rafmacalaba/data-use-mentions-extended huggingface dataset:
# Household Structure

In the PSF survey, a household is defined as the set of co-residing individuals who recognize the authority of a given household head. Table 1 describes the main characteristics of Senegalese households. In the PSF sample they are large, with about eight members on average and a dependency ratio nearly equal to 50%. Households are typically multigenerational and extended both horizontally and vertically, with 28.2% of members that are neither the head, nor one of his wives or children.<sup>5</sup> Two thirds of households include such “extended” family members.

Polygamous unions are common, with 24.7% of married men and 38.7% of married women engaged in such unions. Most of these comprise a husband and two wives (only 20% of polygamous unions have more than two wives). We find that 31% of polygamous men have non-cohabiting wives. In this work, we ignore the consumption of non-co-residing spouses of the household head.

Field interviews conducted at the early stages of the PSF project showed that within Senegalese households, it is possible to distinguish sub-groups of household members that are at least partly autonomous, in particular with respect to their budget management. To best capture intra-household structure and resource allocation, the survey was designed to record this internal household organisation. Each household was divided into groups, called “cells”, whereby the head forms a cell with unaccompanied dependent members (a widowed parent, a child whose mother is not present in the household, an unmarried sibling when no parent coresides...); each wife of the head, her children, and any other dependents then form separate cells, as do any other adults with dependent (such as a married brother or a married son, for example). A similar approach had already been used to structure households in the 1988 Senegalese census (van de Walle and Gaye, 2006) and seemed very intuitive to enumerators 7
the event, the _βk_ are event-time indicators, the _ψi_ are worker fixed effects, the _νt_ are event-time fixed effects, the _γl_ are calendar-year fixed effects. We cluster standard errors at the event level. Because of the staggered timing of these events, we use the weighting approach proposed by Sun and Abraham (2021).<sup>11</sup>

> 10We use actual skill levels as reported in the Education Database as well as imputed skill values using an inverse Mincer equation (see Appendix B). Low-skilled refers to workers who have up to and including a low (junior) secondary education. Medium-skilled refers to workers with upper-secondary or post-secondary education excluding tertiary education. Workers designated as high-skilled have tertiary education.

> 11As demonstrated in Goodman-Bacon (2021) two-way fixed effects models (TWFE) can produce biased estimates in

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would suggest. By mapping WBES firms onto the natural city dataset, the relationship between firm innovation and urban agglomeration in developing Asia can be examined. Further, an international ranking of universities and academic disciplines or major fields of study is utilized to investigate whether the presence of high-quality universities has any bearing on firm innovation.

This paper finds that innovative activity by firms is concentrated spatially within several large Asian countries. The share of firms that innovate in just a few cities is substantially greater than the share of urban population these cities account for. Further, the data shows that the population size of a natural city is strongly correlated with the propensity of firms in the city to engage in innovative activities. Specifically, the estimates suggest that larger city size is significantly associated with increases in the propensity of firms to introduce product and process innovations and undertake R&D. A doubling of city size is associated with increases in firms’ propensity to engage in product innovation, process innovation, and R&D by 4.3, 3.7, and 2.8 percentage points, respectively, or by 13.5%, 8.5%, and 14.3% against the respective benchmark propensities. Since the biggest natural city is much larger than the smallest one in the data, the effects of agglomeration on firm innovation can be quite large.

> 2 Most research from the developing world focuses on agglomeration effects concerning labor and firm productivity. Duranton (2008), Overman and Venables (2005), Henderson (2005), and Duranton (2014) provided detailed review of this research.

2
ascending trend, or at best, a plateau. In this way, over ten years, Argentina's spending on social benefits increased from 14 percent of GDP in 2008 to 18 percent in 2018.

# **4. Data and Estimation**

We analyze asymmetric cyclical behavior in emerging and advanced economies using the IMF's World Economic Outlook dataset from April 2019. We chose this dataset over newer versions due to its detailed disaggregation of government expenditure, which is no longer available in more recent publications. The frequency of the data is annual. The panel dataset spans from up to 1980 to 2018 and includes 71 countries from various regions and income classifications. Our findings contrast evidence from up to 49 emerging economies and 22 advanced countries. A comprehensive list of these countries is provided in Table A1 in the appendix. We employ a non-linear econometric model to estimate the elasticities of public consumption relative to aggregate output throughout the business cycle. Table 1 presents descriptive statistics for the growth rates of the specific categories of public spending under analysis, as well as the real GDP used in our regressions.

**Table 1: Descriptive Statistics**

|**Advanced Economies**||||||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. Dev.|Min|Max|
|GDP Growth|836|.024|.022|-.086|.126|
|Compensation to Employees|469|.018|.028|-.166|.129|
|<br>Growth||||||
|Goods and Services Growth|471|.023|.039|-.16|.255|

|**Emerging Markets**||||||
|---|---|---|---|---|---|
|Variable|
and Korea**

The descriptive patterns shown above make use of data from early stages of longitudinal studies from China and Korea which are modeled on the Health and Retirement Study (HRS) from the US and the English Longitudinal Study on Aging (UK). New survey efforts, like CHARLS in China, promise to facilitate the study of retirement and labor supply behavior in regions where pension and social security systems are not well established, and population aging is occurring at a rapid pace. In the analytical models that we estimate, we make use of the cross-sections from

> 19As earlier, we use non-parametric locally weighted regression (LOWESS) to smooth the averages across the age distribution.

> 20This has long been true of China’s rural elderly. A classic study of the rural elderly in the 60s and 70s referred to their lives as one of “ceaseless toil” (Davis-Friedman, 1991), and Pang et al (2004) characterize the retirement decision in rural China as “working until dropping.”

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The present study relates to a large body of empirical literature on mining and development in Africa. Addison et al. (2015) found that, overall, mining activities increase district-level inequality and show differential effects on inequality based on such characteristics as minerals values, scale of mining operations, and the nature of mining activities. Moreover, our study echoes the recent debate on natural resources and management in developing countries (see Venables, 2016; Van der Ploeg 2011; Beny and Cook, 2009). We also add to the recent growing literature on the local economic effects of natural resource wealth (e.g. Aragón and Rud, 2013; Caselli and Michaels, 2013; Fafchamps, et al., 2015; and Aragón, et al., 2015; and Chuhan-Pole, et al., 2015); in contrast to earlier cross-country studies on the resource curse (e.g. Sachs and Warner, 1995, 1997, 2001; Gleason, Herbert son and Sega, 1999; Mehlum et al., 2006; Brunnschweiler, 2008; Chambers D, and Guo J, 2009; and Arezki and Van der Ploeg 2011).

Our paper is closest to that of Andersen et al. (2014), who estimated that the increase in average annual real economic growth in sub-Saharan Africa induced by China’s accession to WTO was approximately 14.6 percent of total (actual) growth. In contrast to our findings, Bhattacharyya et al. (2015) found that mineral production significantly improved district-level economic development in sub-Saharan Africa between 1992 and 2012. Similarly, Tolonen (2015) combined panel data on industrial mines with DHS household survey data for women aged 15-49 and found that the opening of a new gold mine increases women’s income earning opportunities by 41 percent within the service sector and halves infant mortality rate, despite risks of environmental pollution from gold mining. Kotsadam and Tolonen (2013) showed that gold mines increase local female employment in service sectors when a mine opens up, but that
**Figure 6:  Latin American Economic Performance Over the Last Century**

<!-- Start of picture text --> Ratio of LAC to USA Per Capita GDP<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> Source: Author‘s calculations based on data from Maddison.

The net result is that, during the 20<sup>th</sup> century, very few countries managed to progress from low income status to middle income status and then to high income status. Part of this history was explained in the previous sections, and hopefully, the recent rise of a number of large MICs marks the beginning of a better century for developing country convergence. That said, the table below summarizes how only a handful of developing countries have succeeded in reaching high levels of prosperity, and many of them are in Western Europe. The few developing economy success stories – with the exception of a few small oil rich countries<sup>7</sup> --are generally located in East Asia and achieved rapid industrialization by following comparative advantage export dynamism, helped by the facilitating role of the state.<sup>8</sup>

> 7 This is the case for Equatorial Guinea, Trinidad and Tobago and Oman.

> 8 See Lin, 2012c.

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= 0.004|
||Arellano-Bond test for|Arellano-Bond test for|
||AR(2) 0.86 Pr > z = 0.391<br>Sargan test of overid.<br>restrictions: chi2(172) =|AR(2) = Pr > z = 0.339<br>Sargan test of overid.<br>restrictions: chi2(172) =|
||171.71|193.86|
||Prob > chi2 = 0.492|Prob > chi2 = 0.121|

The growth of the official development assistance to the education sector seems to not be associated with a significant increase of education expenditure (Tables 3 and 4). A large proportion of education spending is provided to countries through project modalities and may not be counted as part of government education expenditure.<sup>5</sup> In addition, education aid represents a small proportion of education financing. Because of the relatively small contribution of aid to the education budget, an increase of the amount of aid may not be enough to drive a substantial increase of overall government expenditure on education. Tables 3 and 4 also show that GDP growth is associated with higher government expenditure on education. GDP growth drives higher government revenue which in turn contributes to increased education expenditure. This evidence is in support of the Wagner's law on the causal effect of GDP growth on government expenditure (Chandran et al., 2011; Dogan & Tang, 2006; Ebaidalla, 2013; Loizides & Vamvoukas, 2005; Ahuja and Pandit, 2020).

> 5 In 2019, only 3.5% of education ODA was provided through budget support modalities according to the OECD Creditor Reporting System: https://stats.oecd.org/Index.aspx?DataSetCode=crs1.

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is based at Purdue University. The GTAP Center provides four key resources to the trade community. First is an integrated and consistent international database for trade policy analysis. The current version is composed of 87 country and region groupings and 57 economic sectors. The second is a publicly available global trade model, also known as the GTAP model. (Note The LINKAGE model is distinct from the GTAP model, though it uses the same underlying database.) The third is an annual course in applied trade modeling. Finally, GTAP organizes and cohosts the annual Conference on Global Economic Analysis. More information on the GTAP Center and project can be found at http://www.gtap.agecon.purdue.edu.

> 7. More information on the MAcMap database is available at http://www.cepii.fr/anglaisgraph/bdd/macmap.htm and from Bouët and others (2004).

> 8. Details on the aggregation are available in appendix A of this paper.

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- 69 -

Algeria Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1987.

Central Government employment figures do not include Defense data or social security. They are drawn from B. Pecheur's report (draft 09/05/95). Central Government is defined as Central Government excluding local government, PTr personnel, career armed forces and judiciary. Military includes 90,000 conscripts. Data on Health workers are taken from a May 17, 1995 IMF Algeria Background paper by E. Bell and Feler (p. 19), which specifies that health workers are 0.64% of population (.64 per 100 inhabitants) for the year 1994. It does not include a number of health workers who are under contract or work for semi-autonomous hospitals (32). In Algeria, education is the sole responsibility of the central government and private education is illegal. Employment in State-owned enterprises is taken from IMF Report SM/96/135 Selected Economic Issues, dated June 13, 1996. Military employment data include 90,000 conscripts, but do not include paramilitary units, such as the Gendarmerie, (24,000), the National Security Forces (16,000), both under the command of the Ministry of Interior and the Republican Guard (1,200).

Wages and Salaries are taken from IMF Report No. 95/108 of May 17,1995 and relate to 1994. In Algeria, Education and Health are the sole responsibility of the Central Government. Accordingly, the data on Consolidated Central Government employment is inclusive of Education and Health employment. Data on wages in manufacturing (monthly basis) are taken from a report from the Algerian National Statistical Office in Algeria: 'Les Salaires Bruts Moyens en Algerie" and refer to 1993.

Bahrain Central Government employment, education and health employment are taken from IMF Report SM 94/85 of April 1, 1994 and relate to 1993. There is no local Government structure in Bahrain
products for which there was available price data spanning both sub-periods. We report the state capitals in which the differences in prices volatility were statistically significant.<sup>15</sup> Whenever there was a significant change in price volatility after the border closure, this change implied an increase in volatility. For the case of broad beans, for example price volatility more than tripled in Bor, it doubled in Juba, and increased by almost 10 percentage points in Malakal. Sorghum is the product for which the increases in price volatility were more widespread spatially (similarly for groundnuts). Volatility roughly doubled in Aweil, Bor and Rumbek; increased by 75 percent in Juba, and by 17.5 percent in Wau.

> 14 Statistically, the test results from the comparison of the sample variance of price changes pre-closure with the sample variance of price changes post-closure. Both sample statistics are distributed Chi-Squared, and its ratio is distributed as an F.

> 15 Given the short sample period, a cut off of 20 percent significance is considered. The full set of results for this volatility comparison as well as figures showing the evolution of the standard deviation of price changes for each state capital and product are presented in the Appendix.

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Policy Research Working Paper 9758

# **Abstract**

To what extent do the behavioral choices of Zambian smallholder farmers influence the negative effects of climate shocks, and what impact do these choices have on vulnerability and resilience? This paper uses nationally representative, three-wave household-level panel data to investigate these questions. The empirical estimation employs an instrumental variable probit regression model, which also controls for the endogeneity of key choice variables. There are four main empirical findings. First, droughts are the most prevalent climate shock rural smallholder farmers in Zambia face, but the extent of exposure differs spatially, with the Southern and Western Provinces being the hardest hit. Nationally, about three-quarters of all smallholder farmers are vulnerable and only about one-quarter are resilient. Second, increased climate shocks correlate with both increased vulnerability and reduced resilience, with short- and long-term deviations in seasonal rainfall worsening vulnerability and resilience. Third, higher asset endowments and education level of the household head reduce vulnerability and increase resilience among smallholder farmers. Female-headed households are more vulnerable and less resilient, on average. Fourth, the use of climate-smart agricultural practices—namely, minimum tillage and use of inorganic fertilizers or hybrid maize seed— significantly improves household resilience in the short term. The paper draws two main policy implications from the findings. First, the results point to an urgent need to invest in research and development for climate shock–tolerant crop varieties and in broader climate-smart agricultural technologies to scale out and scale up context-specific practices through innovative digital platforms. Second, more investment is needed in risk mitigation strategies such as weather indexed insurance, targeted social cash transfers and how to make these work effectively for smallholder farmers. Other important complementary elements include investment in innovative digital platforms that can facilitate - timely delivery of climate information services and facili tating asset accumulation and education that can enable farmers to improve climate shock resilience over time.

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
._93|6_._808|0_._108|1_._472|
|Employed|0_._216|0_._0416|0_._0533|0_._435|1_._956|
|Married|0_._460|0_._0246|0_._110|0_._823|2_._373|
|Region: NCR|0_._198|_−_0_._0340|0_._0487|0_._485|1_._834|
|Region: Luzon|0_._681|0_._0118|0_._0585|0_._840|1_._857|
|Region: Visayas|0_._0862|0_._0138|0_._0365|0_._706|1_._844|
|Region: Mindanao|0_._0345|0_._00837|0_._0242|0_._729|1_._825|
|Test batch 1|0_._0259|_−_0_._0187|0_._0164|0_._255|1_._819|
|Test batch 2|0_._144|_−_0_._0755|0_._0775|0_._329|2_._268|
|Test batch 3|0_._276|0_._0710|0_._0991|0_._474|2_._245|
|Test batch 4|0_._366|0_._00947|0_._107|0_._929|2_._413|
|Test batch 5|0_._181|_−_0_._00961|0_._0481|0_._842|1_._911|

Data for households in survey sample. _N_ fail( _s<_ 0) = 460, _N_ pass( _s_ ⩾0) = 439. _μs↑_ 0 is the mean of the local regression using data for test-failers only, evaluated at _s_ = 0. NCR = National Capital Region. “Luzon” omits NCR. “Anyone” means any household member. Optimal bandwidth selected following Imbens and Kalyanaraman (2012). Triangular kernel.

36
These 366 households are substantially more prosperous than the overall population of Addis Ababa. Many poor households in this capital city share either an electricity connection, a piped water connection, or both—or have no water connection. Yet for households with a shared water connection, we are unable to estimate their water use and monthly water expenditures with sufficient accuracy to estimate the subsidies they receive. Nevertheless, we believe it is worth examining the subsample of 366 households with private connections to both the electricity grid and the piped water network because scholars can rarely estimate the magnitude of both electricity and water subsidies received for a given household.

We derive these estimates by using data from four sources: (1) our own estimates of the total average costs of water and electricity services in Addis Ababa, which are mainly based on utility financial reports; (2) customer billing records from the electricity utility; (3) customer billing records from the water utility, which we match with data from the electricity utility; and (4) in-person interviews with respondents in the sample households. We are able to match household electricity and water billing records with the household demographic and socioeconomic data collected via our household survey. In a companion paper (Cardenas and Whittington 2019) we report the magnitude and distribution of electricity-only subsidies across the entire sample of 987 households.

Our analysis makes three contributions to the literature on the incidence of electricity and water subsidies. First, most previous research on the distribution of such subsidies is limited to only one of the utility services and so it is not possible to assess either the magnitude of the cumulative subsidies that different household wealth groups receive or how these subsidies interact with each other. Second, few previous studies match actual customer billing records with household socioeconomic data—and none match both electricity and water customer billing records with such data. Third, most previous studies do not base their subsidy calculations on the actual cost of service 3
**Figure 10: Herfindahl-Hirschman Index for Russia's exports in 2012**

<!-- Start of picture text --> 0.350<br>0.300<br>0.250<br>0.200<br>0.150<br>0.100<br>0.050<br>0.000<br><!-- End of picture text --> _Source_ : COMTRADE data.

_Note_ : The index is calculated over three-digit categories of the SITC (Standard International Trade Categories).

0
producers /2|11.02|11.75|11.84|11.41|11.19|
|Major exporters /3|14.23|14.48|14.61|14.38|13.53|
|Actual Market Prices||||||
|Raw cane sugar /4|13.53|12.23|11.21|10.71|7.05|

1/ Measured in nominal U.S. cents per pound, ex-mill, factory basis. 2/ Average of five producing regions (Australia, Brazil–Center/South, Guatemala, Zambia, and Zimbabwe). 3/ Average of seven countries (Australia, Brazil, Colombia, Cuba, Guatemala, South Africa, and Thailand). /4 Raw cane sugar prices are is U.S. cents per lb, July-June average of monthly prices, fob Caribbean ports (note that production costs are ex-mill while the actual market prices are fob prices).

Source: LMC International as reported in Sugar and Sweetener Situation & Outlook, Economic Research Service, USDA, September 2001. Actual market prices are from World Bank databases.

National average production costs do not reflect cost differences within a country and these differences can be substantial. In Jamaica for example, the cost of the private sector estates are about one-half of those of the state-owned and managed estates. These differences are due partly to variations in natural climatic conditions for sugar cane production, but mostly to better management. Thus, it may be possible for part of an industry to be competitive while other parts are uncompetitive.

Most Caribbean sugar industries have accumulated large debts from years of unprofitable operation. This debt reduces competitiveness by increasing interest costs, constraining equipment maintenance and replacement, and limiting application of production inputs such as fertilizer. Deferred equipment maintenance and replacement leads to more frequent equipment failure and increases factory downtime. Debt of cane growers, in those countries that have private sector cane growers, constrains their