rafmacalaba/data-use-mentions-extended huggingface dataset:
We assigned interventions to _at least_ one of 12 intervention categories.<sup>10</sup> Around 60 percent of programs included a component focused on teachers’ professional development, with more of a focus on this kind of component in low- and middle-income countries. This is not surprising as many interventions that aim to improve the quality of preprimary education—such as a change in curriculum or pedagogy—will require teachers to receive some training prior to program implementation. Similarly, offering a new service will also initially entail training. Close to half of all programs provided subsidized or free access in studies from high-income countries, compared to only 3 percent among studies from low- and middle-income countries. In both contexts, around 70 percent aimed to target a disadvantaged population.

These interventions include both small-scale pilots implemented by non-governmental organizations (NGOs) and nationally scaled government programs. Daycare programs with education components represented very few evaluated programs; formal preschool was the dominant program in both high-income and low- and middle-income contexts, although community preschool accounted for more than 40 percent of programs in low- and middleincome contexts. While a majority of programs in both contexts took place in publicly managed schools, a sizeable fraction had a combination of public and private management. A higher fraction of programs in studies from high-income countries took place in classrooms with a teacher who was formally qualified to teach preprimary students than in studies from low- and middle-income countries, where teachers were formally qualified to teach in preprimary settings in only half of the studies in the sample.

# **_3.1.3.3 Outcomes_**

We focused on outcomes related to school participation and child development, extracting average treatment effects and their precision for each intervention (Table 4). When available, we also captured outcomes of teachers, such as classroom practice or self-reported measures of wellbeing. We extracted a total of 798 outcomes at the child and teacher levels. In one study that relied on a very large administrative dataset with a panel structure (Rossin-Slater
On the other hand, teacher turnover rates were significantly higher in private schools. Between February and October, around 17 percent of public school teachers had left the school, while nearly 39 percent of private school teachers had left the school. It is possible that control over teacher dismissals allows private schools to enforce higher effort among teachers than public schools. However, due to this and lower salaries, private schools may suffer from unstable teacher tenures which may influence attrition rates and quality of service delivery.

# 6.3 Financing

The information presented in this section, which comes from 2015, is likely to be somewhat outdated, as FBEP – which eliminates all school fees for lower secondary public schools – was introduced in 2016. However, it is instructive because it helps contextualize the education market for secondary schools in Tanzania.

Over half (57 percent) of public schools and nearly all private schools in our 2015 Morogoro data charge tuition. Also, private schools charge higher non-tuition fees than their public school counterparts. These fees cover registration/admission, food, transportation, textbooks, and extracurricular activities. Families of students in private schools pay close to 9.5 times more in nontuition fees at the pre-primary/primary level and 2.1 times more at the secondary level ( **Table 3** ). However, based on self-reported data, nearly 65 percent of private schools claim to provide scholarships to marginalized students, compared to 17 percent of public schools. This number stands at 53 percent at the pre-primary/ primary level and 74 percent at the secondary level for private schools.

While private schools do not receive government funding, they appear to be financially sustainable. Nearly, 69 percent of private schools own the land on which the school resides and 71 percent own the school building. Of those which do not own the school building, only 56 percent pay a rent. Only 11 schools reported currently having outstanding debt, credit or loans, and an equal number of private schools reported plans to borrow money in
where _s;,_ = _Li_ is the ratio of labor employed in sector i to total labor employed in the economy. Taking the differential of (5) we obtain: Figure 5 shows the relative contributions of sectoral labor productivity growth and labor reallocation between sectors to economy-wide labor productivity growth. It can be seen that labor productivity growth within sectors has been the dominant contributor. The impact of labor reallocation which was negative till 1987-88 has been positive since then. Given that Malaysia has accumulated capital quite rapidly relative to labor, the increase in sectoral labor productivity and its large contribution should not be surprising.

# **IV.** EXPLANING TFP GROWTH

In theory, a large number of policies and factors can contribute to output, capital and TFP growth. There is an extensive cross-country empirical literature that has identified macroeconomic, trade and financial sector policies as being significant factors in explaining output growth [Fischer (1993), King and Levine (1993), Summers and De Long (1993)]. Given the cross-country nature of these studies over relatively long time intervals they tend to capture long-run relationships between the chosen variables. In contrast with the earlier literature we employ time-series data for an individual country rather than cross-country data. In what follows, we would not interpret our results as capturing long-run relationships, rather what we capture are shorter run dynamics in the specific Malaysian setting. We discuss below some of the key policies that influenced TFP growth, at least in the short-run. The longer run relationship between these variables may or may not be similar.

Three structural features of Malaysia stand out. First, its openness. The ratio of exports and imports to GDP for Malaysia at 190% in 1997, is high compared to Korea (77%) and Thailand (93%), Mexico (33%) and Chile (49%). Second, capital inflows. FDI inflows into Malaysia averaged nearly 5% of GDP during 1994-97, while it was less than 2% for Korea and Thailand. Third
Policy Research Working Paper 10127

# **Abstract**

This paper uses a hedonic property price function to estimate the relationship between flood risk and rents in four Sub-Saharan Africa cities: Accra, Antananarivo, Dar es Salaam, and Addis Ababa. The analysis relies on household survey data collected after flood events in the cities. Flood risk is measured with self-reported data on past flood exposure and perception of future risk of flooding of households. The study finds that flood risk is associated with lower rents in Accra, Antananarivo, and Addis Ababa, ranging from 14 to 56 percent lower. In contrast, risk is associated with higher rent in Dar es Salaam, which could be potentially attributed to a combination of lack of awareness of flood risk among renters, high transaction costs and omitted variable bias. For example, only 12 percent of households living in flood-prone areas were aware of the flood risk when they moved in. In Antananarivo, job density is associated with higher rents while in Accra and Addis Ababa, higher job density is associated with lower rents. Results are negative but not significant in Dar es Salaam. When interacting job density with flood risk for each city, the negative effect of job density on rents is higher (in absolute value) when flood risk is high in Accra and Addis Ababa, and the positive effect of job density on rents becomes negative when flood risk is high in Antananarivo. This relationship is not found in Dar es Salaam. The finding seems to suggest that access to jobs is a factor driving people to settle in flood-prone areas.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aerman@worldbank.org.

_The Policy Research Working
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determines how much de jure exemption of food can redistributive. Because food Engel curves are downward set reduced rates or typically sloping, many governments fully exempt food products for redistributive purposes.<sup>26</sup> Previous research has studied the magnitude and approximate log-level linearity of the food Engel curves.<sup>27</sup> We combine our core data with microdata from the Global Consumption Database (discussed in section 2.3) to estimate food Engel curves in a uniquely large sample of 89 low and middle income countries.

In the top panels of Figure 4, we present the aggregate food budget share (Panel A) and food Engel slope (Panel B) against countries’ GDP per capita. The aggregate budget share spent on food decreases with development, but the percentage-point drop over development is less pronounced for food than for informal consumption. Food Engel curves are typically downward sloping and there is no relationship between the magnitude of the slope and development. In Panels C and D of4 we show the _formal_ food aggregate budget share and Engel curve slopes, respectively. The aggregate formal food budget share is small on average (11%) but increases with development. The slope of the formal food Engel curve is small in magnitude but positive on average in low-income countries; it becomes negative in upper-middle income countries.

Panels A and B show that the levels and of food curves are Finally, slopes Engel similar between our core and extended for countries at the same very sample sample, level of development. This could suggest that while we can only characterize informal consumption patterns in our core sample, our results may be relevant for developing countries more broadly.

# **4 Progressivity of Consumption Taxes**

The consumption patterns described in Section 3 determine the progressivity of consumption taxes both in the average developing country and across development, which we turn to in this section. We say a tax policy is progressive if the effective tax rate (ratio of taxes paid to household income) increases with household income
possibility by performing additional estimations in which we control for alternative measures of culture.

First, Global Findex reveals that religious belief is one of the top reasons for voluntary financial exclusion, with 6 percent of respondents citing it. By the same token, Demirgüc-Kunt et al. (2014) demonstrate that Muslims are significantly less likely than non-Muslims to own a formal account or save at a formal financial institution, mostly due to the fact that conventional financial products violate the Sharia. Moreover, evidence shows that religion-based financial exclusion is disproportionately more prominent among women (Cicchiello et al., 2021). As our sample does not allow us to control for respondent’s religion, we consider the CIA World Factbook country-level data to create four dummy variables ( _Catholic, Protestant, Muslim and Buddhist_ ), each of which takes the value 1 if the religion represents more than 50% of the population in the country (highest percentage of practicing population) and 0 otherwise. We drop _Buddhist_ to avoid perfect collinearity.

Second, Lu et al. (2021) suggest that a country’s cultural attributes – particularly its level of individualism – are linked to its inhabitants’ financial inclusion. Overall, the effect of culture on the decision-making process of individuals has been widely investigated in the economics and finance literature (Guiso et al., 2006). Therefore, we consider additional cultural dimensions using the six traditional indices provided by Geert Hofstede, namely _Individualism_ , _Power distance_ , _Masculinity, Long-term orientation, Indulgence_ and _Uncertainty avoidance_ . Variables are described in detail in **Appendix A** .

The reason why additional cultural measures and religion where not included in the main estimations is that they may potentially introduce multicollinearity in our model. Indeed, culture has been shown to exert an influence on the development of laws, especially those related to gender (Bu, 2015).

**Table 6** reports the results of the regressions with these additional control variables included. We first add them separately in Columns (1) and (
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to either reducing inequality or reducing poverty **.** The large marginal contributions to inequality reduction stemming from public basic education is expected since poor households have more children that access basic public schools. The model could, however, overestimate the inequality reduction stemming from in-kind public health benefits considering that benefits were imputed based on access to public-health insurance rather than actual usage of public health services (not available in the PECS 2016/17). Yet human capital outcomes as measured by the SES-Disaggregated Human Capital Index are relatively even across socioeconomic quintiles compared to data for other countries,<sup>81</sup> suggesting that public spending on education and healthcare has been effective at reducing gaps in quality.

**The VAT and customs duties are important tools for domestic revenue mobilization, but they represent the main drivers of poverty increase in the West Bank and Gaza.** While VAT and customs duties were progressive overall, the large burden they cause explains why they represent the largest marginal contributions to the national increase in poverty. Pension contributions and the PIT also increase poverty, although to a lesser extent due to their smaller size.

**Strengthening the social protection transfer system could improve poverty and inequality reduction.** Cash transfers from social protection (CTP) and pension income contribute most to poverty reduction in

> 80 The marginal contribution of the in-kind benefits at Consumable income is 2.9 Gini points, while the CTP has a marginal contribution at Consumable income of 1.2 Gini points. 81 Avitabile et al (2020).

60
**for human capital accumulation** . Using data from Spain, De La Roca and Puga (2017) show that workers living in bigger cities see faster growth in earnings over the life cycle, consistently with the view that they acquire more valuable experience; this conclusion is supported by the evidence in Eckert et al. (2022), which leverages the quasi-random allocation of refugees across local labor markets in Denmark. In the context of developing countries, Monge-Naranjo et al. (2018) argue that the formation of slums (a key driver of urban growth in many countries) has accelerated human capital accumulation and provided a stepping stone to low-income households.

**At the same time, both developing and developed countries face several challenges in ensuring that cities remain environments where human capital can easily form and thrive.** The living conditions and standards of urban services are still very low in the developing world, which might constitute a barrier to skill accumulation and participation in urban labor markets. Another key issue is represented by the growth in residential segregation by income, which is amplified by and contributes to differences in educational opportunities across neighborhoods (Fogli and Guerrieri, 2019). Tackling these issues is crucial for urbanization and human capital growth to continue to be as intertwined as they have been in the recent past.

# **5 Conclusions**

**The research presented in this paper provides some important lessons for the role of human capital for economic development. First, the accumulation of human capital is a dynamic and multifaceted process, which involves different types of investments across all stages of the life cycle, as well as the accumulation of a wide variety of skills and traits.** Individuals in developing countries face significant obstacles in all these dimensions, calling for a holistic approach when designing human capital policy for economic development. Increasing educational attainment is only one of the many possible margins for policy action in this context, and likely not very effective if not associated by improvements in educational quality and the relaxation of
|· Malawi<br>·|Rwanda|
||· Myanmar||· Mauritania||
|**_Under-five mortality rate_**|None||· Bangladesh<br>·|Madagascar|
||||· Eritrea<br>·<br>· Lao PDR|Nepal|
|**_Access to safe drinking_**|· Afghanistan|· Malawi|· Benin||
|**_water_**|· Burkina Faso|· Nepal|· Cambodia||
||· Comoros||· Guinea||
||· Gambia, The<br>· Ghana<br>· Korea, Dem. Rep.||· Uganda||
||· Kyrgyz Republic||||
|**_Acces to sanitation_**|· Lao PDR<br>· Myanmar||· Rwanda||
|’|<br>· Tajikistan||||

_Source:_ Authors’ calculation based on World Development Indicators database (as of March 2011). _Note:_ List of low-income countries is based on fiscal year 2011 World Bank classification; see table A1.13 in World Bank (2011a).

**Table 2. Lagging countries are surprisingly close to getting on target**

||**Average di**<br>**All ff tt ti**|**stance to getting on target (gaps, %)**<br>**Countries that are**|
|---|---|---|
||**o arge counres**|**close to the target**<br>**far from the target**|
|**MDG 1.a Extreme poverty**|39(96)|17<br>67|
|**MDG 1.c Hunger**|19(60)|9<br>35|
|**MDG 2.a Primary
"development" is strongly associated with improvements in mortality is neither surprising nor new (Caldwell, 1986, World Bank 1993). What is perhaps surprising is the _strength_ of the relationship as essentially all (95 percent) of the cross national variations in either under-5 or infant mortality can be explained by five factors: the level of income and its distribution, the extent of female education, the extent of ethnolinguistic differences within a country, and whether it is predominately Muslim. While there are poor countries with exceptionally good health status, properly accounting for determninants besides income reduces the unexplained differences in health outcomes and leaves little to be explained by independent variations in health policy.

Second, there is an enormous gap between the apparent _potential_ of public spending to improve health status and the actual _performance._ Reviews of the cost effectiveness of preventive and primary curative interventions suggest that a significant fraction of under five deaths could be avoided for as little as SI 0, and in many cases under $1000, per death averted (Jamison and others, 1993). However, differences in public spending on health account for essentially _none (0.15_ percent) of the cross-national differences in health status. The extremely small actual impact of public spending that we estimate from the cross-national data implies that the typical public spending on health per child death averted is $50,000 to $100,000, a striking discrepancy between the apparent potential and actual performance.

Why is public spending on health ineffective at improving health status even though relatively cheap and effective medical interventions exist? Addressing that complicated question in detail is left to a companion paper (Filmer, Hammer, and Pritchett, 1997) and here we only suggest three likely explanations: (1) cross-national differences in the efficacy of the public 3