rafmacalaba/data-use-mentions-extended huggingface dataset:
elaboration of the other surveys discussed, but nevertheless of considerable value. (For instance it revealed a sharp decline in the number of bank account holders (from18 to 6 per cent of the population) in the nine years since the previous survey. Research exploring the correlation between banking and other household characteristics would be possible, though (as is all too often the case), this does not seem to have been carried out so far.

# _Stand-alone financial access surveys_

Recently, increased interest in financial access has prompted a number of stand-alone financial access surveys. Evidently, such surveys can probe more deeply questions such as “why do you have a deposit account?”. On the other hand, as mentioned, cost may make them thinner in terms of control variables. We mention three important recent exercises.

A World Bank initiative to improve information on household access to finance in selected Latin American countries is exemplified by work on _Brazil_ (Kumar, 2004)<sup>16</sup> surveyed about 1250 urban households asking similar questions on deposit and credit as in the general survey examples given above from the LSMS, with perhaps more detail, for example on the motivation for household choices and the reasons for being refused credit (though only 9 respondents answered this question!). It also extended into the different purposes for which different retail payments media were used by households. Quite a few control variables were obtained by asking questions about such matters as education, labor force status, housing and ownership of key durables. It proves important to include country-specific institutional detail in some of the questions. For example, more of the respondents reported that they used as their main financial institution not the banks, but the lottery outlets of the Caixa Economia Federal, which provides limited banking services. This is a point that might have been missed if a standard template was imposed, or in the case of if a limited set of financial questions being added to a general household survey.

Econometric analysis of the survey data threw light
among the phone owners applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 29.3%, 31.4%, 32.0%, and 31.9%, respectively. While the last three poverty rates with adjusted weights are higher than the poverty rate of the phone owner sample

> 7 In the COVID-19 HFPS for Rwanda, we prepare models for urban and rural areas separately; for Saint Lucia and Uganda (refugees), we prepare only one national SWIFT model.

> 8 This analysis is added based on suggestions from peer reviewers of the World Bank’s quality enhancement review.

12
42 items were foodstuffs for every quintile group. Expenditures on all food items were deflated using a Laspeyres index provided in the survey data set.

The survey asked a wide range of questions concerning fuel use, allowing an unusually rich picture of LPG use:

1. What is the main primary type of appliance used for cooking? The options include a gas cooker.

2. What are the household’s main and secondary sources of cooking fuel? Collected firewood was the most commonly used cooking fuel, followed by kerosene and charcoal. The secondary choice of fuel for cooking was not identified by two thirds of households, but kerosene and charcoal were the most frequently cited alternatives (Figure A2.2).

3. In the last year, has your household used LPG?

4. During the last month, how many units of LPG did you use? Units are defined by a range of specified cylinder sizes. This question elicited an integer for every response, suggesting that the respondents might have cited the number of cylinders the household had in operation (or possibly owned) during the recall period rather than an estimate of the actual amount of LPG consumed. Table A2.9 summarizes the data on the distribution of LPG cylinder use by size in kg and quintile group. LPG use was almost entirely confined to the top quintile. The spread of cylinder sizes used was relatively even and suggests that households could vary their choice of cylinder to suit their convenience and budget. Households reporting the cylinder size used also reported the prices paid. The unit price was highest for the smallest cylinder size, as expected, but for other sizes there was no trend with relation to size.

5. What was the cost of those units that you used during the last month?

6. During the last month how much (expenditure and quantity) was purchased of LPG?

Figure A2.2: Main sources of energy for cooking across all households in Kenya <!-- Start of picture text --> Collected firewood 58<
Mabsout, Ramzi and Irene Van Staveren. 2010. “Disentangling Bargaining Power from Individual and Household Level to Institutions: Evidence on Women's Position in Ethiopia.” _World Development_ 38 (5): 783-96.

Maluccio, John and Rafael Flores. 2005. Impact evaluation of a conditional cash transfer program : the Nicaraguan Red de Protección Social. Research Report 141. Washington, DC: International Food Policy Research Insititute.

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Qian, Nancy. 2008.” Missing Women and the Price of Tea in China: The Effect of Sex-Specific Earnings on Sex Imbalance,” _The Quarterly Journal of Economics_ 123 (3): 1251-85.

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Quisumbing, Agnes R. 1994. “Intergenerational transfers in Philippine rice villages: Gender differences in
. The regression analysis includes public and publicly guaranteed external debt owed to official creditors (bilateral and multilateral lenders), and to private creditors (bonds, commercial banks, and other private creditors). The data is collected from the IMF’s World Economic Outlook, and the World Bank’s International Debt Statistics.

_Policy Response._ To capture the policy responses to the pandemic, we use the Oxford COVID-19 government response tracker (OxCGRT) and the different components in this index. The overall government response index summarizes information on 17 indicators of government responses (8 indicators on containment and closure policies, 4 indicators on economic policies and 5 indicators on health system policies). We further look at two sub-indices: (a) a containment and health measure, which includes lockdown restrictions and closures), and (b) an economic support index, which captures measures such as income support and debt relief. Finally, we use the OxCGRT stringency index that captures the strictness of lockdown measures affecting people’s behavior — including school closures, workplace closures, and travel bans, among others. The data is obtained from Hale et al. (2020).<sup>19</sup>

> 19 Note that these indexes record the number and strictness of government policies rather than their effectiveness.

11
# **Table A.1.3:  Age of Child and Cognitive Burdens for Recall Survey data**

||**Farm**<br>**activities**|**Worked**<br>**Hours**|
|---|---|---|
||(1)|(2)|
|Self-reported (SR)|0.010|-1.573|
||(0.083)|(4.940)|
|Age|0.069***|3.946***|
||(0.004)|(0.283)|
|Girls|-0.046|4.732|
||(0.063)|(3.645)|
|SR*Girls|0.047|4.796|
||(0.108)|(6.237)|
|SR*Age|-0.000|0.339|
||(0.007)|(0.486)|
|Girls*Age|-0.013**|-1.401***|
||(0.006)|(0.359)|
|SR*Girls*Age|0.002|-0.457|
||(0.010)|(0.617)|
|Observations|5142|5409|

**Source:** Authors’ analysis based on agricultural household surveys conducted in 2015 and 2016. **Notes:** Robust standard errors clustered at the household level in parenthesis. Control covariates are described in Table 3. Proxy respondents include head of households and spouses. *p<0.1, **p<0.05, ***p<0.01

29
83

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As Table 8 shows, simple household FE results are not far off from alternative estimation methods that control for time-varying heterogeneity.<sup>20</sup> In some cases, the FE results are strengthened somewhat while in others they are marginally weakened. In any case, the direction of the change in outcomes remains the same, although the coefficients are slightly changed. For example, according to the simple FE method, men’s participation increases household net worth by 18.8 percent, compared with 13.8 percent according to propensity-score weighted FE, where the coefficient is adjusted downward. But this is not the case for many outcomes. For example, according to the simple FE method, women’s participation increases net worth by 5.3 percent, compared to 8.9 percent according to the weighted FE model. This is a case of a slightly improved coefficient of participation. We therefore conclude that, even after controlling for possible bias due to time-varying heterogeneity, microcredit borrowing matters and matters more for women than for men, thereby reducing poverty and increasing household net worth without making borrower households over-indebted relative to assets in the process.

# **7. Conclusions**

This paper has examined a long panel survey in Bangladesh over a 20-year period beginning in 1991/92. The panel data used in this analysis contains rich household- and individual-level information from a panel survey of more than 1,500 households first surveyed in 1991/92, again in 1998/99, and lastly in 2010/11. The data set is unique in terms of its content and coverage for investigating the long-term consequences of microcredit expansion in rural Bangladesh. Earlier analysis of the first two survey rounds demonstrated a positive attribution for microfinance in poverty reduction. The addition of a third survey round provided a unique data set to validate the

> 20 The basic regression results of alternative models are presented in the Annex (Tables A1 and A2).

22
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|
||(0.019)|(0.011)|(0.006)|(0.006)|
|Observations|22,567|40,687|24,913|71,149|

Source: Calculations based on Bangladesh labor force surveys. Note: Standard errors are in parentheses. Statistically significant coefficients at the 10,5, and 1 percent level are indicated by one, two, and three asterisks respectively.

We present results from our Mincerian wage equations in Table 5 and Table 6. We observe that in 2005, on average, women in Bangladesh earned 60 percent less, on average, than males. As expected, substantial increases in demand for Bangladesh exports in the female-intensive garments sector significantly reduced this wage differential throughout the following years: the female-male wage gap decreased to -12 percent in 2010 and -11 percent in 2013. Despite a reverse in this trend in the next years, when exports fell, the female-male wage gap still remained substantially below 2005 levels. Similarly, high-skilled wage premiums relative to low-skilled workers decreased during the same time, from 70 percent in 2005 to 52 percent in 2016 (see Table 6).

**Table 5. Mincerian Wage Premium Estimates, by each year**

|||Logarithmo<br>|f realwages<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Only2005|Only2010|Only2013|Only2016|
|Female|-0.56***|-0.09***|-0.06***|-0.15***|
||(0.01)|(0.01)|(0.01)|(0.00)|
|Textile and Apparel|0.57|0.15|0.16*
larger gains in purchases of durable goods than households with overseas migrants. The null of coefficient equality for the durable goods index is weakly rejected with a _p-value_ of 0.21.

Table 5 displays the results of several identification tests derived from the 2SLS estimation. Focusing on column 1, which shows the validity of the instruments used for the number of international and domestic migrants between survey rounds, one sees that the null hypothesis that coefficients in the first-stage regressions are jointly zero is strongly rejected in each case. Underidentification tests reveal that there are a sufficient number of instruments, and moreover, that weak identification is not an issue. The Hansen’s J-test fails to reject the null hypothesis that overidentification restrictions are valid for each case. This confirms that the instruments I used can be validly excluded from the welfare change regressions. The hypothesis that one or more of the instruments is redundant is clearly rejected in each case. Finally, the null hypothesis that entry into migration is exogenous is firmly rejected in all cases. Overall, the identification tests reported in Table 5 indicate that the instrumental variable estimation that I conducted was needed, given that the choice of migration is endogenous, and that the instruments that I selected for the study were appropriate.

# _C. Components of the Asset Index_

Drilling down further, I show the impact of migration on the individual components of the housing quality (Table 6) and durable goods indices (Table 7). Here the impression is slightly more nuanced. I focus on results from the 2SLS estimation (Panel B), as the endogeneity of migration is apparent from the identification tests reported in Table 5. The results suggest that households whose members left to work overseas after 1998 experienced qualitatively larger asset gains—in terms of better housing and purchases of durable goods—than households with domestic migrants.

All households with one or more members migrating away from Kerala after 1998, both domestically and internationally, displayed upward mobility in terms of housing type (Table 6, Panel B, columns 2 to
2 is important for policy purposes. It is not uncommon in developing countries to implement redistribution schemes from large land owners to the landless. The formulae derived here yield a way to analyze the impact of (marginal) targeted transfers by group on the overall Gini.

The third contribution of the paper is substantive. Little research has been done on income and consumption inequality in Bangladesh'. Osmani (1982) presented empirical work based on surveys conducted between 1963-64 and 1973-74. The contributions of Khan (1986), Rahman and Haque (1988), and Rahman (1988) were based on surveys up to the early 1980's. Rahman and Huda (1992) considered inequality between occupational groups using the 1983-84 Household Expenditure Survey (HES) of the Bangladesh Bureau of Statistics (hereafter BBS). The BBS (1995) itself does report more recent estimates of inequality, but only for the urban and rural sectors. Moreover, these measures are over-estimated because the BBS does not adequately take into account price differentials between the two sectors and between areas within each sector (Wodon, 1997). Using group data from the 1991-92 HES and urban/rural price deflators, Ravallion and Sen (1996) found that the urban and rural Ginis for per capita consumption in 1991-92 were equal to 0.319 and 0.255. But they could not investigate in any detail the impact of household characteristics such as education, land ownership, and occupation on inequality in the absence of household level data. The unit level data of the 1991-92 HES survey have been made available for this study, which makes it possible in this paper to investigate the determinants of inequality in much more detail using a nationally representative sample.

The structure of the paper is as follows. The first section presents YL's unidimensional group decomposition of the Gini. The second section presents a multidimensional extension to the decomposition. The third section derives formulae for the impact on the overall Gini of
infrastructure disruptions, this analysis combines firm-level microdata and natural hazard exposure data. Enterprise Survey observations are used for 13 major African cities. Flood hazard data is drawn from the highest-quality flood map available in each city. Using the nonrandomized ES locations, each firm’s exposure is extracted using the local flood map. For the analysis, the flood exposure is then reclassified into the binary variable Flood-threatened that takes the value 1 if a firm is exposed to significant flood risk and 0 otherwise.

At the firm-level, there is no link between firms’ flood exposure and experience of infrastructure disruptions. Table 3 displays the result from regressions on power outages, water disruptions, and transport problems in 13 African cities. The Flood-threatened dummy variable has no significant effect on the three types of disruption. The absence of a visible relationship between natural hazard exposure and infrastructure disruptions can be explained by two factors: other stressors and network effects.

Infrastructure disruptions are not only caused by natural hazards. Instead, a wide-range of other factors inhibit the functioning of infrastructure systems. Systems in developing countries are usually less robust and exhibit a wider range of vulnerabilities. Thus, the systems in the African cities analyzed here might be under greater relative pressure from other stressors that hides the influence of natural hazards (Rentschler et al., 2019).

Furthermore, infrastructure disruptions caused by natural hazards are likely to affect the whole system as infrastructure systems are large networks. The effects of a flood destroying power distribution infrastructure will not be limited to those firms located in the flood zone but will be felt by all firms in a city. Consequently, firm location is an insufficient proxy to pick up the true influence of natural disasters on infrastructure.

10
|8.96|14.29|<br>12.40|11.41|14.97|<br>6.26|0.33|
|Monthly returns, full sample|0.54|0.47|3.74|0.65|0.58|3.66|<br>0.35|9.33|

_Note:_ Table 3 shows average ex-post investor returns in our total sample of 91 countries and 200 years, as well as for different subsamples. The country composition is changing over time (see Appendix B1). All returns are yearly averages, except for the last line, which shows a monthly frequency. Returns and standard deviations shown are based on a global portfolio that includes all outstanding foreign-currency sovereign bonds at each point in time. Excess returns and Sharpe ratios are computed using US/UK bonds as benchmark.

In the full sample from 1815 to 2016, foreign-currency bonds show an annual ex-post real return of 6.85%, including spells of turmoil due to defaults, wars, and revolutions. The average is slightly higher (7%)

> 39 As we explain in Section 3.1. and in Appendix B1, the 1970s and 1980s were a period dominated by syndicated bank lending to sovereigns. Barely any sovereign bonds were issued abroad. As a result, the number of countries with actively traded bonds dropped to fewer than 10 in the 1980s, making the global portfolio unrepresentative.

23
|
|Wholesale|78.4|78.0|37.8|69.5|57.1|77.8|66.4|
|Retail|59.8|54.4|47.6|57.9|70.4|87.3|62.9|
|Hotel and restaurants|71.2|40.7|34.3|67.4|54.9|91.9|60.1|
|Transport Section|97.7|62.4|52.8|61.9|69.2|100.0|74.0|
|IT|91.1|92.2|-|90.9|100.0|100.0|94.9|

Source: Author’s own elaboration from enterprise surveys _Larger, younger and medium-aged, international traders and foreign owned firms are more likely to use computers._ As Table 4 suggests, larger and medium sized firms are more likely to use computers. Medium aged firms are also more likely to use computers on average, although in Kenya young firms show high computer use and in Tanzania young firms show little computer use. Also, as expected more internationalized firms, via trading or foreign ownership, have larger prevalence of computer use. Firms that are only importers show a higher percentage of computer use than only exporters (see Table 4); although two-way traders are the firms with more computer use in most countries. Overall computer use appears to be quite high among all kinds of Kenyan firms, while computer use is low among Tanzania and DRC firms.

14