rafmacalaba/datause-encoder-data huggingface dataset:
The change in world demand coincided with a significant change in the composition of exports. Jute and jute goods dominated export earnings in the 1970s and 1980s but gave way to ready-made garments (RMG). Jute and jute-goods dropped from 68.5 percent of total export earnings in 1980– 81 to less than 5 percent by 1999–2000. The share of export earnings from RMG products over the same period rose from 0.4 percent to more than 75 percent. Nearly 90 percent of the total export growth of Bangladesh is due to apparel (Figure 6).

**<mark>Figure 6. Textiles and apparel leading South Asian export growth</mark>**

_(Industry-wise contribution to exports growth in South Asia, 2000–16 (%)_

|**Bangladesh**|**I**|**ndia**|
|---|---|---|
|92.10%<br>Textiles and apparel|Chemicals|29.94%|
|2.08%<br>Fabricated metals|Fabricated metals|21.88%|
|1.77%<br>Chemicals|Other manuf. Industries|14.64%|
|1.56%<br>Food, beverages and tobacco|Textiles and apparel|9.96%|
|0.76%<br>Agriculture and mining|Basic metal industries|7.95%|
|0.46%<br>Fishing, forestry and logging|Food, beverages and tobacco|6.68%|
|0.38%<br>Basic metal industries|Agriculture and mining|4.39%|
|0.31%<br>Other manuf. Industries|Fishing, forestry and logging|2.01%|
|0.23%<br>Wood|Non-metallic minerals|1.34%|
|0.20%<br>Paper|Wood|0.64%|
|0.16%<br>Non-metallic minerals|Paper|0.58%|
|**Pakistan**|**Sri La**|**nka**|
|56.65%<br>Textiles and apparel|Textiles and apparel|33.81%|
|18.09%<br>Food, beverages and tobacco|Agriculture and mining|20.89%|
|6.86%<br>Agriculture and mining|Chemicals|18.05%|
|6.61%<br>Chemicals|Food, beverages and tobacco|12.80%|
|4.46%<br>Fabricated metals|Fabricated metals|6.88%|
|2.92%<br>Non-metallic minerals|Paper|2.24%|
|1.87%<br>Fishing, forestry and logging|Fishing, forestry and logging|1.59%|
|1.30%<br>Basic metal industries|Other manuf. Industries|1.28%|
|0.93%<br>Paper|Wood|0.99%|
|0.65%<br>Wood|Non-metallic minerals|0.77%|
|-0.35%<br>Other manuf. Industries|Basic metal industries|0.69%|

Source: Artuc et al., (2019)

# Labor Market Data and Characteristics

Our main source of labor market data is the labor force survey (LFS) provided by Bangladesh Bureau of Statistics. Bangladeshi labor force surveys are cross-sectional containing detailed household and individual information about key labor market characteristics, household characteristics, and individual demographic characteristics with units of analysis as individuals and households. For the purpose of this paper, we use the LFS for 2005 (10th round), 2010 (11th 13
Lokshin and Ravaillon (2000) examine the impact of the **1998 crisis in Russia** on household income and consumption using household panel data from 1996 and 1998.<sup>9</sup> This study also documents a strong labor market impact on households: average household income fell by 20% in real terms and the share of households below the poverty line rose from 22% to 33%. Earnings from the formal labor market declined strongly so that the share of salary earnings in total income fell from 41% to 36%. The share of household income originating from home production (16% to 20%) and government benefits (27% to 31%) rose during the crisis. Household expenditure fell stronger for households in urban areas than in rural areas. Lokshin and Ravaillon (2000) document that the response of the Russian social safety network – by targeting benefits more to the poor - substantially reduced the number of households which fell below the poverty line during the crisis. They also suggest that households also experienced substantial wealth losses due to the collapse of the banking system. However, they do not document which households were most hit by this wealth effect.

The above studies suggest that the main impact of banking crises on households runs through the labor market: Households face worse employment prospects and lower real wages, especially when the crisis coincides with a currency crisis and spiraling inflation. There is only limited evidence (from Korea) of a credit crunch on households in the crisis, reducing their ability to smooth income. The product market channel also seems to affect households when the crisis coincides with a currency crisis. However, as suggested in the previous section, rural households seem less affected by changes in relative prices than urban households. Finally, there seems to be no direct evidence that a cutback in public services or safety nets amplify the impact of banking crises on the poor. If anything, the evidence from Russia suggests that the public sector partly cushioned the impact of the 1998 financial crisis.

# **3 The Impact of Recent Banking Crises in the ECA Region**

In this section we employ household-level data from the 2<sup>nd</sup> wave of the EBRD / World Bank _Life in Transition Survey_ (LiTS) carried out in 2010 to examine the impact of the recent wave of banking crises in the ECA region.<sup>10</sup> As documented above, among the 25 countries which experienced a banking crisis during 2008-2011 22 countries are located in the ECA region. The 2010 LiTS survey covers eleven of these countries, including five high-income countries from Western Europe and six

> 9 See also Lokshin & Yemtsov (2004)

> 10 See Christelis et al. (2011) as well as Hurd and Rohwedder (2010) for an analysis of the impact of the crisis on US households.

12
<!-- Start of picture text --> Figure 7 Payments of Taxes and Benefits of Public Spending by Figure 8 Payments of Taxes and Benefits of Public Spending<br>Gender Earner category (Million JOD) by Household by Gender Earner Categories (Percentage of<br>Market Income)<br>2,000 100<br>1,500 80<br>1,000 60<br>500 40<br>0 20<br>-500 0<br>-1,000 -20<br>Female Male earner No earner No labour Female earner Male earner No earner No labour<br>earner income majority majority income<br>Millions<br>Fiscal Revenue / Expenditure (JD)<br>Revenue / Expenditure (Percentage of Market Income)<br><!-- End of picture text --> <!-- Start of picture text --> Female<br>earner<br>majority<br>(>60%<br>income)<br><!-- End of picture text --> <!-- Start of picture text --> Male earner<br>majority<br>(>60%<br>income)<br><!-- End of picture text --> <!-- Start of picture text --> No earner<br>majority<br><!-- End of picture text --> <!-- Start of picture text --> No labour<br>income<br><!-- End of picture text --> <!-- Start of picture text --> majority<br>(>60%<br>income)<br><!-- End of picture text --> <!-- Start of picture text --> majority<br>(>60%<br>income)<br><!-- End of picture text --> <!-- Start of picture text --> majority income<br><!-- End of picture text --> Source: 2017-18 Household Expenditure and Income Survey (H ~~EI~~ S) and World Bank calculations.

# Poverty and Inequality Impacts

Official poverty is measured using the household per capita consumption aggregate and results in a poverty rate for Jordanians of 15.7 percent.<sup>17</sup> Overall, poverty falls slightly from market income to disposable income (when accounting for the impact of direct taxes and transfers) and then plateaus when moving to consumable income (accounting for indirect taxes and subsidies). For most care, fiscal and gender earner majority groups this pattern is repeated (Figure 9 and Figure 10). There are three groups for which poverty continues to fall from disposable to consumable income: Other Care Categories, Single Adult with Dependents and No Labour Income. For the second group, the fiscal system more than halves poverty. There is also two group for which the fiscal system increases poverty, albeit by a small amount: Pensioners and Male Earner Majority. For the most part, we do not observe that groups that started with higher poverty rates experience a substantially higher poverty reduction through the fiscal system. Rather, the rank across the different groups is preserved through the system (e.g. which is the group with the highest, second highest, third highest, etc. poverty rate), with the one exception of the ‘Single Adult with Dependents’ care group.
20 Table **2: Ten** Percent **Productivity Increase in Production of** Manufactures **for Export a**

||EA|SA|LAC|SSA|MENA|OE|EER|
|---|---|---|---|---|---|---|---|
|Region Alone||||||||
|Manuf Exports|16.6|16.5|16.6|16.7|16.6|16.8|16.0|
|Manuf Export Price|-6.0|_-5.5_|-5.6|-5.6|_-5.6_|-5.7|-7.0|
|Manuf Import Price|-0.4|-0.1|-0.6|-0.3|-0.2|-0.1|-4.3|
|Nontraded Price|-0.1|0.0|0.0|0.0|0.0|0.1|-0.9|
|Welfare Increase|0.65|0.17|0.28|0.28|0.08|0.52|0.48|
|Output Increase|1.4|0.34|0.47|0.53|0.14|1.2|_0.58_|
|Welfare/Output Ratio|_0.5_|_0.5_|0.6|_0.5_|0.6|0.4|0.8|
|All Developing Regions||||||||
|Manuf Exports|16.4|_15.5_|16.0|16.2|15.8|16.4|15.6|
|Manuf Export Price|-6.4|-6.2|-6.1|-6.3|-6.3|-6.3|-7.1|
|Manuf Import Price|-1.2|-2.0|-1.7|-1.6|-2.4|-1.2|-4.7|
|Nontraded Price|-0.3|-0.2|0.0|-0.2|-0.1|-0.3|-0.6|
|Welfare Increase|0.79|0.43|0.49|0.56|0.39|0.77|0.6|
|Output Increase|1.35|0.34|0.47|0.53|0.14|1.17|0.58|
|Welfare/Output Ratio|0.6|1.3|1.0|1.1|2.8|0.7|1.0|

a Please see Table **1** for definitions of regions and variables.
# **Table A6: Attrition test – Balanced sample**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health scores|-0.048***<br>(0.010)|-0.200***<br>(0.065)|
|_First stage of 2SLS (dependent varia_|_ble is standardized m_|_ental health scores)_|
|Trauma experience*Year after<br>arrival|-0.044***<br>(0.011)|-0.063***<br>(0.020)|
|Kleibergen-Paap F stat.|17.627|19.635|
|AR 95-CIs|[-0.094, -0.019]|[-0.469, -0.124]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|2,497|2,497|
|Number of individuals|1,003|1,003|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

38
Product and Factor Market Distortions: The Case of the Manufacturing Sector in Morocco Jean-Pierre Chauffour and Jose L. Diaz-Sanchez<sup>_∗_</sup> The World Bank _JEL Code_ : O11, 047, 055, L60 _Keywords_ : TFP, Market Distortions, Resource Misallocation, Productivity, Manufacturing.

> _∗_ Respectively Lead Economist for the Maghreb region and Economist, World Bank. The views expressed in this article are those of the authors and do not necessarily represent those of the World Bank. We would like to thank Abdessamad Saidi, Anta Ndoye, Fernando Parro, Addisu Lashitew, Ragbi Aziz, and participants of a research seminar of the Bank Al-Maghrib for useful comments and discussions.
Table S3: Proxy: No Country Time Trend.

||ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|ln(GDP)|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|ln(NO2)|0.348***|||||0.307***|0.165***|0.164***|0.080|
||(0.061)|||||(0.060)|(0.055)|(0.060)|(0.053)|
|ln(Lights)||0.109***||||0.100***|0.053***|0.049**|0.055**|
|||(0.027)||||(0.027)|(0.020)|(0.021)|(0.023)|
|ln(CO2)|||0.316***||||0.281***|0.246***|0.221***|
||||(0.035)||||(0.036)|(0.035)|(0.037)|
|ln(Electricity)||||0.207***||||0.052|0.035|
|||||(0.055)||||(0.046)|(0.049)|
|ln(Survey)|||||0.468***<br>(0.083)||||0.345***<br>(0.059)|
|Observations|2,752|2,752|2,472|2,548|2,303|2,752|2,472|2,371|2,071|
|Countries|178|178|174|170|154|178|174|167|146|
|Years|05-20|05-20|05-19|05-19|05-19|05-20|05-20|05-19|05-19|
|R-Squared|0.049|0.068|0.313|0.159|0.256|0.105|0.341|0.344|0.490|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes._ All specifications include country and year fixed effects, but no country-specific time trend. When using nights lights, fixed effects for either OLS (pre-2014) or VIIRS (2014-2020) included. Robust standard errors, clustered by country, in parentheses. R-Squared is adjusted and within all fixed effects.

all specification, whereas NO2 eventually drops off. This is likely due to the fact that GDP and lights trend increase across most countries over time, whereas NO2 is trend decreasing in many countries during the sample (Table 1, Panel B). Thus, lights are a valuable indicator of economic growth, while NO2 is perhaps preferable when considering cyclical fluctuations.

Table S4 does the same without country time trends by-grade, comparable with the withtime trends Table 3 in the text. Column 1 is equivalent to Column 6 in Table S3. From a trend growth perspective, NO2 is primarily useful in higher-graded countries, whereas lights are primarily useful for lower-graded countries. The basic difference when including time trends in Table 3 is that NO2 also has explanatory power for cyclical fluctuations in nations with lower-graded statistical systems.

Table S5 repeats the analysis pertaining to data manipulation summarized in Table 4, now considering the effects of omitting country-specific time trends. Column 1 of Table S5 replicates Column 3 of Table 4, which showed no evidence of data manipulation with respect to cyclical fluctuations. This is indicated by the counterintuitive negative interaction between lights and the FiW index of autocracy. However, when in Column 2 we omit the country time trend, the positive interaction reappears. We also find that there is a difference between crisis and non-crisis years in this case. Thus, the issues we uncover with respect to lights’ unreliability concerns off-trend fluctuations, specifically.

S12
# **Table 2: Descriptive statistics**

|**Variable Name**|**#Obs.**|**Mean**|**Median**|**Standard Deviation**|**Min.**|**Max.**|
|---|---|---|---|---|---|---|
|Local stock market returns in US$ (%)|86|-1.57|-1.36|8.85|-25.93|26.18|
|**Trade Channel**|||||||
|Exports to US/GDP (%)|75|4.52|2.46|4.79|0.05|20.20|
|Exports/GDP (%)|77|46.95|44.88|22.03|14.37|116.74|
|(Export+Import)/ GDP (%)|76|73.79|68.45|34.66|21.8|187|
|Fuel Exports/Total Exports (%)|78|17.21|6.00|24.95|0.053|92.56|
|Agricultural Exports/Total Exports(%)|75|1.68|1.17|1.59|0.003|6.40|
|**Financial Channel**|||||||
|US Holdings of Foreign Equity/Local Market Capitalization (%)|74|8.52|6.95|8.15|0|33.96|
|Foreign Holdings of US Equity/Local Market Capitalization (%)|72|5.18|1.62|7.78|0|39.11|
|Chin-Ito Index of Financial Openness|80|0.70|0.88|0.31|0.16|1|
|FDI Inflows/GDP (%)|77|6.02|3.88|6.31|0.07|29.29|
|Portfolio Inflows/GDP (%)|76|3.60|2.17|4.19|0|19.84|
|Other Inflows/GDP (%)|75|7.95|4.40|8.70|0.11|35.89|
|Stock Market Turnover (%)|82|53.48|40.33|49.19|0.35|172.26|
|Stock Market Capitalization/GDP(%)|79|71.27|49.79|59.92|2.50|285.57|
|**Banking Sector Vulnerabilities**|||||||
|Capital Adequacy Ratio (%)|83|9.66|9.16|4.81|1.12|25.53|
|Liquidity Ratio (%)|82|4.25|2.94|3.99|.27|16.26|
|Private Credit to GDP Ratio (%)|76|71.39|62.48|46.87|11.36|183.84|
|BankingActivityComposite Index|72|10.11|10|2.46|4|16|
|**Corporate Sector Vulnerabilities**|||||||
|Total Debt to Assets Ratio (%)|61|25.47|25.56|8.64|2.22|45.68|
|Short-term Debt to Assets Ratio (%)|61|10.58|9.09|6.26|0.43|24.79|
|Return on Assets (%)|61|3.54|3.08|1.87|0.97|9.62|
|Cash to Total Assets Ratio (%)|60|4.30|4.00|2.34|0.81|12.53|
|Firms with Interest Coverage<1(%)|61|18.72|18.89|11.00|0|47.51|
|**Macro and Sectoral Vulnerabilities**|||||||
|Budget Surplus or Deficit/GDP (%)|72|-0.11|-0.29|3.99|-10.78|9.86|
|Current Account Balance/GDP (%)|76|0.009|-0.48|9.63|-25.45|27.78|
|<br>Financial Account Balance/GDP (%)|74|2.16|2.38|9.87|-27.53|30.78|
|Total Reserves/GDP (%)|76|18.30|15.78|14.53|0.38|72.62|
|Mortgage Debt to GDP (%)|47|32.42|23.50|29.76|0.50|100.8|
|<br>Mortgage Debt to GDP Growth Rate 2003-2006(%)|39|121.95|29.99|207.41|-9.36|875.00|
|Avg. Annual Mortgage to GDP Growth Rate 2003-2006(%)|50|20.73|7.62|30.55|-4.45|134.34|
|Change in Real Estate Price 2003-2006(%)|33|16.08|12.09|22.58|-11.76|87.69|

25
Rubin, D.B. 1974. “Estimating causal effects of treatments in randomized and nonrandomized studies.” _Journal of Educational Psychology_ 66: 688–701 Sathar, Z. A. and S. Kazi. 1997. Women's Autonomy, Livelihood and Fertility. A Study of Rural Punjab. Islamabad: Pakistan Institute of Development Studies.

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34
to 2005 in South America. Sustained poverty reduction in sample countries in North America has also been limited, except in 11-15.

There is a strong presumption in economics that growth and poverty reduction go hand in hand. To examine whether this relationship bears out in our data, Table 1 compares instances of sustained poverty reduction to instances of sustained growth in real GDP per capita, the latter of which is considered to occur when real GDP per capita does not contract at all during a period. To prevent higher frequency observations of GDP per capita from affecting our results, we look only at GDP per capita in the years in which the poverty headcount is also measured, and interpolate GDP per capita between years, as we have done for the headcount. What is apparent from Table 1 is that sustained GDP per capita growth does seem to be positively correlated with sustained poverty reduction, with 76 percent of instances of poverty reduction also having sustained GDP per capita growth, and only 24 percent not having it. More interestingly however, despite this positive relationship, there are 52 percent of the periods without sustained poverty reduction experience sustained real per capita GDP growth. Poverty reduction and GDP growth are positively related, but growth by itself does not always imply poverty reduction.

**Domestic market size** The second variable we construct is a measure of domestic market size, which depends on the income distribution. Consistent with the underlying conceptual framework, we identify “domestic market size” with the share of the population in the global middle class. We define the global middle class following Kharas (2010) and Kharas (2017), who proposes bounds at $11-110/day PPP in 2011 US dollars of consumption, on the basis that the lower bound is the average of the national poverty lines in Portugal and Italy, and the upper bound is twice the median income in Luxembourg. That is, to be in the global middle class, one cannot be poor in the poorest rich countries, and cannot be rich in the richest country. We apply these bounds and estimate the share of the middle class as the headcount ratio for the upper bound minus the headcount ratio for the lower bound.

This middle class share variable combines measures of both equality and average income. To measure equality, we use the average Gini coefficient for each country reported (Milanovic, 2013). For average income, we use data on real GDP per capita from the Penn World Tables 9.1 (Feenstra, Inklaar, and Timmer, 2015). A difference between our approach to measure the global middle class and that of Kharas (2010) is that he uses average household consumption for average income, whereas we use GDP per capita. GDP includes expenditure in the investment sector (i.e. construction, machinery and equipment), thus increasing average income and making our estimates of the 15
# **Methods**

## **Data**

We use data from High-Frequency Phone Surveys (HFPS) in five countries in Sub-Saharan Africa: Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda. The surveys were conducted by study countries’ national statistical organizations (NSOs), supported by the World Bank’s Living Standards Measurement Study (LSMS). Since May 2020, the LSMS-supported HFPS have collected cross-country comparable longitudinal data on a wide range of topics, focused on COVID-19 impacts on households and individuals.

Vaccine hesitancy related information was collected from in 2020, 2021, and recently in 2022, depending on the study country. This study uses four rounds of data for Malawi (Oct-Nov 2020, Apr 2021, Feb 2022, Jul-Aug 2022) and Uganda (Oct-Nov 2020, Feb 2021, Sep-Nov 2021, Aug-Sep 2022), three rounds in Burkina Faso (Dec 2020, May-Jun 2021, Apr-May 2022) and Nigeria (Oct 2020, Feb 2021, Dec 2021-Jan 2022), and two rounds of data for Ethiopia (Sep-Oct 2020, Feb 2021).

## **Sampling and sample representativeness**

The HFPS have national coverage and draw their samples from nationally representative samples of households interviewed in pre-pandemic face-to-face surveys of the LSMS-Integrated Survey on Agriculture (LSMS-ISA) series. In the settings at hand, mobile phone coverage is not universal, and the sample selection of phone surveys may not yield samples fully representative of the general population. Phone surveys also experience an extent (albeit limited) of non-response and attrition. The HFPS therefore use recalibrated sampling weights (based on propensity score and post-stratification methods), which mitigate sample selection biases. **[18–20]** In each household, one main respondent over the age of 15 was interviewed, selected to be knowledgeable of the household and its members. This purposive selection has been found to overrepresent certain population groups. **[20]** The sample sizes for each country are shown in Table A1.

## **Survey instrument and variables**

Survey instruments were harmonized across countries while allowing for a degree of contextualization where necessary. In the early rounds, before COVID-19 vaccines were available, vaccine acceptance questions were posed in the hypothetical, asking respondents if they would be willing to get vaccinated if an approved COVID-19 vaccine was available. When vaccines became more widely available, questions were changed to reflect this development, asking respondents if they were planning to be vaccinated. Whenever respondents said they were unsure or unwilling to be vaccinated, the survey enquired about the reasons for their hesitancy or unwillingness.

## **Estimation methods**

Reported estimates are population-weighted means with their 95% confidence intervals where applicable, computed using the recalibrated phone survey weights. To explore how individual and household characteristics correlate with the willingness to get vaccinated, we make use of a multivariate logit regression setup, again using recalibrated phone survey weights.

3