rafmacalaba/data-use-mentions-extended huggingface dataset:
2016).

The results suggest that maintaining macroeconomic stability as well as investing in human and physical capital would not only accelerate overall economic growth, but benefit more particularly the poorest segments of the population. This paper confirms the central role overall economic growth should play in any strategy to reduce poverty. Its results suggest, however, that in addition policy makers may have instruments to tweak the distribution of the benefits of faster economic growth in favor of the households at the bottom of the income distribution, ensuring that everyone can live up to their potential. This paper shows that there thus not need be a trade-off between inequality and growth. Economies can foster faster growth while also increasing inclusiveness.

The paper proceeds as follows: Section I provides some characteristics of Haiti; Section II discusses the literature on income drivers; Section III presents our approach, the data, and the

> 1 The World Bank has adopted the Twin Goals of ending extreme poverty (defined as a fall in the percentage of people living with less than $1.25 a day to no more than 3 percent globally by 2030) and promoting shared prosperity (defined as income growth of the bottom 40 percent of the population in every country).

> 2 Ravallion (2013).

> 3 See Singh and Weber (1997), for example, for a discussion on the influence of the composition of public spending on growth in Switzerland based on time-series going back to the 1950s.

-2-
- Hallward-Driemeier, M., & Rijkers, B. (2013). Do crises catalyze creative destruction? Firmlevel evidence from Indonesia. _Review of Economics and Statistics_ , _95_ (5), 1788–1810.

- Harasztosi, P., & Lindner, A. (2019). Who pays for the minimum wage? _American Economic Review_ , _109_ (8), 2693–2727.

- Harrigan, J., & Reshef, A. (2015). Skill-biased heterogeneous firms, trade liberalization and the skill premium. _Canadian Journal of Economics/Revue canadienne d’ ́economique_ , _48_ (3), 1024–1066.

- Harrison, A., Meyer, M., Wang, P., Zhao, L., & Zhao, M. (2019). _Can a tiger change its stripes? reform of chinese state-owned enterprises in the penumbra of the state_ (Working Paper No. 25475). National Bureau of Economic Research.

- Helpman, E., Itskhoki, O., Muendler, M.-A., & Redding, S. J. (2017). Trade and inequality: From theory to estimation. _The Review of Economic Studies_ , _84_ (1), 357–405.

- Hummels, D., Jørgensen, R., Munch, J., & Xiang, C. (2014). The wage effects of offshoring: Evidence from Danish matched worker-firm data. _American Economic Review_ , _104_ (6), 1597–1629.

- Hyman, B. G. (2018). Can displaced labor be retrained? Evidence from quasi-random assignment to trade adjustment assistance. In _Proceedings. annual conference on taxation and minutes of the annual meeting of the national tax association_ (Vol. 111, pp. 1–70).

- Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings losses of displaced workers. _The American Economic Review_ , 685
the payment system, making transactions more difficult and expensive. These mechanisms suggest that fragile banks hinder economic activity (the credit crunch hypothesis).

On the other hand, there are several channels through which exogenous adverse shocks to the economy might cause a decline in credit and economic activity even if the banking sector itself is relatively healthy. For instance, adverse shocks may trigger a fall in aggregate demand, leading firms to cut production and investment and, consequently, credit demand. Increased uncertainty may also cause firms to delay investment and borrowing decisions. Finally, adverse shocks might worsen agency problems and complicate lending relationships, for instance by reducing the net worth of borrowers. This, in turn, might cause banks to abandon high risk borrowers (flight to quality) or raise lending spreads. So output and bank credit may decelerate around banking crises even if there is no feedback effect from 14 bank distress to credit availability.

Existing studies of individual country experiences have found conflicting evidence on the relationship between bank distress and real activity. In a study of the so-called capital crunch in the United States in 1990, Bernanke _et al_ . (1991) argue that a shortage of bank capital had little to do with the recession. Domaç and Ferri (1999) reached the opposite conclusion for Malaysia and Korea during 1997–8. They found small and medium-sized firms to have suffered more than large firms during the crisis. Since these firms are usually more dependent on bank credit than large firms, this is evidence of a credit crunch. Data from a survey of Thai firms, on the other hand, suggest that poor demand rather than lack of credit caused the decline in production, although many firms complained about high interest rates (Dollar and Hallward-Driemeier, 2000). For Indonesia and Korea, Ghosh and Ghosh (1999) test an aggregate model of credit demand and supply and find evidence of a credit crunch, but only in the first few months of the crisis. Finally, using firm level data from Korea, Borensztein and Lee
**Table A7: Country-Specific Proportion of Sibling Correlations Explained Explained by Intergenerational Transmission Estimates (1990 Cohort)**

|**Country**|**Prop.**|**S.E.**|**N**|**Country**|**Prop.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.659|0.020|1751|Benin|0.809|0.031|1915|
|Indonesia|0.774|0.023|4540|Burkina Faso|0.727|0.095|241|
|Philippines|0.810|0.026|4301|Burundi|0.639|0.033|2484|
|Vietnam|n.a|n.a|n.a|Cameroon|0.871|0.032|1502|
|||||Chad|0.677|0.024|1058|
|Europe & Central Asia||||Congo, Rep.|0.728|0.061|291|
|Albania|0.762|0.080|1115|Cote d'Ivoire|0.732|0.078|154|
|Armenia|0.555|0.084|428|Ethiopia|0.679|0.023|1935|
|Kyrgyz Republic|0.501|0.159|304|Gabon|0.775|0.095|249|
|Tajikistan|0.587|0.053|1744|Ghana|0.796|0.066|577|
|Turkey|0.701|0.
financial health; and suppliers of differentiated products use trade credit to signal their willingness to invest in relationship-specific capital.

Trade credit can be especially relevant in countries/regions where the rule of law and the legal system are weak. Weak institutions would, in principle, prompt greater use of trade credit, which requires less formal contract enforcement institutions and mechanisms than bank loans. Informal institutions such as belonging to the same social networks (such as hometown connections between CEOs of borrowing firms and their suppliers) or personal knowledge about suppliers/customers have facilitated trade credit relationships, as observed in Vietnam, Eastern European countries, and China. Such countries have relied more heavily on trade credit than others, and the positive effects on firm growth tend to be stronger where industry demand for credit is greater. Furthermore, where the level of social trust is low, informal institutions such as hometown connections loom even larger in facilitating trade credit. Countries with stronger formal legal foundations—for example, the court systems in Eastern European countries in the 1990s were further along than that in transitional Vietnam—can rely on those institutions to facilitate greater provision of trade credit by suppliers (Johnson, McMillan and Woodruff 2002). Better formal institutions also enable customers to switch suppliers, which facilitates competition among suppliers and thus improves trade credit terms for borrowers. Finally, in economic systems where bank credit allocation is heavily tilted toward (inefficient) state-owned enterprises, the trade credit extended by profitable private firms could improve credit allocation in the spirit of the Coase Theorem (Coase 1960) that resources would tend to flow to usages where efficiency improvement is possible.

The patterns that emerge from evidence around the world suggest a sweet spot or ‘Goldilocks’ region where mid-size firms and those in countries at middling levels of development tend to rely relatively more heavily on trade credit as a source of finance that can help spur productivity.

An implication for further studies is that better designs of firm surveys or better combinations of complementary datasets could facilitate more direct tests of various theoretical predictions
- 1 For example, see Balk et al. (2009), who use satellite mapping techniques to document population growth in areas subject to climate change and natural disasters.

- 2 World Bank (2005) examines ten index insurance case studies in countries as different as India, Malawi, Nicaragua, and Ukraine.

- 3 Index insurance was first proposed by Halcrow (1948) and Dandekar (1977) in the context of area yield insurance, which has since been tried on a subsidized basis in Canada, India, Sweden, and the United States (Miranda 1991; Skees, Black, and Barnett 1997).

- 4 This accumulation of indigenous knowledge over thousands of years is reflected in literature, folk songs, and proverbs or sayings. For example, farmers use the color of the sky, the shape and color of the clouds, the direction of the winds, the appearance of certain insects or migratory birds, and the like to update the probability that the monsoon has arrived (Fein and Stephens, 1987).

- 5 Some adjustments are made to accumulated rainfall when constructing the rainfall index used to calculate payouts. If daily rainfall exceeds 60 millimeters, only 60 millimeters is counted towards the cumulative rainfall index. Also, rainfall less than 2 millimeters is ignored. These adjustments reflect that heavy rain may generate water runoff, resulting in a less than proportionate increase in soil moisture, while very light rain is likely to evaporate before it soaks into the soil.

- 6 Although we also have data on BASIX insurance sales in other states, we focus on Andhra Pradesh for this analysis to avoid confounding effects associated with the introduction of subsidized insurance by AIC under WBCIS in some other states. Policies sold in Andhra Pradesh are not subject to government subsidies.
# 1. **Introduction**

This paper undertakes a comparative analysis of poverty in three East European countries (Bulgaria, Hungary, Poland,) and three countries of the Former Soviet Union (FSU) (Estonia, Kyrgyz Republic, Russia). To that effect, we constructed a comparative data set, whereby household survey data from the six countries were carefully checked, cleaned and made comparable. The resulting data set has been dubbed HEIDE (Household Expenditure and Income Data for Transitional Economies) and its content and construction method are described in detail in Ackland et al (1997).

Although our analysis of the HEIDE data found elements in common, the most striking result is how different the post-Soviet experience with poverty and targeting is from the East European one. Overcoming the Soviet legacy has not been as easy as the generally positive East European prototypes would have suggested. Poverty correlates for the FSU are not as sharp nor as well-defined as in Eastern Europe, yet poverty levels are also higher in the FSU, presenting a larger challenge to governments as they try to reduce poverty and improve targeting.

We have set ourselves three tasks in this paper. First, we construct a profile of the incidence and depth of poverty in the six countries, using aggregate poverty indexes. The aim is to find out what the common elements are in the profile of poverty in Eastern Europe and the FSU, and which aspects of poverty are country-specific or bi-modal (e.g. the immediate Soviet legacy of the FSU vs. the more diluted Soviet legacy of Eastern

**1**
uncertainty. This aspect is crucial to understanding the political economy of reforms and why most governments in the region have put subsidy reforms at the top of their agenda in recent years.

Figure 2: Commodities and Oil Prices, January 1992–May 2016 <!-- Start of picture text --> 300 160<br>140<br>250<br>120<br>200<br>100<br>150 80<br>60<br>100<br>40<br>50<br>20<br>0 0<br>Commodity Food and Beverage Price Index ‐ Monthly Price (left scale)<br>Commodity Fuel (energy) Index ‐ Monthly Price (left scale)<br>Crude Oil (petroleum); Dated Brent ‐ Monthly Price (right scale)<br>Jan‐1992 Jan‐1993 Jan‐1994 Jan‐1995 Jan‐1996 Jan‐1997 Jan‐1998 Jan‐1999 Jan‐2000 Jan‐2001 Jan‐2002 Jan‐2003 Jan‐2004 Jan‐2005 Jan‐2006 Jan‐2007 Jan‐2008 Jan‐2009 Jan‐2010 Jan‐2011 Jan‐2012 Jan‐2013 Jan‐2014 Jan‐2015 Jan‐2016<br><!-- End of picture text --> _Sources:_ Elaborated from http://www.indexmundi.com/ and World Bank Commodities Prices database.

The next issue to consider is the regulated prices that MENA countries were confronted with when they were forced to introduce reforms. Table 2 shows the average regional prices for four energy products—gasoline, diesel, kerosene, and LPG—comparing the main regions of the world. It is striking how the MENA region distinguishes itself from the others by having, by far, the lowest average regional prices. For example, the price of gasoline, on average, was US$ 0.67, about half the price of other regions. The price of LPG was US$ 0
defense production to emerge from a severe recession and become one of the nation’s fastest growing, diverse and knowledge-based economic regions (Porter and Monitor Group 2001).<sup>29</sup> With a focus on research, the city now has

> 28 A somewhat different concept from quality of place, quality of life may refer to such important issues as quality public education, reliable health care, assured public safety, and a clean and attractive natural environment, as shown by results of a survey of high-tech communities in the U. S.  In addition, the availability of recreational opportunities is another top driver of quality of life (IntelliQuest 1999).

> 29 During the early 1990s, the region experienced a recession that was attributable to the basic restructuring of the local economy.  See SANDAG (2001).  But today, most agree that, San Diego ranks just behind San Francisco and Boston as a biotech center in the U.S. (Newscientistjobs.com 2003b).

18
as the treatment group in these calculations.

Table 3 illustrates numerically Implications 1-6, and Implication 10. Comparing the sample sizes in Panel A and B, we see Implication 1 and 3. When there is only one baseline, difference-indifferences requires larger sample sizes than post analysis when ρ<0.5, the same sample sizes for ρ=0.5, and lower sample sizes for ρ>0.5. The difference in sample size can be large. For example, with a single baseline and follow-up, when ρ=0.25, one would need a treatment group size of 2355 with difference-in-differences, compared to 1570 with post analysis. Adding a second pre-treatment survey reduces the sample size for difference-in-differences to 1766, 18
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- For the lower estimate, we introduce a decline in the LPFR based on the latest ILO‐ modelled estimates and projections that in turn draw on the most recent UN population estimates and regional GDP growth.<sup>19</sup> Other possible alternatives for this, though probably less satisfactory, are to: (i) assume the same decline in the participation rate as in the previous decade, but this would have likely been an unsustainable path; or (ii) use an earlier average growth, namely for 1991‐2000 (‐0.6%), which would be quite arbitrary.

- For the upper estimate, we use a higher I/Y of 23.5% since the IMF data presented in Section 2.1 indicates roughly a 2 ppt increase in average gross capital formation (from 23.1% over 2001‐2010 to 25.2% over 2011‐2016) and we assume the same for gross _fixed_ capital formation. We also use a higher long‐term average growth rate of 1.5% for human capital based on both CSL and BL data.

**<u>Table 2: No‐Conflict Simulation –</u> Upper and Lower Estimates for Projected Variables**

|**Variable**<br>**Note**|**Input value**|
|---|---|
|**Lower estimate onprojected variables(average, 201**<br> <br>|**1‐2030)**|
|Labor forceparticipation rate, growthg�<br>_(1)_|‐0.003|
|**Upper estimate onprojected variables(average, 2010**<br> <br>|**/11‐2030)**<br>|
|Investment‐to‐output ratioI/Y<br>_(2)_|0.235|
|Public investment‐to‐output ratioI
Evidence from panel data studies also indicates that the effect of inequality on growth might depend crucially on the level of the development and the time horizon of the growth spells (short term vs long term). Brueckner and Lederman (2018) find that income inequality may be beneficial for transitional growth in poor countries but becomes harmful for growth in economies with high average income, contradicting the results by Barro (2000). Regarding the time horizon, Halter, Oechslin, and Zweimüller (2014) find that higher inequality is beneficial for economic performance in the short term, but in the long term the net effect of the relationship tends to be negative. Inequality reduces the duration of growth spells (Berg, Ostry, and Zettelmeyer 2012; Berg and Ostry 2017), with most of the results coming from cross-country differences rather than changes over time.

Banerjee and Duflo (2003) find a nonlinear relationship between changes in inequality and growth. In particular, growth is an inverted U-shaped function of changes in inequality such that a change in the Gini coefficient in either direction is correlated with lower future growth. This empirical result strongly rejects the standard linear specification of cross-country growth regressions and suggests an explanation for the seemingly contradictory results obtained in the literature. However, the non-linear relationship could also reflect omitted variables in the empirical model. For instance, Aiyar and Ebeke (2020) show that the negative effect of inequality on growth largely depends on the degree of intergenerational mobility. In countries with higher intergenerational mobility, the negative impact of income inequality can be more easily reversed because the poor have more opportunities to improve their living standards. In particular, they show that in their specification, the nonlinear term proposed by Banerjee and Duflo (2003) is not statistically significant, suggesting that intergenerational mobility could be capturing the nonlinear relationship between inequality and growth.<sup>15</sup> The lack of robust results on the effect of inequality of growth using panel data may reflect not only issues of correct specification but also an insufficient way
and Klasen [2015] estimate the eect of Vietnam's war on FLFP but they use a cohorts approach to nd that the "added worker" eect is positive for people directly aected by the war and smaller for those individuals entering working age after the end of the conict. Although these last two articles depart from our specic focus on terrorism, they are still relevant because they (i) clearly develop the argument of the "additional worker eect" that induces women to participate in the labor force as men are displaced from the household, dead or disabled; (ii) focus on countries with similar work morale, culture, and income level; and (iii) focus at the sub-region level and have similar methodological concerns as we do.

This paper focuses on four countries with particularly low FLFP rates. Terrorist attacks have been persistent in these countries after the end of the crudest phase of civil conicts experienced decades ago. Terrorist attacks in these countries are motivated mainly by political and religious reasons that, fortunately for our empirical approach, are not caused by FLFP per se. Indeed, our identication strategy rests on the fact that terrorist attacks are typically focused in specic places within countries and are not a function of an individual woman's decision to work. Due to data limitations, we are not able to provide a formal account of the motivations for each attack. However, we know that the main driver of the India-Bangladesh conict is rooted in India's 1947 geographic partition, which led to creation of an Islamic majority population in Bangladesh and Pakistan. This issue has remained unsolved, and borders have been disputed and subject

> 3Khan et al. [2017] use individual level data to capture characteristics of the labor force but use district level data for violence measures. We argue this is awed, as it delivers articially signicant estimates of the eect, and deal with it explicitly in our estimation approach.

3
the reweighting has not factored variable-specific missing values into account, if indicator values are not missing at random, estimates may be biased.

These biases suggest the need for caution in interpreting the findings from HFPS data. In particular, direct comparisons between employment and labor force participation rates in HFPS with those obtained from conventional surveys can be problematic. However, the caveats are less of a concern when it comes to tracking indicators like job and income losses over time among those who were working before the pandemic and comparing these losses across different population groups. Using evidence from five countries, Kugler et al (2021) show that the bias is of similar magnitude across gender, education, and urban/rural groups, meaning that HFPS data does give accurate picture of group disparities in employment rates following the onset of the pandemic. Thus, for all its flaws, HFPS data is a valuable source for identifying and measuring inequalities in the socioeconomic impact of the pandemic on households.

**4. Short-term impacts on inequality – what we know from phone survey data**

In addition to widening gaps in well-being across countries (see Section 2 above), the pandemic might have affected global inequality through its short-term impact on within-country inequality in developing countries. To understand the distribution of economic impacts within countries in the first three months of the pandemic, we present evidence below on the distribution of job and income losses, food insecurity, and continued learning, based on harmonized HFPS data from 52 developing countries and up to 47,000 respondents. The findings are based on an update of earlier work by Bundervoet et al (2021), which was

> 5 <u>https://www.worldbank.org/en/data/interactive/2020/11/11/covid-19-high-frequency-monitoring-dashboard. The</u> dashboard currently includes 126 indicators covering 15 topics for 64 countries, with multiple rounds of data for some countries.

> 6 See Bundervoet et al (2021) and Kugler et al (2021) for detailed discussions