rafmacalaba/data-use-mentions-extended huggingface dataset:
Mueller, B. (2015). Tanzania’s rural labour markets: the missing link between development and poverty reduction. In _Rural Wage Employment in Developing Countries_ (pp. 166‐192). Routledge.

Mueller, B., & Chan, M.‐K. (2015). Wage labor, agriculture‐based economies, and pathways out of poverty: taking stock of the evidence. _Leveraging Economic Opportunities (LEO) Report, 15_ .

NISR. (2018). Labour Force Survey Trends February 2018 report, June 2018. _National Institute of Statistics of Rwanda_ .

Oya, C. (2013). Rural wage employment in Africa: methodological issues and emerging evidence. _Review of African Political Economy, 40_ (136), 251‐273.

Ruppert Bulmer, E. (2018). Defining informality vs mitigating its negative effects. _IZA World of Labor_ .

Seymour, G., Malapit, H., & Quisumbing, A. (2017). Measuring time use in development settings. _Working Paper, World Bank._ Szirmai, A., Gebreeyesus, M., Guadagno, F., & Verspagen, B. (2013). Promoting productive employment in Sub‐Saharan Africa: a review of the literature.

Thomsen, I., & Villund, O. (2011). Using register data to evaluate the effects of proxy interviews in the Norwegian Labour Force Survey.

Van den Broeck, G., & Kilic, T. (2018). Dynamics of off‐farm employment in Sub‐Saharan Africa: a gender perspective. _Working Paper, World Bank._ Verick, S. (2018). Female labor force participation and development. _IZA World of Labor_ .

Walsh, K. (2018). Measuring women’s and men’s work: The 19th ICLS, purpose and progress. _Presentation prepared for the 20th ICLS_ .

World Bank. (2013). World Development Report 2013: jobs.

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<!-- Start of picture text --> Figure 14: State Level Share of Children of School-going-age Not in School – Northern Regions<br>a) b) c)<br>Share Not in School at School going Age Share Not in School at School going Age Share in School at School going Age<br>- by State in North Central - MICS - by State in North East - MICS - by State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Benue FCT Abuja Kogi Kwara Nasarawa Niger Plateau Adamawa Bauchi Borno Gombe Taraba Yobe Jigawa Kaduna Kano Katsina Kebbi Sokoto Zamfara<br><!-- End of picture text --> <!-- Start of picture text --> d) e) f)<br>Urban Rural - Boy Girl - Share Not in School at Urban Rural - Boy Girl - Share Not in School at Urban Rural - Boy Girl - Share Not in School at<br>School going Age - by State in North Central - MICS School going Age - by State in North East - MICS School going Age - by State in North West - MICS<br>80% 80% 80%<br>70% 70% 70%<br>60%50% 60%50% 60%<br>40% 40% 50%<br>30% 30% 40%<br>20% 20% 30%<br>10%
included 1,951 small businesses operating out of a physical structure in and around Cape Town, South Africa.

In stage two, we used the data collected in the screening survey to further narrow our sampling frame. This screening stage involved ranking businesses based on questions covering formal education levels, years in business operation, formal registration status, motivation and commitment, as well as several interviewer impression questions evaluated by the RA (e.g., business aspirations, English level, literacy, and numeracy). Entrepreneurs were then ranked on their composite score.

In stage three, beginning in September 2012, the top 1,500 businesses on our composite scale were invited to attend a registration session to learn more about the next steps for their training and complete additional forms. This number was chosen for two main reasons: first, based on statistical power calculations, we were aiming for an initial sample of 750 businesses (approximately 250 in each of the three groups). Second, we conservatively anticipated a 50% take up rate between the invitations and registration attendance, meaning it was important to ensure the program was oversubscribed.

During the notification call, each of the 1,500 invited participants was told that they had qualified for a free scholarship to receive a two-month business training course offered by our partner organization, Business Bridge, but that they had to attend a registration session in person to pick up their scholarship letter. In total, 852 small business owners attended these registration sessions and completed the baseline survey. This survey was administered in person at the registration sessions.

Randomization was done after the baseline by computer, so that any pre-treatment differences between the groups are due to pure chance. The three groups were not perfectly equal in size because we performed a stratified randomization in order to balance our sample on several variables (gender, education, firm size, and formalization status). Our partner organization, Business Bridge, also indicated a capacity constraint of 13 classes of 20 students each for both finance and marketing courses
estimates based on historical schooling enrollment ...gures when Censuses are not available.

Combining Census and Survey data on immigration, we obtain the emigration stock for almost all the countries of the world (191 countries in 2000, 170 in 1990). For countries where Barro and Lee measures are missing (about 70 countries in 2000), we use the World Bank data on the population aged 25 and over and transpose the skill sharing of the bordering country with the closest GDP per capita. Note that the World Bank database provides the GDP per capita in PPP value for 155 countries. For a limited number of countries, the population aged 25 and over has been estimated by applying the share of the 25+ observed in the geographical area to

> 10For some countries, immigrants often travel back and forth between their new and old countries (e.g. Mexico). They are likely to be counted as still being residents in their home country. For that reason, Carrington and Detragiache (1998) provide an upper bound (m = M=N ) and a lower bound (m = M=(N + M )). Since the upper bound is not interpretable for a large number of countries (higher than one), we only report the lower bound.

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_permisionarios_ do) are more likely to join cooperatives. Revenue may be paid out in annual bonuses, which may be based on fishermen’s total annual catch, equal for all members, or determined by some other rule (McGuire 1983). Finally, in some cooperatives, members also enjoy income security through sources such as retirement benefits or credit (McCay et al. 2014; G. Hinojosa-Arango, pers. com., Feb. 2012).

In the theoretical model below, we model these benefits received by cooperative members in two ways: (i) a lump sum payment reflecting discounts on equipment (including boats and motors), credit, or annual bonuses from the cooperative leadership; (ii) a factor reducing the costs of fishing reflecting access to fishing permits and state subsidies for fuel, as well as the absence of costs associated with searching for buyers. To the extent that the size of an annual bonus is dependent on annual catch, the bonus is not appropriately classified as a lump sum payment, as it affects incentives for effort. We do not have information on how often such catch-dependent bonuses occur, but it is important to note from the above discussion that other forms of compensation also make up the lump sum payment.

While the features above are generally shared by many cooperatives in the Gulf of California area, there are key differences among the three cooperatives for which we have daily logbook data and conduct empirical analysis. Figure 1 shows the location of these cooperatives. Pichilingue is located on the outskirts of La Paz, the largest city in the state and a major market in the region, and Sargento is a short drive away from La Paz. Punta Abreojos, a member of the federation of cooperatives in Northwest Baja California known as FEDECOOP, is on the Pacific side of the peninsula, adjacent to other cooperatives from FEDECOOP. For the purposes of the empirical work below, we utilize the fact that Abreojos effectively has more secure property rights than Pichilingue and Sargento and can therefore manage its own
these findings. Moreover, the distribution of tourism benefits within local communities remains unknown, warranting future exploration.

It also appears that AP’s positive conservation impact does not impair economic development, in contrast to the traditional view that conservation and development are competing objectives [90]. This trade-off between conservation and economic development may be occurring prior to AP management, as indicated by the upward pre-transference trend in asset wealth (indicating economic development) concurrent with the downward pre-transference trend in elephant poaching (indicating reduced conservation). However, asset wealth remains stable and elevated in protected areas following their transference to AP, even as elephant poaching rates decline substantially. It is important to emphasize that the absence of evidence of an economic development cost does not imply that AP management increases economic development; the upward pre-transference trend in asset wealth precludes that inference.

Finally, our results highlight a continued need for careful evaluations of privatelymanaged protected areas, particularly regarding their impacts on nearby communities. People living near protected areas have much to gain or lose from private management, yet their voices are seldom captured in the observational data used to gauge protected area effectiveness. Large field-based data collection projects enabling the careful measurement of local peoples’ experiences of protected area management will be critical for future research. For example, such data would provide greater certainty 13
# **Introduction**

Paid work without legal contract is a phenomenon closely related to such fields of economic and social studies as shadow economy, tax evasion, trust in and efficiency of institutions, labor demand and labor supply, self-employment, worker mobility, labor market flexibility, social exclusion, social security, and many others. Understanding determinants of the size of informal workforce is thus important both for policy making and for design of institutional reforms. Yet research in this field, especially in European context, has been limited due to lack of data.

In this paper we compare the prevalence of informal employment in 30 European countries using data from the European Social Survey (2004-2009), further referred to as ESS. Our analysis excludes under-declared work (envelope wages) and does not distinguish declared and undeclared output. In other words, we focus on dependent workers without contracts, as well as on self-employed (a further classification of self-employed into ̳formal‘ and ̳informal‘ will be suggested below).

We address the following questions:

- How strongly do European countries, as well as Northern, Western, Eastern and Southern Europe<sup>1</sup> differ from each other in terms of levels of informal employment observed in the first decade of the 21<sup>st</sup> century? Does a stable ranking emerge?

- How does prevalence of works without contract among wage earners depend on their human capital and other characteristics? In particular, how do minorities, first and second generation immigrants compare to native workers?

- Is informal wage employment found only in small establishments in selected sectors, or is it more common?

- How are the levels of informal dependent employment and  informal self-employment related to the economic cycle (and, in particular, how did they respond to the current crisis)?

- In what ways is the profile of an  ̳informal worker‘ different from that of a person employed under a contract, on one hand, and of informal self-employed, on the other
to that the flip side of the coin, the primarily nonfarm workers who work seasonally in agriculture, to arrive at the total share of the rural labor force moving seasonally between farm and nonfarm activities. Given that as much as 75 percent of rural nonfarm workers may participate in agriculture (Mbithi and Chege, 1973), this results in an additional 7.5 to 15 percent of the rural laborforce (.75 x 10 to 20 percent primarily engaged in nonfarm activity). Thus the total flow lies between about 15 (7. + 7.5) and 57 percent (42 + 15). Within this interval, we believe that 20 to 40 percent represents the most likely norm.

Considerable evidence indicates that 60 percent of rural consumption involves cash purchases, while the remaining 40 percent derives from own consumption of home-produced food (see Section V.A). Combining this with rural consumption data from Sierra Leone (King and Byerlee, 1977) and Nigeria (Hazell and Roell, 1983) results in the following stylized disaggregation of rural consumption: 40 percent non-marketed, home-produced food; 30 percent purchases of locally produced food; 5 percent imported foods; and 25 percent nonfood goods and services. Given these parameters, the share of locally produced food in total rural cash expenditure stands at 50 percent [30/,30 + 5 + 25)]. Specific computation for Sierra Leone results in a 40 percent estimate. while use of the Gusau, Nigeria data leads to an estimate of 58
additional reason for doing this is that tariff data contain many missing values; using average values allows maximizing the number of observations. We however provide robustness exercises using time-varying tariffs.

> 30We confirmed that our main results of incomplete pass-through still hold despite the lower number of observations by re-estimating column (1) of Table 3 on this smaller sample. Results were unchanged (the exchange rate coefficient is estimated at 0.12, very close to our baseline of Table 3).

16
Policy Research Working Paper 5878

# **Abstract**

This paper estimates average and marginal returns to schooling in Indonesia using a non-parametric selection model estimated by local instrumental variables, and data from the Indonesia Family Life Survey. The analysis finds that the return to upper secondary schooling varies widely across individual: it can be as high as 50 percent per year of schooling for those very likely to enroll in upper secondary schooling, or as low as −10 percent for those very unlikely to do so. Returns to the marginal student (14 percent) are well below those for the average student attending upper secondary schooling (27 percent).

This paper is a product of the Development Research Group; and East Asia and Pacific Region, Social Protection Unit. 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 numapathi@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
.0<br> 1.0<br> -<br>RC/RWA Equity/TA<br><!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus. Note: We report the effect on the Z-Score of moving from increasing regulatory capital (RC/RWA) and simple leverage (Equity/TA) by 10%. The estimates are obtained after controlling for bank size (log(TA)), bank liquidity (LiquidA/TA), bank profitability (ROA), reliance on short-term funding (ShortFund/TA), and loan ratio (Loans/TA).

We also examine the impact on bank risk of having a higher proportion of bank capital in the form of Tier 1, which is captured by the coefficient on the variable Tier 1 Capital over Regulatory Capital (Tier 1C/ RC). In the second specification we capture the impact of having a higher portion of riskweighted assets, which is captured by the coefficient on the variable RWA/TA. We use the same bank control variables in both specifications reported in Table 1. All capital ratios and controls are lagged by one year. The coefficient on the Tier 1C/ RC variable captures the impact of having higher proportion of capital in the form of Tier 1. Since we control for the overall level of regulatory capital, the coefficient 15
Policy Research Working Paper 8859

# **Abstract**

This paper examines the growth effects of different dimensions of international trade integration—notably, volume, diversification, and natural resource dependence—in Sub-Saharan Africa. First, the paper documents the recent trends in these foreign trade dimensions for the region and the traditional sources of growth. Second, it empirically estimates the impact of trade integration on growth per worker and the sources of growth; that is, growth of capital per worker and total factor productivity growth. To accomplish this task, the analysis uses a sample of non-overlapping five-year period observations for 173 countries from 1975 to 2014. The econometric evidence shows that increased trade openness, greater export production diversification, and reduced export dependence from natural resources will have a positive causal impact on economic growth. These effects will be mainly transmitted through faster capital accumulation or enhanced total factor productivity growth. Finally, the paper finds that, despite the progress exhibited in trade openness and diversification over the past decade, there are still potential benefits that can be accrued if countries were to deepen their integration to world trade.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 ccalderon@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/
positively associated with the migration of a family member for work. The asset gains are also statistically similar between households whose members migrated overseas and those whose members moved within India for

> 6 The 1998 and 2003 KMS data reported that 95 and 90 percent, respectively, of household members in a foreign labor force were residing in the Arabian Gulf.  The Arabian Gulf countries consist of the Kingdom of Saudi Arabia, United Arab Emirates, Qatar, Bahrain, Oman, and Kuwait.

3
0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Sub-Saharan Africa SSA Oil Rich Countries SSA Metal Rich SSA Non-Resource Rich<br>(SSA) Countries Countries<br>Output Physical Capital Human Capital<br>percent<br><!-- End of picture text --> _Notes: Averages are population weighted averages. The human capital index_ H�exp�φ�s���� _is characterized by the relationship between human capital (H) and the years of schooling (s) as_ φ�s����φ� ∙s�� _for each country i in period t —where the returns to education (_ φ� _) are heterogeneous across countries. The returns to education are measured from the estimation of Mincerian wage regressions on schooling. We use the Mincerian returns estimated by Montenegro and Patrinos (2014) and the total years of schooling for each country and year to construct the human capital index H. Source: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._ Figure 2 plots the sources of growth for the Sub-Saharan Africa region as well as the different groups according to the extent of natural resource abundance —that is, oil rich, minerals and metals rich, and non-resource rich countries during the period 1961-2014. The average annual growth rate of output per worker in the region (1.1 percent) is _page 5_
type of selection exists, the test score distributions of treated and control children tested in school are not comparable. In order to address this issue, we follow Lee (2002) and Angrist et al. (2006) to construct nonparametric upper bounds of program effects (for the matching analysis) on learning by symmetrically truncating the two distributions at some specific quantile. In contrast, unadjusted (selection-contaminated) comparisons of test scores –conditional on positive scores– provide lower bound estimates of program impacts (see Appendix B for details on the methodology for nonparametric bounds of program impacts).

# **_Regression Discontinuity Design_**

The second research design employed in this paper exploits variation in assignment to treatment arising from the discontinuous rule that determines eligibility to the program. As noted before, a proxy-means test designed with the goal of identifying the most vulnerable population that qualifies for various social programs was used to define the target population of FA. The index ( _Sisben_ ) varies between 0 and 100 and is the result of an algorithm that weights households‘ variables associated with their socio-economic wellbeing. Households placed in the first bracket of _Sisben_ (level 1), namely those with scores below 18 and 36 in rural and urban areas, respectively, were considered eligible for the first phase of the program. In principle, the expected discrete change in participation produced by the rule offers an opportunity to estimate the causal effects of the program on education outcomes with a Regression Discontinuity Design (RDD). The intuition behind this strategy is that households that lie just below and just above the threshold are statistically comparable except for their participation in the program. As a result, any discontinuity in the conditional distribution of high school graduation rates and tests scores at the cutoff could be interpreted as the effect of FA.

We used data from SIFA, an information management system that compiles information on a number of operational aspects of the program including historical records of all beneficiaries. When merging it with the _Sisben_ scores, these databases allow identification of the universe of
with our findings. The right-wing populism index is the only factor that significantly affects the difference in willingness to pay between the third and fourth rounds of the LiTS (Specification 2). The effect of this variable comes from the changes in “endowment,” or, in other words, from the increased share of respondents who report embracing a right-wing populist ideology. Neither the economic conservatism index nor the environmental risk indicators have a meaningful effect on that difference (Specifications 3 and 4).

> 11 We have administrative data for all countries in our sample except nine countries in the Middle East, North Africa, and Western Europe.

14