rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.1 Employers, own-account, and employees**

ILO offers exhaustive employment and employee statistics, based on nationally representative labor force surveys combined with household surveys and population censuses<sup>4</sup> . ILO data includes the number of employers, employees, and own-account workers by country and year. Specifically, employees who have an explicit or implicit contract of employment with an employer are distinguished from the self-employed, for which remuneration is directly tied to the profits from goods and services that they produce. The self-employed include employers, who continuously have engaged one or more persons to work for them in their business, and own-account workers, who do not employ other workers.

The number of employers, own-account and employees by country constitute the key statistics from which we estimate the number of firms per size and formality status. ILO reports two versions of these variables. First, ILOSTATS is a data repository with information from labor force and household surveys worldwide. The reporting of employment information is harmonized to allow country comparison, however data gaps remain. Not all countries produce labor or household surveys regularly. This creates space for the second version of employment data, stored in a second repository, ILOEST (ILO, 2023). ILOEST produces estimates of labor force variables using a series of cross-validated statistical models selected based on pseudo-out-of-sample comparisons. In some cases, models are country idiosyncratic.

According to ILO (2023), the initial modeling predicts country-level labor force participation rate using economic and demographic variables across nine estimation groups split based on economic and geographical proximity. Relevant to our setting, ILO further models the distribution of employment by status. In a similar procedure, first, the shares of each category (employer, employee, own account) are modeled against a set of country characteristics such as per capita income, economic structure, and model-specific variables such as the ”work for an employer” index from the Gallup World Poll. Second, the models estimate the evolution of these employment category shares based on
further confirmed by looking at the main occupations in the agriculture sector for the two groups: In the United States, half of the workers (47%) are "Agricultural and forestry production managers", while in the LICs, half of the workers are employed as "Subsistence mixed crop and livestock farmers" (31%) and "Crop farm laborers" (16%). In sum, we recognize the caveat that the AIOE results for LMICs are likely to provide an upper bound, but the change in occupations and AIOE across income groups gives us confidence in our approach.

To summarize the characteristics associated with AI occupation exposure, we pool data from the latest survey for each country and apply the following simple regression model at the level of the individual worker with AIOE as the independent variable: = X tt,cc,ii tt,cc,ii tt,cc,ii yy xx + εε Where _i_ is worker _i_ in year _t_ and country _c_ , and is the AI occupation exposure for that worker. The matrix _X_ has dimension _n_ x _p_ , where _n_ refers to the number of workers and _p_ to the variables. tt,cc,ii yy The variables include 1-digit ISCO occupation information, sector, gender, region of residence, employment status, age group, survey year, and country. All results use individual weights to ensure country representativeness. Robust standard errors are clustered at the level of country (c) and survey year (t), and all analyses are restricted to workers aged 15-64. The role of the regression is to describe the descriptive relationship between the AIOE and the independent variables in a multivariate framework. Its values are of a correlational nature and so is its interpretation.

For bivariate representations we divide the values of AIOE in quartiles with cuts at 23, 35, 51 and 82: “Low exposure”, “Moderate low exposure”, “Moderate high exposure”, “High exposure”. The sample consists of 4 low-income countries, 12 lower-middle-income countries
# **1 Introduction**

Small farms (with fewer than 2 ha) represent 84% of the estimated 570 million farms worldwide (Lowder et al., 2016). In view of the prevalence of smallholder farming across the developing world and the linkages between agricultural and welfare outcomes, one of the targets of the Sustainable Development Goal of Ending Hunger has been identified as doubling the agricultural productivity and incomes of small-scale food producers. One measure of agricultural productivity is crop yield, which is the amount of crop production harvested per unit of cultivated land. Increasing crop yields has been a long-standing goal of African governments and is key to feeding a growing global population while managing natural resources efficiently (Lobell et al., 2009; van Ittersum et al., 2013; Zhang et al., 2016).

Despite the seemingly simple formula for computing crop yields, and its frequent use in agricultural economics research, obtaining accurate measures of crop yields is underlined by a host of challenges in low- and middle-income countries, mainly due to reliance on household and farm surveys and measurement errors in self-reported information on crop production and cultivated area. Research has demonstrated the effects of these measurement errors on our understanding of the inverse-scale productivity relationship (IR) in agriculture – i.e. the finding that the productivity measures, including crop yields, are, on average, higher on smaller farms and plots vis-`a-vis their larger counterparts.

The first strand of research has focused on the denominator in the formula for crop yields: cultivated land area, and has documented systematic discrepancies between GPS-based and self-reported plot areas (Carletto et al., 2013; Carletto et al., 2015; Kilic et al., 2017). The cross-country comparable finding across these studies is that on average, farmers tend to over-report plot areas, and more so at the lower end of the plot area distribution. As a result of using GPS-based plot area measures, as opposed to their self-reported counterparts, research has shown that the IR,
17 would bring down the price of local calls, intensify reported telephone faults, and improve labor productivity. With respect to the price of local calls, the results suggest that an IRA is important to ensure local users that the potential gains from technological progress are not offset with the increase in local tariffs often associated to tariff rebalancing.<sup>15</sup> Regarding quality, the deterioration associated with the existence of an IRA probably reflects the fact that an IRA does a better job at measuring quality than a self-regulated operator. Thus, reform policies initially may end up increasing measured faults, even if they ultimately improve quality.<sup>16</sup> Finally, results suggest that the strongest effect of the introduction of an IRA is the enhancement of labor productivity.

When comparing the effects of an IRA in developing and developed countries, table 6 shows that these effects have the same sign in both groups but are stronger in developing countries in terms of labor productivity and quality, and are stronger in developed countries in terms of prices. Somewhat surprisingly, the introduction of an IRA has no statistically significant impact on access and on the fixed component of the tariff paid by users, whether residential or business.

# **6. CONCLUDING REMARKS**

We first reported basic data on telecom reform policies and performance outputs which hinted at a number of correlations. Based on this very basic data analysis, countries with private capital and/or an independent regulatory agency (IRA) seem to have, on average, more subscribers, lower local call price, lower fixed costs, lower faults, and higher labor productivity. We also observed that countries that have reformed present, on average, better performance outputs than countries that have not reformed. However, there seemed to be differences in the impact of reform policies between developing and developed countries.

We then provided a more formal assessment of these apparent correlations through an econometric analysis to refine the conclusions emerging from the basic data analysis. More specifically, we could
, Cameroon, Ghana, and Rwanda to identify four stylized facts: (i) there exists a drop in the employment share accounted for by the middle range of the size distribution, providing some support to the missing middle hypothesis; (ii) informal firms are necessary for the missing middle to appear; (iii) the size distribution of formal firms looks somehow more like the size distribution in developed economies; and (iv) the missing middle does not arise from entry but instead reflects firm growth patterns. To the best of our knowledge, our contribution is the first that embraces detailed and comprehensive censuses of establishments in the non-agriculture sectors of Sub-Saharan countries in a comparative fashion.

The controversy about the existence of a missing middle appears to be definitional. A binary existence statement necessarily rests on identifying assumptions that arguably obscure the relevance of the matter. In that sense, we argue that the missing middle is an elusive concept. This paper steps back from the controversy and establishes a number of stylized facts regarding patterns in the size distribution.

The analyses underline the important consequences of informality and question the common perception that the missing middle arises from the size distribution of entrants. Additionally, methodically studying the size distribution to identify empirical regularities is useful and policy-relevant. However, the possibilities are currently rather limited because of the issue of data availability. This calls for conducting comprehensive establishment censuses that include informal businesses.

The policy-relevant question here is whether a missing middle is a cause for concern. In some ways, it is: a firm size distribution exhibiting a missing middle means that there is a scope for shifting 38
in poverty and inequality in Egypt over 2005-2008, and a broad picture of economic change are presented in section IV, showing that the growth was not benefitting the poor relatively more than the richest parts of the distribution. Fifth section discusses growth incidence curves based on panel data, and shows that growth was indeed pro-poor and that the lowest groups of the distribution benefited the most from the growth process. Section VI attempts to reconcile two opposite assessments of pro-poorness of growth in Egypt. Section VII concludes.

# **II. Pro-poor growth analysis: Review of the selected studies**

There is a growing consensus in the literature on the conclusion that sustained and rapid economic growth translates into poverty reduction<sup>3</sup> . However, there is a wide disparity in the extent of poverty reduction a growth process can achieve. The relation between growth, inequality, and redistribution is among the mostdebated topics in the economic analysis of development since the 1950s. This is because the link between growth and inequality is not unequivocal”. Changes in the distribution are not necessarily directly linked to growth and may reflect different economic factors that are specific to individual country experience<sup>4</sup> . Inequality may rise or fall temporarily for reasons which are not necessarily linked to growth. A World Bank study (2005) analyzed in some depth the relationship between changes in growth and inequality in eight countries<sup>5</sup> in the 1990s and found that growth typically was associated with growing inequality. By contrast, the experience of many high income (OECD) countries suggests that income growth is often pro-poor, both in absolute and relative terms, reducing not only poverty but also inequality (e.g. Smeeding 1990).

There are some facts in the dynamic relationship between growth, poverty and inequality that are well established. Rising inequality tends to reduce the growth elasticity of poverty reduction, i.e. weakening the impact of growth on poverty. Also the higher the initial level of
Box 2 provides more examples of the strong and unmet demand for Islamic microfinance in selected OIC countries.

# **Box 2 – Strong demand for Islamic microfinance in different countries**

- In the West Bank and Gaza, more than 60% of low-income survey respondents prefer Islamic products to conventional products, and more than half of them still do if the Islamic products come at a higher price. (PlaNet Finance 2007)

- In Jordan, 24.9% and 32%, respectively, of those interviewed cite religious reasons for not obtaining conventional loans. 18.6% of those interviewed rank religious reasons as the single most important factor in obtaining a loan. (IFC and FINCA 2006)

- In Algeria, a 2006 study by Bankakademie International revealed that 20.7% of micro enterprise owners do not apply for a loan primarily due to religious reasons. (Bankakademie International 2006)

- In Yemen, it is estimated that 40% of the poor demand Islamic financial services, regardless of a higher price. (source: Phone interview with Executive director of the National Microfinance Foundation, Yemen)

- In Syria, an IFC survey revealed that 43% of respondents considered religious reasons to be the largest obstacle to obtaining a microcredit. In addition, 46% of respondents who had never applied for a loan stated that religious reasons were the primary reason they had never applied. Nearly 5% of current borrowers said they would not apply for another loan for religious reasons. (IFC/ The World Bank 2007)

- In Indonesia, according to a 2000 Bank Indonesia report, 49% of the rural population of East Java considers interest prohibited and would prefer to bank with Sharia-compliant financial institutions.

- In Lebanon, the success in outreach of Islamic programs relative to conventional _-_

- MFIs strongly suggests that large numbers of poor people prefer Sharia compliant finance.<sup>1</sup> In addition, microfinance practitioners report that many of the Lebanese poor refuse financial services unless they are Sharia-compliant. (Hamze
vocational school completer working in a matched occupation, and -0 if otherwise), and VOC.U (-1 if he is an unmatched vocational school completer, and -O if otherwise). The reference group is thus workers who have completed general secondary schooling. A positive (negative) coefficient on one of the VOC sub-categories would indicated higher (lower) average earnings than for those individuals that had completed an academic secondary school. The regression model specification is otherwise parallel to that of Regression 2 in Table 3; a set of occupational dunwmies replace the subject-of-study dummies used in the regressions in Table 7 (the Census did not collect information on the latter for academic secondary school completers).

The reported coefficients on the VOC.M and VOC.U variables in the regressions in Table 8 are significantly positive and nonsignificant, respectively. The implication of these results is clear. They indicate that while there is no difference in earnings between academic school completers and those vocational school completers that work in occupations unrelated to vocational courses studied at school, the earnings of workers employed in matched occupations exceed those of workers who attended academic schools (by over 8 per cent in the regression relating to wider matchings and by approaching 10 per cent for direct matchings).22/ 22 These results may, at first glance, appear to be in conflict with the data reported in Table 7, where the coefficient on the VOC.M term is larger for the wider matchings regressions; here the opposite is the case. However, the samples and the reference group differs in the two sets of regressions. Table 7 regressions relate to vocational school completers only and compare the earnings of the two sub-groups within this category. In contrast, the Table 8 regressions include, in addition, academic school completers, and compares the matched and non-matched vocational school completer sub-groups separately, with their counterparts from the academic schools.

- 34 -
Collier, Paul, Anke Hoeffler, and Dominic Rohner. "Beyond Greed and Grievance: Feasibility and Civil War." _Oxford Economic Papers_ 61, no. 1 (2009): 1-27.

Englebert, Pierre, and James Ron. "Primary Commodities and War: Congo-Brazzaville's Ambivalent Resource Curse." Comparative Politics 37, no. 1 (2004): 61-81.

Fearon, James D. "Primary Commodity Exports and Civil War." _Journal of Conflict Resolution_ 49, no. 1 (2005): 483-507.

- ———. ―Governance and Civil War Onset.‖ _World Development Report_ 2011.

Fearon, James D. and David D. Laitin. "Explaining Interethnic Cooperation." American Political Science Review 90, no. 4 (1996): 715-35.

- ———. "Ethnicity, Insurgency, and Civil War." _The American Political Science Review_ 97, no. 1 (2003): 75-90.

- Fjelde, Hanne. "Buying Peace? Oil Wealth, Corruption and Civil War, 1985-99." _Journal of Peace Research_ 46, no. 2 (2009): 199-218.

- Gleditsch, Nils Petter, Peter Wallensteen, Mikael Eriksson, Margereta Sollenberg, and Havard Strand. "Armed Conflict 1946-2001: A New Dataset." _Journal of Peace Research_ 39, no. 5 (2002): 615-37.

- Humphreys, Macartan. "Natural Resources, Conflict, and Conflict Resolution." _Journal of Conflict Resolution_ 49, no. 4 (2005): 508-37.

- Keefer, Philip. "Insurgency and Credible Commitment in Autocracies and Democracies." _The_ World Bank Economic Review 22, no. 1 (2008): 33-61.

Keefer, Philip. " Why follow the leader? Collective action, credible commitment and conflict."

- Prepared for the _Oxford Handbook of the Economics of Peace and Conflict_ . Michelle Garfinkel and Stergios Skaperdas, editors, 2010.

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flows, DLM includes 34% of pairs of countries with zero values.

What do these large numbers of zero values truly reflect? For a group of country pairs, the zero values might be the result of a statistical truncation process. For instance, for reasons of statistical confidentiality, national statistical agencies might prefer not to report some low number of migrants of country o in country d. This is reported to be the case for provincial data of international migrants in Canada (see Wagner et al., 2003). Under 5 recorded migrants, the statistical offices are expected to report a zero to preserve the anonymity of the migrants. Similarly, due to imperfect sampling, many smaller and positive migrant stock and flows might not be fully captured in censuses or labor force surveys. Also, it is possible that a number diplomats are not counted in the official stock of migrants following international conventions.

In majority of the cases, a large number of zero values in the migration datasets reflect true zeroes. Like in international trade, many bilateral migration corridors are not ’profitable’ so that there are simply no migrants to observe and record. Ignoring such zero values would be highly detrimental to assess the relevance of the determinants of international migration patterns. Zero values imply that the costs of migration is too high for any potential migrant to move from country o to country d. Among those factors, The absence of a network at destination might be a leading factor that deters potential migrants from choosing that particular destination.<sup>2</sup> Therefore, it is important in the empirical investigation of the network effect to employ methods that properly account for those zero migration flows. For example, for the size estimation, possible methods include Poisson regressions, 2-step Heckman approach and Tobit. For the selection and relative concentration, however, Tobit and Poisson regression methods are not possible.

# 3.3 Stocks vs Flows

A critical choice in the investigations of the network/diaspora effect is the appropriate dependent variables. For instance
Income (percentage points)**|

Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: IPTU stands for Imposto sobre a Propriedade Territorial Urbana, while ITR stands for Imposto sobre a Propriedade Territorial Rural. BPC stands for Benefício de Prestação Continuada. Notes: Moderate and extreme poverty are based on poverty lines of BRL 499 and BRL 178, respectively.

# 4. What are the implications for vulnerable demographic groups?

We complement this incidence analysis by disaggregating the results by the demographic composition of households. In particular, we look into how the headship structure, and the presence of both children and elderly can be determinants of the net effects of fiscal policies in Brazil. In this exercise, children are considered all members aged 15 or less who are neither the household head nor the household head’s spouse, while an elderly member is an individual aged 60 or more.

Table 4 presents the households’ demographic characterization. There are twelve categories, four for each type of household headship. The rationale for such categorization is three-fold. First, headship structure is likely to be deeply connected with economic agency, as it is a major determinant of since the benchmarks are not the same. Bolsa Família’s evaluations focus on families that are potentially poor after (potentially) receiving other government transfers. Here, the relative contribution to poverty reduction uses as a benchmark a pre-fiscal situation so that larger transfers (such as pensions) can have larger effects on poverty reduction than programs like Bolsa Familia.

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Policy Research Working Paper 8801

# **Abstract**

This paper presents a structural general equilibrium model to analyze the effects on trade, welfare, and gross domestic product of common transport infrastructure. Specifically, the model builds on the framework by Caliendo and Parro (2015)—a Ricardian model with sectoral linkages, trade in intermediate goods and sectoral heterogeneity—to allow for changes in trade costs due to improvements in transportation infrastructure, financed through domestic taxation, connecting multiple countries. The model highlights the trade impact of infrastructure investments through cross-border input-output linkages. This framework is then used to quantify the impact of the Belt and Road Initiative. Using new estimates on the effects on trade costs of transport infrastructure related to the initiative based on Geographic Information System analysis, the model shows that gross domestic product will increase by up to 3.4 percent for participating countries and by up to 2.9 percent for the world. Because trade gains are not commensurate with projected investments, some countries may experience a negative welfare effect due to the high cost of the infrastructure. The analysis also finds strong complementarity between infrastructure investment and trade policy reforms.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Office of the Chief Economist, Middle East and North 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/research. The authors may be contacted at fdesoyres@worldbank.org, amulabdic@worldbank.org, and mruta@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