rafmacalaba/datause-encoder-data huggingface dataset:
households may have been able to protect these expenditures somewhat in the context of dramatic cut-backs in overall household income. **6** The fall in public expenditures on education during the crisis may have had an attendant effect on quality. This, in turn, could eventually affect the productivity and wages of cohorts of children who received their education in crisis years." One way to test for these effects would be to supplement standard Mincerian wage regressions with measures of crisis exposure, as well as (possibly) with an interaction term between schooling and exposure. The 1988-82 crisis in Peru is too recent to assess these possible long-term consequences, as many of the affected cohorts are still in school. However, the possible negative effect of the crisis on school quality is a caveat to my conclusion that the 1988-92 contraction had a "positive" overall effect on human capital accumulation.

Finally, migration is a concern because the sample is limited to Lima, and the urban areas of the coast and sierra. In Peru, migration has traditionally been from rural to urban areas, although there appears to have been an important migration from urban to rural areas in the mid1990's. This reverse migration was a result of the increase in security in the countryside which followed the lower levels of activity of Shining Path and MRTA after 1992, as well as of a deliberate policy designed to encourage resettlement of abandoned rural areas. (This policy included the creation of special government programs, such as PAR, the Programa de Apoyo al Repoblamiento.) Given lower mean incomes and lower adult education levels in rural areas, a reasonable assumption is that the underlying propensities to attend school and be on-track are lower-or at least no higher-among the migrants than the sedentary population. Another way to think about this is that the migrant populations are likely to have lower "ability" to attend school and make satisfactory grade progress. Rural-to-urban migration would tend to diminish ability in the sample in 1991 relative to 1985/86, while urban-to-rural migration could also diminish ability in 1991 relative to 1997. To the extent that this is the case, the estimates of the effect of the crisis I report would be downward-biased: In the absence of migration, the estimated coefficients on the measures of crisis exposure would have been even larger.

> **16** Data from countries affected by the 1998 East Asian crisis, for example, show a varied picture: In Indonesia, private expenditures on education fell both in absolute terms and as a share of total household expenditures, whereas in Korea expenditures on education fell by less than overall household spending (World Bank 2000, p. 121).

> **17** I thank Francisco Ferreira for this point.

16
(mainly agriculture) or wage work, 71 percent of females report staying at home without a primary occupation. One of the reasons for the limited female labor supply may be the prevalence of social norms about the role of women as caregivers.

We explore this hypothesis by examining self-reported time allocation during the day prior to when the follow-up survey took place. Women do spend a lot more time in household chores than men (6.6 hours for women compared to 2.3 for men) and about half as much time in the business than their male counterparts (2.9 versus 5.4 hours among business owners). The spouses of CO members behave along similar gender lines, that is, female spouses of male CO members show similar hours in household chores and the business as female CO members and vice versa.

Finally, the same social and cultural norms that restrict female labor supply also affect women’s mobility outside the home. In a study of female entrepreneurs in Pakistan, Roomi (2005) finds that the social unacceptability of females interacting with unrelated males is responsible for the low number of female borrowers (less than 40 percent in Pakistan in 2009) compared to more than 85 percent in India or Bangladesh. The lack of mobility also affects women’s involvement in the business. Since women cannot sell products or purchase inputs in the market, their decision-making power is limited. In our data, 40 percent of female CO members involved in a business report that all business decisions are made by their husband. Indeed, not only do women spend less time in the business, the share of time devoted to managerial activities is also lower (16 percent for women compared to 27 percent for men). Therefore, even if one argued that business training could have improved business performance because better decisions about production and marketing, etc. may not require additional time, the fact is that women show no improvement because they have little control over the businesses they are involved in.

# <u>5.4 Labor Supply</u>

We have argued that female labor supply is restricted, and indeed Panel B of Table 6 shows that female labor supply in the business does not respond to any of the treatments, either for females or their male spouses. Women in the _LW_ and the _BT_ and _LW_ groups do reduce the labor supply in agriculture but the overall impact is small because women spend only 0.4 hours on average in agricultural activities. Male CO 24
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for the whole sample, but only 0.54 for the restricted sample of poorer countries. As with the first indicator, the results suggest that although equity in education follows a predictable relation to the per capita GNP, differences in policy probably also account for part of the observed diversity across countries.

# **2.6 Summary of the relation between country wealth and education**

In the foregoing discussion we have considered how selected features of the education system relate to country wealth, in terms of both the structure and the closeness of the fit between each indicator and the per capita GNP. With regard to structure, the patterns fall into four categories: (a) rising consistently against per capita GNP; (b) falling consistently against it; (c) flat against it; or (d) rising against it over some income range and falling over other ranges (or vice versa). With regard to the closeness of the fit, we discern on the basis of the RP statistic associated with the regression equations, that some relationships show a tight fit, while others reveal a moderate to loose fit, and yet others, hardly any fit at all. The data indicate that the relationships are almost always less tight among lower income countries (i.e. those below $4,000 in 1993) which imply generally greater diversity among them.

Of the indicators pertaining to the sector context, three strong patterns emerge: a continuous easing of the demographic pressures on education as countries grow rich; a steady rise in the educational attainment of the adult population, which flattens out as incomes continue to rise beyond $10,000; and a sharp shift in employment out of agriculture. All three variables follow a highly predictable path as incomes rise.<sup>19</sup> The other variables describing the sector context show the following behavior: the government's fiscal capacity is flat against the per capita GNP up to around $1,500, after which it shows a gradual but steady rise, while the share of education in the public budget rises then falls as incomes grow. Neither variable shows a close fit to the per capita GNP.

On the production of education services, significant shifts occur as countries grow rich, as indicated by the following trends: shifts in the allocation of spending away from primary education to the other two levels; a generally rising per pupil-spending (expressed as a percentage of the per capita GNP) at the primary level, accompanied by sharp drops in the pupilteacher ratio and average teacher salaries (again expressed as a percentage of the per capita GNP); and a sharply declining per pupil spending in higher education at low incomes which then flattens out as incomes continue rise beyond $3,000. The shares of spending on pedagogical

> 9 The relation between the demographic burden and the per capita GNP is less tight among countries with for income below $4,000, and reflects their diverse history of demographic transition experienced.
bank-based or market-based financial systems”<sup>1</sup> (La Porta et al., 1997, 1998). Some empirical studies, such as Beck and Levine(2002), Demierguc-Kunt and Maksimovic(2002), and Levine(2003) etc., find results surprisingly consistent with the third and fourth views, showing that financial structure is not a first-order concern in understanding the process of economic growth after controlling for the level of financial development.

# **II.2.2 Banking structure and economic development**

When it comes to banking structure, the literature mainly discusses whether competitive or monopolistic banking structure is better for economic growth. Traditional wisdom suggests that monopolistic banks may extract too much rent from firms, pay lower deposit interest rates, and thus lead to more severe credit rationing, which has very negative effects on economic growth. But some studies argue that monopolistic banks have more incentive to collect information, screen and monitor borrowers, and form long-term relationships with borrowers; therefore investment projects have more chances to get financed. In a competitive banking sector, borrowers can more easily shift between lenders, so banks may have less incentive and less capability to forge such long-term borrower-lender relationships. Such borrower-lender relationships are especially valuable to start-ups and new firms. Thus these studies suggest that monopolistic banking structure is beneficial to the establishment and growth of new firms. Empirical results in this area are far from conclusive. Some studies show that lower banking concentration leads to more new establishments and thus has positive implications for economic growth (Jayaratne and Strahan, 1996; Cetorelli and Strahan, 2006; etc.; World Bank 2007). Others find that new firms grow faster in economies with a more concentrated banking sector but old firms benefit from a more competitive banking structure (Jackson and Thomson, 1995; Petersen and Rajan, 1995; etc.).

# **II.2.3 Some comments**

It seems that the empirical results on the growth impacts of bank-based and market-based financial structure are inconclusive. The basic fact mentioned at the beginning of this subsection that financial markets tend to be more active relative to banks in countries with

> 1 Cited from Levine (2005).

5
our purposes as it is by far the largest household survey-based data source that we use in the WGI. We use questions from this survey in our Voice and Accountability, Government Effectiveness, Rule of Law, and Control of Corruption indicators. The Open Budget Index and the Global Integrity Index are two new sets of expert assessments produced by Washington-based non-governmental organizations. The Open Budget Index, produced by the International Budget Project, has for the first time in 2006 produced detailed assessments of access to government budget information for a set 59 countries worldwide. We use their overall index as a component of Voice and Accountability. The Global Integrity Index has been produced by Global Integrity, for 2003 and 2006, for samples of 25 and 41 countries respectively. It is an expert assessment that provides ratings of the statutory existence and practical effectiveness of a long list of public sector accountability mechanisms. In keeping with our practice of relying exclusively on perceptions data, we draw on the "in practice" subcomponents of selected indicators in Voice and Accountability, Rule of Law, and Control of Corruption.

We have also added two data sources that are not new, but that we have learned about since our last update and have decided to include this year. The African Economic Outlook is an annual report produced by the OECD Development Center. It contains two indicators of democracy and politically-motivated violence that we use in our Voice and Accountability and Political Stability and Absence of Violence indicators. These are based on counts of the frequency of news reports in these two areas, drawn from a weekly newsmagazine, Marchés Tropicaux et Mediterranéens, and are available since the beginning of our indicators in 1996. We have also learned about the Rural Sector Performance Assessments produced by the International Fund for Agricultural Development, a multilateral aid agency based in Rome and focused on financing agricultural investments in developing countries.<sup>5</sup> These assessments are similar in content and process to the Country Policy and Institutional Assessments that are produced by the World Bank, the African Development Bank, and the Asian Development Bank, and have been produced since 2004. The key difference however is in their emphasis on policies and institutions that affect the rural sector. We use this source in five of our six of our aggregate indicators (all except Political Stability and Absence of Violence).

> 5 We are grateful to Brad Parks of the Millennium Challenge Corporation for drawing our attention to this data source.

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noted before, data from two other sources – the National Demographic and Health Survey and the Family Income and Expenditure Survey – can also be used for constructing multidimensional poverty measures. However, their more limited frequency – once every five years for NDHS and once every three years for FIES – implies that only 2-3 rounds of these surveys are available every decade. By comparison, for the 2004-13 period covered by this study, we are able to use six rounds of APIS for the years 2004, 2007, 2008, 2010, 2011 and 2013.

A notable benchmark in terms of empirical practice is the global Multidimensional Poverty Index (MPI) adopted by UNDP and now estimated for over 100 countries – including the Philippines. The UNDP’s MPI is based on the counting approach proposed by Alkire and Foster (2011), and uses ten indicators covering three broad dimensions related to education, health and what is dubbed as the “standard of living”. Education and health have two indicators each, and there are six indicators of the standard of living related to access to services (especially, electricity, water and sanitation) and asset ownership (see UNDP, 2014 for details).

The choice of indicators used in this paper broadly follows the global MPI. While the same three main dimensions of health, education and standard of living are identified as in the global MPI, the specific indicators under each had to be adapted to what could be implemented with the data available from the APIS surveys. In particular, this paper presents results using the set of indicators as shown in Table 1.

The two indicators for education deprivation – viz., if no one in the household has completed six years of schooling, and if any school-age child in the household is not attending school – are essentially the same as have been used in the global MPI, and do not require further explanation. In relation to health indicators, as noted above, APIS does not collect anthropometric information, nor information on child mortality. Instead, there are two other potential health indicators available from the APIS. First, as APIS has information on consumption, the adequacy of a household’s per capita food consumption can be assessed relative to the official food poverty thresholds. The indicator we use (“ _health1_ ” in Table 1) deploys a stringent criterion of nutritional deprivation defined as per capita food consumption of the household falling below 80% of the food poverty threshold.

The second potential health indicator (“ _health2_ ” in Table 1) is defined in terms of past (self-reported) illness or injury of household members. There is however a data comparability issue related to this 6
Appendix C presents maps illustrating the regional variation in the number of newspapers in circulation and the number of universities in the 46 Russian oblasts of our sample.<sup>12</sup> Third, to account for unobserved heterogeneity that can account for the relationship between financial literacy and our set of outcomes, we use both years of data and estimate individual random effects and fixed effects models.

# **5.2** **_Empirical Estimates: Financial Outcomes_**

Our first set of estimates examines the correlates of the use of bank accounts by respondents in our sample. Due to the low number of new bank accounts for the year 2009 in the sample, panel models cannot be estimated. Table 4 presents probit estimates. The dependent variable is a dummy equal to one if the individual reports having a bank account in 2009 and equal to zero otherwise. Explanatory variables are dated as of 2008 in order to mitigate as much as possible simultaneity problems. Marginal effects and robust standard errors are presented throughout the table.

Column 1 of Panel A shows the baseline probit estimates, which exclude measures of financial literacy. We find that individuals who are older, more educated, and have higher income are more likely to have a bank account, consistent with findings in other countries (Christelis, Jappelli, and Padula, 2010; Cole, Sampson and Zia, 2011). Columns 2, 3, and 4 include our two measures of financial literacy (an index and the number of correct responses to the financial literacy questions). Both measures show a significantly positive effect (at the 10% level) of financial literacy on the likelihood of

> 12 In terms of federal regions (figures available upon request), the Central federal region, Volga, and the Southern region have the highest newspaper circulation, while the Urals, the Far-Eastern, and the Siberian region have the lowest numbers of newspapers in circulation. Moreover, the Southern region has the highest number of universities, with the next highest being the North-Western and the Central regions. The lowest numbers of universities are found in the Urals, the Far-Eastern, and the Siberian federal region.

20
# Table 9: COVID-19 and Electricity Consumption in Non-Lockdown Communities: Ghana

|||||logof Co|nsumption||||
|---|---|---|---|---|---|---|---|---|
||A|ll|Resid|ential|Non-Re|sidential|HeavyI|ndustries|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|DID (Month> April=0)|0.438***<br>(0.001)|0.439***<br>(0.001)|0.460***<br>(0.001)|0.462***<br>(0.001)|0.224***<br>(0.004)|0.225***<br>(0.004)|-0.229***<br>(0.052)|-0.224***<br>(0.052)|
|DID (Month> April=1)|0.179***|0.186***|0.201***|0.208***|-0.027***|-0.025***|-0.098*|-0.100**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.004)|(0.051)|(0.051)|
|DID (Month> April=2)|0.161***|0.142***|0.178***|0.158***|-0.001|-0.010***|-0.049|0.003|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.004)|(0.050)|(0.054)|
|DID (Month> April=3)|0.142***|0.149***|0.155***|0.163***|0.019***|0.025***|-0.054|-0.104**|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.004)|(0.047)|(0.051)|
|DID (Month> April=4)|0.145***|0.106***|0.153***|0.111***|0.076***|0.062***|-0.021|-0.003|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.004)|(0.052)|(0.057)|
|DID (Month> April=5)|0.198***|0.169***|0.211***|0.179***|0.086***|0.075***|0.001|0.014|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.004)|(0.051)|(0.054)|
|DID (Month> April=6)|0.135***|0.105***|0.146***|0.114***|0.035***|0.022***|-0.026|0.018|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.004)|(0.050)|(0.053)|
|DID (Month> April=7)|0.191***|0.182***|0.204***|0.194***|0.068***|0.067***|0.010|-0.012|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.004)|(0.004)|(0.050)|(0.052)|
|DID (Month> April=8)|0.372***|0.369***|0.473***|0.471***|0.200***|0.202***|0.030|-0.005|
||(0.015)|(0.015)|(0.025)|(0.025)|(0.018)|(0.018)|(0.051)|(0.053)|
|Climate Ctrls|No|Yes|No|Yes|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.691|0.691|0.676|0.676|0.753|0.753|0.836|0.836|
|Mean dep. Var|214.740|214.740|99.257|99.257|288.256|288.256|179514.430|179514.430|
|Obs|33321054|33321054|29970907|29970907|3334122|3334122|13427|13427|

Notes: Dependent variable is the log of monthly electricity consumption. DID (Month> April= _ω_ ) represents the DID estimates for the _ω_ months since the start of COVID lockdown (April, 2020). The DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly average</sup> temperature and total precipitation . Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 35
# **Table of Contents**

# **Introduction**

# **Part I: Policy foundations for sub-national debt markets development**

# Chapter 1: Agency problems in sub-national debt markets

Chapter 2: Key constraints hampering the development of sub-national debt markets Chapter 3: The evolution of sub-national debt markets as part of the transition from closed to open financial systems Chapter 4: Establishing the prerequisites for sub-national debt market development: a framework for policy reform

# **Part II: Building the architecture for sub-national bond markets**

Chapter 1: The relationship between the development of the domestic bond market and of the sub-national bond market Chapter 2: The regulatory and supervisory framework for the sub-national bond market Chapter 3: The relationships between bond market players and instruments for credit enhancement and pooling Chapter 4: Priorities for strengthening sub-national bond market architecture in developing and transition countries

# **Part III: Implications for the design of country assistance strategies**

Chiapter 1: Support strategies in pre-market conditions Chapter 2: Support strategies in emerging markets iii
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Niger’s Internet market compares poorly with other ECOWAS countries**

a. Internet service trends, Niger <!-- Start of picture text --> 0.8 8<br>0.7 7<br>0.6 6<br>0.5 5<br>0.4 4<br>0.3 3<br>0.2 2<br>0.1 1<br>0.0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> b. Internet service, ECOWAS, 2008 <!-- Start of picture text --> 14 350<br>12 300<br>10 250<br>8 200<br>6 150<br>4 100<br>2 50<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br>Sierra Leone Liberia Niger Guinea-Bissau Mali Burkina Faso Mauritania Gambia Senegal Benin Ghana Cote d'Ivoire Cape Verde Togo Nigeria<br><!-- End of picture text --> _Source:_ World Bank, including AICD analysis. _Note:_ ECOWAS = Economic Community of West African States.

# Financing Niger’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Niger needs to expand its infrastructure assets in key areas (table 16). The targets outlined in table 16 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting targets that can be modified or delayed as needed to achieve financial balance.

34