rafmacalaba/datause-encoder-data huggingface dataset:
that the instrumental variable does not impact consumption per adult equivalent by another channel than the presence of refugees, the so-called exclusion restriction. With our distance-based shift-share instrument, one of the main threats to the exclusion restriction is the fact that other time-varying factors would be correlated with the proximity to the border. One major threat would come from conflict in neighboring countries driving both forced migration and the economic lives of people living in areas close to borders. For instance, trade channels have been shown to have non-trivial impact across borders (Bayer & Rupert, 2004; Glick & Taylor, 2010). Spillovers may also arise from the so-called peace dividends. For instance, the return of South Sudan to relative stability coincide with large increase of exports in bordering areas in North-Western Uganda (Brenton & Isik, 2012). Therefore, controlling for conflict spillovers can help to show that the distance variable in our IV is an excludable weighting measure allowing the IV to properly predict the number of refugees being hosted in particular localities, without capturing alternative channels such as changing trade.

We use the Armed Conflict Location & Event Data (ACLED) to create a conflict fatalities index which we apply as a proxy for conflict spillovers (Raleigh et al., 2010). The assumption is that conflicts resulting in fatalities capture the intensity of the violence towards potential migrants and large disruption to trade or economic activity in neighboring countries/regions. The conflict spillover index measures the number of fatalities from the conflict events in neighboring countries, in a particular year, taking place in the areas nearest to the refugee hosting country, weighted by the distance from the conflict area to the clusters in the refugee hosting country. Initially we construct this conflict spillover index by restricting the conflict source countries to the neighboring countries including refugee source countries (i.e. Burundi, DRC, Eritrea, Ethiopia, Rwanda, Somalia, South Sudan, Sudan, Tanzania, Kenya). Later we further restrict the sample to only include the closest neighbors and closest trade partners that are, Burundi, DRC, Rwanda, South Sudan, Tanzania and Kenya.

Table 4 shows that controlling for conflict spillovers does not significantly affect the main coefficient of interest. This is suggestive evidence that the exclusion restriction is likely to be satisfied.

22
based work and therefore at higher risk are largely concentrated among lower wage deciles. This includes personal care, food services, and production jobs (figure 3). Occupations that are intensive in face-to-face interactions that relate most closely to physical proximity are similarly concentrated among lower wage deciles. However, the correlation is stronger for HBW than for F2F because several well-paid occupations, such as professors and managers, ae amenable to remote work, but ideally require intense F2F interactions.

Lastly, a larger share of women’s employment is also accounted for by occupations that are intensive in these face-to-face interactions (figure 4). The gender exposure measure is constructed by taking the F2F occupational scores and multiplying them by the share of employment by gender in each occupation as a share of total employment by gender. For example, a value of 100 for female workers would imply all women are employed in the occupation with the highest F2F score, while a score of 0 would mean all women are employed in the occupation with the lowest F2F score. The results clearly indicate much stronger effects for women than for men. It is also worth noting that women are more exposed according to the F2F index than the HBW measure. The reason is that women in the United States are less likely to work in manufacturing, mining, and construction occupations, which cannot be performed at home, but are less intensive in F2F interactions. Hence, as lockdowns are lifted, the longer lasting effects may be on female employment, as discussed above.

Overall, our results indicate the COVID-19 outbreak is likely to disproportionately affect vulnerable groups and could exacerbate inequality.

**Figure 4: Women are more exposed to face-to-face interactions than men**

<!-- Start of picture text --> 60<br>52<br>50<br>39<br>40<br>30<br>20<br>10<br>0<br>Male Female<br><!-- End of picture text --> Source: Authors’ calculations based on the O*NET database and US. Bureau of Labor Statistics.

9
Because the paper uses at a large number of measures of the war definition, natural resources and structural conditions, the research design searches for robust results. <mark>Table 4 summarizes the direction and statistical significance of</mark> the coefficients on the interacted variables, showing both a good amount of support for our hypotheses and that it is very unlikely that the totality of our results would be due to pure chance. In addition, we have explored a shorter and longer time window (than 5 or 10 years) for the history of coup d’état and riots and our results remain robust. While we have taken as given the other independent variables and the model specification, Fearon and Laitin (2003) and the subsequent literature have shown that their results are robust to a substantial number of checks, including a different lagging structure, different definitions for the independent variables, adding other variables, and different modeling choices. Therefore, finding consistency in our results represents strong support for the theoretical hypotheses.

# [Table 4 about here]

The magnitude of the effect of key variables is not immediately apparent from the individual coefficients. Table 5 shows the marginal effects of state weakness (anocracy, riot history, coup risk or military regime), when varying oil production and rents while keeping all the other variables at observational mean. We compute the results by varying the Humphreys (2005) oil production and the Ross (2008) oil and gas rents for the models with a statistically significant interaction term, for the Prio \ Uppsala definition of civil war.

[Table 5 about here] The midpoint predicted probabilities offer a stark picture that is worth discussing because of the relative magnitude of the effects. For example, at medium levels of coup d’état risk increasing the Humphreys oil production from the 75<sup>th</sup> to the 99<sup>th</sup> percentile leads to a five times higher likelihood of civil war. Because of the negative interaction term, at high levels of coup 23
Table 1: Mask manufacturing intensity and mask manufacturing exposure are uncorrelated with sub-District characteristics

||||_Dependent v_|_ariable:_|||
|---|---|---|---|---|---|---|
||Mask manuf|acturing intensity||Mask manufact|uring exposur|e|
||(1)|(2)|(3)|(4)|(5)|(6)|
|log EBM turnover, RwF Bn|_−_0.000|_−_0.080|_−_0.023|_−_0.026|_−_0.021|_−_0.025|
||(0.076)|(0.096)|(0.015)|(0.016)|(0.017)|(0.019)|
||[0.997]|[0.408]|[0.120]|[0.120]|[0.216]|[0.202]|
|log EBM input, RwF Bn|_−_0.115|0.017|0.054|0.062|0.057|0.064|
||(0.124)|(0.151)|(0.033)|(0.035)|(0.039)|(0.040)|
||[0.361]|[0.910]|[0.109]|[0.077]|[0.148]|[0.121]|
|log population density|_−_0.104|_−_0.080|_−_0.040|_−_0.046|_−_0.032|_−_0.042|
||(0.068)|(0.066)|(0.039)|(0.037)|(0.045)|(0.042)|
||[0.136]|[0.234]|[0.312]|[0.210]|[0.483]|[0.326]|
|% completed primary school|0.015|0.000|_−_0.001|_−_0.001|0.001|0.001|
||(0.018)|(0.019)|(0.002)|(0.002)|(0.002)|(0.003)|
||[0.392]|[0.999]|[0.684]|[0.706]|[0.703]|[0.706]|
|% employed|_−_0.012|0.009|_−_0.013|_−_0.004|_−_0.009|0.001|
||(0.024)|(0.026)|(0.009)|(0.010)|(0.010)|(0.011)|
||[0.628]|[0.732]|[0.167]|[0.719]|[0.390]|[0.962]|
|log textile manufacturers|0.183|0.169|_−_0.022|_−_0.016|_−_0.013|_−_0.006|
||(0.131)|(0.128)|(0.037)|(0.035)|(0.044)|(0.043)|
||[0.173]|[0.198]|[0.560]|[0.661]|[0.765]|[0.893]|
|log population|0.069|0.055|_−_0.016|_−_0.036|_−_0.039|_−_0.049|
||(0.163)|(0.145)|(0.051)|(0.065)|(0.055)|(0.072)|
||[0.677]|[0.708]|[0.757]|[0.577]|[0.477]|[0.497]|
|Province FE||X||X||X|
|Residualized shocks|||||X|X|
|# observations|43|43|86|86|86|86|
|# clusters (sub-Districts)|43|43|86|86|86|86|
|Omnibus F|0.629|0.993|1.123|0.999|2.124|0.942|
||[0.728]|[0.454]|[0.358]|[0.439]|[0.051]|[0.480]|

_Notes:_ Columns 1 and 2 report coefficients from regressions of mask manufacturing intensity on sub-District characteristics, while Columns 3 through 6 report coefficients from regressions of mask manufacturing exposure on sub-District characteristics. Standard errors are reported in parentheses, and p-values are reported in brackets. Columns 2, 4, and 6 control for Province fixed effects, while Columns 5 and 6 residualize leave-out mask manufacturing intensity by regression on Province fixed effects, log population density, and log number of textile manufacturers before constructing mask manufacturing exposure.

controls, and we graph these estimates in Figure 3. All impacts are relative to March 2020, the month before the licensing of domestic textile manufacturers to produce high-quality masks was announced.

21
# **DISTRIBUTION OF INCOME AND INCOME TAX BURDEN IN BULGARIA**

# **±eljko Bogetit and Fareed Hassan**

# **I. Introduction**

Under the previous centrally planned system in Bulgaria, taxes were but one, and perhaps not the main instrument of massive redistributions. Indeed, non-market pricing and extensive explicit and implicit subsidies were equally important mechanisms for creating and maintaining an economy based on the principle of extreme egalitarianism. The shift to a market economy, which in Bulgaria accelerated in 1991, changed all that. Market-based relative prices now guide most of the economic activity, private sector is a significant part of the economy, and taxes are increasingly becoming similar in form and effects to those in market economies'.

Furthermore, taxes are becoming an important characteristic of the overall economic environment determining incentives to engage in economic activity, and level and l/ For recent surveys of Bulgaria's market reforms see, for example, World Bank (1994a,b), and European Commission (1994).
**Figure 12**

# **High alpha, high beta pattern**

This figure presents log(EM Asset) and log(Global Asset) during 2005-2010. EM assets are MSCI EM, FX-EM and EMBI Global. FX-EM is an index that averages dlog changes across EM' FX. Global assets are the S&P, the Broad Dollar Index and the US HY corporate spread. Source: Bloomberg, MSCI, GEM World Bank, US Treasury.

<!-- Start of picture text --> 5.5<br>6<br>5<br>5<br>4.5 4<br>4 3<br>ln(MSCI EM) ln(MSCI World) ln(EMBI Global) ln(US HY)<br>4.8<br>4.7<br>4.6<br>4.5<br>4.4<br>4.3<br>4.2<br>ln(FX-EM) ln(Broad Dollar Index)<br><!-- End of picture text -->
- iv -

|3.6<br>3.7|Government Structures<br>LAC Countries vs. Industrial Market Economies||
|---|---|---|
|3.8|Relative Urbanization and Industrialization|Table 1.2|
|3.9|Public Sector Dominance?|Table 3.9 (a)&(b)|
|3.10|Are LAC Countries Centralized?|Tables 3.10 (a&b) and<br>|
|||3.10 (c&d) in Annex|
|3.11|Are LAC Governments Welfare Minded?|Tables 3.11 (a, b&c)|
|3.12|Inter-Regional Disparities||
|3.13|Intra-Regional Disparities||
|3.14|Regional.<br>Equalization Policies||
|3.15|Spatially Uniform Development?||
|3.16|Top-Down Transfers|Table 3.16|
|3.17|The New Federalism||
|3.18|Rethorics of Decentralization||
|3.19|Ambiguities of Decentralization||
|3.20|Fiscal Reform Initiatives|Boxes I & 2|
|3.21|Summing Up||
|IV.<br>THE F|ISCAL GAP OF LAC SUBNATIONAL GOVERNMENTS||
|4.1|Reasons of Fiscal Imbalance||
|4.2|Deficitary Trends|Table 4.2|
|4.3|Revenue Side Factors|Table 4.3 (a&b)|
|4.4|Main Revenue Sources|Tables 4.4 (a), and 4.4|
|||(b, c, d,&e) in Annex|
|4.5|Taxes on Goods and Services||
|4.7|Taxes on Property|Table 4.8 (a&b)|
|4.9|Other Taxes||
|4.10|Non-Tax Revenues||
|4.11|Intergovernment Transfers and Revenue Sharing|Tables 4.12 (a & b)|
|4.12|Dependence on Transfers|Box 3 (a & b)|
|4.13|Borrowing (Mexico, Brazil, Colombia Cases)|Table 4.14 and Box 4|
|4.15|Expenditures Side Factors||
|4.16|Subnational Participation in Public Expenditures|Table 4.16 (a)&(b)|
|4.17|The Composition of Subnational Expenditures|Tables 4,17 (a), and 4.17|
|||(b, c, d,&e) in Annex|
|4.18|Constraints to Reducing Expenditures|Table 4.18 (a, b, c&d)|
|||in Annex|
|4.19|Spatial Cost Differentials||
|4.20|The Fiscal Gap of LAC Subnational Governments|Table 4.20 (a&b)|
|4.21|Current Strategies to Relieve Fiscal Stress||
|4.22|Changing the Scope and Sc4le of Expenditures||
|4.23|Restructuring the Territorial Base of Jurisdictions||
|<br>4.24|<br>Adjusting the Level of Fees and Charges||
|<br>4.25|<br>Implementing Tax Reforms||
|<br>4.26|<br>Relving on Intergovernment Transfers|Box 5|
|<br>4.27|<br>The Use**ui**<br>Transfers|Boxes 6&7|
|4.28|Stabilization Objectives||
|<br>4.29|<br>Allocation Objectives||
|<br>4.30|<br>Distribution Objectives||
|4.31|The Design of a Transfer System|Boxes 8&9|
|<br>4.32|<br>Summing Up||
|**Ta**|**ble 4: Perc**|**entage of households re**<br>Domestic<br>|**ceiving remittances by division **<br>International<br>|
|---|---|---|---|
||2000|2005|2000<br>2005|
|Barisal|37.2|29.5|8.2<br>5.2|
|Chittagong|16.1|25.3|20.7<br>24.2|
|Dhaka|17.5|13.5|8.2<br>7.8|
|Khulna|21.0|24.1|1.8<br>3.9|
|Rajshahi|13.6|27.0|2.2<br>1.3|
|Sylhet|33.3|10.4|17.4<br>15.7|
|Total|18.9|21.1|8.6<br>8.8|

_<u>Source</u>_ : HIES 2000, 2005

**Table** **<u>5: Increase in number of microfinance members and poverty reduction</u>**

|_Increase in no. of members_<sup>_1_</sup>|_Poverty Headc_<br>2000|_ount Rate (%)_<br>2005|Change(%)|
|---|---|---|---|
|Less than 20%|46.6|42.7|-3.9|
|20% to 30%|46.8|40.0|-6.8|
|30% to 40%|50.9|38.4|-12.6|
|More than 40%|54.4|41.1|-13.3|

_Note_ :<sup>1</sup> indicates percentage change in no. of microfinance members in Thana between 2000 and 2005. _<u>Source</u>_ : HIES 2000, 2005; PKSF 26
25 three error terms. Denote by  1 and  2 the covariances between _u_ and  1 and _u_ and  2, and by  12 and  22 the variances of  1 and  2. The general case where  1≠  2 and  12 ≠  22 is consistent with what Heckman et al. (2006) call ‘essential heterogeneity’, i.e. there are unobserved individual-specific returns to the project and these affect the placement of the 2 2 project. A special case of interest is where  1=  2 (but  1 ≠  2 ): this is the case where there are unobserved individual-specific returns to the project but these do not affect the placement of the project (cf. e.g Basu _et al._ 2007). A further special case of interest is 2 2 where  1 =  2 (and  1=  2): this is the case where there are no unobserved individualspecific returns to the project, which is the case handled by the traditional Heckman (1978) endogenous dummy variable model.

# **Data and variables**

The two years we use are 1997 and 2003, both years when the Demographic Health Survey (DHS) was fielded in Vietnam.<sup>14</sup> The fieldwork for the 1997 DHS was undertaken in July-October, after the two Bank projects had started (Committee for Population Family and Children of Vietnam and ORC Macro 2003). However, both projects were slow to start disbursing, and little had happened by the time the 1997 DHS went into the field; for example, as of October 1997, the health support project had disbursed only 3% of the total amount disbursed by the end of the project (World Bank 2007). The two DHS’s were fielded in the same 205 communes in both years<sup>15</sup> ; these communes fell in 41 of Vietnam’s 64 provinces, 19 being population and family health

> 14 This is not by accident: the DHS was funded by the population and family health project with the intention that the two surveys would be used to monitor and evaluate the project. In the event, the only public evaluation of the project (Fritzen 2007) was undertaken using an altogether different survey.

> 15  Different commune identifiers were used in the two surveys, however.
(ii) (iii) variable for the plant being less than four years old. The variables predict plant usage based upon plant activity itself. As local conditions may also be important, we model district level traits: median manufacturing wage (weighted by employment); percent of plants in a district that are located in urban sites; population density; percent of villages in a district with telecom, power supply, paved roads, and safe water access (separate variables); distance of the district centroid to the closest of the largest three cities of India; percent of households with telecom; percent of villages with electricity available for agricultural use; percent of villages with electricity available for domestic use; and percent of households with electricity access in a district. These traits correspond to the year 2000, which is the mid-point of our sample. Most of these variables are taken from the 2001 Census survey, except for the median wage and urban plant shares that are constructed directly from the ASI and NSSO surveys. The imputation regression is run separately for each “cell” composed of sector (informal vs. formal), year (1989, 1994, 2000, 2005, 2010), industry (two-digit NIC codes, 15-36), and states (30 different states). Due to the localized nature of the regressions, the reported results use unweighted imputation regressions, and we have separately confirmed alternative approaches using sample weights for regressions.

Missing or zero-valued electricity expenses are replaced with the predicted values based upon their traits and the appropriate regression coefficients for their given sector, industry, year and state. However, in some cases, only a few establishments are sampled in a given cell, due to both small local representation and/or sampling procedures noted above. If the reporting number of establishments for a given cell is below 20 in any year, we instead run the imputation procedure at the national level using sector, industry, and year data. This alternative calculation is applied in every year for a given cell to ensure a consistent approach, even if the establishment count described above is only below 20 reporting plants for one or two survey years.

(iv) Since predicted values can be negative, we note the occurrence of negative values in predicted electricity expense. We replace any value of electricity expense below US $0.1, either in predicted or in raw data, by US $0.1.

21
**Figure 8: Average Weekly Hours Worked in Manufacturing. United States, 1890-1991**

<!-- Start of picture text --> Hours<br>65<br>60 -<br>55 _<br>50 _<br>45-<br>40 -<br>35-<br>30-<br>25-<br>20 I l l l l l I I I I<br>1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990<br>Years<br>Sources: Bureau of the Census, Historical Statistics of the United States: Colonial Times to 1970, pt 1<br>(U.S. Department of Labor, 1975), pp. 168-70; Bureau of Labor Statistics. Handbook of Labor Statistics<br>(August 1989), p. 304; and Monthly Labor Review (May 1992), p. 76.<br><!-- End of picture text --> _United States_ The first statutory rights were the minimum wage and overtime provisions of the Fair Labor Standards Act, which became fully effective in 1940. Note that this is five years after the NLRA which guaranteed the right to unionize and bargain collectively. Because the set standard made the 41 st hour of work much costlier, it was expected that the use of overtime would decline. As Figure 8 shows, after 1940, average weekly hours in manufacturing converged around 40. Recently, however, costs for medical insurance, social security contributions, and other benefits have become a much bigger percentage of the typical wage bill, thus reducing the impact of overtime premium on the choice of hours.

There are several factors that explain the effectiveness of overtime provisions. First, the law did not force a major change in practice since, as the figure shows, average hours worked had declined before the passage of the law. Second, the right created under the law seems to be an accepted practice, is unambiguous, reporting is minimal, and enforcement is straightforward. In addition, workers have an incentive to abide by the law, since overtime represents an increase in their pay.

33
|**Variables**|**No Shock**|**Mean**|**Shock**|**Mean**|**Mean Difference**|
|---|---|---|---|---|---|
|Per capita other expenditure (in SDGs)|2,548|1.32|5,365|0.903|0.418***|
|Total per capital expenditure (in SDGs)|2,548|177.35|5,365|156.89|20.469***|
|Household asset index|2,548|42.16|5,365|36.64|5.516***|
|Household tropical livestock index|2,548|1.08|5,365|2.66|−1.575***|
|Household size|2,548|5.75|5,365|6.37|−0.626***|
|Male-headed household|2,548|0.83|5,365|0.82|0.01|
|Age of the household head|2,548|45.74|5,365|45.85|−0.11|
|Total income in previous month (in SDGs)|2,544|1,672.69|5,356|462.88|1,209.81|
|Total income in previous year (in SDGs)|2,544|7,483.45|5,356|6,524.2<br>5|959.20|

_Source_ : Authors’ calculation using 2009 NBHS data. *** p<0.01, ** p<0.05, * p<0.1 Table 2: Test of Means between Households Affected by Shocks and Those Not Affected, 2014/15

|**Variables**|**No Shock**|**Mean**|**Shock**|**Mean**|**Mean Difference**|
|---|---|---|---|---|---|
|Urban (=1)|5,840|1.645|6,113|1.752|−0.107***|
|Poor (=1)|5,840|0.305|6,113|0.357|−0.052***|
|Extremely poor (=1)|5,840|0.21|6,113|0.257|−0.047***|
|Per capita education expenditure (in SDGs)|5,840|84.07|6,113|74.054|10.016***|
|Per capita health expenditure (in SDGs)|5,840|355.398|6,113|390.416|−35.018**|
|Per capita clothing expenditure (in SDGs)|5,840|175.497|6,113|178.322|−2.825|
|Per capita utilities expenditure (in SDGs)|5,840|126.527|6,113|124.198|2.328|
|Per capita transport expenditure (in SDGs)|5,840|393.269|6,113|361.97|31.299***|
|Per capita personal care expenditure (in SDGs)|5,840|199.746|6,113|182.771|16.975***|
|Per capita house related expenditure (in SDGs)|5,840|218.84|6,113|186.886|31.955***|
|Per capita recreational expenditure (in SDGs)|5,840|47.559|6,113|49.777|−2.218|
|Per capita other expenditure (in SDGs)|5,840|39.972|6,113|34.494|5.478|
|Food Insecurity (=1)|5,840|0.081|6,113|0.103|−0.022***|
|Household size|5,840|5.506|6,113|6.162|−0.656***|
|Household asset index|5,840|26.638|6,113|24.063|2.575***|
|Household tropical livestock units|5,840|4.292|6,113|6.678|−2.385**|

_Source:_ Authors’ calculation using 2014/15 NHBPS data. *** p<0.01, ** p<0.05, * p<0.1

# 2.3 Covariate Shocks

In addition to idiosyncratic shocks, households may also be exposed to covariate shocks that potentially affect all households in a community such as a town or state. In this section, we describe the exposure of Sudanese households to covariate shocks at the state level. We consider deviation of rainfall from historical averages to identify covariate flood/drought shocks and the number of conflict/crime events. Rainfall data at the state level were obtained from the Center for Hydrometeorology and Remote Sensing (CHRS)<sup>9</sup> . The conflict/crime data are obtained from the Armed Conflict Location and Event Database (ACLED)<sup>10</sup> . We focus on more recent data of these variables to better inform policy.

> 9 See http://chrsdata.eng.uci.edu/.

> 10 See https://www.acleddata.com/data/

24
# **The Next Wave of Deaths from Ebola? The Impact of Health Care Worker Mortality**

David K. Evans Markus Goldstein World Bank World Bank Anna Popova World Bank JEL Classification: I15, I18, O15 Keywords: Ebola, epidemics, health economics, maternal mortality, health care workforce <u>Acknowledgments: The authors thank Trina Haque, Patricio Marquez, and Shiyong Wang for their help in securing</u> the data without which this work would not have been possible, as well as Kathleen Beegle and Tim Bulman for their suggestions.