rafmacalaba/datause-encoder-data huggingface dataset:
# **Child Ability and Household Human Capital Investment Decisions in Burkina Faso**<sup></sup>

Richard Akresh University of Illinois at Urbana-Champaign Emilie Bagby University of Illinois at Urbana-Champaign Damien de Walque The World Bank Harounan Kazianga Oklahoma State University .

_Keywords_ : Education; Sibling Rivalry; Child Ability; Household Decisions; Africa _JEL classification_ : O15, J12, I21, J13

* The data used in this paper were collected for an ongoing project evaluating social protection strategies in Burkina Faso. The project greatly benefited from the support and guidance of Marie-Claire Damiba, Seydou Kabré and Victorine Yameogo from the Secrétariat Permanent du Comité National de Lutte contre le SIDA et les Infections Sexuellement Transmissibles (SP-CNLS-IST) in Burkina Faso and Hans Binswanger, Nono Ayivi-Guedehoussou, Ousmane Haidara, Timothy Johnston, Mead Over and Tshiya Subayi-Cuppen at the World Bank. The data collection was supervised by Robert Ouedraogo, Jean-Pierre Sawadogo, Bambio Yiriyibin and Pam Zahonogo from the University of Ouagadougou,  Department of Economics. This project is funded by the NBER Africa Project and grants from the following World Bank trust funds: Strategic Impact Evaluation Fund (SIEF), Bank-Netherlands Partnership Program (BNPP), Gender Action Plan (GAP), Knowledge for Change Program (KCP) and Luxembourg Poverty Reduction Partnership (LPRP). The authors would also like to thank Kathy Baylis, Don Fullerton, Craig Gundersen, Christopher Ksoll, Nolan Miller, Elizabeth Powers, and participants at the T. Paul Schultz Festschrift conference and seminars at the University of Illinois at Urbana-Champaign for helpful comments on earlier drafts. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

Richard Akresh, University of Illinois at Urbana-Champaign, Department of Economics, 1407 West Gregory Drive, David Kinley Hall, Room 411, Urbana, IL 61801.Email: akresh@illinois.edu; Emilie Bagby, University of Illinois at Urbana-Champaign, Department of Economics, 1407 West Gregory Drive, David Kinley Hall, Room 410, Urbana, IL 61801. Email: <u>ebagby2@illinois.edu;</u> Damien de Walque, The World Bank, Development Research Group, 1818 H Street, N.W., Washington, D.C., 20433. Email: ddewalque@worldbank.org; Harounan Kazianga, Oklahoma State University, Department of Economics, 327 Business Building, Stillwater, OK 74708. Email: harounan.kazianga@okstate.edu
**Table** 7: **Number of phones due and installed by December 31, 1999**

|**Year**|**Phones due**|**Phones installed**|**Percentage installed**|
|---|---|---|---|
|**1997**|**265**|**265**|**100**|
|**1998**|**1,551**|**1,436**|**92,6**|
|**1999**|2,574|**2,350**|**91.3**|
|**2000**|**1,078**|**211**|**19,6**|
|**2001**|**448**|||
|**Total**|**5,916**|**4,262**|**72,0**|

Source: Memoria 1999, Fondo de Desarrollo para las Telecomunicaciones, Memoria 1999, Subtel.

A second problem has been the quality of the service - particularly its reliability. The successful bidder takes on the commitment to maintain the public phone for ten years, which is not a minor issue since some of the phones are in remote areas, and maintenance and billing costs are quite high. There are indications that service quality, especially among phones located in isolated areas, is not always the best: hence the need to monitor service quality, especially its continuity, in order to guarantee fulfillment of the standards established in the regulations. One way of doing this would be through user surveys. Local authorities are closer to the subsidy beneficiaries and so in the best position to carry out such surveys; municipalities should therefore be active in monitoring public phones.

In some rural phone concessions the franchise-holder delegates responsibility for collecting payments to an administrator. Despite being public phones, they operate like private telephones in terms of billing, which raises two problems. Firstly, it makes it hard for the administrator to charge for long-distance calls, since invoices arrive with a delay. Secondly, if the administrator fails to pay the invoices on time, the service is cut off, thereby harming the entire community. Since 1999 it has been obligatory to install coinoperated phones, but this does not solve the problem of the telephones already installed. Another problem is that coin-operated telephones sometimes break down because the 32
Comparing Figures 3, 4 and 5 what is striking is that states which experienced rapid structural change also experienced faster rural and urban poverty reduction. In short, structural change and growth appear to matter for poverty reduction.<sup>6</sup> Simple regression analysis helps to bring this point home. In Table 1 we show regressions of poverty measures on output per capita measures using panel data on the 16 main Indian states for the 1958-2000 period. Column (1) shows that increases in the levels of non-agricultural output are associated with significant falls in overall poverty. In contrast, changes in agricultural output do not affect poverty. We find a similar pattern when we break poverty into its urban and rural components in columns (2) and (3) – it is the growth of non-agricultural output which is exerting a significant effect on urban and rural poverty. In columns (4) and (5) we look at the influence of registered and unregistered manufacturing.<sup>7</sup> In column (4) we see that increases in per capita registered manufacturing output reduce urban but not rural poverty. This makes sense, as these larger firms are located mainly in urban areas. In contrast, increases in output from firms in the unregistered manufacturing sector is significantly negatively associated with rural poverty but not urban poverty (see column (5)). The entry and growth of small businesses is important for explaining the pattern of rural poverty reduction.

We have established that structural change matters for economic growth and poverty reduction in India. We now turn to the question of which institutions and policies account for different rates of structural change across Indian states. We focus, in particular, on manufacturing. The fact that states, within a centrally planned economy, performed so differently in terms manufacturing is striking (see Figure 4). Much of the literature so far has

> 6 Using Indian state level data Ravallion and Datt (1996) have established a robust link between income growth and poverty reduction. They find that the elasticity of the incidence of poverty with respect to net domestic product per capita was –0.75 for the period 19581991. This figure is close to the elasticity of –0.76 which Besley and Burgess (2003b) find using cross-country data for developing economies.

> 7 Over the period the registered sector makes up about 9 percent of state output and the unregistered sector 5 percent.

24
consumption than HFCE.<sup>14</sup> The second factor is the recovery from the 2008 financial crisis, which is distinctly visible in the 2009 national accounts data. Survey-based consumption, however, may have taken longer to recover. Both factors could have contributed to the unusually large increase in mean consumption observed between 2009-10 and 2011-12, which counteracted the usual tendency for HFCE growth to exceed growth in mean consumption. We know of no reason to believe, however, that growth in HFCE will accurately reflect growth in mean consumption going forward. Turning back to the model predictions, we consider three alternative specifications that impose more restrictive assumptions. These estimate a slower poverty decline, with estimated headcount rates ranging from 17.4 to 19.1 percent. In contrast, the traditional line-up method shows a much more rapid decline in poverty, from 21.6% in 2011-12 to 10.9% in 2014-15. This shows that the choice of modeling assumptions matters, at least in this case.

The final step is to use the estimates for 2014-15 from the econometric model to generate the poverty estimate for India for 2015 used in the global estimates. This was done by estimating an appropriate fraction, or “pass-through rate”, to apply to growth in HFCE, thereby assuming that only a portion of the HFCE growth is passed through to growth in mean welfare in the household survey. Pass-through rates were selected to match the $1.90 headcount poverty estimates from the survey-to-survey imputation model for 2014-15, which were 10% in urban areas and 16.8% in rural areas. These were then used to calculate estimates for 2015, based on the scaled down growth rate of HFCE.<sup>15</sup> The rest of this paper is organized as follows. The next section reviews the data used for the analysis. Section III turns to the methodology. It discusses the estimation method and the process used to select the set of predictor variables, and examines the results of four alternative specifications to the model. Section IV compares the preferred model’s predictions for 2014-15 to the usual method used to project poverty forward, and investigates which specific variables are accounting for the change in average welfare predicted by the model. Section V shows that the estimated poverty rate implies a reasonable elasticity of poverty reduction with respect to growth, verifies that implied poverty reduction is greater in fast-growing states, and examines the model’s predictions at higher poverty lines. Section VI briefly describes how the 2014-15 estimates were used to generate poverty estimates for 2015, and Section VII concludes.

> 14According to the Economic Survey 2017-18 of the Ministry of Finance in India, agriculture employs over half of Indian workers but only contributes 17 to 18 percent of India’s GDP.

> 15The 2015 estimates can be viewed at http://iresearch.worldbank.org/PovcalNet/povDuplicateWB.aspx. See http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx for further details.

6
**| Pol!ey, R search, and External Affairs**

**Ppliton, Hoslth, and Nutrition**

<!-- Start of picture text --> WPS 478<br><!-- End of picture text -->

- This paper a product of the Population, Health, and Nutrition Division, Population and Human Resources Department-is part of a larger effort in PRE to examine family consequences of high fertility. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Susan Cochrane, room S6-139, extension 33222 (52 pages, including tables). Using Egyptian data from 1975-80, Marcotte and more careful about childcare and children's diet Casterline found that weaning children in and hygiene after weaning. infancy inc.eases the risk of death for children under five. Early weaning is responsible for up Replacement behavior in response to to 29 percent of Egyntian children's deaths. children's death accounts for up to 18 percent of pregnancies. It is not actual mortality but Children whose mothers become pregnant perceptions of a child's chances for survival that again are more likely to die if the pregnancy drive fertility. As infant deaths become less begins while the child is still an infarl. Ending common, the proportion of replacement pregnanbreastfeeding is responsible for up to 41 percent cies should decline. of pregnancies -52 percent among women who do not use contraceptives. An important feature of this analysis was

Using Egyptian data from 1975-80, Marcotte and Casterline found that weaning children in infancy inc.eases the risk of death for children under five. Early weaning is responsible for up to 29 percent of Egyntian children's deaths.

An important feature of this analysis was that fertility (represented by pregnancy) was examined simultaneously with child survival and breastfeeding, as three components of a system. The analysis involved regression models for the hazard, or risk, of three events occurring after a live birth: another pregnancy, weaning, or the death of the child.

Breastfeeding lasts an average 17 to 18 months in Egypt, so policy should probably not encourage all women to breastfeed longer, but women who breastfeed for only short periods should probably be encouraged to breastfeed longer. And parents should be encouraged to be The PRE Working Paper Series disseminates the findings of work under way **in** the Bank's Policy, Research, and Extemal AffairsComplex. An objective ofthe series is to get these **findings** outquickly. even if presentations areless than fullypolished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the PRE Dissemination Center
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An important limitation of this type of survey is that it mainly captures rules and regulations that are on the books rather than actual implementation. IMF and the World Bank financial sector assessments have often found implementation to be lacking, particularly in lowincome countries, so that cross-country comparisons of what is on the books may hide substantial variation in the quality of supervision and regulation. BCP assessments have the advantage of taking into account implementation. Of course, assessing how rules and regulations are implemented and enforced in practice is not an exact science, and individual assessments may be influenced by factors such as the assessors’ experience and the regulatory culture they are most familiar with. Nevertheless, although it is difficult to eliminate subjectivity completely, assessments are based on a standardized methodology and are carried out by experienced international assessors with broad country experience.

Cihak and Tieman (2008) analyze the quality of financial sector regulation and supervision using both Barth, Caprio and Levine’s survey data and BCP assessments. They find that regulation and supervision in high-income countries is generally of higher quality than in lower-income countries. They also note that the correlation between survey data and BCP data tend to be low, always less than 50 percent and in many cases in the 20-30 percent range, suggesting that taking into account implementation may indeed make an important difference.

A number of papers also use BCP assessments to study bank regulation and performance. Sundararajan, Marston, and Basu (2001) use a sample of 25 countries to examine the relationship between an overall index of BCP compliance and two indicators of bank soundness: nonperforming loans (NPLs) and loan spreads. They find BCP compliance not to be a significant determinant of these measures of soundness. Podpiera (2004) extends the set of countries and finds that better BCP compliance lowers NPLs. Das et al. (2005) relates bank soundness to a broader concept of regulatory governance, which encompasses compliance with the BCPs as well as compliance with standards and codes for monetary and financial policies. Better regulatory governance is found to be associated with sounder banks, particularly in countries with better institutions.

In this paper, as already discussed we rely on the Z-score to measure bank soundness. While the Z-score has its limitations, we believe it is an improvement over measures used in
# <u>Brazil</u>

The Brazil data includes a sample of 1,641 firms distributed across 13 states (Table 5). To make the analysis comparable with China, 930 firms from five industry groups: Textiles, Apparel, Shoe and Leather Products, Electronics and Auto Parts, were selected. Table 6 shows that unlike the China sample, almost half of the observations are from a single industry group: apparel. Also, unlike the China sample, only a very small share of firms is state-owned.

As Table 7 shows, with the exception of the Auto Parts industry group, most firms do not export. Most notably, about 85% of the firms in the garment industry group produce exclusively for domestic production. This lack of export orientation is found even in the most economically advanced states of S **ã** o Paolo and Rio de Janeiro (Table 8).

**Table 5:  Brazil Observations by State**

|**State**|**Number of**<br>**Firms**|**Percentage of**<br>**Total Firms**|
|---|---|---|
|**São Paulo**|359|21.9|
|**Rio de Janeiro**|122|7.4|
|**Minas Gerais**|232|14.1|
|**Santa Catarina**|175|10.7|
|**Rio Grande do Sul**|191|11.6|
|**Paraná**|179|10.9|
|**Goiás**|82|5.0|
|**Mato Grosso**|38|2.3|
|**Ceará**|90|5.5|
|**Paraíba**|47|2.9|
|**Maranhão**|22|1.3|
|**Bahia**|77|4.7|
|**Amazonas**|27|1.7|
|**Total**|1,641|100.0|

9
# TAXATION, PUBLIC SERVICES, AND THE INFORMAL SECTOR IN A MODEL OF ENDOGENOUS GROWTH*

Juan Braun Pontificia Universidad Catolica de Chile and Norman V. Loayza The World Bank JEL 011, 041, H26, H41 Keywords: Growth, Informal Economy, Tax-Evasion, Congestion Effects, Public Goods

-We thank Alberto Alesina, Robert Barro, John Driscoll, Edward Glaeser, Debraj Ray, Jaume _Ventua,_ and participants in the Economic Growth Workshop at Harvard University for helpful suggestions. "Address: The World Bank, 1818 H Street, N.W., Washington, D.C. 20433. Phone: (202) _458-_ 4767. Fax: (202) 522-1151.
<!-- Start of picture text --> A: Mean Profits by Gender and Time Period<br>Men Women<br>328 390 337 350<br>350 257 268 248<br>o 245 236 256 300<br>=° 300 191 =o 250 tee 18° 171 195 173178<br>&gy 250 | &fe} 200 154 157<br>© 2015 0 | id§ 150 | | |<br>100 100<br>50 50<br>0 2013 (Baseline) 2016 (2-Year) 2021 (7-Year) 0 2013 (Baseline) 2016 (2-Year) 2021 (7-Year)<br>MME Personal Initiative HE Traditional Training Control<br>B: PI Quantile Treatment Effects on Total Profits<br>2 Short-run Profits 2 Long-run Profits<br>os 68s<br>as / a°<br>28 i Zo<br>So 2°<br>cw cn<br>oT [e}<br>=s = g<br>£3cv = icof y><br>(3) —_——_— 3} ae ee .<br>ae —— a0 —<br>= 5140 2 30 40 50 60 70 8 9095 = 510 2 30 40 50 60 70 80 9095<br>Quantile Quantile<br>—_ Ven ——-—- Women<br><!-- End of picture text -->
# Table 9. Continued.

|||1992 Value|of OECD Impo|rts(US$ millions)|||Share of|OECDInports|Covered by Nontar|iff Measures|
|---|---|---|---|---|---|---|---|---|---|---|
|Exporter|All<br>Goods|Foods<br>and<br>Feeds|Agricultural<br>Materials|Ores and<br>Non-Ferrous<br>Metals|Manufactures|All<br>Goods|Foods<br>and<br>Feeds|Agricultural<br>Materials|Ores, and<br>Non-Ferrous<br>Metals|Manufactures|
|Sierra Leone|354|47|1|86|220|0.1|0.3<br>|_5.5_<br>|0.0<br>|0.0<br>|
|Somalia|19|16|2|-|1|9.0|11.6|0.0|43.1|0.0|
|South Africa|11,132|1,799|609|4,711|2,251|13.0|57.1|0.6|8.4|0.4<br>|
|Sudan|149|39|102|1|6|12.4|31.9|0.0|0.0<br>|0.0<br>|
|Tanzania|266|153|36|34|40|3.8|5.8|0.0|0.0|5.3|
|Togo|121|31|14|64|7|0.5|1.6<br>|0.1<br>|0.0<br>|0.2<br>|
|Uganda|162|143|16|0.3|3|0.0|0.0|0.0|0.0<br>|0.0<br>|
|<br>Zaire|1,277|80|64|472|395|0.0|0.0|0.0|0.0|0.1<br>|
|Zambia|630|16|6|579|28|0.7|29.8|0.0<br>|0.0<br>|0.6<br>|
|Zimbabwe|875|386|61|324|102|20.3|30.3|0.1|19.4|8.0|
|SSA|44,791|8,022|2,719|7,521|6,524|13.1|23.4|0.3<br>|5.7<br>|5.6<br>|
|SSA excl S.Africa|33,412|7,822|2,608|7,503|6,369|13.1|18.6|0.2|2.6<br>|6.5<br>|
|Developing Countries|705,634|79,053|20,303|42,227|383,871|16.6|17.1|1.3<br>|10.1<br>|18.8<br>|
|Developed Countries|1,986,779|190,602|53,386|116,438|1,499,800|10.2|24.6|1.3|13.6|8.5|

Source: Trade data from United Nations COMTRADE files.<sup>Nontariff barrier information from World Bank-UNCTAD SMART Data Base.</sup> See the notes to Table 2 for the components of the product groups shown in this table<sup>defined in terms of the SITC (revision 1) classification system. The notes to Table 3 indicate which nontariff measures have been</sup> employed for the computation of the trade coverage ratios.

0
8 According to a survey on German firms' investment behavior in developing countries done by the Ifo-Insti.tute in Munich dating back to 1980 "the most important individual factor for German investors is the difficulties in dealing with the state authorities (bureaucracy) in devc oping countries. Only just under one-fifth of tha ratings classitied this as a minor factor ( Pollak and Riedel (1984, p. 29))." Obtaining a complete picture of this issue for all the countries included in the investigation is, of course, beyond the scope of this paper. There are countries which are known to have a particular tedious approval proceduge, for example Nigeria and Kenya. On the other hand there are a few countries which have a codified, straightforward approval process; in Korea for example many investment projects are approved automatically. For the purpose of this investigation the scoring of the approval process is in most cases taken from outside sources.

Apart from the three issues discussed above there are many others which are crucial for the investment climate in a developing country. Countries which are restrictive with respective to the above mentioned factors often have numerous exceptions from their rules for specific purposes. Among those are export processing zones, tax exemptions, and high degree of industry protection. Other countries, like Chile and recently Korea, rather rely more on deregulation for foreign and domestic investors alike. With respect to the various exemptions from taxes, tariffs and duties particular preferential treatment is often available for joint ventures (Algeria, Egypt, Mexico). There are on the other hand some popular disincentives like quotas mandating employment of host country's --tionals (Malaysia) and performance requirements like local content requirements (Venezuela). The pattern of incentives and disincentives often varies substantially between different sectors.
|Dependent variable:|Need of fisc|al adjustm|ent based o|n Escolano|et al.(201|4)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.037***|0.033***|0.038***|0.032***|0.041***|0.033***|0.021|0.028***|
|(% of GDP)|(0.006)|(0.005)|(0.006)|(0.005)|(0.007)|(0.010)|(0.013)|(0.009)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.928***||-1.829***|-1.749***|-1.824***|-1.820***|-1.841***|
|||(0.413)||(0.420)|(0.409)|(0.424)|(0.416)|(0.424)|
|Inflation (t-1)||-0.090||-0.053|-0.049|-0.053|-0.051|-0.056|
|||(0.074)||(0.066)|(0.062)|(0.066)|(0.067)|(0.067)|
|U.S. Interest rate||-0.012||-0.015|-0.017|-0.015|-0.017|-0.015|
|||(0.010)||(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|**Political factors**|||||||||
|Gini|||0.007|0.005|0.005|0.006|0.005|0.005|
||||(0.005)|(0.005)|(0.005)|(0.007)|(0.005)|(0.005)|
|Rule of law|||0.002|0.012|0.004|0.012|-0.053|0.013|
||||(0.029)|(0.030)|(0.029)|(0.030)|(0.050)|(0.030)|
|Margin of Majority|||0.092|0.059|0.066|0.059|0.063|-0.094|
||||(0.123)|(0.124)|(0.124)|(0.124)|(0.125)|(0.231)|
|Election's year|||0.021|0.028*|0.029**|0.028*|0.028*|0.028*|
||||(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.017*<br>(0.010)||||
|Gini * Rigidity||||||-0.000<br>(0.000)|||
|Rule of law * Rigidity|||||||0.002<br>(0.002)||
|Majority * Rigidity||||||||0.007<br>(0.012)|
|Constant|-0.840***|-0.607***|-1.212***|-0.884***|-0.874***|-0.913**|-0.618*|-0.796***|
||(0.163)|(0.136)|(0.308)|(0.277)|(0.275)|(0.346)|(0.327)|(0.297)|
|Observations|1,521|1,500|1,360|1,342|1,342|1,342|1,342|1,342|
|R-squared|0.126|0.182|0.139|0.186|0.193|0.186|0.188|0.187|
|Number of ifscode|84|84|75|75|75|75|75|75|
|CountryFE<br>Robust standard erro<br>*** p<0.01, ** p<0.0|Yes<br>rs clustered<br>5, * p<0.1|Yes<br>by country|Yes<br>in parenth|Yes<br>eses|Yes|Yes|Yes|Yes|

**Table 4**

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