rafmacalaba/data-use-mentions-extended huggingface dataset:
�� � ������ ����� ������ ��� ����� �4� Where:

- ��� is per capita GDP growth for country _i_ in year _t;_

- ��� is the hurricane damage index for major events for country _i_ in year _t_ (SS ≥ 1 or 3);

- ��� is a matrix of control variables;

�� are year fixed effects; �� are country fixed effects;

- is time trend.

For each country, the dependent variable is per capita GPD growth. As we discussed in the previous section, we adopted three different measures of the intensity of the damage ( ��� ): MSWS, IHD and WEI. MSWS corresponds to the unweighted wind speed aggregated at the country level per year, IHD relates to the population‐weighted wind speed, while WEI relates to the area‐weighted wind speed. Each index has its strengths and weaknesses highlighting the trade‐off between exogeneity and measurement error.

The control variables ( ��� ) mirror those in Strobl (2012) and include: investment as a percent of GDP, population growth, openness (the sum of exports and imports as a percentage of GDP), and the log of GDP per capita. All these variables are lagged one period to capture the contemporary economic impact of hurricanes in the region. Additionally, a regional time trend ( � ) was included to account for common factors to all countries.

> 17 Results available upon request.

> 18 Nicaragua: Joan (1998), Cesar (1996), Felix (2007). Honduras: Mitch (1998).

11
rounds adjusting for inflation between midline and endline, which was particularly important for food items (Online Appendix Text 3 presents details on real value estimates).

2. _Household durable assets._ An index proxy for wealth generated using principal component analysis of the number and types of durable household assets (excluding land/property), following Filmer & Pritchett (2001), for the durable assets available in the DHS (2015) data. The index is then normalized to the unit standard deviation of control, with control mean equal to zero.

3. _Value of livestock._ Total value of livestock is calculated as the total number of TUP livestock (cows, goats, sheep) and chickens owned by the household, multiplied by unit price, for each livestock type. The unit price comes from the valuation given by the households for each type of livestock in nominal USD.

4. _Financial inclusion index._ A standardized index including the following outcomes: (i) anyone in the household has savings; (ii) household value of total savings; and (iii) the household’s ability to access formal credit (i.e., from a bank or microfinance institution) for emergency purposes.

5. _Psychological well-being index._ A psychological well-being index is computed for women using the standardized weighted average of scores on the Center for Epidemiologic Studies Depression (CES-D) seven-point scale (Radloff, 1977) (negatively coded), and the World Values Survey (WVS) questions on happiness and life satisfaction.<sup>15</sup> The index is computed using the procedure outlined in Anderson (2008), by subtracting the mean and dividing by the standard deviation of the control group for each unit of analysis, computing the covariance matrix, inverting the matrix, adding up the rows of the matrix, and weighting each variable with its corresponding entry in the summed inverted covariance matrix. As a last step, the index is normalized again by dividing by the sum of the weights.

6
0.9|1.4|
|Free choice of work hours and place|2.3|1.6|3.2|1.4|
|Can combine business w/ housework|2.4|3.2|5.0|2.9|
|Traditional line of business of family|2.3|1.4|3.0|1.5|
|Other|1.7|0.9|2.0|0.4|
|Total|100|100|100|100|

_Source: Calculations based on the ILFS 2000/01 and 2006 data_ 18. For those engaged in HEs as their secondary activity, the primary reason for doing so was their family‟s need for additional income. This is not surprising as one would expect this need to be a strong motivation for seeking a second job generally. Informal sector arrangements are also more conducive to part-time work, which requires lesser time commitment than a primary occupation. The share of households driven into the HE sector as a secondary source of income rose by 12 percentage points during the 2000-2006 periods, indicating its increasing role as a source of livelihood.

19. More than two-thirds of HEs in the urban areas were formed because of lack of any other job opportunities, in particular wage employment providing adequate remuneration. The dominant factor in the rural areas, on the other hand, was the need for a secondary source of income by households whose main activity was farming (Box 2.1).

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- 15 -

**Figure 11. Estimated time-specific fixed-effects**

**Figure 12. Estimated commodity specific fixed-effects**

<!-- Start of picture text --> Clothing, apparel,<br>texitile<br>Crops, food products<br>Consumer and<br>other goods<br>Minerals, metals,<br>other materials<br>Equipment<br>-.2 -.1 0 .1 .2 .3<br>Coefficient and Confidence Interval<br>.6<br>.4<br>.2<br>0<br>-.2<br>Coefficient and Confidence Interval<br>-.4<br>2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 2016m1 2017m1 2018m1 2019m1 2020m1 2020m12<br><!-- End of picture text -->

# **V. Heterogeneity and Robustness**

32. As discussed above, there seems to be a systematic difference in freight costs across commodities. To investigate potential heterogeneity in freight cost determinants, the sample data is divided into five groups: (i) clothing and apparel, (ii) equipment and vehicles, (iii) agricultural commodities and food, (iv) minerals and metals, and (v) consumer goods. The logarithmic model is separately estimated by applying OLS ( **Table 4** ).<sup>6</sup> The results are broadly consistent with the above, but some coefficients are different among groups of commodities. For instance, driving speed or road conditions matter most for equipment and minerals ( **Figure 13** ). For other types of shipments, i.e., food products and consumer goods, the elasticities are small. It can be interpreted to mean that production-oriented capital goods are more sensitive to delivery efficiency than consumer goods.

33. For shipping distance, it is found that the freight cost increases with distance for all kinds of merchandises but equipment. The
)|(0.04)|(0.0)|(0.0)|(0.0)|
|**Number of observations**|**1179**|**4644**|**711**|**701**|**698**|**1412**|**1409**|**1399**|

**Note:** The standard errors are in parentheses, and the differences are estimated considering the complex survey design. * p < 0.1, ** p < 0.05, *** p < 0.01. The population weights are applied. The consumption data are expressed in monthly basis and are spatially and temporarily deflated within each round. The consumption data in NPS 2019/20 and in NPS 2020/21 are deflated to 2022 prices using the annual WB deflator.

32
household units. Likewise, we stratified the population according to the gender of the household head as there may be gender differences in preferences and attitudes toward child labor. However, more than 90% of heads of households are males since we did not oversample female heads of households to keep the same observed male-female head of household ratio as in the population. Our representative sample is comprised of 1203 households.

One-third of the sample is randomly allocated to the self-response survey design group, while the remaining two-thirds to the proxy-response survey design group. The selection of the proxy respondent is limited to the household head or the spouse thereof to reflect the common practice of interviewing an informed household member. This statistical design yields 401 and 802 household units in the self-reported and proxy-reported groups, respectively. A statistical power analysis of a two-sample mean difference based on a two-sided 5 percent-level test and an effect size of 10 points shows a statistical power above the conventional threshold of 80 percent.<sup>5</sup>

# **2.2 Survey Design and Questionnaire**

To investigate the effects of survey design on child labor statistics, we apply the same survey instrument to randomly selected respondents: children answer the questions themselves in the selfreported group and heads of households or spouses in the proxy group. Field surveyors ask the randomly selected respondent a specific labor-market module about child labor activities in the last 30 days before the survey (a child is defined as 6-14 years of age). There is no distinction in the wording nor in the sequencing of the questions across groups to avoid the possibility of bias in the way information is elicited. We ask the same questions in three agricultural seasons and make sure that the selected respondent in each group does not change over time. The overall compliance 9
of traffic and pollution employ engineering models calibrated for developing-country cities with plentiful information about the spatial distributions of vehicle traffic, emissions, and atmospheric conditions (Jerrett et al. 2005). For urban researchers and planners in developing countries who use these imported models, the scarcity of supporting information often requires extensive spatial and temporal interpolation to cover gaps in local data.

In the second category, local measurements of traffic and air pollution along road corridors are used to estimate vehicular emissions intensities, the local effects of meteorological variables, and the attenuation of pollution with distance from roads (Brauer et al. 2003; Gualtieri et al. 1998; Walker et al. 1999). These studies provide useful information for local zoning in developed countries. In developing-country cities, where regulatory enforcement is often difficult, they have played a useful role in promoting awareness of vehicle-based air pollution as a serious health hazard for young children and elderly adults. However, the limited scope of road corridor studies makes it difficult to extrapolate their empirical results to metropolitan scale.

The third research category has grown rapidly with the development of Geographic Information System (GIS) technology. It uses georeferenced data to assess relationships between the spatial distributions of vehicle traffic and air pollution (Briggs et al. 2010). Until recently, the spatial coverage and resolution of these studies have been greater for developed-country cities with more plentiful spatial information on vehicle traffic and air pollution. Recently, however, higher3
Ghana Microfinance Institutions Network (GHAMFIN). 2014. “Performance Monitoring and Benchmarking of Microfinance Institutions in Ghana: Trends in the Industry during the 2000s (2006-12)”, Accra.

Gneezy, A., U. Gneezy, G. Riener and L.D. Nelson. 2012. “Pay-what-you-want, identity, and selfsignaling in markets”, _<mark>Proceedings of the National Academy of Science</mark>_ <mark>s 109(19): 7236-7240.</mark> Gross DB, Souleles NS. 2002. Do liquidity constraints and interest rates matter for consumer behavior? Evidence from credit card data. _Quarterly Journal of Economics_ 117(1): 149-185.

Grossman S. 1981. “The Informational Role of Warranties and Private Disclosure About Product Quality.” _Journal of Law and Economics_ 24(3): 461-483.

Gurun U, Matvos G, Seru A. Forthcoming. "Advertising Expensive Mortgages," _Journal of Finance_ .

Hastings JS, Hortaçsu A, Syverson C. 2015. “Sales Force and Competition in Financial Product Markets: The Case of Mexico’s Social Security Privatization.” Unpublished manuscript, Brown Univ.

Iyer, R., Schoar, A. 2012, “The Importance of Hold-up in Contracting: Evidence from a Field Experiment”. Unpublished manuscript, MIT.

Jovanovic B. 1982. “Truthful disclosure of information.” _Bell Journal of Economics_ 13(1): 36-44.

<mark>Loewenstein, G., Sunstein, C. and Golman, R. (2014). Disclosure: Psychology Changes Everything.</mark> _<mark>Annual Review of Economics</mark>_ <mark>6: 391-419.</mark> Lusardi A, Mitchell O. 2011. “Financial literacy and planning: implications for retirement welling.” <mark>Oxford University Press, 2011, pp. 17-39.</mark> Lusardi A, Mitchell O. 2014. “The Economic Importance of Financial
For the sake of completeness, Figure 4 plots the volatility CDF of primary commodities based on the IMF commodity price table data, the previously defined group of manufactured products and primary commodities (based on the NAICS-IMF overlap sets) and a more narrow set of manufactured goods classified as “computers”. The latter appear to have the highest volatility distribution, followed by the large group of all manufactured goods.

Thus, the data on price volatility at the level of individual products suggests that manufactured goods prices are more volatile than that of commodities. This result is at odds with Figure 1. We argue that the use of aggregate indices in comparing prices across classes of goods is subject to an aggregation bias. That is, some price swings in one direction cancel out swings in the other direction, which makes for an overall index that looks more stable than its components. Of course that same effect is also at play in commodity price indices, but there are far fewer commodities than manufactures, so fewer prices cancel each other out. According to NAICS, manufactures account for more than 90 percent of the goods in our data set.<sup>7</sup> Nonetheless, since the analysis compares the whole distribution of volatilities within categories of goods, we next need to establish that the observed differences in the CDFs are statistically different.

> 7 More formally, it can easily be shown that using a variance operator to compute measures of volatility for two different price indices will bias the measure of volatility upward for the index which comprises more subcomponents compared to the one with less.

9
to cover the start-up expenses of self-employment and entrepreneurial activities. Second, prior to their move abroad, migrants have self-employment rates that are very similar to those of non-migrants across all ages (blue and red lines in Figure 6). These two groups of workers may differ in various dimensions, yet their propensity to become self-employed is almost identical until after the migration experience. Third, and most importantly for this paper, the rate of self-employment of return migrants is considerably higher than the rate among non-migrants, and the rate for the same migrants prior to their migration experience. This difference emerges at any age (green line versus the red and blue lines). The gap in selfemployment rates between return migrants and non-migrants is largest at younger ages.<sup>17</sup> For instance, 65 percent of 30-year-old return migrants in our sample are self-employed, compared to 28 percent of workers of the same age who have not migrated. While this difference has been pointed out in different contexts, our data facilitate a comparison not only with nonmigrants, but also with migrants’ own activity prior to migration. This within-individual comparison strengthens the case that migration is instrumental in business creation in the origin country. Across all ages, the rates of self-employment for return migrants are high, reaching levels of between 60 and 70 percent. The gap in self-employment between those who did and did not migrate narrows with age, but it never closes. Data from the BRMS support the pattern. They show that over 90 percent of the enterprises owned by former migrants were established after their return from overseas work. In other words, self-employment patterns are not driven by other forces, such as returning migrants taking over the family businesses. This descriptive evidence highlights the role played by temporary migration in accelerating transitions of workers into self-employment. Faster asset accumulation is the main channel behind this observation (see Section 4 for detail).

While self-employment can be a last resort for workers in certain contexts (Gindling and Newhouse 2014), evidence from Bangladesh
especially in Bolivia, Honduras, and Peru, and much larger differences in poverty rates between urban and rural areas within geographic regions in Ecuador, Guatemala, and Nicaragua.

Third, territorial income disparities pose serious policy challenges in both developing and developed countries. In the Middle East and North Africa, they have been associated with political instability and the Arab Spring uprisings (Ianchovichina, 2018; World Bank, 2020). In advanced economies, they have been linked to growing political and social polarization. In the UK, support for Brexit was strongest in the country’s lagging regions (Rodriguez-Pose, 2018), which were also hit harder than other areas by the austerity policies implemented in response to the financial crisis in 2009 (Dorling and Thomlison, 2019). Crafting policies that address such challenges requires a good understanding of the size and dynamics in territorial income differences.

The paper makes several contributions to the literature. Previous studies look at issues of territorial income differences and dynamics separately, cover one or limited number of countries, employ disparate data sources – either national accounts or household surveys – and rely on value added or expenditure data at the first administrative level up to the early 2000s. Therefore, such studies may not be able to provide a complete or consistent regional picture on the size and evolution of territorial income differences within the Latin American countries in more recent years. This paper explores labor and place productivity differences and convergence dynamics during the pre-pandemic period between the early 2000s and the late 2010s using labor income data from a harmonized set of micro surveys for most countries in the region. We employ household labor earnings, deflated to address price variations across time and space, in the estimation of location premia at the 1<sup>st</sup> and 2<sup>nd</sup> administrative levels, following the approach of Li and Rama (2015). We then categorize 2<sup>nd</sup> level administrative units (i.e. municipalities) based on their
Policy Research Working Paper 8879

# **Abstract**

This paper outlines changes that have been made in the 2019 version of the Health Equity and Financial Protection Indicators database. On the financial protection side, the changes include an increase in the number of indicators from five to 14; revisions to several previous data points, reflecting the analysis of new surveys (or adaptations thereof); and refinements to the estimation of out-of-pocket expenditures. On the health equity side, the 2019 database includes 198 more data points than the 9,733 in the 2018 database, reflecting the addition of 535 new datapoints, and the dropping of 337 previously included data points now considered to be substandard.

This paper is a product of the Development Research Group, Development Economics and the Health, Nutrition and Population 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 awagstaff@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
terms than their better-off counterparts, especially after 1992. Moreover, geographic targeting has clearly improved over time. By 1996, households living in the wealthiest quartile of districts were receiving virtually no investments in school facilities from FONCODES at all.

Figure 5.5 presents comparable figures for expenditures made by FONCODES in 1995 in three sectors: education, electrification, and water and sanitation. The corresponding curves for FONCODES projects in other sectors- community centers, health, agriculture, and transportation-are not included to avoid cluttering the pictures, but the results are very similar for other years and other sectors. Figure 5.5 suggests that education projects may be somewhat better targeted to the poorest districts than some of the alternatives in the FONCODES portfolio, although these differences do not appear to be very large.'<sup>0</sup> Figure 5.6, finally, compares district-level expenditures made by FONCODES, the educational infrastructure program INFES, and the feeding program PRONAA in 1995. T-he results show that the geographic distribution of expenditures made by FONCODES was clearly more pro-poor than that of INFES or PRONAA. In sum, the geographic targeting of FONCODES projects in the education sector is progressive, has improved over time, and is noticeably more pro-poor than that of two other large public sector programs in the sector.

(ii) Household-level targeting: In Peru, there is considerable heterogeneity in the intradistrict distribution of welfare: for example, a simple decomposition of the variance in per capita income into inter- and intra-district components suggests that only 24% of the variance is explained by differences across districts. Reaching poor districts is therefore only a weak proxy for reaching poor households. This section looks at the targeting record of FONCODES investments in education at the household level.

The basic approach we use calculates the household-level incidence of FONCODES benefits with information from the 1996 INEI survey on per capita income, access to educ;.tional infrastructure, and access to school breakfast programs financed by FONCODES. Two (pc ssible) concerns with this approach
60 Figure 3: Cumulative numbers of publications in EconLit in health economics and education economics <!-- Start of picture text --> 35,000<br>Health economics<br>Education economics<br>30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Cumulative no. publications in EconLit<br><!-- End of picture text --> Note: Health and education publications in EconLit are identified using the health and education JEL codes.