rafmacalaba/data-use-mentions-extended huggingface dataset:
component of the exercise.

**Step 3:** Test the quality of the model by validating the simulated fiscal policy elements against administrative statistics.

**Step 4:** Build the income aggregates according to CEQ concepts.

**Step 5:** Construct poverty and inequality measures for each income concept.

In the analysis fiscal policy elements are allocated to individuals or households based on simulation and imputation. Household data usually do not focus specifically on the value of individual taxes paid by households and often collect aggregate information on direct transfers. In the CSES 2019/20 case, the only exception is property tax which is recorded in the data. This means that most of the modeled fiscal interventions need to be simulated using other data provided in the survey. The most important simulation assumptions which have been applied are the following (the Technical Appendix provides a detailed description of the assumptions applied in the analysis):

- A) Income taxes, health, and social security insurance values have been simulated using the detailed regulations based on the information on employment earnings and self-employment income; this information is combined with an imputation of employment status to ensure that taxes are only levied on formal employment. The tax on property rental is simulated based on the regulations

12
50.41|(4.38)|46.97|(4.07)|49.18|(3.15)|50.61|(4.16)|
|Mean household size|5.79|(1.13)|5.61|(1.14)|5.55|(0.95)|5.95|(1.14)|5.69|(1.13)|
|Female head|0.06|(0.06)|0.07|(0.08)|0.07|(0.08)|0.06|(0.05)|0.06|(0.07)|
|Mean education level|4.77|(2.23)|3.70|(2.05)|2.77|(2.10)|4.58|(2.02)|5.97|(1.94)|
|Observations|4405||511||381||2085||1428||

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ : The table reports the mean for each variable. Standard deviations are in parentheses. The first column reports the mean for the full sample. Columns 2 to 4 divide the sample into castes groups. SC stands for “Scheduled Castes,” ST for “Scheduled Tribes,” OBC for “Other Backward Classes.” at neighbors with farming as their main activity, neighbors are really similar to the sample of farmers. The only difference is on the education level, which is lower for the sample of farmers. This may be due to the fact that the definition of farmers is different in the _sample_ and in the _listing_ . In the sample a farmer is
of countries in our sample. As an alternative, we rely on OECD inputoutput tables by country and use the WBES identification of the main product sold (ISIC 4D) to construct a product–country–specific matrix of input-output relationships.<sup>20</sup> For each firm in our sample, we construct two weighting matrices: one reflecting dependence on input purchases and another on output sales. By interacting these matrices with the level of conflict exposure experienced by firms in the supplier and buyer networks, we test whether a firm—beyond being directly affected by conflict—is also indirectly impacted through its production linkages. Table A13 replicates our main results while accounting for potential spatial spillovers of conflict transmitted through the domestic production network. Across all outcomes, the indirect effect of conflict exposure through suppliers

> 19When _δ_ = 1, the selection on unobservables is assumed to be as important as selection on observables.

> 20The OECD does not provide input-output matrices for several countries, including Afghanistan, Angola, Albania, Armenia, Azerbaijan, Benin, Bosnia and Herzegovina, Bolivia, Bhutan, Botswana, Democratic Republic of the Congo, Dominican Republic, Ecuador, Ethiopia, Georgia, Ghana, Guatemala, Honduras, Kenya, Kyrgyzstan, Lebanon, Liberia, Lesotho, Moldova, North Macedonia, Mali, Montenegro, Mongolia, Malawi, Niger, Nicaragua, Nepal, Panama, Paraguay, Palestine, Rwanda, Sierra Leone, El Salvador, Serbia, Suriname, Chad, Togo, Tajikistan, Timor-Leste, Tanzania, Uganda, Uruguay, Uzbekistan, Venezuela, Kosovo, Yemen, Zambia, and Zimbabwe. Therefore, data are unavailable for 53 countries in our sample of 89. For these countries, we substitute missing matrices with an average of input-output matrices from available OECD countries within the same region (Africa; East Asia and Pacific; Europe and Central Asia; Latin America and the Caribbean; Middle East and North Africa; South Asia). Moreover, to ensure consistency with available classifications, input-output tables and the WBES product
that find greater trade openness to be associated with higher female employment include, for example, Ozler (2000), Wood (1991), Cagatay and Ozler (1995), Cagatay and Berik (1990), Kasnakoglu and Dikbayir (1997), Chen et al. (2013), Juhn et al. (2014), Ederington et al. (2010), Aguayo-Tellez et al. (2010), Spieldoch (2004), Gaddis and Pieters (2012), Pradhan (2005), Bussman (2009), Rocha and Winkler (2019), World Bank (2020) and United Nations (2009). In contrast, studies showing no effect or a negative effect or contradictory effects of trade liberalization on female employment in developing countries include, for example, Meyer (2006), Wamboye and Seguino (2014), Joekes (1995), Gray et al. (2006) and Cooray et al. (2012).

Our study provides several contributions to the literature. First, we use firm-level survey data for 44,539 manufacturing firms across 91 (mostly) developing economies covering multiple waves of surveys between 2006 and 2017. Second, regressing female employment in a firm on exporting activity of the same firm is fraught with endogeneity problems. Thus, we follow the broader literature and proxy firms’ exporting activity by the average exporting activity of all other firms in the same country-year-industry cell (“cell average”). As discussed below, use of cell averages has been made in the literature and it helps to considerably reduce the possible spurious correlation problem. We go beyond and exploit the repeated cross-section nature of the data. That is, the identification of our main results comes from changes in exporting activity of firms within a country-industry pair over time and the associated change in female employment. This helps limit several avenues of omitted variable bias that plague pure cross-sectional regressions.

Third, we test for several predictions or implications of the theoretical models in the related literature. These tests not only serve
the purpose of the case study. For the MAMS application to the Republic of Congo, private and government production are each disaggregated into six sectors, for the government corresponding to different functions – three types of education (primary, secondary and tertiary), health, infrastructure, and other government services.<sup>4</sup> The development of the MAMS for Congo required extensive data collection. The key component of the database is a social accounting matrix (SAM), complemented by elasticities (related to production, consumption, trade and MDG functions) stock data (covering factors, population, and school enrollment), productivity impacts of changes in infrastructure capital stocks, as well as various education and MDG indicators.<sup>5</sup> The model has been carefully calibrated to ensure the robustness of the results.

# **4. The impact of more efficient investment on growth and employment**

MAMS is here used to assess the impact of increased efficiency on growth and employment. A baseline scenario serves as benchmark to measure the effect of higher productivity and efficiency of investment as well as the timing of the investment program. The baseline scenario assumes that past trends will continue, but also takes recent information into account, especially on government finances.

Different scenarios have been developed assessing the impact of productivity gains, the timing of scaledup investment and improved government efficiency in managing public investments. The scenarios are defined as follows; a detailed description is given in the following sections:

> 3 Appendix 2 provides a non-technical description of MAMS. For more technical descriptions, see Bourguignon et al. (2008), Lofgren et al. (2011), and Lofgren (2010).

> 4 See Table A2.1 for a full account of the disaggregation of the Congo database.

> 5 The necessary information has been obtained, among others, from the IMF, the PRSP, the World Development Indicators, from World Bank (2010a), Guetsop Nangue (2009), budget information and
the cumulative effect of its predicted 623 imported product – trading partner TTBs (see again Figure 4) translate into 14.0 percent of EU non-oil imports.<sup>28</sup> Estimates from Bown (2011a) put the tradeweighted share of US and EU imports subject to the existing stock of TTBs at only 2-3 percent immediately preceding the crisis in 2007. The implication is that TTB coverage of imports was predicted to increase 5-7 times above pre-crisis levels.

27 To clarify the thought experiment, the import data used to construct these shares are all taken from 2007, to remove any potential contamination associated with the 2008-9 trade collapse. Nevertheless, the products in focus would not become subject to TTB policies until 2008:Q4-2010:Q4. The mean value of annual imports in 2007 for one of the 94 product – trading partner combinations that were actually confronted with new US TTBs between 2008:Q4 and 2010:Q4 was $133.4 million. The estimate of 15.4 percent is derived by taking this $133.4 million, multiplying it by 1558 product – trading partner combinations, and dividing by $1.34 trillion – i.e., the total US non-oil imports from these 15 trading partners in 2007. The estimate is an upper limit given that product – trading partner combinations affected by US TTBs in the data ($133.4 million) are much higher than the mean product – trading partner combination in the entire sample ($30.2 million) which includes non-TTB affected products.

28 Even though the EU had a much lower predicted count of TTBs (623 versus 1558), the mean value of EU annual imports in 2007 for the 84 relevant product – trading partner combinations was $247.3 million. Finally, EU non-oil imports from its sample of 15 trading partners in 2007 were $1.1 trillion.

19
_Toilet construction._ Toilet construction was measured using households’ self-reported ownership of toilets, and toilet technology as observed by the survey team. In line with the Joint Monitoring Programme (JMP) for Water Supply and Sanitation, coordinated by the World Health Organization and United Nations Children’s Fund, we have defined toilets in three categories. _Safely managed_ toilets are those involving piped sewer systems, septic tanks (with or without soakage pits), and leach pit latrines. _Basic_ facilities include safely managed toilets as well as pit latrines with a slab. _Limited_ facilities are improved toilets shared among several households. We also included data on toilets under construction.

_Toilet use_ . Toilet use was measured based on the self-reported use of survey respondents. Respondents were asked whether some members of their household practiced open defecation. So, our measurement of open defecation implies open defecation by any member of a household.

_Hygiene awareness and hygienic behavior._ Hygiene awareness was measured using respondents’ correct identification of the critical times when handwashing is required, the benefits of using soap, and the risks of defecating in the open. Hygienic behavior was measured using the following: (i) percentage of critical times that the respondent washes hands; (ii) percentage of critical times soap is used; (iii) whether the respondent washes hands with soap after defecation and before eating; (iv) the availability of soap in the household; (v) the treatment of water for drinking; and (vi) the disposal of child feces (in a toilet). Similar hygiene awareness and behavior outcomes were measured among children aged 6 to 14 years.

# 4.6 Design Validation

We compared the baseline characteristics of the three treatment arms and the control arm to check for any imbalances in the observed characteristics. Since we had randomized the selection of GPs as well as of households, we expect any imbalances in baseline characteristics to be purely due to chance. We did not test for significance in these baseline differences. As has been argued by various
the problem by producing commune-level estimates of the prevalence of malnutrition in Cambodia. The estimates can be projected onto maps, which allow policy-makers to visually identify areas of severe child malnutrition, analyze the current situation of malnutrition and formulate geographic targeting policies aimed at assisting the neediest people in a more efficient and transparent manner.

To derive the commune-level estimates, we combine the CDHS 2000 dataset with individual level Cambodian Population Census data for 1998. The former includes information on child nutrition status but has a limited number of observations, while the latter covers virtually everyone in Cambodia but lacks any specific information on child nutrition status. The approach we take builds on the small-area estimation technique developed by Elbers, Lanjouw and Lanjouw (2000; 2002; 2003a, hereafter ELL). We extend their methodology to jointly estimate multiple indicators and allow for a richer structure of error terms, a critical step to address issues unique to nutrition indicators.

While the commune-level estimates of the prevalence of malnutrition are in themselves of interest for policy-makers, we take the analysis one step further and illustrate three distinct but related applications of the methodology. First, we investigate the relationship between consumption poverty, inequality and health at the commune level. While we find no simple relationship, we find that non-linear effects of consumption on the nutritional status of children are important for understanding the relationship. Second, we decompose health inequality indicators into between group and within group components by geographic information. This application is useful for elucidating the significance of the geographic information in explaining overall inequality. We propose two decomposable inequality indicators that are useful for this purpose. One is based on the analysis of variance. The other uses the concentration curve, which is similar to the Lorenz curve but orders individuals on the horizontal axis according to the group they belong to instead of the individual ranking. Concentration curves have been used to analyze the health inequality across different socioeconomic groups and 2
negative effect is not statistically significant for overall labor use, it is significant for narrower categories of farming activities that are especially hired/kuu labor intensive, namely land clearing and planting. In quantitative terms, multiplying the number of burials per 1,000 population by 10 reduces the intensity of kuu labor in overall labor used for planting by 2.7 percentage points. This disruption in land clearing and planting labor is reflected in a significantly lower share of cultivated land devoted to rice in high Ebola prevalence areas (see Table 2, column 1). Rice yields were also significantly lower in these areas (column 2); a tenfold increase in the number of fatalities per 1,000 population reduces rice yields by 6%.<sup>12</sup> As a reality check, we run the same specifications in Tables 1 and 2 using the full sample of farming households, which includes households interviewed later in 2016 and who are thus likely to be reporting on 2016 rice production activities. By 2016, the epidemic in rural Liberia had largely run its course. Thus, as expected, the estimated effects of Ebola mortality on the use of hired/kuu labor and on rice cultivation are substantially attenuated (see Appendix Tables A2.1 and A2.2).

> 12 A rapid assessment made by Mercy Cops in November 2014 reported an even larger reduction of rice yields, on the order of 10-25% due to the disruption of the _kuu_ system.

14
decision estimation. Each variable is ranked with 1 assigned to the smallest value and ties are assigned the same value, so that the sum of the ranks is preserved. That is, for a given variable an observation’s rank is 1 plus the number of values that are lower than that observation’s value. Five variables are ranked at the zonal level for the 36 zones in the sample and one variable is ranked within zones. For zones, the ranked variables are the size of the population, the degree of urbanization (measured as the percent of the population who live in urban areas) and the percentage of adults with various levels of education (none, primary or 1-6 years, and 7-8 years). These ranks are all based on data from the 1994 Census. The means of the rankings are not all equal to 19 because not all zones have the same number of communities and because weights are applied to calculate the means. The communities are ranked within each zone by their population size. The maximum in 1990. We do test robustness of the results to other cut-off years.

9
*|**3.3**|

Source: Based on the estimated education term of equation (5) Notes: Col.2 from Psacharopoulos and Patrinos (2004), average of social rates of return, full method Col. 3 from World Bank indicators Col. 4 = (Col. 2) * (Col. 3)/100 Col. 5 re-estimation based on a 1 percentage point increase in education expenditure Col. 6 = (Col. 5 - Col. 4) Col. 7 from Maddison (2010) Col. 8 = (Col. 6 / Col. 7) * 100
must be a reasonable amount of variation in tenancy duration. But variation in doc does not necessarily imply variation in μoc, or in θ for that matter. To see why, take the case where θ, the subjective probability of retention, does not vary across tenants. Since actual contract renewal is a stochastic process, we would still observe a nondegenerate distribution of elapsed tenancy durations, but no relationship between duration and investment. It is thus important to assess the extent to which variation in tenancy duration re fl ects heterogeneity in the objective factors underlying μoc, like tenant mobility and landlord behavior.<sup>31</sup> To do so, we use data collected from the landlords in our sample. For each plot that

> 30 Note that, since E [μocLci] = 0, the estimate of α is robust to measurement error in μoc.

> 31 Match quality, which by de fi nition is not speci fi c to the landlord or to the tenant, cannot be distinguished from the error term ξoc.

17
Revenga, Ana (1992): ”Exporting jobs? The impact of import competition on employment and wages in U.S. manufacturing,” _Quarterly Journal of Economics_ , 107(1): 255-284.

Silva, Joana, Rita Almeida and Victoria Strokova (2015): ”Sustaining employment and wage gains in Brazil: A skills and jobs agenda.” _Directions in Development_ , World Bank: Washington DC.

Verhoogen, Eric (2008): ”Trade, quality upgrading, and wage inequality in the Mexican manufacturing sector,” _Quarterly Journal of Economics_ , 123(2): 489-530.

Yeaple, S.R. (2005): ”A Simple model of firm heterogeneity, international trade, and wages,” _Journal of International Economics_ , 65(1): 1-20.

# **A. Appendix**

## **A.1. Definition of skill groups**

We consider four occupational groups to proxy the skill level associated with the worker’s occupation, as defined in the ISCO-88 classification. Table A.1 presents the definition of skill groups.

## **A.2. Summary statistics, worker-firm data**

## **A.3. Further results**

19
16 _Table 3: Use of services and inequalities in service use by income*_

||Allwester|ndoctors|Western<br>Generalpr|doctors<br>actitioners|Speci|alists|Traditiona<br>medi|l Chinese<br>cine|Inpatie|nt care|
|---|---|---|---|---|---|---|---|---|---|---|
||Average<br>percapita|CI|Average<br>percapita|CI|Average<br>percapita|CI|Average<br>percapita|CI|No. per 100<br>persons|CI|
|Hong Kong|||6.99|0.0573|1.27|-0.1580|||||
|(China)|8.27|0.0241|||||2.55|0.0237|14.00|-0.0954|
|Japan|16.00||||||||9.57||
|Korea, Rep.|9.76|-0.0234|||||1.38|-0.0321|7.00|-0.0734|
|Singapore|||||||||12.00||
|Taiwan (China)|8.44|-0.0534|||||2.02|0.0401|10.00|-0.1470|
|OECDaverage|7.15||3.62|-0.0904|1.62|0.0105|||16.40|-0.1516|

* Data are from Lu et al.<sup>35</sup> , except in the case of Japan and Singapore, where the numbers are from Imai<
. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. The critical value of the F-test from Stock and Yogo (2005) is 16.38. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

47
Each household is endowed with three types of labor: skilled, semi-skilled and unskilled. These are distinguished by educational attainment of the worker<sup>7</sup> with semi-skilled workers having a middle- or high school education, and skilled workers having an educational attainment beyond high school<sup>8</sup> . Households are also endowed with profits from family-owned agriculture and non-agriculture enterprises, property income and transfers. Agricultural profits represent returns to family labor, land and capital. However, as noted above, the off-farm labor supply decision is a function of the combined return to labor and land in agriculture, owing to the absence of an effectively functioning land market in many rural areas.

Specification of the value of the off-farm labor supply elasticity draws on the econometric work of Sicular and Zhao (2004). Those authors report results from a household labor supply model estimated using labor survey data from the 1997 CHNS data set for nine central provinces. This survey measures the labor supply of individuals within each household to farm and non-farm activities. Sicular and Zhao estimate the implicit wage for each individual in the sample if they were to work in agriculture or non-agricultural self-employment, and they also estimate the non-agriculture wage that this person could obtain. They then estimate labor supply equations for self-employed agricultural labor, self-employed non-agricultural labor, and wage labor. From these equations, it is possible to calculate elasticities of labor transfer from farm to non-farm activities. They report a variety of elasticities in their paper.9 We adopt their estimate

> 7 We would prefer to base this split on occupation – what they actually do – versus their potential as determined by education. However, the rural household survey does not support this type of  labor split. 8 Since the rural survey only reports the highest educational attainment of the household we do not have endowment by worker. This biases the skill level of rural households upwards. However, since the vast majority of rural households are unskilled, this is less of a problem in practice. 9
Pittau, and R. Zelli. “A Dwindling Middle Class? Italian Evidence in the 2000s.” _Journal of Economic Inequality_ 7 (2009a): 333-350.

- Massari, R., M. G. Pittau, and R. Zelli. “Caos calmo: l'evoluzione dei redditi familiari in Italia.” In _L'Italia delle disuguaglianze_ , edited by L. Cappellari, P. Naticchioni and S. Staffolani, 19-28. Rome: Carocci editore, 2009b.

- Milanovic, B. “Global Inequality Recalculated and Updated: The Effect of New PPP Estimates on Global Inequality and 2005 Estimates.” _Journal of Economic Inequality_ 10 (2012): 1-18.

- Milanovic, B. “True World Income Distribution, 1998 and 1993: First Calculation Based on Household Surveys Alone.” _Economic Journal_ 112 (2002): 51-92.

- Muller, E. N. “Income Inequality, Regime Repressiveness, and Political Violence.” _American Sociological Review_ 50 (1985): 47-61.

- Niño-Zarazúa, M., L. Roope, and F. Tarp. “Global Inequality: Relatively Lower, Absolutely Higher.” _Review of Income and Wealth_ 63 (2017): 661-684.

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