rafmacalaba/datause-encoder-data huggingface dataset:
weighted sample. The weighting estimator is obtained by a weighted least squares regression of _Yi_ = α + β _Di_ + ε , where the weights are unity for the treated units and _P_<sup>ˆ</sup> ( _Z_ /)1 − _P_<sup>ˆ</sup> ( _Z_ ) for the comparison units (Hirano and Imbens 2002; Imbens 2004).<sup>14</sup> Hirano et al. (2003) shows that this estimator is efficient.

These methods attempt to control for observed pre-project heterogeneities that may lead to selection into the project as well as influence outcomes and the growth path of the communes. We assume that any selection bias due to unobserved factors is timeinvariant and so can be differenced out. Accordingly, the DD PS-matched estimator is given by: ( ∑ ( _Yi_ 1 − _Yi_ 0 ) − ∑ _Wij_ ( _Y j_ 1 − _Y j_ 0 /) _N T_ . Similarly, the DD PS-weighted _i_ ∈ _T j_ ∈ _C_ estimator is given by regressing the change in outcome on the treatment indicator: Δ _Yi_ = α′ + β′ _Di_ +ξ _it_ , with weights equal to unity for the treated and _P_<sup>ˆ</sup> ( _Z_ /)1 − _P_<sup>ˆ</sup> ( _Z_ ) for the comparison communes.

As a further test for robustness, we use the PS to create two additional samples over which we calculate the estimates. In one we trim three non-project communes with PS lower than the lowest treatment’s PS. We refer to this as ‘treatment support.’ In the second we further trim the six project communes with PS that are higher than any of the non-project communes’ thus limiting the sample to the region of common support. This provides tighter matching but also results in a loss in our ability to make inferences about the population of project villages. We report these estimates as a test of the sensitivity of the full sample results.

Our estimates will be biased if there are omitted variables that influence both placement and the changes in outcomes. Selection bias on unobservables can never be fully ruled out in any observational study. While an array of initial conditions, including the initial state of infrastructure, will be controlled for in the logit model used to calculate the PS, we can never rule out the possibility of omitted initial conditions, or changes over time in those variables that are correlated with placement or outcomes. We will comment on possible omitted variables.

> 14 If we wish to estimate the average treatment effect for the population, the weight is 1/ _P_<sup>ˆ</sup> ( _Z_ ) for the

> treated and 1/1 − _P_<sup>ˆ</sup> ( _Z_ ) for the non-treated (Hirano and Imbens, 2002).

13
**Table 11: Components** **<u>of IDEB Elementary schools</u>**

||(1)<br>Prova<br>Brasil-Port<br>Elem (SD)|(2)<br>Prova<br>Brasil-Port<br>Elem (SD)|(3)<br>Prova<br>Brasil-Mat<br>Elem (SD)|(4)<br>Prova<br>Brasil-Mat<br>Elem (SD)|(5)<br>Approval<br>rate-Elem|(6)<br>Approval<br>rate-Elem|
|---|---|---|---|---|---|---|
|Single-Shift * Post|0.227<br>[0.058]***|0.134<br> <br>[0.062]**|0.237<br>[0.060]***|0.153<br>[0.065]**|0.038<br>[0.006]***|0.023<br> <br>[0.005]***|
|School of Tomorrow|-0.077|-0.073|-0.019|-0.027|0.01|0.003|
||[0.057]|[0.058]|[0.068]|[0.069]|[0.006]|[0.005]|
|More Education|0.014|0.029|0.029|0.041|0.006|-0.002|
||[0.035]|[0.036]|[0.035]|[0.036]|[0.003]*|[0.003]|
|Mean items in the house||0.032||0.051||0.005|
|||[0.020]||[0.019]***||[0.002]***|
|% Mothers w/ HE||0.376||0.36||0.004|
|||[0.138]***||[0.139]***||[0.010]|
|Participation rate-Prova|||||||
|Brasil Elem||0.000||0.021||0.005|
|||[0.019]||[0.019]||[0.002]***|
|Age-grade distortion||-0.014<br>[0.003]***||-0.012<br>[0.003]***||-0.002<br>[0.000]***|
|Constant|-0.685|-1.006|-1.039|-1.155|0.877|0.932|
||[0.020]***|[0.154]***|[0.019]***|[0.150]***|[0.002]***|[0.013]***|
|Observations|3114|2690|3114|2690|3114|2690|
|Number of schools|519|538|519|538|519|538|
|R-squared|0.67|0.67|0.72|0.65|0.18|0.21|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Restricted Sample<br>Robust standard errors in brac|No<br>kets|No|No|No|No|No|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notes: Prova Brasil Portuguese & Math 5th Grade. Approval rate index from 1st through 5th grades. Years analyzed: 2007, 2009, 2011, 2013 and 2015. Single-Shift includes CES in this model. Standard errors clustered in school level.

30
2 government intervening in the labor market to benefit "insiders" is at all related to the nature of the regime. Are authoritarian regimes or democratic ones more likely to succumb to interest group pressure and use labor policies to give these groups (especially organized labor) special privileges?

In this paper, we use data for ninety developing countries in an attempt to draw general lessons, and to link the probability of governments adopting different policies to the nature of the political regime. The paper is divided into six sections. After this introduction, section 2 presents a general discussion of the possible links between political structure and labor policies. A more rigorous exposition of the underlying analytical model is presented in section 3. Section 4 derives estimating equations and describes the data used, and section 5 presents the results of the empirical tests. Section 6 summarizes our conclusions.

# 2. Political systems and labor policies

The existing literature linking economic growth to political regimes mostly avoids an exact definition of "democratic" and "authoritarian". This is understandable, considering that the two terms are inexact at best when applied to the range of political systems existing in the world today. While realizing that authoritarianism and democracy exist in a continuum (a fact that we explicitly build into our empirical work), our understanding of the terms is, perhaps, closest to the way they have been used by Przeworsky (1991). He notes that: " ... authoritarian regimes abhor _independent_ organizations; they either incorporate them under centralized control or repress them by force" (p. 55, emphasis added). Less authoritarian (and thus more 3 Przcworsky's definition of democracy is pithier: "Democracy isa system in which parties lose elections." (Przeworsky 1991, p. 10). He does go on to discuss how the key feature of democraciei **s** that they are populated by collective organizations (p. 12).
|ML- Governance and Budget Decentralization Technical Assistance|PRagnvald Michel Maellberg|
|---|---|
|ML- Governance and Budget Decentralization Technical Assistance|PRagnvald Michel Maellberg|
|ML- Governance and Budget Decentralization Technical Assistance|PRagnvald Michel Maellberg|
|ML- Governance and Budget Decentralization Technical Assistance|PRagnvald Michel Maellberg|
|Climate Observations and Regional Modelingin Support of Climate|Amal Talbi|
|Climate Observations and Regional Modelingin Support of Climate|Amal Talbi|
|Climate Observations and Regional Modelingin Support of Climate|Amal Talbi|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|China Wuhan Second Urban Transport|Arturo Ardila Gomez|
|Policyand Institutional Reform for Mainstreamingand UpscalingSL|Anupam Joshi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Second Customs Reform and Trade Facilitation Project|Hasan Afzal Zaidi|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
|Kabeli Transmission Project|Rabin Shrestha|
at 0.181, compared with the in-sample value of 0.094. In terms of root mean squared deviation, the household and sub-area models are more accurate than the area-level estimates, which have a root mean squared deviation of 0.198.

Finally, the bottom row shows the correlation of municipality-level poverty with the relative wealth index (RWI) created by Chi et al. (2022) and part of Meta’s Data for Good project.<sup>20</sup> This wealth index is available for 56 different low- and middle-income countries across the globe. The correlation between the RWI and poverty rates is worse than any of the other estimates. However, this is not an indication that the RWI is incorrect; instead, it is an important reminder that wealth does not always correlate well with poverty. Interestingly, however, the correlation between the RWI and poverty is more than 12 percent lower in out-of-sample municipalities, even though the RWI was created with completely different data. In other words, the out-of-sample municipalities may simply be systematically different from in-sample municipalities.

Turning to coverage rates, the household model moderately underestimates mean squared error, which is reflected in estimated coverage of 76.9% in sample and 82.5% out of sample. This underestimate of uncertainty stems from the household model failing to account for uncertainty in the estimated variance of the sample. The sub-area model performs substantially better on coverage, due to its higher MSE estimates. Meanwhile, the coverage rate of the area-level model falls between the household and area-level model for in-sample municipalities and is the lowest for out-of-sample municipalities.

While the coverage rate is lowest for the household model, for in-sample municipalities the rate is only 9 percentage points lower than the Horvitz-Thompson direct survey estimates, and nearly twice as large as the very low coverage rate of the direct estimates when using standard clustered variance estimates. Overall, the results indicate that the household-level model is the most accurate and the most precise of the three estimators for in-sample municipalities and is equally accurate as the other models for out-of-sample municipalities. While coverage rates are moderately underestimated, they remain respectable.

Finally, Figure 3 presents boxplots of the relative bias of the estimators, a statistic not reported in the tables. The relative bias is defined as the difference between the predicted poverty rate and the

> 20 https://dataforgood.facebook.com/

24
erarchy.<sup>30</sup> We can see that the mean and the median regarding number of layers is larger for exporting firms. Apex’s treated firms also have more layers.

|**Total Firms**|**N**|**mean**|**sd**|**p50**|**p10**|**p90**|
|---|---|---|---|---|---|---|
|Size (Employees)|946,455|30.21|291.67|6.00|1.00|41.25|
|Firms’ age|946,581|12.06|9.10|9.91|3.16|23.08|
|Employee’s age|946,455|34.43|7.26|33.65|26.00|43.74|
|Schooling|946,422|8.79|2.06|9.04|5.79|11.04|
|Wage|946,455|885.39|635.49|742.33|503.50|1,386.43|
|Layers|917,765|2.23|1.18|2.00|1.00|4.00|
|Manager exp L1|946,581|0.002|0.04|0.00|0.00|0.00|
|Manager exp L2|946,581|0.002|0.04|0.00|0.00|0.00|
|Manager exp L3|946,581|0.005|0.07|0.00|0.00|0.00|
|Export-spillovers|946,581|26.10|53.91|4.00|0.00|77.00|
|Multi|946,581|0.01|0.08|0.00|0.00|0.00|
|Import Dummy|946,581|0.06|0.23|0.00|0.00|0.00|
|Export Dummy|946,581|0.05|0.22|0.00|0.00|0.00|
|Apex|946,581|0.02|0.14|0.00|0.00|0.00|
|**Exporters**|||||||
|Size (Employees)|47,151|296.67|1,230.53|61.17|6.83|550.08|
|Firms’ age|47,153|21.73|13.93|18.63|6.24|41.91|
|Employee’s age|47,151|34.34|4.55|34.00|29.13|39.89|
|Schooling|47,151|9.61|1.85|9.78|7.14|11.68|
|Wage|47,151|1,698.85|1,324.47|1,326.14|706.51|3,027.30|
|Layers|46,654|4.03|1.20|5.00|2.00|5.00|
|Manager exp L1|47,153|0.02|0.12|0.00|0.00|0.00|
|Manager exp L2|47,153|0.02|0.13|0.00|0.00|0.00|
|Manager exp L3|47,153|0.04|0.19|0.00|0.00|0.00|
|Export-spillovers|47,153|49.19|72.94|19.00|2.00|154.00|
|Multi|47,153|0.11|0.32|0.00|0.00|1.00|
|Import Dummy|47,153|0.60|0.49|1.00|0.00|1.00|
|Apex|47,153|0.17|0.38|0.00|0.00|1.00|
|**Apex’s treated frms**|||||||
|Size (Employees)|18,685|257.62|1,451.88|30.08|3.42|413.58|
|Firms’ age|18,687|18.91|13.29|15.71|4.99|38.08|
|Employee’s age|18,685|33.96|5.06|33.50|28.18|40.12|
|Schooling|18,683|9.28|1.71|9.43|7.06|11.11|
|Wage|18,685|1,163.50|761.54|955.94|568.73|1,975.07|
|Layers|18,531|3.52|1.36|4.00|2.00|5.00|
|Manager exp L1|18,687|0.01|0.10|0.00|0.00|0.00|
|Manager exp L2|18,687|0.01|0.10|0.00|0.00|0.00|
|Manager exp L3|18,687|0.02|0.14|0.00|0.00|0.00|
|Export-spillovers|18,687|32.10|52.80|10.00|0.00|103.00|
|Multi|18,687|0.02|0.15|0.00|0.00|0.00|
|Import Dummy|18,687|0.34|0.48|0.00|0.00|1.00|
|Export Dummy|18,687|0.44|0.50|0.00|0.00|1.00|

Table 3: Characteristics of manufacturing, exporting and Apex’s treated firms (2007-2010) In addition, as emphasized by Koenig (2009) there might be geographically driven shocks that affect export performance, such as neighbourhood-effect, spillovers and other externalities (think

> 30In order to build this index and the management’s dummies, I used RAIS at employee level (which provides yearly information for about 8 millions of employees on manufacturing sector - 7,756 millions in 2010) and first, counted the number of employees according to their CBOs. Then I generated a dummy variable by firm-layer and summed them up for building a proxy for the level of hierarchy of each firm, which is presented in the variable “layer.” For further details see Cruz et al. (2013).

11
# **I Introduction**

Intergenerational mobility (IGM) has gained interest in the economic literature among other things thanks to its importance for equity, social cohesion, and economic growth. Its observed correlation with income inequality, commonly named “the Great Gatsby Curve”, has contributed to the desire for understanding IGM given the documented rise of inequality over the last decades in rich countries (see Corak, 2013).

In the case of the developing economies, the Latin America and the Caribbean (LAC) region is of particular interest because of its historically documented high levels of income inequality relative to other regions of the world.<sup>1</sup> However, the scarcity of high-quality data (e.g. long panel data sets or tax records with linked generations) has limited the study of IGM in income. These constraints are also common in other regions (e.g., Africa), so efforts to document IGM on a global scale have taken an alternative path given by the measurement of mobility in education. These measures are of interest in and of themselves, but they are also a proxy for economic status given the close relationship between education and income.

Recent studies have used household and public opinion surveys with retrospective information about parents’ education to document the levels of IGM in education in LAC at the country level (for an example, see Hertz et al., 2007; Narayan et al., 2018; Neidh ̈ofer, Serrano, & Gasparini, 2018). However, analyzing IGM at a more geographically disaggregated level, as argued in Narayan et al. (2018), is valuable because it can help researchers understand the importance of localized patterns and drivers of IGM, as shown for the case of developed countries. Along these lines, Chetty, Hendren, Kline, and Saez (2014) state that the United States can be better described as a collection of societies, some of which are “lands of opportunity” with high rates of mobility across generations, and others in which only a few children escape poverty. Thus far, this type of analysis has not been conducted in LAC countries as a whole due to the inadequacy of most survey data for this purpose. This paper fills that gap in the literature by generating estimates of IGM in education at smaller

> 1See for example Messina and Silva (2019) for an analysis of wage inequality over the last two decades.

2
Policy Research Working Paper 9448

# **Abstract**

The water supply and sanitation sector remains heavily subsidized around the world. Yet, the accounting of water supply and sanitation subsidies globally has proved challenging due to utility-level data limitations and their often implicit nature. This paper develops a methodology to estimate water supply and sanitation subsidies that is adaptable to data scarce environments, while accounting for differences among service providers such as population served (to account for economies of scale), coverage of water and sanitation services individually, and their level of operational efficiency in terms of water losses and staffing. This methodology is based on Chile’s _empresa modelo_ (model firm) approach to cost-reflective tariff estimation and uses utility-level data from the World Bank’s International Benchmarking Network for Water and Sanitation Utilities database. The results suggest that the cost of subsidies associated with the operations, maintenance, and major repair and replacement of existing water supply and sanitation infrastructure in much of the world (excluding, notably, China and India) is an estimated $289 billion to $353 billion per year, or 0.46 to 0.56 percent of the countries’ combined gross domestic product. This figure rises, shockingly, to 1.59 to 1.95 percent if only low- and middle-income economies are considered, an amount largely due to the capital subsidies captured in the estimation. Subsidies of operating costs account for approximately 22 percent of the total subsidy amount in the full sample and for low-income economies separately. Annual subsidy amounts by region range from 0.05 to 2.40 percent of gross domestic product, and low-income economies are generally at the high end of this range. The estimations do not include capital expenditure for infrastructure expansion— which tends to be fully subsidized—or environmental costs. Therefore, the actual global magnitude of networked water supply and sanitation subsidies is much greater than the estimation.

This paper is a product of the Water Global Practice. 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 Landres@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
Figure 7: Forecasts by groups of respondents <!-- Start of picture text --> Pooled sample Sanctions arm Procedure denial arm Tax morale arm Pooled sample Sanctions arm Procedure d enial arm Tax morale arm<br>(a) Filing probability (b) Declared gross income<br>Pooled sample Sanctions arm Procedure denial arm Tax morale arm<br>(c) Declared taxable income<br>AcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workers AcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workers<br>AcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workers<br>15 30<br>10 20<br>5 10<br>0 0<br>p.p. change in filing probability<br>% change in declared gross income<br>-5 -10<br>20<br>15<br>10<br>5<br>% change in declared taxable income 0<br><!-- End of picture text --> _Note:_ This figure presents means and 95% confidence-intervals for predictions of treatment effect-size on each of the primary outcomes. See Appendix E for details on sample restrictions.

36
19 a major role. The quantitative effect of this factor, between 1983-91, was evidently to raise domestic interest rates by about 6 percentage points above what would have been predicted otherwise.

34. Policy Conclusions. Some countries, such as Indonesia, evidently face high "country-risk' perceptions in international financial markets, raising their domestic interest rates well above world market rates. It is evident that a reduction in domestic interest rates in Indonesia would benefit from efforts to strengthen the prudential regulatory framework, both in the financial and the real sectors of the economy. Their relative absence, or more accurately, perceptions of their relative absence, carries high costs. In the broader context of this paper, it is also evident that real interest parity holds strongly in a diverse sample of both industrial and developing countries. Consequently, the pursuit of independent domestic interest rate policies are increasingly limited. In industrial countries, this appears to be particularly the case. Policies that seek to ease domestic credit policies or to reduce domestic interest rates directly (through central

**Table 3: Determinants of Interest Rates in Indonesia, 1983-92**

|Indep.<br>Variable:<br>Interest<br>Rates)|World<br> <br>Int. Rate<br>(a; b)|Cur. A/c<br>Balance<br>(z)|C. Bank<br>Dis. Rate<br>(Y)|Domestic<br>Credit Policy<br>(x)|Share of<br>Private Bank<br>in Assets<br>(m)|**R2**<br>|
|---|---|---|---|---|---|---|
|**(a) Reai**|**Domestic Inte**<br>0.37<br>(1.82)*|**rest Rates:**<br>0.24<br>(0.87)|0.87<br>(5.17)***|**-0.04**<br>**(-1.84)**|**0.24**<br>(4.37)***|**0.99**|
|**(b) Nom**|**inal Domestic I**|**nterest Rates:**|||||
||0.42<br>(2.52)*|-0.01<br>(-0.03)|0.78<br>**(5.64)*****|**-0.04**<br>(-2.06)*|0.22<br>**(6.47)*****|0.99|

Note: The values reported in parentheses are t-values, *** denotes **significance at the 1%** level or better; * **denotes** significance at **the** **_15%_ level or better; R2 is the** coefficient of determination adjusted for degree of freedom.