rafmacalaba/data-use-mentions-extended huggingface dataset:
.0014|0.0011|-0.0030|0.0122|0.0241|-0.0083|
||(0.0115)|(0.0132)|(0.0133)|(0.0131)|(0.0150)|(0.0153)|
|Muslim|-0.0121|-0.0109|-0.0051|-0.0082|-0.0109|0.0001|
||(0.0089)|(0.0102)|(0.0103)|(0.0105)|(0.0121)|(0.0123)|
|Catholic|0.0051|0.0057|0.0009|0.0065|0.0078|0.0007|
||(0.0063)|(0.0072)|(0.0073)|(0.0072)|(0.0083)|(0.0084)|
|Orthodox|0.0020|0.0085|-0.0059|0.0003|0.0087|0.0094|
||(0.0058)|(0.0066)|(0.0067)|(0.0067)|(0.0076)|(0.0078)|

_Notes:_ Differences in means (mean of the relevant group minus mean of the rest of the population) and their standard errors (in parentheses). * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%. The sample of mothers consists of 7480 Albanian women aged 20 to 49 years old with two or more children aged 18 or younger. The sample for fathers consists of 5496 husbands of the married women with two or more children aged 18 or younger. The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and
22 such information were available for Brazil. There was little alternative to attempting some estimate of the general equilibrium effects of food price changes or, at least, that part of the general equilibrium effects corresponding to the short-run pass-through from product prices to agricultural wages.

Using detailed information on the type of output produced by each agricultural worker in the PNAD 2006 survey, we mapped workers to their farm sectors and estimated market income effects under two different pass-through assumptions – one corresponding to a competitive labor market in the short run, and another to a market where only half of the price increase is passed on to workers. These market income effects were considered alongside the standard expenditure effect, calculated as a first-order approximation to the compensating variation for each food consumer, and a transfer income effect that captured the increase in benefit values for two social assistance programs, Bolsa Família and Benefício de Prestação Continuada.

According to our estimates, the overall effect of the price increases was to raise both extreme and moderate poverty in Brazil, despite the country‟s position as one of the main food exporters in the world. Even with full pass-through to agricultural wages, and despite increases in social assistance benefits, the incidence of extreme poverty increased from 11.0% to 12.3% as a result of higher food prices. However, these increases in poverty were much less pronounced than if the income effects had not been taken into account. Naturally, higher incomes arising from a greater value of agricultural production were particularly important in rural areas where, under the scenario of 100% pass-through, incomes rose for the bottom half of the population, reducing every measure of poverty.

In large urban areas, though, few people benefit directly from agriculture, so the standard expenditure effect (reduction in consumer surplus) dominated. And since Brazil is 80% urban, the aggregate picture for the country as a whole was one of reductions in average welfare as a result of higher food prices. Behind the average impact, however, the distributional consequences of
||(0.513)|
|Observations|5624|5624|3638|3638|
|Pseudo _R_<sup>2</sup>|0.165|0.165|0.117|0.117|

Standard errors in parentheses.<sup>_∗_</sup> _p < ._ 1,<sup>_∗∗_</sup> _p < ._ 05,<sup>_∗∗∗_</sup> _p < ._ 01 Table 5: Ownership Carrying out the analysis at the industry level allows us to get more insight into these results. The annex tables present these results industry by industry. Industry-level regressions show that for some industries it is not possible to conclude if there exists a significant difference because there are not enough observations of the two types of firms. One also finds that in the case of the steel and sugar industries, with enough data of both types, there is a significant difference, with private firms being more in compliance. While one needs to be cautious in interpreting these results, this calls for the attention of public firm managers and regulators in better understanding of how the interaction between industry and ownership affects compliance. In some sectors with enough observations of both types of firms like aluminum and thermal, there is no statistical difference. The case of mines 16
# **f. Unions**<sup>**33**</sup>

Unions differ greatly in character throughout Latin America, but commonalities emerge in their impacts on: the profitability of the firms, investment and technology, wage and employment determination, and the size of the unregulated informal labor market in Latin America.

**_Unions decrease firm profitability:_** Several studies for Latin American countries are consistent with the OECD literature that in advanced countries union power tends to harm business profitability. Saavedra and Torero (2002) show decreasing profitability for firms in the presence of unions for Peru during the period 1994-1996  Similar evidence if found for Brazil by Menezes-Filho, Zylberstajn, Chahad and Pazello (2002) where they use the Annual Industry Survey pooled for the period 1988-1998. However, the results become inconclusive when observing the performance over time. Cassoni, Labadie and Fachola (2002) present preliminary evidence of a negative impact of unionization in profitability for Uruguay, though they acknowledge further work needs to be done. Urizar and Lee (2003) argue that in spite of low union density, productivity in coffee producers firms in Guatemala suffer greatly in the presence of Unions.

**_The impact on aggregate employment is unclear, but there is evidence of efficient bargaining._** The mainstream “efficient bargaining” models stress how in the presence of firm monopoly power, unions may choose to direct their negotiated share of rents toward either higher wages or employment. Hence, their impact on total employment may differ from standard “right to manage” models where unions raise wages and firms move along the demand curve to reduce employment. Maloney and Ribeiro (2001) find a strong impact on employment in Mexico, but little on wages, consistent with an extreme “efficient bargaining” model.  Both Saba Arbache (2002) and MenezesFilho et al (2002) find wage effects, and the latter work finds positive employment effects of unions as well.  Menezes also finds a concave relationships where some union concentration raises
# 1 On-line Appendix

## 1.1 Data for Cross-Country Analysis

Table A1 presents the detailed de...nitions and data sources for the variables used in the analysis. Summary statistics for the country level variables are provided in Table A2 while their correlations are shown in Tables A3 and A4.

The sectors in the EORA database are de...ned as follows. Agriculture includes ...shing; mining and quarrying is a separate sector; manufacturing includes food and beverages; textiles and wearing apparel; wood and paper; petroleum, chemicals and non-metallic minerals; metal products; electrical and machinery; transport equipment; and other manufacturing. Services covers recycling; electricity, gas and water; construction; maintenance and repair; wholesale trade; retail trade; hotels and restaurants; transport; post and telecommunications; ...nancial intermediation and business activities; public administration; and education, health and other services. We exclude from our analysis the following three sectors: private households; others; and re-exports and re-imports.

Regarding factor endowments, all variables are measured as a ratio to real GDP. Lowskilled labor endowments follow the ILO’s de...nition of low skill (skill level 1), based on the International Standard Classi...cation of Occupations. Likewise, medium/high-skilled labor refers to the ILO’s de...nition of medium and high skill (skill levels 2-4). We use the real capital stock from the Penn World Tables to measure capital endowments. Regarding natural resource endowments, we use the country’s land area as well as the rents from natural resources, both taken from the World Development Indicators (WDI).

We use the average manufacturing tari¤ rate from WDI and the level of FDI in‡ows from UNCTAD. Geography is captured by a country’s total distance (in kilometers) to the main global GVC hubs, China, Germany, and the US from CEPII. For domestic industrial capacity we use manufacturing value added from
_... And volatile_ But there have also been very low real interest rate observations in developing countries, and, despite a low (negative) mean value, the mean of 25 developing country monthly standard deviations (excluding Argentina and Brazil) in our sample is 877 basis points, compared with just 18 7 basis points for the eight control industrial countries. And this is not just due to some outliers: the smallest of developing country standard deviations is 221 basis point fDr Singapore.

# 2.2 Negatively skewed distribution of $-adjusted rates

Volatility and extreme values are also evident in Table 3, which compares the 1980-97 average statistics of monthly wholesale returns for developing and industrial countries adjusted for actual change in exchange rates against the US dollar. Although the mean of developing country $-adjusted money market rates was on average much lower than for industrial countries, this mainly reflected the wider variation over time of exchange-rate adjusted interest rates for developing countries, and in particular the negative skewness (influence of extreme negative observations). In other words, occasional sharp devaluations were not fully compensated-for by a sufficient excess return or peso premia in developing country interest rates in normal times.

Table 3: _Ex post $-adjusted Money Market Rates: Industrial and Developing Countries, Monthly Data: 1980-97_

|_% per annum_|26 Developing Countries|12 Industrial Countries|
|---|---|---|
|_mean over countries of:_|||
|mean (standard error)|0.49 (9.0)|7.34 (1.1)|
|median|3.04|7.56|
|maximum|107.3|86.2|
|minimum|-490|-94.9|
|standard deviation|53.3|28.9|
|skewness|-3.95|-0.1|
|_median over countries of:_|||
|median|_6.95_|6.
off the streets when they might otherwise be left alone and exposed to external risks (Gottfredson et al., 2004; Jacob and Lefgren, 2003; Newman et al., 2000). This will be the _protection mechanism_ or, as previous papers have called it, the “involuntary incapacitation” channel (Heller et al., 2017). Although this experimental design does not allow us to perfectly disentangle the two mechanisms, in this section we provide suggestive evidence that students are indeed learning social skills. Therefore, the first mechanism is more likely driving the effects.

First, we exploit the availability of baseline data on adult supervision after school hours to test for differences between both mechanisms. Our assumption is that treated students who reported being without adult supervision after school receive both effects from the intervention, and that the effects for students who are with an adult after school may be caused only by the learning 31
Table 5: Net ODA received (% of GNI), average 2000-2003

|Country|ODA (% GNI)|rank|
|---|---|---|
|Benin|9.77|5|
|Ivory Coast|4.33|7|
|Cameroon|5.54|6|
|Ethiopia|14.67|2|
|Mali|13.33|4|
|Mauritania|20.17|1|
|Uganda|14.09|3|
|South-Africa|0.40|8|

_Source: our elaboration on WB opendata_

# **7 Conclusion**

In a global scenario where rich countries are facing increasing budget constraints, the debate on the optimal allocation of aid has become more important than ever. Exploiting at best new opportunities offered by the widespread availability of micro data, the paper has developed a concept and a measure of the monetary capacity of a country to reduce its own poverty defined as the Income Lever (IL). We constructed various income lever indexes based on different distributive criteria and used such indexes to rank countries according to their own monetary poverty reduction capacity. As shown in the empirical application, this methodology can provide an efficient, equitable and transparent tool to rank countries or regions when it comes to budget or aid allocations, whether it is the allocation of social funds within the European Union (North-North transfers) or the allocation of aid from rich to poor countries (North-South transfers).

26
where _s_ denotes industry, _c_ denotes country, _t_ denotes time, _EFD_ denotes external finance dependence of an industry, and _sd_ ( _log_ _~~T~~ FPR_ ) _s,c,t_ is the standard deviation of log(TFPR/ _TFPR_ ) within an industry-country. _real_ and _fin_ denotes real and financial distortions, respectively. To attenuate concerns about reverse causality and endogeneity, we consider specifications of the estimating equation where the explanatory variable of interest, the standard deviation of the log of financial distortions, is lagged to the previous period’s value.

The external finance dependence within an industry is computed as an average of the following firm-level indicator: That is, following Rajan and Zingales (1998), we define the external finance dependence of the firm _i_ in sector _s_ as the fraction of its capital expenditures that could not be financed with internal sources of funding. The total cash flow, in turn, is computed as an aggregate of total funds from operations, plus increases in accounts payable, decreases in inventories, and decreases in receivables. Importantly, these cash flows are independent of the internal equity of the firm, which we treat as a source of external finance in the measurement of finance misallocation. The sector-wide external finance is the average across firms in the industry. As said, for identification, we apply this indicator to the U.S. economy based on Compustat data. Finally, we classify an industry as highly dependent on external finance if its indicator is above the median across all industries.

Table 7 presents the results from estimating equation 23. We report a positive and statistically significant coefficient for the effect of finance-based on real-based _TFPR_ dispersion. Moreover, we also document a positive and statistically significant coefficient on the interaction between financial misallocation and the high external finance dependence indicator. In industries with a higher reliance on external credit, 29
, it is less so in emerging markets where the HtM phenomenon is largely driven by poor HtM individuals. On average, the share of poor HtM is 17.1 and 39.1 in developed and emerging markets, respectively.

# 2.3. Main characteristics of HtM individuals

We now analyze whether the capacity to deal with a ...nancial shock varies across several plausible relevant economic and demographic individual and household characteristics available in Global Findex. Table A.2.1 shows the share of HtM individuals by household income quintile, education level, age, sex, and labor status, in particular depending on whether the individual is part of the workforce and his/her employment status. Table A.2.2 reports marginal e¤ects from probit regressions in which the dependent variable equals 1 if the individual is HtM and 0 if he/she is not HtM; both one-characteristic-at-a-time and all of them jointly. Both tables report these relevant data for all countries as well as for emerging and developed countries separately.

The ...ndings support that individuals with higher household income, greater educational attainment, male, and employed report a higher capacity to deal with a ...nancial shock, both in developed and emerging countries (with even similar R<sup>2</sup> values among these groups). The main discrepancy between developed and emerging countries is related to the relevance of age. While in emerging markets the older the individual, the lower their capacity to deal with a ...nancial shock, in developed countries, respondents aged 15 to 34 report themselves as being more ...nancially vulnerable. All in all, the relevance of this set of economic and demographic characteristics as well as their ability to predict HtM individuals is comparable to previous studies. These ...ndings are broadly consistent with those of other studies focusing on the developed world (e.g., Lusardi et al., 2011). Moreover, if

> 40 See, for
Next, we explore whether there are differences in coping and adaptation mechanisms among poor and non-poor vulnerable households. To do this, we calculate the above household profiles, differentiating them by poor status.

In addition, we carried out a counterfactual exercise. In this exercise, we estimated the households' vulnerability in a scenario where all households face the lowest possible hazards and compare the estimated vulnerability rates with the actual vulnerability incidence. Thus, we set to all households the minimum hazard they can face, that is, the minimum number of events that occurred among districts and a value of very low damage (more details are presented in Appendix B).

# **3. Data and descriptive statistics**

The datasets for the analysis are the national Household Surveys of each country of the study from the SEDLAC database. This database provides harmonized information of coping and adaptation mechanisms at the household level and poverty status for different countries of the region. The analysis focuses on seven countries: Brazil, the Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, and Peru. We use household survey data, pre-dating Covid19, from 2018 for Mexico and from 2019 for the rest of the countries. Except for Brazil survey, the surveys allow for the identification of the district where households reside. This enables the imputation of hazard and exposure aggregate data at the district level for each household. In the case of Brazil, the survey design led to impute at the state level. However, despite the imputation being carried out at the district level in most countries, the results need to be analyzed at the level of survey representation, which is at the state level.

To identify poor households, we utilize a monetary poverty measure assuming a poverty line of 6.85 dollars a day for the per capita total household income adjusted by 2017 PPP. The household poverty rate is 20% in Brazil, 15% in the Dominican Republic, 21% in Ecuador, 23% in El Salvador, 45% in Honduras, 26
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Type.8.Diabetes.Me|llitus.(T8DM).outcomes||||0.83, 95% CI 0.71, 0.99), and<br>vegetable(OR 0.83,95% CI 0.70,0.99)|
|B. Li et al. (2023)|Meta-analysis 72 articles of<br>prospective cohort studies,<br>11 in Asia, 11 in Europe. 46<br>in the United States|Adults|SSB,ASB, and fruit juice<br>consumption|T2DM, hypertension|T2DM risk was associated with SSB<br>(RR 1.27; 95% CI 1.17, 1.38), ASBs,<br>(RR 1.32; 1.11, 1.56) and fruit juices<br>(RR 0.98; 0.93, 1.03). Intakes of SSBs<br>and ASBs were signifcantly associated<br>with risk of hypertension.|
|Neelakantan et al.<br>(2021)|SR of 17 studies (9<br>prospective, 7 cross-<br>sectional, 1 clinical), Hong<br>Kong SAR, China; Republic<br>of Korea; Thailand;<br>Singapore;Japan|Adults, Asian<br>population<br>(n=114 208)|SSBconsumption|T2DM|High SSB consumption was associated<br>with greater T2DM risk (RR 1.38; 95%CI<br>1.09-1.73)|
|Mishali et al.<br>(2019)|SRM
Policy Research Working Paper 5979

# **Abstract**

This paper examines locational factors that increase the odds of a firm’s entry into export markets and affect the intensity of its participation. It differentiates between two different sources of spillovers: clustering of general economic activity and that of export-oriented activity. It also focuses on the effect of the business environment and that of institutions at the spatial unit of districts in India. The study disentangles the within-industry effect from the within-firm effect. A simple logit specification is used to model the probability of entry. The analysis is based on a panel of manufacturing firms in India, which allows for the introduction of firm-specific controls and a battery of fixed effects. The findings suggest that exporterspecific clustering, general economic agglomeration, and institutional factors affect firms’ export behavior.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at m.mukim@lse.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 by the Research Support Team
_and_ vulnerable poor, so an inclusive poverty rate would not be the simple sum of the individual poverty rates. In addition, the pattern of other dimensions of poverty will likely differ from the pattern of CCED poverty.

# _C) An example: Prospect poor_

To give an illustration of an expanded definition of poverty, we use easily measured indicator in a possibly expanded definition of poverty, i.e. the prospect poor. In this case, we define a household as "prospect poor" if it has at least one child older than 6 but less than 18 years, who is currently not enrolled in school, and has not completed lower secondary education level (SLTP). Table 8 shows a headcount of poverty of this is used in addition to the usual CCED definition.

Table 8: Headcount of Consumption and Prospect Poor

||Februa|ry 1996|Februa|ry 1999|
|---|---|---|---|---|
||Headcount<br>_(%)_|Relative to<br>CCED|Headcount<br>_(%)_|Relative to<br>CCED|
|Consumption poor|15.74|100|27.10|100|
|Prospect poor|15.70|100|12.49|46|
|Consumption or prospect poor|26.80|170|33.52|124|

The table shows that in February 1996, the prospect poor at 15.7 percent is quite sizable, they are as large as the consumption poor with only less than 30 percent of households overlapping and in both categories. Therefore if "broad 34
, Baird, McIntosh, and Ozler (2010) show significant impacts of 0.14 standard deviations on English achievement tests and 0.12 on math achievement tests after two years of operation of a CCT program in Malawi.

Our study builds on two recent studies which use the ENLACE data to study impacts of PROSPERA. Acevedo, Ortega and Székely (2018) use several sources of variation to estimate impact of PROSPERA on achievement tests, including comparing beneficiaries with nonbeneficiaries and comparing beneficiaries with less time in the program to beneficiaries with more time in the program. Depending on the method used, they find varying sizes of impacts, ranging from effects on the order of 0.3 SD to insignificant effects. Our study uses different methodologies to estimate impact as well as combining socio-economic characteristics and school characteristics as conditioning variables and as potential sources of program impact heterogeneity.

De Hoyos, Estrada, and Vargas (2018) analyze a longitudinal panel of ENLACE data for a group of students that took the test in sixth grade in 2007 and then again in grade 9 and in grade 12. They also merge the grade 12 test scores with a special module of the Mexican labor force survey (ENOE) applied to individuals aged 18 to 20 years old in 2010. They find that the ENLACE test scores have strong predictive power for future education and labor market outcomes. They show that grade 6 learning outcomes are an important predictor of lower- and 8