rafmacalaba/datause-encoder-data huggingface dataset:
main regression results are provided in Section 3. In section 4, we discuss some extension of the main results. The concluding section summarizes the main findings of the paper.

# **2. Data and Main Variables**

The main data source we use consists of firm-level surveys for 87 developing countries conducted by the World Bank’s Enterprise Surveys between 2007 and 2009. Table 1 contains the list of countries in our sample along with the sample size (number of firms) for each survey. These surveys were conducted in some cases across the whole region (such as Latin America and Eastern Europe and Central Asia) and sometimes in individual countries. However, a common sampling methodology – stratified random sampling – was followed in all the surveys along with a common questionnaire. The sample for each country was stratified by firm-size, sector of activity and location within the country. Weights are provided in the survey to ensure that the sample is representative of the non-agricultural private sector of the economy. We note that Enterprise Surveys cover manufacturing as well as services sectors but certain services such as education and health are not covered. Also, the primary sector, which encompasses agriculture, mining, forestry, etc., is also excluded from the survey. Finally, the sample we use is a pure cross section of firms with one observation per firm. We also use the latest round of the survey conducted in each country. Enterprise Surveys does provide panel data with more than one observation per firm but currently, these data are limited in size and are available for only a handful of countries.

The regression results discussed below are obtained from logit estimation with HuberWhite robust standard errors and clustered on the country. Marginal effects from the logit estimation discussed below are calculated at the mean value of the all the explanatory variables.

5
# **Table A10: Two Stage Least Squares Regressions, 2005**

Financial Vulnerability Regressions (Marginal Effects)

|||**finburd**|**en**<br>|||**finvul**|||**arrear**|**s1**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**p(Mortgage)**|**p(Rent)**|**Pseudo r**<sup>**2**</sup>|**t-test**|**p(Mortgage)**|**p(Rent)**|**Pseudo r**<sup>**2**</sup>|**t-test**|**p(Mortgage)**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|-1.0385 *|-0.1352|0.06|-1.60|0.0848 *|-0.0649|0.12|1.08|0.0638 **|0.14|
|Estonia|-0.1015|-0.0054|0.11|-0.45|-0.1785 **|-0.0012|0.09|-1.38|-0.1905 *|0.05|
|Hungary|0.0209|0.0017|0.06|0.17|-0.3833 **|0.0853|0.10|-2.57 **|0.1145|0.08|
|Latvia|-0.6449 **|-0.0988|0.08|-1.64|-0.2680|0.0983|0.09|-1.34|0.0612|0.06|
|Lithuania|-0.2587|0.1828|0.09|-1.80 *|0.1173|0.1942 *|0.07|-0.32|-0.1849|0.13|
|Poland|-0.7706 ***|0.0828|0.06|-6.17 ***|-0.4515 ***|-0.2443 **|0.08|-1.53|0.5092 **|0.12|
|Slovak Republic|0.6020|-0.2389 ***|0.04|1.50|0.0386 *|0.0557|0.04|-0.35|0.1167|0.09|
|_EU-15 and Cyprus_|||||||||||
|Austria|-0.0062|0.0096|0.03|-0.39|0.0435|0.0034|0.11|1.28|0.0359 **|0.11|
|Belgium|0.0935 **|0.0365|0.05|0.91|0.0074|0.0595 ***|0.15|-1.61|0.0332|0.14|
|Cyprus|0.3377 **|0.0266|0.05|1.27|0.0601|-0.0989|0.10|1.59|0.1264|0.10|
|Denmark|-0.0174||0.07||-0.0811 ***||0.15||-0.1000 ***|0.16|
|Finland|0.0448|0.0986|0.04|-0.55|-0.0184|0.1578 ***|0.14|-3.07 ***|-0.0189|0.11|
|France|0.0763 **|0.0094|0.04|1.51|-0.0835 *|0.1083 ***|0.14|-3.99 ***|0.0135|0.10|
|Greece|-0.0316|-0.0286|0.04|-0.02|-0.0350|0.0993|0.07|-0.70|0.0654|0.10|
|Ireland|0.0709|0.0998 ***|0.08|-0.37|0.0162|0.0612 ***|0.20|-0.90|0.0582|0.13|
|Italy|-0.2132 **|0.1285|0.04|-2.28 **|-0.0944 **|0.2026 ***|0.11|-4.75 ***|-0.0714|0.12|
|Luxembourg|0.1585 ***|0.0309|0.06|1.56|0.0047|0.0111|0.12|-0.15|0.0798 **|0.12|
|Netherlands|-0.1789 ***||0.04||-0.0455 ***||0.11||-0.0380 ***|0.11|
|Portugal|0.1000 ***|0.0074|0.07|1.33|0.0362|-0.0092|0.05|1.04|0.0359|0.09|
|Spain|0.0772|-0.0826|0.03|0.92|0.0160|0.0809 *|0.08|-1.30|-0.0076|0.05|
|Sweden|0.2170 **|0.0903 ***|0.05|1.10|0.4231 ***|0.0745 ***|0.11|2.49 **|0.3083 ***|0.13|
|United Kingdom|0.1668 ***|0.1053 ***|0.05|1.28|0.0503 **|0.0900 ***|0.14|-1.65 *|-0.0012|0.11|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

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from the informal sector to obtain bank credit for the first time. In terms of costs for borrowers, Azteca’s annual percentage rate (APR) has been significantly higher than traditional banks’: its APR in 2005 was 130% compared to 40% for regular banks. Nevertheless, it is lower than its competitors; . in this same year, pawn shops charged on average an APR of 220%<sup>1</sup> Panel data suggest that households whose members were employed in the informal sector had significant changes in their saving and consumption patterns after an Azteca branch appeared in their municipalities. The household data consist of two waves that were collected in 2002 and 2005. By the time of the first wave, Banco Azteca had not opened its branches, but by 2005, Banco Azteca’s presence varied across municipalities. I exploit this variation over time and across municipalities of Azteca’s branches to compare household outcomes before and after the entrance of Banco Azteca.

I find evidence that relative to households from municipalities where this bank did not open, informal households in Azteca municipalities were more likely to borrow from banks, less likely to obtain loans from pawnshops and less likely to hold savings. In addition, the fraction of informal households owning durable goods increased as did the value of these goods. Moreover, in municipalities where Azteca opened, the data suggest that informal households were more likely to increase their consumption during bad economic times, such as illness of family members, unemployment or failure of the family business. Importantly, I find no substantial change in the saving or consumption behavior of households from municipalities where Azteca entered until 2006, suggesting that changes in these outcomes are associated to the opening of Azteca branches.

The model includes important features from the Mexican economy. To capture heterogeneity among municipalities in Mexico, the economy in this model consists of M municipalities that differ in their composition of households, population size, percapita income and presence of credit suppliers. As in the Mexican credit market, households can borrow from traditional banks, pawn shops, Banco Azteca and friends or relatives.

In the model, Banco Azteca is a for-profit institution that selects the municipalities for branch openings. The bank locates in municipalities where its expected profits are high enough to cover its operating costs. In this economy, credit suppliers differ in their costs, availability and requirements for clients. The APR of each institution is set to the average rate observed from the data, with pawn shops being the most expensive and friends the least costly. When borrowing from Azteca and pawn shops, households need to own collateral to back up their loans. Durable goods serve as households’

> 1Information on the APR of different institutions was obtained from the National Committee for the Defense of Users of Financial Institutions (CONDUSEF) and the Federal Bureau of Consumer Interests (PROFECO).

3
coverage in 9 of Mali’s 10 regions. The 10th region, Kidal, is not included due to insecurity in the zone. The sample size is large enough to estimate commune-level inequality and 90% of the communes in the data set are sampled more than twice, giving us a panel at the commune level over time. Within a commune, the choice of household is random, such that at the household level the data represents a repeated-cross section. Furthermore, the EMOP data are largely representative at the commune level.<sup>4</sup> Combined with the sampling strategy at the commune level, these data provide us with consistent data to estimate effects at the commune level including both urban and rural communes.<sup>5</sup> The commune-level IDP data come from the Displacement Tracking Matrix (DTM) provided by the IOM (IOM, n.d.). These data derive from approximately monthly surveys on the number of IDPs in host communities from 2014 to the present. With this data set, we are able to calculate the total yearly number of IDPs and households in each host community identified by the IOM, and also construct an indicator variable of IDP presence. While the data collection involves multiple layers of data validation, this data set will mostly reflect the number of IDPs recognized by regional authorities, NGO representatives, civil society organizations, who are potentially beneficiaries of humanitarian assistance, as the IOM relies on such local actors as major informants. Therefore, IDP households who temporarily live with relatives to escape violence without seeking assistance are unlikely to be counted in the data.<sup>6</sup> The ethnic diversity measure we use as an instrument in some specifications is calculated

> 4The average number of sampled households in the EMOP data is 15 households. There are, however, two communes out of the 370 communes surveyed in 2019 with only one household interviewed in each village. These villages are Didieni in the Djidieni arrondissement in the Koulikoro region, and Niantaga in the M’pessoba arrondissement in Sikasso region.

> 5We have tested aggregating the data to the arrondissement level and find similar effects as reported here, though they have less precision because our measures are necessarily less precise at a higher level of aggregation. The EMOP data we use show 46% of the households as urban, which almost exactly matches national statistics on urbanization rates, suggesting that our data are broadly representative of both urban and rural areas.

> 6This implies that our measure of IDP presence and population should be considered as a measure of formally recognized IDPs who are likely receiving services. To the extent that there might be large numbers of IDPs in communes who are not receiving services, our data do not measure them. The available evidence that we can glean from the EMOP survey suggests that the number of people who fit such a category is very small.

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- 1, indicating that businesses owned by women that cross over to male‐dominated sectors perform

similarly to male‐owned businesses in those sectors.

**Figure 2: Distribution of (log) monthly profits for women crossovers and men in same sectors – Kassida data set**

<!-- Start of picture text --> Profit Distribution (Crossover Sectors)<br>.4<br>.3<br>.2<br>.1<br>0<br>10 12 14 16<br>logmthprofit<br>Female crossover<br>Male<br>kernel = epanechnikov, bandwidth = 0.4290<br>Density<br><!-- End of picture text --> In order to examine the differences between women who cross over and those who do not in more depth, we now turn to a set of basic statistics in Table 3.

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where poverty is increased and others where poverty is decreased. The notable exceptions are most African countries where yield impacts of climate change are severe and no single stratum experiences significant poverty reductions.

The study by Hertel et al (2010) provides a promising approach for studying the impacts of climate change taking into account general equilibrium effects between agricultural productivity, cost of living, and earnings effects. However, as is the case for most models, there are serious trade-offs associated between the tractability of the general equilibrium effects and the heterogeneity incorporated into the model.

# **_3.2 Heterogeneity Galore_**

The study by Jacoby, Rabassa, and Skoufias (2011) applies a flexible framework for quantifying the distributional impacts of climate change in rural economies. In this study welfare is measured by consumption per capita and is modeled as being determined by the resource endowments of the household, such as land and labor, and the returns from farm and non-farm activities.<sup>18</sup> Using a comparative statics framework, the impacts of climate change on household consumption can be expressed as the impact of changes in temperature on the returns to land (used as a summary measure of agricultural productivity) multiplied by the proportion of income derived from owned land, the impacts of temperature on the returns to labor multiplied by the proportion of income derived from labor, and the impacts of climate change on the price of food multiplied by the net consumption ratio, i.e. the value of the net marketed surplus of food by the household.<sup>19</sup> Using micro data representative for all India and following the Ricardian approach proposed by Mendelsohn et al. (1994) the authors estimate the impacts of climate change in 2040 on agricultural productivity and wages taking into account adaptation (i.e. using district-level cross sectional data) and assuming imperfect mobility of labor.<sup>20</sup> They are also able to estimate

> 18 Additional heterogeneity is introduced into the model by distinguishing between the type of land owned by households (irrigated vs. non-irrigated) and different types of labor (skilled and unskilled). Each of these endowments may have different returns and responses to climate.

> 19 It should also be noted, that the impacts of climate change are derived based on the current stock and distribution of endowments of land and labor.

> 20 The effect of climate change on the price of cereals in India is obtained from the ENVISAGE (Environmental Impact and Sustainability Applied General Equilibrium), a multi-sector computable general equilibrium model developed at the World Bank for assessing climate change effects and policies. The model predicts that cereal prices will rise approximately 10 percent by 2040 due to warming.

14
Hsu, M., Bhatt, M., Adolphs, R., Tranel, D., & Camerer, C. F., (2005). Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making. _Science,_ 9, 1680 – 1683.

Hulme, M. (2009). _Why we disagree about climate change: Understanding controversy, inaction and opportunity._ Cambridge University Press, Cambridge, UK.

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Keysers, C., Cohen, J., Donald, M. _et al_ . (2008). Explicit and implicit strategies in decision making . In: C. Engel and W. Singer (eds) , _Better than Conscious? Implications for Performance and Institutional Analysis_ . Strungman Forum Report. Cambridge, MA: MIT Press.

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<!-- 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>Rwanda Nigeria Malawi Bhutan Cambodia Afghanistan Kenya Lao PDR El Salvador West Bank and Gaza Guatemala Timor-Leste Turkey Ghana Paraguay Bolivia Philippines Panama Bangladesh Nicaragua Peru Yemen, Rep. Vietnam Thailand Nepal Sri Lanka Ecuador Dominican Republic Brazil Mexico Chile Argentina Georgia Pakistan Kyrgyz Republic Malaysia Mauritius Mongolia Azerbaijan Egypt, Arab Rep. Armenia Macedonia, FYR Uruguay Kazakhstan Bosnia and Herzegovina Latvia Poland Ukraine Serbia Romania<br><!-- End of picture text --> _Source: Authors based on ASPIRE, see Annex_ The reduction of inequality as measured by the Gini index is also quite dramatic (Annex 4), even though properties of this measure of inequality mean that impacts will tend to be smaller: on average the Gini index is reduced by 10 percent by social protection programs, led by ECA averaging over 30 percent, while the other regions all experience a reduction of less than 5 percent in the Gini. Figure 5 shows a close relationship between the scale of inequality reduction and the poverty gap reduction by social protection programs, even though the relationship is not one-to one (in some countries inequality is even increasing as a result of social protection transfers- something impossible with our definition of poverty measures). Note that for several countries the standard of $1.25 / day as a poverty line means the complete elimination of poverty, while they achieve very different results in terms of inequality reduction (top of the right panel). Using relative cut-off (left panel) gives a closer relationship, and again shows much stronger effects on poverty than on inequality. In what follows we will focus on the poverty impact at the absolute $1.25/day line as the most direct and visible measure for social protection’s contribution to eliminating extreme poverty.

# **FIGURE 5: GINI INDEX REDUCTION VS POVERTY GAP REDUCTION**

<!-- Start of picture text --> With poverty line as cut off for bottom Quintile With Poverty defined at $1.25/day<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>-10% 0% 10% 20% 30% 40% 50% -10% 0% 10% 20% 30% 40% 50%<br>("-" for increase) Gini coefficient reduction (% of pre-transfer value) ("-" for increase) Gini coefficient reduction (% of pre-transfer value)<br>Poverty gap reduction (% of pre-transfer value) Poverty gap reduction (% of pre-transfer value)<br><!-- End of picture text --> _Source: Authors based on ASPIRE, see Annex_ <u>3.2 Decomposing the Poverty Impact: Poverty Reduction, Efficiency and Budgetary Adequacy</u> 15
sources when seeking to measure broad concepts of governance as we do. Moreover, the likelihood of encountering an extreme outlier in a country's aggregate governance score is commensurately lower than in any individual source.

# **3.1  Cross-Country Comparisons of Governance Using the WGI**

We use Figure 1 and Figure 2 to emphasize the importance of taking these margins of error into account when making comparisons of governance across countries and over time. In the two panels of Figure 1, we order countries in ascending order according to their point estimates of governance in 2007 on the horizontal axis, and on the vertical axis we plot the estimate of governance and the associated 90% confidence intervals. These intervals indicate the range in which it is 90 percent likely that the true governance score falls.<sup>14</sup> We do this for two of the six governance indicators, Political Stability and Absence of Violence/Terrorism, and Control of Corruption. The size of these confidence intervals varies across countries, as different countries appear in different numbers of sources with different levels of precision. The resulting confidence intervals are substantial relative to the units in which governance is measured. From Figure 1 it should also be evident that many of the small differences in estimates of governance across countries are not likely to be statistically significant at reasonable confidence levels, since the associated 90 percent confidence intervals are likely to overlap. For many applications, instead of merely observing the point estimates, it is therefore more useful to focus on the _range_ of possible governance values for each country (as summarized in the 90% confidence intervals shown in Figure 1), recognizing that these likely ranges often overlap for countries that are being compared with each other.<sup>15</sup>

> 14 A x% confidence interval for governance can be obtained as the point estimate of governance plus or minus the standard error times the (100-x)/2<sup>th</sup> percentile of the standard normal distribution.  For example, the 90% confidence intervals we report throughout the paper are the point estimate plus or minus 1.64 times the standard error.

> 15 Of course, asking whether 90% confidence intervals overlap or not corresponds to a hypothesis test at a significance level that is more stringent than 10%.  The assumptions underlying our statistical model imply that the standard error of the difference between two country scores is the square root of the sum of the squared standard errors of the two sources, which is always smaller than the sum of the two standard errors themselves.  It is more convenient -- and more conservative -- for users to simply inspect confidence intervals and see whether they overlap.

18
43

# gBAm u VI MSO J.zgL. KMI Is=

Balancing short-term reform and long-term growth objectives within an environment of budgetary restraint placed issues of size and compositions of government expenditures high on the development agenda of the eighties. In particular, the renewed focus on long-term human capital formation and poverty reduction, coupled wlth the concern that burden of Inevitable public expenditure cuts during the periods of stabilizatLon and adjustment may disproportionately fall on the social sectors, resulted In heightening the development concerns about efficient and equitable allocation of social expenditures. These issues have particularly been pressing in the developing countries where the public sector is the major financier of social servlces and the inadequate provision of social services as well as the associated infrastructure base remain inadequate. The developments mentioned have, on the whole, drawn increasing attention to trends in social sector expenditures and the data bases necessary for monitoring these trends.

Government expenditure data are a key to the analysls of public expenditure programs and the deslgn of policies for rationalizing these programs. However the data remain inadequate especially for purposes of cross country comparisons. The World Bank has, in recent years, conducted public expenditure reviews (PERs) for an increasing number of developing countries. While these reviews are a useful source of data on overall government expenditures at the country level, they tend to have limited focus on social sector expenditures. The IMF Government Finance Statistics (IMFGPS) - a data