rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3.1 Pay as you go-time series

Perhaps the most famous study in all the literature is Feldstein (1974). His contribution was to extend the consumption function popularized by Ando and Modigliani (1963) to include pension wealth as one of the key determinants (the other determinants were permanent income and wealth). The estimates shown in the paper are based on aggregate annual U.S. data and a sample of almost 40 observations. Feldstein (1996) ran the same regressions of the original paper adding 20 observations. After coping with some autocorrelation issues, he obtained the following results (standard errors in parenthesis): where C denotes the value of real per capita consumption, Y D real per capita disposable income, W real per capita personal wealth. The level of disposable income in the previous year is included in an attempt to approximate permanent income. The last variable is social security wealth, SSW , that is de...ned as the present value of retirement bene...ts.<sup>12</sup> What are the implications of these estimates? In 1992, SSW was equal to $14,246 billion. The fall in personal saving that could be accounted by pension wealth is :028 � $14; 246<sup>�</sup> = $400 billion. Moreover, in 1992, pension taxes were equal to $279 billion. Then, the fall in personal saving that could be accounted by a 12The original paper also included a measure of corporate retained earning as one of the determinants. Feldstein (1996) shows that the results remain almost invariant when this variable is included.

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‐cognitive skills that are generally accepted by the profession (aggregating non‐cognitive to just three measures). But these skills – stability, plasticity, and grit – are too aggregated to be policy relevant. Future work will require the survey teams to define _ex ante_ the skills that they wish to correlate with labor market outcomes and design surveys such that a minimum of three measures can be used to construct the latent variables in the structural methodology. Further, since non‐cognitive skills develop over time, and current labor status is a result of choices and actions taken in the past, panel data are needed to provide more robust results.

While Peruvian schools already teach cognitive skills, these findings suggest that the Peruvian government is well justified in incorporating non‐cognitive skill development into the school curriculum. Although the non‐cognitive skills explored in this paper only encompass personality traits, which are commonly assumed to not be malleable to interventions, emerging research is finding that, indeed, interventions do have short‐run effects on personality (Magidson et al. 2014; Jackson et al. 2012). However, there is a significant body of research that quantifies the positive impact of program interventions on specific behaviors that can be linked to the personality traits explored in this paper.<sup>38</sup>

> 38 These program designs are varied and too numerous to present here, but Guerra, Modecki, and Cunningham (2014) provides such a mapping and links to the mapping a list of programs with empirical evidence that show a positive impact of these behaviors in various countries throughout the world.

**17 |** P a g e
corr.|market corr.|score|
|2009-2022|6.21%|15.33%|-21.28%|0.18|0.33|0.71|
|2014-2022|4.9%|11.83%|-19.08%|0.26|0.57|0.76|
|2017-2022|6.5%|12.4%|-18.35%|0.24|0.35|0.77|
|FCS median|4.92%|11.83%|-24.17%|||0.87|
|FCS St.Dev|13.02|5.10|10.36|||0.072|

_Note:_ The first column reports the study period, the second column reports the data coverage across the full study period, the third column reports the data coverage in the last 12 months, the last columns reports the number of markets modeled out of the total number of markets included in the data snapshot. _Source:_ The statistics have been prepared by the author for this paper based on end-of-July (2022) food price data from IFPRI. The FCS median and St.Dev have been calculated from table A4 in Andr ́ee (2021), who present identical estimates for 25 FCS countries (excluding PNG).

The average food price inflation rates are in a reasonable range. For instance, the annualized official CPI price inflation rate for the full 2009-2022(Q2) period was 5.33%. Note that a direct comparison with food price inflation would be interesting, but would have to account for the differences in basket specification.

Considering the high price volatility, two to almost three times annualized inflation, it is also reasonable to assume that any direct measurement of prices would face difficulties with precision. For instance, consider 12 _._ 4%<sup>1</sup><sup
cycles as in government education) and a private health sector.<sup>8</sup> The database also includes twelve non-service (agricultural and industrial) sectors. The sectoral structure of Yemen as pictured in the SAM is described in Table 3.2.

Table 3.2: Economic sectoral structure in 2004

|Sector|VAshr|PRDshr|EMPshr|EXPshr|EXP-<br>OUTshr|IMPshr|IMP-<br>DEMshr|
|---|---|---|---|---|---|---|---|
|Agriculture|11.4|12.8|33.3|3.1|3.7|14.9|16.0|
|Oil|29.5|16.8|6.6|86.0|78.9|1.6|6.4|
|Manufactures|11.5|23.5|5.0|4.2|2.8|71.7|33.6|
|Services non-government|36.4|39.0|28.7|5.1|2.0|10.7|4.0|
|Servicesgovernment|11.3|7.8|26.4|1.6|3.2|1.1|2.2|
|TOTAL|100.0|100.0|100.0|100.0|13.5|100.0|15.5|

Vashr = value-added share (%) PRDshr = production share (%) EMPshr = share in total employment (%) EXPshr = sector share in total exports (%) EXP-OUTshr = exports as share in sector output (%) IMPshr = sector share in total imports (%) IMP-DEMshr = imports as share of domestic demand (%) Source: Yemen SAM 2004.

The factors of production include
for specific countries (Egli 2004), or panel data (Dijkgraaf and Vollebergh 2004; de Bruyn, van den Bergh, and Opschoor 1998). While studies focusing on sulfur dioxide, nitrogen oxide, suspended particulates, and an aggregate measure of air pollution tend to support the existence of an EKC (Grossman and Krueger 1992; Markandya, Golub, and Pedroso- Galinato 2006), papers studying carbon dioxide emissions (Aslanidis 2009) or water pollution are less conclusive (Hettige, Mani, and Wheeler 2000). The EKC may vary with country-specific characteristics, but studies supporting the EKC hypothesis suggest that the turning point<sup>5</sup> . ranges from US$2,805 (Halkos 2003) to US$9,239 (Stern and Common 2001) According to these studies, with the exception of Costa Rica, all countries in Central America would currently be placed in transition or in the increasing part of the EKC<sup>6</sup> .

Empirical studies testing for the direct effects of trade on the environment are less conclusive. For example, Gamper-Rabindran and Jha (2004) empirically analyze the relationship between trade liberalization and the environment in the Indian context. Their findings indicate that exports and foreign direct investment grew in the more-polluting sectors relative to the less-polluting sectors between the preand post-liberalization periods. Mani and Jha (2006) and Akbostanci, Ipek Tunc, and Türüt-Asik (2004) find similar results for Vietnam and Turkey, respectively. Dean (2002) supports the idea that trade liberalization had both a direct and an indirect effect on emission growth in China and these effects could be opposite in sign. In contrast to this works, Grossman and Krueger (1993) found no evidence that a comparative advantage is being created by lax environmental regulations in Mexico. This result is also confirmed by Stern (2005), who finds only small pollution effects of NAFTA on Mexico shortly after the agreement, followed by an improvement in environmental quality afterwards. Gale and Mendez (1998)
: financial development in other (neighboring) countries.7 This approach provides us with a time- variant external instrumental variable to take advantage of our panel setting (Madsen, Islam, and Doucouliagos 2018; Davidson and MacKinnon 2006).<sup>8</sup> In the next subsection, we

> 6 See Ohnsorge, Okawa, and Yu (2021), Medina and Schneider (2018), and Ulyssea (2020) for recent reviews on determinants of informality. See Fergusson (2006) and Voghouei, Azali, and Jamali (2011) for reviews of factors contributing to financial development. Both informality and financial development are associated with institutional quality and economic development (typically captured by per capita incomes).

7 Distance-discounted financial development in foreign countries was used as an instrumental variable (IV) for domestic financial development in studies such as De Haan et al. (2021) and Pleninger and Sturm (2020) in different contexts, but our study is the first to use it to establish a causal link between financial development and informality 8 As suggested by Madsen, Islam, and Doucouliagos (2018) and Davidson and MacKinnon (2006), the benefits of using long panel data are that the fixed effects estimator becomes more consistent 7
above, people’s behavior can be changed by the energy efficiency program. On one hand, the CFL bulb distribution—either free of charge or at deep discounts—could raise awareness with respect to energy efficiency and global warming, motivating people to conserve more energy on other occasions. For example, the Brazilian temporary electrification rationalization is found to have had a long-term effect. The temporarily limited electricity supply encouraged customers to purchase more energyefficient home appliances, leading to long-term energy savings even after the supply constraint eased (Costa 2012). On the other hand, rebound or backfire effects may occur, as observed in many cases related to energy-efficient technologies. For instance, a more fuelefficient car may motivate people to actually drive more, not less.<sup>4</sup> 9. The current paper casts light on these issues by evaluating the energy-saving impact of a free CFL distribution program in Ethiopia. To remove possible self-selection bias, the fixed-effects model is applied for the panel data. To investigate the distributional impact, a two-step fixed-effects quantile regression (Canay 2011) is also used. The following sections are organized as follows: Section II briefly explains recent developments in the Ethiopian

> 3 The average wattage of the old lamps was 58W. These were replaced with 20W CFL bulbs. With a usage factor taken into account, it is estimated that 31MW of peak demand was reduced.

> 4 Evidence shows that the short- and long-term rebound effects of vehicle fuel efficiency are 4.5 percent and 22 percent, respectively (Small and van Dender 2007). The literature reports even larger rebound effects of about 50-60 percent (Frondel and Vance 2010; Frondel, Ritter, and Vance 2012). In a much broader context, the rebound effect for various GHG abatement actions is estimated at 34 percent in the UK (Druckman _et al_ . 2011).
Table 9: Squared Semi-Partial Correlation Coefficients of Poverty from Table 8

|Variable|Correlation Coef.|
|---|---|
|Insurance|0.0001|
|Retirement|0.0013|
|Tenure|0.0000|
|Contract|0.0003|
|Permanent|0.0006|
|Single|0.0000|

_Notes_ : Authors’ calculations using SEDLAC data. Each value represents the proportion in variance of poverty explained by that control variable.

as shown by the partial correlation coefficients in Table 9. Having a contract or a permanent job are also large and significant predictors of living above the poverty line. Tenure and having only one job appear to have very little impact on poverty.

As a robustness check, column 3 lists the results from the regression where missing values are dropped from the sample instead of being coded as zeroes. All of the coefficients on the job quality dimensions are similar to the main results in column 2 except for having a single job, which is no longer negatively correlated with not being poor.

The relationship between job quality and poverty is explored on a country-bycountry basis in Table 10. Both tenure and having a single job are the weakest predictors of poverty in many of the countries. The coefficient on having only one job is actually negative for most countries. The two variables with the strongest and most consistently positive coefficients are having health insurance and retirement benefits, which is what we would expect from previously reported results. It is a toss-up as to which is a stronger predictor as it varies across the countries. Similarly, 34
Policy Research Working Paper 10360

# **Abstract**

While the impact of COVID-19 on Sub-Saharan African labor markets is well documented, there is suggestive evidence that urban households may have fared particularly poorly. This paper uses data from high-frequency phone surveys in 27 Sub-Saharan African countries to investigate which kinds of urban household enterprises were most affected, what coping strategies were utilized, and heterogeneity by sociodemographic characteristics in the short and medium run. Using linear probability models, the paper finds that households that relied on income from non-farm enterprises were hit particularly hard during the early stage of the crisis, with 20-26 percent reporting income declines, and women experiencing even greater losses. Few coping strategies were utilized in the short run to counterbalance the loss of enterprise income. As the crisis progressed, wage employment recovered more quickly than self-employment, with faster gains for non-farm household enterprises, less poor households, and those headed by males and adults. Women, adults, and non-poor self-employed household heads were more successful at leveraging external sources of support early in the pandemic, but these supports largely dropped off by August 2020. These results demonstrate the vulnerability of non-farm household enterprises in urban Sub-Saharan Africa to the COVID-19 shock and highlight the need to expand publicly and privately financed coping mechanisms, particularly for women, youth, and poor household heads who are self-employed.

This paper is a product of the Social Protection and Jobs Global Practice and the Poverty and Equity Global Practice. 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 atWcunningham@worldbank.org and dnewhouse@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
) for 2008-2013.<sup>7</sup> For higher education graduates, we restrict entry age in the labor market to be at least 20 years. Our datasets do not include labor market information for students who did not enroll in higher education, enrolled but dropped out, or enrolled but were still in college by 2013. For these students, we use Colombia’s Integrated Household Survey (GEIH, in Spanish) from the Socioeconomic Database for Latin America and the Caribbean (SEDLAC) to impute labor market participation and wages between 2008-2013. We estimate regressions of formal labor market participation and wages as a function of gender, age, household characteristics, an indicator of urban area, and region fixed effects for the cohort of individuals who were 14-24 years old in 2005, and use predicted labor market outcomes for the imputations (see Appendix A.3 for further details).

> 7About 3% of students who enroll in higher education graduate after 2013 and we therefore lack OLE data for them. We classify students who graduate after 2013 as having incomplete higher education. Dropping them from the estimation sample does not affect our results (not shown).

7
indirectly through booming local businesses, this indicator is used as a proxy for the level of decentralization or the extent to which sub-national governments are involved in raising revenues or deciding over parameters of the tax system. We thereby estimate whether the assessment of government performance of respondents living near a mining area varies according to the level of taxing rights granted to sub-national authorities in each country.<sup>2</sup> Measuring the variables of interest respectively at the regional (local institutions) and national level (local government taxing rights) reduces the probability of a bi-directional relationship between these variables and individual assessment of public goods and services.

# **3.4. Additional Covariates**

The empirical estimations account for countries, regional and individual heterogeneity. At the individual level, we consider an array of demographic and socio-economic characteristics, including gender, age, education, employment status, residential area (urban or rural) which are likely to shape their views on public services. Since the Afrobarometer surveys do not include the income level of the respondents, we create a composite index of living standards by relying on questions such as “ _How often have you gone without food (or water, medicine, cooking fuel)?”._ The question points to the level of deprivation of essential public goods and services, including food, water, medication, cooking fuel and cash. The answers are on a Likert-scale as follows: _0=never, 1=just once or twice, 2=several times, 3=many times, 4=always_ . The categorical variables are aggregated using polychoric correlation to derive a single indicator which reflects the level of poverty and neediness of each respondent's in basic necessities.

At the regional or local community level, the models also account for regional fixed-effects in addition to the local quality of institutions described above. At the country level and in addition to the measurement of decentralization, the empirical models include proxies for the overall corruption control with data from the World Governance Indicator and the
production, etc.) and livestock (cattle, sheep, fish farming, etc.). For each of the settlements, summary statistics are presented in Table 1. On average, settlements derived 71% of their income from agricultural activities, with a minimum of 0% and a maximum of 100%. We also tried to include additional measures of economic activity, such as the average income level in the settlement and the share of employment in the service sectors (both only available for 2020). None of these yielded significant results and were hence not included in the regressions.

Yet limitations in the data available reduce our ability to fully disentangle the interaction between displacement flows, economic growth, and NTL evolution. One should then refrain from making strong inferences in terms of causality, as this paper mainly captures complex correlations.

# 4. Regression Analysis

In this analysis, we regress the NTL growth on the total level of displacement in the settlement, represented by the cumulative net inflow of displaced persons over the 2018-2020 period. The linear regression will return information on the general evolution of NTL, i.e., whether and how cumulative net inflow of displaced persons correlates with higher NTL growth.

## 4.1. Benchmark NTL growth

This section explores the NTL growth rate observed in Badghis, i.e., the NTL evolution over time, before accounting for potential deviations from displacement patterns. NTL patterns indicate improvements in overall electrification or an increase in other ambient light sources, like fires in previously electrified areas, and increases or appearance of electrification in areas with little or no historic light emittance. These findings can be shown by Li et al. (2020), which used global NTL time-series data (1992–2018) to show that the NTL time series experienced a trend of both increasing NTL and continuous spatial expansion for high luminance pixels, both in urban centers and fringe locations. This indicates that the NTL growth rate for a given area will primarily exhibit an increase in overall average NTL
.05, * p _<_ 0.1 Note: The treatment corresponds to 1 while the control group is 0. ATET denotes the weighted average treatment effect on the treated. ATET estimates are adjusted for sector/panel effects and time effects and controlled for Covid-19 global lockdown. The sample period is 2019q1 to 2022q3.

Source: Author estimations.

# **Accounting for Data Seasonality and Endogenous Investment Policy Effect**

The FDI investment policy is an endogenous intervention, which means that FDI inflows are likely to be influenced by other industry growth, among other factors. Furthermore, the FDI data exhibit some seasonality. While our model for the main results largely addresses these, we further adjusted for the suspected effect by including GDP and nominal exchange rate variables as covariates in our main estimation. Controlling for GDP and exchange rate, we found that our results remain consistent (Table 8). There is an increase in the fully liberalized sectors in realized FDI, realized DDI, and planned FDI.

22
as the top manager, compared to the average of 15 percent in Sub-Saharan Africa. Female participation in firm ownership in Sudan is 8 percent, which is substantially lower than the Sub-Saharan Africa average of 36 percent. Access to finance is cited as the biggest obstacle to firm growth for 10 percent of femaleled firms compared to 6 percent of male-led firms.<sup>22</sup> A study of 142 female business owners in Sudan, who represent a diversity of geographic locations, industries, and sizes, confirms that one of the greater obstacles to starting or expanding their businesses was their inability to rely on banks for credit (Musa 2012). Many of the businesswomen were asked to provide guarantees in the form of real estate or other assets which they did not have, and the cost of loans was often greater than 10 percent of the amount loaned. As mentioned earlier, having a land title might help one access credit from a bank, as the land title could serve as collateral, but women face difficulties obtaining land titles. Some of the businesswomen also reported that the banks were reluctant to lend to those with whom they did not have personal connections, or _wasta_ , which was discussed in the preceding sections of this report. Consequently, 66 percent of the businesswomen used their own savings to start their businesses and 17 percent borrowed from family or friends, while only 17 percent sought financing from a bank. Similarly, 63 percent used their own savings or earnings for working capital, and only 37 percent sought financing from a bank for such purposes. It is noteworthy that the businesses these women started were impressive on multiple fronts, not least of which was the fact that 84 percent reported positive net profit margins (Musa 2012).

Access to finance is not the only hurdle that women encounter when starting and growing a business. Campos et al. (2019) identify a number of constraints that female entrepreneurs in Sub-Saharan Africa face in the realm of endowments as well as at the household level. These include