rafmacalaba/datause-encoder-data huggingface dataset:
# **Can You Help Someone Become Financially Capable? A Meta-Analysis of the Literature**<sup>**1**</sup>

Margaret Miller Julia Reichelstein Christian Salas Bilal Zia<sup>2</sup>

**JEL Codes:** D12, D14, I22, O12 **Sector Board:** FSE

> 1 This paper is part of the background research conducted for the 2014 World Bank _Global Financial Development Report_ on Financial Inclusion. The authors would like to acknowledge comments received on this document from Martin Cihak, from peer reviewers Annamaria Lusardi, David McKenzie, Florentina Mulaj, Valeria Perotti, Oya Pinar and Siegfried Zottel and from Caio Piza and other participants at a May 15 GFDR seminar on this topic. Research assistance was also provided by Hendrick Chan, Meghan Conway, and Anisha Mudaliar.

> 2 All authors are from the World Bank. Corresponding authors: Margaret Miller (mmiller5@worldbank.org) and Bilal Zia (bzia@worldbank.org)
with slightly larger magnitudes (Table 6). The fact that the formal sector work decrease is larger than the labor force participation decrease shows that there is some switching from formal to informal work.

Despite a large descriptive literature on the 2008 reform, thus far only a few studies have attempted to isolate the behavioral responses to the 2008 Chilean pension reform using either quasi-experimental or structural estimation methods.<sup>40</sup> Behrman, Calderon, Mitchell, Vasquez, and Bravo (2011) analyze the effects of the PBS (the Basic Solidary Pension) on household income as well as on outcomes related to household work, health status, expenditures on alcohol and cigarettes, health insurance and ownership of consumer durables. They use a difference-in-difference approach to compare the change in income/outcomes over time for treated families that qualify for the PBS (by virtue of being poor and having a family member over age 65) and households that are either non-poor or do not have a family member over age 65 and therefore do not qualify for PBS. The pre-treatment year is 2006, two years before the reform, and the post-treatment year is 2009, one year after the reform.<sup>41</sup> Behrman et al. (2011) find that PBS eligible households had 2.4 percent more household annual income relative to non-targeted households, with little evidence of crowding out of private transfers. In addition, targeted households report more leisure hours, which is consistent with the lower labor force participation we predict at older ages.

Using the same data, Encina (2013) applies a difference-in-difference with matching methodology to PASIS recipients, and finds that receiving the more generous PBS benefit causes them to reduce their labor force participation by 18 percentage points relative to eligible households who do not report receiving PBS. This negative wealth effect in a sample of individuals above 65 is qualitatively consistent with our simulations. The larger magnitude is not surprising since the estimate corresponds to the subsample of the elderly whose increase the most.

> 40Packard (2002) was the first to study the frequency of contributions over the life-cycle and their determinants in Chile, using limited dependent variable models of self-reported contribution densities elicited in PRIESO, a cross-sectional household survey.

> 41An implicit assumption of Behrman et. al.’s (2011) difference-in-difference approach is that households who do not qualify for the program at a point in time do not anticipate that they may qualify at some future time period, which could affect their current behavior even if they are not actively receiving benefits. The dynamic structural modeling framework used in this study explicitly incorporates such possible anticipatory

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**Figure 3.1 Green dictionary capturing green tasks**

<!-- Start of picture text --> a. Green dictionary, total terms b. Total green and green potential task<br>statements captured<br>Total terms Green task statements<br>0 25 50 75 100 125<br>0 10 20 30 40 50 60 70 80<br>Agriculture, forestry, and fish...<br>Agriculture, forestry, and fish production<br>Clean Energy Clean energy<br>Climate change Climate change terms<br>Energy efficiency<br>Energy efficiency<br>Environmental Certifications<br>Environmental Knowledge GHG reduction and pollution...<br>Environmental Regulations and... Low-carbon mobility<br>GHG reduction, Pollution reduction &... Natural Resource Conservation<br>Low-carbon mobility<br>Recycling and reuse of waste and...<br>Low-polluting construction<br>Natural Resource Conservation Multiple<br>Recycling and reuse of waste and... Agriculture, forestry, and fish...<br>Agriculture, forestry, and fish production Energy efficiency<br>Energy efficiency<br>Low-carbon mobility<br>Environmental Knowledge<br>Low-carbon mobility Natural Resource Conservation<br>Low-polluting construction Recycling and reuse of waste and...<br>Natural Resource Conservation<br>Repair<br>Recycling and reuse of waste and...<br>Multiple<br>Repair<br>Terms present in the occupational database<br>Terms not present in the occupational database<br>Source : authors<br>Table 3.3 Select examples of green and green potential task captured by the green dictionary<br>Task type Task statement<br>Green providing environmental engineering assistance in network analysis, regulatory analysis,<br>tasks and planning or reviewing database development;<br>assisting in the development of environmental pollution remediation devices under the<br>direction of an engineer;<br>analyzing workforce utilization, facility layout, operational data and production<br>schedules and costs to determine optimum worker and equipment efficiencies;<br>Green advising on techniques for improving the production of crops, livestock and fish, and<br>potential alternative production options;<br>task fitting, adjusting and repairing electrical parts in domestic appliances, industrial<br>machines and other appliances;<br>driving and tending street tramcars transporting passengers;<br>Green<br>Green<br>Green potential<br>Green potential<br><!-- End of picture text --> _Source_ : authors 20
# **1 Introduction**

The gender disparity in commuting barriers is pervasive, affecting women in developing and developed nations. Recent OECD findings reveal a substantial gender commuting gap, with men averaging 33.4 minutes per day compared to women’s 21.9 minutes, indicating a 31.1% difference (OECD, 2016). Developing countries exhibit even more pronounced patterns; Indian women, on average, spend a mere 8 minutes daily on employment-related travel, compared to men’s 36 minutes.<sup>1</sup> ILO (2017) reports that lack of transportation decreases women’s probability of participating in the labor market by 16.5 percentage points among developing countries. Given the established inof barriers on women’s labor a natural arises: can fluence commuting supply, inquiry reducing these barriers influence women’s labor supply decisions? Whether policies directly addressing commuting costs would help increase female labor supply or not is far from obvious: social norms regarding household chores and traveling alone also significantly impact female labor supply, as evidenced by prior studies (Fanning Madden, 1981; Turner and Niemeier, 1997; Lee and McDonald, 2003; Abe, 2011; McQuaid and Chen, 2012).

Our study sheds light on the trade-offs women face when commuting cost is reduced by exploiting the rollout of a free busing scheme, the _Pink Slip Program_ , in two states of India. India is a pertinent setting for exploring this issue. There is an overwhelming gender gap in commuting time and modes used. The 2011 Census of India reveals that 30.2 % of women travel to work on foot, and only 24.6 % use any transportation, highlighting the limited access to transportation options for many women. In contrast, only 20 % of men travel on foot, and more than 50 % of men use some transportation. There is also evidence that women use slower modes of transportation to commute to work as faster modes are usually more expensive (Anand and Tiwari, 2006). Concurrently, female labor force participation is low in India, at a rate of 28.2 % of the working-age population as of 2019, significantly lower than the average of 46 % in low- and middle-income countries.<sup>2</sup> We leverage the _Pink Slip programs’_ state-wide roll-out in Punjab and Tamil Nadu in April and May 2021, respectively. While Delhi also introduced this program in November 2019, data limitations preclude us from including it in our sample.<sup>3</sup> Several media

> 1India’s 2019 National Time-use Survey (https://mospi.gov.in/time-use-survey).

> 2World Bank’s Gender Data portal (https://tinyurl.com/4a8rm5ye). According to the most recent female labor force survey by India’s Ministry of Statistics and Program Implementation, it increased to 37% (http://tinyurl.com/mu2vx7m3).

> 3The CMIE, our data source in this paper, started collecting information on individual time usage after Delhi started implementing the _Pink Slip Program_ . So, we are unable to include Delhi in our sample.

1
are combined. The surveys follow a repeated cross-sectional design and are not panels (i.e. the same administrative units, not the same people, are re-sampled), so responses are aggregated to the _groupement_ level, the lowest level at which the project consistently collect representative data. Table 2 provides a summary of the dates, sizes, and percent of respondents who report being displaced within each survey wave. This aggregated temporal analysis can show, associations between fluctuations in displacement and perceptions of social cohesion over space and time at the _groupement_ level. Question coverage varies across survey waves, but a battery of core questions enables consistent observation of how many individual respondents self-report being displaced at the time of the survey and being involuntarily moved within the past year.

|**Poll**|**Date**|**N**|**% Current**<br>**Displaced**|**ly**<br>**% Displa**<br>**Last Yr**|**ced**<br>**% Hosting**<br>**Displacees**|
|---|---|---|---|---|---|
|#11|July 2017|5834|4.35|7.42|–|
|#12|September-October 2017|4013|1.62|2.62|–|
|#13|December 2017|4883|3.50|7.97|–|
|#14|March-April 2018|1933|4.97|8.85|31.35|
|#15|June-July 2018|5951|3.70|8.35|30.33|
|#16|October 2018|1112|6.47|4.68|–|
|#17|December 2018|5918|5.86|11.20|–|
|#19|July-August 2019|5961|5.12|10.45|–|
|#20|December 2019|5752|4.71|8.14|–|
|#21|November 2020|2627|4.19|5.14|–|
|#22|February-March 2021|5847|6.86|9.30|–|
|**Overall**|**July 2017-March 2021**|**49831**|**4.64**|**8.19**|**30.58**|

Table 2: Details on Surveys and Displacement Trends Second, the paper conducts an individual-level analysis of two cross-sectional surveys of 1,933 and 5,951 individuals conducted in March-April 2018 and June - July 2018, respectively, to probe the relationship between hosting displacees and social cohesion in more detail. This survey wave 22
In contrast to droughts, floods tend to have a positive effect on economic growth. The response of agricultural growth is significantly positive one year after but not on the same year of the event. The positive response of non-agricultural growth appears even later, which suggests the importance of transmission mechanisms based on supply chain relationships across sectors. The cumulative positive effect of floods on GDP growth is 0.5 pp and on agricultural growth, 0.6 pp.

Earthquakes do not seem to have a significant effect on GDP growth. However, there are some noteworthy results regarding sectoral growth. Earthquakes appear to have a negative impact on agricultural growth, rendering a negative cumulative effect of about 1.4 pp. In contrast, earthquakes elicit a positive mean response of non-agricultural growth one year after the event of 0.7 pp. This positive effect is consistent with the reconstruction activity that follows an earthquake in residential housing, public infrastructure, and production plants.

Storms tend to have a negative effect on GDP growth and non-agricultural growth the same year of the event. The effect is short-lived and small, however. In fact, the negative impact of storms amounts to 0.3 pp of GDP growth and 0.4 pp of nonagricultural growth. In the following years, particularly for non-agricultural growth, there is a growth rebound representing most likely reconstruction efforts.

In our opinion, future research should concentrate in exploring and clarifying the mechanisms through which the heterogeneous impacts of natural disasters on economic growth are produced. This paper has contributed to describing this heterogeneity, but much remains to be done in explaining it. For this purpose, both panel and individual country analysis should prove to be useful.

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The datasets are based on stratified random samples of firms, which as much as possible are drawn from the universe of eligible firms. Stratification is by size, location, and sector. Extensive meta-data for each survey documents in detail the sampling frame and aspects of the survey implementation. In some countries, only firms in the manufacturing sector are sampled, but in most cases service sector firms are included as well. An important advantage of this source of information is that all the data (including meta-data) are easily accessible online. Surveys are conducted in several years for most countries which makes it possible to trace country-level patterns over time. The surveys include a large number of variables, making it possible to research a broad range of topics. One disadvantage is that, in contrast with the original RPED sampling frame that had an annual frequency, it is not possible to follow firms over time. As a result, it is not possible to estimate firm-level productivity controlling for the fundamental simultaneity problem of productivity and input choice (Van Biesebroeck, 2008).

In Table A.1 in the Appendix, we show the number of firms sampled in each survey. The average over all 94 surveys in sub-Saharan Africa is 382 observations. It ranges from a low of 75 in Lesotho in 2003 to a high of 2,676 in Nigeria in 2014. The earliest survey is in 2002 and every year new surveys are added to the database. In the average year, 5.5 surveys are organized, but they are not spread equally over time. In 2008 and 2012 not a single country was surveyed. When we started our work to harmonize the data, only a single survey for 2018 could be included.<sup>7</sup> While these surveys are not specifically aimed to be nationally representative, due to the three dimensions of stratification, these samples are likely to cover a substantial fraction of total exports. Freund and Pierola (2015) have documented the remarkable concentration of trade in very few enterprises for a sample of 32 developing countries. They show that, on average, the top 5 firms account for one third of aggregate exports, and this fraction is even higher for manufacturing products.

Using the information in the UN Comtrade dataset, we calculate total exports and total manufacturing exports for each country-year in which we observe a firm-level survey. In Table 1, we report the ratio of the sum of exports of the sampled firms divided by aggregate exports. The average fraction across all surveys is 19%. In only 23 of the 94 (country-year) surveys is the average below 5%, while in 24 cases it exceeds 25%. It underscores that by surveying a relatively small number of firms, a significant fraction of a country’s exports can be accounted for. We therefore expect our results to be broadly representative of the experience of exporters and importers in many these countries.

> 7 Only counting the years with survey activity between 2002 and 2017, there is an average of 6.6 surveys in each year.

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**Table 3.6. Sector structure in 2022 and for base in 2030 (%)**

||**Value**<br>**added**|**Produc-**<br>**tion**|**Employ-**<br>**ment**|**Exports**|**Imports**|**Export/**<br>**output**|**Import/**<br>**demand**|
|---|---|---|---|---|---|---|---|
|**2022**||||||||
|**Agriculture**|18.9|18.8|28.8|11.9|11.1|2.7|14.3|
|**Industry**|19.7|31.6|10.2|81.8|79.5|11.2|45.8|
|**Mining**|5.6|3.9|0.2|59.0|1.4|68.9|26.8|
|**Manufacturing**|9.9|16.4|4.7|22.8|77.8|5.3|57.8|
|**Other**|4.2|11.3|5.3|0.0|0.3|0.0|0.8|
|**Services**|61.4|49.6|61.0|6.3|9.4|0.6|4.9|
|**Private**|45.7|37.7|39.2|5.5|8.9|0.7|6.1|
|**Government**|15.7|11.9|21.8|0.9|0.5|0.3|1.0|
|**Total**|100.0|100.0|100.0|100.0|100.0|4.3|24.5|
|**2030**||||||||
|**Agriculture**|21.1|20.1|28.9|14.2|11.6|3.0|14.4|
|**Industry**|19.9|31.8|10.6|78.4|79.4|10.2|45.7|
|**Mining**|5.3|3.7|0.1|50.9|1.5|60.8|24.8|
|**Manufacturing**|10.5|17.1|5.2|27.5|77.6|6.0|57.4|
|**Other**|4.1|11.1|5.2|0.0|0.3|0.0|0.8|
|**Services**|59.0|48.1|60.5|7.4|9.0|0.7|5.0|
|**Private**|44.0|36.6|38.8|6.4|8.5|0.8|6.2|
|**Government**|15.0|11.5|21.7|1.0|0.4|0.4|1.0|
|**Total**|100.0|100.0|100.0|100.0|100.0|4.2|24.7|
|**Δ(2030-2022)**||||||||
|**Agriculture**|2.2|1.3|0.2|2.3|0.5|0.2|0.1|
|**Industry**|0.2|0.2|0.4|-3.4|-0.1|-1.0|-0.1|
|**Mining**|-0.3|-0.2|0.0|-8.1|0.1|-8.2|-2.0|
|**Manufacturing**|0.6|0.7|0.5|4.7|-0.2|0.7|-0.4|
|**Other**|-0.1|-0.2|-0.1|0.0|0.0|0.0|0.0|
|**Services**|-2.4|-1.5|-0.5|1.1|-0.4|0.1|0.1|
|**Private**|-1.7|-1.1|-0.4|0.9|-0.4|0.1|0.1|
|**Government**|-0.7|-0.4|-0.1|0.1|0.0|0.1|0.0|
|**Total**|0.0|0.0|0.0|0.0|0.0|-0.2|0.2|

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Such a correction, albeit temporary, would have an important bearing on the probability of isolated or more diffused crises under different macroeconomic scenarios. This issue is addressed in the last section of this paper.

### **Box 5  Surges, stops and aggregate capital inflows**

As discussed, in the main text, capital inflow surges tend to precede financial crises, and crises tend to occur at the same time as sudden stops. The surge in capital inflows in the pre-crisis period was typical (Figure 5. 1), as some 80 percent of developing countries in the sample suffered a sudden stop in its aftermath. The post-crisis rebound, which also classifies as a surge, was again followed by an increased incidence in stops, with 15 percent enduring such episode during 2012-13. The methodology used here to identify surge and stop episodes at the individual country level is based on Forbes and Warnock (2012), with the threshold being defined as changes in flows larger than one standard deviation around a five-year rolling mean.

The link between aggregate capital inflows to developing countries and the proportion of these countries going through either surge or stop episodes can be approximated empirically using a simple vector autoregression model approach. Over the period 2000Q1 to 2013Q2, the relationship can be summarized with the accumulated impulse response presented in Figure 5. 2.

Overall, a decline of one standard deviation in the ratio of aggregate capital inflows to GDP (corresponding to a decline of about 2.7 percent of GDP), tends to increase the proportion of countries experiencing sudden stops to 22 percent after four quarters. In the “overshooting” scenario presented in the text, capital inflows are predicted to decline by 3.5 percent of GDP, implying that more than a quarter of developing countries could experience sudden stops in such scenario.

# **4. Disequilibrium risks**

The preceding analysis suggests that in the long run, the withdrawal of quantitative easing and a return to a tighter monetary policy in high-income countries will have a relatively small impact on capital inflows, reducing them from 4.6 percent of developing-country GDP in 2013Q3 to 4.0 by the end of 2016. However, the path to this new normal level of flows will matter.

If market reactions to tapering decisions are precipitous, developing countries could see flows decline by as much as 80 percent for several months. That would raise the likelihood of abrupt stops at the country level, with more than 25 percent of individual economies experiencing such an episode in these circumstances (box 5).

While this adjustment period might be short-lived, it is likely to inflict serious stresses on the financial and economic conditions in certain countries–potentially heightening crisis risks.

## **A brief history of crises in developing countries**

According to data compiled by the International Monetary Fund (Laeven and Valencia 2012), there were some 147 financial crises globally between 1970 and 2011 (Figure 9). Of these, 123 occurred in what are now classified as developing countries, and 95 developing countries had at least one crisis.

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# **2. Contribution to the Literature**

This paper makes two contributions to the empirical literature on inequality and growth: one is conceptual, the other is methodological.<sup>2</sup> The conceptual contribution is to examine how the effect of inequality on transitional growth differs depending on countries' initial incomes. For that purpose, an econometric model is specified and estimated that includes an interaction term between inequality and initial income. The estimates from the model allow a comparison of subnational estimates for specific countries, such as the United States, with estimates based on cross-country time-series data. Second, the econometric model allows a test of the theoretical model of Galor and Zeira (1993). The theoretical model of Galor and Zeira predicts that the effect of inequality on transitional growth differs depending on the average wealth in the economy.

Panizza (2002) uses state-level panel data for the United States during 1940-1980. His GMM estimates show a significant negative effect of the Gini on transitional GDP per capita growth. Specifically, column (9) of Table 7 in Panizza shows that a 1 percentage point increase in the Gini decreases GDP per capita growth by around 4 percentage points. In order to compare that result to the estimates of this paper, one needs information on the average income of the United States during the sample period analyzed by Panizza. According to the World Development Indicators (2017) the United States had in 1960 (the mid-point of Panizza's sample period) a GDP per capita of around US$17,000; equal to around 9.7 logs. According to the estimates shown in Table 4 of this paper -- for an initial income equal to 9.7 logs -- the effect of a one percentage point increase in the Gini on GDP per capita growth over a five-year period is around -2 percentage points.

Forbes (2000) was the first paper in the literature to estimate an effect of inequality on transitional GDP per capita growth using a dynamic panel model that includes country fixed effects. Her sample spanned the period 1966-1995 and covered 45 countries. Forbes found that inequality

> 2 For a review of mechanisms through which inequality may affect growth, see Galor (2011).

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