rafmacalaba/data-use-mentions-extended huggingface dataset:
income group has the highest percentage of countries with all 5 measures followed by countries in Latin America and the Caribbean.

The dataset collected through the survey provides information on a number of dimensions of financial consumer protection across countries using consistent methodology. Ideally, we would like to explore the links between the scope of financial consumer protection across countries and market outcomes, such as the level of financial access. However available data does not allow for such an investigation for a number of reasons. First, we only have a cross-country sample of data but no historical information, making it impossible to conduct any impact analysis due to endogeneity problems. Second, the specific impact of consumer protection regulations is likely to vary greatly within the economy—from individual to individual, and as such, it is best understood through micro-level studies. Consumer protection regulations may also only have an impact on a subgroup of the population, and not affect country-level statistics in a significant manner. Third, the survey mostly collected data on the nature of financial consumer protection regulations but not on whether they are implemented in practice. The data is also self-reported by the regulators. Even though we try to collect information to proxy for the effectiveness of enforcement, these measures are limited. Fourth, many of the regulations are very recent and may not have had the time yet to “work through” the system.

More importantly, there is a fundamental challenge to measuring the outcome of the financial consumer protection at the country level due to the complexity of transmission channels for this policy. One theory suggests that better consumer protection, including effective disclosure, fair treatment and reliable dispute resolution, would encourage greater use of financial services and lead to greater access to credit. This effect, however would only apply to the previously underserved groups. For the already-served groups the impact may be the opposite, where greater transparency on the available products, for example a credit card with high rates and fees, would induce clients to reduce the use of services. At the country level, the
from August until November 2006 and a follow-up survey from April until July 2008.<sup>5</sup> We also designed a household level survey that

> 5 The study team submitted the research protocol to the Rwanda National Ethics Committee, which approved the research design, methodology and methods for informed consent. Because of the sensitive nature of the sample and survey, the data collection was managed by the National University of Rwanda School of Public Health with guidance from the National AIDS Control Commission (CNLS). The follow-up surveys were also reviewed by the National Institute of Public Health in Mexico Institutional Review Board (IPF Code 3627801).

7
WPS 3464

# **Microeconomic Evidence of Creative Destruction in Industrial and Developing Countries**

**By Eric Bartelsman, John Haltiwanger and Stefano Scarpetta**<sup>**1**</sup>

## _Abstract_

_In this paper we provide an analysis of the process of creative destruction across 24 countries and 2-digit industries over the past decade. We rely on a newly assembled dataset that draws from different micro data sources (business registers, census, or representative enterprise surveys). The novelty of our approach is in the harmonization of firm-level data across countries, which enables international comparisons and the identification of country-specific factors as opposed to sector and time effects. All countries display a massive reallocation of resources, with the entry and exit of many firms in all markets, the failure of many newcomers and the expansion of successful ones. This process of creative destruction affects productivity directly by reallocating resources toward more productive uses, but also indirectly through the effects of increased market contestability. There are also large differences across groups of countries. While entry and exit rates are fairly similar across industrial countries, post-entry performance differs markedly between Europe and the United States, a potential indication of the importance of barriers to firm growth as opposed to barriers to entry. Transition economies show an even more impressive process of creative destruction and, those that have progressed the most toward a market economy show better outcomes from this process. Finally, Mexico shows large firm dynamics with many new firms entering the battle but also many failing rapidly, while Argentina resembles Continental Europe with smaller flows and less impressive post-entry growth of successful firms._ _JEL classification: L11, G33, D92,_ _Keywords: entry, exit, survival, firm size, productivity, micro data_

> 1. Respectively: Free University Amsterdam and Tinbergen Institute; University of Maryland,  U.S. Census Bureau, and NBER; The World Bank.   We are grateful to the World Bank for financial support of this project and to Karin Bouwmeester, Helena Schweiger and Victor
graduation status between 1982 and 2008.<sup>15</sup> Columns (ii) and (iii) in Table 4 report, respectively, random-effects and fixed-effects logit models. Fixed-effects logit estimates are based only on the 14 countries (accounting for 169 observations) for which in the dependent variable varies over time— countries that either graduated or degraduated during our sample period.<sup>16</sup> In these random- and fixed-effects tests, Political Freedoms and Institutional Capacity (and export concentration) remain significant, with higher odds ratios than in regression 1. Creditworthiness is significant (and positively associated with graduation) in random effects but not in fixed effects. The time trend remains significant in both tests, with each passing year reducing the odds of graduating by more than 30 percent. The banking crisis dummy is significant in fixed effects and positively associated with graduation. However, this estimate is based on only a single crisis (Korea in 1997) in regression (iii), while the negative association of banking crises with graduation in our 55-country sample is based on 11 crises. The small island dummy shows even less variation in the 14-country sample: its coefficient estimate in regression (iii) is based entirely on Oman, which had a population under 1.5 million in its first year (1984) in the sample but over 1.5 million thereafter. The transition dummy drops out of regression (iii) as it does not vary at all over time.<sup>17</sup>

# _Alternative institutional indicators_

As noted above, it is difficult to know how to operationalize the phrase “key institutions for economic and social development.” In Table 5 we therefore test alternative institutional development indicators.<sup>18</sup> Regression (i) includes the democracy index from the Polity dataset, commonly used in political science research. In contrast to the Freedom House indicator, the Polity index does not measure _outcomes_ (such as individual rights) often associated with democracy
Figure 4. Around 3.4 percent of the population residing in Mexico in 2015 migrated to another state during 2015-2020.

_Internal migrant population (five-year period) by sex, 2020_ <!-- Start of picture text --> 3,936,960<br>1,974,716 1,962,244<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

# 4.2. Patterns of internal migration

**Internal immigration rates in 2020 display a heterogeneous pattern across states, ranging from 1.1 to 11.4 percent.** The states of Quintana Roo (11.4 percent), Baja California Sur (9.5 percent), Querétaro (8.9 percent), Baja California (7.3 percent), Yucatán (5.6 percent), and Chihuahua (5.6 percent) account for the highest proportion of internal migrants relative to the total state population in 2020 (immigration rate). The lowest immigration rates are registered in Guerrero (1.9 percent) and Chiapas (1.1 percent), as observed in Figure 5. Gender gaps in immigration rates are especially relevant in Baja California Sur (10.1 percent for men and 8.8 percent of women) and Sonora (2.7 percent for men and 2.3 percent for women).

Figure 5. Quintana Roo, Baja California Sur, Querétaro, Colima, and Yucatán are the states that account for the highest immigration rates _Percentage of internal migrants by hosting state (immigration rates), 2020_ <!-- Start of picture text --> Men Women All<br>11.4<br>12.0<br>9.5<br>10.0<br>8.9<br>8.0 7.3<br>5.6<br>6.0<br>4.
49 The pattern is reversed for nonclinical methods. In every country but the Dominican Republic, comercial providers are the most important source for resupply nethods. The discrepancy between clinical and nonclinical methods provision Is consistent and quite large for most countries. In some countries the difference in the proportion in each category is dramatic. For instance, in Korea, government finances 83 percent of all clinical methods but only 13 percent of nonclinical methods. Thus the aggregated figures in Table III.1 are strongly influenced by the distribution of methods vithin each country.

The breakdowns for NGOs vary across countries vith no consistent pattern emerging. In some countries (e.g., Nepal, Sri Lanka, Liberia and Barbados) NGOs are a more important source of resupply methods; hovever, in countries like Lebanon, Colombia and Liberia, they are a key source of clinical method supply. Thus although comercial outlets dominate the resupply methods, and government is the major provider of clinical contraceptive methods, NGOs appear to be the flexible provider that fills the supply gaps. Alternatively, NGOs may be the subsidized competitor to the commercial sector that can claim users because of their lov or zero prices. Available data do not allow a resolution of this controversy.

# Socioeconomic Characteristics and Contraceptive Source

Vho uses public and private services is even less vell understood than the aggregate distribution of users across sources. This subsection revievs the available country-specific data on contraceptive source and location of residence; voman's age; voman's education; and, household's/husband's income/ employment. Ideally these variables should be analyzed jointly to control for each set of factors. Thus, this preliminary description merely sets the parameters of possible indepth analysis, vhich could shed more light on the

**al.w:saily Plamin Ptar**
||[0.033]***|[0.173]***|[0.010]*|[0.030]|[0.032]***|[0.173]**|
|---|---|---|---|---|---|---|
|Observations|1120|669|1120|669|1120|669|
|R-squared|0.170|0.371|0.032|0.605|0.150|0.247|

Source: Own estimations using IES Mozambique and Zambia. Notes: Linear probability models estimated by Ordinary Least Squares. Demographic controls include: owner age dummies, gender, and education dummies. Firm controls include: firm age dummies, sector, location of business, and use of bank account, and if the firm is among the top 10% in productivity. Robust standard errors in brackets. * significant at 10%, ** 5%, *** 1%.

**Table 5: Urban Infrastructure and Services Correlates with Urban Informal Types of** **<u>Workers</u>**

||Unpaid w|orkers|Low-edu<br>emp|cated Self-<br>loyed|Informal Wa|ge employees|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|Access to|-0.056|-0.001|-0.061|-0.024|0.010|0.013|
|electricity|[0.005]***|[0.006]|[0.008]***|[
11 agricultural primary production, food processing, and other manufacturing) and therefore than non-agriculture in total. In the absence of those policies, agricultural value added would have been 5.7 percent higher on average, and higher in each of the six developing country regions shown in that table relative to non-agriculture in total (compare with the numbers in parentheses in the final column of Table 3). There is considerable regional variation though: the averages are 12 percent higher in Latin America, 10 percent higher in East Asia (excluding Korea and Taiwan which, with Hong Kong and Singapore, we classify as high-income), 3 percent higher in SubSaharan Africa, and less than 2 percent higher in the three other developing country regions.

Turning to the top left-hand numbers of Table 3, it is clear that most of that gain to developing country farmers would come from the removal of agricultural tariffs and subsidies in high-income countries.<sup>8</sup> Net farm incomes in all developing country regions, even those that are net food importers and those receiving preferential access to protected markets in high-income countries, would be boosted by such reform. By contrast, according to the GTAP Version 6 database and GTAP-AGR model used here, agricultural value added in all developing country regions would be reduced by the reform of agricultural and food policies in developing countries themselves. This is not surprising, given the high protection to agriculture in both high-income and developing countries reported in Table 1(a). Columns 3 and 4 of Table 3 show that loss would be partly offset by reform in non-agricultural sectors though, with services reform making

> 8 Decomposition of those results reveals that more than two-thirds of the gains to developing country farm incomes from high-income country agricultural policy reform would come from removal of tariffs, and that domestic rather than export subsidies contribute most of the rest. See the discussion of Table 10 below.
) and its derivatives together with empirical employment levels for each sector. The loss function is then the sum across sectors and across years of the square of each sector’s predicted wage minus mean wage in the data, plus the square of the sector’s predicted minus its actual share of GDP. (The sector GDP figures are from the BEA, but the wages are from our sample.) In addition, we assume that all workers have identical Cobb-Douglas preferences, using consumption shares from the BLS consumer price index calculations for the consumption weights. The values of _ψ_<sup>_ik_</sup> are given by the average skill premium within each cell. The calibration results are presented in Table 8. Note that,

> 5We examined a more flexible CES within-occupation aggregation of the two types of labor, but our estimates of the elasticity of substitution between the two types of labor within occupations produced very high values, 60 and above. Perfect substitutability within each industry-occupation cell seems like a good approximation. Please note that this is different from the elasticity of substitution between high- and lowskilled workers examined by other authors in models without occupations, such as Ottaviano and Peri (2012, Table 5), for example. Their estimates of the elasticity of substitution between high- and low-skilled labor take a value around 2, but since generally high- and low-skilled workers have different mixes of occupations, this should be thought of as picking up substitutability across occupations as well as across educational classes.

23
/u> _<u>.</u> The data on fuels and kerosene are from the International Energy Agency (IEA)_ _<u>(https://www.iea.org/statistics/statisticssearch/report/?country=Ethiopia&product=oil, 2015). Emission factors are from FAO (http://www.fao.org/faostat/en/#data, 2017b), and Intergovernmental Panel on Climate Change (IPCC)</u>_ _<u><mark>(http://www.ipccnggip.iges.or.jp/public/2006gl/vol4.html,</mark></u> 2017)._ Figure 3.1 shows that livestock accounts for 74.86 million tones (54.53 percent of total emissions), and forestry and firewood together emit 52.8 tons of carbon (38.46 percent of total carbon emissions), respectively. In contrast, the use of petroleum (fuel and kerosene) accounts for only 6.28 percent (i.e., 8.62 million tons of carbon) of overall emissions. Emissions from fertilizer use in the agriculture sector are based on 650,000 tons of fertilizer used in 2012 (Rashid et al, 2013).

Each of the carbon emission sources mentioned above results from either direct consumption by households or use of intermediate inputs in the production of other goods and services. Using statistics on total outputs and inputs of different sectors in the Ethiopian economy, we can compute the direct and indirect carbon intensities and emissions shown in Table 3.1 that result from the different sources of carbon emissions.<sup>1</sup> Table 3.1 shows that firewood, coal, and livestock have respectively the highest carbon

> 1 The mathematical formula used to arrive at the figures presented in Table 3.1 is given in Appendix C.

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during the 1960's. Time overruns have become more likely and greater In number. Cost overruns have fluctuated, but delays in disbursemenis have Increased steadily. The financial perfermance of utilities also has been poor as measured by Indicators such as the rate of return on revalued assets, self-financ6ig ratio, operating ratio, debt service ratio, and days receivable. Declines In performance over time were already evident well before the first oil crisis took place in 1973-74. Errors In demand forecasting have sigrNificantly increased during the study period. Except In supply constrained systems, the overestimation of demand In many countries has encouraged overinvestment and aggravated financial strains.

While some of the major indicators of armance have deteriorated, others have shown Improvement. For example, most utilities appear to have met Institutional development targets over the perlod. Furthermore, conformity with non-financial covenants, many of which are related to Institution building goals, was good. Nevertheless, progress on the organizational front has not been able to reverse the overall performance decline In the sector, probably because the Institutional and non-financial targets were too modest or wrongly directed, and any gains were overwhelmed by more Important exogenous factors. The Implication for future action by the Bank and its borrowers Is that preoccupation with covenants and conditionalities in non-critical areas, and satisfaction derived from meeting such targets, should not be allowed to divert attention from more fundamental problems that have led to overall erosion of the sector. These problems can be assessed In terms of specifli performance indicators, discussed In the next section.

# GLOBAL PERFORMANCE INDICATORS

The study h'6nz confirmed the general Impression that while access to service has improved in terms of the average kilowatt hours generated per capita and the percentage of population served, the quality of service Is poor and has shown no sign ot Improvement. In many cases, service has deteriorated as utilities place unduly great emphasis on
. Notably, they found limited correlation between the percentages of underweight and stunted children across woredas.

Other studies employed SAE to estimate dynamic changes in nutritional outcomes. In fact, to estimate the trends in age-specific chronic childhood undernutrition in all of Bangladesh's 64 districts spanning from 1997 to 2018, Das et al. (2023) employed advanced small area time-series models. These models integrated direct estimates derived from seven Demographic and Health Surveys, combining crosssectional, temporal, and spatial data to make predictions for all districts during both survey and nonsurvey years. At the national level, their findings revealed a significant reduction in stunting prevalence, decreasing from approximately three in five children to one in three. However, the study also exposed substantial disparities in chronic undernutrition across the districts, with certain areas experiencing less pronounced declines. Moreover, these disparities became more apparent when examining stunting prevalence at the district-by-age level, where only districts in more socio-economically advantaged regions consistently reported declines in stunting across all age groups.

In our present study, we present small area estimates both of monetary poverty and stunting, allowing us to uncover a distinct dimension of undernutrition known as "hidden hunger". Our research unveils the prevalence of hidden hunger and employs a novel bivariate mapping approach to rank and categorize hidden hunger across various communes. This approach is valuable for facilitating location5 | P a g e
skills on wages. The paper finds that an additional standard deviation in the overall non‐cognitive skill measure increases earnings by 9 percent, similar in magnitude to the effects of individual cognitive skills. Specifically, emotional stability<sup>6</sup> and perseverance of effort are associated with 5 and 8 percent higher earnings respectively; while agreeableness is associated with 8 percent lower earnings. Lavado, Velarde, and Yamada (2014) complement the ENHAB with longitudinal data on national test scores and find that differences in non‐cognitive skills explain gender gaps in returns to abilities, schooling, employment and occupation.

The case of Peru is interesting not only from a data, but also from a policy, point of view. The country is engaged in a debate about which skills are the key to labor market success, including a discussion about the role of non‐cognitive skills. Since the beginning of the millennium, Peru has experienced impressive growth rates, rising from 2 percent in 2001 to more than 9 percent in 2008, returning to over 6 percent by 2013; largely due to a boom in export products in newly developed markets.<sup>7</sup> Drawing from evidence from the US and Europe, the Peruvian Ministry of Education designed and is pilot‐testing a non‐cognitive skills development program. Furthermore, since November 2013, the Ministry of Labor has been implementing a series of free short workshops that

> 4 http://www.oecd.org/site/piaac/

> 5 http://siteresources.worldbank.org/EXTHDOFFICE/Resources/5485726‐1281723119684/TEP_Skills_Measurement_Brochure_Jan _2012.pdf

> 6 The concept “emotional stability” captures the depth of reaction to emotions, particularly negative emotions. Those who have more moderated reactions are rated as having higher emotional stability. Those who are more reactive are rated as being less emotionally stable, or the (unfortunate) parlance of “neuroticism.”

> 7 World Development Indicators, accessed on June 13, 2014.
herding is expected to decrease with the transparency of the assets for a given level of risk.<sup>2</sup> In other words, securities for which information is widely available and that entail less risk are less likely to induce herding patterns. We thus exploit our dataset to analyze herding by asset type by the same institutional investors. In particular, we calculate herding in corporate bonds, financial institutions bonds, government bonds, mortgage bonds, and equity.

Second, herding might also be explained by managers following similar trading strategies like momentum (Froot et al., 1992 and Gompers and Metrick, 2001). Momentum trading, defined as the purchasing (selling) of assets whose returns are positive (negative), is a popular investment strategy and has been often found in developed and developing countries. Its presence among US institutional investors (primarily mutual funds) has been widely documented in the literature.<sup>3</sup> Our data on returns allows us to test whether, to the extent that there is herding, it is driven by momentum strategies.

Third, managers might herd as a way to reduce risk. While traditional theories of asset allocation focus on the problem of an isolated investor whose goal is to maximize wealth or consumption at some point in time, several papers study the incentives schemes that arise in the context of financial intermediation. In particular, the conflicts of interest between fund managers and the underlying investors can affect manager risk-taking behavior (Sharfstein and Stein, 1990; Shleifer and Vishny, 1990; Chevalier and Ellison, 1999; Graham, 1999; Stein, 2003, 2005;

> 2 Although theoretical models of herding behavior do not focus on the characteristic of the assets as a determinant of herding, they typically imply a relation with opacity. For instance, in Sharfstein and Stein (1990), herding can only occur when there are “systematically unpredictable factors affecting the future state that nobody can know anything about,” that is, under the presence of an