rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5:  Programmatic Parties and Project Ratings for Public Sector Reform (Country Fixed Effects)**

||Base Model<br>Full Model<br>Inst. Dev. Impact Rating|Base Model<br>Overall Outc|Full Model<br>ome Rating|
|---|---|---|---|
|Programmatic|7.269<sup>*</sup><br>27.97<sup>**</sup>|2.608|23.02<sup>+</sup>|
|Parties|(0.026)<br>(0.004)|(0.382)|(0.063)|
|Log GDP per|0.123<br>0.0350|0.446|0.163|
|capita|(0.234)<br>(0.133)|(0.452)|(0.502)|
|Log Population|10.52<br>1414.6<sup>+</sup>|0.0111|0.381|
||(0.486)<br>(0.084)|(0.158)|(0.818)|
|Log Land Area|0.785<br>6.987|284915.5|4.135|
||(0.916)<br>(0.371)|(0.122)|(0.895)|
|Funding Amount|0.994<sup>+</sup><br>0.993<sup>+</sup>|0.997|0.994<sup>*</sup>|
||(0.053)<br>(0.052)|(0.151)|(0.023)|
|Project Duration|0.964<br>1.077|0.801<sup>+</sup>|0.922|
||(0.757)<br>(0.608)|(0.081)|(0.687)|
|Loan % Allocated|1.002<br>1.004|0.994|0.994|
|to PSR|(0.872)<br>(0.754)|(0.574)|(0.716)|
|IEG Quality at|1.900<sup>*</sup>||9.324<sup>**</sup>|
|Entry|(0.015)||(0.003)|
|ICRG|0.483||0.510|
|Bureaucratic|(0.327)||(0.382)|
|Quality||||
|CountryFE|Yes<br>Yes|Yes|Yes|
|Observations|148<br>130|196|168|
|Pseudo_R_<sup>2</sup>|0.167<br>0.265|0.148|0.430|

Odds ratios reported; p-values in parentheses + p < 0.10, * p < 0.05, ** p < .01, *** p < .001 GDP per capita and population are averaged over the project period; progression to secondary school is a country average; programmatic parties and ICRG bureaucratic quality are measured at the project start year

# **Selection effects and the association of programmatic parties and successful public sector reform**

Selection bias would be a serious issue for the analysis here if we were asking whether countries are better able to reform their public sector if they receive a World Bank 39
TABLE 5: Impact of leaders’ exposure on Early Childhood Development outcomes

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|_Panel A ITT of leaders’ exposure_||||
|ITT Leader|-0.11<sup>_∗∗∗_</sup><br>(0.04)|-0.02<br>(0.05)|-0.14<sup>_∗∗∗_</sup><br>(0.05)|
|_Panel B Heterogeneity by caregiver education_||||
|ITT Leader|-0.20<sup>_∗∗∗_</sup><br>(0.06)|-0.22<sup>_∗_</sup><br>(0.12)|-0.18<sup>_∗∗∗_</sup><br>(0.07)|
|ITT Leader X Medium educ (4-6 years of education)|0.17<sup>_∗∗_</sup><br>(0.07)|0.35<sup>_∗∗_</sup><br>(0.16)|0.09<br>(0.08)|
|ITT Leader X High educ (more than primary education)|0.08<br>(0.07)|0.17<br>(0.14)|0.01<br>(0.10)|
|Observations|2051|624|1427|
|_P-values joint signifcance tes_t||||
|ITT Leader + ITT Leader X Medium educ = 0|0,58|0,20|0,13|
|ITT Leader + ITT Leader X High educ = 0|0,03|0,49|0,05|

_Note:_ Sample includes only non-leader households in 92 villages with opinion leaders. Five villages without leaders were excluded from the estimation. All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months old and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, Memory and self-control) of children aged 36 to 83 months. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, child age and gender, and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 39
small effect of donor support for PFM reform on PEFA scores, a result robust to different estimation methods and several sample restrictions. de Renzio et al. (2011) acknowledge that causation could run in both directions, but report that attempts to find “adequate instrumental variables” were unsuccessful. Therefore the authors note that their findings should be treated with caution.

A few studies specifically focus on World Bank support. A large-scale evaluation of PSG lending during 1999-2006 by IEG (2008) – combining a with-without approach, com– parison to objectives, before-after analysis with case study findings suggests that the Bank has been successful in supporting changes in public financial management and tax administration, without much progress however in controlling corruption and improving the functioning of civil service. Cruz and Keefer (2013) show that World Bank public sector reform projects are more likely to be rated as successful in countries with more programmatic political parties. Their explanation is that politicians in those countries have stronger incentives to pursue public policies that require well-functioning public administrations, and therefore to assist rather than block the Bank’s reform efforts. Blum (2014) similarly investigates the country and project-level factors associated with higher IEG ratings for PSG projects, finding that they perform better in more democratic and aid-dependent countries. Moloney (2009) estimates the impact of public sector projects on – – the quality of governance as measured by Kaufmann et al. (2006) covering the period 1996-2005. Controlling for GDP per capita and aid over GNI in a random effects model, the author claims that World Bank projects have a positive effect on regulatory quality, but a negative impact on control of corruption. Moloney (2009) does not correct for endogeneity, however, and acknowledges that PSG projects may target the most problematic countries. Moreover, due to limited availability of the CPIA data we use in this study, her study relied on other governance indicators “that the Bank itself does not use . . . to evaluate its clients.” In all of these studies the majority of PSG projects considered are 5
# ACKNOWLEDGMENTS

_The project otn which a previous version of this paper was based was conducted in China during 1989-1990. The National Natural Science Foundation of China provided financial support for the project. Ren Ruoen (the first author), invited by Professor Stanley Fischer, refined this work in the Department of Economics at MIT, supporledfinandially by the Edhcation Committee of China and the World Economy Laboratory at MIT Finally, ken Ruoen finalized this paper in the SocroEconomic Data Division of the WorldBank as a resident consultant_ _In this research Ren Ruoen benefited considerablyfrom the advice of a number of eminent schoklrs, but the most profound influence has been that of Professor Stanley Fischer. He was most generous with his time so that Ren Ruoen had opportunities to discuss this topic with him extensively and received many valuable suggestions Ren Ruoen also would like to thank him for assistance in obtainingfundig through MIT._ _Professor Dwight Perldns of Harvard University made penetrating observations on a previous &raft and took keen interest in the results of this work_ _Ren Ruoen would like to tha Professor A. Bergson of Harvard University for suggestons and Professor J Poterba and W Wheaton ofMITfor discussions andforproviding datix_ _The colleagues in the World Bark Mr. John O'Connor, Mr. Sultan Ahmad, Mr. Jong-goo Parkread aprelimminy drf of the paper and gave many valuable comments and suggestions. Ren Ruoen also would like to thank his colleapes Mr. Bons Bimic-Metzner, Mr. Jitendra Borpujari and Mr. Yonas Biru for discusson and help. Furthermore, Ren Ruoen wishes to ackiowledge comments from Professors AlanI Heston of the Umversily of Pensylwmk4 Professor Angus Madisan Bart van Ark Adam Szirmai and Dirk Pilat of the University of Groningen, Professor Clopper Almon of the University ofMa,yland4 andMr. Peter Hanrold andMr. Zafer Ecevit of the World Bank_ _The paper was edited by William Prnce of the WorldBank and Cheng Guangkm of MIT_ _Professor Han Yue e of the Beiying University of Aeronautics and Astroniauics made her conribuions to this project when it was conducted in China The authors are gratefid to Professor Cui Shuwaang of the Central Institute of Economics and Finance in Beijing acnd Professor Mao Yusiu of the Social Science Academy of China for heoful comments and to Jiang Wei, Zhang Xiaobin, Zeng Zhohui, Liang Mingjie and Cheng Jun for research assistance._ iv
**Figure 6. Decomposition of percentage change of (sales-weighted) average markup, 2008–17**

_Source:_ World Bank staff calculations based on SBS data set.

**This result could be interpreted as a sign of consolidation of market power among existing firms enabled by remaining product market regulations that restrict competition, despite previous reforms.** Several policy-related factors can explain this result, among them, government interventions and regulations that restrict competition by reinforcing dominance or limiting entry, facilitating collusive outcomes or increasing the cost to compete in markets, and discriminating and protecting vested interests.<sup>24</sup> In this regard, Pop and Ore Monago (2020) provide illustrative examples about the presence of regulations that still restrict competition in Romania, drawing from the OECD–World Bank Group (WBG) product market regulation (PMR) indicators data set, which provides information across 70 countries.<sup>25</sup> The assessment uses the latest PMR round for Romania from 2013 and includes Romania’s performance projected on the basis of information updated in 2017. Results suggest that the regulatory framework as it is in the books in Romania is less restrictive than that of countries such as Poland and the total sample average, but it is more restrictive than that of the OECD and EU15 countries. However, from an economywide perspective, the overall restrictiveness of regulations in Romania is mainly associated with the state control of the economy and several remaining barriers to entry and rivalry (see figure 7).

> 24 In principle, greater competition outcomes are enabled through an effective competition policy framework, which involves three key pillars: (a) fostering pro-competition regulations and government interventions, (b) promoting competitive neutrality and non-distortive public aid, and (c) enabling effective competition law and antitrust enforcement.

> 25 The current PMR database used for this study includes Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Bulgaria, Canada, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, the Czech Republic, Denmark, the Dominican Republic, Ecuador, Arab Republic of Egypt, El Salvador, Estonia, Finland, France, Germany, Greece, Guatemala, Honduras, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Jamaica, Japan, Kenya, the Republic of Korea, Kuwait, Latvia, Lithuania, Luxembourg, Malta, Mexico, the Netherlands, New Zealand, Nicaragua, Norway, Panama, Paraguay, Peru, the Philippines, Poland, Portugal, Romania, the Russian Federation, Rwanda, Senegal, the Slovak Republic, Slovenia, South Africa, Spain, Sweden, Switzerland, Turkey, Tunisia, Ukraine, Uruguay, the United Kingdom, and the United States.

20
_Figure 1. Most U.S. job growth is in the middle‐ and high‐skill categories; low‐skill jobs declining._ <!-- Start of picture text --> 60<br>50<br>40<br>30<br>2000<br>2011<br>20<br>10<br>0<br>Very low Low Medium High Very high<br>Average college attainment in the occupation<br>Millions<br>Workers<br><!-- End of picture text --> Source: Authors’ calculations based on U.S. Census, 2000 Census of Population and Housing long form and 2011 one‐year American Community Survey, extracted from IPUMS‐USA (Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine‐readable database]. Minneapolis: University of Minnesota, 2010.).

Metro areas where many young people lack access to good K‐12 education, community colleges, and four‐year degrees therefore are unlikely to share in national job growth unless they have other features that can attract in‐migrants. Focusing on education, at least through community college, is therefore a necessary ingredient not only because it allows workers to get access to better jobs but simply because it equips them to qualify for jobs in the only segments of the labor force that are still growing. Most critically of all, perhaps, access to education allows current workers to keep up with new entrants to the labor force, who are increasingly likely to have at least a year of college.

# _2. Cities with low‐skill job concentrations can grow but are not safe from decline._

As shown in Figure 2, U.S. metropolitan areas where most of the labor force worked in occupations requiring low levels of college attainment in 2000 (averages less than 45 percent of the total) ranged from employment growth of over 40 percent between then and 2011 (in the border metros of McAllen and Laredo, Texas, where cross‐border consumer 14
health and remittances. In addition, the detailed community level information on infrastructure and access to facilities are collected for the rural psus. We utilize these data to construct both the dependent and explanatory variables. The dependent variable of our empirical analysis is the log of real per capita household expenditure (LRPCE) measured in 2005 prices. For the purpose of poverty assessment, two separate price indices are de fi ned. They relate to the “upper” and “lower” poverty lines.<sup>8</sup> As the incidence of poverty is estimated using the upper poverty line, we used price index for the upper poverty line for de fl ating per capita expenditure.<sup>9</sup> A critical step in the estimation of equation(5) is the identi fi cation of the integrated and less integrated regions. Perhaps because of its smaller geographical size and very high density of population, Bangladesh does not have a clearly marked “lagging” region, though the North-West region has been historically known as a region with a higher incidence of poverty. However, with the spread of irrigated agriculture, the region has become the bread basket of the country in recent years (Diop, 2005).<sup>10</sup> In the context of Bangladesh, metropolitan cities of Dhaka and Chittagong have emerged as the main growth centers. The urbanization process as well as economic growth in Bangladesh has been dominated by these metropolitan cities - Dhaka, the capital city with a population of 10 million and Chittagong, the main port city with a population of 3.4 million. Together these two cities account for 88 percent of the population in metropolitan areas and 41 percent of the total urban population. Estimates based on HIES 2000 and 2005 indicate that the average real per capita income in these cities is about 40 percent higher than that of the other metropolitan areas. As a result of the higher living standards, Dhaka and Chittagong cities have acted as magnates for migrants experiencing more than 5 percent growth in population.<sup>11</sup> These two cities also act as the main domestic and international trading hubs and

> 8 The upper and lower poverty lines di ff er in terms of allowances for non-food expenditure. For detail on the construction of poverty lines, please see Narayan and Yoshida (2007).

> 9 The lower poverty line is used to de fi ne the incidence of extreme poverty. It should be noted that for each year, 16 area speci fi c poverty lines are constructed.

> 10 Despite this progress, there exists still smaller areas with very high incidence of poverty such as the marsh land.

> 11 The overall rate of population growth is about 1.5 percent according to the Population Census, 2000.

11
# **Figure 3: Wealth and natural capital components over time**

We compare how natural capital and its constituents developed throughout the sample period. The first row shows the breakdown of total wealth and the second row shows the components of natural capital. The percentages denote the growth of the respective component since 1995.

<!-- Start of picture text --> Low income B-rated issuers (BBB+ to BB-) A-rated issuers (AAA to A-)<br>8.0 +110.7% 175 +72.6% Human capital +93.0% since 1995<br>7.0 800 Produced capital<br>150 Natural capital<br>6.0<br>5.0 125 600<br> +69.7% 100<br>4.0<br> +66.1% 400<br>3.0 75<br> +34.0% +85.3%<br>2.0 50<br>200<br>1.0 25 +52.5%<br> +86.0%<br>0.0 0 0<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>2.5 +34.2% +34.7% 20 + 54.7% 40 Subsoil assets Protected areas<br>2.0 + 30.1% Agricultural landForests +93.5%<br>15 +38.1% 30 + +82.8%83.3%<br>1.5 +33.1%<br>1.0 +27.7% 10 20 +95.3%<br>0.5 5 +23.4% 10<br>0.0 0 0<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br>US$ trillion, constant 2018 US$<br>US$ trillion, constant 2018 US$<br><!-- End of picture text --> 2014 from the World Bank’s Open Data platform (Lange et al., 2018). See Figure 3 for a visual depiction of how individual wealth components evolved since 1995.7 We then linearly interpolate the annual values to obtain monthly figures. Given the slow-moving and persistent process behind wealth, the linear interpolation is reasonable. Throughout this paper, wealth variables are transformed with the base-10 logarithm.

# **2.2 Bond data**

Sovereign bonds of developing markets and middle income countries are sometimes issued in both foreign and local currencies. This study focuses on local currency bonds. This choice is primarily motivated by the larger size of sovereign bond markets in local versus foreign currencies. According to the debt securities statistics of the Bank for International Settlements, the average amount outstanding between 2009–2018 for central government debt is US$ 19.7 trillion for domestic currencies and US$ 78.3 billion for foreign currencies. If we exclude the United States and Eurozone countries, the numbers reduce to US$ 6.6 trillion for domestic and US$ 49.0 billion for foreign currencies. This is also reflected by the broader bond data coverage across countries and time for local currencies.

One concern for this choice might be the “original sin” (Eichengreen et al., 1999; Eichengreen et al., 2002), describing the inability of developing countries to adequately borrow domestically or internationally when issuing debt in their own currency. Instead, countries issue debt denominated in USD or EUR, which insulates the creditor from domestic inflation, liquidity or solvency concerns of the issuing country. However, Hausmann et al. (2003) do not find evidence that these concerns

> 7The data used in this paper was obtained from the World Bank’s wealth team and is subject to change prior to the release of the new wealth data described in Lange et al. (2021).

8
Table 3: Returns to Schooling from OLS (male employees 22-45 years)

|Variable|(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Years of schooling|0.090|0.068|0.076|0.069|
||(8.0)|(4.9)|(5.5)|(4.7)|
|Experience|0.012|0.011|0.012|0.008|
||(0.8)|(0.7)|(0.8)|(0.5)|
|Experience squared|-0.0002|-0.0002|-0.0003|-0.0002|
||(0.6)|(0.4)|(0.6)|(0.4)|
|Standardized IALS|-|0.128<br>(2.7)|-|-|
|Standardized SCS|-|-|0.111|0.111|
||||(2.3)|(2.3)|
|Standardized NSCS|-|-|-|0.075<br>(1.4)|
|Father >= Post-Secondary|0.236|0.194|0.251|0.127|
||(2.8)|(2.3)|(2.9)|(0.9)|
|Constant|5.34|5.56|5.48|5.57|
||(26.6)|(25.8)|(25.1)|(24.1)|
|Adj. R<sup>2</sup>|0.197|0.207|0.228|0.228|
|N|490|490|467|467|

Note: Theoretical sampling weights included; _t_ -values in parentheses In Tables 4 and 5 we use the specifications in columns (1)-(4) of Table 3 and estimate IV regressions using the binary instrument involving a group of compliers who were of basic school age in 1981. The first two columns are the same as in Table 1 and 2 with the addition of father’s education in the controls. The third column replaces full cognitive skills with _SCS_ while the fourth includes both _SCS_ and _NSCS_ . The findings in Table 4 confirm the earlier conclusion that for this group of compliers, the coefficient of years of schooling is mainly a measure of the noncognitive return to schooling. The coefficient remains largely unchanged when we control separately for schooling cognitive skills and non-schooling cognitive skills which enter the equation with coefficients which are statistically near zero.

25
Table 9: Does politics matter?

||paid bribe|bribe<br>bur-|informal|bribe%sales|corr.<br>per-|
|---|---|---|---|---|---|
|||den|gift||ception|
||(1)|(2)|(3)|(4)|(5)|
|dec.index|-0.81**|-53.58***|-2.39***|-38.25***|30.86**|
||-0.39|-13.82|-0.6|-10.35|-12.97|
|LG exec. el.|0.14***|0.04|-0.10***|-2.12***|0.54|
||-0.02|-0.87|-0.02|-0.51|-0.82|
|dec.index*LG exec. el.|-3.60***|50.14***|2.92***|36.93***|-0.82|
||-0.45|-16.21|-0.4|-10.32|-14.13|
|LG indep.|-0.07***|-3.24***|-0.13***|-1.67*|2.21*|
||-0.02|-0.69|-0.03|-0.94|-1.12|
|dec.index*LG indep.|-1.56***|18.88**|0.79*|28.24**|-16.59|
||-0.26|-8.99|-0.41|-13.17|-13.61|
|LG direct dem.|-0.04|-3.03***|0.27***|1.22|-0.46|
||-0.03|-1.03|-0.04|-0.92|-1.05|
|dec.index*LG dir.dem.|0.74**|-7.54|-5.97***|-13.24|14.84|
||-0.31|-11.15|-0.72|-16.19|-11.69|
|LG leg. el.|-0.25***|-2.18**|0.08***||-0.06|
||-0.03|-1.01|-0.02||-0.57|
|dec.index*LG leg.el.|4.58***|-9.97|0.12||-20.79|
||-0.69|-22.78|-0.54||-14.07|
|LG area|-0.02**|1.20***|-0.01***|-0.04***|0.01|
||-0.01|-0.29|0|-0.01|-0.01|
|dec.index*LG area|0.15|-21.35***|-0.25***|0.15|4.06***|
||-0.12|-4.37|-0.05|-1.86|-1.46|
|1 tier|0.17**|-17.95***|-0.02**|0.55|-0.26|
||-0.07|-3.12|-0.01|-0.45|-0.46|
|2 tiers|0.12*|-18.46***|-0.06***|-0.28|-0.18|
||-0.07|-3|-0.01|-0.36|-0.32|
|3 tiers|0.1|-17.67***||||
||-0.07|-3.01||||
|UK legal origin|-0.04***|-2.98***|0.04**|0.81|-0.12|
||-0.01|-0.45|-0.02|-0.57|-0.35|
|fractionalization|0.04***|-0.50***|0.02***|0.31***|-0.07|
||0|-0.16|0|-0.1|-0.12|
|start business|-0.01***|-0.06|0.01***|0.08**|-0.10*|
||0|-0.04|0|-0.03|-0.05|
|enforce contract|0.00***|-0.04|0.01***|0|-0.03|
||0|-0.03|0|-0.02|-0.03|
|log (GDP/capita)|-0.07***|-0.19|||0.31|
||-0.01|-0.22|||-0.24|
|Observations|33925|33459|27217|28773|72|
|R-squared|0.1|0.05||0.05|0.93|
|# countries|54|56|56|56|101|

Note<sup>∗</sup> - significant at 5% level,<sup>∗∗</sup> - significant at 3% level,<sup>∗∗∗</sup> - significant at 1% level. Dependent variable: column (1) - paid bribe; columns (2) - paid bribe; columns (3) - informal gift; columns (4) - bribe%sales; column (5) - corruption perception, 2007 (see definitions in Table 2). Estimation method: columns (1), (2), (4), (5) - linear probability (OLS); column (3) - probit (average partial effects are reported). Standard errors are reported in brackets: columns (1), (2), (5) - heteroscedasticity robust; columns (3), (4) - clustered by country. Additional controls are included in all regressions, but not reported here due to a lack of space. These are regional dummies, year dummies, individual and firm controls, population, openness, # of units, LG population size.

30