rafmacalaba/data-use-mentions-extended huggingface dataset:
, EMP is a sector’s labor share in the urban areas of country _c_ c. 2000. Since most censuses are centered around the year 2000, we use information from 2000 for MFGSERV in country _c_ , the mean of NRXGDP in country _c_ in the period from 1960 to 2000, and DEINDU in country _c_ between 1980 and 2000 (a positive value indicates deindustrialization). To compare countries with _similar_ levels of urban economic development, we control for the urban share (URB) in 2000. We can then ignore the initial conditions in 1960 but still add the controls ( _X_ ) for area (2000), population (2000), small islands, and urban definitions. We use country populations in 2000 as weights. Finally, we examine the differences between coefficients _β_ and _γ_ , and then between _β_ and _δ_ .

**Sectors.** IPUMS has information on 16 industries from which we construct 10 sectors. As can be seen in Table 3, for a given urban share, urban areas in industrialized MFGSERV countries have more employment in tradables (MFG, FIRE and SUM = MFG + FIRE) and
different from either the effect of the reform on the random person, or its effect on the “treated.” There are very few studies for developing countries dealing with the issues surrounding the endogeneity of education and the implications of estimating returns to education from instrumental variables; exceptions are the studies by Maluccio (1997) and DuFlo (2001). Maluccio’s study for the Philippines uses distance to the nearest high school as the main instrument (in some specifications supplemented with parental education and wealth). His study, however, uses panel data from Bicol region in the rural Philippines (one of the poorest in the country and, therefore, not representative) and relies on 250 or less observations for estimation of returns to schooling from instrumental variables. DuFlo (2001), on the other hand, uses a set of unique data and examines the effects of the schooling construction programs in Indonesia on education and earnings. Using the variation in schooling generated by the policy as instrumental variables, she generates estimates of economic returns to education ranging from 6.8 to 10.6 percent.

# **2. Education in Venezuela**

The issue of free, public and compulsory education at the primary level in Venezuela first arose during the independence struggle. Simon Bolívar – the liberator of Bolivia, Colombia, Ecuador, Panama, Peru and Venezuela – having lived in France, was greatly influenced by the French educational system (such as the centralization and rigid structure of curricula), and issued a series of decrees concerning free education. The real beginning of free public education, however, did not come until 1870, when then President Antonio Guzmán Blanco issued a decree in which he recognized compulsory 4
also mark-up differences (Cosar, Grieco and Tintelnot, 2015).

A rare attempt to overcome these challenges by Atkin and Donaldson (2015) applies the price-gaps methodology to estimate trade costs in Ethiopia and Nigeria. The authors use data on prices of a number of staple consumer goods collected by statistical agencies for constructing the consumer price index, addressing the first challenge. Complementing the price data with the origin information for the goods, they overcome the second challenge.<sup>8</sup> Moving away from perfect competition and featuring oligopolistic intermediaries, their model addresses the third challenge. Finally, to allow a comparison to a developed country, they also estimate trade costs using U.S. data.

The results suggest an economically large difference between trade costs in the two African countries relative to the US. The costs of trading goods to the most remote compared to the least remote location is 9 cents in Ethiopia and 13 cents in Nigeria. In the US, the same distance costs only 2 cents. In terms of the marginal trade cost with respect to geodesic distance, the cost is 3.53 times higher in Ethiopia and 5.26 times higher in Nigeria than in the US, respectively.

Noting that these estimates encompass not just transportation but other costs of trading, a natural question for the purposes of this review is how to unpack the contribution of freight costs to this inter-country difference. In one exercise, the authors replace their geodesic distance metric with the fastest travel time measure. Since the U.S. is better endowed with higher quality roads, a change in relative costs is informative about the role of infrastructure. As a result, the estimated marginal cost difference between the two African countries and the U.S. drops to 2.46 for Ethiopia and to 4 for Nigeria. Comparing with full costs at face value, infrastructure explains about 24-30% of these countries’ cost gap with the U.S. While this is not a trivial magnitude itself, it is likely to
in allowing adults to adjust working hours after a change in the childcare burden; participation in household agriculture was also less affected by the pandemic.

Agricultural households drive average labor impacts. Poorer households—based on an index of housing and assets—increase work hours by more than wealthy ones but not significantly so. They are more likely to be engaged in agriculture and may have had fewer resources to deal with increased childcare needs during closures. We observe no differences in impacts between rural and urban locations. Relatively high participation in agriculture even in ‘urban’ areas and low participation in wage employment or take-up of paid childcare in our sample mean that key differences that could affect how changes in childcare burdens affect labor supply by location are not present in this context.

Surprisingly, the impacts are not significantly different by sex, contrasting with evidence of more negative impacts of the COVID-19 pandemic on mothers’ labor hours in high-income contexts (Alon et al. 2021; Amuedo-Dorantes et al. 2020; Collins et al. 2021; Hansen, Sabia, and Schaller 2022; Heggeness 2020) and expectations based on women’s role as primary caregivers in most Kenyan households. One reason for the lack of difference is that in our sample, both women and men contribute to childcare and increased childcare hours during school closures. A second reason is that in this setting, school-age children are both _recipients_ of childcare and _contributors_ to household productive activities, including childcare to siblings and household agriculture. We find that women’s labor supply responds relatively more to changes in childcare burdens while men respond more to changes in child agricultural labor, leading to offsetting impacts.

The effects of the partial school reopening on adult childcare and work hours vary with household composition—in particular, the presence of below-school-age children—consistent with evidence of economies of scale in childcare and of an important role of siblings in childcare (Jakiela et al. 2020). Whether the returning student is a net provider or recipient of childcare in the household determines whether the reopening is
# 5 Conclusion

Achieving universal primary education remains an elusive goal in many developing countries. While governments can choose to invest greater amounts in providing and subsidizing the costs of public schooling, the budgetary implications of such a task are daunting. Private educational provision is an increasing presence, particularly in developing countries, with shares exceeding 20 percent at the primary level in a large number of countries.

The crucial question is whether the market can oer aordable and quality education at a scale that can complement the public sector in achieving universal enrollment goals. This paper underscores that for this to happen local supply-side constraints need to be alleviated. While not surprising at the aggregate level, the result that (teacher) supply curves are not perfectly elastic at the village level can generate poverty traps in credit-constrained environments (Ljungqvist 1993 and Banerjee 2004).<sup>23</sup> Higher returns to education may perversely lead to declines in the provision of education if the returns increase as a consequence of higher wages in non-teaching professions. Moreover, locally upward-sloping supply curves have consequences for the pricing of voucher schemes. Depending on the elasticity of supply, increases in demand through vouchers may lead to simultaneous increases in prices, a decline in quality (in price-capped schemes), or both.

In contrast to calls for larger primary school investments at the expense of secondary schools, our ndings suggest that both play a role. Public investments in secondary schools increase the supply of potential teachers locally and foster the growth of private schools, potentially leading to a virtuous cycle of human capital accumulation.

The changes documented in this paper represent more than just a sectoral realignment from public to private schools. Work in Pakistan and other countries suggests that the

> 23An upward sloping supply curve at the local level reects supply constraints in the educational sector as it arises due to local labor market restrictions. There is a natural parallel with the literature on credit constraints. Evidence for such constraints is whether the cost of borrowing increases with the amount for individual
<!-- Start of picture text --> Direct Taxes and SIC Indirect Taxes<br>30.0 30.0<br>25.1<br>25.0 24.0 25.0<br>s00 19.6 20.0 a<br>16.1<br>15.0 9.9 10.8 15.0 9.0 na- 126 13.3<br>10.0 a1 8.1 100 6.9 7.0<br>5.2<br>0.0 e . 0.0 ry ° °<br>-5.0 e e -5.0 > ve > o<br>-10.0 -10.0<br>TIK KYG GEO ARM RUS MDA BLR UKR MDA = RUS UKR ARM _ KYG GEO BLR TJK<br>TM% of GDP © Gini index (pp) © Poverty headcount (pp) TM% of GDP Gini index (pp) Poverty headcount (pp)<br><!-- End of picture text -->
|Square Ln Population<br>Ln NTLs per capita<br>Constant|3.270***|5.699***|-0.0880***<br>(0.00553)<br>1.453***|3.860***|-0.615|0.0432**<br>(0.0132)<br>9.400***|
|---|---|---|---|---|---|---|
||(0.218)|(0.424)|(0.204)|(0.119)|(0.402)|(2.768)|
|FE|City + Year|City + Year|City + Year|City + Year|City + Year|City + Year|
|Controls|No|No|No|No|No|No|
|R-squared|0.415|0.328|0.420||0.466|0.477|
|N. of obs|28352|18856|27636|27460|2495|2495|

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001

# 4.2  The effects of income and population growth on vertical layering

Cities can add space by expanding their built-up surface as well as by building higher. We use the subsample of almost 400 cities, representative of the larger GHSL sample to assess the income and population elasticities of city height.<sup>8</sup> Height is measured as the average height of the city’s buildings within 5 kms of the highest point in the city.<sup>9</sup> Similar analysis is conducted using the average over the whole urban extent as defined by GHSL, or using quantiles
value added – and statistically significant. In other words, once the initial productivity level differences caused by self-selection of weaker firms into CIMO were controlled for, participation in the program was associated with a positive impact on performance.

Tables 5 and 6 report the corresponding production functions results using the alternative measures of output, but estimated for the 1993-95 period (first panel), and for the full five-year 1991-95 period when a sub-sample of treatment and control group firms from the first study were added (second panel). Like the previous results, CIMO had a negative impact on the productivity of participants when the production function was estimated **in levels** . The estimated θ parameter was not only negative and large (-53 to -60 percent), but statistically very significant as well. **In first-differences** , however, estimates of the θ parameter became much smaller (-5 percent) or not statistically different from zero, depending upon the value added measure used. When the data were augmented to include some firms from the first study (the 1991-95 panel), the estimated θ parameters of CIMO were not statistically different from zero. It is unclear if this absence of an impact is a reflection on the program’s lack of effectiveness, or the selection of an inappropriate control group for the CIMO firms.

**Table 5. Production Function Estimates for 1993-95 and 1991-95 Dependent variable: Log(Value Added)**

|**Variable**|_L_<br>Coeffici|_evels_<br>ent|**1993-19**<br> <br>z-statistic|**95**<br>_First d_<br> Coeffici|_ifferences_<br>ent<br>z-<br>statistic|_L_<br> Coefficie|**1991-**<br>_evels_<br>nt z-statistic|**1995**<br>_First_<br>Coeffici|_differences_<br>entz-statistic|
|---
I **POIICY RESEARCH WORKING PAPER** 1661

# **Summary findings**

Randolph, Bogetic, and Heffley empirically study factors that influence public investment in transportation and communication infrastructure. Using pooled crossnational and time-series data for 1980-86 for 27 lowand middle-income economies, they assess the influence on public infrastructure spending of a government's objectives (especially its commitment to poverty alleviation), the nature of the domestic economy, and the flow (and composition) of external assistance. Their findings:

* Per capita spending on infrastructure responds most strongly to changes in the level of development, the urbanization rate, and the labor force participation rate.

* Spending is greater in countries with large foreign sectors and is positively influenced by sectoral imbalances between rural and urban areas (reflected in migration rates). Moreover, as the stock of infrastructure increases, so does per capita spending on it.

D If total flows of foreign savings increase, there is a small positive response in per capita spending. The composition of foreign savings matters: when commercial bank flows represent proportionately more of such flows, infrastructure spending is greater.

* With higher population densities, consolidated government spending declines. Central government

spending increases initially, but decreases as population densities rise.

* Central budget spending is positively associated with improved institutional development, whereas consolidated budget spending falls as institutional development improves (when levels of institutional development are low).

> * The size of the budget deficit appears not to influence central budget spending but is positively associated with consolidated budget spending.

* Greater outward orientation is positively associated with increased consolidated budget spending but seems to bear no relationship to central budget spending on infrastructure.

* Governments that are not committed to alleviating poverty, or that are extremely committed to it, spend less from the central budget on infrastructure. Governments with only limited commitment to alleviating poverty adopt strategies to increase the productivity of the poor by investing in infrastructure. But as their commitment intensifies, their strategy shifts to improving human capital or strengthening the social safety net,
***|0.287***|0.354***|0.374***|0.130**|
|**Hazard**|-0.000|0.094|-0.085|0.362**|0.132|0.040|
|**Constant**|10.640***|9.973***|10.747***|9.942***|9.930***|10.394***|
|**Number of observations**|4,706|3,000|7,068|2,294|1,287|3,800|

_Source:_ Authors’ calculations based on Cambodian Socio-Economic Surveys.

_Note:_ The grand mean effects of the industries are calculated; additional controls include age squared, hours of work, industry, and occupation dummies. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1.

**Table 4 Wage Premium Regressions forSri Lanka**

||**2002**||**2006**||**2008**||
|---|---|---|---|---|---|---|
|**Female**|-0.401|***|-0.553|***|-0.443|***|
|**Age**|0.111|***|0.154|***|0.118|***|
|**Education**|0.040|***|0.039|***|0.041|***|
|**Textiles and apparel**|0.196
21 OECD (2010), _Producer and Consumer Support Estimates, OECD Database 1986-2009_ .

<u>http://www.oecd.org.</u> Thompson, S.R., P.M. Schmitz, N. Iwai and B.K. Goodwin (2004), ‘The Real Rate of Protection: The Income Insurance Effects of Agricultural Policy”, _Applied Economics_ 36: 1-8.

Tovar, P. (2009), ‘The Effects of Loss Aversion on Trade Policy: Theory and Evidence’, _Journal of International Economics_ 78(1): 154-67, June.

Tyers, R. (1991), ‘On the Neglect of Dynamics, Risk and Market Insulation in the Analysis of Uruguay Round Food Trade Reforms’, _Australian Journal of Agricultural Economics_ 35(3): 295-313, December.

Tyers, R. and K. Anderson (1992), _Disarray in World Food Markets: A Quantitative_ _Assessment_ , Cambridge and New York: Cambridge University Press.

World Bank (2010), _Global Commodity Markets: Review and Price Forecasts,_ Washington DC: World Bank.
***|1.385***|
||(0.0876)|(0.0645)|(0.312)|(0.124)|(0.0845)|(0.0697)|(0.292)|(0.138)|
|Tertiary education|1.657***|1.250***|3.031***|1.626***|2.364***|0.958|3.167***|1.770***|
||(0.158)|(0.0902)|(0.590)|(0.189)|(0.157)|(0.0805)|(0.564)|(0.198)|

15 The relative risk ratio (RRR) measures the association between an explanatory variable (factor) and the outcome variable. In this case, the RRR represents the change in the likelihood that a woman or men had migrated due to any of the reasons established associated with a one-unit change in the corresponding explanatory variable. A relative risk ratio (RRR) equal to 1 indicates that exposure to the factor does not affect the relative probability, a RRR greater than 1 indicates a positive association and that exposure to the factor increases the relative probability, and a RRR less than 1 indicates a negative association and that exposure to the factor decreases the relative probability.

> 16 The variable “Causes of Migration” available in the Census data allows distinguishing between economic reasons, family reunification, crime, studies, natural disasters, and other reasons. As shown in section 5.2.2, the category “Other” in the multinomial model includes studies, natural disasters, and other reasons. We explored the possibility of having studies and natural disasters as independent categories. However, the number of observations in each subgroup does not allow for having them
market income, percent Share of market income, percent<br><!-- End of picture text -->

# **4.2. Progressivity of Taxes and Transfers**

We assess the progressivity and pro-poorness of interventions by comparing the cumulative distribution of a tax burden or transfer benefit with the cumulative distribution of market income and the cumulative share of total population ranked by incom <mark>e (Duclos and Araar 2006).</mark> Concentration curves are used to show the impact of tax and transfer policies by mapping the cumulative share of taxes paid or benefits received from a particular category of taxes or transfers on the vertical axis against the cumulative share of the population, ordered by pre-fiscal income, on the horizontal axis <mark>(Duclos and Araar 2006)</mark> . The progressivity of fiscal interventions is also examined using the Kakwani coefficient (Annex 3). Figure 3 illustrates the progressivity of different taxes and transfers measured using the Kakwani index.<sup>8</sup> A positive Kakwani coefficient

> 8 The marginal contribution analysis for poverty reduction is based on the absolute poverty line, and the redistribution effect is measured using the Gini index.

10
Integrating an ethnography into the design of an impact evaluation can provide a robust way of examining processes and mechanisms. In 2007, I worked with colleagues to test an intervention to improve the quality of citizen engagement in Indian village governments (Rao, Ananthpur and Malik 2017). The intervention provided two weeks of training to village residents on participatory planning, village government regulations, budgets and administrative systems, which was followed by monthly follow-up visits by facilitators over a two-year period to mobilize citizens and translate their demands and needs into actions. We picked a hundred villages at random, and then randomly assigned 50 of them to the intervention. Quantitative data from surveys of villages, households and key informants were conducted prior to the intervention in 2007 and after the two-year period in 2009. A 10% sub-sample of the quantitative sample, five treatment and five control villages, were selected for the ethnography, which is a large sample for participant-observation. We developed an “embedded reporter” approach where five trained ethnographers were hired to live in these villages (each reporter was assigned to one treatmentcontrol pair) to conduct participant-observations. They prepared monthly reports of everything they observed – including economic changes, social changes, and political events. These 240 monthly reports constituted our qualitative data. Early rounds of the qualitative data helped inform the development of the quantitative survey instruments.

The quantitative data showed considerable improvement over time across a variety of indicators, but the difference in outcomes between treatment and control villages were not significantly different from each other. The ethnography allowed us to unpack the reasons why the intervention “failed” highlighting the role of variations in the quality of facilitation, lack of top-down support, and difficulties in confronting the stubborn challenge of persistent inequality. We found that while surveys have the advantage of being able to measure predictable outcomes with measurable effects, major events sometimes occur that are best investigated on the ground, during the moment they happen, by participant observers. Important shifts occur during points of conflict, or during periods of mobilization and coordination.
the association between social capital and child health, given the high rate of both child malnutrition and social participation.

In order to test if social capital is substitutable or complementary to human and physical capital, I allow for heterogeneous effects of maternal social capital on child nutritional status for different levels of maternal education and household wealth status. I find evidence of the substitutability between social and human capital. Maternal social capital is positively associated with 1 year old children’s height-for-age for those children whose mothers have no education.

Consistent with Cunha and Heckman’s (2007) criticism of the assumption of perfect substitutability between investments made at different ages, I find that early investments are more effective during the first year of life than in later childhood. Indeed, I do not find evidence for an association between maternal social participation and nutritional status at 5 years of age.

This paper contributes to the debate from an empirical perspective, addressing three gaps in the literature on social capital and health. First, most of the literature focuses on adult health and uses mainly data from developed countries (for reviews, see Macinko and Starfield, 2001; Almedom, 2005).

Second, several empirical analyses ignore the potential reverse causality issue arising when looking at maternal social capital and child health. Plausibly, poor child health might prevent mothers from participating actively in community life or conversely, might encourage mothers to participate in order to have access to health services and advices. On the other hand, maternal 2
Table _1: Financial development and real per capita GDP in 1985_

|Indicators|Very rich|Rich|Poor|Very poor|Correlation<br>with Real per<br>Capita GDP in<br>1985|(P-value)|
|---|---|---|---|---|---|---|
|DEPTH|0.67|0.51|0.39|0.26|0.51|<br>(0.0001)|
|BANK|0.91|0.73|0.57|<br>0.52|0.58|<br>(0.0001)|
|PRIVATE|0.71|0.58|0.47|<br>0.37|0.51|<br>(0.0001)|
|PRIVY|0.53|0.31|0.20|<br>0.13|0.70|<br>(0.0001)|
|RGDP85|13053|2376|754|<br>241|||
|Observations|29|29|29|<br>29|||

_Key:_ Very rich: Real GDP per Capita > 4998 Rich: Real GDP per Capita > 1161 and < 4998 Poor: Real GDP per Capita > 391 and < 1161 Very poor: Real GDP per Capita < 391 DEPTH = Liquid liabilities to GDP BANK = Deposit money bank domestic credit divided by deposit money bank + central bank domestic credit PRIVATE = Claims on the non-financial private sector to total domestic credit PRIVY = Gross claims on private sector to GDP RGDP85 = Real per capita GDP in 1985 (in constant 1987 dollars) _Source: King and Levine (1 993a)_