating and analyzing face-to-face IPV data from diverse settings, lest context-specific misreporting bias results.

Policy makers and social scientists measuring sensitive topics could use similar randomized survey experiments to compare IPV reporting rates between the list experiment and direct methods to identify levels of stigma, the types of people most likely to misreport, and the risk of generating biased treatment effects. For example, social scientists researching the impact of interventions on IPV might use such an experiment to sign potential reporting bias in their treatment effect estimates. Also, given Demographic and Health Surveys’ large sample sizes and use in multi-country analysis, perhaps such surveys should measure several IPV questions using the list method as well as the face-to-face method. This would allow data users to assess the likely extent of reporting bias by context, allowing them to make adjustments to inferences about true levels and correlates of IPV.<sup>33</sup> Future research could be conducted in other contexts and on other types of violence and sensitive topics to better understand what drives misreporting. Other potential areas for additional research include studying how to best design and administer the list experiment to minimize bias, and developing alternative measurement methods for efficient causal research on IPV which, according to face-to-face survey data, is estimated to affect one in three women globally (García-Moreno et al., 2013).

> have made more of a difference. See Appendix B for a further discussion on IPV measurement method trade-offs.

> 33DHS data on IPV cover more than 60 developing countries and are cited in more than 5,000 papers on google scholar. If DHS IPV data are measured with substantial, systematic, and context-specific bias as this paper suggests is likely, this has significant implications for the use of such data.

21
Guatemalan and Indian children who were near the 50 ' percentile of the reference growth charts prior to puberty, ended up near the 25th percentile by the end of adolescence.

The World Health Organization has long recommended the use of a reference population for the assessment of the nutritional status of children (Waterlow _et al._ 1977; WHO 1979; WHO 1983; WHO Working Group 1986). The emergence of ethnic differences in growth attainment during adolescence justifies the upper limit of 10 years fixed by the WHO on the age for appropriate comparisons with the WHO/NCHS/CDC growth charts.

# **2.3 Standard Deviations Scores**

In order to be able to compare the growth attainment of children of different ages by sex, we converted the EGSF anthropometric measurements into three indexes: height-for-age, weight-for-age, and weight-for-height. Using the WHO/NCHS/CDC curves we then expressed the growth attainment of each as standard deviations from the median (z-scores) (Waterlow _et al._ 1977; WHO Working Group 1986). The standardizing calculations were made with ANTHRO (Software for Calculating Pediatric Anthropometry), Version 1.01, provided by the Division of Nutrition of the Centers for Disease Control and the Nutrition Unit of the World Health Organization.

The z-score measures the degree to which a child's measurements deviate from what is expected for that child, based on a reference population. The formula for the calculation of the height-for-age z- score is: Z, = (yis,a - H s,a)/,s,a where zi is the z-score for child i; **y.S,a** is the measured height (in cm) for child i of sex s and age a; H sa _iS_ the median height (in cm) for children of sex s and age a in the reference population; and as7a is the standard deviation in height (in cm) for children of sex s and age a in the reference population.

# III. **DATA THE GUATEMALAN SURVEY OF FAMILY HEALTH**

The data for the empirical analysis come from the Guatemalan Survey of Family Health (EGSF) conducted in Guatemala between May and October 1995.2 The survey focused on a large set of determinants of the health treatment process including the family's economic situation and social support network and women's health beliefs on the nature and causation of illness. The data collection process comprised an ethnographic study of four rural communities, an individual survey of 2872 women of ages

> 2 See Peterson, Goldman and Pebley (1995) for a thorough description of the EGSF.

3
Kurtz, Jon, Beza Tesfaye and Rebecca Wolfe. (2018). “Can Economic Interventions Reduce Violence. Impacts of Vocational Training and Cash Transfers on Youth Support for Political Violence in Afghanistan.” Washington DC: Mercy Corps.

- Kurtz, Jon. (2015). “Does Youth Employment Build Stability?” Mercy Corps Evidence Paper. Washington DC: Mercy Corps.

- Lazear, Edward (1989). “Pay Equality and Industrial Politics”. _Journal of Political Economy_ 97(3), 561-580.

- Lyall, Jason, Yang-Yang Zhou and Kosuke Imai (2019). “Can Economic Assistance Shape Combatant Support in Wartime? Experimental Evidence from Afghanistan.” _American Political Science Review_ 114(1): 126-143

- Murard, Elie (2021). “Mass Refugee Infow and Social Cohesion in the Long-Run: Evidence from the Greek Population Resettlement.” _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group

- Nowak, Martin and Karl Sigmund (2005). “Evolution of Indirect Reciprocity.” _Nature_ , 437, 1291-1298.

- Okunogbe, Oyebola (2016). “Does Exposure to Other Ethnic Groups Promote National Integration? Evidence from Nigeria.” _World Bank Policy Research Paper No. 8606_

- Pham, Phuong, Thomas O’Mealia, Carol Wei, Kennedy Bindu, Anupah Makoond and Patrick Vinck (2021). “Hosting New Neighbors: Perspective of Host Communities on Displacement and Social Cohesion.” _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group.

- Pixley, Jocelyn J. (2018). “Employment and Social Identity: Theoretical Issues.” In _European citizenship and social exclusion_ (pp. 119-134). Routledge, Taylor and Francis Group.

- Allen, William, Isabel Ruiz and Carlos Vargas-Silva, C. (2021). “Refugee Return and Social Cohesion.” _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group.

- Scacco, Alexandra., and Shana Warren (2018). “Can Social Contact Reduce Prejudice and Discrimination? Evidence from a Field Experiment in Nigeria.” _American Political Science Review_ 112(3), 654-77.

Tan, Carol (2015). “The Syrian Refugee Crisis: Conflicts in the Making.” _IEMed. Mediterranean Yearbook 2015_ (pp: 307-311) Taylor, J. Edward, Mateusz Filipski, Mohammad Alloush, Anubhab Gupta Ruben Rojas Valdes and Ernesto Gonzalez-Estrada, E. (2016). “Economic Impact of Refugees.” 28
# 5.2 Detailed RPSR Indices

## A. Global reform index

The standard package of reforms prescribed by international donors in the 1990s included four principal components: restructuring (vertical and horizontal unbundling of power utilities); private sector participation; creation of an independent regulator; and competition in power generation.

In order to aggregate across the four dimensions of power sector reform considered in this study, a simple Power Sector Reform Index is constructed. The index gives each country a score on an interval of 0 to 100 on each dimension of power sector reform. The scores are based on giving equal weight to each step on each dimension of the reform continuum (see tabulation below). The average of the four 0-100 scores is used to provide an overall summary of the extent of reform.

|**Regulation**|No regu<br>= 0|lator<br> <br>Regulator<br>= 100|
|---|---|---|
|**Restructuring**|Vertically integrated<br>= 0|Partial vertical<br>unbundling<br>= 33<br>Full vertical<br>unbundling<br>= 67<br>Vertical & horizontal<br>unbundling = 100|
|**Competition**|Monopoly<br>= 0|IPPs<br>= 25<br>Single Buyer<br>Model<br>= 50<br>Bilateral<br>Contracts<br>= 75<br>Competitive<br>market<br>= 100|
|**Private Sector**<br>**Participation**|0.5*(Percent<br>0.5*(Percent|age of generation capacity with private sector participation)<br>+<br>age of distribution utilities withprivate sectorparticipation)|

60
**Figure 3:** _Internet use and capacity to innovate_

<!-- Start of picture text --> 6<br>World EU12 EAP12 Eurasia<br>5.5<br>5<br>4.5<br>4<br>CHN<br>3.53 RUS UKR<br>TAJ ARM<br>2.5 AZE<br>2<br>1.5 KAZ<br>MOL GEO<br>1 KYG<br>3 3.5 4 4.5 5 5.5 6 6.5<br>Extent of business Internet use (1-7)<br>Capacity for Innovation (1-7)<br><!-- End of picture text --> Source: World Economic Forum (2012) Note: Annual Executive Opinion Survey, ranking out of 144 economies and scores measured on a 1-7 scale.

Effective and efficient movement across borders and along transport corridors is essential to advance the contribution of trade to economic growth. Many Eurasian countries have vast remote areas with poor connections to other domestic markets, as well as to international sea and air gateways. They struggle with small domestic markets that are remote from world markets and poor transport infrastructure. Unless traded goods are transported expensively by air, they must transit through at least one neighboring state, and frequent changes result in high transaction costs. Infrastructure quality and transport costs were found to be the leading determinants (along with tariffs) of cross-country variations in trade flows in the region after controlling for distance (AsDB 2012). In terms of availability and quality of transport infrastructure, Russia and Kazakhstan rank at par with China (with a score of 4.67 and 4.46, respectively relative to 4.49 for China, on a scale of 1 to 7), but other countries lag behind (the Kyrgyz Republic had a score of 3.81) (WEF, 2012). In terms of transportation costs, we find that the entire region is off track. For example, the real cost of exporting a container is 429 man days (Uzbekistan), compared with 20 days in China (Table 1). It also takes longer to prepare goods for export or import from Eurasia—about 45 days compared with 20 days in China. Transit time and transport costs can be reduced by improving transport connectivity and procedures for trade facilitation.

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# 5. Conclusion

This paper examined the impact of Russia’s invasion of Ukraine on firms in Central Asian countries, focusing on firms with pre-invasion trade links with Russia as a measure of exposure to the invasion shock and firms with pre-invasion digitization gains as a potential mitigant of the shock to explain possible heterogeneity in the Ukraine invasion effects. The study found that Central Asian firms that had prior export ties to Russia experienced significant negative effects on their sales and employment after the invasion shock. The analysis also considered firms' pre-invasion digitization as a potential mechanism for coping with the invasion's repercussions. However, no significant mitigation effect of digitization on firm performance is found, possibly due to mixed effects on firms that did not export to Russia.

The paper's findings have important policy implications, highlighting the potential trade-off between the efficiency gains associated with concentrated export links and the importance of geographic export diversification for resiliency to geopolitical shocks. Based on unstable results for digitization as a mitigant of risks from concentrated exports given possible geopolitical shocks, we encourage future research into the possible trade-off that firms' digitization in particular areas can deliver between firms’ efficiency in normal times and coping with unexpected large economic shocks.

More recently, countries in Central Asia boosted their trade thanks to the intermediary (transit) trade in goods from Western countries to Russia (EBRD, 2023 and ADB 2023).<sup>9,10</sup> After sanctions were introduced, the trade turnover of the European Union with Russia’s neighbors to the East increased many times, while their Russia trade also increased. In 2022, the intra-EAEU trade was up 40 percent. ADB (2023) reports that, for Central Asian countries, reexports accounted for close to 50 percent of the rise in exports to Russia. Moreover, Central Asian countries have replenished their own budgets through an influx of labor, capital, and remittances. In addition, the countries increased the sale of their own oil and gas, the demand for which has hiked after the large drop in EU gas supplies from Russia. With the relocation of Russian business to Central Asian countries, the arguably temporary inflow of skilled labor from Russia, and a short-lived windfall reexport margin (ADB 2023), the total effects of Russia’s invasion of Ukraine on Central Asian business—and its dynamics over time—needs to be further studied using updated firm-level data and rigorous empirical methods.

> 9 <u>https://www.russia-briefing.com/news/ebrd-report-anti-russian-sanctions-are-boosting-central-asianeconomies.html/</u>

> 10 <u>https://www.adb.org/sites/default/files/publication/863591/ado-april-2023-special-topic.pdf</u>

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(q-value = 0.03), which represents a 24% decrease in food assistance relative to control. This is a nontrivial proportion of households’ budgets: it is equivalent to over 10% of households’ total monthly labor income pre-pandemic (as control households reported 218 USD PPP in average monthly food assistance and pre-pandemic monthly labor income of 503 USD PPP). It is worth noting that this measure of assistance includes both formal assistance, such as from the UN High Commissioner for Refugees and the World Food Program, as well as informal assistance from local organizations and community members. Regardless of the channel, this substantial decrease in the household’s food budget seems likely to have contributed to worse household food security. This is in line with conversations that the research team had with several humanitarian aid organizations during the pandemic, in which some implementers sought to allocate emergency assistance to households that were not already benefiting from large forms of assistance, such as the HSP.

The midline survey data also indicates that there were several other unexpected short-run changes in household outcomes. For instance, it appears that changes in household composition may have further contributed to household hunger (Panel C, Table 5). Treated households at midline report significantly more children than control households (point estimate 0.3 children, q- value=0.03), and this effect is driven by an increase of 0.16 more adolescent boys (q-value=0.33). This movement between households can be understood as a form of redistribution within the Syrian refugee community, by which treated households shared some of the program benefits of the HSP but at the potential cost of food security to the full household. Despite the possible negative effect on household food security, the study cannot rule out that recomposition might have improved the well-being of the new household members who moved in (and the households they came from), if those adolescents came from relatively worse-off households. (Similarly, the targeting of food assistance away from HSP treatment households noted above could have benefited other refugee households that received additional assistance.) Nonetheless, these individuals’ joining the treated households appears to have likely come at a cost for other treated household members. The effects of the program on household recomposition, however, is not fully robust when correcting for non-random attrition (Panel C, Table A.2), and as such should be viewed as suggestive.

24
# **_5._ Politically Feasible Reform**

|**5.1.**|**Political Feasibility of Liberalization**<br>43<br>Removal or Relaxation of Cotton Export Quota? / Timing of Liberalization / Deregulation<br>of Long-staple Cotton Exports|
|---|---|
|**5.2.**|**Mobilizing the Political Power Structure**<br>46<br>Spinning Mills' Involvement in Cotton Production / Deployment of Mill Workers to Other<br>Sectors / Handloom Conversion Program / Diversifying Handloom Weavers' Activities /<br>Promoting Hank Yarn Reeling Business|
|**6.**|**Conclusions**<br>49|
|**Appe**|**ndix**A:**Technical Notes on the Elasticities for the Model**<br>51<br>Ratio Among Elasticities and Cost Share / Changes in the Elasticities Over Time|
|**Appe**|**ndix**B:**Sensitivity Analysis under Different Elasticities**<br>58<br>Different Sets of Elasticities / Similar Results from Different Elasticities|
|**Refer**|**ences**<br>60|

ii