rafmacalaba/data-use-mentions-extended huggingface dataset:
The ENIGH data set does not contain any information on ethnic or racial background. Other studies on poverty such as Finan, Sadoulet, and de Janvry (2002) measure the poverty reduction potential of land in rural Mexico using household data gathered in 1997 by the Mexican program for Education, Health, and Nutrition. Their results show for small landholders, an additional hectare of land increases welfare on average by 1.3 times the earnings of an agricultural worker. In addition, the marginal welfare value of land depends importantly on a household's control over complementary assets and on the context where assets are used: For non-indigenous small farmers with at least primary education and access to a road, the welfare benefit of additional land is on average seven times higher than for those without these attributes. Social assets such as ethnicity lower the marginal value of land, whereas households with more education receive a higher return to land. Households that face lower transactions costs as measured by assess to roads, garner a return to land that is two to three times as high as those without access to a road. These findings suggest that land can indeed be an important element of a poverty reduction strategy, but that there are specific conditions that must hold for this to be the case, calling on complementary interventions in Mexico.

Moreover, the ENIGH datasets do not contain any information on ethnic or racial background. Other studies on poverty in Latin America have shown that ethnic or racial background is an important factor contributing to poverty, controlling for other household head characteristics. Mestizos and Indians are two broad ethnic groups based on cultural rather than racial differences cohabiting in Mexico. Each group has a distinct cultural viewpoint and perceives itself as different from the other. At the same time, however, group allegiances may change, making measurement of ethnic composition problematic at best. Originally racial designators the terms _Mestizo_ and _Indian_ have lost almost their entire previous racial connotation and are now used entirely to designate cultural groups.9 Although matzos and indians may both reside in
**Figure 1. Evolution of employment to working-age population ratio in Korea and India**

<!-- Start of picture text --> Log workers per working-age population<br>0.0<br>-0.1 Korea India<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>-0.8<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> Sources: World Development Indicators (WDI); Penn World Tables.

19
**Table A.2.1: Transition Matrix Before and After Economic Shock by Welfare Quintile**

|**Quintile Pre \**||**Transition**|**Matrix**|||
|---|---|---|---|---|---|
|**Post**|**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|
|**Q1 - poorest**|19.8%|0.2%||||
|**Q2 - poor**|1.7%|18.0%|0.2%|0.0%||
|**Q3 - middle**||2.3%|17.5%|0.2%||
|**Q4 - rich**|||2.2%|17.7%|0.1%|
|**Q5- richest**||||1.4%|18.6%|

Source: Authors’ calculations using 130 harmonized household surveys (GMD) and Macro Poverty Outlook June 8th 2020 update (https://www.worldbank.org/en/publication/macro-poverty-outlook), with the regional average imputed if no country value was available.

44
- **ISIE, Instance Sup ́erieure Ind ́ependante pour les Elections** , “R ́esultats finaux par circonscription pour les  ́elections l ́egislatives 2014,” `https://bit.ly/30AEnQ5` 2015. Accessed: 2021-09-20.

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less important than other criteria such as the ability to change and adapt to new technology and demands.

# **VIII.  Conclusions**

The results of our analysis yield valuable insights regarding the role of investment climate variables, in addition to firms’ internal characteristics, that explain variations in TFP across manufacturing firms in China and Brazil.

# <u>Investment Climate Variables</u>

Among the investment climate variables, **_customs clearance time_** has a strong negative effect on TFP for both China and Brazil. A particularly interesting result is the strong positive effect of **_email usage_** on productivity. This indicator is probably a proxy for e-commerce and e-trade (ongoing and potential), critical in the context of global trade. Poor utility services have the expected negative impacts in both countries.

# <u>Firm Characteristics</u>

Regarding firm characteristics, ownership, age and location affect productivity. In the case of China, a single firm characteristic, state ownership, is shown to have profound effect on TFP. Productivity is 43-68% lower for state-owned firms when compared with private firms in the same industry group. This difference indicates a rather severe polarization in all industries and makes a strong case for private ownership and management. A similar, but less stark, polarization is evident for geographical location in comparing between firms in the rapidly growing coastal cities and firms in the interior city of Chengdu. In this case it is unclear whether the productivity gap will ultimately be resolved via the transfer of resources away from the interior or via the transfer of technology-efficient business practices and favorable investment climate from the coastal cities to the interior.

It is harder to draw broad conclusions based on the Brazil survey data because the results are more variable across industries. Focusing first on the apparel industry, which accounts for almost half of the observations in our data, older firms with lower market shares have higher productivity. In other words, mature firms in competitive markets do 24
preferences, _how_ these changes translate into investment decisions is then likely to be influenced by the same set of economic, political and institutional factors.

We are primarily interested in identifying the factors that explain the increase in expenditures between pre- and post- _Pilkada_ periods in the districts that belong to the treatment groups most relevant for our analysis – districts that held direct elections in 2005. However, for the purpose of comparison, we also report the estimates of the same regression on the sample of the districts that did not have direct elections until 2008 and later, i.e. the districts used as a comparison group thus far. The explanatory variables used in the analysis are obtained from the Village Potential series (PODES) and the National Socio-Economic Survey (SUSENAS) both administered by Central Bureau of Statistics‘ (BPS). The PODES is a village census that collects detailed information every three years on a range of characteristics – ranging from infrastructure to village finance – for all of Indonesia‘s villages and neighborhoods (about 69,000). The survey is implemented by sub-district level statistical agents, who work for BPS, and information is typically provided by the village heads and neighborhood heads. The SUSENAS survey is an annual household survey covering around 205,000 households from all over Indonesia that is representative at the district (Kabupaten) level. The survey collects household level data, such as household age and gender composition, consumption expenditures in major food and non-food categories, as well as individual level data, including school attendance and educational attainment, use of health facilities, participation in the labor market, earnings and fertility-related questions for women.

The dependent variables in the regressions are the _difference between post- and pre- Pilkada_ expenditures (total, development and routine) for education, health and all sectors combined and budget deficit for all sectors combined.<sup>24</sup> Education and health sectors are considered separately given that these represent key public services that are likely to matter for human development outcomes.
of the tax that would be paid for this value.

The direct household burden of the tax is estimated by multiplying total household expenditures (excluding self-production and gifts<sup>24</sup> ) by the effective tax.<sup>25</sup> We make no further adjustments, for example, to account for informality or evasion. For items where GST and SST apply, we calculate each tax separately and add the totals. For GST-exempt items we calculate the indirect effect using an IO table uprated to 2016 and a cost-push model, where the higher producer prices will be pushed to consumers in the final sale price of the product. The amount of tax collected from GST and SST is scaled to match the effective tax rate in national accounts.<sup>26</sup>

# Allocating cash transfers from social assistance

In the first instance, we use the household self-reported information in HEIS about receiving NAF or cash assistance from the Ministry of Social Development (MoSD) to determine who is obtaining this benefit.<sup>27</sup> This identifies 77 percent of the households receiving NAF benefits in 2018 according to the administrative data. To assign the remaining beneficiaries from NAF, we use a simple prediction model based on

> 22 This gap is estimated with respect to the scaled budget (as a share of private consumption).

> 23 This is a common problem in surveys around the world. For example, in an earlier HEIS, every household in West Amman, an affluent neighbourhood, refused to participate (Department of Statistics, Ministry of Planning and International Cooperation and World Bank, 2012).

> 24 For non-food items, it is not possible to discriminate between the expenditure corresponding to purchases, and the estimated value of non-purchased ones. If the entirety of the item was obtained as a gift, we do not calculate any tax. If a household obtained the item with a combination of methods (purchases and gifts), we calculate the tax on the full value as
wage regressions, also explain wage changes (as well as, in part, an increase in schooling). The program also tends to keep female students in school, thus increasing their educational attainment. Though we found that improved school quality significantly increased female wages, female labor supply was reduced too. This result shows that an increase in opportunity cost did not offset a reinforced incentive to advance schooling among females.

# **7. Conclusion**

This paper examined the long-term impacts of improved school quality at the elementary school stage on subsequent schooling investments and labor market outcomes using unique data from a recent survey that tracked students from the areas that experienced (and did not experience) a large-scale school intervention in the Philippines. We find that improved school quality enhanced female advantage (or decreased female disadvantage) in subsequent schooling investments, migration, and labor market earnings. That is, females study more (relative to males) and tend to migrate and earn more if they receive high-quality educational investments at an early stage.

The above specific results are consistent with the observation that returns to schooling are significantly higher among females than males, especially after high school completion. A labor market imbalance, represented by a significant gender gap in the returns to schooling, seems to create a unique situation in which the impact of improved school quality is biased in favor of females. This result is also supported by the behavior of parents, who were found to prioritize investments in the schooling of their daughters in the face of financial constraints. Our example demonstrates the importance of linking education and labor markets when predicting the impact of any intervention in schooling.

# **Appendix**

The first-step probit regression results are reported in Table A2. The results suggest that the probability of the wage being observed is positively correlated with age (in a decreasing manner), being female in the TEEP cohort, and height (which captures physical endowment). It is intuitive that older and/or taller individuals were more likely to participate in the labor
The variables serving as indicators are, first, the rate of value-added tax (VAT) evasion and, second, the percentage of the non-agricultural labor force does not contribute to social security. VAT evasion data are taken from an IMF study conducted in 1993 by Silvani and Brondolo. Data on the percentage of non-agricultural labor force that does not contribute to social security are taken from the World Bank study _Averting the Old Age Crisis_ (1995). These indicators reflect the two most important aspects of the informal sector in Latin America, namely, non-compliance with taxes and labor market regulations. Whereas the rate of VAT evasion proxies directly for the share of informal production, the percentage of non-agricultural workers not covered by social security proxies for the share of informal employment.

# **The MIMIC Model**

The MIMIC model of a latent variable (Joreskog and Goldberger, 1975) is specified as follows, _and E(qi') = 0',_ **_E(6_**<sup>**_2_**</sup> **_)_** _= oE, E(pt') = Q, a diagonal matrix_ In the first equation, the latent variable _I is linearly determined,_ subject to a disturbance, by a set of observable exogenous causes X. In the second equation, the latent variable _linearly determines,_ subject to disturbances, a set of observable endogenous indicators Z.

A reduced-form equation is obtained by substituting the first into the second

-22-
<!-- Start of picture text --> Addis Ababa Dakar<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>South of CBD North of CBD East of CBD<br>0% 0%<br>‐25 ‐22 ‐19 ‐16 ‐13 ‐10 ‐7 ‐4 ‐1 3 6 9 12 15 18 21 24 ‐4 ‐2 1 3 5 7 9 11 13 15 17 19 21 23 25<br>Buit‐up Paved Roads Open Space Built‐up Paved roads Open Space<br>Source: Author’s calculations using land classification from high resolution satellite imagery (see<br>annex A for more details) and Felkner, Lall, & Lee (Forthcoming). European cities were analyzed using<br>Urban Atlas data layers published by the European Environment Agency (EEA)<br>(http://www.eea.europa.eu/data‐and‐maps/data/urban‐atlas)<br><!-- End of picture text -->

# LAND COVER CLASSIFICATION

Poorly managed, dense urban areas can become congested, polluted, and poorly equipped to provide basic services for their inhabitants. In Africa, evidence of this challenge is apparent in the growth of informal settlements; tightly packed small houses that have been constructed from nondurable materials. Households in these dense neighborhoods often lack sufficient living space, sanitation, electricity, and safe water. Historically, many governments have been reluctant to grapple with such settlements, weary of the social and political risks associated with displacing vulnerable populations.<sup>21</sup> Furthermore, a lack of up‐to‐date and accurate spatial data on these areas adds to the challenge. In order for policy makers and planners to make well informed decisions, high quality maps are essential.

This study
# **_5.2 Prenatal environment: Measures of maternal health, nutrition and care_**

Given extensive evidence about the importance of the prenatal environment for subsequent child nutrition and health outcomes, a set of 10 indicators that are proxies for the likely nutritional status of the mother during pregnancy as well as the health care services that were available to - and used by - mothers during their pregnancy were identified in the NNS. Of these, five are considered necessary evidence for an adequate prenatal environment for the child: normal birthweight (greater than or equal to 2.5kg),<sup>44</sup> maternal height above 145 cm, mother’s BMI > 18.5 and at least four prenatal visits and birth assistance during delivery. Three additional ideal measures in this domain that were not available in the data include whether the child was born preterm, whether the child was born small for gestational age and whether the mother had anemia while she was pregnant.

**Table 3**

|Category|Dataset|Driver|Definition|Available<br>in NNS<br>dataset?|Required<br>for<br>adequacy<br>in<br>domain?|
|---|---|---|---|---|---|
||NNS Form 1.6|**Low birth weight**|**Child weighted less than**<br>**2.5kg at birth**|**Yes**|**Yes**|
|Birth<br>outcomes|Not in questionnaire|Preterm delivery|Child is born before 37 weeks<br>of completed gestation|No|No|
||Not in questionnaire|Small for gestational age|Child is as smaller than the<br>10th centile for fetal growth.|No|No|
|||**Short maternal stature**|**Mother’
all these elements of the economy on a daily basis they are the most capable of grading and gauging which areas affect their growth the most.

# **Data Description**

The objective of this paper is to analyze whether aid targets areas which firms in developing countries have identified as obstacles for their growth. We also examine whether greater aid flows in the past are reflected in better firm perceptions today. With these objectives in mind we classified aid flows from the OECD’s Creditor Reporting System (CRS) database into seven areas—access to finance and land, business environment, infrastructure, labor force, rule of law (courts, corruption and crime), economic and political stability, and trade—and matched them to firm perceptions of these seven areas from the World Bank Enterprise Survey Database.

The World Bank’s Enterprise Survey is intended to capture business perceptions of the biggest obstacles to enterprise growth, the relative importance of various constraints to employment and productivity, and the effects of a country’s business environment on its international competitiveness. This unique and comprehensive survey collects data from key manufacturing and service sectors in every region of the world. It uses standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across the world’s economies. This new database is such a rich source of homogeneous information across countries and regions that it has already been used for studies relating to labor markets, export markets, and institutions.<sup>3</sup> The Enterprise Survey is an ongoing project by the World Bank; it is continuously surveying countries and updating the database. The data used for this

> 3 Go to www.enterprisesurveys.org/researchpapers/ for a complete list of recent research papers that use Enterprise Survey data.

8
# Table 8: First-stage results using 1994 industrial conditions from female-owned businesses

|||Incumbe|<br>nt industrial con|ditions in 2000 a|nd 2005||
|---|---|---|---|---|---|---|
||o tota|o|o tota|o|aor|nut-outut|
||<br>incumbent<br>employment|female-owned<br>incumbent<br>businesses|<br>incumbent<br>employment|female-owned<br>incumbent<br>businesses|market<br>strength,<br>female-owned<br>businesses|market<br>strength,<br>female-owned<br>businesses|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Log total incumbent employment in|**0.177+++**|0.093+++|**0.174+++**|0.091+++|0.022++|0.029+++|
|district-industry in 1994|**(0.016)**|(0.020)|**(0.016)**|(0.020)|(0.010)|(0.010)|
|Log female-owned incumbent businesses|0.094+++|**0.286+++**|0.071+++|**0.259+++**|0.027+++|-0.000|
|in district-industry in 1994|(0.014)|**(0.024)**|(0.016)|**(0.027)**|(0.011)|(0.010)|
, external provision of low-cost information can have impacts on the magnitude of officials’ errors equivalent to or greater than the design of incentives. However, improving the underlying incentives for information acquisition would yield superior information outcomes without external intervention.

# **5 Conclusions and Further Research**

This paper investigates the scale of errors in the beliefs of public officials and their determinants across a large, hierarchical public sector. It measures the subjective beliefs of public officials as to the characteristics of the citizens that they serve at all three tiers of government and across five major sectors. By comparing these beliefs with benchmark objective data from surveys and administrative data, we identify which officials have the most accurate perceptions of the characteristics of their constituents. The paper presents results from the experimental provision of an evidence briefing that aims to improve the accuracy of public officials beliefs. We find that such evidence briefings, now a common tool of economics researchers who aim to influence public policy, can improve the accuracy of officials’ beliefs. However, in line with our descriptive evidence and consistent with theory, this result is significantly mediated by organizational 29
internet access.<sup>5</sup> The average delegation has a 35 percent (male) internet access rate; the aggregate nation-wide rate is 39 percent because more densely populated urban areas tend to be more connected. Figure 1b shows how geographically heterogeneous internet access is; metropolitan areas closer to the coast have high access rates, while these tend to be low in the interior of Tunisia and in delegations abutting the Algerian border.

To match census data with our Daesh recruit data, we aggregate the census data to a level below the delegation, i.e. the cell level. Recall that a cell is characterized by the delegation _d_ , education level _e_ (primary or below, secondary, and tertiary and higher), and a five-year age group _a_ (starting from at 15-19 through 60-64). We construct demographic, unemployment, and internet and media usage variables for each cell by taking the mean among men in that cell. Our data set thus consists of 7890 observations, but the population in 12 of the _{d, e, a}_ cells is equal to zero, so they are excluded them from the estimation sample, which eventually consists of 7878 cell-level observations. Definitions and sources of the variables used in our analyses are provided in Appendix Table A1 and summary statistics in Table 1.

**Instrumental variables** As an instrumental variable for internet access, we use the average lightning strike intensity per square km per year over the period 1995-2010 obtained from Manacorda and Tesei (2020). They use data collected by the U.S. National Aeronautics and Space Administration (NASA) and processed by the Global Hydrology Resource Center (GHRC). Figure 1c shows the spatial distribution of lightning strikes. The map shows a high degree of spatial correlation, underscoring the need to accommodate this feature in the econometric analysis. Lightning strikes are least prevalent in the south, which is covered by the Sahara desert, and most prevalent in the Chaambi mountains close to the Algerian border and