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68
sheds light on health outcomes, specifically, diarrhea and cough prevalence. Notably, data on these health indicators were exclusively available for children, constituting 25.28% of the overall sample. For children aged 0-4, data on diarrhea was accessible for nearly 99.81% of the 30,003 children in the sample, while data on cough was available for approximately 99.84% of the same group. Panel C offers insights into relevant covariates and household characteristics, encompassing toilet access (available for 100% of the sample), refrigerator ownership (accessible for approximately 99.97% of the sample), education level, household size, and urban residence. Education in Cambodia DHS is defined for six levels: people with no, incomplete primary, complete primary, incomplete secondary, complete secondary, and higher education. Our dummy variable encompasses the last three.

> 4The DHS includes a displacement of coordinates to preserve anonymity. It is standard practice in the literature to include buffer sizes corresponding to the DHS displacement to account for it. We apply this method in our study and set the size of the buffers to equal the size of the maximum displacement.

> 5We focus on average air pollution in this paper. Air pollution peaks could also have acute consequences on health, but their study requires temporally disaggregated data that we do not have.

6
# III. MECHANISMS

We now use additional information gathered from both program operational surveys and administrative sources in order to shed some light on the interpretation behind the patterns uncovered in section 3. The finding of spillovers on school enrollment operating over short distances supports a simple model of peer effects on program take-up decisions of eligible households.<sup>22</sup> As we do not have measures of the occurrences of interactions of beneficiaries from different neighboring villages, we cannot report direct evidence of this. Hence, we conduct several indirect checks for the presence of such interactions. On the other hand, some spatial variations in the local implementation of the program could also a priori explain the observed relationship between the local density of the treatment and program impacts. We thus also test for the presence of such spatial variations in the implementation of the intervention under study.

# _Knowledge Spillovers Among Program Participants_

In spite of the emphasis placed on informing the potential participants about the objectives, design, and requirements of the intervention, concerns have been expressed by those involved in the initial phases of the implementation regarding the effectiveness of the diffusion of information about the program among targeted households (Adato et al. <u>2000).</u> To further corroborate this anecdotal evidence, we use information from an operational follow-up survey conducted among eligible households in the evaluation treatment-group villages in May 1999 (i.e., 14 months after the inception of the program). Program beneficiaries were asked to identify three sets of benefits distributed by _Progresa_ : (i) scholarships and school supplies; (ii) food stipends and nutritional supplements; and (iii) preventive healthcare and health check-ups. Most of the respondents who were to receive the transfers were mothers. While 98 percent of the respondents were able to spontaneously

> 22. Non-market interactions may affect take-up decisions through two channels: information and social norms. While conceptually different, these two forms of social behaviors can hardly be distinguished empirically. We thus broadly refer to the influence of
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vary by agro-ecological zone. The analysis in this paper focused on differences across country, which is the level at which data are collected and organized in the LSMS-ISA. Economics researchers is frequently conducted using nationally representative data or regional data from within a single country. However, the relevant organizing structure of weather is not the political boundaries of a country but the geographies that create different agro-ecological zones. We plan to replicate much of the current analysis, ignoring crosscountry differences, and instead focusing on differences in agro-ecological zone. Our objective with this question is to determine if some weather metrics or some remote sensing products perform better in one agro-ecological zone versus another. If this is the case, it suggests that researchers using national data may need to use different metrics to measure weather for households in different regions of the country or may need to use data from different remote sensing sources depending on which agro-ecological zone a household lives within.

A second research question which we plan to explore is the degree to which the inclusion of household fixed effects impact results. Here, the integration of cross-sectional data from Mali is critical. Additionally, we will incorporate two waves of cross-sectional data from Malawi, and a 47
level of exports in the first year the plant has exported. Unfortunately, we cannot use a reciprocal functional form in our regressions because both indexes of plant export experience are zero for the majority of observations in the sample.

Given our definitions of output and export experience, we need to restrict our main estimating sample to plants born in 1981 or later, the first year when information on exports is included in the census.<sup>11</sup> Besides limiting our sample to plants born in or after 1981, we require plants to have a minimum of three years of data and have positive values for the key variables output, labor, intermediate inputs, capital, and wage premium. We exclude plants that do not report data in some year between their first and last year in the survey and plants belonging to industries with less than 100 plant-year observations. In addition, given that our output and export experience measures depend on cumulative output and exports up to _t-1_ , we exclude from the estimating sample the first observation of each plant. Applying these criteria, we obtain a sample of 3,324 young plants and 16,706 plant-year observations. Finally, since our estimation procedures are sensitive to outliers, we reduce further our sample to 3,091 plants and 15,457 plant-year observations. The criteria for the elimination of outliers are described in Appendix B. To compare the effect of export experience on productivity in young and old plants, Section 6 uses a larger sample including both young and old plants. The latter plants appear continuously in the Colombian manufacturing census since 1974. This sample includes 6,171 plants and 46,574 plant-year observations.

> 11 A limitation of this procedure is that some of the “new” plants in 1981 could have actually been born before 1981, but were smaller than the cutoff level of ten employees required to fill out the census form. Similarly, if a new owner acquires a previously operating plant and registers it under a different name, it might be coded in
Policy Research Working Paper 10141

# **Abstract**

Measurement of food access typically relies on a consensus of different indicators. However, there is a growing list of surveys in which the Food Insecurity Experience Scale is one of the few food access indicators captured, likely because it is an official measure for tracking progress toward the Sustainable Development Goal of zero hunger. This paper uses a nationally representative, multipurpose household survey conducted in Nigeria to investigate the validity of the Food Insecurity Experience Scale. It compares the Food Insecurity Experience Scale to monetary poverty and a widely used food access metric that has been more extensively validated, the Food Consumption Score. Although it is possible for food access metrics to be poorly aligned and capture different dimensions of poor food access, empirically supported assumptions in standard consumption models result in many dimensions of poor food access being concentrated among the poorest segments of the population. However, the paper demonstrates that the Food Insecurity Experience Scale does not appear to correctly identify the population with poor food access—it finds little difference in the share with poor food access among poor and nonpoor Nigerians. Moreover, even the very richest and very poorest households have a similar prevalence of poor food access, according to the Food Insecurity Experience Scale. These patterns are in stark contrast to the Food Consumption Score, which suggests that food access is significantly lower for poorer Nigerians. Combined, the results demonstrate the importance of measuring food access with more than one indicator, and they call into question the notion of using the Food Insecurity Experience Scale alone, despite the measure being a key Sustainable Development Goal food security indicator.

This paper is a product of the Poverty and Equity 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 jlain@ worldbank.org,
We use the microsimulation model EUROMOD based on EU-SILC data for the EU-28 countries to compute the Gini coefficients for market income, market income plus pensions, and disposable income in order to measure redistribution. Next, we use counterfactual decomposition techniques to assess the change in redistribution and to isolate the effects of policy changes. In addition to vertical redistribution, we pay special attention to horizontal redistribution between socioeconomic groups. We find that the majority of EU member states have experienced an increase in overall redistribution between 2007 and 2014, with the exception of some Eastern European countries that effectively flattened their income tax schedule. The drivers of these changes differ across countries: in certain cases, increased redistribution results from changes in market incomes, in other cases from tax-benefit changes, sometimes linked to tax-based budget consolidation, in particular in Southern Europe.

When assessing the incidence of taxes over time, our results show that in Southern and Western Europe both automatic stabilization effects and active changes in the tax system have worked in the same direction and alleviated the burden on younger generations, who have been hit especially hard following the financial crisis, while in Eastern Europe, discretionary policy changes had the potential to increase intergenerational tensions.

No clear pattern emerges when analyzing across occupations and job types. Some countries, such as Germany, have actively compensated for job polarization while other countries, such as Poland, have predominately favored individuals in non-routine cognitive occupations benefitting from job polarization. In most other countries, policy makers have yet to act upon increasing job polarization and tax incidence is mainly driven by changing market incomes.

We also assess developments when differentiating by benefit-recipient status. Most countries have implemented progressive policy changes that lead to increasing transfers to households depending on transfers. Policies vary much more once households with income earners are considered. In particular in Eastern Europe, increasing efficiency has dominated the redistributive motive and many countries have actively lowered the tax burden on single and dual earner households, i.e. the households with traditionally large tax burdens. In contrast, tax burden increases
benefits, it is clear, particularly in the Israeli context, that they are real and tend to raise the underlying social rates of return to vocational education.

# 3. Vocational Education, Occupational Choice and Earnings

The previous section has been concerned with a comparison of the earnings of vocational and academic secondary school graduates. In this section we pose the qu stions: to what extent are vocational school completers employed in occupations related to the main subject area studied at school? Are there significant differences in the earnings of those employed in jobs related to subject studied at school and those not working in subject-related occupations?

# Education-occupation matchings:

The Census questionnaire was unusual in addressing a specific question to those individuals whose formal education terminated at the agricultural or vocational secondary school, concerning the main subject of study, whether in Agriculture, Electricity, Electronics, Metal work, Automechanics, Clerical and book-keeping, Sewing and fashion, and Hotel management.

For each vocational school completer, we compared subject studied with current job held (using 2-digit occupational codes) to see if vocational education received was related to occupation. Two alternative

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- Tesfaye, W., G. Blalock, and N. Tirivayi (2021). Climate-smart innovations and rural poverty in Ethiopia: Exploring impacts and pathways. _American Journal of Agricultural Economics 103_ (3), 878–99.

- Tzachor, A., C. Richards, M. Gudoshava, P. Nying’uro, H. Misiani, J. Ongoma, Y. Yair, Y. Mulugetta, and A. Gaye (2023). How to reduce Africa’s undue exposure to climate risk. _Nature 620_ , 488–91.

- Uganda Bureau of Statistics (UBOS) (2012). Uganda National Panel Survey 2005-2009. Public Use Dataset. Ref: UGA ~~2~~ 005-2009 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/1001` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2014a). Uganda National Panel Survey 2010-2011. Public Use Dataset. Ref: UGA ~~2~~ 010 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2166` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2014b). Uganda National Panel Survey 2011-2012, Wave III. Public Use Dataset. Ref: UGA ~~2~~ 011 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2059` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2016). Uganda National Panel Survey 2013-2014. Public Use Dataset. Ref: UGA ~~2~~ 013 ~~U~~ NPS ~~v~~ 02 ~~M~~
digitalization and skills requirements across the four countries we study. We use the SEAD data set to understand the distribution of skills across the four countries of interest. We also generate occupation-weighted data sets over several years for Malaysia, Thailand, and Vietnam to understand how the distribution of skills across each country evolves over a 4-6 year period.<sup>32</sup>

> 30 We repeat this analysis for unweighted occupations, and weighting for the Cambodian, Thai, and Vietnamese occupational distributions. Again, results are similar to those when using the Malaysia-weighted data.

> 31 By definition, orthogonal factors are not correlated with each other.

> 32 We were unable to access multiple years of the Cambodian SES.

22
|-0.27|
|Out-of-pocket spending in 1997/98|-18.744|-0.96|
|Inpatient expenditure in 1997/98|8.636|1.08|
|<br>Non-inpatient out-of-pocket spending 1997/98|-27.380|-1.67|

Jowett et al. (2003) also found that VHI reduced out-of-pocket payments, though there are some differences between their study and ours are worth noting: their survey was not nationally representative; VHI coverage was restricted to voluntary coverage (their data were collected in the city of Hai Phong that succeeded for a while in enrolling adults as well as school children in the voluntary scheme); out-of-pocket payment data were collected only from those reporting illness; and their results refer only to those reporting positive payments. Trivedi (2003) found some evidence from the 1997/98 data of VHI _increasing_ out-of-pocket expenditures<sup>18</sup> , but the effect was not significant when commune fixed effects were included in the regression model.

> 17  The 1992/93 survey also asked about spending in the previous 4 weeks, but only of those reporting illness and only in respect of first visits to each provider type.

> 18  Spending was defined in Trivedi’s analysis as spending over the past 4 weeks _plus_ spending over the past 12 months.

17
.

'<sup>2</sup> The possibility exists that exports to third markets (particularly of agricultural products) may have been greatly constrained by high tariffs and nontariff measures. In these cases, RO index values could rise because of restrictions in third markets and not the more favorable tariff treatment of member countries in inter-block trade. There is reason to believe, however, that this situation occurs infrequently. World Bank-UNCTAD records show that most of the products listed in Table 4 do not encounter major OECD restrictions (the processed foodstuffs are an exception). In addition, this study shows (see Table 7) that Mercosur provides sizeable trade preferences on intra-trade. It is also possible that idiosyncracies in demand patterns and in the ability to produce certain varieties of goods made trade between Mercosur countries increase disproportionately fast as a result of the MFN liberalization. This is sufficiently unlikely, however, that the burden of proof must lie with advocates of this view.
- 5 -

may reflect Nepal’s fiscal year cycle, which runs from July 16 through July 15. There may be some administrate pressure to execute the budget toward the end of each fiscal year.<sup>4</sup> On the other hand, there is also another peak to start public works in the first quarter of the fiscal year.

In Nepal many road contracts are made before the end of the fiscal year (July) and the actual works tend to be started after the rainy season. Rural road works can last several months to more than one year (Figure 4). For technical reasons, the works can be affected by the climate, depending on work specification. Concrete or bitumen work cannot be implemented during the rainy season. The data indicate that cost overruns and project delays seem to depend on when the work is started. Suppose that the sample is divided into two seasons: rainy (May-September) and dry (October-April). More cost overruns and delays occurred in the rainy season. The distribution of project delays in the rainy season has a relatively thicker tail on the right hand side (Figure 5).

**Figure 2. Average monthly precipitation for the past 10 years**

<!-- Start of picture text --> 1200<br>Siraha<br>Makwanpur<br>1000<br>Banke<br>800<br>600<br>400<br>200<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>Monthly precipitation (mm)<br><!-- End of picture text --> Source: Author’s calculation based on data from the Ministry of Environment, Nepal.

> 4 For some of the sample projects, there is a view that the work schedules in contracts are determined by the remaining number of days in the current fiscal year, regardless of the work period estimate from the engineering