rafmacalaba/datause-encoder-data huggingface dataset:
Because stipends are given to all girls attending public middle school grades (despite the fact the target districts in the stipend program have the lowest literacy rates), it may be of general interest to look at equity implication of these conditional cash transfers. As seen in table 14, the school participation rates for these 10-14 year old girls vary significantly across per capita household consumption quintiles. More importantly, there was a large improvement in this indicator in stipend districts between 2002 and 2005, particularly for poorer quintiles. For instance, participation rate among 10-14 year old girls in the poorest quintile rose impressively from a 10% to 37% in that period. Regarding impact heterogeneity across welfare quintiles, the estimates indicate that the net program effects were stronger for the bottom two (poorest 40%) and the highest (richest 20%) quintiles, suggesting some kind of a U-shaped effect where stipends may have had a real effect on the poorer quintiles and the impact on the richest quintile is just picking up its disproportionate share and growth in private school attendance.

In terms of benefit incidence, we also see a noteworthy change from pre-stipend period to poststipend period. Aggregating all public middle school grade girl enrollments in the stipend program districts, PIHS 2001/02 and PSLM 2004/05 indicate that the current stipend transfers are neither pro-poor nor pro-rich. 50% of all stipends (and all other public resources in those public schools) go to girls from the poorest 50% of the population and the rest to the richest 50% (see table 15)<sup>23</sup> . Prior to the stipend program, the poorest 50% of girls were receiving only 38% of the government subsidy.

# **7. Conclusions**

This study evaluates the impact of a stipend program on female enrollments in government middle schools in the Punjab province of Pakistan. It makes use of a panel dataset of schools constructed from the provincial school census for 2003 (before the program) and 2005 (after the program). Because stipends for enrollees in grades 6-8 are targeted to girls in the 15 lowest literacy districts (out of 34) in the province, any simple naïve impact estimator (such as female enrollment grew by some y percent) is likely to suffer from a substantial program placement bias. The study considers estimates obtained using various empirical approaches including doubledifferencing (DD) and triple-differencing (DDD) in combination with regression-discontinuity design (RDD) and controlling for covariates. The results, not surprisingly, vary with

> 23 Sample size does not allow dis-aggregation by quintiles.

22
Table 1. Skill Content by Occupational Groups

|||Non-routine||<br>Ro|<br>utine|
|---|---|---|---|---|---|
|||||||
||Analytical|nerper-<br>sonal|Manual|oune<br>manual|oune<br>cognitive|
|Administrative and managerial workers|0.80|1.11|-1.24|-0.63|-1.31|
|Professionals and technicians|1.45|1.29|-1.15|0.33|-0.96|
|Clerical workers|0.27|0.08|-0.10|-1.69|-0.04|
|Sales and services workers|-0.83|-0.92|0.96|0.78|1.36|
|Agriculture, animal husbandry, forestry workers, fishermen, and hunters|-0.62|-0.71|0.87|0.99|0.61|
|Production workers, transport and equipment operators, and laborers|-1.06|-0.85|0.65|0.22|0.34|

Source: Authors’ calculations based on the 2015-2016 Philippines STEP survey. Notes: The skills measures are constructed based on Acemoglu and Autor (2011) methodology described in the Appendix.

Table 2. Summary Statistics on ICT Use, Skill Measures, Aggregate Labor Market Indicators, and Province and Industry Characteristics

||20|05|20|10|2|011|2|015|
|---|---|---|---|---|---|---|---|---|
||Mean|Std. Dev|Mean|Std. Dev|Mean|Std. Dev|Mean|Std. Dev|
|Panel A: ICT use|||||||||
|Cellphone|20.31|12.13|71.56|12.78|75.69|13.46|85.96|9.86|
|Computer|3.45|2.83|11.22|5.05|12.15|5.93|19.85|6.40|
|Internet access|3.22|2.81|22.61|8.65|25.08|10.14|33.33|11.13|
|Panel B: Skill measures|||||||||
|Non-routine analytical|-0.83|0.30|-0.77|0.35|-0.76|0.36|-0.77|0.34|
|Non-routine interpersonal|-0.73|0.24|-0.68|0.31|-0.66|0.32|-0.67|0.30|
|Routine manual|0.31|0.45|0.29|0.43|0.30|0.42|0.29|0.42|
|Routine cognitive|0.42|0.27|0.39|0.32|0.38|0.33|0.38|0.30|
|Non-routine manual|0.64|0.26|0.58|0.31|0.57|0.32|0.58|0.31|
|Panel C: Aggregate labor ma|rket indica|tors|||||||
|LFP rate|65.85|6.07|67.37|4.29|65.64|3.98|66.19|4.15|
|Employment rate|62.05|6.94|63.97|5.16|62.99|4.52|63.00|4.81|
|Unemployment rate|5.92|2.72|5.13|2.22|4.09|1.64|4.90|1.65|
|Panel D: Provinces character|istics||||||||
|Age group 15-24|28.76|2.99|25.54|2.50|26.31|2.45|24.68|2.51|
|Age group 25-49|52.84|3.36|54.07|3.00|53.37|2.99|53.05|3.75|
|Age group 50-60+|18.40|4.07|20.39|4.53|20.32|4.34|22.27|4.36|
|Urban population|38.88|16.17|38.26|17.90|43.14|18.31|45.94|18.47|
|Panel E: Industries characteri|stics||||||||
|Low-educated workers|64.20|33.09|59.46|31.65|59.94|32.03|56.20|31.57|
|High-educated workers|35.80|33.09|40.54|31.65|40.06|32.03|43.80|31.57|
|Female workers|31.96|31.12|33.62|30.43|30.85|27.17|30.01|27.27|
|Male workers|68.04|31.12|66.38|30.43|69.15|27.17|69.99|27.27|

Source: Authors’ calculations based on Susenas 2005-2015, Sakernas 2005-2015, and 2015-2016 Philippines STEP survey.

23
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE between 1998 and 2006, a level comparable with the average in resource-rich countries. However, high and increasing reliance on wells and boreholes does not always guarantee safe water quality, as many wells and boreholes are unprotected and, hence, do not provide safe water.

**Table 9. Cameroon’s water and sanitation indicators benchmarked**

||Unit|Resource-<br>rich<br>countries||Cameroon||Middle-<br>income<br>countries|
|---|---|---|---|---|---|---|
|||2005|1991|1998|2006|2005|
|Access to piped water|% pop|13|13|17|20|61|
|Access to stand posts|% pop|12|24|31|23|22|
|Access to wells/boreholes|% pop|47|27|23|47|5|
|Access to surface water|% pop|27|37|32|9|11|
|Access to septic tanks|% pop|13|6|9|12|48|
|Access to improved latrines|% pop|37|53|48|46|34|
|Access to traditional latrines|% pop|22|27|33|35|7|
|Open defecation|% pop|28|14|9|7|11|
|||||2005|||
|Domestic water consumption|liter/capita/day|115||—||196|
|Revenue collection|% sales|60||—||99|
|Distribution losses|% production|40||37||29|
|Cost recovery|% total costs|67||56||86|
|Operating cost recovery|% operating costs|94||79||121|
|Labor productivity|connections per employee|96||—||203|
|||**Cameroon**<br>(2009)|Co|untries with non-<br>scarce water<br>resources|Other<br>re|developing<br>gions|
|Average effective tariff|US$ per m3||0.8|0.6||0.03–0.6|

_Source_ : AICD water supply and sanitation database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note_ : Access figures from Demographic and Health Surveys (1991 and 1998) and Multiple Indicators Cluster Survey (2006) as reported by WHO 2010a and 2010b. A country is considered to have non-scarce water resources if renewable internal freshwater resources per capita are greater than 3,000 mm.

— = data not available.

Open defecation has also declined, if slowly, with the expansion of traditional latrines (figure 15b). The practice declined from 9 percent to 7 percent between 1998 and 2006, both figures being only about a quarter of the typical level of open defecation in resource-rich countries (table 9). Over this period, use of traditional latrines increased from 33 percent of the population in 1998 to 35 percent in 2006. But because the overall level of access to improved sanitation in 2006 remained just 47 percent, the country was far behind the goal of achieving the Millennium Development Goal of 74 percent of population with access to improved sanitation (AMCOW 2010b).

26
institutions.<sup>6</sup> To avoid results being biased by omitted variables, we run the following panel fixed-effect estimation: INFit = α+ βFDit + γXit + ci + yt + εit (1) where INFit is the extent of informality in country _i_ and year _t_ , FDit captures the level of banking sector development, and ci and yt control for country fixed effects and year fixed effects. The estimation includes a range of controls (Xit) that can influence both informality and financial development. In particular, these are real GDP per capita (in constant 2010 U.S. dollars, in logarithms) and indicators of institutional quality (control of corruption, regulatory quality, and the rule of law). Data on real GDP per capita are drawn from the World Bank’s WDI. The indicators of institutional quality are obtained from the World Bank’s _Worldwide Governance Indicators_ , with higher values corresponding to better institutional quality.

As shown in Table 3, greater banking sector development is associated with less output informality in a statistically significant manner, even when controlling for other aspects of underdevelopment (such as poor institutions and low per capita incomes). This relationship remains statistically significant when using lagged financial development.

While this establishes a correlation between financial development and informality, it does not establish causality. As discussed in the introduction, several studies argue for causality running in either direction. To establish causality, we next turn to an instrumental variable approach.

# **3. Causal link from financial development to informality**

We conduct the two-stage least squares estimation with a novel instrumental variable that has not yet been used in the literature on informality but that captures a well- established, quantitatively important driver of domestic financial development: financial development in other (neighboring) countries.7 This approach provides us with a time- variant external instrumental variable to take advantage of our panel setting (Madsen, Islam, and Doucouliagos 2018; Davidson and MacKinnon 2006).<sup>8</sup> In the next subsection, we

> 6 See Ohnsorge, Okawa, and Yu (2021), Medina and Schneider (2018), and Ulyssea (2020) for recent reviews on determinants of informality. See Fergusson (2006) and Voghouei, Azali, and Jamali (2011) for reviews of factors contributing to financial development. Both informality and financial development are associated with institutional quality and economic development (typically captured by per capita incomes).

7 Distance-discounted financial development in foreign countries was used as an instrumental variable (IV) for domestic financial development in studies such as De Haan et al. (2021) and Pleninger and Sturm (2020) in different contexts, but our study is the first to use it to establish a causal link between financial development and informality 8 As suggested by Madsen, Islam, and Doucouliagos (2018) and Davidson and MacKinnon (2006), the benefits of using long panel data are that the fixed effects estimator becomes more consistent 7
# **Table S11:** Attrition Analysis for Primary Round 4 Outcomes <u>(Baseline Schoolgirls: CCT vs. UCT)</u>

||Depen|dent Variable<br>Sch|L<br>: =1 if Unco<br>oolgirl|U<br>nditional|
|---|---|---|---|---|
|_Outcomes_|Original|IPW|ower<br>Bound|pper<br>Bound|
||(1)|(2)|(3)|(4)|
|Highest Grade Completed|-0.021|-0.020|-0.005|-0.027|
||(0.129)|(0.129)|(0.127)|(0.129)|
|Competencies Score (Standardized)|0.029|0.032|0.039|0.015|
||(0.072)|(0.073)|(0.070)|(0.072)|
|=1 if Ever Married|0.011|0.009|0.013|0.006|
||(0.049)|(0.050)|(0.049)|(0.049)|
|Age at First Marriage|0.536**|0.527**|0.536**|0.527**|
||(0.220)|(0.223)|(0.220)|(0.221)|
|=1 if Ever Pregnant|0.010|0.010|0.013|0.006|
||(0.046)|(0.047)|(0.046)|(0.047)|
|Number of Live Births|-0.053|-0.052|-0.051|-0.061|
||(0.053)|(0.054)|(0.054)|(0.053)|
|Age at First Birth|0.088|0.086|0.088|0.042|
||(0.186)|(0.189)|(0.186)|(0.176)|
|= if HIV Positive|0.003|0.003|0.004|-0.006|
||(0.029)|(0.029)|(0.029)|(0.028)|
|=1 if Anemic|-0.074*|-0.075*|-0.072*|-0.086**|
||(0.042)|(0.041)|(0.041)|(0.042)|
|Opportunity Cost of Time|-0.040|-0.035|-0.037|-0.119|
||(0.106)|(0.103)|(0.107)|(0.095)|
|Typical Daily Wage in Last Three|0.079|0.077|0.080|-0.058|
|Months|(0.108)|(0.108)|(0.109)|(0.046)|
|Proportion of Hours Spent in Self-|-0.003|-0.003|-0.003|-0.013*|
|<br>Employment or Paid Work in Past|(0.009)|(0.010)|(0.009)|(0.007)|
|Super Index of Overall|-0.221**|-0.214**|-0.206*|-0.243**|
|Empowerment (Standardized)|(0.106)|(0.107)|(0.108)|(0.104)|
|<br>Change in Subjective Wellbeing|-0.134|-0.134|-0.073|-0.203|
|_Notes:_Regressions are OLS models wi<br>All regressions are weighted to make th<br>study EAs. Baseline values of the follow<br>regression analyses: age indicators, stra<br>attended, an indicator for never had sex,<br>phase of the development of the testing<br>who were surveyed during the latest hou<br>program (Round 4). Parameter estimate<br>(**), and 90% (*) confidence.<br>from Five Years Ago to Today|(0.221)<br>th robust sta<br>em represent<br>ing variables<br>tum indicato<br>and whether<br>instruments.<br>sehold surve<br>s statistically|(0.221)<br>ndard errors c<br>ative of the t<br>are included<br>rs, household<br>the responde<br>We restrict t<br>y conducted t<br>different tha|(0.202)<br>lustered at th<br>arget populati<br>as controls i<br>asset index,<br>nt participate<br>he sample to<br>wo years afte<br>n zero at 99%|(0.219)<br>e EA level.<br>on in the<br>n the<br>highest grade<br>d in the pilot<br>respondents<br>r the<br>(***), 95%|

91
Table 1 offers a partial list of _some recent surveys_ done within the Bank. The table reveals four main types of surveys: (1) Comprehensive surveys (RPED, ICS); (2) Surveys on transition (Russia: Economic and Civil Society; Corporate Governance in Poland, Hungary, and Czech Republic; Large Scale Privatization in Mongolia; The Emergence of Private Sector Manufacturing in Poland, in Hungary; Industrial Enterprises and Adjustment in Russia; Private Service Firms in Russia-St. Petersburg.); (3) Surveys of the market environment, including regulation and govemment-industry relations (World Business Environment Survey); and (4) Topic-specific surveys, such as on training (Enterprise Training in Developing Countries, Tan and Batra), or on SME problems (CECPS, "Small-and-Medium Industry Impact Evaluation," 1995; and "Enterprise training in developing countries," Tan and Batra).

How well do World Bank surveys meet the data needs outlined above? We begin to address that question below. One lesson from the Bank experience is that surveys are extremely difficult. Thus, while we make the comments below with the ideal data set in mind, we recognize that in practice surveys may not be able to meet this ideal. Longitudinal surveys are costly and take too long for many Bank operations. Some data, meanwhile, simply do not exist. Nonetheless, it is instructive to consider what would have made these surveys more useful, and use that knowledge when designing future surveys.

**1. Data consistency:** Although World Bank surveys are usually designed to address a particular issue, each could be more broadly useful by consistently including a small set of standard questions on firm performance, such as total output and profitability. The survey should also collect price data on the firm's main products, inputs, and investment goods. In addition, the survey becomes more valuable when it contains enough information to estimate production functions, including value added, number of employees, capital stock, and material inputs. This information would help to isolate the contribution to productivity changes of various production, technological, and institutional factors.<sup>5</sup> Only a few surveys, such as RPED and ICS, include consistent productivity measures.

5Consider the Bank's general interest in Small and Medium-sized Enterprises (SMEs). To understand if SMEs are as efficient as other firms, we must hold constant capital and labor and then compare productivity to that of different types of firms.

5
The implications of the above are that, in estimating the impact of regulatory governance variables on outcomes, we concentrate on:

- (i) Electricity capacity levels in developing countries, excluding transition economies as well as OECD countries;  and

- (ii) Efficiency measures in developing countries, insofar as they are available.

We discuss the precise statistical measures of these in the next section. However, the key point to note here is that we have chosen our sample so that it includes only countries where there is good reason to believe that there are significant amounts of unsatisfied electricity demand because of capacity constraints.

.

# **2.3 Results from Studies of Regulation on Developing Country Telecommunications Outcomes**

The approach outlined above is echoed in a rapidly growing literature on the impact of regulation on telecom outcomes.

The main empirical papers in this area (e.g. Fink, Mattoo and Rathindran (2003), Wallsten (2002) and Gutierrez (2003)) estimate the effects of regulation on

- (i) mainline penetration rates (a standard measure of capacity) and

- (b) efficiency (e.g. mainlines per employee).

They typically estimate panel data models (primarily fixed effects models) with one or other of the outcome measures as the dependent variable, and include regulatory variables as independent variables along with competition and privatisation variables, as well as standard control variables. We follow this approach in estimating the impact of regulation on electricity industry outcomes.

The standard model estimated in these papers (e.g. by Gutierrez) is Yit = Xitβ + Ditδ + αi + εit, i =1, ..., N; j = 1, ..., T (Equ 1) where X is a vector of exogenous variables, D is a vector of dummy variables, αi is a country specific fixed effect and εit, is an error model.

The X vector includes both regulatory variables and standard control variables The approach of Gutierrez (2003) is particularly relevant to this paper. He constructs a _regulatory governance index_ for his sample of 22 Latin American and Caribbean countries. This 7-element index (derived from the Stern-Holder typology) is 7
poverty rates for this group of households are overestimated and consequently are overestimated in all urban areas; and iii) inequality was _overestimated_ when the unadjusted welfare aggregate is used, but the national‐level inequality is adjusted downwards only slightly due to the counteracting effects of lower within‐region inequality and higher between‐region inequality.

# **5. Conclusions**

In this paper, we aim to shed light on the implications of rental regulation in the measurement of welfare and related indicators. The case of Egypt is well‐suited to study this phenomenon. Having started their rental regulation since World War I, a third of all dwellings in Egypt are estimated to be under a strict form of rent control. This control has led to a dual rental market where tenants of the Old‐law and the new‐law coexist and many times actually end up living very close to each other. Nonetheless, tenants with rental contracts under the 1977 law pay substantially lower rents than those under the New‐law. The differences in rents paid are not explained by dwelling characteristics, and there is no strong evidence of important differences in location either.

Failure to account properly for the differential in rental prices of different households creates an inconsistency in the approach to measure welfare. If two households (of the same size) are enjoying the same type of dwelling with similar amenities, the valuation of housing services should be the same. Thus, using the rent paid as collected by the household survey at face value for welfare valuation is incorrect. The study uses a hedonic model approach and illustrates how not correcting for the implications of the rental regulation leads to an underestimation of welfare for a sector of the population, the overestimation of the overall poverty rate and inequality (as measured by the Gini coefficient). This last finding becomes of particular significance at a time when understanding inequality and its role in recent social movements and political shifts is ever important. Further research is warranted to understand how the interplay of rental regulation and other better‐known factors affects inequality estimates.

15
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- [16] Besley, Timothy, Thorsten Persson, and Daniel Sturm (2010). “Political Competition and Economic Performance: Evidenced from the United States,” _Review of Economic Studies_ , 77(3), 1329-52.

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- [18] Bueno de Mesquita, Bruce and Alastair Smith (2008). “Political Survival and Endogenous Institutional Change.” _Comparative Political Studies_ , 42, 167–197.

- [19] Bueno de Mesquita, B. and Smith, A. (2010). “Leader Survival, Revolutions, and the Nature of Government Finance.” _American Journal of Political Science_ , 54(4), 936–950.

- [20] Bueno de Mesquita, Bruce, Alastair Smith, Randolph M. Siverson, and James D. Morrow (2003). _The Logic of Political Survival_ . Cambridge: The MIT Press.

- [21] Coale, A. J. (1981). “Population Trends, Population Policy and Population Studies in China.” _Population and Development Review_ , 7, 85-97.

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54
<!-- Start of picture text --> PPP Investment PPP Investment<br>US$ Billions as percent of<br>(US 2015 $) GDP<br>$160 1.6%<br>$120 1.2%<br>Restof EMDE<br>—— Investment as % ofGDP<br>(tight scale)<br>$80 0.8%<br>SSA Investment<br>as % of GDP.<br>(right scale)<br>$40 0.4%<br>$F~»~ KEKE© © » FRESA 2 FFP> Y ESP5 FRESEiS s S- ES© » ESE»-<br>WM sub-Saharan Africa Rest of EMDE<br>Source: World Bank PPI Database — 7 Feb 2017<br>*Note: % of GDP for rest of EMDE uses GDP for all countries except Sub-Saharan Africa<br>Data excludes telecom, divestitures, merchants, and management and lease contracts.<br><!-- End of picture text -->