rafmacalaba/datause-encoder-data huggingface dataset:
labor supply and earnings of adult family members, the health and education of children, the consumption and poverty levels of the household, and fertility choices. However, the above illustrates some of the many ways that migration is itself determined by different aspects of the family that then may differ between otherwise identical individuals with different migration statuses. This complicates identifying the impacts of migration if these family characteristics that determine migration also are likely to affect the outcomes of interest.

Let us illustrate with an example that often captures considerable policy attention when discussing migration’s impacts on families: the impact on children left behind. Save the Children in Sri Lanka (2006) claims there is a “clear negative impact that migration of women has on the education of children” as well as an emotional and psychological impact. UNICEF (2022) argues that while “children who remain behind while their parents migrate may benefit from improved living conditions and access to education and other services due to remittances [...]. Migration also brings potential emotional harm as a result of separation from one or both parents”. Such policy statements typically are based on comparisons of children in migrant and non-migrant households. Bilsborrow (2016) also recalls that the World Bank started including a module with retrospective questions on the occurrence of migration episodes in the LSMS out of concern for the children left behind.<sup>11</sup> For example, consider a regression that attempts to examine the relationship between an outcome _Yj,h_ for child _j_ in household _h_ , such as child education or emotional state, and household participation in migration _Mh_ , after controlling for observed characteristics of the child and her household **_X_** : _j,h_ For this type of comparison to identify the causal impact of migration we require _E_ ( _Mh × εj,h|_ **_X_** _j,h_ ) = 0. That is, after conditioning on observed characteristics in **_X_** _j,h_ , the migration decision is uncorrelated with other determinants of the outcome that appear in Eq. (2). a group of individuals related by marriage and consanguinity. [...] Families typically consist of multiple households forming a network of kith and kin, related by blood or marriage but not necessarily living together.” (pp. 3189–90).

> 11“The LSMS survey of Ecuador in 2005-2006 [...] included a module on emigrants from the household, recording their current age, sex, relationship, education, and whether the emigrant left minor children under age 18 behind (there being special concern at the time, following the surge of emigrants to Spain in 1997–2003, about who was taking care of them following the emigration of a parent, often the mother).” (Bilsborrow, 2016, p. 125)

10
# **3.3 Estimation Methods**

It is widely known that informal firms are small, more likely present in certain services sectors with a higher level of participation of women in these sectors, among others. Therefore, we correlate the adverse effect of the pandemic (or the adjustments response of firms) with informality by itself once we control for these firm-level characteristics. We estimate the model shown in equation 2: The main controls included in **_Xit_** are indicator variables to identify microenterprises (less than nine employees), age, sectors, and a female-leadership. We also control for country and time-fixed effects, the severity of the lockdown (using the Google mobility indicator), and a dummy variable for whether the firm is a panel firm. The latter controls for any recall bias in retrospective variables and differences in new firms’ characteristics. Our main outcome variable is the change in sales since the start of the pandemic which measures the size of the shock.<sup>10</sup> We implement a Shorrocks-Shapley decomposition for the change in sales to analyze the relative impact of informality against other firm-level characteristics.<sup>11</sup> To understand the heterogeneity in the impact of the shock and the adjustment responses among informal firms, we estimate the model in equation 3: _Zit_ is each of the different groups of variables included in vector **_Xit_** taken separately.

Finally, we estimate split regressions, to allow for different coefficients for different types of firms (necessity, De Soto, parasite, and fully formal).<sup>12</sup>

> 10In addition to the change in sales, we also consider other outcome variables such as the firm’s adjustment response to the pandemic through technology adoption and innovation in products and services.

> 11Such decomposition has been widely used to understand inequality and poverty measures (See Shorrocks and Shorrocks, 1999; Shorrocks, 2013; Sastre and Trannoy, 2002).

> 12To analyze the statistical significance of the differences between coefficients, we also estimate a fullyinteracted model of the types of informality categorical variable, which is equivalent to the split regressions setting.

10
# **Table 7(d): Hypotheses Tests for the Beta Vector**

|rm2|Zero|Chi^2(1)|5.6395 [0.0176]*|
|---|---|---|---|
|rgdp|Zero|Chi^2(1)|3.9278 [0.0475]*|
|INFL|Zero|Chi^2(1)|1.5497 [0.2132]|
|Interestrt|Zero|Chi^2(1)|25.429 [0.0000]**|
|DISC|Zero|Chi^2(1)|24.227 [0.0000]**|
|Constant|Zero|Chi^2(1)|1.4148 [0.2343]|
|Joint Significance for betas (including constant)|Zero|Chi^2(5)|32.104 [0.0000]**|
|Joint Significance for betas (excluding constant)|Zero|Chi^2(4)|32.063 [0.0000]**<br>[strong evidence that rm2 is non<br>stationary]|
|**Joint Significance for Betas for (INFL and**<br>**Constant)**|**Zero**|**Chi^2(2)**|**4.9499 [0.0842]**|
|Joint Significance for Betas for (INFL and<br>Constant) and alpha for (rm2)|Zero|Chi^2(2)|10.602 [0.0141]*|

50
Policy Research Working Paper 6822

# **Abstract**

Using detailed survey data on management practices, this paper uses recent advances in unconditional quantile analysis to study the changes in the within country distribution of management quality associated with country convergence to the managerial frontier. It then decomposes the contribution of potential explanatory factors to the distributional changes. The United States emerges as the frontier country, not because of better management on average, but because its best firms are far better than those of its close competitors. Part of the process of convergence to the frontier across the development process represents a trimming of the left tail, much is movement of the central mass and, for rich countries, it is actually the best firms that lag the frontier benchmark. Among potential explanatory variables that may drive convergence, ownership and human capital appear critical, the former especially for poorer countries and that latter for richer countries suggesting that the mechanics of convergence change across the process. These variables lose their explanatory power as firm and average country management quality rises. Hence, once in the advanced country range, the factors that improve management quality are less easy to document and hence influence.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
In a few instances, the fraction is relatively low using aggregate exports, but higher if we calculate the ratio limited to manufacturing firms in the numerator and to manufactured products in the denominator. For example, the firms surveyed in Angola account for less than 1% of aggregate exports, which are dominated by oil. Limited to manufacturing, the ratio is 16.8% in 2006 and 2.4% in 2010.

The rich information in the enterprise surveys opens up a wealth of opportunities for research. Information is collected for the following broad modules, which focus on the composition of inputs and outputs, interactions with the external environment, and the identification of obstacles to operation or growth. The standard questionnaire contains the following modules:

- A. Control information: stratification variables, type of accounting, interviewer,...

- B. General information: owner, year of establishment, quality certification,...

- C. Infrastructure and services: problems, connection requests,...

- D. Sales and supplies: type and value of sales

- E. Degree of competition

- G. Land: also questions related to construction permits

- I. Crime

- J. Business-government relations: type spent, incidence of corruption,...

- K. Finance: source and value for different types

- L. Labor: employment by type

- M. Investment climate constraints: rank the severity of various obstacles

- N. Performance: cost components

# **2.2 Extent of firm-level integration in GVCs**

The different modules of the questionnaires allow one to zoom in on a variety of topics that are relevant to GVC integration. For example, the questions make it possible to assess the importance of the following issues:

- Growth constraints: some frequently-mentioned constraints, e.g. lack of demand, could be much less of a problem for firms active in GVCs.

- Conflict resolution: weak domestic institutions are often an impediment to business development. For firms active in GVCs, relational contracts (i.e. incentives coming from the continuation value of a relationship) could be effective substitutes for formal institutions (as in Van Biesebroeck, 2014)

- Employment/Capital: the surveys contain very detailed information on the structure of employment and types of capital which could be used to investigate necessary conditions for effective integration in GVCs.

More specifically, Taglioni and Winkler (2014) describe how some of the measures that have been calculated in the GVC literature using a variety of data sources in a variety of countries, can also be calculated using the WBES information. An important advantage of this data source is that the necessary questions have been posed identically and in the exact same situation in all the different countries. An additional advantage is that the data represent a 8
capital cost of the fuel source, the characteristics of the household, and the future benefit from utilization of that fuel source. Conditional on choosing a particular fuel source, we estimate the monthly demand for that type of energy and use these estimates as an input in the discrete choice model.

We estimate our model using data from Uganda. The electricity industry in Uganda was restructured starting in 1999, and the reform process has been one of the most advanced in Africa. It included the vertical separation and partial privatization of the incumbent utility (Mawejje et al., 2013). The system operator, Uganda Electricity Transmission Company Limited (UETCL), is responsible for electricity purchases from independent generators. Electricity distributors, including the primary distribution utility Umeme, pay a regulated price to buy electricity from UETCL. This feature of the restructured industry is useful for our analysis. The regulated wholesale price makes the cost for a distribution utility of adding a customer more transparent than it would be in the case of a vertically-integrated firm.

The Electricity Regulatory Authority in Uganda sets tari↵s using a rate-of-return methodology (Electricity Regulatory Authority, 2006).<sup>5</sup> The revenue requirement for each firm is the sum of operating and maintenance costs, lease costs, and costs related to capital investment, including an allowed rate of return on invested capital. The revenue requirement is split by customer category and divided by electricity volumes to calculate the unit price (P ̈oyry Management Consulting, 2012). Under rate-of-return regulation, firms have an incentive to keep the connection and fixed charges low to encourage new connections and increase their rate base (Averch and Johnson, 1962; Davis and Muehlegger, 2010). However, this result may not hold if regulators cannot credibly commit to setting future tari↵s that allow for recovery of past capital investment (La↵ont and Tirole, 1986). For example, in Uganda the President has directly intervened in favor of particular energy projects, overriding existing contracts. This type of interference has undermined investor confidence (Whitley and Tumushabe, 2014, p.25). Another issue is that bureaucratic red tape may increase the cost of projects but not be recoverable through additions to the rate base.

Despite our choice of setting, we should emphasize that the regulatory distortion created by low retail prices is not an issue in Uganda. Instead, we will show that the regulated price in Uganda is sufficient to recover the costs of supplying electricity, and given this regulated price, the connection charge set by Umeme is close to optimal. Along with the Seychelles, Uganda is one of only two countries in Sub-Saharan Africa that Trimble et al. (2016) describe as having

> 5The regulator reviews the rate-of-return tari↵s annually for each firm. There is a quarterly adjustment to allow for pass-through of changes in fuel prices, inflation, and exchange rates.

3
Alkire, S. and M. E. Santos (2013), “Measuring Acute Poverty in the Developing World: Robustness and Scope of the Multidimensional Poverty Index”, The Oxford Poverty & Human Development Initiative Working Paper No. 59, University of Oxford.

Alkire, S. and J.M. Roche (2013), "How MPI Went Down: Dynamics and Comparisons" , Oxford Poverty & Human Development Initiative, University of Oxford **.**

- Alkire, S. and  J.M. Roche (2011),  “Beyond headcount: Measures that reflect the breadth and components of child poverty”, _OPHI Working Paper_ 45.

- Atkinson, A. B.  (2003), “Multidimensional deprivation: Contrasting social welfare and counting approaches”, _Journal of Economic Inequality_ , l (1): 51-65.

- Atkinson, A. B. and M. A. Lugo (2010),  “Growth, poverty and distribution in Tanzania”, London School of Economics, London:  International Growth Centre, Working Paper 10/0831.

Bernard, Tanguy, Stefan Dercon, Kate Orkin, and Alemayehu Seyoum Taffesse (2014), “The Future in Mind: Aspirations and Forward Looking Behavior in Rural Ethiopia”, _Centre for Study of African Economies Working Paper_ 16, 1-48.

- Bevan, P., C. Dom, and A. Pankhurst (2014), _Long term perspectives on development impacts in rural Ethiopia: WIDE stage 3_ . Mokoro Limited: Oxford.

Carranza, E. and J. Gallegos (2013), “Ethiopia Trends of Wellbeing 2000-2011”, Poverty Reduction and Economic Management, Africa Region, World Bank.

Central Statistical Authority [Ethiopia] and ORC Macro, (2001). _Ethiopia Demographic and Health Survey 2000._ Addis Ababa, Ethiopia and Calverton, Maryland, USA: Central Statistical Authority and ORC Macro.

27
were destroyed in the agriculture sector over the last decades and the service sector did not generate enough jobs to absorb those displaced workers.

During the last decade, transition economies have also experienced a large worker and job reallocation due to the significant destruction of jobs in privatized companies and in the agriculture and over-staffed industrial sector. Hence we might expect high start-up costs to prevent firm entry and therefore increase unemployment.

Administrative burdens on firm creation refer to issues such as the number of procedures to register a business, the time and cost to undertake those procedures and the minimum capital requirements. However, as one can see in Box 1, other “unofficial burdens” such as informal payments or bribes required to get business licenses, can have also a large impact on the administrative entry barriers for new businesses.

# **Box 1: What the official data on entry barriers do not show: the case of Romania**

Measurement matters. Different ways of measuring the same concept can yield different conclusions. The case of the start-up costs in Romania is a good example of that.

According to _objective_ data from the World Bank, opening a business is easy in Romania: no more than 6 procedures are required to start a business, one can go through the process in less than a month (37 days in average in Europe) and the cost of the process and minimum capital requirements are among the lowest in the ECA countries.

However, when one looks at similar indicators of start-up costs based on _subjective_ opinions, in this case of managers of large corporations, the situation is quite different. According to the evaluation given to the sentence “Starting a new business is generally easy” by the Global Competitiveness Report, Romania is one of the countries where starting a business is most difficult (figure 2.1).

The differences between the _objective_ and _subjective_ indicators could lie on the existence of wide-spread corruption in Romania, which is not included in any of the objective measures of start-up costs compiled by the World Bank. Indeed, when one looks at the GCR’s evaluation of the sentence “irregular additional payments connected with import and export permits, business licenses, exchange controls, tax assessments, police protection or loan applications are very rare” (figure 2.2), Romania along with Turkey occupies the last position.

Hence objective indicators have to be complemented sometimes with subjective impressions and anecdotal evidence to get a complete picture of the business environment in each country.

6
- [17] Chiswick, Carmel, Barry Chiswick and Paul W. Miller (1985). "Are Immigrants and Natives Perfect Substitutes in Production?" International Migration Review, Winter, pp. 674-685.

- [18] Cortes, Patricia (2008), "The Effect of Low-Skilled Immigration on U.S. Prices: Evidence from CPI Data", The Journal of Political Economy, Vol. 116, No. 3, pp. 381-422.

- [19] Del Carpio, X., C. Ozden, M. Testaverde and M. Wagner (2013). "Local Labor Supply Responses to Immigration," mimeo.

- [20] Docquier, Frederic, Caglar Ozden and Giovanni Peri (forthcoming) "The Wage Effects of Immigration and Emigration," The Economic Journal.

- [21] Dustmann, Christian, Tommaso Frattini and Ian Preston (2013). "The Effect of Immigration along the Distribution of Wages," The Review of Economic Studies, Vol. 80, Issue 1, pp. 145-173.

- [22] Friedberg, Rachel M. (2001), "The Impact of Mass Migration on the Israeli Labor Market", The Quarterly Journal of Economics, Vol. 116, No. 4, pp. 1373-1408.

- [23] Grossman, Jean (1982), "The Substitutability of Natives and Immigrants in Production," The Review of Economics and Statistics, Vol. 64, No. 4, pp. 596-603.

- [24] Hanson, Gordon and Craig McIntosh (2010). “The Great Mexican Emigration,”Review of Economics and Statistics, Vol. 92, No. 4, pp. 798-810.

- [25] Katz, Lawrence and Murphy, Kevin (1992). "Changes in Relative Wages, 1963-1987: Supply and Demand Factors," The Quarterly Journal of Economics, Vol. 107, No. 1, pp. 35-78.

- [26] Lach, Saul (2007), "Immigration and Prices" Journal of Political Economy, Vol. 115, No. 4, pp. 548-587.

49
Policy Research Working Paper 5114

# **Abstract**

Much of the developing world has experienced a decline in mortality, while fertility often has remained high. This has produced youthful populations in many countries, generally referred to as “youth bulges.” Recent empirical research suggests that youth bulges may be associated with increased risks of political violence and conflict. This paper addresses ways that education may serve as a strategy to reduce the risk of political violence, particularly in the context of large cohorts of young males. The authors use a new education dataset measuring educational attainment. The dataset is constructed using demographic back-projection techniques, and offers uninterrupted time-series data for 120 countries. The empirical analysis finds evidence that large, young male population bulges are more likely to increase the risk of conflict in societies where male secondary education is low. The effect on conflict risk by low education and large youth populations is particularly strong in low and middle-income countries. This is especially challenging for Sub-Saharan Africa, the region facing the youngest age structure and the lowest educational attainment levels. Although quantitative studies generally find a strong relationship between indicators of development and conflict risk, the results suggest that poor countries do have some leverage over reducing conflict potential through increased educational opportunities for young people. There is further evidence that the interaction of large youth cohorts and low education levels may be mediated by structural economic factors. The study supports broad policy interventions in education by relaxing concerns about the consequences of rapid educational expansion.

This paper—a product of the Post Conflict & Social Development Unit, Africa Region, with support from the Trust Fund for Environmentally and Socially Sustainable Development and the Norwegian Ministry of Foreign Affairs—is part of a larger effort to build a solid body of theoretical and applied research on the links between youth exclusion and political violence. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Bilal.Barakat@oeaw.ac.at and henriku@prio.no.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team