rafmacalaba/datause-encoder-data huggingface dataset:
In order to target poor communities, the program relies on self-selection into participation by schools serving poor families. The level of the subsidy at $90 per student is well below the level of tuition charged by those schools serving children in upper income quintiles, and as such those schools self-select out of the program. On the other hand, many schools serving poor children stood to benefit from participation, by potentially earning more per child through the program than through continuing to charge low levels of tuition.

# **3. MEASURING RESULTS**

# **_Initial Randomization, Identifying Schools Across Years, and School Survival_**

In the school year 2008-2009, due to a limited amount of program financing, the Ministry of Education and the World Bank agreed that only approximately 100 schools from each of the 5 newly participating departments in the country would be selected, among the 1,034 qualifying schools that applied.<sup>5</sup> A total of 547 schools were selected to participate: 110 in Centre, 115 in Grand-Anse, 112 in Nord-Est, 111 in Nord-Ouest, and 99 in Nord. The randomization was conducted so that at least one school from each commune of each of the departments was represented in the sample, in order to favor rural areas and maximize the geographical distribution of the program.<sup>6</sup> Education indicators have historically been worse in rural areas; for example, as of 2012, children living in rural areas are two percentage points less likely to be in school and 19 percentage points more likely to be over-age for their grade compared to children living in urban areas (World Bank 2014).

We identify the causal impacts of program participation on school-level characteristics through the national school census. The available census waves (2002-3 and 2011-12) provide information on school staffing and infrastructure, as well as the size of the student population.<sup>7</sup> Because the schools were randomly selected when the program was expanded in the 2008-9 school year, causal estimates of the program’s impacts on these measures can be made. A direct measure of the program’s results in terms of its primary objective – increasing the number of

> 5 Centre, Grand-Anse, Nord-Est, Nord-Ouest, and Nord.

> 6 The geographic divisions of Haiti are department, arrondissement, commune, and section communale.  There are 140 communes in the country.

> 7 The 2011-12 census was conducted less than two years after the January 20, 2010 earthquake.  While the five departments studied here were not directly affected, population movement following the earthquake may have impacted the schools in the study.  This is particularly true in Centre, the closest of the five departments to Ouest.

6
Treasury's net worth if the following conditions are met: the sale value of the public enterprise, plus the present value of future taxes from the latter, minus government expenditures to put the enterprise on the market exceeds the present value of future losses (if not privatized).

Conversely, privatization may have no effect on debt sustainability. If the enterprise was profitable and the net income flowing from the stock to be privatized was included in the fiscal primary balance, there would be no difference in the debt sustainability exercise if that income flow is reduced, but the proceeds from privatization are used to reduce the stock of debt<sup>8</sup> .

For most countries the flow of income from public sector enterprises is not included in Treasury revenue flows. Many of these enterprises either generate losses or their profits accrue only to the enterprise<sup>9</sup> .

# _Debt: gross or net?_

Another issue is the correct measurement of debt. For example, if the central bank issues debt in order to sterilize capital inflows, should there be any change in relevant debt statistics? What if debt is issued to buy a non-financial asset? This is not a minor issue. Country after country in Latin America, in one way or another, has issued a significant amount of debt in order to sterilize capital inflows. Brazil and Chile are prime examples, engaging in zealous sterilization practices.

In the case of foreign exchange purchases, an advisable approach would be to deduct them from gross debt, given that in the primary surplus, interest earned on those reserves is ignored. Non-financial assets are different. They could be useful when assessing government solvency but would complicate matters if net out in the debt sustainability exercise.

Moreover, implicating non-financial assets may muddle calculations. There are instances in which it may be difficult to value the asset counterpart of debt. For example, the central bank grants a rediscount to troubled banks, assumes a portion of the latter's assets as a guarantee, and sterilizes the monetary effect by issuing debt. Should this debt increment be offset by the seizure of commercial bank assets? What percentage of the rediscount would be paid back? In this example, the difficulty comes from the lack of indicators of the market value of commercial bank assets seized by the central bank.

Regarding the use of net or gross figures for debt analysis, a distinction should be drawn between financial and non-financial assets.

> 8 Given the interest rates, any stock can be converted into a flow and vice-versa.

- 9A classic counterexample would be Venezuela where a large share of PDVESA profits go to the government coffers. This is not the usual case in Latin America.

6
such as disaster relief or reconstruction, driving the observed increase in bank credit to the private sector.

Last but not least, disaster episodes are associated with a decrease in liquid assets relative to deposits. One year after an episode with multiple types of disasters, the liquid assets-to-deposits ratio decreases by 0.82 percentage point on average. This may be due to depositors and other investors withdrawing funds from their banks after a severe disaster, both to cover post-disaster expenses and for fear of solvency problems with banks, especially if they believe banks have substantial exposures to a vulnerable area.

Our preliminary results on the effects of climate and environmental related natural disasters on banking sector variables other than the NPLs should be interpreted with caution. First, our results on banking sector profitability and liquidity are tentative: aside from our results on net interest margins, we do not find a significant relationship between disaster episodes and other important measures of profitability, such as interest margin to gross income and non-interest expenses to gross income; we also fail to find a meaningful relationship between disaster episodes and liquid assets to total assets. Second, our results depend on the type of severe disaster we consider (official declaration or multiple types) and on the horizon.

**Table 7: Estimated effects of disaster episodes on other banking sector variables**

|Disaster: Ofcial Declaration|||||
|---|---|---|---|---|
|Dependent variable|NIM|Capital Adequacy|Private Credit to GDP|LADR|
|0 year|-0.02|-0.18|0.43|-0.68|
||(0.08)|(0.12)|(0.31)|(0.53)|
|1 years|0.12|-0.27**|1.28***|-0.07|
||(0.09)|(0.13)|(0.45)|(0.68)|
|2 years|-0.06|-0.18|1.01**|0.14|
||(0.10)|(0.16)|(0.50)|(0.77)|
|3 years|-0.14|-0.09|1.46***|1.14|
||(0.10)|(0.20)|(0.50)|(0.79)|
|Country FE|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|
|Disaster: Multiple Types|||||
|Depedent variable|NIM|Capital Adequacy|Private Credit to GDP|LADR|
|0 year|0.18*|0.05|-0.02|0.77|
||(0.10)|(0.14)|(0.28)|(0.57)|
|1 years|-0.06|0.38**|0.21|-0.82*|
||(0.10)|(0.17)|(0.31)|(0.48)|
|2 years|0.16|0.21|0.07|-0.26|
||(0.16)|(0.23)|(0.33)|(0.57)|
|3 years|0.04|0.76***|-0.14|0.32|
||(0.13)|(0.25)|(0.48)|(0.69)|
|Country FE|Yes|Yes|Yes|Yes|
|<br>Time FE|Yes|Yes|Yes|Yes|

The table displays point estimates for the effect of climate-related natural disasters on selected banking sector variables: NIM (net interest margins), Capital Adequacy(Regulatory Capital to Risk-weighted Asset Ratio), Private Credit to GDP and LADR (Liquid Assets to Deposits). Standard errors(in round brackets) are two-way clustered by country and time. Significance levels: *0.10, **0.05, ***0.01.

14
36

- Ferreira, F.H.G. and J.A. Litchfield (2001): “Education or Inflation?: The Micro and Macroeconomics of the Brazilian Income Distribution during 1981-1995”, _Cuadernos de Economía_ , **38** (141), pp.209-238.

- Ferreira, F.H.G. and R. Paes de Barros (1999): “The Slippery Slope: Explaining the Increase in Extreme Poverty in Urban Brazil, 1976-1996”, _Brazilian Review of Econometrics_ , **19** (2), pp.211-296.

- Foster, J., J. Greer, and E. Thorbecke, 1984, “A class of decomposable poverty measures.” _Econometrica,_ **52** , pp.761-65.

- Hoffman, R. (1989): “Evolução da Distribuição da Renda no Brasil, Entre Pessoas e Entre Famílias, 1979/86”, in Sedlacek, G.L. and R. P. de Barros (eds.) _Mercado de Trabalho e Distribuição de Renda: Uma Coletânea_ (Rio de Janeiro: IPEA, Série Monográfica No. 35).

- Instituto Brasileiro de Geografia e Estatística (IBGE) (1993): _Pesquisa Nacional por Amostra de Domicílios: Síntese de Indicadores da Pesquisa Básica - 1990_ , (Rio de Janeiro: IBGE).

- Jenkins, S.P. (1995): "Accounting for Inequality Trends: Decomposition Analyses for the UK, 1971-86", _<u>Economica</u>_ , **62,** pp.29-63.

- Lara Resende, A., D. Funaro, J. Sayad, L. G. Belluzzo, P. Arida and A. Solnik (1987): _Por Que Não Deu Certo_ (São Paulo: L&PM Editores).

- Leme, M.C.S. And S. Wajnman (2001): “Diferenciais de Rendimento por Gênero” in M.B. Lisboa and N.A. Menezes-Filho (eds.) _Microeconomia e Sociedade no Brasil_ (Rio de Janeiro: Contra-Capa Livraria).

- Litchfield, J.A. (2001): _Welfare and Income Distribution in Brazil, 1981-1995_ , Ph.D. Dissertation, London School of Economics.

- Mookherjee, D. and A. Shorrocks (1982): "A Decomposition Analysis of the Trend in UK Income Inequality", _Economic Journal_ , **92,** pp.886-902.

- Neri, M. (1995): "Sobre a Mensuração dos Salários Reais em Alta Inflação", _Pesquisa e Planejamento Econômico_ , **25** , no. 3, pp.497-525.

Ramos, L. (1993): _A Distribuição de Rendimentos no Brasil: 1976/85,_ (IPEA: Rio de Janeiro).

- Rocha, S. (1993): “Poverty Lines for Brazil: New Estimates from Recent Empirical Evidence”, IPEA - Rio de Janeiro, mimeo.

- Romer, C.D. and D.H. Romer (1999): “Monetary Policy and the Well-Being of the Poor”, _Federal Reserve Bank of Kansas City Economic Review_ , First Quarter, pp.21-49.
in 1984 to 26.5 million in 1994 and further to 33 million in 2005—and is projected to increase to 81.9 million by 2030. The industrial sector has much potential to create the many jobs that will be required in the coming years. But the descriptive statistics suggest that many cites attract capital-intensive firms, and that there is scope to increase the contribution of labor-intensive manufacturing. The move towards capital-intensive manufacturing could be a function of whether Ethiopian workers have the right skills – whether acquired through traditional academic and/or vocational education. The right mix between the two is subject to debate – for instance, Biavaschi et al (2012) argue that school education is the foundation and that vocational education and training is an add-on component only. Shaorshadze (2012) argues that Technical and Vocational Education and Training (TVET) in Ethiopia has been mostly supply driven. Figure 16 presents shows that the cities with high levels of school education also have high levels of vocational education. This very likely because in the both are provided by the public sector. This is unlike in successful countries such as Germany, where vocational education is supported by the private sector and is complementary to traditional schoolbased education. In a developing country like Ethiopia, given that policy makers might have to make cost-based choices, research (Tsang 1997) indicates that secondary vocational education is more expensive than secondary academic education, indicating a larger role for private sector involvement.<sup>8</sup>

**Figure 17: School education versus vocational education**

<!-- Start of picture text --> 80 8.0<br>70 7.5<br>7.0<br>60<br>6.5<br>50 6.0<br>40 5.5<br>30 5.0<br>4.5<br>20<br>4.0<br>10 3.5<br>0 3.0<br>Share of labour received TVET (%) Average Years od Education<br>(%)<br>METU SODO NEGELE BUTAJIRA AXUM AGARO ADIGRAT JIJIGA GONDER SEBETA DILA DIRE DAWA JIMMA HARAR NEKEMTE BEDELE MEKELE AMBO ASELA ADWA HOSAENA Average years of Schooling<br>Share of labour received TVET SHASHEMENE ZEWAY/BATU ADDIS ABABA<br><!-- End of picture text --> Source: UBAEUS, CSA

**Does infrastructure matter?** Industrialization, urbanization and growth are all infrastructure-intensive processes.  Hulten (1996) shows that a country’s stock of infrastructure and its ability to use it efficiently accounted for a large part of growth rates, and that low and middle-income countries did not use infrastructure efficiently. Indeed, the stock and the efficient use of infrastructure in most African countries lags behind other countries.

Foster and Morella (2011) found that the recent infrastructure build up in Ethiopia contributed to 0.6 percentage points to annual per capital GDP growth. However they also concluded that there remained a large scope for improvements in infrastructure.

> 8 In fact, pre-employment vocational training is much more expensive than in-service training.

18
maternal antenatal care, delivery, and newborn exam, and lower rates of use of private health facilities for these services. For example, mothers of 27% of slum children obtained antenatal care from an NGO health facility, compared to 17% for nonslum children; mothers of 20% of slum children obtained the same service from a private health facility, compared to 51% for nonslum children.

The 2013 BUHS asked the mother the reason for her choice of delivery site for the youngest child born in the three years before the survey. Table 5 reports the distribution of responses. In the pooled sample, the main reason that mothers chose to deliver in a health facility was due to pregnancy or delivery complications (55% of cases), followed by safety of the health facility (29%). The main reasons that mothers reported for choosing a specific health facility were safety (33% of cases), prior knowledge about the provider (17%), and receipt of antenatal care there (14%). Mothers reported cost as the reason in 6% of cases, and proximity of the health facility to home in 10%. For children who were delivered at home, mothers predominately reported that they felt that it was not necessary to deliver at a health facility (70% of cases). Cost or lack of money was reported only for 12% of cases.

Slum mothers were more likely to report cost and distance as the reasons for their choice of a specific health facility than nonslum mothers (cost: 9% versus 4% of cases; distance: 11% versus 7% of cases). Among those who delivered at home, slum mothers were also more likely to report cost or lack of money as the reason for their choice than nonslum mothers (13% versus 9% of cases).

_Effects:_ The type of health facility for antenatal care or delivery may influence child growth, if, for example, it is associated with quality of care. Mothers may choose to obtain antenatal care or deliver at a specific type of health facility because they expect to obtain better care there for any pregnancy or delivery complications. Even if they obtain better care, complications may induce a negative association between a higher-quality health facility or provider type and child growth.

Table 6 presents the estimated effects on HAZ scores of the use of maternal and child health services at a facility (Panel A), and at a facility by type (Panel B). The regressions control for the base set of factors. Of note, the regressions control for household wealth, which could influence whether households use free or subsidized public or NGO health services, or fee-based 24
On the other hand, market size has a limited impact on the quality of the investment climate. Large states have almost the same quality of the investment climate than small states. Interestingly the regulatory environment in the 16 states analyzed in this paper does not seem a discriminating factor of the overall investment climate. States that perform poorly on the investment climate composite indicators show the same average ranking in the quality of their regulatory environment than states that have a good investment climate. Since the regulatory environment index measures the _de jure_ business environment while the ICI measure the _de facto_ assessment of such

**Figure 4. Quality of the Investment Climate by typology of states**

<!-- Start of picture text --> Better IC<br>Worse IC<br>Level of Growth Market Regulatory FDI Infrastructure<br>Development Performance Size Environment Destination Quality<br>Low High Low High Small Large Poor Good Unattractive Attractive Poor Good<br><!-- End of picture text --> environment, this result seems to indicate that there is a gap between what is seen as the statutory environment of Indian states and the institutional environment in which these rules are applied. Furthermore, even though most of the sample interviewed in the India Surveys includes domestic firms, states with a better investment climate attract more FDI. Foreign investors share the view of domestic investors on the quality of the investment climate in Indian states. Finally, and not surprisingly,<sup>18</sup> the quality of infrastructure has a large impact on the quality of the investment climate. Using the infrastructure quality index developed by the GoI in 2000, states that show a good quality of ‘social and economic’ infrastructure have a much better investment climate than states with poor infrastructure.

> 18 As we will show later, the main determinant of the ICI in India is infrastructure.

14
- 29 -

# V. GENERALIZABLE LESSONS

5.01 Brazil's experience with urban property taxation yields generalizable lessons on both the practice of property tax administrationi and the process of implementing administrative reforms.

# A. ImDroving Administrative Procedures

5.02 Virtually all developing countries present a similar set of challenges to the administration of the property tax. The effectiveness of administrative procedures in responding to these problems determines how well the property tax performs. (For a summary of these common problems, and Brazil's response to them, see Box 2.)

# SUMMARY OF PROCEDURAL SOLUTIONS

<!-- Start of picture text --> i Proble Brazilian ResRonse<br>-<br>Rapid urban growth Cross-referencing<br>-<br>Voluntary compliance<br>-<br>Ongoing field surveys<br>-<br>Episodic general surveys<br>Scarce skills - Simple valuation method<br>- CIATA technical assistance<br>Inaccessible market data - Use of construction data<br>- Land value "commissions"<br>Inflation - Indexed valuations<br>-<br>Disputed liability Broad definition of liability<br>Slow judicial processes - Tax clearances<br>Box 2<br><!-- End of picture text -->

# Rapid Urban Growth

5.03 In a rapidly growing city, a one-time survey of parcels and property characteristics is obsolete as soon as it is completed. Property tax procedures must be capable of discovering and incorporating new construction onto the tax rolls on an ongoing basis.

5.04 In Brazil, municipalities employ four techniques to obtain updated information on property characteristics: cross-referencing, voluntary compliance, ongoing field surveys and episodic general resurveys. The taxclearance form of cross-referencing appears to be effective up-ier certain
Table 4: Lagged Shift in Non-Farm Enterprise Sales

||Non-farm En|terprise (NFE)||NFE|Sales||
|---|---|---|---|---|---|---|
||(0|/1)|Naira|(IHS)|Naira|(log)|
|_δlag_|0.149*|0.0952|-1.174|-1.899*|-0.585|-0.617|
||(0.0854)|(0.113)|(0.748)|(1.004)|(0.387)|(0.527)|
|_βlag,pv_||-0.0405||-0.159||0.205|
|||(0.0418)||(0.392)||(0.303)|
|_δlag,pv_||0.259||1.979*||0.226|
|||(0.192)||(1.156)||(0.737)|
|Constant|0.148***|0.151***|11.65***|11.66***|11.12***|11.10***|
||(0.000990)|(0.00300)|(0.00822)|(0.0275)|(0.00430)|(0.0198)|
|Obs.|3,882|3,882|2,002|2,002|1,978|1,978|
|R<sup>2</sup>|0.243|0.244|0.264|0.264|0.321|0.321|
|_δpv −βpv_||0.299||2.138||0.0213|
|S.E.||0.201||1.327||0.887|

_Notes_ : * p _<_ .1, ** p _<_ .05, *** p _<_ .01. Household-level data. All columns include fixed effects for if a household was located within 10 km. of a population center with at least 20,000 inhabitants, the state-level administrative center, and a market. S.E.s allow for clustering at the level of enumeration area (EA). Borno state is excluded.

33
by FDI inflow (Schultz, 1975), thus enabling female entrepreneurs. Better female education should therefore have the direct effect of increasing women’s entrepreneurship. However, education itself does not directly affect women’s access to critical resources to compete with FDI—as demonstrated by rapid female advances in education but the lack of advances in top jobs that are mentioned in the introduction. Female education thus likely has no significant interactions with FDI in determining female entrepreneurship.

The FDI effects likely differ among firms in manufacturing and in services. Femaleled firms have comparative advantage over male-led firms in service industries due to the relative importance of interpersonal relationships, and the relative lack of importance of brawn (vs brain) (Pitt et al., 2012; Zhang and Xu 2016). Furthermore, since entry into the service sectors likely requires less capital input, female entrepreneurs, facing stronger financial constraints, are more likely to enter service sectors. Thus, _the FDI-sectoral-bias hypothesis_ is that the FDI effect on female entrepreneurship in service sectors should be positive, or at least more positive than in manufacturing sectors.

The FDI effects should also differ among firms of different sizes. Perhaps due to the lack of access to critical inputs, female-led firms tend to be smaller. Since FDI firms tend to have stronger spillover effects on smaller firms that serve FDI firms in various capacity, and there are relatively more female-led ones among small firms, we expect FDI inflow in general to have stronger spillover effects on female entrepreneurship among the small firms than among the medium-and-large firms. The intra-industry FDI effects on female entrepreneurship among small firms (relative to other larger firms) is ambiguous: the spillover effect is positive, but the horizontal competition effect is negative due to female-led firms’ lack of competitiveness with respect to larger FDI firms. The _FDI-sizebias hypothesis_ is thus the following: on female entrepreneurship, FDI should have a larger general spillover effects among small firms than among medium-and-large firms, but an ambiguous intra-industry effect when comparing the effects among the two types of firms.

# **3. Data set**

The main data source is the World Bank’s Enterprise Surveys (WBES) in 709 cities of 128 countries. The WBES data are collected by the World Bank to benchmark the business 7