rafmacalaba/datause-encoder-data huggingface dataset:
and societal ‘improvement’) and distinctive social policies (the Poor Law, which sought to both promote labor mobility and provide a ‘portable’ minimum income for the lower classes).

Circumstances are obviously different for today’s poorest countries and peoples and, to be sure, a host of key measurement questions remain to be answered; but articulating and implementing effective policy agendas for promoting economic opportunity and mobility – about which, in stark contrast to its empirical sophistication, MDMIW is exceedingly cautious – is surely the priority issue for the coming decades. A research agenda focused on securing clearer understandings of how we got here, and why – within and between countries – certain sub-groups have fared so much better (or worse) than others, offers a possible pathway for being both rigorous and relevant.

# **References**

<mark>Alesina, Alberto, and Edward Ludwig Glaeser (2004)</mark> _<mark>Fighting Poverty in the US and Europe: A World of Difference</mark>_ <mark>New York: Oxford University Press</mark> Alesina, Alberto, Sebastian Hohmann, Stelios Michalopoulos, and Elias Papaioannou (2021), `Intergenerational mobility in Africa’, _Econometrica_ , 89(1): 1-35 Alvaredo, Facundo, Lucas Chancel, Thomas Piketty, Emmanuel Saez, and Gabriel Zucman (2017), `Global inequality dynamics: New findings from WID.world’, _American Economic Review: Papers & Proceedings_ , 107(5): 404–409 Asher, Sam, Paul Novosad, and Charlie Rafkin (2022), `Intergenerational mobility in India: New measures and estimates across time and social groups’, _mimeo_ Banerjee, Abhijit and Thomas Piketty (2005), `Top Indian incomes, 1922-2000’, _World Bank Economic Review_ , 19(1): 1-20 Berg, Andrew, Jonathan D. Ostry, Charalambos G. Tsangarides, and Yorbol Yakhshilikov (2018), ‘Redistribution, inequality, and growth: New evidence’ _Journal of Economic Growth_ 23 (3): 259– 305 Bourguignon, Francois and Christian Morrisson (2022), `Inequality among world citizens: 1820– 1992’, _American Economic Review_ , 92(4): 727-744 Brunori, Paolo, Francisco H. G. Ferreira and Guido Neidhöfer (2023), `Inequality of opportunity and intergenerational persistence in Latin America’, WIDER Working Paper 2023/39 Brunori, Paolo, Francisco H. G. Ferreira, Vito Peragine, Patrizio Piraino, Moris Triventi, Roy Van der Weide, Francesco Bloise, Rakesh Gupta, Leonardo Gasparini, Christoph Lakner, Francesca Luppi, Daniel Mahler, Ambar Narayan, Guido Neidhöfer, Flaviana Palmisano, Teresa Randazzo, Tina Rampino, Laura Serlenga, Joaquín Serrano (2021), `Equal chances: Equality of opportunity and intergenerational mobility around the world’, _mimeo_ 9
of the financial and nonfinancial incentives, respectively. Moreover, including the interactions of deviations on covariates with _Fg_ and _NFg_ gives the ATEs that are unbiased and consistent even in the presence of heterogeneity along these covariates including the pre-treatment outcome (Lin, 2013).

# **5.4 Secondary analysis on within-GP inequity**

We use the design of the two incentive schemes to also test the predictions from the theoretical framework in section 4 on electorally-driven policy inequities. While the proposed equity analysis was not the primary goal of the study, it provides useful insights on the mechanisms that might underly the effects of our financial and non-financial rewards on GP presidents, who are also electorally motivated.

The hypotheses derived from the theoretical insights in section 4 are as follows. First, disseminating credible information on president quality to voters across all hamlets as part of our nonfinancial reward, should redress within-GP inequity in resource allocations made by GP presidents during their tenure (in anticipation of receiving such a reward prior to their next election). Second, similar equity effects are not likely to exist, on the other hand, for GP presidents who received financial incentives, insofar as such a reward does not provide credible information on president quality to voters.

For this purpose, we focus on new investments made by the GP president during the evaluation period across four public infrastructure goods and the IAY scheme. We examine the effects on within-GP resource allocation across different types of hamlets that differ in the ability of hamlet residents to accurately observe their GP president’s quality, which—in the context of our study— are likely to be correlated with hamlet residents’ physical proximity to the GP president. We use hamlet-by-program level data to estimate OLS with the following specification: _Ygsp,_ 1 is an indicator variable that takes the value of one if hamlet _s_ in GP _g_ received program _p_ during the evaluation period, and zero otherwise. _Ps_ is a dummy variable that equals one if hamlet _s_ is the GP president’s hamlet of residence, henceforth referred to as pres-hamlet. _H_<sup> ̄</sup> is the vector of hamlet-level characteristics—that specifies the location of the GP office and hamlet population size.

16
<!-- Start of picture text --> $100,000 ==Delivery<br>$90,000 reimbursements<br>==Programme costs i<br>$50,000 (central)<br>$70,000<br>$60,000<br>$90,000<br>$40,000<br>$30,000 ick<br>$20,000<br>$10,000<br>$0<br>Q4 Qt @ 8 24 Qi @ 8 24 QI<br>2008 2009 2009 2009 2009 2010 2010 2010 2010 2011 2vti<br><!-- End of picture text -->
|**ECA country**|**Renewable**<br>**share 2005**|**EU**<br>**Directive**<br>**2020**<br>**Target**|**Year FIT**<br>**enacted (years**<br>**updated)**|**Renewable sources covered**|
|---|---|---|---|---|
|Russian<br>Federation|||--||
|Serbia*|||2009|Wind, PV, biomass, biogas, landfill gas,<br>geothermal,small hydro|
|Slovak<br>Republic|6.7%|14.0%|2001 (2005,<br>2009,2011)|Wind, PV, biomass, cofiring biomass,<br>biogas, geothermal,CHP|
|Slovenia|16.0%|25.0%|2002 (2004,<br>2006)|Wind, PV, hydro, biomass, CHP,<br>biogas,landfillgas|
|Tajikistan|||--||
|Turkey|||2005 (2010/11)|Wind, PV, hydro, geothermal, CSP,<br>biomass,landfillgas|
|Turkmenistan|||--||
|Ukraine|||2008 (2009)|Wind, PV, small hydro, biomass|
|Uzbekistan|||2010?|Wind, PV, others|

Source: World Bank, 2011.

In recent years, many ECA countries have also passed national legislation related to energy efficiency or adopted energy efficiency national action plans (World Bank, 2010). In making energy efficiency investments more attractive, these policies also help create jobs related to energy auditing, manufacturing of energy efficient equipments, and energy service companies etc. The cost savings from energy efficiency improvement could also be recycled into investment in the economy, further generating job gains.

In summary, the development of new green industries could create the basis of a new green employment market. Therefore, government policies that support the development of low-carbon industries may also have important employment benefits. However, as discussed in previous sections, it is also important to provide training and education programs to prepare the labor force will skills required for these new “green” jobs.

38
# **Appendix D: Data Sources and Attrition**

In this section, we discuss attrition in more detail and show that it is unrelated to both the Tuungane and the gender parity treatment status. **Table A3** gives, for each data source, an overview of the number of targeted observations, the number of observations actually used for analysis, and possible imbalances between treatment conditions. We discuss each data source in turn.

# **Villages Visited**

Tuungane was implemented between 2007 and 2012 in 280 clusters comprising 1,250 villages across four provinces of the Democratic Republic of Congo: South Kivu, Maniema, Tanganyika and Haut Katanga. In 2010 and 2011, Humphreys et al. (2019) targeted to collect data from two randomly selected villages in each of the 560 clusters (280 treatment, 280 control). In total, 816 villages out of the targeted 1,120 villages were visited. There are no differences in attrition by treatment condition (Humphreys et al. 2019). In 2015, we targeted the same villages as visited Humphreys et al. (2019), excluding the Maniema province for logistical reasons. Specifically, we targeted 781 villages (286 in Haut Katanga, 208 in Tanganyika, 287 in South Kivu). In total, 735 of the 781 villages (94%) were visited. Attrition took place because of inaccessibility of villages. **Table A3** shows that almost the same number of villages are missing in treatment and control communities. Related to the gender parity treatment, a total of 190 villages out of the 781 participated in the gender parity lottery. We again find no differences in attrition by treatment status.

# **Village Chief Survey**

Among the 735 villages that we visited, we conducted a survey with the village chief in 714 villages (97%). Among the 180 villages that participated in the gender parity treatment and were visited, a total of 176 village chief surveys were conducted. We find no differences in attrition by treatment status.

# **Household Survey**

Per village, we targeted five randomly selected households. Within households we randomly selected the (adult) respondent in such a way as to ensure that each gender was represented equally within the sample. Given that 735 villages were visited, the study should make use of 3,675 6
household attended insurance marketing meeting but did not purchase insurance (233 households) and (iii) household did not attend marketing meeting (252 households). The total sample size is 752 households. The sample of 267 purchasers represents a large fraction of the 315 total households that purchased insurance in 2004.

# **Table 4: Sampling Methodology, Marketed Villages**

“Unweighted observations” is the number of households appearing in the sample. “Weighted observations” is the number of households in the underlying population represented by the sample.

||**observ**|**ations**|
|---|---|---|
||Unweighted|Weighted|
|**Sample size**|752|5805|
|**Did not attend marketing meeting**|252|5205|
|**Attended marketing meeting**|500|600|
|Purchased insurance|267|267|
|Did not purchase insurance|233|333|

Weighted statistics in Table 4 reflect the size of the underlying population from which each sample is drawn, based on the landowner census and BASIX administrative records. The underlying population is 5805 households across 25 villages; since 266 households purchase insurance, the insurance takeup rate is 4.6%.

Note that we use “purchased insurance” as the dependent variable in most of our regressions. Since we also stratify on this variable, our sampling approach is an example of choicebased sampling (Manski and Lerman, 1977). Following Manski and Lerman, we estimate a weighted probit regression using the sampling weights from Table 4 to recover consistent estimates of the slope coefficients. (If instead stratification was based on right-hand-side variables, either weighted or unweighted regressions would provide consistent estimates).

# _5.1 Summary statistics and variable construction_

Summary statistics for the sample are presented in Table 5. Full-sample averages are weighted by population weights, and thus are close to non-buyer averages, given the low takeup rate. For 25 15
# Table 3. Brazilian Industries Facing AD Duties

|Three-digit ISIC Industry|Number of<br>cases|
|---|---|
|311 Food products|1|
|313 Beverages|0|
|314 Tobacco|0|
|321 Textiles|1|
|322 Apparel except footwear|0|
|323 Leather products|0|
|324 Footwear except rubber or plastic|0|
|331 Wood products except furniture|0|
|332 Furniture except metal|0|
|341 Paper and products|2|
|342 Printing and publishing|0|
|351 Industrial chemicals|3|
|352 Other chemicals|0|
|353 Petroleum refineries|0|
|354 Miscellaneous petroleum and coal products|0|
|355 Rubber products|0|
|356 Plastic products|0|
|361 Pottery, china, earthenware|0|
|362 Glass and products|1|
|369 Other non-metallic min. products|1|
|371 Iron and steel|12|
|372 Non-ferrous metals|0|
|381 Fabricated metal products|1|
|382 Machinery except electrical|0|
|383 Machinery electric|3|
|384 Transport equipment|0|
|385 Professional and scientific equipment|1|
|390 Other manufacturedproducts|1|

_Source:_ Bown (2010) 20
25 data, particularly because the respondents differed on the two dates.) 3 This applies to per-case payments to providers also, but in some countries these are used simply as a salary substitute and not as a staff incentive.

# Refererces

Abel-Smith, Brian. 1985. 'Global perspective on health service financing." Social Science and Medicine 21, 9:957-963.

Arellano, Jose Pablo. 1987. La Salud en los Anos 80: Analisis desde la Economia, Corporacion de Investigaciones Economicas para Latinoamerica. Note Tecnica 100.

Cornia, Andrea, Richard Jolly, and Frances Stewart. 1988. Adiustment with a Human Face: Protecting the Vulnerable and Promoting Growth. New

# York: Oxford University Press.

Isaacs, Stephen L. 1981. PoRulation Law and Policy. Human Sciences Press. Lapham, R. J. and W. P. Mauldin. 1984. "Family planning program effort and birthrate decline in developing countries." International Family Planning Perspectives 10, 4:109-118.

-----. 1985. "Contraceptive prevalence: The influence of organized family planning programs." Studies in Family Planning 16, 3:117-137.

Nortman, Dorothy. 1985 (and prior years). PoDulation and Family Planning Programs: A Compendium of Data through 1983. 12th Edition. New York: The Population Council.

Ridker, Ronald. 1971. "Savings accounts for family planning: An illustration from the tea estates of India." Studies in Family Planning 2, 7:150-152.
# **D. Regression Results**

# **_Basic model_**

Table 1 reports the results of estimating Equation 3 separately for child mortality and school enrollment. The initial levels of child mortality and school enrollment turn out to be very important in their respective regressions. They are statistically significant at the 1% level of confidence and they have the largest standardized coefficients among all independent variables by far.

**Table 1: Determinants of changes in child mortality and school enrollment (Full sample)**

|Variable|Child mortality|School<br>enrollment|
|---|---|---|
|Ln Initial child mortality|0.373***<br>(0.110)||
|Ln Initial school enrollment||-0.771***<br>(0.038)|
|Health spending per capita|0.144**<br>(0.063)||
|Education spending per capita||0.070***<br>(0.026)|
|GDP per capita growth|0.069**|0.054***|
||(0.027)|(0.012)|
|Undernourishment|-0.013**|-0.013***|
||(5.50e-03)|(2.30e-03)|
|Population density|5.00e-04*|1.00e-05|
||(3.00e-04)|(1.00e-04)|
|Control of corruption|-0.062|0.013|
||(0.103)|(0.037)|
|Constant|-0.958|3.238***|
||(0.688)|(0.171)|
|Observations|79|78|
|Adjusted R-squared|0.226|0.885|

Notes: Regressions are run on global samples including developing and high-income countries. The dependent variables are the absolute log changes in child mortality (reduction) and secondary school enrollment (increase) over the period 1980-2014. Asterisks denote significance levels as follows: *** p<0.01, ** p<0.05, * p<0.1.

The positive sign on initial child mortality shows that the higher is the initial child mortality in 1980, the greater is the reduction during 1980-2014. The negative sign on initial school enrollment shows that the is higher the initial level of secondary school enrollment in 1980, the smaller is the increase over time. Thus, in both cases, convergence is at work: countries with worse child mortality and education levels in 1980 improve these indicators at a faster rate over the next three decades than countries with better initial levels.

10
25 Data available from INTA (1998) for seven extension-providing companies indicates a range of cost of extension provision for 1997/98 that varies between $70.98 and $88.43 per producer, based on the company. The average cost for 1997/98 of $79.67 is significantly lower than that of 1996/97 ($88.83), and that of 1995/96 ($101.18). The cost effectiveness trend over the last three years of ATP2 operations is a reflection of both the experience gained by the private companies, and by the competition regulated by INTA.

Table I 1: Extensionists' allocation time for extension in the 7 private companies of ATP2 as of 10/1996

||Numberof|Working Days||Distribut|ion of Tec|hnicians|' Time am|ong Act|ivities||
|---|---|---|---|---|---|---|---|---|---|---|
|Company|Technicians|per Month|Visits of|Producers|Technican|'s Training|Office|Work|Mi|sc.|
||||Days|%|Days|%|Days|%|Days|%|
|I|10|24|20|83.3|2|8.2|1|4.2|1|4.2|
|2|7|24|20|83.3|2|8.3|2|8.3|-|-|
|7|6|23|18|78.2|2|8.7|2|8.7|1|4.4|
|6|8|24|20|83.3|3|12.5|1|4.1|-|-|
|3|11|22|18|81.8|1|4.5|2|9.1|1|4.5|
|4|10|22|18|81.8|2|9.1|2|9.1|-|-|
|5|8|22|17|77.3|2|9.1|3|13.6|<br>-|-|

Another trend worth-mentioning is the producer payment schedule. First-year participating producers pay 20% of the extension cost; second-year participating producers pay 30% of the cost, and so far, third year participating producers pay 50% of the cost. This schedule is expected to reach 100% cost recovery in five to eight years. Finally, the total cost of the extension provision in 1997/98 will be borne as follows: producers 31.7%, the government of Nicaragua 18.5%, and a World Bank loan 49.8%.
microfinance firms account between them for more than 90 per cent of the clients served worldwide by the 234 top firms (and hence for more than three-quarters of those served by all of the 2572 firms reporting to the Microcredit Summit).” While we cannot make a similar comparison here, Honohan’s evidence suggests that during the sample period the banks here served over half of all microfinance customers worldwide.

An important feature of our data is qualitative information on the lending style employed by the MFI, the range of the services it offers, its profit status, ownership structure, and sources of funds. These detailed data enable us to offer a more complete analysis of MFI performance by lending type than has been possible before.<sup>7</sup> Lending methods vary across regions, as shown in Table 3. There are no village banks in East Asia in the sample, for example. Individual-based lending predominates in East Asia and the Pacific, while institutions in South Asia and Sub-Saharan Africa tend to lend through group mechanisms. Institutions in Eastern Europe and North Africa do not strongly favor either individual-based or group lending.

Summary statistics at the bottom of Table 1 indicate that, with the possible exception of Latin America and the Caribbean (LAC), our sample is reasonably balanced across regions. Seventeen percent of the institutions come from Eastern Europe and Central Asia, another seventeen from Sub-Saharan Africa (AFR). South Asian (SA) institutions comprise ten percent of the sample, while institutions from East Asia and the Pacific (EAP) and the Middle East and North Africa (MENA) comprise nine and seven percent, respectively.<sup>8</sup> Institutions from Latin America and the Caribbean comprise forty

> 7 For example, Hartaska and Nadolnyak (2005) use a subset of the MIX data to investigate whether the regulatory environment affects the operational self-sustainability and outreach of MFIs, but they are unable to disaggregate by lending type and their self-sustainability measure is unadjusted. 8 The regional classifications and acronyms are those employed by the World Bank.

8
15 Infrastructure. Figures 16 and 17 show scatter diagrams of the transport and communication employment share against **per capita** incomes in the "world economies" and OECD respectively. As in the case of value added, there is no relationship between this share and incomes. Almost all the Soviet republics lie above the norm for infrastructure services, and Kazakhstan, Estonia, and Russia head the list. In Figure 17, only Norway and Iceland are comparable. Tadzhikistan, Uzbekistan, Armenia, and Moldova are the Soviet republics with the lowest infrastructure employment shares and are about at the "world economies" and OECD averages.

**Business and consumer services.** Figure 18 shows the scatter diagram for the wholesale and retail trade employment share in "world economies". The former Soviet republics are far below international norms for comparable levels of income. There is the usual problem that the international data include restaurants and hotels, while the Soviet data include only restaurants. Thus, we compare the Soviet trade data to OECD data for trade, restaurants, and hotels in Figure 19 and to OECD data for wholesale and retail trade only in Figure 20. Figure 19 shows trade services in the Soviet republics to be substantially below OECD norms; Figure 20 shows them to be only slightly below OECD norms. Unlike value added, there is a slight tendency for trade employment shares in the Soviet republics, as well as in OECD countries, to rise with per capita income.

Figures 21 and 22 compare the banking and insurance employment shares in the Soviet republics with "world economies" and OECD employment shares respectively. Employment in the Soviet financial sector is far below international norms by either measure, with the gap increasing as incomes rise. In Figure 23, we compare Soviet data on community services, which include housing services, with OECD data on consumer services, which here exclude housing as well
Policy Research Working Paper 9191

# **Abstract**

Using a comprehensive and proprietary data set on international private equity activity, this paper studies the determinants of buyout investments across 61 countries and 19 industries over 1990–2017. The study finds evidence that macroeconomic conditions, development of stock and credit markets, and the regulatory environment in a country are important drivers of international buyout capital flows. The paper shows that countries with low unemployment, more active stock and credit markets, and better rule of law receive more buyout capital. A difference-in-differences approach is used to explore the regulatory reforms some countries have adopted over the sample period. The findings are that countries receive significantly more buyout capital following investor protection and contract enforcement reforms. The impact of regulatory reform is more pronounced in countries with better corporate governance standards and education. Buyout investment responds to these factors more so than foreign direct investment and gross domestic fixed investment.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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://www.worldbank.org/prwp. The authors may be contacted at saldatma@gmu.edu, gregw-brown@unc.edu, and ademirguckunt@worldbank.org.

_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
# Table of Contents

||Introduction|2|
|---|---|---|
|1.|A Simple Portfolio<br>Model|3|
|2.|The Flexible<br>Accelerator<br>Model ofInvestment|8|
|3.|TheIntroduction<br>ofthe Public Sector|10|
|4.|AnEmpirical<br>Test : theCase ofArgentina<sup>(1961-82)</sup>|13|
|S.|Concluding Remarks|16|
|**Biblio**|**gra:phy**|17|
|Tables||19|

I am grateful to M. Blejer, A. Fishlow, **J.** de Melo and two anonymous referees for helpful comments and suggestions.