rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.3.2 Regulations Affecting Firm Ownership Structures**

Policies or institutional settings that condition the ownership structure of firms can have implications on labor market outcomes by altering the rent-seeking conflict between employees and shareholders, distorting incentives of firm owners, or affecting management efficiency. The policies most relevant for increasing within-firm production efficiency vary greatly country by country. In all cases, however, it is key to achieve high management efficiencies—either through the appropriate selection of managers or the right incentive structure.

There is abundant evidence about how good management benefits firm performance and employees. Bender et al. (2018) use highly detailed employer-employee data from Germany to show that plants with higher management scores are more productive. This relationship holds true even after accounting for the fact that high-productivity firms also feature higher average worker skills and are strongly driven by the human capital of the (top) managers. Estimates suggest that a one standard deviation increase in the management score is associated with a 15–21-percent increase in labor productivity. Such well-managed firms are also able to pay their employees higher wages relative to what the market as a whole could offer these workers. The evidence from this and related papers justify a deeper investigation into how more efficient and skilled management teams can be achieved. While such high-quality data do not exist for most LMICs, the main message about good management improving labor productivity also holds in their context. For example, Bloom et al. (2013) find a large causal role of management practices for productivity in Indian textile plants by implementing a randomized controlled trial. Reviewing the evidence for a larger set of LMICs, La Porta and Shleifer (2014) note that unproductive, informal establishments are predominantly run by poorly educated entrepreneurs. To the extent that education improves managerial skills, this is further evidence of the importance of good practices for efficient production and favorable work conditions.

As noted earlier, the type of PMR that are most crucial for aggregate labor market outcomes depends fundamentally on their political and economic context.
ingrained in the agriculture sector **.** Nearly two-thirds of extremely poor workers aged 15 and above reported that their primary job is in the agricultural sector; and extreme poverty rates among these workers is more than four times higher than among non-agricultural workers (Figures 5 and 6).<sup>24</sup> The significant gap in poverty rates between the agriculture and non-agriculture sectors is consistent with the well-documented earnings penalty faced by agriculture labor.<sup>25</sup> While the proportions of the extreme poor and moderate poor living in rural areas are roughly similar (80.1% vs. 76.0%, or a 4.1 percentage point difference), the moderate poor are much less likely than the extreme poor to work in the agriculture sector (64.7 vs. 52.0 percent, a 12.7 percentage point difference). This is consistent with growth in non-agricultural employment facilitating movement from extreme to moderate poverty.

Non-agriculture employment, however, is far from sufficient to escape poverty. Of the extremely poor workers in rural areas, nearly 24 percent work in non-agriculture jobs, and just under 40 percent of moderately poor workers in rural areas work outside of agriculture (Figure 7). This suggests further analysis to better understand why these non-agricultural workers in rural areas remain poor, and whether either facilitating migration to urban areas or greater investment in infrastructure would raise their productivity. Further analysis of the GMD, for example, can

> 22 Future work analysis will examine trends in the poverty profile using a comparable sample of countries. 23 An additional source of bias, if the population of interest is the entire developing world, results from the nonrandom selection of countries into the sample. In general, results can be interpreted as representing the 89 sample countries, which as noted above represent 86.5 percent of the developing world population.

> 24 The total poverty rate in Figure 6 is lower by 2.4 percentage points than in Figure 4. This is
intermediated flows remain the most volatile. Bank-intermediated flows, which rose in the mid2000s, are especially volatile. They decline most sharply during country-specific sudden stops and global stop episodes. These results may not be surprising, but their constancy is surprising. Empirical regularities in international finance that stand the test of time are the exception, not the rule.

But outflows from emerging markets behave differently. In contrast to the findings for inflows, we document important changes since the turn of the century and in the most recent decade, in particular, in the behavior of outflows. FDI outflows from emerging markets have grown and become significantly more volatile. Similarly, there is a significant increase in the volatility of “other” (mainly bank-intermediated) capital outflows from emerging markets since the turn of the century. Those other outflows are twice as volatile again as FDI outflows, as measured by the coefficient of variation of gross flows scaled by GDP, in the most recent period, 2011-2015. In terms of shocks to the capital account of the balance of payments, our findings underscore that outflows from emerging markets, both FDI and bank-related flows, have come to play a growing role and deserve greater attention from analysts and policy makers.

# **2. Literature Review**

The majority of previous papers in this area use annual data, as noted above, typically from 1980 through the early- to mid-2000s. Recent contributions tend to focus on gross flows rather than netting inflows and outflows. An example is Bluedorn et al (2013). Working with annual data on gross flows since 1980 for a sample of 150 advanced, emerging and developing countries, they argue that capital flows are volatile for all countries (advanced and emerging) and across all points in time. Flows exhibit low persistence regardless of country and type. Flows into emerging markets in particular rise when global financing conditions are relatively easy.

Levchenko and Mauro (2007) are another good example of the genre. Using annual data on gross flows for a panel of 142 advanced
improved their ratings, while 9 remained unchange (of these 4 are ‘high’, 2 ‘moderate’ and 3 ‘low’) and only two deteriorated – Mozambique and Sudan. Further, of the 20 countries that show an improvement in their risk ratings more than half (65 percent) improved by more than one step (went from ‘high’ to ‘low’), while 7 improved one step (6 from moved from ‘high’ to ‘moderate’ and 1 from ‘moderate’ to ‘low’). The rating deterioration from ‘low’ to ‘moderate’ for Mozambique is due to increased external borrowing for infrastructure investments around natural gas exploration and liquefaction. However, as noted in the DSA these investments are broadly seen as important for the country’s development, and the increased risk of debt distress is deemed appropriate.

**Figure 4.1: DSA Based Debt Distress Level Change**

(Percentage of total sample) <!-- Start of picture text --> 80 74<br>55<br>60<br>40<br>29<br>16<br>20 10 13<br>3<br>0<br>0<br>Low Moderate High In debt distress<br>2009‐2011 2012‐2014<br><!-- End of picture text --> 15
participate. We attempt to overcome the potential identification issues that may arise from non-random assignment and voluntary participation with two different quasiexperiments, each of which are explained below in more detail.

# **_Matching Analysis_**

The first research strategy builds on the design of the first short-term impact evaluation of the program. This evaluation was based on a non-experimental design that compares eligible households from municipalities covered by the program with potentially eligible households (also classified as _Sisben_ level 1) from selected comparable areas not targeted by the program.<sup>19</sup> We follow different cohorts of children in treatment and control municipalities who were interviewed as part of the baseline survey carried out in 2002 and that could have finished high school during the period 2003-2009. Hence, the location, age and grade of the children at baseline determine their treatment status and length of exposure to the program for the treated. Given that 2003 is the first year in which the program was broadly implemented in the samples for the matching analysis, the pool of treated individuals includes children with program exposure that ranges roughly from one to seven years.<sup>20</sup> A limitation of this research strategy is the lack of baseline measures of the outcome variables, making it difficult to test for differences between groups at the pre-program time. Given that high school graduation and test scores are in reality observed only once for the same child, program

> 19 Municipalities were grouped based on the number of eligible families that reside in each of them to form 639 Primary Sampling Units (PSU). Twenty-five strata were then defined based on geographic location, level of urbanization, number of eligible families, and indexes of quality of life and availability of school and health facilities in the municipality. Fifty PSUs (two within each stratum) corresponding to 57 municipalities targeted by the program were selected and then matched to 50 ―control‖ PSUs (equivalent to 63 municipalities) that were relatively similar to ―treatment‖ PSUs. Matching was done within each stratum
.033)|(0.05)|(0.047)|(0.051)|
|**Characteristics of the household**|||||
|Quintile 2|0.084*|0.037|-0.037|0.112*|
||(0.044)|(0.076)|(0.056)|(0.065)|
|Observations|2,688|881|873|934|

Note. Results are presented in margins and clustered standard errors in parentheses. Individual-level survey weights used. Statistical significance at: *** p<0.01, ** p<0.05, * p<0.1. Enumeration areas fixed effect included. The Educational gap is measured as the difference between aspiration to complete university or higher degree and the current education of the youth at the time of the interview. We also controlled for the following variables: Started working <14 years, Started working 23-25 years, Interesting job to do, Working in STEM related jobs, Size of the HH, and Urban area, Consumption Quintiles 3, 4 and 5. Results for these variables were either not statistically significant or not discussed in the analysis. Full results are available under request.

**Regarding aspirations to migrate, we find that female youths generally have lower aspirations to migrate compared to their male counterparts.** Specifically, female youths are 11.6% less likely to express a desire to migrate than their male peers. This pattern is in line with the literature (i.e., Migali and Scipioni, 2019) and consistent across all the countries examined, although there are some variations. For example, in Nigeria, the gender difference is more pronounced, with female youths being 16.8% less likely to aspire to emigrate to another country ( **Table 5** ).

**Additionally, our analysis reveals that aspirations to migrate tend
We include firms that were active, and thus included in the business registry, in at least two years over the sample period (between 2006 and 2015). Further, we include firms that remained located in the same district for every entry in the business registry. We then combine this firm panel with transaction-level customs data provided by the Serbian Customs Administration. The higher frequency customs data is aggregated at the firm-level to reflect the imports of each firm by origin country on an annual basis. The firms are identified by a unique tax identification number that allows us to merge the balance sheet data with the customs data. In the main results, we restrict the set of import partners to the top 150 countries that Serbian firms source from (aggregating all firms) over the sample period.<sup>2</sup> We define a firm’s neighbors as firms in the same industry and district (importing firms are observed in 484 NACE 4-digit industries and all 25 districts).<sup>3</sup> We group firms by district in order to capture the geographic variation in sourcing choices observed in Serbia (see Figure 4). Similarly, since the combination of inputs a firm sources are dependent on its production process, grouping firms by NACE 4-digit industry to define neighboring firms ensures that only the neighbors with relevant experiences for firm learning are being included in the analysis.

While we define the signals a firm receives from its neighbors in an analogous manner to Fernandes and Tang (2014), it is important to note that, in contrast, we do not trim our panel to exclude firms that receive exceptionally strong or weak signals because we do not want to artificially censor the learning signal.<sup>4</sup>

> 2The set of import partners is restricted to the top 150 countries so that it was feasible to calculate the neighbor learning variables. The top 150 countries make up 99.6% of Serbia’s import values.

> 3The district is equivalent to the smallest
– <u>Figure 6 Perceptions of equality and Gini index, 2009</u> Source: own elaboration based on ISSP Social Inequality data set; Gini indices from Bussolo et al. (2018) and Milanovic (2018) Note: Net equality perception is equal to the percentage of people believing theirs is an equal society minus the percentage believing theirs is an unequal one. National weights used. Gini index estimated on per capita household income.

A similar weak correlation is also found when comparing demand for redistribution and objective inequality (Figure 7). Individuals in countries with similar levels of income inequality have strongly different levels of demand for redistribution. Portugal and the United Kingdom have roughly similar levels of income inequality, but in the former the percentage of people that agree with redistribution being a government responsibility is 50 percentage points higher than that of those who disagree, while in the United Kingdom that difference is below 20 percentage points. Slovenia and Portugal have a very similar demand for redistribution, but in Slovenia actual income inequality is 10 Gini points lower than Portugal.

19
Formal education in Burkina Faso consists of six years of primary school, four years of lower secondary school, and three years of upper secondary school, followed by tertiary education. A national law officially makes school mandatory until age 16. However, this law is not enforced because the government lacks the resources to provide education to all school age children. In principle, children can attend public primary schools free of charge. However, parents still have to pay for various fees and school supplies. These expenses must be paid in cash and in a timely manner, with most being paid at the start of the academic year, thus representing a significant constraint for cash-strapped households. Cash transfers reduce this cash-constraint in addition to compensating for the opportunity cost of child time.

The pilot cash transfer program was conducted in Nahouri province in southern Burkina Faso, located approximately 100 miles from the capital, Ouagadougou. Households in the province are predominantly subsistence farmers growing millet, sorghum, and peanuts. On average, there are 6.5 members in each household (Table 1) of whom 3.4 are children ages 0-15 (1.5 between 0 and 6 and 1.8 between 7 and 15).

Of the children aged 7 to 15, 61.3 percent are reported by their parents to be enrolled in school, but when enrollment is measured using the information available in school rosters, the enrollment rate is lower at 49.1 percent. School attendance conditional on enrollment as measured from school rosters is higher at 98.3 percent, suggesting that, once enrolled children are very likely to attend classes. The attendance rates are consistent with other research in Africa using administrative school data (Miguel and Kremer, 2004 in Kenya; Benhassine et al., 2015 in Morocco; Kazianga, de Walque, and Alderman, 2012 in another region of Burkina Faso). Furthermore, at least for Burkina Faso where enrollment is low, they are suggestive of an environment where parents strategically choose which children they enroll and then make
Peeples, L. (2020) News Feature: How air pollution threatens brain health. _Proceedings of the National Academy of Sciences (PNAS)._ June 23, 2020. 117(25): 13856-13860 Peters, R., Ee, N., Peters, J., Booth, A., Mudway, I., Anstey, K. (2019). Air pollution and dementia: a systematic review. _Journal of Alzheimer's Disease._ Vol. 70. 145-63.

- Ramanathan, V., Feng, Y. (2009). Air Pollution, Greenhouse Gases and Climate Change: Global and Regional Perspectives. _Atmospheric Environment_ . Vol. 43(1): 37–50.

- Reames, T., Bravo, M. (2019). People, Place and Pollution: Investigating Relationships between Air Quality Perceptions, Health Concerns, Exposure, and Individual- and Area-Level Characteristics. _Environment International._ Vol. 122(Jan.): 244–55.

- Rao, N.D., Kiesewetter, G., Min, J., Pachauri, S., Wagner, F. (2021). Household contributions to and impacts from air pollution in India. Nature Sustainability. Vol. 4: 859–867

- Rentschler, J., Salhab, M. (2020). People in Harm’s Way Flood Exposure and Poverty in 189 Countries. _World Bank Policy Research Paper Series_ . WPS 9447

- Smith, A., Bates, P., Wing, O., Sampson, C., Quinn, N., Neal, J. (2019) New estimates of flood exposure in developing countries using high-resolution population data. _Nature Communications._ Vol. 10: 1814

- Southerland, V., Brauer, M., Mohegh, A., Hammer, M., van Donkelaar, A., Martin, R., Apte, J., Anenberg, S. (2021) Global urban temporal
relationship for Turkey and a set of twelve other emerging economies. On average, across these emerging economies, new import protection through flexibilities embodied in its temporary trade barrier policies (discussed in more detail in section 3.2 below) are associated with recent appreciations in the real exchange rate. I.e., for the case of Turkey, this is consistent with a strengthening Turkish lira – that makes imports suddenly more price-competitive with domestic production – subsequently resulting in additional demands for new import restrictions. There is also evidence from emerging economies that new trade barriers are associated with negative shocks to domestic real GDP growth and rising unemployment. The timing of the shocks that Turkey has faced in the Great Recession period, especially

> 8 Knetter and Prusa (2003) and Bown and Crowley (forthcoming) provide evidence on the macroeconomic determinants of these relatively formal instruments of import protection for high-income economies; the latter paper uses quarterly data for the period 1988-2010 and thus covers the inception of the Great Recession.

8
4.93|
|Uruguay|3.91|8.50|9.44|2.11|7.19|2.93|9.08|1.98|
|USA|2.98|4.28|10.62|4.93|5.30|2.55|10.25|5.30|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18. Children defined as 14 years of age or less except for Pakistan and South Africa where they are defined as 6 years of age or less.

33
the other two states, and hence the NPCs in UP are higher. It should be noted, however, that this result follows almost entirely from the particular assumption made about the geographical distribution of sugar under free trade. An alternative plausible assumption is that UP sugar would not be exported and would compete with imported sugar in UP itself and in other inland areas in the north west and north east. In that case transport costs to these inland markets would be added to the landed duty free price of imported sugar at the ports, and NPCs in UP would tend to be lower rather than higher than the NPCs in the other two states.

# _Nominal protection: khandsari_

# Methods.

As noted above, there are no complete all-India statistics of khandsari production. However, the study by Kalra and Gulati (1992) reports statistics of cane crushed in UP by khandsari units for the period 1981-90. Assuming an average sugar extraction rate of 6 percent, annual khandsari production in UP during this period would have varied between about 600,000 and 800,000 tons which would correspond to all-India production of between 1 million and about 1.3 million tons.<sup>3 0</sup> . Wholesale prices of khandsari at Bareilly in UP are published and can be used to estimate ex-factory prices. The details of these estimates are shown in Annex 8. Annual average wholesale prices are available for the 1965-94 period, except for 1965-69 and 1976-80. The missing prices for these years were interpolated by applying the wholesale price index for sugar, khandsari and gur to the recorded khandsari prices for 1970-75 according to the formula _Pi = WPIi / nj Pn / WPIn_ where Pi is the estimated price in year i, WPIi is the _n_ wholesale price index for sugar, khandsari and gur in year i, and Pn, n= 1970... 1975, is the average wholesale price of khandsari in year n. Excise taxes plus a marketing margin estimated at 1 percent of the wholesale price are then deducted to give an
cooperatives and credit unions. In New York City, one of the more highly concentrated urban markets in the U.S, the five largest banks account for 54 percent of all deposits. Competition for deposits is also more varied in the United States. Large commercial banks compete with community banks, thrifts, and credit unions, all of which are regulated and offer deposit insurance. In Mexico, non-bank savings institutions are much smaller and many have traditionally been largely unregulated. Second, computerized credit histories for consumers and automated credit risk assessments are highly developed in the U.S. and cover nearly all working adults. In Mexico, such institutions are far less comprehensive and sufficiently less developed that there can be little meaningfully predictive automated credit risk assessments for most lower-income Mexican households. Third, labor costs are significantly less in Mexico compared to the U.S. This means that labor-displacing financial service technologies employed in the U.S. may not be appropriate in the Mexican context. Despite these differences, however, this paper identifies many parallels in the issues that the two countries face in trying to improve financial services for lower-income households, even if the scale and context of the problem differs.

# Who are the unbanked?

In both countries, the unbanked are characterized by lower incomes and lower education levels than the population at large, and they also tend to be marginalized in socio economic terms. In Mexico this is inferred by the high percentage of unbanked dependent on the informal sector, while in the U.S. it is defined by the disproportionate representation of immigrant and minority groups. (According to US Federal Reserve Bank reports based on surveys of consumer finance from 1997, 54% of the unbanked are Latin American migrants, compared with 15.1% over the population as a whole<sup>3</sup> .) However, although in line with income and education levels overall, the percentage of unbanked is markedly greater in Mexico than in the U.S. Surprisingly, home ownership among