rafmacalaba/datause-encoder-data huggingface dataset:
Table 1b focuses on baseline summary statistics for school enrollment and attendance. Columns 1 to 6 disaggregate those statistics by gender, age group, and ability level as measured by the Raven’s raw score.<sup>12</sup> For enrollment using both self-reported and school-based measures and for attendance, we observe that, at baseline, girls are less likely to be enrolled and attend school than boys. A similar pattern of lower enrollment and attendance is observed for younger children<sup>13</sup> (ages 7 to 8) compared to older children (ages 9 to 13) and for children with lower cognitive ability (a Raven’s score below the sample mean) compared to higher ability children (those with a Raven’s score above the mean). All of those baseline differences are statistically significant. These observations at baseline confirm our description of girls, younger children, and lower ability children as categories of “marginal” children in our conceptual framework.

_3.2 Experimental Design: Burkina Faso Nahouri Cash Transfers Pilot Project_ The 75 villages in Nahouri province that each have a primary school were randomly allocated to the following five groups as illustrated in Panel A of Figure 2: (i) conditional cash transfers given to the father, (ii) conditional cash transfers given to the mother, (iii) unconditional cash transfers given to the father, (iv) unconditional cash transfers given to the mother, and (v) a control group.<sup>14</sup> There were 15 villages in each treatment arm and in the control group, and only poor households were eligible to receive a cash transfer.<sup>15</sup> After villages were randomly assigned

> 12 We use the Raven’s Colored Progressive Matrices (CPM) to measure a child’s cognitive ability. The Raven’s CPM is a measure of fluid intelligence or problem solving ability, and it does not require formal schooling to be able to answer the questions (Raven, Raven, and Court, 1998). The test does not depend heavily on verbal skills, making it relatively “culture free” (Borghans, Duckworth, Heckman, and ter Weel, 2008). In the Raven’s test, the child respondent is asked to select the image that is missing in order to complete a picture.

> 13 Age seven is the official school starting age in Burkina Faso, but many children start school at a later age. 14 Due to the low primary school enrollment rates in Burkina Faso, the program intervention focused exclusively on primary schooling as opposed to also covering secondary schools.

> 15 Immediately prior to the baseline survey, we conducted a household census in every village to collect information from each household about living structure (flooring, access to latrine), ownership of assets (plow, cart, draft animals, motorcycle, radio), whether the household head ever attended school, whether the household grows cotton, and whether there is a weekly village market. We combined this information with a Burkina Faso nationally representative household survey (INSD Burkinabe Survey on Household Living Conditions – 2003) to calculate a

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||||**Stock Deficit**|**– 2019**|**Flow Deficit -**<br>**2030**|
|---|---|---|---|---|---|
|**Country**|**AHI–**<br>**National**<br>**Average**|**Households**<br>**(in million)**|**Inadequate**<br>**Housing**<br>**Units (in**<br>**million)**|**Inadequate**<br>**Housing**<br>**Units per**<br>**capita**|**Inadequate**<br>**Housing Units**<br>**projected to**<br>**2030 (in**<br>**million)**|
|Morocco|0.91|7.69|0.69|0.02|0.74|
|Mozambique|0.41|4.85|2.82|0.09|3.69|
|Myanmar|0.57|11.93|5.07|0.09|5.37|
|Nepal|0.64|5.90|2.14|0.07|2.43|
|Nicaragua|0.70|1.50|0.44|0.07|0.48|
|Niger|0.27|3.56|2.59|0.11|3.84|
|Nigeria|0.55|36.63|15.56|0.08|20.00|
|Pakistan|0.68|34.55|10.81|0.05|12.90|
|Peru|0.88|9.11|1.10|0.03|1.17|
|Philippines|0.72|23.42|6.47|0.06|7.30|
|Romania|0.92|7.34|0.53|0.03|0.49|
|Russian Federation|0.93|54.08|3.69|0.03|3.64|
|Rwanda|0.55|2.92|1.32|0.10|1.68|
|Senegal|0.64|1.81|0.63|0.04|0.82|
|Sierra Leone|0.41|1.40|0.81|0.10|0.99|
|South Africa|0.85|15.28|2.29|0.04|2.53|
|Sri Lanka|0.80|5.73|1.12|0.05|1.14|
|Sudan|0.57|6.28|2.70|0.06|3.41|
|Tanzania|0.51|12.55|6.06|0.10|7.92|
|Togo|0.52|1.69|0.81|0.10|1.01|
|Tunisia|0.95|2.91|0.14|0.01|0.14|
|Turkey*|0.92|24.68|1.91|0.02|2.04|
|Uganda|0.51|9.98|4.93|0.11|6.49|
|Ukraine|0.91|17.22|1.52|0.03|1.37|
|Uzbekistan|0.94|7.06|0.35|0.01|0.39|
|Vietnam|0.88|25.40|3.00|0.03|3.03|
|Zambia|0.52|3.48|1.65|0.09|2.15|

**Note** : Number of inadequate households extrapolated from the AHI, average HH size, and overall number of population (Source: Authors’ calculation based on countries’ available household consumption and expenditure survey and WDI (2019)). * Due to absence of rural / urban differentiation in HCES, stock deficits are calculated based on overall AHI, total population, and total average HH size for these countries.

# **_5.2 Flow Deficit_**

Given unprecedented population growth rates that are projected to add millions of people by 2030, many more additional HHs will require adequate housing within the next decade. Based on projected urbanization rate and population growth, we estimate that at least 40 million _additional_
also worth noting that a focus on multidimensional poverty has the potential to draw attention to “government failures” too, especially in relation to provision of public goods and how markets function.

While these conceptual arguments for an independent focus on measuring multidimensional poverty apply to all countries, there is also a significant framing issue that is especially relevant for the Philippines. During 2003-14, the Philippine economy grew at average rate of 5.4% per year. While there was fluctuation in growth over this period, per capita GDP grew by 48% and per capita consumption grew by 37% in real terms or at average annual growth rates of 3.5% and 2.9% respectively. However, the official (income) poverty estimates over this period largely indicate a flat line, changing little from 28% in 2003 to 26% in 2014 (see Figure 1).<sup>4</sup> Why economic growth during 2003-14 did not translate into significant income poverty reduction has been puzzling. Of course, a decade is a relatively short period of time, and some of the investment activity may augment household incomes with a lag. Most recent estimates based on the 2015 Family Income and Expenditure Survey indicate a fall in poverty incidence to 21.6% (PSA, 2016). Nonetheless, this experience begs the question of how non-income indicators of poverty – including those related to health, education, quality of housing and access to services – have progressed over this period. This question has already initiated an investigation of trends in multidimensional poverty based on a range of non-monetary indicators of welfare. For instance, using data from three different sets of household surveys – the National Demographic and Health Survey (NDHS), Family Income and Expenditure Survey (FIES), and the Annual Poverty Indicators Survey (APIS), Balisacan (2015) constructs a series of multidimensional poverty indices. Defining poverty as deprivation in at least one-third of the chosen indicators, he finds that rates of multidimensional poverty reduction were higher than income poverty reduction during 1998-2011/12,<sup>5</sup> leading him to conclude that “aggregate poverty, seen from the lens of multidimensional deprivation, actually declined as the economy expanded during the past decade” (p. 463).

> 4 These official estimates are based on per capita incomes from the first-semester Family Income and Expenditure Surveys (FIES) for 2003, 2006, 2009 and 2012, and the Annual Poverty Indicators Surveys (APIS) for 2013 and 2014. The APIS surveys are only conducted for the first semester of the survey year.  Since 2013, the APIS has used the same income module as the FIES.

> 5 Balisacan (2015) in fact presents estimates for a longer period for some of his data sources.  In particular, the study uses FIES data for the years 1998, 1991, 1994, 1997, 2000, 2003, 2006, 2009 and 2012; NDHS for 1993, 1998, 2003 and 2008; and APIS data for 1998, 2002, 2004, 2007, 2008, 2010 and 2011.

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model can be applied to a joint analysis of all the infrastructure responses of firms. With appropriate modification, the model can also be applied to the behavior of households and nonmanufacturing businesses.

As a fourth objective, we used our estimated microeconomic model to shed light on a number of policy issues regarding the pricing of publicly supplied electricity. Although we recognize that pricing is a small part of the big policy picture, it is nevertheless an important piece of it. While pricing policy should not be contemplated as separate from the broader issues of industrial organization, there is no possible industrial reorganization of infrastructure which does not carry with it strong implications for an appropriate pricing policy.

As a fifth and final objective, we have placed our findings regarding the pricing of public infrastructure services, into the broader context of industrial organization policy [Baumol and Lee, 1991]. In particular, the theory of contestable markets is the appropriate theoretical backdrop against which the organization of private and public infrastructure markets, including questions of pricing, should be considered.

The support for our recommendations for policy actions in the industrial organization front are supported by extensive evidence we have gathered in field observations, by case studies of deregulation of utilities in developing countries and by some of the qualitative and quantitative responses of the firms in our samples of manufacturers in the three countries.

# EMPIRICAL FINDINGS

Our empirical findings are grouped into three parts: those which are supported by the descriptive analysis of the data in the three surveys as well as our field observations [Report No.
# **Section 1: Introduction**

Adequate transportation infrastructure is an essential ingredient for economic development and growth. Beyond simply facilitating cheaper and more efficient movements of goods, people, and ideas across places, transportation infrastructure impacts the distribution of economic activity and development across regions, the extent to which agglomeration economies and efficient sorting can be realized, the levels of competition among industries and concomitant reallocation of inputs towards productive enterprises, and much more. Rapidly expanding countries like India and China often face severe constraints on their transportation infrastructure. Many business leaders, policy makers, and academics describe infrastructure as a critical hurdle for sustained growth that must be met with public funding, but to date we have a very limited understanding of the economic impact of those projects.

We study the impact of the Golden Quadrilateral (GQ) project, a large-scale highway construction and improvement project in India. The GQ project sought to improve the connection of four major cities in India: Delhi, Mumbai, Chennai, and Kolkata. The GQ system comprises 5,846 km (3,633 mi) of road connecting many of the major industrial, agricultural, and cultural centers of India. It is the fifth-longest highway in the world. The massive project began in 2001, was two-thirds complete by 2005, and mostly finished in 2007. Datta (2011), a study that we describe in greater detail below, finds that GQ upgrades quickly improved the inventory management and sourcing choices of manufacturing plants located in non-nodal districts along the GQ network by 2005.

This paper investigates the impact of the GQ highway upgrades on the organization and performance of the organized manufacturing sector for India. Several studies evaluate the performance of Indian manufacturing, especially after the liberalization reforms (e.g. Kochhar et al. 2006, Ahluwalia 2000, Besley and Burgess 2004). Some authors argue that Indian manufacturing has been constrained by inadequate infrastructure and that industries that are dependent upon infrastructure have not been able to reap the maximum benefits of the liberalization’s reforms (e.g. Gupta et al. 2008, Gupta and Kumar 2010, Mitra et al. 1998).

We employ plant-level data from the years 1994, 2000, 2005, and 2007 to study the impact of highway infrastructure investments on Indian manufacturing. We study how proximity to GQ in non-nodal districts affected the organization of manufacturing activity using establishment counts, employment, and output levels, especially among newly entering plants that are making location choice decisions before or after the upgrades. This work on the organization of the manufacturing sector also considers industry-level 2
allocated to tobacco is roughly 1 acre (among club members). In consequence, with gains in yields of 40-74 percent, the average tobacco farmer that belongs to a club would earn between 20 and 37 percent more than the average tobacco farmer that does not belong to a club. Additional simple calculations reveal that these income gains would be equivalent to the benefits generated by increases in tobacco prices of between 37 and 54 percent.<sup>21</sup>

# **3.3 Robustness**

A potential concern with our analysis is sensitivity to the age cut-off for our instrument. The 45 years old cut-off was first selected based on testimonies gathered during the pilot phase of the Tobacco Clubs Survey and then tested with a specific question included in the final questionnaire of the TCS. Even with all our efforts to gather evidence to support the age cut-off, there remain some elements of arbitrariness behind it. In rural Malawi, it is not easy to determine the age of women because most of them are not registered at birth and do not have an ID issued by the government. Hence, the 45-year cut-off suggested by the respondents of the TCS is just a reference number. It reflects the intention of clubs to choose a number high enough to minimize the probability of a new marriage while at the same time ensuring that the female candidates are strong enough to be able to grow tobacco productively.

In our robustness analysis, we re-estimate Model 4 of Table 6 using different cut-offs for the instrument. Results are reported in Table 8. The exercise shows that our main results are robust to various age cut-offs ranging from 41 to 47 years old. The instrument remains highly significant in the first stage, with associated F-tests always above 10. Similarly, the effects of club membership on the four tobacco outcomes (output per acre, sales per acre, unit value and land share) are also robust. The statistical and economic significance of the coefficients remains unaltered—in the cases of output and sales, however, the impacts somewhat increase when the age cut-off is reduced.

> 21To see this, take a second order approximation (thus allowing for supply responses) to the income generating equation of a typical farmer. This is given by _cvp_ = _<u>sd</u>_ ln _p_ +<sup><u>1</u></sup> 2<sup>_εp_</sup> _<u>s</u>_ ( _d_ ln _p_ )<sup>2</sup> _,_ where _cvp_ is the income gain, _<u>s</u>_ is the average share of income earned in tobacco, _εp_ is the elasticity of tobacco supply for tobacco growers, and _d_ ln _p_ is the proportional price change. Finally, solve for the price changes that cause income gains in the range 0.2-0.37 (assuming a unitary supply elasticity).

33
Policy Research Working Paper 8198

# **Abstract**

This paper introduces a novel corporate financial vulnerability index that tracks financial conditions of the non-financial corporate sector. Using the balance sheet information of 14,207 listed non-financial firms in 69 emerging markets and developing economies, the index shows that, at the global level, corporate vulnerability sharply increased since 2013 and stabilized in 2016. Regional trends are more heterogeneous, pointing to significant corporate vulnerabilities in Eastern Europe and Central Asia, as well as a deterioration of firms’ financial conditions in Latin America, the Middle East and North Africa, and Sub-Saharan Africa in recent years. The energy sector has exhibited the fastest deterioration, especially since 2014, in part driven by the decline in oil prices. However, if currently relatively benign global funding conditions and higher commodity prices endure, companies may have an opportunity to strengthen their balance sheets. The paper also finds that the index has leading indicator properties for socioeconomic outcomes, such as a rise in unemployment and an economic recession, and outperforms a commonly used “debt at risk” approach.

This paper is a product of the Equitable Growth, Finance and Institutions Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at efeijen@worldbank.org, nfiess@worldbank.org, and izuccardi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
S stagnation of such growth since 19808; this expansion appears to have been accompanied by increased diversity of curriculum, administration, unit costs by type of school, Inequitable allocatlon of resources across schools, and school quality differences, which are partly the result of a long term lack of public policy with regard to secondary education. There has been an expansion of nonformal programs provided by the major training institutions Serviclo [' ^Ional de Aprendizagem Industrial (SENA) and Serviclo Nacional de Aprendizegem Commerclo (SENAC) over the last two decades.

Brazilian vocatlonal educatlon at the secondary school level Is provided by all three levels of govemment: federa!, state and municipal as well as outside the formal education system by SENAI and SENAC. Over 96 percent of public secondary school enrollment and 75 percent of total secondary school enrollment Is located In state/municipal schools. These schools offer academic and vocational programs leading to a general secondary diploma. Vocational students may specialize In the following major fields: 4 agriculture, Industrial, accounting, pedagogical, and para-medical. Most state schools In Brazil offer vocational and academic curriculum fields in the same schools and the unit costs of the former are likely to be similar to those of academic education, particularly In those vocational education fields that do not require special machinery or equipment.

Table 2 Public Secondary Schooling In Brazil Enrollment and Expenditures by Type of School (1985)

|Type of School|Enrollme<br>1985|nt<br>%|Number<br>of<br>Schools|Per student<br>cost<br>US$|
|---|---|---|---|---|
|Federal Technical<br>|67,657|<br>3.4|137|1,759|
|Agricultural<br>|13,568<br>|0.7<br> <br>|-|2,727<br>|
|industrial|54,089|<br>2.7|-|1,516|
|State/Municipal*|1,912,488|96.2|5,059|249|
|State|1,780,155|89.6|4,421|257|
|Municipal|132,333|6.7|638|136|
|SENAI Secondary|7,543|0.4|-|1,880|

Source: World Bank (1989)

- Not available.

* cost figures relate to both technical and academic schools.

3 World Bank (1989), p.i.

4In detail, the fields offered are: industrial electrical, industrial mechanics, industrial chemical, industrial textile, architectural design, agriculture, accounting, secretarial, administratlon, insurance, and para-medical.
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
||10 in Asia, 2 in Africa, 3 in<br>America|(n=193 203)||Overweight/obesity (9<br>studies)|1.44). OWOB (efect size:1.29, 95%<br>CI: 1.09,1.53)|
|Rouhani et al.<br>(2014)|SRM of 21 studies, 1 in the<br>Islamic Republic of Iran,<br>rest in HICs|Adults,<br>n=1,135,661|Red and processed meat<br>consumption|Obesity|consumption of higher quantities of<br>red and processed meats was a risk<br>factor for obesity (OR 1.37; 95% CI<br>1.14,1.64)|
|Feng et al. (2022)|SRM of 42 articles of 52<br>cohort studies in 4 Asian<br>countries|Adults|Dairy consumption|Obesity, overweight,<br>hypertension, T2DM|For overweight/obesity, risk reduction<br>was 25% (total dairy), 7% (high-fat<br>dairy), 12% (milk), and 13% (yogurt)<br>per specifed increase. Hypertension<br>had a nonlinear association with total<br>dairy, while low-fat dairy and milk<br>showed a 6% reduction. T2DM had<br>nonlinear associations; total dairy and<br>yogurt showed 3% and 7% lower risk<br>per 200-g/d and 50-g/d increase.|
|Schwingshackl et<br>al. (2016)|SRM of 22 longitudinal<br>studies, 1 in the Islamic<br>Republic of Iran, 1 in China,<br>rest in HICs|Adults|Dairy consumption|Body weight, waist<br>circumference, risk of<br>overweight, risk of obesity|Found an inverse association between<br>body weight and yogurt (beta -40.99<br>g/year, 95% CI:-48.09, -33.88), and a<br>positive association for cheese (beta -<br>10.97 g/year, 95% CI: 2.86, 19.07)<br>Highest dairy intake reduced risk of<br>abdominal obesity (OR 0.85; 95% CI<br>0.76, 0.95) and overweight (OR 0.87;<br>95% CI 0.76,1.00)|
|W. Wang, Wu, and<br>Zhang (2016) †|SRM of 17 studies for total<br>dairy products (14 cross-<br>sectional, 2 case-control, 1<br>cohort) and 16 for milk (12<br>cross-sectional, 4 cohort),<br>10 in Asia, 2 in South<br>America|Adults 18+ y (19<br>studies)|Milk and dairyproducts<br>consumption<br>[assessed using FFQ<br>(n=12) and/or 24HR (n=7)<br>or questionnaire (n=9)]|Obesitydefned using<br>waist circumference or<br>BMI (with cutofs of≥25,<br>28 or 30 kg/m<sup>2</sup>)|Lower risk of obesity in adults<br>associated with consumption of dairy<br>products (pooledOR 0.75;95% CI<br>0.69, 0.81) and milk (pooledOR 0.77;<br>95% CI 0.68, 0.87)|
|Schwingshackl et<br>al. (2015)|SRM of 17 cohort studies, 1<br>in the Islamic Republic of<br>Iran, rest in HICs|n=563 277|Fruit and vegetable<br>consumption|Body weight (8 studies),<br>waist circumference (3<br>studies), BMI (2 studies),<br>overweight/obesity (7<br>studies)|Higher fruit intake was linked to weight<br>decrease (beta -13.68 g/year; 95% CI -<br>22.97, -4.40) and reduced WC (beta -<br>0.04 cm/year; -0.05, -0.02). Lower risk<br>of adiposity associated with highest<br>intake of combined fruit & vegetable<br>(OR 0.91,95% CI 0.84,0.99),fruit(OR|

39