rafmacalaba/datause-encoder-data huggingface dataset:
_Data_ The core data set is that of Summers and Heston (1991), updated to 2000 and the trade variables were constructed as in table 1. We construct panels of five year periods extending from 1975 to 1999. We lose one observation to instruments leaving a twenty year span to estimate from 1980 to 1999. Because we are interested in seeing how sensitive the results are to estimating technique, we use the same sample for both the cross section and panel exercises. Table 2 presents the summary statistics of both the cross section and panel data sets.

Tables 3a and 3b present the cross sectional and panel results respectively. The tables report the coefficient and significance level on the particular trade variable in a regression containing the control variables listed in the first column. Hence, the next column reports the coefficient on the Leamer measure first for the basic conditioning variables, then with human capital and investment, then with terms of trade, and so on. This is done for each variable as we move across the top of the table. Below the double line (under section labeled "Additional Controls"), we combine the variables of interest along with the full conditioning set as tests of possible channels through which the principal variable of interest works. For instance, we add the export Herfindahl to the Leamer regression as a test of whether whatever effect resource abundance has on growth may work through export concentration.

The diagnostics for the panel are those suggested by Blundell and Bond (1998): the Sargan test for overidentifying restrictions, implicitly a test of specification, and tests for second order serial correlation.<sup>9</sup> In both the OLS and panel exercises, the key conditioning variables entered either with the expected sign or statistically insignificantly (results available on request). For instance, in most specifications initial GDP per capita enters negatively and significantly;

> 9 With regressions in differences, however, first-order serial correlation is to be found by construction, so the relevant specification test is that of second-order serial correlation, which does support the reported results.

10
with the many Copenhagen Consensus studies that attempt to make benefit-cost comparisons consistent across many types of public spending (Wong and Dubosse 2019). Finally, very few people report more than 18 years of schooling, so we capped the total at 18.

# _Results_

Table 1 and figure 7 present the regression estimates. The parametric and nonparametric estimates for the years-of-experience variables are very similar. Coefficients on place of residence vary more, but they also have large standard errors. The coefficients on father’s education attainment are not significantly different from zero<sup>15</sup> except for fathers who are university graduates; they have a strong positive effect on labor earnings in all the regression models except the regression for women. The coefficients on mother’s education attainment are also mostly insignificant, though they are large for university graduates, with very large standard errors. The important exception is that women whose mothers completed primary, junior secondary, or senior secondary schooling have significantly higher labor earnings.

The parametric estimates on years of schooling in the first two columns are somewhat lower that those reported in Patrinos and Psacharopoulos (2020) for Africa, but similar to all developing countries. They are also somewhat lower than the results in Montenegro and Patrinos (2014) for Ghana in 2005 and 2012 (10.3 percent and 12.5 percent per year, respectively) but much higher than their estimate for 1991 (5.3 percent per year). This may reflect the improved macroeconomic and labor market conditions over time and, for 2012, the effect of the single spine salary scheme. Note that adding the control variables decreases this coefficient only slightly in our estimates, unlike the results in Glewwe (1996).

In figure 7, we see, however, that the returns to an additional year of schooling<sup>16</sup> increase sharply for men at 13 years of school, the beginning of postsecondary schooling, and at 10 years for women, the beginning of secondary schooling. This is the main motivation for the more complicated calculation of lifetime benefits in equation (5). Note also that the labor earnings pattern is almost identical regardless of whether we exclude public sector workers from the sample.

> 15 But recall that these estimates are conditional on the individual’s schooling, captured through the nonparametric estimate.

> 16 Because the vertical axis is the natural log of labor earnings, the slope of the function is approximately the percentage increase in labor earnings for an additional year of schooling.

18
patriarchal preferences, and Ross (2008) maintains that in the Middle East the presence of substantial oil rents (that protect patriarchal norms), and not Islam, is the reason that women have made so little progress in terms of asserting their rights. Another reason to question the absolute link between Islamic law and legal gender restrictions is that, as Noland (2005), Bowen (2013) and Barlas (2019) note, laws derived from readings of the Quran are subject to many interpretations – as is the case with the reading of many sacred texts – and there are wide divergences in terms of practice. This may be why, as noted in King and Mason (2001), there is such a large variation in the autonomy of Muslim women in different contexts.

Mashhour (2005) argues that the restrictions placed on women’s rights in many Muslim countries are not the result of Islamic law, but rather the result of patriarchal norms in these societies. Indeed, the author notes that the main aim of Sharia is to maintain justice and promote public welfare. By comparing the cases of Tunisia and the Arab Republic of Egypt (which, it is worth noting, had WBL scores in 2019 of 70 and 45, respectively), Mashhour (2005) argues that the liberal case of Tunisia should be considered as a model for how to incorporate gender equality in the family sphere within the bounds of Sharia. He argues that, as Sharia is partly divine (coming from the Quran and Sunna) and partly human‐derived, it is not static but, rather, is constantly evolving. He concludes that common ground can indeed be found between Islamic law and gender equality.

# **Stylized Fact 4:  The past five decades have seen tremendous progress, but the pace of reform has differed across regions.**

While the current state of legal gender equality is what matters to women now, it is also interesting to chart the progress of reform over the five decades covered by the WBL data. The unweighted global average WBL score increased from 46.48 to 75.23 points between 1970 and 2019, but different regions have made progress in terms of legal gender equality at different paces. As Figure 2 illustrates, while OECD economies are currently at the top of the ranking, in the 1970s, women in the Europe and Central Asia region faced fewer legal gender restrictions than their OECD counterparts. On the other hand, while regional average scores in the Latin America and Caribbean (LAC) and East Asia and Pacific (EAP) regions were very close in 1970, at 49.34 and 48.75 points respectively, economies in LAC have made greater progress towards legal gender equality over time, achieving a score of 79.18 by 2019, compared to 71.30 in EAP. Another interesting comparison is between the Sub‐Saharan Africa and South Asia regions; while legal gender restrictions in 1970 were fewer in South Asia (WBL score of 43.67) compared to Sub‐Saharan 10
Semykina, Anastasia, and Jeffrey M. Wooldridge. 2010. “Estimating Panel Data Models in the Presence of Endogeneity and Selection.” _Journal of Econometrics_ 157 (2): 375–80.

Stark, Oded. 1991. _The Migration of Labor_ . Oxford: Blackwell.

Stark, Oded, and David E. Bloom. 1985. “The New Economics of Labor Migration.” _The American_

- _Economic Review_ , 173–78.

- Stark, Oded, and Robert Lucas. 1988. “Migration, Remittances and the Family.” _Economic Development and Cultural Change_ 36: 465–81.

- Strobl, E., and M.-A. Valfort. 2015. “The Effect of Weather-Induced Internal Migration on Local Labor Markets. Evidence from Uganda.” _The World Bank Economic Review_ 29 (2): 385–412.

- Tacoli, Cecilia. 1998. “Rural-Urban Interactions: A Guide to the Literature.” _Environment and Urbanization_ 10 (1): 147–66.

Uganda Bureau of Statistics. 2010. “The Uganda National Panel Survey (UNPS) 2009/10: Basic

- Information Document.” Kampala: Uganda Bureau of Statistics (UBOS).

- ———. 2013. “Uganda National Panel Survey 2011/12: Wave III Report.” Kampala: Uganda Bureau of Statistics (UBOS).

- ———. 2014. “National Population and Housing Census 2014: Provisional Results.” Kampala: Uganda Bureau of Statistics (UBOS).

- World Bank. 2006. “Uganda Poverty and Vulnerability Assessment.” No. 36996-UG. World Bank.

- ———. 2008. _World Development Report 2009: Reshaping Economic Geography_ . The World Bank. http://elibrary.worldbank.org/doi/book/10.1596/978-0-8213-7607-2.

- ———. 2011. “Weather Index Insurance for Agriculture: Guidance for Development Practitioners." Agriculture and Rural Development Discussion Paper no. 50. Washington, DC: World Bank.

- ———. 2012. “Uganda: Promoting Inclusive Growth: Transforming Farms, Human Capital, and Economic Geography.” Report No. 67377–UG. Kampala: World Bank.

- ———. 2015. “World Development Indicators.” http://data.worldbank.org/data-catalog/worlddevelopment-indicators.

- Yang, D., and H. Choi. 2007. “Are Remittances Insurance? Evidence from Rainfall Shocks in the Philippines.” _The World Bank Economic Review_ 21 (2): 219–48.

- Yang, Dean. 2011. “Migrant Remittances.” _Journal of Economic Perspectives_ 25 (3): 129–52.

37
Policy ReseaRch WoRking PaPeR 4881

# **Abstract**

A majority of sub-Saharan Africa’s population is not connected to electricity and piped water networks, and even in urban areas coverage is low. Lack of network coverage may be due to demand or supply-side factors. Some households may live in areas where access to piped water and electricity is feasible, but may not be able to pay for those services. Other households may be able to afford the services, but may live too far from the electric line or water pipe to have a choice to be connected to it. Given that the policy options for dealing with demand as opposed to supply-side issues are fairly different, it is important to try to measure the contributions of both types of factors in preventing better coverage of infrastructure services in the population. This paper shows how this can be done empirically using household survey data and provides results on the magnitude of both types of factors in explaining the coverage deficit of piped water and electricity services in urban areas for a large sample of African countries.

This paper—a product of the Development Dialogue on Values and Ethics in the Human Development Network and of the Sustainable Development Department in the Africa Region Vice Presidency—is part of a larger effort in the Network and Region to document the access to, and affordability of basic infrastructure services. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at sbanerjee@ 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 5: Nightlights Dynamic Panel Estimations

||(1)<br>OLS|(2)<br>Fixed<br>Eects|(3)<br>First<br>Dierence|(4)<br>Dynamic<br>Panel|(5)<br>Dynamic<br>Panel IV|(6)<br>Dynamic<br>Panel IV|
|---|---|---|---|---|---|---|
|L.Nightlight||||0.966***<br>(0.009)|0.413***<br>(0.133)|0.374***<br>(0.134)|
|Mining 0-10 km|0.578***<br>(0.062)|0.213***<br>(0.051)|0.166***<br>(0.035)|||0.242*<br>(0.138)|
|Mining 10-20 km|-0.035|-0.003|0.015*|||-0.000|
||(0.021)|(0.014)|(0.008)|||(0.081)|
|Mining 20-30 km|-0.033|0.005|0.015|||0.064|
||(0.026)|(0.010)|(0.013)|||(0.105)|
|Mining 30-40 km|-0.051**|-0.009|0.004|||0.027|
||(0.022)|(0.010)|(0.010)|||(0.045)|
|Mining 40-50 km|-0.012|-0.015|0.001|||0.244**|
||(0.040)|(0.014)|(0.008)|||(0.107)|
|Mining 50-60 km|0.016|-0.006|0.008|||-0.098*|
||(0.034)|(0.009)|(0.012)|||(0.057)|
|Mining 60-70 km|0.049|-0.003|0.005|||0.051|
||(0.052)|(0.022)|(0.011)|||(0.078)|
|Mining 70-80 km|0.018|0.003|0.019**|||-0.058|
||(0.027)|(0.010)|(0.007)|||(0.046)|
|Mining 80-90 km|0.033|0.042***|0.027***|||-0.070|
||(0.042)|(0.015)|(0.010)|||(0.089)|
|Mining 90-100 km|0.001|0.025**|0.024***|||-0.111|
||(0.021)|(0.010)|(0.009)|||(0.092)|
|Constant|0.927***|0.957***||0.352***|||
||(0.303)|(0.094)||(0.052)|||
|N|235410|235410|223020|223020|210630|210630|
|AB AR(1) test||||0.000|0.000|0.000|
|AB AR(2) test||||0.012|0.208|0.298|

*** p _<_ 0.01 ** p _<_ 0.05 * p _<_ 0.10.All regressions contain dummies for every time period. Yearly data for12,390 EA from 1992-2010. Column(5) uses instruments the dierence equation with the 4th lag of Nightlight. Column (5) additionally instruments the dierence of gold production with its 2nd lag. AB AR( _p_ ) test reports p-values of the Arellano-Bond (1991) test for serial correlation of order _p_ .

1
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The state of the CAR’s infrastructure and the price of fuel are highly correlated. After a notable decline in the retail price of diesel and super gasoline during the mid-1990s, these prices have been steadily increasing. In 1998 the price of diesel, at 64 cents per liter, reached its lowest point between 1991 and 2010. The retail price in 2010 was 169 cents per liter—an increase of about 260 percent (figure 4a). Meanwhile, diesel prices in the CAR are the highest in Central Africa (figure 4b). This is due to the high cost—in both money and time—incurred by importing oil from Cameroon (via the Doula-Bangui corridor) and the Democratic Republic of Congo (via the Oubangui River). Higher oil prices raise transportation costs and impact the cost of thermal power production. High power generation costs in turn scale up the price of energy, which can only be afforded by the highest quintiles of the population. Scarcity of fuel is also responsible for constant power outages as thermal production is curtailed, which in turn affects water treatment.

**Figure 4. Diesel and super gasoline retail prices**

<!-- Start of picture text --> a. The CAR’s diesel and super gasoline retail prices b. Central African countries diesel retail prices<br>180 180<br>160 160<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0<br>0<br>Diesel Super Gasoline<br>US cents per liter US cents per liter<br><!-- End of picture text --> _Source:_ GTZ 2008. _Note:_ Diesel retail prices for Central African countries as of mid-November 2008.

The CAR’s infrastructure network would benefit from closer interconnection and collaboration with neighboring countries. The Central African Backbone has the potential to connect the CAR to the submarine cable landing in Cameroon, reducing the cost of telecommunications in the CAR. Upgrading the Cameroonian portion of the Bangui-Douala corridor is critical, as it could decrease the time and cost of transport goods from the CAR to the port of Douala. As far as the energy sector is concern, estimates suggest that as long as the CAR is in a position to develop its own significant domestic hydropower, the country will not necessarily benefit from regional trade.

The main achievements and challenges in each of the CAR’s major infrastructure sectors are summarized in table 1. The table outlines the need for upgrading infrastructure in all subsectors, the obstacles to enhancing the coverage and quality of infrastructure services, and the ambitious reform efforts in this area. In particular, there is a need to improve the operational and financial performance of state-owned enterprises (SODECA, ENERCA, and SOCATEL). The following section discusses these achievements and challenges in more detail, by sector.

8
Table B.2. Validity of the Local Continuity Assumption – Non-RAMV Referrals

|Variables in STD.|Knew job<br>opportunity<br>before<br>migrating [=1]|Ever<br>worked [=1|]<br>Employed at<br>private<br>frm [=1]|Employed<br>with<br>gov. [=1]|Self-employed or<br>employer [=1]|Written<br>contract [=1]|Gap between<br>last job and<br>migration<br>(months)|**Years**<br>of educ.<br>before<br>migration<br>|HH. Ven.<br>parents or<br>siblings [=1]|HH. Ven.<br>partner or<br>spouse [=1]|HH. Ven.<br>others [=1]|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|1[PEP_i_ = 1]|-0.124|0.380|0.169|0.444|-0.219|0.458|-0.168|0.959**|-0.560**|0.045|0.229|
||(0.250)|(0.308)|(0.275)|(0.209)|(0.268)|(0.256)|(0.218)|(0.294)|(0.196)|(0.241)|(0.248)|
|q-values|[1.00]|[0.686]|[1.00]|[0.193]|[1.00]|[0.264]|[1.00]|[0.012]|[0.028]|[1.00]|[1.00]|
|Obs. left|190|213|179|212|199|200|194|176|182|190|222|
|Obs. right|530|600|512|753|580|587|568|436|455|530|724|
|Observations|2,589|2,589|2,483|2,483|2,483|2,483|2,477|2,589|2,589|2,589|2,589|
|Variables in STD.|Migrated for<br>health<br>reasons [=1]|Friends/<br>family in<br>Col. [=1]<br>s|Had<br>martphone [=1]|Owner of<br>dwelling in<br>Venezuela [=1]|Electricity in<br>Venezuela [=1]|Running<br>water in<br>Venezuela [=1]|Sewage in<br>Venezuela [=1|]<br>Female [=|1]<br>Age<br>(years)|Number of<br>children|Time in<br>Colombia|
||(12)|(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|
|1[PEP_i_ = 1]|-0.138|0.080|0.632**|-0.045|-0.401|0.125|0.249|0.197|0.025|-0.331|0.015|
||(0.316)|(0.233)|(0.189)|(0.393)|(0.204)|(0.252)|(0.387)|(0.233)|(0.210)|(0.190)|(0.114)|
|q-values|[1.00]|[1.00]|[0.012]|[1.00]|[0.231]|[1.00]|[1.00]|[1.00]|[1.00]|[0.264]|[1.00]|
|Obs. left|204|208|214|168|144|204|145|269|221|219|185|
|Obs. right|574|588|617|405|352|574|373|881|687|679|509|
|Observations|2,589|2,589|2,589|2,589|2,589|2,589|2,589|2,589|2,586|2,589|2,543|

_Notes:_ The table depicts the sharp RDD model results of pre-migration and pre-RAMV controls used as outcome variables. These variables correspond to all the controls specified in our preanalysis plan.
non-issuing firms is held fixed throughout the paper. Non-issuing firms are those that did not have any issuance activity between 2003 and 2011. In the SDC-Orbis data, 18,342 firms are issuers (16,198 firms are equity issuers and 5,134 are bond issuers) and 27,185 are non-issuing firms.

We classify the countries in the sample into developed and emerging economies following the World Bank classification of countries. In particular, developed countries are those with a gross national income (GNI) per capita in 2009 above $12,195 (U.S. dollars). All other countries are classified as emerging economies. The final matched data set comprises firms from 20 emerging countries and 31 developed countries. Appendix Table 1 reports the list of countries in the sample.

Our analyses focus on firm size and growth, measured by the level and growth rate of total assets, sales, and the number of employees. Firm assets and sales are measured in constant 2011 U.S. dollars, using the U.S. consumer price index (CPI) to discount nominal values. The analysis also examines firm age (measured in 2011), firm profitability, and other financial indicators such as return on assets (ROA), leverage (including bank and other types of financing), and the maturity profile of liabilities.

We also match the SDC-Orbis data set with the SDC Platinum database on mergers and acquisitions (M&A) to assess whether firms with M&A activity around their capital raisings display a similar performance than other firms raising capital. This helps us analyze whether the patterns we uncover are driven by M&A activity and whether the expansion of firms comes from their own internal growth. To do so, we identify whether issuing firms engage in some M&A activity as the acquirer firm in the year of the capital raising or the following year. Of the issuing firms in the sample, 8,919 firms conducted an M&A transaction (about 50% of the issuing firms). The results using the M&A data are mentioned in the text but not reported to save space.

Although in this paper we use firm-level data for 2003-2011 only due to the wide coverage of firms by Orbis, we also match the data on security issuances from SDC Platinum with balance sheet 9
_“I wanted my own house after my marriage and shouldn’t be dependent on anyone.”_ Female (Ahmedabad) _“We took in joint name because my mother also paid some money.”_ Male (Bengaluru) _“I had invested in this property from my salary and savings. That is why it is in my name also.”_ Female (Mumbai) _“Things were not very good when I got married. I didn’t get any property from my in-laws. I worked very hard and got this property here.”_ Female (Ahmedabad) _“Both of us contributed to buy land. I felt that there should be an equal right for both of us.”_ Joint owner, male (Bhubaneswar) Reflecting the composition of India’s labor force,<sup>25</sup> only one of the females in the FGDs was the primary earner for her family; many were not earning at all, though some had finances to invest in the property. Many females voiced their husbands’ perspectives about the necessity of home ownership, rather than state their own views. Often, this viewpoint was couched in terms of the husband’s concerns about inheritance of the property, should there be an accident or illness, or for the family’s general future security. Males, too, voiced this concern.

_“My husband feels that even I must be part of it. He thinks about me. He feels that if our children don’t take care of us then what will happen in future. I see the main motive and he takes care of me. So, he registered in my name also.”_ Joint owner, female (Mumbai) _“My husband has three brothers. We have bought this house after marriage. If my husband is the owner, his brothers can claim it and that will be a family problem. So, it is in my name claiming that it is ours and your mother’s or your brother’s.”_ Female (Ahmedabad) _“By default, the property must go to her if something happens to me.”_ Male (Ahmedabad) Some males also commented that female ownership was simply part of a woman’s equal rights: _“She has the rights and should know everything in case there’s an accident.”_ Male (Bhubaneswar) _“Nowadays there is gender equality and in our home we give priority to the women.”_ Male (Bhubaneswar) An additional reason was given for female ownership related to income tax declarations. The income tax benefits to female owners are described in the legal review section. The impact on income taxes was noted across cities and was mentioned particularly by females.

> 25. The World Bank, for example, reports that <u>female labor force participation in India fell from 33.7% in 1991 to 20.8% in 2018.</u>

24