rafmacalaba/data-use-mentions-extended huggingface dataset:
analysis as they may have a different development trajectory and may thus introduce endogeneity. Figure 1 maps the distribution of refugees in 1928 at the community level.

# **4.1.2 Contemporary social cohesion at the local level**

I use three main indicators of social cohesion at the local level: participation in voluntary associations, political fragmentation, and crime.

**Sports clubs** To measure participation in voluntary associations, I use the register of sports clubs inventoried by the General Secretariat of Sports (GGA) in the early 2000s. The dataset provides the geolocation of more than 22,000 sport association across the country. The geolocation is precise enough to identify the number of association within each of the 5775 communities that existed in Greece in 2001.

**Political fragmentation** To measure political fragmentation at the local level, I follow the political science literature (Gross and Sigelman, 1984) by using an index of fragmentation of votes between different political parties:

This index simply measures the probability that two people drawn at random in the population have voted for the same party at the last election. This index is lower when people tend to vote for the same party and higher when votes are scattered between different parties. Since the Greek political landscape has many parties that sometimes hold relatively similar ideological position, I also used a fragmentation index where parties are grouped into a leftist coalition and a rightist coalition.The electoral data I use covers the legislative elections of June 2012 and January 2015 and provides the number of votes for each of the 325 municipalities of Greece at that time.<sup>17</sup> **Crime data** I draw on crime data from Vasilakis (2018) who collected the data from the Greek police. This data comprise the total number of robberies, drug arrests and burglaries in 2015 for each of the 56 departments of Greece.

**Matching places over time** Out of the 5,042 municipalities that existed in 1928, 4,845
Asian economies, including both those more and less advanced. The sample of comparators is dictated by data availability.

# **2 Data**

The use of comprehensive data on the productive activities where individuals obtain their income, either as self-employed or as salaried workers or firm owners, is crucial to our research question. For this reason, we rely on microdata from the surveys used in different countries to generate official labor market statistics, which we refer to as ”employment surveys” even though in some countries they are labeled as household surveys. Given their nature and purpose, they are designed to be representative of the entire labor force in each country. Beyond collecting information on the work status and income of each adult individual in the household, the surveys frequently ask for the size, in number of workers, of the business where the person works or, in the case of employers, the number of people they employ.

These data are crucial for questions on how attributes of the business sector impact development or inequality. Their unparalleled strength is their comprehensiveness in covering all 4
, this share is estimated at 78 percent (Scheil-Adlung, 2015).<sup>26</sup> Fewer than 20 percent of the health care facilities across the country are providing services to more than 75 percent of the rural population (World Bank, 2015).<sup>27</sup> There appears to be a persistent “urban bias” in the government health care workforce (Ahmed et al., 2011). Moreover, there is also a significant gap in terms of the quality of water and sanitation facilities available between health care facilities in the urban and rural areas. Health care facilities in the rural areas, for example, are more susceptible to water shortages (World Bank, 2018). While urban health care facilities have almost universal sanitation coverage, the coverage for rural health care facilities is 85 percent. Thus, both the differences in availability and quality of health-care services across urban and rural areas could have confounded the effect of poor sanitation on school enrollment. The availability of health care services in urban areas, for example, would make it easier for parents to seek medical help, preventing their children from suffering longer-term health deficits.

> 26 Based on data collected in 2011.

> 27 It was also shown that the doctor-to-population ratio is 1:1,500 in urban areas, but it is 10 times worse in rural areas – 1:15,000 (Mabud 2005).

18
Policy Research Working Paper 7732

# **Abstract**

This paper assesses the impact of the demobilization, reinsertion and reintegration program in post-war Burundi. Two major rebel groups benefited from cash and in-kind transfers, the CNDD-FDD from 2004, and the FNL from 2010. A panel data of households collected in 2006 and 2010 is combined with official records from the National Commission for Demobilization, Reinsertion and Reintegration. Regression analysis shows that the cash payments received by FNL demobilized households had a positive impact on consumption, nonfood spending and investments. The program also generated positive spillovers in the villages where FNL combatants returned. Ex-combatants indeed spent a large part of their allowance on consumption goods and clothing, thereby generating a short-run economic boom in villages. However, the long-run evolution of consumption indicators is negative for CNDD-FDD households, as well as for villages where CNDD-FDD combatants returned, suggesting that the direct impact and the spillovers of the program vanished in the long run.

This paper is a product of the Operations and Strategy Team, 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 authors may be contacted at olivier.sterck@economics.ox.ac.uk.

_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
find a total effect of the area of city lights of -0.715. Combined with the results of Table 3, this suggests that 87% of the total big city effect is via induced town growth, and 13% is direct. However, it should also be noted that the R<sup>2</sup> has fallen from 0.631 to 0.546 in comparing columns 1 of Tables 3 and 4. This suggests there is a sizeable share of the variance that may be a direct effect of town growth, independently of city growth. Moreover, the tests of the nesting restrictions needed to derive the ‘big city only’ or ‘small town only’ models show that the models that drop the variables for smaller towns are not consistent with the data, whereas one would not reject the restrictions needed for dropping the big city variables.

To summarize, our key finding is that the lights data indicate that the effects of urban economic growth on rural poverty are almost entirely attributable to towns rather than cities.

# _5.3 Distributional Effects of City and Town Expansion_

Do the effects we have seen on rural poverty measures entail significant systematic changes in the extent of inequality within rural areas? A simple test for such effects is to 18
Figure A.8: Number of firms acquired by German shareholders by location <!-- Start of picture text --> year<br>Border region No border region<br>500<br>400<br>300<br>200<br>100<br>Number of firms with a German Shareholder 0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. Border regions are: Dolnoslaskie, Lubuskie and Zachodniopomorskie.

Figure A.9: Event study: the effect of pre-determined measure of German comparative advantage on the acquisition of Romanian firms.

<!-- Start of picture text --> .3<br>.25<br>.2<br>.15<br>.1<br>.05<br>0<br>-.05<br>-.1<br>-.15<br>Year # export share Germany (log) -.2<br>-.25<br>-.3<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Notes: Authors’ calculation based on the Orbis database. The sample includes only Romanian firms. The plot is created by regressing a dummy variable indicating German ownership on a full set of event time indicators interacted with the pre-determined measure of German comparative advantage and controlling for firm and year fixed effects. The omitted baseline is the last pre-treatment year, 2003. The vertical bars indicate 95% confidence interval. Results include only firms established before 2004. Germany’s comparative advantage is measured by average sector-level export shares over the period 1994-2004.

30
|Child faced illness in last 2 months|0.01|0.104|0.01|0.107|0.01|0.101|0.01|0.113|0.00|0.059|
|Child is stunted|0.07|0.262|0.07|0.251|0.08|0.273|0.08|0.266|0.07|0.251|
|Unsafe source of drinking water|0.32|0.468|0.32|0.468|0.32|0.467|0.40|0.490|0.04|0.206|
|Unimproved toilet facility|0.56|0.496|0.56|0.497|0.57|0.495|0.65|0.476|0.24|0.428|
|No access to electricity|0.76|0.424|0.75|0.431|0.78|0.418|0.93|0.255|0.16|0.370|
|Four or more people per room|0.42|0.493|0.41|0.492|0.42|0.494|0.46|0.498|0.25|0.435|
|Floor: natural, non-permanent material|0.86|0.344|0.86|0.350|0.87|0.338|0.96|0.200|0.52|0.500|
|No television/ radio/mobile phone/ fixed phone<br>**_Source:_**_Authors’ calculations; Datafrom ESS 2018/19._|0.42|0.493|0.43|0.495|0.41|0.491
published results. However, there are notable exceptions for cases in which the estimates are derived entirely from the FH model (i.e. those districts in which there were no observations in the underlying survey). The root mean square errors for these cases are higher than in the case for most ELL maps and this is one of the key advantages of the ELL approach (because such analyses use census microdata, there are no missing districts that need to be estimated out-of-sample). However, unfortunately, in this application the ELL approach is not possible.

**Figure 33: Comparison between ELL and FH**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>ELL Maps Lower ELL Maps Upper CA FH Lower CA FH Upper<br><!-- End of picture text --> The improvements in precision (as measured by root mean square error) over direct survey estimates from the Fay Herriot approach are also substantial. Improvements are concentrated in those locations that had the least precise estimates before the exercise. The following figure (34) illustrates the improvement in terms of the root mean square error of the mean for poverty at $5.5/day.

67
|1,594|0.2|3,930|

Source: Sierra Leone Population and Housing Census, 2015.

# _Farm Size_

The 2009 National Sustainable Agriculture Development Plan 2010-2030 gives the average farm size as 1.63 hectares, based on the 1985 census. However, the 2015 Population and Housing Census indicates that 1,694,309 hectares of land is under rice cultivation, comprising 1,133,925 hectares of upland and 560,384 hectares of lowland rice. The total estimate is slightly higher than the estimate from the Ministry of Agriculture (Table 2). The 2015 Census puts the average farm size at 2.46 hectares. This suggests that the average farm size increased by 51 percent over the 30-year period between 1985 and 2015.

# _Labor availability use and earnings_

According to the 2014 Sierra Leone Labor Force Survey, the working-age population is just over 3 million. The overall labor force participation rate is 65 percent, with the male participation rate at 65.7 percent and the female rate slighter lower at 64.5 percent. The overall labor participation rate is much higher in the rural areas (69.4 percent) than urban Freetown (53.9 percent). According to the 2014 Labor Force Survey, 59.2 percent of the Labor force is engaged in self-employment in the agricultural sector, with the percentage slightly larger for men than for youth and women (Table 4).

**Table 4: Sierra Leone-Employment by Sector**

|**Sectors**|**Men**<br>**(%)**|**Women**<br>**(%)**|**Youth**<br>**(%)**|**Overall**<br>**(%)**|**Rural**<br>**(%)**|**Urban**<br>**(%)
141|
|$ 200 lottery: Consumption|0.17|0.14|0.20|0.18|0.20|0.21|0.18|0.15|0.17|0.19|
|$ 200 lottery: Working capital|0.40|0.39|0.42|0.44|0.39|0.39|0.46|0.42|0.34|0.26|
|<br>$ 200 lottery: Investment/savings|0.15|0.17|0.14|0.13|0.15|0.15|0.15|0.13|0.17|0.23|
|$ 5,000 lottery: Consumption|0.10|0.09|0.09|0.11|0.14|0.08|0.15|0.08|0.07|0.06|
|$ 5,000 lottery: Working capital|0.65|0.64|0.66|0.69|0.57|0.61|0.67|0.69|0.63|0.43|
|$ 5,000 lottery: Investment/saving|0.19|0.20|0.17|0.17|0.23|0.22|0.19|0.16|0.20|0.35|
|No. of obs. (farms)|2,251|1,059|316|464|124|288|619|790|501|341|

_Source:_ Own computation from MAPF/WB/EU small producer survey. _Note:_ Answers marked by<sup>a</sup> were answered by all farms while the remainder is only for the 82% owner-operated farms that answered the household section of
The remainder of this paper is structured as follows. Section 2 provides a brief review of the existing studies on the welfare impacts of the COVID-19 pandemic and describes the contributions of this paper to the existing literature. Section 3 describes the data used in the analysis, defines the main variables that the study relies upon to examine the different dimensions of welfare impacts, and discusses some of the data-related caveats. Section 4 outlines the main findings related to the patterns of employment losses and subsequent dynamics across different countries and population groups. Section 5 looks at how the broader welfare impacts and the response to the shock could lead to scarring and reinforce inequality in the longer-term if the losses are left unmitigated. Section 6 concludes.

# **2 Welfare impacts of COVID-19: Existing evidence and knowledge gaps**

A number of studies have documented the multifaceted welfare impacts of the COVID-19 pandemic both across and within countries, focusing primarily on the labor market impacts such as job and income losses. These resulted from the restrictions to mobility and economic activity that were imposed by governments in order to contain the spread of the coronavirus as well as the ensuing impact of global demand and supply shocks.<sup>3</sup> In developing countries, analyses mostly rely on HFPS data collected by the World Bank, as many countries had to put on hold the collection of household data through regular surveys such as Household Budget Surveys or Labor Force Surveys during 2020-2021. HFPS data revealed high levels of work stoppages early in the pandemic, particularly in the industry and services sectors, lack of payment for work performed, as well as income losses at the household level that correlate with the degree of stringency of the measures that were imposed to restrict mobility and economic activity (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021). For instance, Bundervoet et al. (2022) find, based on a sample of 31 countries, that more than a third of respondents reported having stopped working
- 22 -

From this equation, one would tend to reject at the 95 percent level of confidence that the elasticity of the debt was larger than one. Before comnenting on the insufficiency of such an equation, it is interesting to report that the price of the debt seems to be very poorly correlated to macroeconomic data related to the country. For instance, the most important of these macroeconomic data (one would guess), such as the non-interest current account or the domestic inflation rate, never appeared to be significantly correlated with the price. On the other hand, arrears or rescheduling data (as we can see from equation (21)) alurays perform extremely well. These rasults are summarized in diagrams 1 to 3. They tend to indicate that the market is extremely sensitive to the "punctuality" of payments and pay little attention to overall macroeconomic performance. Finally, one also sees from equation (21) that a dummy separating the 1986 and 1987 data appears to be significant. This may be a reflection of Citibank's decision to build up $3 billion of reserves against developing country exposure, a move which significantly influenced the market.

Despite its appeal and its simplicity, an equation such as (21) is extremely misleading. First, it leads us to reject the hypothesis that the elasticity of the price with respect to debt is larger than one for the entire sample. But it may very well be the case that only a sub-group of countries was hit by the debt-overhang problem. Running, for instance, the same regression for the sub-sample of countries for which the debt-to-export ratio is larger than 3 (a sub-sample of 16 countries) would yield a larger elasticity, which we estimated to be at 1.183 (with a standard error of 0.339). Second, and perhaps more importantly, an equation such as (21) takes
BERR (2008). “ _High growth firms in the UK: Lessons from an analysis of comparative UK performance_ ”. BERR economics paper 03.

- Bigsten, A., M. Gebreeyesus and M. Söderbom (2016). “ _Tariffs and Firm Performance in Ethiopia, The Journal of Development Studies_ ”, 52(7), 986-1001

- Bigsten, A., and Gebreeyesus, M, Siba, E., and Söderbom, M. (2012). _“Enterprise agglomeration, output prices, and physical productivity: Firm-level evidence from Ethiopia.”_ WIDER working paper. No. 2012/85.

- Bigsten, A., and Söderbom, M. (2006). “ _What have we learned from a decade of manufacturing enterprise surveys in Africa_ ?” World Bank Research Observer, 21(2), 241–265.

- Bigsten, A., and Gebreeyesus, M. (2007). _“The small, the young, and the productive: Determinants of manufacturing firm growth in Ethiopia.”_ Economic Development and Cultural Change, 55(4), 813-840.

- Bigsten, A., and Gebreeyesus, M. (2009). _“Firm productivity and exports: Evidence from Ethiopian manufacturing.”_ The Journal of Development Studies, 45(10), 1594-1614.

- Bigsten, A., Gebreeyesus, M., and Söderbom, M. (2009). _“Gradual trade liberalization and firm performance in Ethiopia.”_ University of Oxford. Department of economics. Centre for the study of African economies (CSAE).

- Birch, David L., (1979). _The Job Generation Process_ . Cambridge, MA: MIT Program on Neighborhood and Regional Change.

- Bogas, P., and N. Barbosa. (2015). “ _High-Growth Firms: What Is the Impact of Region-Specific Characteristics?_ ” In Entrepreneurship, Human Capital, and Regional Development, edited by Rui Baptista and João Leitão,
A first question is whether adaptive sampling improved learning in the IVR experiment. The motivation to use adaptive methods is to increase efficiency and make the most of a limited sample and time. However, asymptotic convergence results for exploration sampling and other best-arm algorithms (Kasy and Sautmann, 2021a; Russo, 2020; Qin et al., 2017) only apply to specific outcome distributions and when the number of waves grows large. In this experiment, we learn only from one prior wave and adapt the assignment shares for half of the sample in a second wave. Possible learning gains from adaptivity are further limited by the fact that the exploration sampling algorithm can only approximate the optimal assignment. In a first exercise below we therefore use simulations to evaluate the gains from adaptive sampling in wave 2, relative to non-adaptive sampling where an equal share of the sample is allocated to each treatment arm (a “standard RCT”). We use the data actually gathered in this experiment. The goal is to quantify the performance of exploration sampling ex post and for the specific context of the IVR experiment. This contributes to an evidence base about the gains from adaptive sampling in policy choice.

A second question is how researchers should ex ante compare and make decisions about research designs based on prior information, and whether such comparisons are reliable. As discussed above, operational and logistical constraints in the IVR restricted the space of possible research designs essentially to either conducting one experimental wave (possibly in only one half of the term) or two waves. With reference to the two scenarios laid out in 2, ex ante, we might have asked whether we should simply conduct a one-wave, non-adaptive RCT with the full sample, or if there are significant gains from holding back half of the sample and adjusting the treatment assignment using exploration sampling in wave 2. Alternatively, after carrying out wave 1 and observing the results, we might have asked whether the learning gains from the second wave make the effort worthwhile. In the second and third exercise below,
the second most progressive tax system, achieves the lowest degree of redistribution because the average tax rate is extremely low.

More generally, the decomposition of the redistributive effect into its progressivity and average tax rate components shows that a highly progressive system on its own is not sufficient to achieve a significant reduction in inequality. It also illustrates that countries can take different approaches to achieve the same degree of redistribution. Consider, for example, Ghana and Uganda which both achieve almost the same degree of redistribution. Uganda achieves this through a highly progressive system that targets only the highest income households, whereas Ghana achieves the same redistribution through a less progressive system applying positive tax rates across most of the income distribution.

# 2.3.2 VAT

Effective progressivity studies can also be undertaken for the VAT if detailed household budget survey (HBS) microdata is available covering a wide range of consumption items (so as to enable modelling of multi-rate VAT structures). While VAT studies have been undertaken for many high-income countries (see, e.g., Thomas, 2022 and 2020, for 27 OECD countries; IFS, 2011, for 9 European countries), as with direct taxes, the data requirements mean that studies for IDA countries are not common.

Furthermore, a range of conceptual modelling difficulties are associated with modelling VAT burdens. In particular, many studies of the distributional impact of the VAT have examined the VAT burdens across 23 A close examination of the results shows some local regressivity in Ethiopia (1st-2nd decile) and Honduras (5th-6th decile). This could be due to the provision of direct transfers incorporated in the CEQ analysis, but potentially also due to measurement or sampling error. While the overall trend in these countries still appears progressive, this also highlights the usefulness of global metrics in providing a framework to weight the regressivity in one party of the distribution against progressivity elsewhere to generate an overall measure. This becomes particularly relevant for analysis of the VAT where effective tax burdens can fluctuate non-monotonically across the income and expenditure distribution.