rafmacalaba/data-use-mentions-extended huggingface dataset:
processing of milk and derivatives, corn, rice, fruits and meat; Small livestock and poultry care (especially chickens and pigs); Home-based agroindustry plants producing tortillas, vinegars, vegetables, soaps, sausages, dairy products and handicrafts; Fish products (salting and drying); Selling of vegetables, fruits, meats, and processed products in local markets; and business activities such as buying of inputs, rental of equipment, and payment of workers (SAG 2000).

In many contexts, there is significant overlap between economic activities that generate production for own-consumption and tasks that are identified as housework. Women doing these tasks might not recognize it as work beyond housework (Shah 1985) as it is informal, unpaid and flexible (Saxena 2012). <mark>This problem is aggravated when women’s</mark> work activity is combined and is intertwined with their household tasks, and as such is viewed as an extension of household duties (Donahoe 1999). Evidence from Bangladesh shows that women’s work on their husband’s farm might be considered part of household work though it generates income (Anderson and Eswaran 2009). One of the findings of the initiative Women’s Work and Employment<sup>9</sup> is that women who often helped with stocking, storing and other farm activities only reported these activities when they were specifically asked about them (Data2x 2017). A qualitative study in Malaysia listed different reasons for why working women called themselves housewives such as: “just helping”; “little business, not serious”; irregularity of income; lack of skills or formal training; not qualified (Franck and Olsson 2014).

Survey design also plays a role in the extent to which these activities are undercounted. The wording of questions meant to measure these activities has been shown to affect female labor force statistics more than statistics for the male population. Researchers have shown that keyword questions such as “work”, “job” or “main activities” produce
We use the terms Eastern Croatia and Slavonia interchangeably. ‘Slavonia’ is used as a shorthand for the region of Slavonija, Baranja & Srijem, consisting of five counties in Eastern Croatia. These five counties are: Brod-Posavina county, Osijek-Baranja county, Pozega-Slavonia county, Virovitica-Podravina county, and Vukovar-Srijem county.

> 4 Apart from data availability, it makes sense to compare Slavonia with the federal districts covered in Russia because, in both cases, the regions are part of a transition economy in Europe, although arguably Russian districts are much larger in size as well as in per capita GDP terms than any region in Croatia. Comparison with Mexico is also revealing in the sense that Mexico’s per capita GDP is closer to Slavonia (but lower than Croatia), but has a strong manufacturing base, and thus offers another perspective for comparison, apart from the United States, which is the benchmark frontier economy (upper bound). In fact, to minimize error margin, we took advantage of access to Mexican survey data on management capabilities at the sample stratification stage and the count of firms to be interviewed by sector within each region is based on an analogous comparator state in Mexico. For example, the sample distribution across sectors in Dalmatia is based on Quintana Roo, a state in Mexico with similar economic structure and relative income ranking as Dalmatia. Likewise, the target sample distribution of firms in Zagreb is based on Mexico City.

> 5 We are concerned that the higher score in Croatia may be a result of a low response rate and a likely selection bias. However, it should be noted that we did not find a significant selection bias based on observable characteristics of firms such as size, age, sector and export status.

3
<u>Table A.3: Augmented Dickey-Fuller (ADF) Unit Root Test – Annual data</u>

||||**Level**||**1**|**st Differenc**|**e**||
|---|---|---|---|---|---|---|---|---|
|**Fundamental**|**Variable**|**None**|**Constant**|**Constant**<br>**and**<br>**Trend**|**None**|**Constant**|**Constant**<br>**and**<br>**Trend**|<br>**Stationarity**|
|REER|reer|-1.22|-1.52|-1.68|-5.00|-5.07|-4.98|I(1)|
||reer_adj|-0.63|-2.36|-2.23|-5.06|-5.00|4.98|I(1)|
|Terms of trade|tot|0.29|-1.17|-4.01|-5.94|-5.94|-5.85|I(1)|
||tbY|-2.16|-2.59|-2.47|-5.18|-5.10|-5.05|I(1)|
|Productivity|tw_gdppc|-1.10|-2.53|-2.60|-5.05|-5.06|-5.03|I(1)|
|differential|tw_gdppc_adj|
penalty of all location circumstances (jointly) on the same HOIs, and in four cases this ratio is fourfold or larger. The only two exceptions to the joint dominance of demographic circumstances occur for sanitation and non-underweight. This is particularly important for sanitation, for which the ratio of the inequality penalty of location circumstances is 2.6 times the penalty associated with demographic circumstances. The fact that the _joint_ unequalizing effect of demographic circumstances is significantly larger than the average of the unequalizing effects of demographic circumstances considered _one by one_ suggests that there is some degree of (perverse) complementarity between unfavorable circumstances. That is to say, a household with two or three unfavorable circumstances would be subject to disproportionate penalties in its access to basic human development opportunities, well beyond the _linear_ proxies given by the IOC matrix.<sup>38</sup> _The Importance of Specific Circumstances across Sectors._ In order to understand whether certain circumstances are more unequalizing within some sector than in others, the same methodology used in the previous sub-section can be applied to analyze the IOC matrix _by sector_ . Table 8 reports, for 2000 and around 2009, the average inequality penalties for the HOIs _within each sector_ , and for each circumstance. Each row lists one of the four sectors and each column corresponds to one of the eight circumstances. In each row the cells corresponding to the three most unequalizing circumstances are highlighted, and additional cells are highlighted if they exceed the mean inequality penalty of the period.<sup>39</sup>

**Table 8. Analysis of the IOC Matrix: Inequality of Opportunity by Circumstance -IOC- within Sectors, Egypt, 2009 ***

|_Sector of opportunities \_<br>_circumstances_|_Gender_|_Number of_<br>_children_|_Presence of_<br>_elderly_|_Presence of_<br>_parents_|_Parents_<br>_Education_|_Income per_<br>_capita_|_Urban-rural_<br>_location_|_Regional_<br>_location_|_Mean_|
|---|-
(in Bln|||||||
|Rp)<br>- % of Central|[...]|[...]|76,978|83,139|88,766|145,644|
|Expenditures|||||22%|23%|

Note: a = as share of Total DAU Pool (Province & Districts) b = OSR is imputed revenue based on a regression of actual OSR against regional income measured by GRDP PBB = shared property tax and BPHTB = shared change of property title tax income STX = Shared Tax Revenue and SDA = Shared Natural Resource Revenue IPOP = Population Index, IPOVGAP = Poverty Index, IAREA = Area Index, ICOSTREL = Cost Index, HDI = Human Development Index and IGRDPPC = GRDP Per Capita Index The Subscript t indicates total and the subscript i indicates the respective kab/kota. For more details see Annex 1 and Lewis (2002a) Source: Ministry of Finance data. See also Arze (2005).

The initial challenge for policy makers was to annually increase the prominence of a formula-based allocation relative to legacy allocations for localities, represented by expenditure on civil servant wages and the amount that localities had received in the previous year. Increasing the share of the formula meant that some localities were potentially bound to get less than the previous year. In a series of “hold-harmless” adjustments, those localities which would have received less than the previous year got 10
et al. 2020). However, associating cost with nutritional status does not directly inform policy, as there are multiple factors which determine nutritional status. Recent work has increased focus on the health and economic consequences of diet since this provides an entry point to examine agrifood policies.

A growing number of economic studies attempt to assess the economic cost of unhealthy diets. Candari and colleagues (2017) reported just six studies from Australia, China, the United Kingdom and the United States that generated evidence on the costs of unhealthy diets, but these varied widely in their definition of unhealthy diets and reported a wide range of cost estimates which were sensitive to the study methodology. The majority of recent studies rely on impacts on health using data from the Global Burden of Disease (GBD) project (Institute for Health Metrics and Evaluation, IHME). These include studies by The Food and Land Use Coalition (2019) and FAO et al. (2023). These estimates face methodological issues discussed elsewhere (Beal et al. 2021; Stanton et al. 2022; Horton et al. 2024).

2
**Figure 9. Shared prosperity numbers for two scenarios including and excluding zero income**

<!-- Start of picture text --> Shared prosperity growth rates in selected LAC countries including and excluding zero income, 2006-2011<br>Period Country with zeros without zeros<br>2006-2011 Bolivia 12.66 12.36<br>Peru 9.94 9.94<br>Paraguay 7.53 7.53<br>Brazil 5.71 6.08<br>Ecuador 4.35 4.43<br>Honduras 4.23 4.41<br>Chile 3.90 3.91<br>Dominican Repub.. 2.32 2.29<br>difference in growth rate of bottom 40, pp total share of zero income earners<br>Difference in growth rate of bottom 40 and share of population with zero income<br>1.6 2.0<br>1.5<br>1.2<br>0.8 1.0<br>0.4 0.5<br>0.0 0.0<br>-0.4<br>total share of zero income earners<br>difference in growth rate of bottom 40, pp<br>Brazil Honduras Ecuador Chile Paraguay Peru Dominican Republic Bolivia<br><!-- End of picture text --> Source: SEDLAC, microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations.

Expectedly, larger absolute differences in obtained estimates (in percentage points) are observed in countries with higher shares of the population with zero incomes. However, the magnitude of the population with zero income is not the only factor behind the impact of excluding zero income earners. For example, impact is stronger in Bolivia than in Ecuador (bars in figure 9) even though the share of zero earners is higher in Ecuador than in Bolivia (lines in figure 9
, which is our cohort’s entry year. Others did exist in 2014 but did not have graduates who were formally employed during our sample period. Table A8 presents descriptive statistics for the subsample of 401 programs matched to individual-level data. It also shows t-tests for mean differences between those programs and the remaining 200. A few means are statistically different. For example, matched programs are more likely to have sufficient infrastructure for practical training or coordinate job interviews with firms than unmatched programs. They also tend to be older, are taught by smaller institutions, and have higher shares of part-time students.

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in a subsequent HFPS wave. Figure 9a shows that households were significantly more likely to lose income in each country if they were larger in size, less educated, the respondent stopped working, and during a period of higher policy stringency.<sup>18</sup> Figure 9b shows that some of the same households were less likely to gain income during the pandemic. Those who returned to work (or did not stop working) were more likely to recover income, implying that the same groups less likely to recover work (Figure 4b) were also less likely to recover income. Moreover, results are consistent with changes in income occurring at the intensive margin of employment, especially for households where respondents have lower education levels. Households with less access to education were significantly less likely to gain or recover income during the pandemic, whereas urban households were better off than rural households in terms of income recovery. Controlling for household characteristics, policy stringency remains a significant determinant of initial income losses and was

> 18Regression results are reported in Table A6.19. Results are robust to alternate definitions of the outcomes or specifications of the model (logit, probit).

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# **_Data_**

The shift-share Bartik analysis draws on labor market indicators from the _Cambodia SocioEconomic Survey (CSES)_ and on trade flows from the United Nations (UN) COMTRADE database. Combines, these surveys provide information on employment, informality, wages, and trade flows in 2009, 2014, and 2019. We include the distribution of employment across provinces in Cambodia in the appendix (Table A1).

Annual bilateral trade flow data come from the UN COMTRADE database. This analysis focuses on Cambodian exports, or its analog (world imports of the rest of the world from Cambodia). We merge these trade data with labor market indicators using the concordance between ISIC rev 3.1 (from CSES) and HS0 – 1988/92 trade classification (used by UN COMTRADE). We include the instrumented change in export exposure between 2009 and 2014 and 2009 and 2019-20 by district in the appendix (Table A2).

# **_Results_**

# <u>Informality</u>

Our Bartik second stage estimates in Table 4.1 show that an increase in trade exposure (measured by exports per worker) correlates with a decrease in informality rates in Cambodia.<sup>3</sup> More specifically, a foreign demand shock of US$100 in the period 2009–2014 is related with a decrease of nearly 0.14 percent in informality rate, which dissipates overtime to 0.03 percent for the 2009–2019 period. These results are consistent with the hypothesis that increasing exports help decrease informality and imply that districts with higher exposure to trade have indeed experienced a decrease in their informality rate. The results for 2014-2019 are not statistically significant.

**Table 4.1: Estimated effect on informality rate from a US$100 increase in exports per worker in Cambodia**

|VARIABLES|**Informality**<br>**rate,**<br>**2014-2009**|**Informality**<br>*
Rao’s Empirical Bayes prediction estimates (Corral, Molina and Nguyen, 2020; Corral et al., 2021).

The current poverty mapping method imputes consumption expenditure for census households based on a model estimated from the household survey by applying the estimated coefficients of the model to the same variables from the census data. Poverty and inequality statistics for each small area are then calculated with the imputed consumption estimates of census households. The method does not only estimate poverty indices but also their corresponding standard errors. This methodology involves three major steps. The first step is to select a set of variables that are common to a census and a household expenditure survey.

The subset of variables that are found both in the census and the survey is used to estimate a regression model of per capita consumption using the survey data. In the second step, the set of parameter estimates obtained from the regression model is applied to the same set of variables identified in the census data to obtain predicted per capita consumption for each census household. Finally, based on the estimated level of per capita consumption, estimates of poverty, inequality and other welfare measures, as well as their standard errors, are calculated for any geographical unit with enough households to obtain reliable estimates. The point estimates and standard errors of the welfare indicators are calculated by MonteCarlo simulations. In each simulation, a set of values for the model parameters are drawn from their estimated distributions, and an estimate of consumption expenditure and poverty rates are obtained.

# **3.1 Poverty mapping in Tunisia**

This section describes the implementation of the method and key findings of smallarea poverty estimation in Tunisia. It provides poverty rates and per capita consumption expenditure at the national level, and disaggregated at regional, governorate, and delegation levels – which constitute official poverty measures – and also disaggregated at the PSU level – which are measures used for implementing the new and original design of the EBCNV 2021.

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quantity of information, and ‘desire’ to acquire information. In contrast, we assume that everyone is exposed to the same degree to the relevant economic context, but ideology plays a role in interpreting the elements of such context. That is, faced with a same context – a high unemployment rate, for instance – individuals with different ideologies may form different perceptions of equality. Similarly, given everything else constant, high earnings individuals might also perceive inequality differently.

In sum and starting from the bottom, the model says that people’s ideology, exposure to a specific economic context (inequality, poverty, unemployment and government expenditures), and their personal characteristics form their perceptions of inequality. These perceptions, in turn, influence, together again

> 14 Data on intergenerational mobility are just beginning to become available for many countries and long time periods. In order to cover all the countries/years of our sample we had to use intergenerational mobility of education. For more details about intergenerational mobility and equality of opportunity see www.equalchances.org.

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_The ABC of Gender Equality in Education: Aptitude, Behaviour, Confidence_ . PISA. Paris: PISA, OECD Publishing. https://doi.org/10.1787/9789264229945-en.

- ———. 2019. _PISA 2018 Results (Volume I): What Students Know and Can Do_ . Paris: PISA, OECD Publishing. https://doi.org/10.1787/5f07c754-en.

- ———. 2020. _Reviews of National Policies for Education: Education in Saudi Arabia._ Paris: OECD Publishing. https://doi.org/10.1787/76df15a2-en.

- ———. 2021. _TALIS: Positive, High-Achieving Students? What Schools and Teachers Can Do_ . Paris: OECD Publishing. https://doi.org/10.1787/3b9551db-en.

- Page, Elspeth, and Jyotsna Jha, eds. 2009. _Exploring the Bias: Gender and Stereotyping in Secondary Schools_ . London: Commonwealth Secretariat.

- Patrinos, Harry A., and Noam Angrist. 2019. “Harmonized Learning Outcomes: Transforming Learning Assessment Data into National Education Policy Reforms.” _World Bank Blogs._ August 12. https://blogs.worldbank.org/opendata/harmonized-learning-outcomes-transforming-learningassessment-data-national-education.

- Reynolds, David, Pam Sammons, Bieke De Fraine, Jan Van Damme, Tony Townsend, Charles Teddlie, and Sam Stringfield. 2014. “Educational Effectiveness Research (EER): A State-of-the-Art Review.” _School Effectiveness and School Improvement_ 25 (2): 197–230. https://doi.org/10.1080/09243453.2014.885450.

- Ridge, Natasha, and Soohyun Jeon. 2020. “Father Involvement and Education in the Middle East: Geography, Gender, and Generations.” _Comparative Education Review_ 64 (4): 725–48. https://doi.org/10.1086/710768.

- Rogers, Halsey F., and Emiliana Vegas. 2009. “No More Cutting
and Cote-d'Ivoire alone." (Tabatabai, p.718 quoting UNICEF, 1984, pp.165-66). Since employment in the "modern" or formal sector was maintained in the period under consideration (see above), much of the emigrant labor came from the traditional activities including agriculture. "Even if only half of the reduction concerned the agricultural sector, the decline in the agricultural labor force would have been of the order of 15-20 per cent between 1975 and the early eighties." (Tabatabai, p.721) While this labor shortage accounts for a decline in food production in the decade, it also implies that the decline in the real income of food producers was not as much as the output figures might tend to suggest. Not only were there fewer people to share in the total output in the farm sector, the natives left behind enjoyed substantial income augmentation through the remittance of the emigrants.

Relative real incomes of different groups in the urban and rural labor markets were also changed by sharp relative movements in different price indices during the general inflationary period. In particular, although the statistical series are somewhat incomplete after 1977, "the conclusion is inescapable that the underlying trend in real wholesale prices of food are at most steady, if not actually downward." (Tabatabai, p.715; see p.714 for more details) The way the divergent movements in the different price series affected the relative real incomes of different groups can be seen in the trends in real returns calculated by Tabatabai in which he deflates the nominal returns of each group by the index of consumer prices most relevant to each. These are portrayed in Figure 4.1.

38
. The EGSF design was such that households were selected randomly in each community. Surveyed households are therefore scattered randomly across the community so that contextual variables are likely to be representative of the whole community and not only of selected areas.

The community-level covariates in our study are divided into six groups that aim to capture the different mechanisms through which the community environment can influence child growth: (1) public

- **14** The only exception is the data on altitude of municipality capitals that were coded from information supplied by the Guatemalan Military Geographic Institute and the National Statistical Institute (Pebley and Goldman 1995).

> **15** The EGSF collected information from three key informants in each community. When there was lack of agreement among the answers given by the three key informants, we reconciled the responses as follows. For _continuous_ variables, such as distances and prices, we computed the median value. For _ordinal_ and _categorical_ variables, when possible, we selected the answer on which two informants agreed. When all responses were distinct, we chose the value which fell between the other two in the first case and the answer given by the mayor in the second case.

14
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Unlike elsewhere, the ICT sector’s contribution was relatively small—it added 0.69 percentage points to the per capita growth rate. The bad quality of the road network actually held per capita growth back, by - 0.56 percentage points. Infrastructure contribution in Niger was also among the lowest in West Africa, only after Guinea- Bissau and Sierra Leone, in a region where the highest contribution of infrastructure took place in Benin (1.6 percentage points) (figure 1a).

**Figure 1. Infrastructure’s contribution to economic growth: Benchmarking Niger against other Sub-Saharan countries**

<!-- Start of picture text --> a. Infrastructure’s contribution to annual per capita economic b. Potential contribution of infrastructure to annual per capita<br>growth in Central African countries, in percentage points, 2001–05 economic growth in Central African countries, in percentage points<br>5<br>5<br>4<br>4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>Roads Electricity Telecom Roads Electricity Telecom<br>Percentage Points Percentage Points<br>n a go r e i us<br>Beni Cote d'Ivoire Mauritius Guinea Burkina Faso Senegal Ghan Mauritania T o Nigeria South Africa Gambia Nig Sierra Leone Guinea-Bissau Niger Togo Nigeria Burkina Faso Guinea-Bissau Sierra Leone Benin Mauritania Gambia Senegal Ghana Guinea Cote d'Ivoire South Africa Maurit<br><!-- End of picture text --> a. Infrastructure’s contribution to annual per capita economic growth in Central African countries, in percentage points, 2001–05 _Source:_ Calderón 2009.

Poor infrastructure is a handicap for business in Niger. Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 35 percent of the productivity gap faced by Nigerien firms, the remainder