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Bozzoli, Carlos, Tilman Bruck and Tony Muhumuza. 2012. _Movers or Stayers? Understanding the Drivers of IDP Camp Decongestion during Post‐Conflict Recovery in Uganda._ DIW Discussion paper 1197.

Bruck, T. 2001. “Mozambique: The Economic Effects of the war”, in Frances Steward, eds., _War and Underdevelopment_ . Oxford: Oxford University Press (Vol. 2, 56‐88).

Bundervoet, T., P. Verwimp, and R. Akresh. 2009. “Health and Civil War in Rural Burundi," _Journal of Human Resources_ 44(2), 536‐563.

Büscher, K. and K. Vlassenroot (2010) “Humanitarian Presence and Urban Development: New Opportunities and Contrasts in Goma, DRC” _Disasters_ 34(s2): S256‐S273.

Calderone, M., D. Headey, and J.‐F. Maystadt. 2014. “Resilience to Climate‐Induced Conflict in the Horn of Africa.” (chapter 8) In _Resilience for Food and Nutrition Security_ , edited by S. Fan, R. Pandya‐Lorch, and S. Yosef, 65‐73. Washington, DC: International Food Policy Research Institute.

Chambers, R. 1986. “Hidden Losers? The Impact of Rural Refugees and Refugee Programs on Poorer Hosts.” In “Refugees: Issues and Directions,” special issue, International Migration Review 20, no. 2:245– 63.

Crisp, Jeff and Karen Jacobsen. 1998. “Refugee camps reconsidered.” _Forced Migration review_ 3: 27‐30.

Currie, A and T.Vogl. 2013. “Early‐Life Health and Adult Circumstance in Developing Countries,” _Annual Review of Economics_ , 5, 1‐36 Danish Refugee Council (DRC) Lebanon. 2014. Unconditional Cash Assistance via E‐Transfer: Implementation Lessons Learned Winterization Support via CSC Bank ATM Card.

De Montclos, M.‐A. P., and P. M. Kagwanja. 2000. “Refugee Camps or Cities? The Socio‐Economic Dynamics of the Dadaab and Kakuma Camps in Northern Kenya.” _Journal of Refugee Studies_ 13, no. 2:205–22.

33
||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|||Weight-for-|age z-scores|||
|Iringa|0.550|0.552|0.548|0.548|0.529|0.532|
||(0.343)|(0.342)|(0.346)|(0.346)|(0.348)|(0.346)|
|Mbeya|0.767**|0.763**|0.760**|0.746**|0.747**|0.740**|
||(0.353)|(0.352)|(0.356)|(0.356)|(0.358)|(0.358)|
|Singida|0.559|0.543|0.590|0.608|0.565|0.573|
||(0.413)|(0.412)|(0.414)|(0.411)|(0.416)|(0.414)|
|Tabora|0.784**|0.782**|0.763**|0.776**|0.746**|0.749**|
||(0.339)|(0.338)|(0.342)|(0.341)|(0.344)|(0.342)|
|Rukwa|0.540|0.535|0.536|0.536|0.520|0.525|
||(0.363)|(0.362)|(0.365)|(0.365)|(0.366)|(0.364)|
|Kigoma|0.303|0.311|0.309|0.297|0.290|0.281|
||(0.337)|(0.336)|(0.339)|(0.339)|(0.339)|(0.341)|
|Shinyanga|<br>0.884***|<br>0.886***|<br>0.889***|<br>0.885***|<br>0.873**|<br>0.876**|
||(0.337)|(0.336)|(0.340)|(0.339)|(0.341)|(0.340)|
|Kagera|0.336|0.346|0.327|0.309|0.313|0.309|
||(0.347)|(0.347)|(0.350)|(0.349)|(0.350)|(0.350)|
|Mwanza|1.013***|1.035***|1.029***|1.015***|1.008***|0.998***|
||(0.335)|(0.336)|(0.338)|(0.338)|(0.338)|(0.339)|
|Mara|0.806**|0.834**|0.809**|0.819**|0.804**|0.780**|
||(0.356)|(0.357)|(0.359)|(0.359)|(0.358)|(0.363)|
|Manyara|0.867**|0.872**|0.869**|0.865**|0.858**|0.846**|
||(0.353)|(0.354)|(0.357)|(0.356)|(0.357)|(0.359)|
|Kusini ungunja|<br>1.279***|<br>1.306***|<br>1.254***|<br>1.285***|<br>1.278***|<br>1.277***|
||(0.365)|(0.364)|(0.369)|(0.366)|(0.366)|(0.365)|
|Mjini / Magharibi ungunja|0.001|0.008|0.001|0.023|0.008|-0.004|
||(0.321)|(0.320)|(0.324)|(0.322)|(0.322)|(0.324)|
|Kaskazini pemba|0.788*|0.807*|0.780*|0.805*|0.795*|0.787*|
||(0.448)|(0.450)|(0.453)|(0.449)|(0.450)|(0.450)|
|Constant|-0.861**<br>(0.417)|-0.861**<br>(0.416)|-0.858**<br>(0.421)|-0.850**<br>(0.421)|-0.853**<br>(0.420)|-0.824*<br>(0.422)|
|Observations|1,101|1,101|1,099|1,101|1,099|1,099|
|R-squared|0.123|0.123|0.124|0.125|0.123|0.124|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in the total food expenditure, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure on total expenditure.

99
Policy Research Working Paper 6866

# **Abstract**

This paper studies channels through which well-known benchmark indexes impact asset allocations and capital flows across countries. The study uses unique monthly micro-level data of benchmark compositions and mutual fund investments during 1996–2012. Benchmarks have important effects on equity and bond mutual fund portfolios across funds with different degrees of activism. Benchmarks explain, on average, around 70 percent of country allocations and have significant impact even on active funds. Benchmark effects are important after controlling for industry, macroeconomic, and countryspecific, time-varying effects. Reverse causality does not drive the results. Exogenous, pre-announced changes in benchmarks result in movements in asset allocations mostly when these changes are implemented (not when announced). By impacting country allocations, benchmarks affect capital flows across countries through direct and indirect channels, including contagion. They explain apparently counterintuitive movements in capital flows, generating outflows from countries when upgraded and with large market capitalization and better relative performance.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at craddatz@bcentral.cl; sschmukler@worldbank.org; tomas.williams@upf.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 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
Figure A.1. Population shares of school-age children (%) <!-- Start of picture text --> 60<br>48.4<br>50<br>43.7<br>41.3 41.0<br>37.7<br>40 33.534.6 32.6 33.1<br>30.1<br>30<br>20 16.718.1 17.8 17.2 16.6<br>12.2<br>7.4 7.9<br>10 5.0 5.2<br>0<br>Boys Girls Boys Girls<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

Figure A.2. Population shares of primary school-age household members (13+) (%) <!-- Start of picture text --> 50 45.2 47.0<br>45 41.0 40.4<br>38.1<br>40 35.9<br>32.7 33.7 32.8 34.5<br>35<br>30<br>25<br>20 17.1 16.0 17.5 15.7<br>13.3 12.7<br>15<br>10 5.8 6.8 6.5 7.3<br>5<br>0<br>Males Females Males Females<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

Figure A.3. Population shares of the working-age population (%) 38
offices and public schools) and rare facilities that require hours of travel (including train stations and government hospitals) present little variation across _Unions_ . However, the availability of bus stations, health clinics, and _Upazila Sadars_ , and banks varies largely across _Unions_ and over time; in addition, travel time and distance varies when services are located outside the respondent _Union_ . The share of respondents who report having these facilities in their _Union_ is 46 percent for a bank, 34 percent for a bus station, 4 percent for health clinics, and 0 percent for _Upazila Sadars_ . When these facilities exist outside the _Union_ , the accessibility varies although they are mostly reachable within two hours of travel or 30 kilometers (Figure 2).

**Figure 2: Time and Distance to Key Infrastructure Outside the** **_Union_**

<!-- Start of picture text --> 0 1 2 3 0 20 40 60<br>hours kilometers<br>bank Upazila Sadar bank Upazila Sadar<br>bus station health clinic bus station health clinic<br>1.5<br>.1<br>.08<br>1<br>.06<br>.5 .04<br>.02<br>0 0<br><!-- End of picture text --> We define easy access to these facilities when they are either in the _Union_ or within 30 minutes of reach, and ��� �1 if the _Union_ has easy access to a bank. We use a linear model to examine the association between the access to a bank and EGPP participation with and without the households’ characteristics. Utilizing the _Union_ level panel structure of the data, we use the _Union_ -time fixed effects model to control for the impacts of time-invariant _Union_ level characteristics that may affect individuals’ EGPP participation. As presented in Table 6, having a bank within 30 minutes of reach has a statistically significant effect on the likelihood of EGPP participation (t=5.6) by 9.3 to 9.9 percentage points. The relationship between access to a bank and EGPP participation remains robust when household characteristics such as the head’s education and health, household size, and productive assets are added as covariates.

20
# **9. Energy poverty and carbon emissions**

Since the bulk of the energy needs of the rural households in Bangladesh are currently met and continue to be met in the foreseeable future by biomass regardless of their electrification status, one issue that still remains is whether biomass energy can be used more efficiently in rural Bangladesh and how this might be related to energy poverty and carbon emission—an indicator of social cost of energy use practice with implications to climate change. So far we talked about the private rates of returns and costs associated with energy consumption and access to electricity. There are also social costs of energy poverty.

Worldwide about 2.5 billion people today rely on biomass energy. Also about 50 percent of the people in developing countries—and more than 90 percent of the rural residents in many countries—use biomass energy, including wood, dung, and agricultural residue, as their main cooking and heating fuel. Altogether, in developing countries about 730 million tons of biomass is burned every year (WHO 2006). This amounts to over 1 billion tons of CO2 emitted into the atmosphere, which accounts for about 5 percent of the global CO2 emissions (Yevich and Logan 2003). In addition, incomplete combustion of other products also has large greenhouse-gas (GHG) impacts. For lighting alone, about 11.5 million tons of kerosene is burned every year in the developing countries, mostly in lamps made of a tin container and a wick. That way, kerosene is very inefficiently burned and because of incomplete combustion it actually produces other greenhouse gases which stay in the atmosphere for decades (WHO 2006; Smith 2000). In spite of this, the role of household energy is often ignored in the discussions of climate change. As a result, a considerable amount of energy consumed by households worldwide is ignored or only given token recognition. In this section, we explore the climate implications of household energy in rural Bangladesh.

32
8.3 percent (10.2 percent) compared the merchants without this technology. I also find evidence that having contactless payments during COVID-19 helps attract more consumers and shift noncard payments to card payments. The acceptance of contactless payment is still promoting sales growth of merchants with the latest digital payment technology, especially small businesses and new entrepreneurs. Additionally, I show that the increase in the limit of contactless payment from 30 euros to 50 euros by French banks significantly accelerates the substitution between contactless payment and other means of payment for merchants with the latest digital payment relative to their counterparties. These results suggest the importance of contactless payment in mitigating the impact of epidemic shocks where social distancing is encouraged, acquiring new clients, and accelerating substitution with other payment methods.

Overall, the findings of the paper contribute to the literature on fintech and the digitization of cashless payments by shed new light on their real effect and ability to mitigate the impact of an epidemic shock. This could be fruitfully used to provide policy recommendations to policy makers, banks, and fintech companies interested in promoting efficient digital payment technologies.

# **References**

- Daniel Aaronson, Agarwal Sumit, and French Eric. The spending and debt response to minimum wage hikes. _American Economic Review_ , 102(7):3111–39, December 2012. doi: 10.1257/aer. 102.7.3111. URL `http://www.aeaweb.org/articles?id=10.1257/aer.102.7.3111` .

- Alberto Abadie and Javier Gardeazabal. The economic costs of conflict: A case study of the basque country. _American Economic Review_ , 93(1):113–132, March 2003. doi: 10.1257/000282803321455188. URL `https://www.aeaweb.org/articles?id=10.1257/ 000282803321455188` .

- Alberto Abadie and Guido W. Imbens. Large sample properties of matching estimators for average treatment effects. _Econometrica_ , 74(1):235–267, 2006. doi: https://doi.org/10.1111/j. 1468-0262.2006.00655.x. URL `https://onlinelibrary.wiley.com/doi/abs/10.1111/ j.1468-0262.2006.00655.x` .

- Alberto Abadie, Alexis Diamond, and Jens Hainmueller. Synthetic control methods for comparative case studies: Estimating the effect of californiaˆa ~~C~~ TMs tobacco control program. _Journal of the American Statistical Association_ , 105(490):493–505, 2010. URL `https://EconPapers. repec.org/RePEc:bes:jnlasa:v:105:i:490:y:2010:p:493-505` .

34
effects of trade liberalization on urban concentration (spatial convergence). Both trade (or imports) and GDP are endogenous variables, and causal economic identification of the effect of changes in trade policy requires exogenous instruments that are correlated with trade, but not with urban concentration. This issue is recognized by Ades and Glaeser (1995), where trade openness (as measured by trade-to-GDP ratio) loses its significance in IV regressions, thereby calling into question its causal effect on urban concentration.

In this paper, the question of causality is taken seriously to differ from the existing literature by avoiding the use of endogenous “outcome” measures (such as trade volume) that do not correspond to any trade policy measure that is directly controlled by policymakers. We tackle these issues by adopting an improved methodology and data set to study the effects of trade liberalization on urban concentration (spatial convergence). Tariff measures that are controlled by policy-makers are looked for and tests are implemented using continuous treatment measures. We attempt to answer the correct policy question and attend to problems of causality and identification while avoiding biases by using a difference-in-difference approach. To put it differently, we are concerned with a treatment-and-control partition of countries based on their engagement in trade liberalization, and we test whether the liberalizers experienced a reduction in urban concentration.<sup>10</sup> Our “policy experiment” approach relies on identification in the time dimension rather than in cross-section. Our trade openness data are the new and detailed Estevadeordal and Taylor (2013) tariff data on consumption, capital, and intermediate goods gathered from primary sources (based on digital sources for recent years as well as archival sources for the 1980s that have not been used previously). Based

> 10We should emphasize that our focus here is squarely on urban concentration and not the more
|Estimating elasticity ................................................................................................................................ 30|
|---|
|Changes in tariff structures and the consumer‟s surplus ............................................................................. 32|
|**References .................................................................................................................................................. 34**|
|**Part III – SUBSIM - The Subsidies simulation Stata Toolkit ............................................................... 35**|
|Introduction ................................................................................................................................................. 35|
|Summary of commands .............................................................................................................................. 35|
|Package installation .................................................................................................................................... 36|
|Initializing the price schedules (pschset) .................................................................................................... 36|
|Initializing the price schedules using the Stata variables (pschsetv) .......................................................... 38|
|Describing the price schedules (pschdes) ................................................................................................... 38|
|Estimating the consumed quantities (trexqua) ............................................................................................ 40|
|The price schedule reform and household wellbeing (imwref) .................................................................. 43|
|The price schedule reform and the government revenue (imrref) ............................................................... 49|
|The price schedule reform and the government revenue according to price change (imrrefpr) ................. 53|
|The price schedule reform and the government revenue according to price elasticity (imrrefel) .............. 54|
|The required increase in prices for a given increase in government revenue (spriceref) ............................ 56|
|ANNEX A: Working with do files ............................................................................................................. 57|
|ANNEX B: Sampling design and Stata ...................................................................................................... 59|

3
We note that the ranking of educational attainment by regions has changed over time. For primar) education, South Asia has apparently stagnated. East Asia has pulled ahead in both primary and secondar\ eduLCition. We also note that the educational structure of Africa is quite ditterent from those of the other regions: Africa :eems to have a much smaller ratio of secondary to primary educated individuals in its labor force.

Psacharopoulos and Arriagada (1986) have estimated mean years of schooling attained by the labor torce fo. different countries around the world that can conceivably be used as a substitute for our estimated educational capital stocks.' However, they only provide **data** for isolated points in time for each country and not a time-series. It has not been possible to generate, from their data, a meaningful time-series for the countries and period covered in this study. Nor has it been possible to apply their methodology to fill in the data gaps, principally because census data are not available for many countries, especially for the early years. In Appendix 2 we present a more detailed comparison between the estimates of Psacharopoulos and Arriagada (1986) and ours.

For almost all of the countries in the sample", data are avalable for all of the variables from 1960 to 1987 and that is the period used for the estimation of the aggregate production function.

'They use attainment rates provided by Kaneko (1986), who utilizes data from reports of the censuses undertaken worldwide from 1975 to 1984.

zThe only exceptions are Hong Kong and Singapore, the education data for which are not available for 1987 at the time of the study.

15
# Index of Figures

|Figure 1. Analytical model for BNPP-funded surveys ............................................. ......._3_|
|---|
|Figure 2. Credible rules, credible policy and predictable resourcing constitute the<br>institutional environment within any perspective ...................... .<br>8|
|Figure 3 The main components of government employment .................... .................... 11|
|Figure 4. Officials say that the whole government is much more corrupt than their own<br>organization ............................................................<br>_15_|
|Figure_5._Construction of indicators for institutional environment in East Caribbean<br>states ...........................................................<br>17|
|Figure 6. Construction of indicators for performance in East Caribbean states .............. 20|
|Figure 7. Performance and Institutional Environment in sampled Bolivian public sector<br>organizations ...........................................................<br>23|
|Figure 8. Rule complexity affects performance in Albania ....................... .................... 25|
|Figure 9. Expected impact of reforms on components of performance in Bolivia ......... 32|
|Figure 10. Effect of institutional environment on results focus in Bolivia ....................... 56|
|Figure 11. Effect of institutional environment on accountability in Bolivia .......... .......... 57|
|Figure 12. Effect of institutional environment on employee morale in Bolivia ......... ...... 58|
|Figure 13. Expected impact of reforms on different components of performance in Bolivia<br>Central Administrations ....................................................<br>_65_|

# Index of Tables

|Table 1 Illustrative prior assertions tested against survey evidence ............. ............|....<br>26|
|---|---|
|Table 2 Reform Interventions and anticipated performance pay offs in the Bolivian<br>public sector .............................................................|31|
|Table 3 Reform Interventions and anticipated performance pay offs in Bolivia Centr<br>Administrations ...................................................|al<br>64|
|Statistical Table I Rule credibility indicator and components (East Caribbean) ..........|. 46|
|Statistical Table 2 Policy credibility indicator and components (East Caribbean) .......|. 46|
|Statistical Table 3 Indicator of resource adequacy and predictability and components<br>(East Caribbean) ............................................|46|
|Statistical Table 4 Indicator of overall institutional environment and components (Ea<br>Caribbean) ...........................................................|st<br>47|
|Statistical Table 5 Results focus indicator and components (East Caribbean)<br>........|......<br>54|
|Statistical Table 6 Accountability indicator and components (East Caribbean) ......... .|.. 54|
|Statistical Table 7 Employee morale indicator and components (East Caribbean) .......|. 54|
|Statistical Table 8 Overall performance indicator and components (East Caribbean)|..._55_|
|Statistical Table 9 Calculating the IRRs (Bolivia) ........................................................|63|