rafmacalaba/datause-encoder-data huggingface dataset:
**Appendix Table 5. Effect of preschool availability on female’s earnings and work hours**

||(1)<br>Salary|(2)<br>Net profit|(3)<br>Income|(4)<br>Work hours|
|---|---|---|---|---|
|**Panel A: Public**|||||
|Public * Eligible|0.001|0.199|0.040|0.001|
||(0.120)|(0.232)|(0.126)|(0.034)|
|Public Preschools|0.046|-0.087|-0.017|0.007|
||(0.079)|(0.169)|(0.078)|(0.038)|
|Eligible Child|0.029|-0.017|0.005|-0.029**|
||(0.029)|(0.044)|(0.030)|(0.013)|
|FDR q-value|0.993|0.993|0.993|0.993|
|**Panel B: Private**|||||
|Private * Eligible|-0.000|-0.002|-0.008|-0.005|
||(0.008)|(0.011)|(0.008)|(0.003)|
|Private Preschools|-0.014|-0.018|-0.016*|-0.000|
||(0.012)|(0.017)|(0.009)|(0.004)|
|Eligible Child|0.029|0.014|0.042|-0.008|
||(0.043)|(0.062)|(0.040)|(0.018)|
|FDR q-value|0.987|0.987|0.987|0.434|
|Observations|20,193|17,804|37,523|48,537|
|Mean|442.201|467.009|457.837|37.890|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dependent variable indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Salary, net profit, income are per month and adjusted for inflation using national consumer price index (CPI) with 2010 base year obtained from FRED. Income is defined as the sum of salary and net profit. Work hours are per week. We apply log transformation to all dependent variables so that estimates can be interpreted as percentage changes; zero values are imputed with log(0.1). Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 4 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. Means are reported in nominal terms. Salary, net profit, and income are reported in IDR 10,000 increments and are adjusted for inflation using national CPI with 2010 base year (FRED). The exchange rate in 2010 was 1 USD for 9,090 IDR (FRED). Earnings and work hours are asked pertaining to survey years and historical employment in round 1-3 but are limited to survey years in round 4-5.

73
# **1 Introduction**

The mining sector in Africa is growing rapidly and is the main recipient of foreign direct investment (World Bank 2011). The welfare effects of this sector are not well understood, although a literature has recently developed around this question. The main contribution of this paper is to shed light on the welfare effects of gold mining in a detailed, in-depth country study of Ghana, a country with a long tradition of gold mining and a recent, large expansion in capitalintensive and industrial-scale production.

A second contribution of this paper is to show the importance of decomposing the effects with respect to distance from the mines. Given the spatial heterogeneity of the results, we explore the effects in an individual-level, difference-in-differences analysis by using spatial lag models to allow for nonlinear effects with distance from mine. We also allow for spillovers across districts, in a district-level analysis. We use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.

The paper contributes to the growing literature on the local effects of mining. Much of the academic interest in natural resources is focused on country-wide effects, and this research discusses whether the discovery of natural resources is a blessing or a curse to the national economy. Natural resource dependence at the national level has been linked to worsening economic and political outcomes, such as weaker institutions, and more corruption and conflict (see Frankel 2012 and van der Ploeg 2011 for an overview). While all these effects can have household-level implications, fewer analyses have, thus far, analyzed the geographic dispersion of such impacts. A recent literature on the local and subnational effects of natural resources contributes to the understanding of such effects (for example Aragón and Rud 2013, 2015; Axbard et al., 2016; Benshaul-Tolonen 2018, 2019; Caselli and Michaels 2013; Corno and de Walque 2012; Fafchamps et al. 2016; Kotsadam and Tolonen 2016; Loyaza et al 2013; Michaels 2011; von der Goltz and Barnwal 2019; Wilson 2012). A growing number of papers explore the mining industry, in particular, see Aragón, Chuhan-Pole, and Land (2015) for an overview. We contribute to this literature by showing the importance of analyzing local level effects in addition to district level effects in a one-country case study.

Aragón and Rud (2013) provided the seminal work exploring the economic effects of one very large mine in Peru. They find that the expansion of the mine had poverty-reducing effects, but 2
Governance Of Public Pension<sup>Funds:Lessons FromCorporate Governance And</sup> International Evidence further burden on the system.<sup>The loansfurthermoreare indexedto inflation,and as they</sup> increase in size government<sup>delaysin the paymentof interest subsidiesto theSSNIT</sup> again increase the overall<sup>burden on the fund.</sup> This paper's survey of public<sup>pension fundsaround the world revealedthat the use of</sup> restrictions and mandates<sup>is widespread.Themost common restrictionis on foreign</sup> investments, with 57 percent<sup>of the surveyed fundsfacing prohibitionon investment</sup> abroad. Other restrictions<sup>include prohibitions on equities (14percent)and loans(19</sup> percent) (see Figure 1).

Figure 1. ]Investment restrictions<sup>(%of fundssurveyed)</sup> <!-- Start of picture text --> Any<br>Restriction<br>Loans<br>Equities<br>-<br>Foreign<br>- ' I' -l -- 1 I- - | -<br>0 10 20 30 40 50 60<br><!-- End of picture text -->

mandates<sup>alsoare common, with 60 percentof thefunds operating</sup> Explicit investment under at least one type of<sup>mandate.Thesemandates include requirementsto invest in</sup> government bonds (including<sup>national,state, provincial,and municipal bonds) (48</sup> percent), in social projects<sup>suchas housing(24 percent),and ingeneral economic</sup> development obligations<sup>(32percent).The use of restrictionsand mandatesfurthermore</sup> may be more widespread<sup>than thesefiguresindicate,as trusteesmay self-impose these</sup> investment practices on<sup>the fund in theabsence of explicit requirements.</sup>

- 12 -
distribution mean and one standard deviation; (c) high-standard-performance teachers, which are those with scores between the mean and one standard deviation above the mean; and (d) highperformance teachers, which are those with scores above one standard deviation above the mean. Based on these definitions, 6.28 percent of all teachers in Ecuador are classified as being in the low-performance group, 44.45 percent are classified as being in the low-standard-performance group, 38.26 percent are classified as being in the high-standard-performance group, and 11.01 percent are classified as being in the high-performance group.

Assessment results by knowledge topic indicate that a large majority of teachers (50 to 70 percent) underperform in mathematics, Basic General Education, and natural sciences (BGE level), and in mathematics, physics, and biology (UGB level). Overall teacher performance is generally better in subjects that pertain to social sciences and literature (table 1).

**<u>Table 1. Knowledge Areas Included in the</u>** **_<u>Ser Maestro</u>_** **<u>Assessment, by Education Cycle</u>**

|||P<br>Standard|erformance<br>Standard|||% Teachers|
|---|---|---|---|---|---|---|
||Low<br>(%)|<br>Low<br>(%)|<br>High<br>(%)|High<br>(%)|Total<br>Sample|Evaluated<br>in the<br>Subject|
|**General Subjects (all levels)**|||||||
|Management|6.67|39.28|33.78|20.27|2.235|2.38|
|Computers|4.84|38.99|42.44|13.73|3.655|3.89|
|Philosophical thinking|5.74|44.93|29.39|19.93|296|0.32|
|Artistic education|6.02|38.44|38.35|17.20|1.163|1.24|
|Special education|6.8|37.62|45.78|9.82|723|0.77|
|Physical education|6.48|33.72|44.25|15.55|4.102|4.37|
|Early education|4.44|45.61|41.66|8.30|14.281|15.20|
|Citizenship education|6.17|42.08|34.95|16.81|827|0.88|
|**Basic General Education (BGE)**|||||||
|Basic general education (grades 2o–7o)|4.71|47.33|39.02|8.94|41.993|44.69|
|Natural sciences|8.64|47.94|30.38|13.03|5.056|5.38|
|Social studies|6.80|34.62|42.69|15.88|4.659|4.96|
|Language and literature (grades 8o–10o)|5.15|27.38|41.89|25.58|2.991|3.18|
|Mathematics (grades 8o–10o)|13.97|55.52|23.88|6.64|3.208|3.41|
|**Unified General Baccalaureate (UGB)**|||||||
|Physics|21.67|51.71|23.38|3.23|526|0.56|
|History and social sciences|18.10|40.10|31.36|10.43|1.591|1.69|
|Language and literature|9.01|36.50|37.66|16.83|2.775|2.95|
|Biology|6.66|47.10|39.33|6.91|811|0.86|
|Mathematics|16.91|56.49|22.28|4.32|2.271|2.42|
|Chemistry|7.81|33.25|38.79|20.15|794|0.85|
|Total<br>_Source:_Author using_Ser Maestro_2016 data.|6.28|44.45|38.26|11.01|93.957|100.00|

_<mark>Source:</mark>_ <mark>Author using</mark> _<mark>Ser Maestro</mark>_ <mark>2016 data.</mark>
market, and (e) the deal environment.<sup>12</sup> Why and how does the business environment matter for firm performance?

Good _infrastructure_ (e.g., roads and power) would reduce transaction, logistics and production costs for firms. For instance, in the case of power, a reliable supply of power would reduce or eliminate the need for individual firms to produce power in house, thereby realizing the economy of scale associated with power production. It also reduces the amount of capital needed for starting a new firm.

The level of _skills_ in a locality may matter for productivity of local firms for several reasons. It makes hiring qualified staff members easier and, therefore, reduces or eliminates the skill bottlenecks for local firms. Moreover, human capital externality at the local level may increase the productivity of individual firms (Lucas 1988), and may also encourage entrepreneurship and innovations. Finally, if product quality is determined by the probability of not making mistakes in each task (the O-ring theory), the level of production efficiency would depend on the distribution of skills for all staffs (Kremer 1993).

We also include the ease with which firms can obtain short-term bank loans, our measure of access to _finance_ , to be part of the determinants of firm productivity; following a vast amount of literature linking financial development to growth (see Levine [1997] for a summary). After all, the development of financial infrastructure allows firms to expand without solely relying on internal saving, reducing transaction costs between firms, and increasing the scope for specialization of firms.

_Labor inflexibility_ likely would reduce productivity (see Xu forthcoming for a summary of recent firm-level evidence). It increases the costs of adjusting a firm’s scale of operation. When hit by adverse demand shocks, firms would optimally reduce their workforces. Firing inflexibility would delay or prevent such adjustments and, therefore, increase operating costs and reduce firm profitability. Anticipating this, firms may become reluctant to expand and, therefore, fail to capture the economy of scale otherwise possible. Indeed, Fallon (1987) and Fallon and Lucas (1993) find that strengthening labor regulation in India was associated with lower labor demand for large firms covered by the regulation, but not for small firms not covered by the regulations. Amin (2009a) find

> 12 For more discussion, see, for example, Stern (2002) and Xu (forthcoming).  There could be other important determinants; our selection is undoubtedly constrained by data availability.  The China and India Enterprise Surveys are both non-standard—that is, they do not use the common survey instruments though they follow the common guidelines.

10
**Table 1: A summary of the literature on growth convergence within India**

||**Find**|**ing**|**Dependent**|**Spatial level**|**Time**|
|---|---|---|---|---|---|
|**Study**|**Absolute**|**Conditional**|**Variable**|**(no.)**|**Period**|
|Cashin and Sahay (1996)|Convergence|Convergence|Income|State (20)|1961–91|
|Bajpai and Sachs (1996)|Convergence|Convergence|Income|State (19)|1961–71|
||Divergence|Divergence|Income|State (19)|1971–95|
|Rao, Shand and Kalirajan (1999)|Divergence|Divergence|Income|State (14)|1961–91|
|Nagaraj, Varoudakis and<br>Veganzones (2000)|Divergence|Convergence|Income|State (17)|1971–91|
|Aiyar (2001)|Divergence|Convergence|Income|State (19)|1971–95|
|Sachs, Bajpai and Ramiah(2002)|Divergence||Income|State (14)|1976–95|
|Datt and Ravallion (2002)|Divergence||Income|State (12)|1992–2000|
|Bandyopadhyay (2012)|Divergence||Income|State (17)|1965–97|
|Rodrik and Subramanian (2004)|Divergence||Income|State (20)|1980–99|
|Baddeley, McNay and Cassen<br>(2006)|Divergence||Income|State (15)|1970–97|
|Kochhar et al. (2006)|Inconclusive||Income|State (14)|1961–2000|
|Purfield (2006)|Convergence|Convergence|Income|State (15)|1976–2005|
|Misra (2007)|Divergence|Convergence|Income|State (14)|1976–2001|
|Kalra and Sodsriwiboon (2010)|Divergence||Income|State (15)|1960–2003|
||Divergence||Income|State (14)|1980–2005|
|Das (2012)|Divergence||Rural expenditure|State (14)|1980–2005|
||Convergence||Urban expenditure|State (14)|1980–2005|
|Kumar and Subramanian (2012)|Divergence||Income|State (21)|1993–2009|
||Divergence||Income|State (21)|2001–09|
|Ghate and Wright (2013)|Inconclusive|Inconclusive|Income|State (15)|1987–2004|
|Tripathi (2013)||Inconclusive|Income|City (52)|2001‐2005|
|Nayyar (2014)|Divergence|Convergence|Expenditure|State (17)|1994–2012|
|Das, Ghate and Robertson (2015)|Divergence|Convergence|Income|District (575)|2001–08|
||Convergence||Nighttime light<br>intensity|State (33)|1992–2013|
|Tewari and Godfrey (2016)|Convergence||Nighttime light<br>intensity|District (618)|1992–2013|
||Convergence||Nighttime light<br>intensity|City (479)|1996–2011|

_Source_ : Authors, building on Kalra and Sodsriwiboon (2010).

_Note: Income_ refers to either GDP per capita or gross value added per capita. _Expenditure_ refers to average household expenditure per capita. Nighttime light intensity is measured per unit of surface.

4
Policy Research Working Paper 8702

# **Abstract**

The impact of urban form on economic performance and quality of life has been extensively recognized. The studies on urban form have focused in developed countries; only a few cities in developing countries have been studied. This paper utilizes nighttime lights imagery and information on street networks, automatically retrieved from OpenStreetMap, to calculate a series of spatial metrics that capture different aspects of the urban form of 919 Latin American and Caribbean cities. The paper classifies these cities into clusters according to these spatial metrics.

It also studies the relationship between the urban form metrics and some factors that can correlate with urban form (topography, size, colony, and economic performance) and performs a spatio-temporal analysis of urban growth from 1996 to 2010. Among the results, the paper highlights the identification of five typologies of cities, the tendency of a group of cities to grow at a steeper slope, some worrying cases of urban growth over protected areas, and a trend toward increasing sprawl in some Latin American and Caribbean cities.

This paper is a product of the Social, Urban, Rural and Resilience 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/research. The authors may be contacted at nlozano@worldbank.org and prestrepocadavid@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
# **2.3 Data**

Data collection began in November 2012 and lasted until September 2015 (Table 1). For each phase, three rounds of data were collected: baseline data at the start of the school year; midline data in the middle; and endline data at the end of the school year. Phase 1 baseline data were collected in November 2012February 2013, midline in August-September 2013, and endline in November 2013. Phase 2 data were collected at corresponding times in 2014. At baseline and endline, students were tested and teachers and head-teachers were surveyed. At midline, attendance data were collected for both students and teachers during unannounced visits. Teachers were observed teaching a class during the midline visits and their behaviors recorded along a number of dimensions using an instrument with 21 items across the following domains: how teachers interact with students; the materials they used during instruction; use of the blackboard; and assignment and review of homework.<sup>19</sup> After phase 2, post-endline data were collected in August-September 2015 during which the phase 2 cohort of students was re-tested only in math. Test scores used in our empirical analysis are all normalized to have a mean of 0 and a standard deviation of 1 for the control group in the relevant phase.

Around 83 percent of the schools in our sample are public schools; 92 percent are O Level only (go up to Grade 11). At time of the Phase 1 baseline (2013), around 36 percent of schools had electricity and almost all schools (99 percent) collected school fees. Around 56 percent of the schools in our sample have less than 10 permanent teachers; 11 percent have more than 20 permanent teachers. The majority of headteachers (85 percent) and teachers (79 percent) are male. Nearly 83 percent of teachers have more than secondary education and 90 percent have received some type of formal teacher training.

# **2.4 Estimation strategy**

We divide our empirical investigation into four main parts. We start by analyzing average impacts on student test scores in phases 1 and 2. Given the random assignment study design, our estimation of treatment impacts is straightforward. All of the estimates described below are intent-to-treat. We regress endline test scores _Testi1_e_ for phase 1 and _Testi2_e_ for phase 2 on the set of indicators capturing random assignment to a study arm. We control for a set of indicators to capture the stratified random assignment

> 19 For each phase, we conducted a separate exploratory factor analysis and retained the top four leading factors. In phase 1, the leading factors, which explain just under 38 percent of the variation across all items are shaped by items related to a) teacher-pupil interactions, b) use of materials such as maps, textbooks or equipment in instruction; c) use of the blackboard to copy lessons and illustrate examples, and d) assignment and review of homework. In phase 2, the four leading factors explain 36 percent of total variation and their dimensionality is explained by the same sets of items as the Phase 1 leading factors.

10
**Table 2.1: Test Scores and Governance Indicators: African and Latin American Countries**

||Without Co<br>|untryFixed E<br>|ffects<br>||With Count<br>|ryFixed Effe<br>|cts|
|---|---|---|---|---|---|---|---|
||Average<br>Score|Average<br>Score|Average<br>Score|Average<br>Score|Average<br>Score|Average<br>Score|Average Score|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Civil Rights|-1.057*|-0.569|-0.454|-0.613|0.028|-1.187|-0.844|
||(0.59)|(0.73)|(0.4)|(0.93)|(1.0)|(0.77)|(0.75)|
|Globalization|-0.204*|-0.164|-0.162**|0.098|-0.055|-0.087|-0.065|
||(0.12)|(0.11)|(0.08)|(0.18)|(0.12)|(0.1)|(0.11)|
|**Democracy**|0.66|1.5|0.387|**1.776*****|**3.200****|**1.592****|**1.555****|
||(0.63)|(1.04)|(0.47)|(0.55)|(1.4)|(0.62)|(0.69)|
|**Economic Freedom**|0.281|0.315**|0.298***|**0.432****|0.15|**0.231****|**0.205***|
||(0.19)|(0.13)|(0.11)|(0.21)|(0.1)|(0.11)|(0.11)|
|Openness (in percent)|-0.008|0.005|-0.003|-0.001|-0.026|0.004|0|
||(0.04)|(0.03)|(0.03)|(0.05)|(0.08)|(0.03)|(0.04)|
|Log Population|0.905|1.704|1.371|-12.679|-28.566|-2.115|14.821|
||(1.34)|(0.91)|(0.8)|(8.03)|-29.28|(6.49)|(15.3)|
|Log GDP per Capita|2.123|-1.274|3.203**|-4.146|-7.948|-8.056|0.184|
||(1.71)|(2.26)|(0.94)|(5.92)|(11.29)|(4.91)|(7.21)|
|R-Squared|0.35|0.416|0.46|0.486|0.802|0.455|0.95|
|Observations|40|28|68|40|28|68|68|
|Number of Countries|24|17|41|24|17|41|41|

**Notes:** Dependent Variable: Score averaged over all test score domains. Columns (1), (2), and (3) report OLS estimations for Africa, Latin America, and both, respectively. Column (4), (5), (6) and (7) report Fixed Effects Estimations. In particular, column (4), (6), and (7) report country fixed effects for Africa, Latin America, and both, respectively. Column (5) reports country fixed and time fixed effects for Latin America. All regressions are estimated with robust standard errors and are clustered on the country level.

# **6. Conclusion**

In this paper, we present the construction of an international database comparable across countries and over time. Our focus is on the inclusion of developing countries, with the goal of evaluating which causal factors contribute most to cognitive skill attainment around the world. In particular, we use the methodology of Altinok and Murseli (2007) to build a data set of comparable test scores from 1965-2010 for a set of 128 countries.

32"