rafmacalaba/data-use-mentions-extended huggingface dataset:
# <u>6.2 Networks</u>

Table 5 (Panel B) reports program impacts on a number of networking proxies, including whether the beneficiaries registered at employment offices, whether they knew an entrepreneur, an employment officer or a banker, and how often he/she interacted with him. Results show that the entrepreneurship training was effective in increasing the business and employment networks of participants. Participants are marginally more likely to be registered at employment offices, an indicator obtained directly from administrative data, not from the follow-up survey. Graduates that were assigned to the entrepreneurship track are also more likely to know an employment agent, even though they do not interact with the employment agent more frequently than graduates from the control group. Furthermore, there is evidence that the program increased the probability of knowing an entrepreneur, as well as knowing and interacting frequently with a banker. Interestingly, however, program participants were less likely to seek advice from a professor to develop a new project idea.

# <u>6.3 Behavioral skills</u>

As discussed in section 2, the entrepreneurship training contained a module “for the person” aiming to develop entrepreneurial traits and behavioral skills. These behavioral skills are likely most malleable among youth (Almlund et al., 2011). In Tunisia, during qualitative interviews, some training facilitators stressed that as part of this module one of their main objectives was to change students’ behavioral skills to contribute to “turn them into entrepreneurs.” We now consider whether the program impacted a range of behavioral skills often associated with entrepreneurship.

First, Table 6 (Panel A) presents estimates of program impacts on risk and time preference parameters. Indicators of willingness to take risk include a direct self-reported measure (“on a scale from 1-10, how willing are you to take risks?”), as well as the certainty equivalent for an imaginary lottery where the respondent would have a 50 percent chance of winning 0 and a 50 percent chance of winning 2000 TND
35 Table 13: Percentage of households citing reasons for not connecting to the grid

|Location|Too<br>costly|No need|Dwelling<br>unsuited|Pending|Discon‐<br>nected|Unreliable<br>service|Other|No<br>reply|
|---|---|---|---|---|---|---|---|---|
|Malawi urban|37|2|4|6|0.0|—|0.1|51|
|Malawi rural|56|3|8|2|0.1|—|4|27|
|Malawi total|49|2|7|4|0.1|—|2|36|
|Nigeria urban|59|6|2|1|—|15|16|—|
|Nigeria rural|63|8|8|5|—|8|9|—|
|Nigeria total|62|7|6|4|—|10|11|—|

_Source_ : World Bank staff calculations using household survey data. _Note_ : No need = no need for electricity; dwelling unsuited = dwelling unsuited for connection to the grid; pending = application for connection pending; disconnected = disconnected for non‐payment; — = reason not among the options cited in the questionnaire.

# No spending on grid electricity

Survey questionnaires are generally not sufficiently detailed to quantify how many households do not pay for electricity. The data collected should ideally be able to separate the absence of reported household spending on electricity for legitimate reasons—such as inclusion of electricity in the rent, or in the compensation package for employment—from evasion of payment, such as theft of electricity or non‐payment without disconnection. No household survey enabled probing of this question at that level of detail.

- In Mali, Niger, and Zambia, the survey
)

||Innovati|on type|R&D a<br>and equi|nd machinery<br>pment upgrades|
|---|---|---|---|---|
||New to the<br>company|New to the<br>market|R&D<br>investment|Machinery and<br>equipment upgrades|
|Trader|10.9|18.5|22.6|44.5|
|Non-trader|6|11.4|10.6|36.5|

_Source:_ the authors’ calculations based on EBRD-EIB-WB Enterprise Survey.

Confirming the idea of self-selection into trade, firms engaging in international trade tend to be more productive. Table 4 shows that there are significant productivity and firm size premia associated with trade participation. We also find that firms that trade and innovate at the same time tend to be much more productive than other firms. Traders are on average 25% more productive and 30% larger than non-traders. Innovators are around 40% more productive and larger. However, the premia are even higher for firms that trade and innovate at the same time. In addition, innovative traders tend to have higher sales growth.

> 10 Table A2 shows that this positive association between trade and innovation also holds in regression analysis in most MENA countries (with the exceptions of Lebanon and the West Bank and Gaza).

> 11 In this analysis, controlling for firm size is important, as larger firms are also more likely to use foreign licensed technology, use a website, have recently upgraded their machinery or use international quality certification.

11
earn 30.2 percent less as compared to men. The only sector which provides higher wages to female workers compared to male counterpart is transport and storage industry (with gender wage gap of 17.4 percent), however, this sector has very low concentration of women workers. Thus, many different factors determine the wage gap in Pakistan, women are generally concentrated in a smaller number of specific industry groups than men, and these occupations usually pay lower wages. In occupations where there are fewer women, they tend to earn relatively higher wages.<sup>20</sup>

# 2.3.4 Sri Lanka

Sri Lanka is the second best among South Asian countries in terms of gender equity with regard to attainment of education, health and other social indicators for women, however, female labor force participation has remained pegged at a low level of 30-35 percent of the workforce for the past two decades. An ILO (2016) study on women’s labor force participation in Sri Lanka highlighted that combination of demand-side constraints such as discrimination in employment, differences in the quality of jobs available to women versus the quantity of such jobs, wage discrimination faced for doing exactly the same job, etc. result in lower participation of women into the workforce than men.<sup>21</sup> In another way, Sri Lanka is distinct compared to other countries in the region is that it has less dependence of women in agriculture and other informal activities. The 2014 labor force survey in Sri Lanka shows that the highest percentage of women workers are in the

> 20 Saeed, Muhammad B. (2017).

21 International Labor Organization 2016. Factors affecting women’s Labor force participation in Sri Lanka, ILO Country Office for Sri Lanka and the Maldives. Colombo.

19
destinations in 2000 and 31 destinations in 1990. In the third step, data collected in steps 1 and 2 are used to estimate the determinants of the size of bilateral migration (Mijt<sup>s)from countryitocountryjintheeducationgroupsatyeart.Then,basedonthese</sup> estimations, the size of the migrant stock from each origin country to the remaining 119 non-OECD host countries in 2000 (and 134 in 1990) for each education level is predicted. The ...nal 195x195 database combines recorded migration stocks (when census and register data are available) and out-of-sample predictions (when o¢ cial statistics are missing).

Table A1 provides summary statistics for migration stocks which are comparable with labor force data. The database characterizes the origin, destination and education level of about 100.5 million adult migrants in 2000, and 80.2 million in 1990. Table A2 distinguishes observed and imputed values for immigrants to countries included in our sample. Table A3 shows stock of immigrants and emigrants in 1990 and 2000 as percentage of the skill-speci...c labor force. Table A4 describes the distribution of the labor force between college graduates and less educated as percentage of total.

> 17Beine et al (2007) have estimated the age-of-entry structure of high-skill immigration and proposed alternative measures of the brain drain excluding those who left their home country before age 12, 18 or 22. The corrected rates are obviously lower than those calculated without age-of-entry restrictions. However, the correlation between corrected and uncorrected rates is extremely high and the country rankings by brain drain intensities are basically una¤ected by the correction. This should mitigate concerns about children migration possibly leading to cross-sectional biases in the brain drain estimates.

24
-- End of picture text -->

**D. Contributions to change in average debt-toGDP ratio, 2023-26**

<!-- Start of picture text --> Percentage points of GDP<br>80<br>60<br>40<br>20<br>0<br>Debt in Growth Primary Interest Other<br>2023 deficits cost<br><!-- End of picture text --> _Sources:_ WEO (database); World Bank.

_Note:_ Contributions to changes in debt-to-GDP ratios. See annex 1 for further details. “Other” factors include exchange rate depreciation, inflation, privatization proceeds, the materialization of contingent liabilities and other ad-hoc changes to debt stocks.

A.-C. Orange bars indicate debt-decreasing contributions and red bars indicate debt-increasing contributions. Sample includes 32 small states.

D. Orange bars indicate debt-decreasing contribution and red bars indicate debt-increasing contribution. The decomposition is based on forecasts from the April 2024 _World Economic Outlook_ . Sample includes 32 small states.

33
Policy Research Working Paper 10510

# **Abstract**

This paper analyzes the effects of power outages and constraints on manufacturing firms’ revenue-based total factor productivity in developing countries. The empirical analysis is based on the World Bank Enterprise Surveys dataset for 84 countries over 2006–2019. The paper starts by showing statistically that firms facing power outages differ and operate in very different environments compared to firms not facing power outages, underlining a potential nonrandom issue of the treatment variable. The matching-based approach (entropy balancing) is designed to contain this type of bias. It shows that power outages negatively and significantly affect firm-level revenue-based total factor productivity, with a 9 percent lower unconditional average productivity for exposed firms compared to nonexposed firms. Moreover, the estimates suggest a connection between the severity of self-reported power constraints or obstacles by firms and the magnitude of revenue-based total factor productivity loss. The results also indicate that the effect of power outages on firm-level revenue-based total factor productivity could be influenced by the stage of economic development (low-income countries, lower-middle-income countries, upper-middle-income countries), and the ability of firms to engage in research and development and purchase backup generators. These findings suggest that to ensure economic development, the government should provide a stable power supply that can mitigate the negative shocks faced by manufacturing firms and enhance their productivity and competitiveness, allowing them to drive economic growth.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at aly6@worldbank.org or aapeti@ifc.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
percent in the Kyrgyz Republic to more than 95 percent in Estonia. However, the difference between reported income and household expenditures is large enough to mean that conclusions about the distribution of consumption inferred from the distribution of income are problematic. This is one of the reasons why the poverty profile in this study is based on household expenditures.

It is also futile to compare exactly the distribution of consumption before and after transition. During the pre-transition period, wages and prices were controlled and food and other consumer goods were allocated by queuing, rationing, and favoritism. Neither money income nor money expenditure reflected adequately the household's real consumption, since much of that real consumption was allocated to the household through non-market, non-money means.- For example, senior workers at larger enterprises received better housing than junior workers at smaller enterprises, and they had shorter queues for purchasing automobiles, etc. Without ever considering their money income, it is clear that senior workers were better off. Unfortunately, there is no reliable way to reconstruct the real consumption of the poor and non-poor prior to transition, due to the absence of reliable, non-biased household data sets.

# **IEL** Who are the Poor After Five Years of Transition?

The working poor predominate in the poverty profile for the FSU countries. By and large, in Estonia, Russia, and the Kyrgyz Republic, the head of household of poor families is employed, most often in the state sector. Results indicate that poverty rates in rural areas are much higher than in urban areas.

44
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Niger’s Internet market compares poorly with other ECOWAS countries**

a. Internet service trends, Niger <!-- Start of picture text --> 0.8 8<br>0.7 7<br>0.6 6<br>0.5 5<br>0.4 4<br>0.3 3<br>0.2 2<br>0.1 1<br>0.0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> b. Internet service, ECOWAS, 2008 <!-- Start of picture text --> 14 350<br>12 300<br>10 250<br>8 200<br>6 150<br>4 100<br>2 50<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br>Sierra Leone Liberia Niger Guinea-Bissau Mali Burkina Faso Mauritania Gambia Senegal Benin Ghana Cote d'Ivoire Cape Verde Togo Nigeria<br><!-- End of picture text --> _Source:_ World Bank, including AICD analysis. _Note:_ ECOWAS = Economic Community of West African States.

# Financing Niger’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Niger needs to expand its infrastructure assets in key areas (table 16). The targets outlined in table 16 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African
that discriminate by gender on natal inheritance and spousal inheritance, the two variables on inheritance are merged into a single variable. We include an additional variable that measures if men and women have equal ownership rights to immovable property. This picks up discrimination by gender not only in the legal ability to own property, but also in the legal treatment of spousal property. Thus, in countries where husbands are granted the administrative right of marital property, this variable would take on a value of 0. The fourth variable in this set relates to pay equality and captures if the law mandates equal remuneration for work of equal value (Figure 3).

The WBL variables under consideration in the empirical analysis broadly reflect the pathways to property ownership by women discussed in section 2. We expect that laws discriminating against women in property use and ownership and, in the workplace, may have a negative association with their ability to own any land or housing, either solely or jointly with someone else. The variable on the valuation of nonmonetary contributions captures two related pathways to asset accumulation – via equal rights for women in marital and spousal assets (depending on full or partial community of property) and recognizing women’s contribution to social reproduction and the care economy. The gendered division of labor and expectations that women are responsible for the home in practically every country constrain women’s ability to fully realize their potential in the labor market which negatively impacts their earnings. Finally, discrimination in the workplace is captured by looking at legislations that mandate equal remuneration (including emoluments in cash or in-kind) for equal work. Such laws increase the likelihood that women in 15
The rest of the paper is structured as follows. Section (2) discusses the related literature and puts the contributions of this paper in perspective. Section (3) is devoted to the conceptual framework that describes the measure of sibling correlation and the decomposition methods for estimating the share of intergenerational transmission in sibling correlation. A special focus here is on the Bingley and Cappalleri (2019) approach. Section (4) discusses the advantages of the Demographic and Health Surveys for our cross-country analysis and provides a brief discussion of the estimation methods. Section (5), arranged in a number of subsections, reports and discusses the estimates of sibling correlation. Section (6) discusses the estimates of the share of the intergenerational transmission across regions and countries, and traces out the evolution over time from the 1970s birth cohort to the 1990s birth cohort. The paper ends with a summary of the results and the contributions of the paper in the conclusions.

# (2) Related Literature

The economics literature on intergenerational mobility is grounded on the seminal contributions of Becker and Tomes (1986) that developed a model of intergenerational persistence in permanent income focusing on the role of human capital. The inequality of opportunity (IOP) strand of the literature builds on the foundation of the theory of distributive justice developed by Roemer (see Roemer (1998), and Roemer and Trannoy (2016)). The inequality of opportunity (IOP) refers to the circumstances a child is born into, and emphasizes that inequality due to the circumstances is unjust and should be the focus of policy interventions. Although these two approaches grew largely independently, there has been an increasing appreciation that they deal with fundamentally the same question.<sup>18</sup> These two approaches can be best viewed as complementary. The IOP provides a theory of justice foundation, but does not identify the economic mechanisms which could be the policy levers. The Becker-Tomes model identies a set of such economic mechanisms. The sociological literature uses occupational prestige and class
# **1 Introduction**

Household businesses comprise the majority of firms in developing countries (Hsieh and Olken, 2014; La Porta and Shleifer, 2014).<sup>1</sup> They employ a large share of the workforce, and matter significantly to the livelihoods of households in low-income countries (Gollin, 2002, 2008; Nataraj, 2011; McCaig and Pavcnik, 2021). Their performance and dynamics are therefore key to understanding the livelihoods of poor households, generation of jobs, and aggregate labor productivity in low-income countries.

In this paper, we document novel stylized facts about entry and exit of household businesses in a developing country context. We utilize a unique tax census data disclosed by the General Department of Taxation that covers the universe of tax-registered household-owned businesses in Vietnam between January 2018 and August 2020, a period spanning the COVID-19 pandemic. The data include monthly information on (permanent) closure filings and (temporary) suspensions of household businesses across all communes in the country.<sup>2</sup> We find that tax-registered household businesses exhibit rates of entry and exit of about 5-6 percent a year. Existing estimates in the literature of the rate at which small firms die (exit) in developing countries range from as low as 3 percent per year (Frazer, 2005; Davies and Kerr, 2018) to over 30 percent annually (Fajnzylber et al., 2006; Nagler and Naud ́e, 2017). McKenzie and Paffhausen (2019) find that the rate of firm exit in developing economies averages 8.3 percent per year. McCaig and Pavcnik (2021) document an entry and exit rate of 14-18 percent for informal sector firms in Vietnam. These estimates are higher than the ones documented in this paper and the difference is likely due to by the nature of the data, which includes household businesses whose larger revenue scales subject them to register and pay annual taxes by law, and are thus less prone to turnover.<sup>3</sup
the Gini coefficient nn nn cc,2015 cc,TT ��εε �kk=1, ... , �εε �kk=1 �. Each draw is generated using mean and variance sample statistics for nn nn cc,2015 cc,TT a specific period. ��uu kk=1, ... , �uu kk=1 Although the simulation exercises are designed to be comprehensive and to project multiple and simultaneous performances of factors affecting the countries’ mean income growth, our model is simple by design and includes some caveats. Four core elements considered when we

> 27 Data were retrieved on February 22, 2019 from PovcalNet (World Bank 2019).

> 28 A priori, given the embedded historical information in the period 1980–2015, country-specific simulations are expected to be much closer to the idea of inequality convergence; this means that inequality tends to decrease in countries with high inequality and increase in countries with low inequality (Benabou 1996, and Ravallion 2003).

Page **17** of **45**
Economics Series Working Papers: 1999-W32. Castellazzi, M. S., et al. 2008. "A Systematic Representation of Crop Rotations." _Agricultural Systems_ , **_97_** (1), 26-33.

Davis, A. S., et al. 2012. "Increasing Cropping System Diversity Balances Productivity, Profitability and Environmental Health." _PLOS ONE_ , **_7_** (10), e47149. Deininger, K., D. Nizalov and S. K. Singh. 2018. "Determinants of Productivity and Structural Change in a Large Commercial Farm Environment: Evidence from Ukraine." _The World Bank Economic Review_ , **_32_** (2), 287-306. Gentry, L., M. Ruffo and F. Below. 2013. "Identifying Factors Controlling the Continuous Corn Yield Penalty." _Agronomy Journal_ , **_105_** , 295. Hendricks, N. P., A. Smith and D. A. Sumner. 2014. "Crop Supply Dynamics and the Illusion of Partial Adjustment." _American Journal of Agricultural Economics_ , **_96_** (5), 1469-1491. Hennessy, D. A. 2006. "On Monoculture and the Structure of Crop Rotations." _American Journal of Agricultural Economics_ , **_88_** (4), 900-914. Hilton, S., et al. 2013. "Impact of Shortened Crop Rotation of Oilseed Rape on Soil and Rhizosphere Microbial Diversity in Relation to Yield Decline." _PLOS ONE_ , **_8_** (4), e59859. Koch, H.-J., K. Trimpler, A. Jacobs and N. Stockfisch. 2018. "Crop Rotational Effects on Yield Formation in Current Sugar Beet Production – Results from a Farm Survey and Field Trials." _Frontiers in Plant Science_ , **_9_** (231). Krasnozhon, L. A. 2013. "Political Economy of Agricultural Market Reform in Ukraine: 'Good Bye Lenin'
of countries and products. Libya has the largest variety of food subsidies, and a few other countries subsidize flour, bread, sugar, or vegetable oil, which are the four products that we analyze across countries. The largest subsidies go to flour and bread. The distinction between flour and bread is not always clear cut in the data. Some countries subsidize the price of flour for mills and then impose regulated prices on the sale of bread. What we observe in expenditure data are direct purchases of flour or bread on the part of households. Therefore, we need to estimate the flour subsidies received by households via the purchase of bread using conversion factors between these two products. As a consequence, the estimates on bread and flour should be taken with some caution. Sugar is also an important subsidized item in three countries, and vegetable oil remains subsidized in two countries.

8