rafmacalaba/data-use-mentions-extended huggingface dataset:
gross income per member is below the Differentiated Minimum Income (DMI) threshold. The DMI threshold varies based on age, health, family, and educational status and is calculated as a proportion of the GMI threshold.<sup>11</sup> Certain types of income are not considered in the means test of the GMI. The benefit amount is the difference between the DMI and the gross family income from all sources, net of specific exceptions. The Council of Ministers set the GMI amount in 2018 at BGN 75 per month (38 Euros), and the level was not adjusted until mid-2023 with the reform. It is important to note that GMI and DMI were not adjusted for price levels or cost of living (Coady et al., 2021; Vaughn & Cabrera, 2022; World Bank, 2020, 2021). Additional eligibility conditions include restrictions on assets, which are also subject to a test, and registration of the unemployed in the Employment Agency (see Annex 2 for details).

**Previous evidence suggests that before the reform, the MSA in Bulgaria had limitations in terms of low spending, limited coverage and generosity due to restrictive eligibility criteria, and limited impact on poverty reduction.**

**Social assistance spending in Bulgaria for low-income families and individuals was low compared to other European and Central Asian countries.** Social assistance spending for low-income families and individuals is at most 0.17 percent of GDP, which is lower than similar social assistance schemes in lowspending ECA countries such as Serbia, Montenegro, or North Macedonia (World Bank, 2020, based on SPEED data). According to estimates based on National Statistical Office information, MSA benefits for 2018 were only 0.03% of GDP. Furthermore, spending on the MSA program has been decreasing in recent years.

**The MSA program in Bulgaria was small, covering only a small percentage of the poorest quintile, and while targeting accuracy was good, the scheme's generosity was particularly low.*
,643|10,800,786|0.16%|
|Senior<br>Secondary|2,461|1,374,981|0.18%|6,057|1,788|2,314|9,885|8,278,110|0.12%|

Source: EMIS and Susenas 2018. Dapodik 2018 is not currently available.

In Indonesia, having a disability significantly increases a child’s likelihood of being out of school. A 2016 study by UNICEF found that in Indonesia school attendance was reduced by 61 percent for boys and 59 percent for girls with disabilities.<sup>34</sup> Data from Susenas 2018 indicate that more than one-fourth (27 percent) of adolescents with disabilities of junior secondary school age (13-15) are out of school compared with less than 1 percent among those without disabilities. For girls, the out of school rate (r 32 percent) is higher than that for boys (23 percent). Additionally, analytical work conducted by UNICEF, MoEC, and BPS has found that while primary school completion rate for children without disabilities is 95.0 percent, for children with disabilities it is only 54.0 percent. Junior secondary school completion rates are also much lower for children with disabilities at 36.6 percent, compared with 85.4 percent for children without disabilities. For senior secondary schooling, the completion rate for children without disabilities is 62.2 percent, while for children with disabilities it is 26.0 percent (UNICEF & MoEC, 2019).

At the district level, we extract the figure of disabled children from the 2010 Population Census.<sup>35</sup> The disability prevalence rates for the school-age population (7-18 years) in the selected districts of Sampang and Bangkalan are both 1.0 percent, whereas in Banjarbaru and Banjarmasin the figures are 1.47 percent and less than 0.1 percent, respectively. As the reported prevalence rate is
relevant labor market indicators<sup>5</sup> , we decided to focus on the Sustainable Development Goals (SDGs) labor market indicators. We do so for four reasons: the SDGs emphasize the importance of productive employment and decent work (e.g. goal 8); monitoring the SDGs is one of the objectives of some of the survey programs we reviewed; the SDG labor market indicators offer the potential to study many topics that go beyond the SDGs; and the SDGs emphasize the need to disaggregate indicators by sex and age.

SDG labor market indicators are composed of a set of concepts that determine the indicators. Table A in the Appendix presents the SDG labor market indicators, while Table B in the Appendix identifies and defines the concepts underlying these indicators. An in‐depth review of the SDG labor market indicators goes beyond the scope of this paper.<sup>6</sup> In this section we only briefly highlight key concepts that have recently been revised.

A major innovation of the 19<sup>th</sup> International Conference of Labour Statisticians (ICLS) in 2013 was the introduction of a definition of work (Benes & Walsh, 2018b). Work is now defined as ‘any activity performed by persons of any sex and age to produce goods or to provide services for use by others or for own use’ (19th ICLS, §6). It consists of five mutually exclusive forms of work: (1) own‐use production work, (2) employment work, (3) unpaid trainee work, (4) volunteer work and (5) other unpaid work activities (not defined in the resolution). While these five forms of work are mutually exclusive, persons can be engaged in several forms of work in a short reference period. The 20<sup>th</sup> ICLS in 2018 revised the International Classification of Status in Employment (ICSE‐18) aligning this classification with the narrowed definition of employment.
Table 2. Descriptive Statistics – Cox’s Bazar Panel Survey

||Average<br>Std. Deviation|Observations|
|---|---|---|
|_A. Measures of the Distancefrom an Individual’s Location to the 33_|_Refugee Camps_||
|Weighted distance to refugee camps|0.325<br>0.205|17,407|
|Inverse of weighted distance to refugee camps|5.042<br>3.874|17,407|
|_B. Control Variables_|||
|Age|26.217<br>18.154|17,032|
|Male [=1]|0.486<br>0.500|17,030|
|Household Head [=1]|0.235<br>0.424|17,037|
|Married [=1]|0.588<br>0.492|13,760|
|Household size|5.861<br>2.336|17,407|
|Enough household income in 2017 [=1, measuredpre-infux]|0.479<br>0.500|17,350|
|_C. Adult and Household Welfare_|||
|Wealth Index|-0.006<br>2.135|12,334|
|Proportion of service access - subcomponent|0.679<br>0.329|17,282|
|Proportion of adequate shelter materials - subcomponent|0.652<br>0.256|17,282|
|Proportion of total assets - subcomponent|0.306<br>0.149|17,282|
|Household income (Takas)|19,357<br>36,964|12,553|
|Household expenditures (Takas)|6,178<br>5,850|12,553|
|Non-food household expenditures (Takas)|1,830
IMF, 2009); constrained investment due to weak corporate balance sheets, tight credit conditions and higher uncertainty in the aftermath of the crisis (Adler et al., 2017).<sup>9</sup> Although external factors may have contributed to the slowdown in China’s productivity growth, we identify several important domestic causes based on evidence from the Industrial Enterprise Survey, as well as aggregate financial sector data. Unfortunately, firm-level data that are representative of the service sector are not available. Given the growing importance of services in China’s economy, this is a significant shortcoming of our analysis.

# **2.1 Firm-level evidence of weaker productivity growth in manufacturing**

First, manufacturing firm data show that market entry and exit have contributed less to productivity growth in recent years compared to the early 2000s. Second, after a notable convergence in returns to capital and labor and TFP between private and state-owned manufacturing enterprises (SOEs) before 2008, SOE productivity has deteriorated since then. We also uncover differences in productivity across China’s provinces.

Aggregate TFP growth can be improved through two mechanisms. One is raising productive efficiency within firms through either innovation or adoption of more efficient existing technologies. The second is reallocating resources between firms, either by moving resources between existing firms or by the entry and exit of firms.<sup>10</sup> Before the global financial crisis, the net new entry of firms accounted for more than two-thirds of TFP growth in China, while the reallocation of inputs to more productive firms was limited (Brandt et al., 2012). Compared with the United States, there were large gaps in the marginal products of labor and capital between manufacturing firms within the same sector in China in 1998–2005 (Hsieh and Klenow, 2009). There were also large gaps in the returns to capital— between SOEs and private firms, between regions, and between sectors within China in 2002– 04 (Dollar and Wei, 2007).
case of dummy variables) and z-srores based on robust standard errors.

Another aspect of asset accumulation is the ability to have savings. While the LLI questionnaire did not record the amount of savings, it did ask whether households had been able to increase savings in the past year. Households with more memberships in local associations were significantly more able to do so than others (Column 2, Table 10). The effect was especially strong from memberships in credit and savings associations indicating that such organizations do in fact achieve their professed objective. The initial wealth position of the household also mattered, as richer
genetic correlation alone, while the persistence in China can be explained away by an ability correlation of plausible magnitude.<sup>15</sup> An important advantage of this approach is that the conclusions refer to the whole population of interest, rather than a subset as usually is the case with other standard approaches such as instrumental variables strategies. 14It is important to recognize that separability in rural China does not imply that nonfarm income did not play any role in rural income inequality; in fact, we cite a substantial literature to the contrary. The evidence in this paper suggests that intergenerational educational linkage is unlikely to be an important mechanism through which the nonfarm sector influenced the observed inequality in rural China in the 1980s and 1990s.

> 15This evidence on rural China is similar to the recent analysis on urban China based on a sample of monozygotic twins by Behrman et al. (2020). They find that the estimated impact of father’s education can be explained fully by genetic correlations and unobserved family endowments.

4
,705|31,466|67,359|34,845|
|Unorganized sector|148,808|165,854|236,822|91,881|90,048|157,592|
|Female-owned establishments|7,128|10,371|18,486|4,615|4,777|7,522|
|Female-owned share|0.06|0.08|0.09|0.06|0.06|0.05|
|||E. District|count where female-o|wned establishments ar|e observed||
|Organized sector|n.a.|n.a.|n.a.|159|117|127|
|Unorganized sector|414|413|415|407|412|412|

Notes: Descriptive statistics taken from the Annual Survey of Industries (ASI) and National Sample Survey (NSS). Gender of owner is not present for organized sector of manufacturing. Organized sector for services is defined to be over 10 employees to mimic manufacturing definition. Female-owned share of establishments, employees, and output is calculated as a share of establishments for which we know the gender of the owner.
requires a dataset of intertemporally comparable indicators of well-being that are statistically representative of the population in each municipality. The availability of such a dataset, however, may entail a trade-off between relatively high precision in the measurement of, say, household income and significant geographical detail. One might exploit household surveys designed to capture all sources of income and thus

> 2Two papers have used a preliminary version of this dataset to examine changes across municipalities. Villalobos Barr ́ıa et al. (2016) have analyzed, through Gaussian mixture modeling, the univariate and joint distribution of human development indicators —income, infant mortality, and years of schooling— and the conformation of development clusters in 1990, 2000, and 2010. Using the same preliminary dataset, Enamorado et al. (2016) study the causal effect of inequality on drugrelated homicides and report a sizable decline in inequality in the majority of municipalities over 1990–2010.

6
Policy Research Working Paper 6204

# **Abstract**

Competing theories increasingly support the positive role of social capital in small loan default costs of group lending; at the same time, potential group collusion may increase loan delinquencies. Findings from the available literature are mixed on the role of the various attributes of group lending. But past studies suffer from estimation bias due to the unobserved sorting behavior of group members and their other attributes. This paper attempts to resolve that estimation bias by utilizing longitudinal data from 297 Grameen Bank groups since their inceptions. A dynamic lagged dependent model with correction for time-varying heterogeneity of group and individual behavior is applied to estimate the effect of group liability in the Grameen Bank. The results suggest that group liability matters in both loan disbursement and repayment, with women less of a credit risk than men and women’s groups more homogeneous than men’s. Finally, the benefits of social capital outweigh the costs of group collusion, especially for women’s groups, thereby reducing overall default rates. The risk-pooling behavior of diverse men’s groups increases men’s repayment behavior. Overall, group lending as practiced by Grameen Bank appears to increase repayment rates.

This paper is a product of the Agriculture and Rural Development 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 skhandker@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
Measures of the degree to which economic growth is inclusive are also affected by the assumption that at least some of the HHS-NAS gap originates from missing top incomes in survey data. In measuring the degree to which growth is inclusive, the World Bank’s twin goals and the UN Sustainable Development Goals (SDGs) monitor growth in income and consumption of the bottom 40 percent in each country, relative to the growth for the overall population (World Bank, 2016). For a recent set of comparable spells used to monitor these goals, 56 out of 88 countries (64 percent) that are available in the Shared Prosperity Database for 2013 to 2018, reported a positive ‘shared prosperity premium’: the growth of mean income or consumption among the bottom 40 percent exceeded that of the overall mean growth. Panel A of Figure 8 shows the growth of the bottom 40 percent on the vertical access, and of the overall mean on the horizontal axis. The majority of observations are above the 45-degree line, indicating that growth was inclusive, and inequality, by this measure, was falling. However, these estimates rely solely on survey data. If we assume that the gap between surveys and national accounts is partly due to missing top incomes in surveys, it may be justifiable to compare the growth of the bottom 40 percent from surveys with the mean of national accounts, assuming that this better reflects overall growth. Under such a comparison, only 40 countries (49 percent of the 81 countries for which we have HFCE data for the spells) had a positive shared prosperity premium where the bottom 40 percent grew faster than the mean as measured by HFCE. Panel B of Figure 8 illustrates the relationship and differences between growth in the bottom 40 percent and growth in the mean with the adjusted distributions. The population-weighted average annualized shared prosperity premium for the period falls by 0.6 percentage points, from a positive 0.5 percent growth to -0.1 percent, inverting the positive global picture of trends in inequality measured
. The main two outcomes are dummy variables indicating whether the firm increased the use of digital platforms, and whether the firm invested in digital solutions. Time _t_ is split into two periods, broadly defined as the first and last rounds in which the survey was conducted. For all specifications the vector of firm characteristics _X_ includes dummies for the size of the firm based on number of workers (small; medium; and large). Estimations also control for 12 sectors and country fixed effects. To control for variation in the severity of the shock within the period the survey is implemented, _Shockict_ includes the severity of the crisis measure using Google mobility trends around transit stations.<sup>7</sup> All variables are interacted with a time measure _t_ to allow for changes in the coefficients between the first and the latest rounds. Analysis estimate the margins at the means for the variable of interest (e.g., size, sector, or country), for which results are compared across time, keeping the distribution of size, sector, and country constant across waves to ensure that the results are not driven by differences in composition. To

> the first round but did not respond to the follow-up survey, and some of them are replacement firms collected in follow-up surveys. The methodology section in Cirera et al. (2021) describes the robustness checks implemented and the handling of the different sample of firms.

> 6For more details on the surveys and aggregate results see Cirera et al. (2021)

> 7Authors construct an indicator of the severity of the crisis that is a weighted average of 30-day periods since the start of the pandemic until the date of the survey. Specifically, the 30-day period average just before the survey has a weight of 1, the average from day 31-60 has a weight of 1/2, the average from day 61 to 90 has a weight of 1/3, and so on until the start of the pandemic. This approach is aligned with Apedo-Amah et al.
# **References**

Acemoglu, D., 2009. In: Introduction to Modern Economic Growth. Princeton University Press, Princeton.

Aghion, P., Bloom, N., Blundell, R., Griffith, R., Howitt, P. (2005). Competition and innovation: An inverted U-relationship. Quarterly Journal of Economics 120, 701-728.

<mark>Aghion, Philippe, Philippe Askenazy, Nicolas Berman, Gilbert Cette, and Laurent Eymard. 2012. “Credit Constraints and the Cyclicality of R&D Investment: Evidence from France.” Journal of the European Economic Association 10, no. 5: 1001–1024.</mark> Aghion, P.; Blundell, R.; Griffith, R.; Howitt, P. and Prantl, S., 2009, ‘The Effects of Entry on Incumbent Innovation and Productivity,’ _Review of Economics and Statistics_ , 91, pp. 20–32.

Alam, Ashraful Moshfique Uddin, and Hassan Yazdifar (2019). “Institutional determinants of R&D investment: Evidence from emerging markets.” Technological Forecasting & Social Change 138: 34-44.

Amin, M. and Y. C. Soh (2020). Does Greater Regulatory Burden Lead to Higher Corruption: Evidence Using Firm-level Survey Data for Developing Countries. Forthcoming _World Bank Economic Review_ . <u><mark>https://doi.org/10.1093/wber/lhaa007</mark></u> <mark>Aterido, R., Hallward-Driemeier, M., & Pages, C. (2011). Big Constraints to Small Firms' Growth? Business Environment and Employment Growth Across Firms.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>, 59 (3): 609 – 647.</mark> Ayyagari, Meghana, Asli Demirguc-Kunt, and Vojislav Maksimovic. (2011). Small vs. Young Firms Across the World.
participants, neighbors, and consumers (i.e., social welfare) are practically impossible to capture in the data. In short, this paper examines the effects of a managerial training program on participating entrepreneurs’ business practices and performance and compares the cost and private benefit of such a program.

# **3. Surveys and training program**

Our study site is Suame Magazine, located in Kumasi, the second largest city in Ghana. Kumasi is a junction of the artery roads connecting the major coastal cities and the major inland cities including Ouagadougou, the capital city of Burkina Faso.<sup>2</sup> Suame Magazine is known in West Africa as a large cluster of garage mechanics, but it is also a cluster of metalwork enterprises producing a variety of metal products, such as bolts and nuts, corn mill machines, threshing machines, and cash safes.<sup>3</sup> The garage cluster has had a long period of quantitative expansion because the demand for car repair services has increased dramatically. As the garage cluster expanded, scrap metal became increasingly available, helping the expansion of the metalwork cluster.

We conducted a survey of metalwork entrepreneurs in early 2005. In those days, most masters, whether garage mechanics or metalwork entrepreneurs, were members of the Suame branch of the Ghana National Association of Garages (GNAG). As shown in Table 1, the number of members in 2003 exceeded 10,000, of which more than 1,000 were metalwork entrepreneurs. The number of metalwork entrepreneurs does not seem to have increased since then. As will be shown in Table 3 below, the profitability in this cluster began decreasing clearly in the early 2000s. The metalwork cluster in Suame Magazine was a typical survival cluster except for the extraordinarily large size of the annexed garage cluster. For the survey, we selected 167 metalwork entrepreneurs 8
# **1 Introduction**

The number of firms operating in Africa and their characteristics remain challenging to ascertain due to the limited and inconsistent availability of establishment censuses and business statistics across the continent. This paper aims to bridge this knowledge gap by mapping existing sources of firm-level data, integrating employer information from household and labor surveys, and applying a simple statistical model to estimate the total number of firms in African countries, categorized by size and formal status. The scarcity of data on the number of firms in Africa starkly contrasts with the extensive, up-to-date, and comparable firm-level information available in advanced economies. For instance, the OECD’s Structural and Demographic Business Statistics and the European Union’s Business Demography data exemplify the comprehensive nature of firm demographic information available for these regions. Previous studies have emphasized the importance of these estimates for Africa (Teal, 2023b,a; Tsaedu et al., 2023), but often focus on a few particular countries for which firm data is available.

Firm demographic indicators are crucial for policymakers as they provide insights into the structure and dynamics of the economy, enabling targeted and effective decision-making. These indicators help identify which sectors and firm sizes drive growth and highlight challenges, such as limited access to technology needed to boost productivity. They are also instrumental in making decisions related to taxation, social security, and policies aimed at fostering job creation. Additionally, firm demographics are vital for assessing economic vulnerabilities, designing tailored crisis responses, and evaluating the long-term impact of policies. A recent article by The Economist (2025) suggests that “Africa has too many businesses, too little business” emphasizing the lack of large enterprises on the continent as a key constraint to economic growth. Better estimates of the distribution of firms by size and formal status, along with the distribution of workers associated with them, can provide greater clarity on the magnitude of this gap.

Estimates on business demographics are also relevant for investors and the private sector. This information can be used to estimate the potential