rafmacalaba/data-use-mentions-extended huggingface dataset:
in the probability of staying minus the probability of leaving. To characterize this, we do not need the flow data between non-conflict locations or inflows to conflict locations, which may not be captured in the absence of a humanitarian situation. However the number of internally displaced people, i.e. flows out of conflict regions, is often well documented by the international organizations to program assistance.

Note that the calculated utility change in Proposition 1 is an upper bound rather than a precise point estimate because it is missing the term<sup>�</sup> _l∈_ Φ<sup>_φl_</sup> �∆ _Ut_<sup>_l−_∆</sup><sup>_C_</sup> _t_<sup>_kl_</sup> � from equation (3), which accounts for the average utility loss in all regions weighted by _φl_ , net of the changes in moving costs. As long as this term is non-positive, Proposition 1 will hold.

The next proposition extends this result to changes in welfare.

**Proposition 2** _The upper bound of change in welfare in conflict location k (i.e., the lower bound of the welfare impact) can be expressed as_

_if the condition_<sup>�</sup> _l∈_ Φ<sup>_φl_∆</sup> � _Ut_<sup>_l−C_</sup> _t_<sup>_kl_</sup> � _≤_ 0 _holds, where φl ≥_ 0 _are weights for destinations such that_<sup>�</sup> _l∈_ Φ<sup>_φl_= 1</sup><sup>_._</sup> Similar to the previous proposition, Proposition 2 only needs the flow of agents out of conflict locations and the migration elasticity parameter to calculate the upper bound of welfare changes. The expression in Proposition 2 calculates the upper bounds of welfare change (lower bound of the welfare impact
Three best practices for applying regression include, first, using time-series data, which contain observation of variables over multiple time periods whenever possible. Regression models typically have better long-term accuracy when the historical patterns of variables are included in the data. When time-series data is not available, these models are best used to estimate short- to medium-term impacts. Second, it is important to evaluate the goodness of fit of a regression model using different indicators such as the coefficient of determination (R-squared), root mean squared error (RMSE), and residual analysis. Third and finally, validation of a regression model generally benefits from sensitivity analysis on the short- and long-term predictions of the model using systematic changes in the variable values.

Regression models, due to their flexibility in explaining statistical correlations between variables, can be used to assess all aspects of impacts listed in the IMO Strategy.

# **5.1.2 Input/Output models**

I/O models have several limitations that make them less preferable when modeling the wider economic impacts of mitigation measures (Bachmann et al., 2014). These limitations include the absence of macroeconomic feedbacks (such as how price-changes would induce changes in demand for commodities), assumptions of linear proportionality between input and output in economic sectors, and a lack of macroeconomic constraints from factors such as labor, capital, and, land. Nevertheless, I/O models have advantages in being relatively simple and able to simulate limited redistribution effects on trade flows due to mitigation measures.

According to Bachmann et al. (2014), I/O models may be used to reduce the burden of a CGE model when: i) The impacts of mitigation measures are not expected to cause a drastic price changes; ii) The main impacts of transport policies are expected to be within the transport networks (such as reduced travel time); and iii) The analysis is focused on changes in trade flow patterns assuming fixed total demand values. As such, I/O models are
The wave of LSLBI triggered by the 2007/08 commodity boom spawned many descriptive case studies. These studies generally support the notion that LSLBI can increase output and create positive external effects for small producers if (i) large and small farms do not compete for land or other resources but complement each other (e.g., by large farms providing ancillary infrastructure) and there are no negative externalities;<sup>9</sup> (ii) large farms increase productivity (Dubbert 2019) or labor demand (Nolte & Ostermeier 2017), e.g., through investment in irrigation (Schuenemann _et al._ 2017), high-value production <mark>(Negash & Swinnen 2013), or</mark> agro-processing; and (iii) institutional arrangements, such as contract farming (Hall _et al._ 2017) rather than increase skills and benefits beyond what is observed for wage labor -though better endowed individuals may still reap disproportional benefits (Boamah & Overa 2016). Case studies also suggest that public agencies may use LSLBI to pursue objectives other than that of increasing productivity<sup>10</sup> and that the type of rights large farms can access and the way to obtain them will affect potential benefits.

As case studies may be subject to selection bias, e.g., by focusing on foreign investors to the detriment of locals (Jayne _et al._ 2019) even if the latter comprise the vast majority of ventures and area (Hausermann _et al._ 2018), complete geo-referenced concession inventories, overlaid with existing household or farm surveys, provide a more representative way of exploring and testing welfare and productivity effects of LSLBI on neighboring households. Studies using this methodology have been conducted in Cambodia (Anti 2021), the Lao People’s Democratic Republic (Nanhthavong _et al._ 2020), Mozambique (Deininger & Xia 2016; Glover & Jones 2019), and Ethiopia (Ali _et al._ 2019a). Notwithstanding an interesting approach
low-income countries, 37% for lower-middle-income countries, 41% for upper-middle-income countries, and 26% for high-income countries. (We note that the set of countries in our data is not representative of regions or country income groups.) In addition to work stoppage, we examine other measures of the labor market effects of COVID-19 available from the HFPS data. We find that a substantial share, 20% of wage workers, report partial or no payments for work performed in the LAC region where this information was collected. In some countries, up to 21% of respondents report changing jobs during the pandemic (on average 9% report changing jobs), another sign of disruptions and a possible coping mechanism. Taking the simple average across countries, 22% of agricultural workers reported stopping work as opposed to 40% for industry and 38% for services. Examining broader measures of income loss, we find that a high share of respondents reported total income loss (62%), as well as loss from farming (62%) and non-farming (75%) family businesses, and wage incomes (49%), conditional on having a specific source of income.

To better understand what the HFPS data are saying about the initial impacts of the crisis on labor market outcomes, we examine the relationship between HFPS measures of economic impact and external measures of crisis impact. We find that work stoppage in the HFPS data exhibits the expected negative relationship with GDP growth projections in the LAC region. In contrast, we find that work stoppage is weakly and positively correlated with GDP growth projections in the SSA region. Furthermore, we see the same pattern when it comes to household farm income reductions: The expected negative correlation in LAC and a positive correlation in SSA. We hypothesize that GDP growth projections may not be accurately capturing income changes in the agriculture sector and the informal economy, with informal labor market arrangements and self-employment, which are prevalent in many countries in the SSA region. This also highlights that the HFPS data, despite
**Based on the conceptualization of energy poverty, we describe these approaches in more detail but rely on approaches we can feasibly measure and monitor with the available data in Bulgaria.** As we are ultimately interested in recommending a measure of energy poverty in Bulgaria that can be operationalized for monitoring and evaluation purposes, we rely on those approaches that we can feasibly measure and monitor through household expenditure surveys and the EU-SILC (see Column 4 in Table 1). We also explore the potential of big data sources. This leaves us with the following approaches: the access-based approach, the consensual-based approach, the outcome-based approach, the big data approach, and the expenditure-based approach. Finally, we analyze the strengths and weaknesses of each of these approaches.

# 2.2. Access-Based Approach

**The most straightforward approach to measuring energy poverty is to analyze the share of the population without access to energy.** The worst form of energy poverty is not having access to energy, which has important implications for overall well-being and monetary poverty. Previous research shows a significant relationship between access to electricity, for example, and poverty rates (Asghar et al., 2022). A common approach to measuring this aspect of energy poverty is to look at the share of the population without access to certain energy services, such as electricity, natural gas, or other forms of energy. There are binary access-based approaches (e.g., having access to electricity or not) and those using a threshold (e.g., having more than 50 kWh of electricity per capita) (Nussbaumer et al., 2012). Others have constructed composite measures such as the Energy Access Index (EAI), which measures access to six essential energy services: lighting, cooking, water heating, space heating/cooling, information/communication, and mobility (Practical Action, 2010).

**Access to energy services can be measured at the extensive but also the intensive margin**<sup>**
0.12|
|No. frms census of industrial production|48<br>6|0.54|0.02|
|Sales census of industrial production|61<br>7|0.32|0.03|
|No. VAT frms|64<br>9|0.63|0.03|
|Employment in VAT frms|62<br>9|0.36|0.03|
|Value added of VAT frms|72<br>10|0.38|0.03|
|VAT network sales|68<br>13|0.35|0.02|
|VAT network outlinks|69<br>11|0.35|0.02|
|VAT network purchases|60<br>9|0.43|0.02|
|VAT network inlinks|65<br>10|0.48|0.02|

The columns for Nairobi and Mombasa report their share of the respective national aggregate figures (e.g., Nairobi’s contribution to Kenya’s GDP). The Pareto exponent _α_ is the estimated coefficient from a county-level regression of each county’s rank (log) on the respective measure _x_ (log): log rank = log _A − α_ log _x_ . All measures reported in the final section of the table are derived from the VAT data. All other measures are based on data sources summarised in Table 1.

without multi-establishments. We find that the excess spatial concentration introduced by multi-establishments does not explain the aggregate concentration patterns of formal private sector activity.

# **Formal firms predominately source from and sell to firms in urban hubs.**

Consistent with the previous section, a plot of county-by-county trade flows in Figure 3 highlights the primacy of Nairobi and Mombasa. The width of each segment on the left reflects the county’s total sales within the network, while segments on the right are proportional to total purchases.
_Normsj_ variable encapsulates 4 different measures of progressiveness: men’s individual beliefs, women’s individual beliefs, men’s perceived community norms, and women’s perceived community norms. Men’s (Women’s) Individual Belief (0-1) is the proportion of men(women) in the country who agreed with the statement that _men and women should have equal opportunities_ measured prior to the COVID-19 pandemic. Men’s (Women’s) Community Norm is the country-level aggregate of the perceived gender norm as reported by men(women) in the sample towards gender equality rescaled to be a continuous variable between 0 and 1 (in increments of 0.1). Higher values of the norms measures all indicate more progressive or egalitarian views within the country i.e. a more liberal society. Alternatively, a lower value indicates more regressive or discriminatory views i.e. a more conservative society. The beliefs and norms were collected before the pandemic in December 2019 and aggregated at the country level among both men and women.<sup>28</sup> Moreover, since this sample is not nationally representative, we also show how these data correlate with a comparable gender equality statement collected in the World Values Survey (WVS). Overall, this correlation is strong, giving us assurance that, in this vein, our sample is not unrepresentative.

# **5 Results**

# **5.1 Gender Differentials in Firm Closure**

Before presenting results on the impact of school closure policies, we examine the overall gender gaps in firm closures and performance during the 2020-2021 COVID-19 pandemic. Tables 1a and 1b show that the COVID-19 pandemic did indeed impact female-led businesses differently than male-led businesses throughout the 2020 survey period and continues to persist into 2021.

The repeated cross-section results, following equation 1, are presented in Table 1a. The time path results suggest that more female-led businesses were closed during COVID-19 than male-led businesses for each wave of the survey. We define closure as when
but this subsequently stagnated and was even reversed following the 2016 recession. Given Nigeria’s large population, this has sizeable implications for regional and global poverty reduction.

This analysis provides useful evidence on how to estimate a poverty trend in contexts where official household consumption survey data are infrequent and where changes to the data collection methodology do not allow comparing survey estimates over time. Rather than proposing a new methodology, the paper shows how different data sources can be used to estimate a trend and how applying different methodologies can help improve the robustness of the results. A similar approach could be replicated in contexts where data on national accounts and/or non-monetary indicators of household welfare are available, but household consumption survey data are not. While this is not uncommon, we should stress that the data environment in Nigeria was particularly rich and this analysis could not have been conducted without these conducive data “pre-conditions”. In particular, the availability of GHS data for an overlapping year with the official NLSS survey was crucial to validate our imputations, before going back throughout the 2010s.

This analysis also shows the importance of regularly collecting _comparable_ data on household consumption. While this work shows that alternative data sources can be useful for estimating long-run poverty trends, it also highlights how many additional assumptions and checks are needed to produce robust evidence. Having direct estimates of monetary household welfare – with trends as well as snapshots – would provide more precise and timely information on poverty. This is particularly relevant in times of economic crises, such as the current COVID-19 pandemic, when rapidly rolling out countervailing policies to help support households is critical.

# 9. References

Atamanov, Aziz, Dean M. Jolliffe, Christoph Lakner and Espen Beer Prydz., "Purchasing Power Parities used in Global Poverty Measurement ", World Bank Group Global Poverty Monitoring Technical Note, no. 5., September 2018.

Beegle, Kathleen, Joachim De Weerdt, Jed Friedman, and John Gibson. "Methods of household consumption measurement through surveys: Experimental results from Tanzania."
definition of an urban area has not changed during this period. As we discuss further below, some sub-units of districts move from being rural areas to being urban areas with the 2001 Census compared to the 1991 Census when the sub-units begin to satisfy the urbanization criteria. This change can influence our measured urbanization levels, and we discuss below the robustness of our documented patterns to these reclassifications.<sup>5</sup> It is important to note that India uses a more demanding set of criteria than most countries to define what is ‘urban’ (e.g., Bhagat, 2005; United Nations, 2001). For instance, substantial parts of U.S. metropolitan areas like Atlanta or Phoenix would be classified as rural in Indian statistical analyses because their population densities fall below 1000 persons per square mile. Thus, our measured urbanization for India will be lower than many international standards. This consideration does not affect, however, the longitudinal consistency of our trends for India.

We primarily measure urbanization of manufacturing activity for a district through the share of manufacturing employment contained in establishments classified to be in an urban location. This choice most closely corresponds to the prior literature and the central concerns of Indian policy makers. We also consider urbanization of plant and outputs for comparison. Table 1 provides basic descriptive statistics on districts, further discussed below, that begin with the average of these measures for 2001.

Table 2 lists the 17 major states from our sample and their urban shares, combining the organized and unorganized sectors. Figure 3a provides a graphical depiction. These states are a subset of India’s 35 states/union territories. Exclusions were due to three potential factors: 1) the state was not sampled across all of our surveys, 2) the small sample size for the state raised data quality concerns, or 3) persistent conflict and political turmoil existed in the region. We discuss below in greater detail the explicit criteria for a district’s inclusion in the regression sample. These exclusions
Policy Research Working Paper 7894

# **Abstract**

This paper investigates the impact of income diversification on farming households’ welfare in Nigeria on two rounds of the Nigeria General Household Survey-Panel, namely the 2010/2011 and 2012/2013. The study finds that income diversification is the norm in Nigeria, with about 60 percent of farmers diversifying away from subsistence farming into non-farm activities and cash crops. In addition, using the panel of farmers interviewed before and after a severe drought that hit Northern Nigeria in particular in 2011, the study finds that diversification increased throughout Nigeria from 60 to 64 percent and in the North from 58 to 63 percent. The study postulates the existence of heterogeneous returns on diversification as a consequence of the drought, and estimates the returns through a non-parametric selection model using a local instrumental variable. The choice of this model is dictated by the necessity to account for both heterogeneous effects of diversification and selection bias related to households’ decision to diversify. Overall, it is found that diversification positively affects consumption in Nigeria. However, who benefits the most is crucially determined by the initial conditions under which diversification is undertaken and the specific agro-climatic context in which households operate.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at ebertoni@worldbankgroup.org,pcorralrodas@worldbank.org, vmolini@worldbank.org, and goseni@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,
importables in the consumption basket across socioeconomic groups or between the poor and nonpoor will have relatively little influence on poverty outcomes. Moreover, in 1993 there was relatively little difference between the poor and nonpoor population in the share of tradables in total household expenditure (table 14). The nonpoor consume slightly less tradables than the poor, given that they have a higher share of expenditure on lodging. The two factors taken together suggest that in terms 16 Based on the expenditure shares generated by the survey, we found that 35.2 percent of household consumption in 1993 was on tradables, compared to 46.5 percent **in** the CPI (table 12). Remittances, which account for 7 percent of the survey basket, but do not figure into the CPI basket, were classified in the same proportion as the remainder of the consumption basket. The difference in the share of tradables between the CPI basket and the survey basket is due primarily to the fact the share of nontradables in total food expenditure is higher in the survey--75 percent compared with 53 percent in the CPI basket. The reason for the smaller percentage point difference in the price indices for tradables and nontradables in the survey basket is due primarily to the larger share of nontradable food in the survey basket. Because of the large seasonal price increases for starchy food, the index for nontradable food prices was actually greater than that of traded food products in April 1995 though by January 1996 the situation had reversed itself (table 13).

15
various technical programs. The mathematics/language and literature assessments include 19 and 23 items, respectively ( **Table 1** ). We compute the outcome variable as the percentage of correct answers in the EAES in the selected subjects. We then standardize it relative to the control group's mean and standard deviation. In the appendix, we present results based on Item Response Theory (IRT) in order to check the robustness of our results.

# **Enrollment and Repetition**

Other key outcomes of interest include enrollment in the third semester and the probability of repeating at least one subject. The first outcome takes a value of one if a student enrolls in the third semester and zero if otherwise. Similarly, the second outcome takes a value of one if a student repeats at least one subject (up to that semester) and zero if otherwise. Both outcomes originate from available administrative data collected by SENESCYT in the second semester of the 2021 academic year.<sup>5</sup>

# _4.2. Baseline Covariates and Balance Results_

We test whether pre-treatment characteristics differ for the treatment and control groups. These covariates were obtained from the administrative enrollment dataset gathered by SENESCYT in the second semester of the 2020 academic year. This dataset collects comprehensive information from each student that ranges from unique identification (e.g., student ID), basic demographics (e.g., date of birth, gender, ethnicity), proxies of socioeconomic status (e.g., whether the student studies and works), whether the family receives cash transfers and parental education (i.e., the “Bono de Desarrollo Humano”), along with other academic information such as the admission score obtained during their higher education application process.

**Table 2** presents the results, with student-level and institute-level characteristics displayed in the top and bottom panels, respectively. In the control group, students are 22 years old and primarily male (60 percent). About 40
Table 4. Results of off-farm job search between May 2009 and August 2009 for the longterm laid-offs (those who lost off-farm jobs after September 2008 and did not find new <u>ones by April 2009) from August 2009 survey of CNRS respondents.</u>

||ByAugust|
|---|---|
|Total sample size (total number of long-term laid-offs in our<br>sample)|124|
|Share of those with off-farm jobs (%)|30|
|By sex||
|Male|33|
|Female|25|
|By age group||
|<=30 years|43|
|30~50 years|24|
|>50 years|17|
|By education level||
|Elementary school or less|23|
|Middle school|32|
|Highschoolor higher|45|

Data source: Authors’ own data (CNRS dataset) 31