rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10976

# **Abstract**

Since 2008, the World Bank’s Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA) program has supported the collection of nationally representative, longitudinal, multi-topic household survey data to inform researchers and policy makers of living standards in Sub-Saharan Africa. The surveys maintain a distinct focus on the agricultural sector, collecting detailed plot-level data and information about agricultural activities, while measuring socioeconomic conditions of thousands of smallholder farmers and households across multiple countries. This paper presents a harmonized panel dataset (HP) from LSMS-ISA surveys from 2008 to 2021 in seven Sub-Saharan African countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda, from 2008 to 2021. It includes more than 200,000 agricultural plot observations, more than 400,000 individuals, and about 59,000 households. The HP allows for in-depth analysis of farm, household, and individual dynamics over time and across countries. It is ideal for researchers interested in studying the dynamics between agriculture, economic development, and welfare outcomes in Sub-Saharan Africa.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tbentze@worldbank.org, _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated
. Isolating the impact of trade reform is complex since cuts in tariffs and quotas have occurred concomitant with, and in fact, as parts of stabilization plans seeking to eliminate the adverse expectational consequences of long standing macroeconomic balances. That said:

**_Increased openness does not appear correlated with unemployment:_** Beyond the initial adjustments there appears to be little evidence to indict trade reforms for causing higher rates of unemployment.  Anecdotally the region’s more aggressive trade integrators - Chile and Mexico - have, if anything, among the most modest rates of unemployment. Marquez and Pages (1998) argue that while liberalization appeared to reduce employment per unit, the expansion of production makes the overall impact of liberalization on employment statistically insignificant. In fact, IADB (2003), using household surveys for 10 countries, argued that it increased employment and did not have any significant impact on unemployment.  Their overall conclusion is consistent with the finding that, internationally, there is no significant relationship between openness of countries (exports plus imports over output) and their levels of unemployment.

**_Or with  informality._** The tendency towards somewhat higher informality does not seem particularly due to trade.  Though the sector has absorbed some of the workers displaced by foreign competition, this is probably not the entire story as discussed below.  Argentina has seen a rise in informal salaried work (Gasparini 2000) but the trend predates liberalization by half a decade suggesting other forces.  More exposed sectors tend to be more formal throughout the region and there is relatively

> 4 This section draws heavily on Maloney (2002).

3
protective dietary factors, in the same way that households with high energy intakes and high levels of ultraprocessed food consumption are likely to consume various of the higher-risk items and too few of the protective items.

Another summary measure based on quantitative diet data focuses on **ultra-processed foods** (Monteiro, Cannon, Lawrence et al. 2019; Monteiro, Cannon, Levy et al. 2019). Ultra-processed foods are defined by food processing methods as defined by the Nova classification and include foods such as convenience foods, fast foods, and sugar-sweetened beverages. Monteiro et al. (2019) summarize the relationship between amount of ultra-processed food in the diet and presence of diet risk factors in the diet (positive relation with saturated fat, sugar, trans fats, sodium and energy density) as well as the absence of protective factors in the diet (negative relation with protein, fiber and potassium). Monteiro et al. (2019) also summarize the relationship of amount of ultra-processed food in the diet, and suggest it is associated with increased obesity or obesity-related outcomes, CVD outcomes, increased prevalence of selected cancers. Recent systematic reviews also have found evidence of an increased risk of overweight and obesity, diabetes and other NCD risk factors associated with higher ultra-processed food consumption (Lane et al. 2021; Moradi et al. 2021).

Metrics for unhealthy diets in children are at an early stage of development and currently rely on the monitoring of food groups that are shown to be associated with increased risk of overweight and obesity. In **young children 6-23 months** , zero vegetable or fruit consumption in the previous day is used to measure diet risk, a practice that is linked to both insufficient intake of nutrients presently as well as low intake later in life (WHO & UNICEF, 2021). Similarly, data on child consumption of sweet beverages and sentinel unhealthy foods<sup>2</sup> in the previous day/time interval are now included as unhealthy eating indicators in largescale surveys (i
Employment shocks caused by firm closures and mass layoffs can lead to lower economic mobility within and across generations. Studies have found that workers who are displaced by unexpected firm closures experience significant and long-lasting reductions in earnings.<sup>24</sup> The long-term impacts of unexpected job loss can extend beyond the effect on one’s own income to the eventual labor market outcomes of offspring, reducing intergenerational mobility. Oreopoulos et al (2008), using Canadian data that follows more than 39,000 father-son pairs from 1978 to 1999, find that sons with fathers whose jobs were displaced by firm closures have significantly lower earnings than similar individuals whose fathers did not experience an employment shock, with the effects driven by families in the bottom quartile of the income distribution.<sup>25</sup> These effects are probably due to the impact of permanent reductions in family income caused by employment shocks on human capital development of children, with other non-monetary costs (such as stress in families) of unexpected job losses likely playing a role as well.

Second, large income losses often compel households to use coping mechanisms with potential longer-run impacts on nutrition, indebtedness, and at times, (the loss of) productive assts. As described in Section 4, the size of the shock and lack of adequate income-replacement programs led to many households, particularly the poor and the vulnerable, to adopt such strategies. Reducing consumption could increase nutritional deprivation for children and mothers, with damaging long-term consequences. High rates of indebtedness can lead to poverty traps, as the poor often pay higher interest rates for emergency loans than do better off households. The sale of productive assets to cope with shocks can be highly damaging when it is adopted, as it can adversely affect a household’s income-generating potential and ability to cope with future shocks.

Third, disruptions to schooling can widen learning gaps between children from different socioeconomic strata, reducing intergenerational mobility. In most low-income countries covered by HFPS, children in fewer than 66 percent of households
measures to alleviate poverty might instead emphasize unemployment insurance or other components of a social safety net that aim to protect against falling into poverty. Thus, data on poverty dynamics at the micro-level can inform our understanding of the drivers of poverty and provide insights into the kind of anti-poverty policies that are needed.

Unfortunately, evidence about such poverty dynamics is scarce in developing countries; this also holds for Malaysia. In general, panel surveys that follow the same households over time are needed in order to make statements about whether poverty is chronic or transitory. Such surveys are typically complex and costly, and as a result they are rare and often involve relatively small sample sizes. In addition, attrition can be a problem as households drop out of the sample for a variety of reasons. To the extent that attrition is non-random, it has consequences for the validity of results. Still, it is notable that in Southeast Asia, both Indonesia and Vietnam conduct panel surveys on household income or expenditure.

A variety of approaches that aim to overcome the constraints posed by the lack of panel data have been explored in the literature. The idea is to draw inferences about poverty dynamics via analysis of far more widely available cross-sectional data. One line of research focuses on pseudo-panel methods, in which insights about poverty transitions are gleaned through scrutiny of cohort averages in cross-sectional surveys.<sup>3</sup> Another approach constructs synthetic panels from cross-sectional surveys through econometric models estimated on the basis of time-invariant household characteristics (Dang, Lanjouw, Luoto and McKenzie, 2014). The empirical analysis in this paper is based on the latter approach. While the method does not permit true panel analysis—in which the trajectories of individual households can be followed over time—it does yield upper- and lower-bound estimates of transitions into or out of poverty at an aggregate level.<sup>4</sup> An appealing feature of the method is that it also allows for delving deeper into the data to construct profiles of the groups who experience
rejected when they applied. Non-credit constrained firms are those that either had no need for credit or whose demand for credit was satisfied.<sup>4</sup>

# _Firm covariates_

Firm-level control variables include the log of firm age (and its square), percentage of employees with a university degree, and dummy variables for whether the firm is publicly listed, a sole proprietorship, an exporter, and whether an external auditor reviews its financial statements. In the main paper, we present estimates without firm covariates; estimates with firm covariates included can be found in the Appendix.

# _Local credit market conditions controls_

To control for local credit market conditions, we use detailed data about the banking sectors in our sample countries. We connect the firm and branch data by drawing circles with a radius of 15km around the coordinates of each firm and then linking the firm to all branches inside that circle. For each firm, we first measure the number of bank branches within a 15km radius. Next, we calculate the branch-weighted average asset size of banks with branches within this radius. This allows us to control for the number and the size of banks that make up the local credit market around each firm.

# Descriptive statistics

Table 1 presents summary statistics (mean, median, standard deviation, minimum, maximum) for the explanatory variables (Figures 7 and 8 show the relative frequencies for the outcome variables). It shows that almost a quarter of firms are credit constrained (24.6%). The standardized green management variable is by construction close to zero on average, but varies between -1.91 and 6.98. Close to a fifth of the firms in our sample are exporters, and 6% of them are publicly listed. Among the firms, 19.7% are sole proprietorships and 39.4% had their annual financial statements checked and certified by an external auditor. On average, 30.9% of the firms’ employees had a university degree. The table also shows
to such a shock. Of course, the opposite would be true if the shock originated abroad. The magnitude of the shock, and relevant time horizon also matter greatly for resilience. For example, the 2011 earthquake in Japan caused severe disruptions in the global automobile industry that lasted for several months after the earthquake. But beyond the automobile industry, the global economy was not much affected, and even the automobile industry eventually recovered. Based on the criterion of “bend but not break”, the global economy proved incredibly resilient to this shock.

Returning to the COVID pandemic, it is plausibly the largest shock the global economy has faced since World War II. It represented both a demand and a supply shock, as unemployment rose and income dropped sharply during lockdowns, while at the same time, production and commerce ceased in many countries. It affected all sectors of the economy, but some more than others (e.g., hospitality services versus finance or tech). It affected most countries on the globe, but not at the same time, as the infection waves were not synchronized across countries in different continents. In some sectors, such as food and medical products, delivery delays and shortages are critical (i.e., life threatening), while in others, such as autos or semiconductor chips, they are simply inconvenient.

How would one judge the resilience of GVCs and the global economy against this background? While, as noted earlier, we lack an agreed-upon benchmark against which resilience can be measured, we can look at how global trade, both in aggregate terms and in some key products, evolved during the pandemic years, keeping of course in mind that the pandemic is not completely over yet.

Figure 4 shows world trade flows since 2018 up to 2021, when the most recent COMTRADE data are available. Values are reported as a multiple of the 2018 world import value in constant prices. There was a negligible drop in all goods trade during the pandemic during 2020, and a
0.359<br>2|
|Poland|0.24|4|0.064|4|0.085|5|244.61|4|0.698|6|0.063|5|0.255<br>6|
|Romania|0.80|1|0.376|1|3.620|1|-|1|-|1|3.750|1|2.606<br>1|
|Slovakia|0.10|7|0.028|7|0.036|7|89.62|7|0.740|5|0.056|7|0.283<br>5|

_Source: our elaboration on EU-SILC 2008 cross-section data. ‘r’ indicates rank._ Table 2 also shows the ranking _r_ of the the set of countries considered, which can be used for the purpose of aid allocation. In general, a donor may want to select the index that best corresponds to widely accepted normative criteria. While proportional and progressive criteria are usually the norm in tax systems worldwide and therefore justifiable in the context of aid allocation, regressive criteria are much less popular and more difficult to defend. One can also look at the consistency across indexes. The Czech Republic, for example, comes consistently in the last place while Romania comes in first place across all 22
You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. “Torrents and Trickles: Irrigation Spending Needs in Africa.” AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. “Underpowered: The State of the Power Sector in Sub-Saharan Africa.” AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

EIA. 2009. International Energy Statistics, United States Energy Information Administration, Washington, DC EIU. 2011. Uganda Country Report, Economist Intelligence Unit, London

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. “Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.” Policy Research Working Paper 4913, World Bank, Washington, DC.

- GTZ. **(** Deutsche Gesellschaft für Internationale Zusammenarbeit), 2009. International Fuel Prices 2009, 6th edition. Eschborn, Germany.

- Keener, S., and S. G. Banerjee. 2005. “Ghana: Poverty and Social Impact Analysis of Electricity Tariffs.” ESMAP Technical Paper 088, World Bank, Washington, DC.

- Okure, Eng. Dr. Mackay A. E. 2008. _EAC Strategy to Scale up Access to Modern Energy Services: Uganda Country Report and Implementation Workplan_ . Mimeo, Kampala.

- Rosnes, Orvika, and Haakon Vennemo. 2009. “Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.” AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

- Tallapragada, Prasad, Maria Shkaratan, Ada Karin Izaguire, Jaakko Helleranta, Saifur Rahman, and Sten Bergman. 2009. _Monitoring Performance of Electric Utilities_ . Washington, DC: World Bank.

- World Bank. 2010a. “Performance
). During Argentina’s financial crisis, for example, job exits increased for both men and women, but women experienced smaller employment losses due to an increase in job entry.<sup>5</sup> In theory, youth could face similar pressures; families facing job loss or a fall in real income may withdraw youth from school, or idle youth may be forced to work. Evidence from the Mexican tequila crisis, however, indicates that most of the burden of the adjustment, in terms of increased labor force participation, fell on wives rather than children (Skoufias and Parker, 2006). In rural areas, employment declines may be smaller, if women and youth joining the workforce find it particularly convenient to enter family businesses, which are more common in rural areas.

_How have these three mechanisms affected the vulnerability of different groups during downturns?_ While several studies have examined households’ vulnerability to crises, fewer have documented their effects on individual workers.<sup>6</sup> Most studies of crisis impacts focus on Europe and the United States (Verick, 2009, OECD, 2010, Elsby et al., 2010,) or highlight the experience of particular groups or countries (ILO 2010a, 2010b, Ha et al., 2010, Sabarwal et al., 2011, Leung et al., 2009).

_The best existing evidence on the effect of past crises on workers’ income and employment exploits longitudinal data from Indonesia and Urban Argentina._ Men and women experienced equally

> 5 See McKenzie (2004).

> 6 Households’ vulnerability and response to economic crises has been explored in Peru (Glewwe and Hall, 1998), Mexico (Cunningham and Maloney, 2001 and Mckenzie, 2004), Russia (Lokshin and Ravallion, 2003), Indonesia (Strauss et al., 2004) and Argentina (Corbacho et al., 2007), among others.

5
/sup>_Ii_)|0.002|0.003|-0.005*|0.007**|-0.001|-0.005**|
||(0.002)|(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Num.Obs.|1114|1114|1114|1114|1114|1114|
|R<sup>2</sup>|0.352|0.343|0.243|0.606|0.543|0.550|
|_S_(lat, lon)||||0.000|0.000|0.000|

Data: IPUMS-International for Brazil (2000-2010), DNIT and ANEEL. Geographic regions are consistent across years. Robust standard errors in parenthesis. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Panel OLS controls for latitude and longitude. Panel GAM shows semiparametric regressions with thin-plate spline in latitude and longitude, _S_ (lat, lon) shows the approximate p-value of the thin-plate spline.

tween the quality of roads and electricity, year fixed effects ( _μt_ ), and the function _S_ (lat _i_ , lon _i_ ) that controls for the possible spatial correlation of the errors. In particular, The first three columns in Table 1 show the OLS estimates of (1) in which the variables latitude and longitude enter the regression linearly. A second set of results in that table, labeled as GAM (Generalized Additive Model), follows Kelly et al. (2023) and considers a thin-plate spline specification of _S_ (lat _i_ , lon _i_ ); see Appendix A for details.

Table 1 shows that locations with larger shares of employment in manufacturing and services tend to have larger populations. Better quality of electricity is
land transfers in response to changed circumstances or competitiveness (Deininger and Byerlee 2011), key aspects of the legal and institutional framework such as the cost of registering transfers and roles of notaries need to be addressed as well (AGREX 2011).

While Eastern Europe‟s experience provides institutional lessons, it gives little guidance on the productivity effects of land fragmentation, an issue that has been explored in great detail in China, a country where the number of plots is well above the levels commonly encountered in Eastern Europe. Most studies use stochastic frontier production functions with the number of plots as a proxy for fragmentation. Results are ambiguous, though: some find a negative impact (Wan and Cheng 2001), others a relationship that is insignificant (Tan _et al._ 2008, Wu _et al._ 2005), or that follows an inverted U-shape (Chen _et al._ 2009). One reason may be that existing arrangements to transfer land at the local level, through market and non-market mechanisms, appear relatively effective in reducing fragmentation over time: the number of plots per household decreased in all of the country‟s regions, from a national average of almost 9 in 1986 to 6 in 1999 (Tan _et al._ 2006). This suggests that, despite a large number of plots, the operation of land markets implies that land fragmentation in China does not pose critical constraints for productivity.

To see whether fragmentation, proxied by the number of plots, affects agricultural production efficiency, we use LSMS data to estimate stochastic frontier production functions for crop, livestock, and total output. In line with the literature (Coelli 1998), let production follow a Cobb-Douglas functional form where, _i_ indexes households, ln( _y_ i) is the value of output in logs, _xi_ a vector of conventional inputs, β a vector of parameters to be estimated, νi is a two-sided white noise error term assumed to be normally distributed with mean 0 and variance σν2 and _u_ i a one-sided non-negative error term
employers with one employee or less are far overrepresented among low-productivity firms. The results for employers could be the result of self-selection: the top 10% is represented by those very productive informal firms that have more workers (6+) and are in sub-sectors that are more typically high-growth (services) rather than simply trading. Finally, LE-SE who work outside the home are particularly productive.

> 60 Grimm et al. (2012) defines firms that are in the top 90 percent of a composite score composed of profits per worker and value of capita stock, as “top performers” in a study of eight African countries. Adoho et al. (2018) follows the same methodology when studying small firms in the Democratic Republic of Congo. This 10 percent is exogenously set by the researchers. A more recent study (Karlen 2023) allows for the top performers to endogenously emerge from data from Togo through a cluster analysis, finding 18 percent are top performers A similar exercise using data from Côte d’Ivoire finds that 11 percent are in the top tier (Karlen and Johanssen 2023). In Aga, Campos, Conconi, Davies & Geginat (2021) 8% of informal firms have similar characteristics and performance than formal firms (hence they have higher productivity levels). 61 As is common in firm data, a large share of firms reports negative profits and the distribution has long left and right tales. The data do not provide sufficient information to determine if these outliers are caused by measurement error or if they reflect the potential of all firm types to earn very high and very low profits. The distribution of profits per capita heavily overlaps between firm types.

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