rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6.1 Data and calibration**

We use the World Input Output Database (WIOD) to calibrate international trade, production functions and labor shares. In the baseline simulation, we use WIOD data for 2005 to calibrate initial trade patterns. We group countries into three broad categories, based on their income per capita, robot density and data availability. The group of countries in the North is composed of Belgium, Germany, Denmark, Finland, France, Italy, Netherlands, Sweden and the United States. The group of countries in the South is composed of Brazil, China, India, Indonesia, Mexico, Turkey and Taiwan, China. Based on these two groupings, we construct the representative countries in the North and South. The group of other developed countries results from the aggregation of other OECD and EU countries for which data are available in WIOD. This group consists of Australia, Austria, Bulgaria, Canada, Czech Republic, Spain, the United Kingdom, Greece, Croatia, Hungary, Ireland, Portugal, the Slovak Republic, Poland, Norway and Switzerland.<sup>14</sup> As detailed in Appendix B3, we have performed robustness checks using a variety of alternative groupings, and found that the results are qualitatively robust.

In the model, workers are perfectly mobile across sectors. Wages are therefore similar across sectors within countries, but differ across countries due to productivity differences. We assume that initial wages are proportional to the average GDP per capita of each group in thousands of U.S. dollars. We set initial wages to 39.5 in the North, 26.7 in Other, and 5.7 in the South. According to our calculations, the average ratio of replaceable tasks in the robotized tradeable sector defined above is approximately 50%, thus we set _K_<sup>_i_</sup> = 0 _._ 5 for this sector and set _K_<sup>_i_</sup> = 0 for the non-robotized sector
for having sons combined with the siblings sex-composition instrument as an exogenous source of variation in the fertility choice. Thus, the analysis focuses on a specific margin: I estimate the impact of having a third child or higher order on parental labor supply outcomes in comparison to those with only two children.

The data used in this paper come from four different surveys conducted in Albania between 2002 and 2012. More specifically, I use the 2002, 2005 and 2012 Albanian Living Standard Measurement Surveys (LSMS) and the 2008-09 Albanian Demographic Health Survey (DHS). The advantage of these surveys is that they contain not only rich information on individuals’ socioeconomic and demographic characteristics, but they also contain detailed information on the family structure of each one of the households, including information about the children no longer living in the household, which makes possible the construction of the entire parental fertility history. For the purpose of this analysis, I construct a repeated-cross section of married mothers and fathers with two or more children whose oldest child is younger than 18 years old.

The main results of this paper argue that an increase in family size has a _positive_ impact on parental labor-force participation in the context of Albania. More precisely, for the sample of mothers I find that having an additional child (third or higher order) increases on average a mother’s likelihood of _working off-farm_ by 5 percentage points and it also increases the working time by around 4.4 _hours per week_ on average. Similarly relevant is the finding that fathers also change positively their labor-market behavior in response to a change in family size. In particular, having an additional child increases fathers’ likelihood of _working off-farm_ by 6.7 percentage points, and also their likelihood of having a _second occupation_ by 4 percentage points.

The second focus of the paper is to pinpoint the channels through which these results operate. I propose two plausible mechanisms behind the positive effect of fertility on parental labor supply for the case of Albania. First, family structure in developing countries, especially
increase proportionally more at the top than at the bottom of the distribution (and the mean of the well-being indicator would increase). Nothing can be stated a priori regarding inequality under scenario (iii). If rent were a growing (decreasing) share of individuals’ welfare, inequality would increase (decrease). Yet, if there is no linear relation between the original welfare distribution and rents, the distributional impact of imputed rents cannot be predicted ex-ante.<sup>5</sup> For example, if rents are higher for some specific subset of the population, such as individuals living in urban dwellings, individuals belonging to that subgroup may also present higher rent shares relative other subpopulations _._ The effect of adding rents on inequality would not be straightforward in this case.

The distributional impact of imputed rents on poverty is assessed analyzing changes in poverty incidence, depth and severity. Recall that poverty measurement involves three steps: defining a welfare indicator (usually consumption or income), setting a poverty line, and finally aggregating all information into a single poverty indicator. Recall, moreover, that the poverty line is usually defined as a function of the welfare indicator itself. This holds true not just in case of a relative poverty line, e.g. 60 percent of median income, but also in case of an absolute poverty line. An absolute poverty line, e.g. based on the cost of basic needs approach, is generally computed in two steps, first by identifying a food poverty line and then by adding to it a non-food poverty line. Seeking simplicity, we can think of the non-food component as determined residually by inflating the food poverty line with the ratio of non-food consumption to food consumption for the reference population.<sup>6</sup> Therefore, if the welfare distribution changes, it should be good practice, whenever possible, to re-estimate the poverty line accordingly. Trivially, if rents are included in the welfare aggregate, but the poverty line is left unchanged
Preserving habitats for endemic species, those with restricted ranges, and species at high extinction risk requires special conservation measures tailored to vulnerable and geopolitically complex regions.

The expansion of species information using GBIF records offers a significant expansion of the objective, comparable and reliable data that are critical for successful implementation of transboundary and fragile/conflict state conservation measures. In this paper, we illustrate the potential contribution of our GBIF database data with results for 38 non-determined legal status area areas, 19 conflict-affected countries, 19 countries with pronounced institutional and social fragility, and 311 international river basins.

5
# **Different Dreams, Same Bed: Collecting, Using, and Interpreting Employment Statistics in Sub-Saharan Africa - The Case of Uganda**

## **Louise Fox**

## **Obert Pimhidzai**

JEL: J21, o55. Y1 Keywords: Employment, Sub-Saharan Africa, labor force participation, labor force surveys, gender and employment Sector Board: Poverty Reduction Authors can be contacted at <u>lfox@worldbank.org</u> and opimhidzai@worldbank.org. Useful comments and suggestions were received from Kathleen Beegle.
the underlying population of shippers or carriers. Notable examples collecting surveybased data on trucking costs in developing countries are Teravaninthorn and Raballand (2009) for Africa, Osborne et al. (2014) for international trade corridors in Central America and Lam et al. (2019) for Vietnam.

Naturally, survey design and sampling presents researchers and practitioners with multiple choices to be made such as whether one is interested in trucking costs throughout the entire country at both urban or intra-city scales, or just in some key corridors. The studies cited above exemplify different choices on this front. To facilitate comparison and guide standardized data collection, survey data is typically collected for a fixed freight scenario that is representative for a typical shipment, such as a 20 ton trailer truck. Many countries have separate fleet inventory surveys that can guide this choice. Similarly, respondents are asked to consider a scenario involving commodities that satisfy certain criteria, such as not being irregularly shaped. The length and detail of the survey is another key design choice: facing a trade-off between detail and response rates, a common approach is to complement questionnaires with in-depth interviews from a small number of respondents.

Most studies in the literature aim to quantify several variables for the case under study: the ton-mile cost of truck shipments to the carrier, the price charged to shippers, the composition of costs to variable versus fixed costs, capacity utilization rates and total vehicle operation costs. As expected, relative and absolute costs differ across countries, but some consistent results also emerge from a range of studies: in developing countries, a larger share of total costs in trucking is borne by variable costs since older vehicles with low fuel efficiency have a larger share in the national fleet. In France and the U.S., two developed economies for which comparable data exists, the cost composition is the reverse, with a higher fraction accounted for by fixed costs. This finding resonates with firm-level models of technology choice that posits a trade-off in operating with a low marginal cost technology at a high
- Look at the description of the budget line to see whether words such as _rehabilitation_ or _refurbishment_ appear.

- Look for expenditures that relate to major repair and restoration of degraded existing assets to their original condition without resulting in any upgrading or expansion of capacity.

- Look for large maintenance activities that span more than a one-year duration.

Also, the IMF economic classification does not provide a definition of maintenance, nor does it provide a breakdown good enough to tailor an accurate derived estimation. Given this structural constraint on the reporting format and therefore the way the data are collected, a proxy for maintenance— as good as any other—is to use the category ―use of goods and services,‖ which essentially comprises all current expenditures, excluding wages and salaries, transfers, depreciation, and subsidies. Despite this drawback, using the GFSM 2001 facilitates the sustainability of the exercise and cross-country comparison .

It is likely that consumption of fixed capital will be difficult to allocate by function, especially if only aggregated figures for total government capital stock and consumption of fixed capital are compiled. In these circumstances, approximations will have to be used. One possibility may be to distribute consumption of fixed capital according to the assets acquired. Thus, the COFOG statistics should be cross-classified at least with total expense and acquisition of nonfinancial assets.

**Table 3.4 Economic classification of government expenditures—capital expenditures**

|**Category/subcategory**|**Definitions**|
|---|---|
|**Buildings, structures,**<br>**machinery, and equipment**|Explicit spending flows allocated to capital investment during the period recorded. Includes flows into new<br>assets and rehabilitation of existing ones.|
|**_Of which rehabilitation_**|If the government do not classify rehabilitation expenditures, it will be useful to state the approximate amount<br>used for rehabilitation. This could be done by estimating capital expenditures on new fixed assets
> 15 Erwann, M-K. & Kunreuther, H. Redesigning flood insurance. _Science_ **333** , 6041 (2011).

> 16 Kunreuther, H. Disaster Mitigation and Insurance: Learning from Katrina. _Ann. Am. Acad. Pol. Soc. Sci._ **604** , 1 (2006).

> 17 World Bank. _Planning, Connecting, and Financing Cities Now: Priorities for City Leaders_ (World Bank, Washington DC, 2013).

> 18 Hallegatte, S. et al. _Shock Waves: Managing the Impacts of Climate Change on Poverty_ (World Bank, Washington DC, 2016).

> 19 Bangalore, M., Smith, A. & Veldkamp, T. Exposure to floods, climate change, and poverty in Vietnam. _Economics of Disasters and Climate Change_ **3** , 7–99 (2019).

> 20 Lin, Yatang, McDermott, T.K.J. & Michaels, G. _Cities and the Sea Level_ (CESifo Working Papers Series 8997, 2021).

> 21 Hallegatte, S. _An Exploration of the Link between Development, Economic Growth, and Natural Risk_ (World Bank, Washington DC, 2012).

> 22 Rentschler, J. et al. _Resilient Shores: Vietnam’s Coastal Development Between Opportunity and Disaster Risk_ (World Bank, Washington DC, 2020).

> 23 Corbane, C., Florczyk, A., Pesaresi, M., Politis, P. & Syrris, V. _GHS Built-up Grid, Derived from Landsat, Multitemporal_ (1975-1990-2000-2014), R2018A. (European Commission, Joint Research Centre, 2018).

> 24 Liu, X., Huang, Y., Xu, X. _et al._ High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. _Nat Sustain_ **3,** 564–570 (2020). 25 DLR(2021) https://geoservice.dlr.de/web/maps/
mix of low- and high-productivity in industry and service sectors, men have benefitted disproportionately. Of the 3.8 million new wage jobs added to the economy by 2018, men took up over two-thirds of those jobs (Ruppert Bulmer, 2020), including jobs in manufacturing, finance, and business services sectors. These outcomes cannot be explained by Nepali women’s education levels – progress in educational attainment of girls is sizable and women enjoy high returns to education (Ruppert Bulmer et al., 2020).<sup>4</sup> Young women, particularly married ones, have low attachment to the labor market. Nearly half (47 percent) of them are not in employment, education, or training (NEET), vs. 22 percent of young men.<sup>5</sup> Over a third of young married women aged 20 and 24 report to not have done any work other than housework in the 12 months before the 2022 Nepal Demographics and Health Survey (DHS), as compared to three percent of young married men within the same age group. Social norms related to the unequal gender roles in families may compel women to shoulder a disproportionate burden of household work, thereby reducing their availability for wage employment and restricting them to jobs with lower employment potential than men. Women are more likely than men to be in unpaid farm work (6 million women compared to 2.8 million men). They also work, on average, 9 hours per week less than men do, which partly explains a gender gap in mean monthly earnings of almost 6,000 Nepali rupees (NPR) in favor of men (Ruppert Bulmer et al., 2020).

2 World Development Indicators (WDI), latest year available. Modeled ILO LFP. The modeled ILO comparable number for FLFP in Nepal is 29 percent in 2016, an increase from 22 percent in 2003.

3 Source: Nepal Labor Force Survey 2017/2018. The labor force participation rate is a measure
IHPS dataset in a way that can gauge the accuracy of the IHS4 data, we focus only on the IHPS individuals that have been subject to individual interviews and correct for non-response. To do the latter, we calculate weights by first running a logistic regression of individual response status among the 5,776 adults eligible for individual interviews. The results from the logistic regression are presented in the Appendix Table A1.<sup>11</sup> Subsequently, we (1) take the inverse of the predicted

> 7 This was an upper limit that only applied to one percent of the sampled household population that had more than four adults. If a sampled household had more than four adult household members, following the preference given to the head of the household, and his/her spouse if applicable, the remaining interview targets (2 or 3 depending on the presence of a spouse) were selected at random from the remaining pool of adult household members.

> 8 For more information on the organization and implementation of the individual-disaggregated data collection as part of the IHPS, please consult the survey’s basic information document, which can be accessed here: <u>https://microdata.worldbank.org/index.php/catalog/2939/download/47216.</u>

> 9 In view of seasonality of work and employment activities in Malawi, the sample is limited to the overlapping fieldwork months to ensuring temporal comparability of the individual-level data solicited in both surveys. 10 Comparing self-reporters versus individuals reported by proxy in the IHS4, we find that self-reporters tend to be significantly older and less educated, more likely to be female, married, to be the household head or spouse, and to live in smaller, poorer households with a higher dependency ratio than respondents who did not report for themselves (results available upon request). These differences are also in line with other studies that compare self-respondents and respondents-by-proxy (Bardasi et al., 2011; Serneels et al., 2017).

>
Other households were only observed in one of the three years. Using this cross‐section data as well and treating all observation as independent observations provides a _Pooled_ data set.

A main concern when fitting models to estimate weather impacts on economic outcomes is endogeneity bias (dell et al., 2014). Reverse causation is unlikely to be a problem as weather conditions are exogenously determined. Yet the model is likely to suffer from omitted variable bias caused by the potential correlation of weather variables with other commune characteristics (e.g. long‐term climate, geo‐graphical, agro‐ecological conditions) that determine living standards. To the extent that such variables cannot be measured, the estimates of β will be biased.

To minimize this omitted variable bias, a fixed‐effects ( _FE_ ) linear model is fitted by using a within‐ regression estimator based on the _Panel_ data set ( _Panel FE_ ).<sup>6</sup> These analyses allow investigating the determinants of income changes between 2010 and 2012 and 2014 due to differences in weather

> 5 The data sets allow to control for land and labor inputs of some income activities. These are, however, not included in the regression analyses due to potential endogeneity biases.

> 6 A within‐regression estimator is always consistent, but a General Least Squares estimator can be more efficient. The GLS –estimator based on a random‐effects model was also tested, but a Hausman test rejects that the GLS estimator is efficient.

6
012|0.510|
|... for upper class|0.034|1.066|
|Observations|21547|21547|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. The sample includes observations from the long-run followup survey (2014). All specifications control for subdistrict (strata) fixed effects, and the pre-treatment level of the outcome as reported (retrospectively) in the 2014 survey. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable equal to 1 if the respondent’s household gave any transfers or loans to any ultra-poor household in her village. The dependent variable in column 2 is the fraction of ultra-poor households living in the respondent’s village who received transfers or loans from the respondent’s household, multiplied by 100 (so that the effects correspond to percentage points).

42
under the VG component), we included a question in our census of 100 study villages that asks whether an individual had “ever heard of TASAF.” We suggest that this variable provides a similar gateway that is necessary, but not sufficient, for an individual to be a program beneficiary: while many people who have heard of the program do not benefit, an individual who has not been informed as to the existence of the program cannot seek to benefit from it. Our data show that, among households eligible for TASAF II according to the exogenous vulnerability criteria, less than half of survey respondents had ever heard of the program. Non-vulnerable households, i.e. those who were ineligible to receive support under this program, were actually eight percentage points _more_ likely to have heard of the program than the eligible nonbeneficiaries. This finding is dismaying, indicating that half of the target population under TASAF II had never heard of TASAF at baseline, by which time, as described earlier, VG groups had been formed and were ready to be funded in each of these 100 villages.

We therefore conclude our empirical analysis by exploiting the village-level census of eligible households provided by the listing exercise in the 100 study villages with VG subprojects and examining the determinants of program awareness among eligible households, and the determinants of being a beneficiary among those who had heard of the program at baseline. This analysis uses the listing data, meaning that the sample size for the analysis is greatly enhanced relative to Table 5. However, detailed consumption data are not available for this larger sample, so we proxy per capita consumption (and poverty) with several simpler questions: the number of days in the past week that a household consumed proteins (meat, eggs, or milk).

In Table 6 we analyze awareness of TASAF within the eligible non-beneficiary sample. In columns (1) through (4), we cumulatively include covariates intended to proxy for wealth (consumption), information (proximity to village center, education, and ownership of
. The mechanisms they identify are sociotropic and relate to the perceived negative impact of influx on culture. Exploiting the quasi-random settlement of refugees in Sweden between 1985 and 1994, Rozo and Urbina (2020) find migration shocks are associated with lower support for immigration. Here, the result appears to be largely driven by attitude change among young males working in low-wage blue-collar occupations. Ajzenman et al. (2020) explore the impact of exposure to mass transit migration in the Eastern Mediterranean during the refugee crisis on the attitudes and beliefs of ‘natives.’ They find that ‘natives’ entrepreneurship falls substantially and anti-migrant sentiment increases in localities that are close to migrant routes compared to those further away. Dustmann et al. (2019) use election data to explore the effect of the quasi-random allocation of refugees to municipalities (or not) on voting shares for anti-immigration parties. They find that in most municipalities, the allocation of larger refugee shares leads to an increase in voting for anti-immigration – parties. However, in large and urban municipalities in which interactions between ‘natives’ and refugees might be assumed to be greater – the pattern is the opposite. Steinmayr (2021) uses exit poll data from the September 2015 elections in Austria to show short-term exposure to transiting refugees increased Far Right voting, whereas sustained contact decreased Far Right voting. Also in Austria, Halla et al. (2017) combine census data and election results at the community-level and find that refugee inflows significantly increased Far Right voting. They find suggestive evidence that voters worry about negative effects 9