rafmacalaba/datause-encoder-data huggingface dataset:
have worked in creating groups which are comparable in terms of baseline characteristics. There is no significant difference in the baseline profits, revenue, capital stock, age, and level of informality of the treated and untreated firms. The age, education, parental background, and risk aversion are similar for owners in both groups. The only significant difference between the two groups occurs for a household durable asset index, with firm owners in the control group having slightly higher mean baseline assets. To test for joint significance of all these variables, we estimate a probit model of whether or not a firm is assigned to treatment as a function of the 16 baseline characteristics given in the Table. The p-value from the chi-squared test is 0.355, so that we can not reject the null hypothesis that these observable characteristics do not help predict whether or not a firm was assigned to treatment.

# **3. Data/Survey**

The baseline survey gathered detail information on the firm and the characteristics of the firm owner. The main outcome variable of interest in this paper is the profits of the firm. Firm profits were elicited directly from the firm by asking: “What was the total income the business earned during March after paying all expenses including wages of employees, but not including any income you paid yourself. That is, what were the profits of your business during March?” The reported mean and median profits in the baseline are 3900 rupees and 3000 rupees respectively. The survey also asked detailed questions on revenues and expenses. Profits calculated as reported revenues minus reported expenses are lower, around 2500 at the mean and 1350 at the median. Profits calculated in this manner are positively correlated with reported profits, with a correlation coefficient of 0.32. This is about the same level as one finds in other microenterprise surveys. In de Mel, McKenzie and Woodruff (2007), we analyze the measurement of profits in some detail, reporting on experiments conducted with different questions, bookkeeping, and monitoring of sales. The biggest reason reported and calculated profits differ is a mismatch of the timing of purchases and the sales associated with those purchases. Some of the expenses in one 9
Lithuania, Portugal, Romania and Slovenia. Finally, Hungary is the only country where we find a U-shaped pattern with a smaller share of the severely affected in the middle of the distribution.

Figure 3 zooms in on those severely affected to assess whether tax-benefit systems have been able to cushion the shock of those with comparably high changes in market incomes. As such, it controls for differences in the share of affected individuals which influences mean outcomes and presents the effectiveness of the social protection system for those who needed it the most.

_Figure 3: Mean change in market vs. disposable incomes of those severely affected (in percent)_ <!-- Start of picture text --> IE FI MT SK AT NL LV DE IT SE EL PL LT SI FR ES BG HU BE PT EE CZ LU DK HR RO CY<br>Market (Scenario A vs. D) Disposable (Scenario C vs. D) Disposable (Scenario A vs. D)<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>-35<br>-40<br><!-- End of picture text --> Note: See Figure A3 in the Appendix for results by income groups. Severely refers to losses in market income of more than 11 percent (Scenario A vs. D).

Source: Own calculations based on EUROMOD I4.0+.

By construction, losses in market incomes are higher than for the total population ranging between 37 percent in Ireland and 19 percent in Cyprus. These pronounced losses in incomes are only partly cushioned by European tax-benefit systems. Although the share of severely affected is very small in the Netherlands, those who face substantial income drops are not cushioned sufficiently by the tax-benefit system and experience losses in disposable incomes of over 15 percent. The situation is similar in Finland, Poland and Malta, although the share of those severely affected is by far higher in Malta than in the other three countries. Countries that stand out in cushioning higher market income losses are Belgium, Denmark and especially Luxembourg, where mean disposable income of those severely affected increases.

Figure A3 in the Appendix shows changes in mean market and disposable income by income quintiles among those severely affected. In terms of market income, in all countries except France, the drop in market income is smaller at the top than at the bottom of the distribution. In France a clear inverted U-shaped pattern is observed. In terms of disposable income, the opposite pattern is observed, with a smaller decrease in disposable income at the bottom relative to the top quintile among those severely affected. The only exceptions are Bulgaria and Sweden. In the former, the drop in disposable income becomes smaller up until quintile four, but then it decreases further in quintile five. In the latter, a somewhat U-shaped pattern is observed.

To grasp better the heterogeneous impact of the pandemic within countries, Table 2 presents the share of severely affected and changes in mean disposable income by population subgroups.

11
# **Table 5:  Significant Changes in WGI Estimates of Governance, 1998-2007, Cont'd**

||**Gov**|**ernance S**|**core**||||**Agree/**||||
|---|---|---|---|---|---|---|---|---|---|---|
||**2007**<br>**(Level)**|**1998**<br>**(Level)**|**Change**|**Agree**|**No**<br>**change**|**Dis-**<br>**agree**|<br>**(agree+**<br>**Disagree)**|**Sources**<br>**Added**|**Balanced**<br>**Change**|**Bal Chng/**<br>**Actual Chng**|
|**Rule of Law**|||||||||||
|ZIMBABWE|-1.67|-0.50|-1.17|9|0|1|0.90|9|-1.17|1.01|
|ERITREA|-1.10|-0.22|-0.89|4|0|0|1.00|4|-0.61|0.69|
|VENEZUELA|-1.47|-0.69|-0.78|10|1|1|0.91|8|-0.79|1.01|
|BOLIVIA|-0.96|-0.30|-0.66|7|2|0|1.00|9|-0.61|0.93|
|COTE D'IVOIRE|-1.54|-0.90|-0.64|6|2|0|1.00|4|-0.59|0.93|
|TRINIDAD AND TOBAGO|-0.22|0.36|-0.59|6|0|1|0.86|4|-0.44|0.74|
|ARGENTINA|-0.52|0.04|-0.56|9|2|1|0.90|6|-0.58|1.03|
|ESTONIA|1.00|0.50|0.49|7|2|0|1.00|8|0.51|1.04|
|TAJIKISTAN|-1.13|-1.75|0.61|3|3|0|1.00|11|0.74|1.21|
|SERBIA|-0.57|-1.30|0.72|3|1|1|0.75|9|0.74|1.02|
|GEORGIA|-0.44|-1.18|0.74|4|2|1|0.80|11|0.75|1.01|
|RWANDA|-0.65|-1.47|0.82|4|1|0|1.00|6|0.74|0.91|
|LIBERIA|-1.06|-2.07|1.01|3|1|0|1.00|5|0.85|0.84|
|KIRIBATI|0.84|-0.69|1.53|1|0|0|1.00|3|0.77|0.50|
|_Average_||||_5.43_|_1.21_|_0.43_|_0.94_|_6.93_|||
|**Control of Corruption**|||||||||||
|ERITREA|-0.60|0.77|-1.37|2|1|0|1.00|4|-1.52|1.11|
|ZIMBABWE|-1.25|-0.38|-0.87|7|0|1|0.88|8|-0.88|1.01|
|COTE D'IVOIRE|-1.09|-0.38|-0.71|4|2|0|1.00|3|-0.64|0.91|
|KUWAIT|0.49|1.11|-0.61|2|2|1|0.67|4|-0.29|0.47|
|POLAND|0.14|0.60|-0.46|6|2|2|0.75|6|-0.45|0.97|
|UKRAINE|-0.73|-1.16|0.44|6|2|1|0.86|9|0.41|0.95|
|ESTONIA|0.94|0.42|0.52|5|1|1|0.83|7|0.26|0.50|
|TANZANIA|-0.45|-1.09|0.64|5|2|0|1.00|7|0.59|0.93|
|SERBIA|-0.41|-1.08|0.67|4|0|0|1.00|9|0.82|1.22|
|ICELAND|2.60|1.92|0.68|2|1|2|0.50|3|0.48|0.71|
|CAPE VERDE|0.76|-0.32|1.08|2|0|0|1.00|4|1.13|1.05|
|LIBERIA|-0.41|-1.72|1.30|3|0|0|1.00|4|1.32|1.01|
|_Average_||||_4.00_|_1.08_|_0.67_|_0.87_|_5.67_|||
|**Overall Average**||||4.35|0.55|0.47|0.91|5.47||0.98|
|||||||||**Cases>1.25**||5|
|||||||||**Cases<0.75**||13|
|||||||||**0.75 < Case**|**s < 1.75**|68|

Note: Shaded countries correspond to increases in WGI estimates of governance, and non-shaded areas correspond to declines.

35
# **Conflict and Girl Child Marriage: Global Evidence***

Caroline Krafft,<sup>a</sup> Diana Jimena Arango,<sup>b</sup> Amalia Hadas Rubin,<sup>c</sup> and Jocelyn Kelly<sup>d</sup> a St. Catherine University b World Bank Group c International Republican Institute d Harvard Humanitarian Initiative

**Keywords:** Conflict; Child marriage; Humanitarian settings; Gender-based violence **JEL codes:** D74, J12, J13, J16

**Funding note** : This work is part of the program “Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership.” The program is funded by UK aid from the United Kingdom's Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.
income effects on participation in casual labor, particularly for women. This specification gives about the same impacts on poverty at low EGS wage rates, rising to about two percentage points at higher wages. At the Rs 40 wage rate, the headcount index fell to 23% while at the Rs 50 wage rate it fell to 18%. This specification also gave higher female participation in the EGS, though the difference was small.

We also tested the sensitivity of our results to using a different cut-off point in identifying those likely to supply casual labor. Recall that we used a cut-off of 0.5 for the predicted probability. Lowering this to 0.4 adds about one percentage point to the impact on poverty; at the Rs 40 wage rate the headcount index drops to 23.3% and at Rs 50 it falls to 18.7%. Raising the cut-off to 0.6, conversely reduces the impact on poverty by similar magnitudes; at the Rs 40 wage rate, the headcount rate is estimated to be 25.5% and at Rs 50 it is 21.9%.

- _4.2 Budgetary cost and comparison with untargeted transfers_

Table 10 gives our estimates of the net labor supply to the EGS. At an EGS wage rate of Rs 40, we expect a daily attendance at EGS sites of 35 million people. This rises to 38 million at a wage of Rs 50. At a living wage anchored to the official poverty line, the EGS would account for 33% of the casual labor force, rising to 35% at the Rs 50 wage rate. It may be noted that the bulk of the excess supply response to higher EGS wage rates comes from men. Indeed, at all except the low EGS wage rates the increments to female take-up of the EGS as the wage rate rises tend to be small. This occurs in large part because of saturation of the number of female workers with characteristics that imply they would participate in the casual labor market.

The last column of Table 10 gives an estimate of the total cost as a percentage of GDP in 1999-00. We assume that the EGS operates for 300 days and we assume a labor share in EGS projects of 60%, in keeping with policy discussions on the proposed national EGS. We find that 19
A Measurement Based on Questionnaire Surveys. Copenhagen: The Rockwool Foundation Research Unit, Study no. 10.

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_Informality: Exit and Exclusion_ . Washington D.C.: The World Bank Pfau-Effinger, B _._ (2009). Varieties of Undeclared Work in European Societies. _British Journal of Industrial Relations_ 47 (1): 79–99 Pissarides, C. (2000). Equilibrium unemployment theory. MIT Press.

Riedmann, A. and G. Fischer (2008). _Undeclared work in the European Union_ . Special Eurobarometer 284: Report.

Schneider, F. (2005). Shadow Economies around the World: What Do We Really Know. _European Journal of Political Economy_ 21, 598–642.

Schneider F., Buehn A., Montenegro C.E. (2010). Shadow Economies All over the World: New Estimates for 162 Countries from 1999 to 2007 (Revised Version). _World Bank Policy Research Working Paper_ No. 5356.

Say, F. (2011), Does employment protection help immigrants? Evidence from European labour markets. _Labour Economics_ (forthcoming, doi:10.1016/j.labeco.2011.01.006 ) Slonimczyk, F. (2011). The Effect of Taxation on Informal Employment: Evidence from the Russian Flat Tax Reform. Moscow: Higher School of Economics, mimeo http://www.iza.org/conference_files/InfoETE2011/slonimczyk_f6969.pdf Torgler, B. (2007). Tax Compliance and _Tax Morale: A Theoretical and Empirical Analysis_ , Cheltenham, UK: Edward Elgar Torgler, B. (2010). Tax Morale, Eastern Europe and European Enlargement. Background paper for _In from the Shadow: Integrating Europe’s Informal Labor_ , a World Bank regional report on the informal sector in Central, Southern Europe and the Baltic countries (Task number P112988).

Train, K. (2003). _Discrete Choice Methods with Simulations_ , Cambridge: Cambridge University Press. Williams, C. C. (2009). Formal and Informal Employment in Europe: Beyond Dualistic Representations. _European Urban and Regional Studies_ 16(2): 147–159 Williams, C. C. and P. Renooy (2008). _Measures to Tackle Undeclared Work in the European Union_ .

Dublin: European Foundation for the Improvement of Living and Working Conditions.

49
surveys containing relevant information on remittances. In fact, there are only 11 countries for which such data are available. Table 1 reports the countries for which the household survey allows an analysis of the poverty impact of remittances, together with some information regarding the year of the survey, and the survey used. Overall, remittances to these countries would have amounted in 2004 to US$33.5 billion, or about 70 percent of total remittances to the region.

**<u>Figure 1. Remittances to Latin America in 2004</u>**

**Panel A. (% of GDP)**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Panel B. (US$ millions)<br>21000<br>18000<br>15000<br>12000<br>9000<br>6000<br>3000<br>0<br>% of GDP<br>Haiti Jamaica Honduras El Salvador Dom. Rep. Nicaragua Guatemala Ecuador Barbados Colombia Paraguay Mexico Bolivia Antigua Costa Rica Dominica Suriname Belize Peru Trinidad St. Kitts St. Vincent Panama Argentina Brazil St. Lucia Chile Uruguay<br>Millions US$<br>Mexico Colombia Brazil Guatemala El Salvador Dom. Rep. Haiti Ecuador Jamaica Honduras Peru Argentina Nicaragua Costa Rica Paraguay Trinidad Bolivia Barbados Panama Uruguay Chile Suriname Belize Antigua Dominica St. Kitts St. Vincent St. Lucia<br><!-- End of picture text --> 7
19 consumption" became flexible, permitting sellers to claim that sale was for further resale.

Evasion was also facilitated by commercial relationships between state enterprises and private firns. Under the turnover tax, goods could be sold by state enterprises to private firms, with the sale designated as for resale. The state enterprises were therefore not obliged to pay the turnover tax, and private firms being new and outside of tne tax system had a comparative advantage in evasion of tax obligations. Incentives for profit shifting from state to private firms left state enterprises with low or no value added and private firrs with high value-added; the VAT thereby potentially subjected private firms to high tax liability. State enterprise managers and private entrepreneurs therefore both had reason to oppose the replacement of the turnovsr tax by the VAT.31 With the VAT finally introduced, could VAT revenue be expected to solve the problem of the transitionary fiscal gap? some indications are available from a study by Bogetie and Hassan (1993), which estimates a relationship between VAT revenues and VAT rates in 22 countries that had single VAT rates in 1988. The estimated relationship predicts VAT revenue for Bulgaria from an 18 percent single rate of approximately 8 percent of GDP. 32

> 31/ See Hillman and Peters (1993) for more extensive discussion.

> 3V This estimate is an upper bound. An important implicit assumption behind this prediction is that Bulgaria's VAT would have a base (or the ntuaber of exemptions) and tax administration capacity similar to the average country in the sample. To the extent that exemptions are more numerous and tax administration
organization provides that information and regions with an active groups making sovereignty claims are coded _sd_ = 1 and 0 otherwise. Most claims are non-violent. We also identify claims that are made violently ( _violent_sd_ ). These are usually low-to-intermediate level armed conflicts and, in a few cases, separatist wars.

We have coded 222 regions as having a movement demanding more sovereignty, 47 of which are violent. Conflict was coded on the basis of encyclopedic sources (Minahan 1996; Dagenhard 1988), news archives (Keesings, Lexis-Nexis) and country case studies, so we are not limited by the sample selection problems that arise in the Minorities at Risk (MAR) database or other comparable sources since we collected data for all countries in our sample and not just those with a significant level of minority discrimination. We have data for small groups such as the Friulians in Italy, or nonviolent groups in developed democracies, such as the Lombardians (again in Italy), which would typically be excluded from MAR and other datasets of conflict.

Two risks associated with including non-violent groups is that we can count even fringe groups that have little popular support; and that it is difficult to establish the start and end-dates of activity for nonviolent movements. One can quibble over which movements are active in a given year, or which cases represent legitimate movements. Our approach is to flag those cases that are potentially ambiguous and discuss them in detail in the supplement, explaining why some were dropped from the analysis and others included. We drop some groups that are mentioned in encyclopedic sources if closer investigation reveals them to be too small (e.g. Koryaks and Itel’men in Russia’s Kamchatskaya oblast); if their political organizations represent minor fringes of groups with extremely low levels of public support in their group (e.g. Newfoundlanders and Westerners in Canada; the Lusatian Sorbs in Germany’s Saxony and Brandenburg regions); or if the group is too diffuse, representing a loose association of a regionally-based identity group (e.g. Southerners in South Carolina). Ambiguous cases can be dropped in robustness tests.

Finally, in a few regions, we find two or more competing movements for sovereignty (e.g. Aborigines and Torres Strait Islanders in Australia’s Queensland province; or Fleming and 21
_Role of NGOs itt Health Delivery Africa Health Policy Study_ _-6-_ NGOs, such a flow of resources may undermine the advantages of NGOs that are sg lauded. More rigorcus assessment and evaluation of NGO capacity, particularly in the health sector, is critical if the sudden surge of resources is not to be under used, perhaps to the detriment of NGOs themselves.

14. The following represents an attempt to outline the distinctive features of NGOs as providers of hcilth services In contrast to public and for-profit providers. The discussion then continues with scrutiny of some of the misconceptions about NGOs and reasons for which caution is advisable when advocating channelling resources through them. Much of the discussion draws on literature on NGOs not specific to the health sector. This, in part, reflects the multi-sectoral nature of NGO activides. It also reflects the lack of literature due to a neglect, until recently, of analysis of the contribution of NGOs to the health sector.

# Defining Characteristics and Comparative Advantages of NGOs

_15._ The economic crisis has put NGOs in the limelight because it Is assumed that they provide a net increase in resources in health care, and because they do not put an additional strain on already over-stretched government budgets. In fact however, this argument is dispumable in manyAfrican countries, where governments frequendy provide NGOs not only with direct subsidies but also "hidden subsidies" (Green 1987) in the forn of relief from import duties, taxes ind other financial obligations. Those who see NGOs as bolster their argument by pointing to the inherent advantages of NGOs in the health field as community-based, responsive and efficient providers of health care. Yet NGOs have been active In the health field in Africa long before the advent of the economic cris, and their persistence is In large part explained by the niche they were able to carve for themselves. The following section attempts to enumerate some of the inherent advantages of NGOs In the health field that help to explain their persistence.

# _Motivation_

16. Perhaps the strongest defining characteristic of non-governmental organizldons is the presence of a shared ideology, be It religious or political, as the main motvadng fictor for NGO activides. As Brown and Korten have argued, In contrast to either the commercial or government sector, NGOs are guided neither by the pursuit of largely political or economic interests (Brown and Korten 1989). In ti 5 African setting, religion has clearly been a dominant value system inspiring the work of NGOs, yet even non. denominational NGOs share an _esprit de corps_ derived from shared commitment to community empowerment which is often lacking among either commercial or govemment insdtutions. It Is such overriding Ideology which contributes to the capacity of NGOs to solicit community voluntarism and membership in their activides. In the delivery of health services, this commitment is likely to be reflected In the quality of care since genuine
remittance prices through their impact on transaction costs incurred by remittance service providers. In particular, we include a proxy for the volume of remittance transactions within corridors: the number (bilateral stock) of migrants residing in country i originally from country j. This data comes from the World Bank.<sup>13</sup> Unlike the flow of actual remittances sent, this variable is less likely to be endogenous to the cost variable. We conjecture a negative relationship between migration and the cost of remittances, as a higher volume might imply scale economies and more competition among service providers. The number of migrants is negligible in the South Africa-Zambia corridor and exceeds 10 million people in the case of the US-Mexico corridor. The average for this variable is 379,199 migrants.

We also include GDP per capita, proxying for the level of economic development and standard of living in a country. This variable comes from the World Bank’s World Development Indicators Database. In countries with higher standards of living the cost of goods and services will be higher, so we expect remittance costs to be higher as a result. On the other hand, economic development may be associated with greater efficiencies and lower cost of financial intermediation (Harrison, Sussman and Zeira, 1999) and, hence, lower remittance costs. In our sample, GDP per capita for receiving countries varies from US$148 in Malawi to close to US$14,000 in Korea. Among remittance sending countries, GDP per capita varies between US$3,640 for South Africa and US$40,200 in Japan. In some estimations, we separately control for the level of financial development by including a measure of liquid liabilities to GDP. This variable comes from the World Bank Financial Structure Database (see Beck, Demirguc-Kunt, Levine 2009). A priori it is unclear whether financial development should have a positive or negative impact on costs. On the one hand, more financially developed systems are likely to be more sophisticated and to offer better services, which might be more expensive. On the other hand, more financially developed systems may be more efficient and might be able to deliver services at lower prices to consumers. The ratio of liquid liabilities to GDP in receiving countries varies from 15 percent for Algeria to 126 percent for Jordan and the average is 48 percent. Among sending countries, the ratio of liquid liabilities to GDP varies from 44 percent in South

> 13 See Ratha and Shaw (2007).

> http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTDECPROSPECTS/0,,contentMDK:21154867~pag ePK:64165401~piPK:64165026~theSitePK:476883~isCURL:Y,00.html

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