rafmacalaba/data-use-mentions-extended huggingface dataset:
detail the contribution of small firms to employment, productivity, and job creation in our data. In Section IV, we present growth regressions and sensitivity analysis and place our results in the context of existing literature. Section V concludes.

# **II. Data and Variable Construction**

In this section, we describe the survey dataset and define the various variables used to describe the relative importance of small firms in different countries. We use the World Bank Enterprise Surveys (ES)<sup>5</sup> that are an on-going initiative of the World Bank to benchmark the investment climate in different countries across the world and to analyze firm behavior and performance. The Enterprise Surveys survey from the universe of eligible firms obtained from the country’s statistical office<sup>6</sup> using stratified random sampling with replacement to generate a sample representative of the whole non-agricultural private economy (so fully government owned firms are excluded from the sampling universe) in the country. The surveys are stratified according to three criteria: _Sector of activity_ (population of industries include manufacturing sectors, construction, services, transport, storage, communications, and computer and related activities), _Firm size_ (the strata include small firms (5-19 employees),<sup>7</sup> medium firms (20-99

> 5 The ES surveys and their precursor, the World Business Environment Survey have  been used to investigate a series of questions in developmental economics including the relation between property rights and contracting institutions (e.g. Acemoglu and Johnson, 2005; Ayyagari, Demirguc-Kunt, and Maksimovic, 2008a),  investment climate and business environment obstacles to growth (e.g. Beck, Demirguc-Kunt, and Maksimovic, 2005; Ayyagari, Demirguc-Kunt, and Maksimovic, 2008b), firm financing patterns (e.g. Beck, Demirguc-Kunt, andMaksimovic, 2008; Cull and Xu, 2005, Ayyagari, Demirguc-Kunt, and Maksimovic, 2010) and dispute resolution via courts (e.g. Djankov, La Porta,
# **Table 9. Percent Market Capitalization and Volume Traded by Trading Patterns**

||Market C|apitalization|Value Trade|d|Turnover|
|---|---|---|---|---|---|
|Year|Total<br>(LE bn.)|Cosmetic<br>(percent)|Total<br>(LE bn.)|Cosmetic<br>(percent)||
|1997|244|9.8|17|1.6|0.07|
|1998|207|13.4|14|12.2|0.07|
|1999|259|45.3|442|9.0|0.16|
|2000|166|51.0|25|9.5|0.15|
|2001|81|29.1|13|5.0|0.16|

Notes: Source CMA (own calculations). This table includes all firms that have been traded (i.e., have non-zero price in the CMA database). Turnover is the ratio of the value traded by market capitalization.
# **TABLES & FIGURES**

**Table 1: Household size by region**

|**Regions**|**Household size**|**Year**|**Households (in millions)**|**Countries**|
|---|---|---|---|---|
|(1)|(2)|(3)|(4)|(5)|
|Sub-Saharan Africa|4.7|2014.7|207|46|
|Middle East & North Africa|4.4|2013.5|71|11|
|South Asia|4.5|2014.8|366|7|
|East Asia & Pacific|3.8|2013.9|169|19|
|**World**|**3.6**|**2014.8**|**1578**|**162**|
|Latin America & Caribbean|3.3|2013.4|176|22|
|Europe & Central Asia|2.8|2015.2|171|30|
|Other High Income|2.4|2016.6|418|27|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD) and Luxembourg Income Study (LIS) Notes: The table shows the distribution of average household size by region. Column (3) represents the year of the</u> surveys on average. The table is arranged in descending order of average household size in column (2).

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IDP situations_**

Profiling of IDP situations is a collaborative process aimed at generating reliable data that can be broadly agreed upon. As a collaborative process, it can be a crucial tool for generating agreement on persistent questions such as who is recognized as internally displaced within a given context, what are the most prevalent vulnerabilities caused by displacement, and how do IDPs fare compared to host populations. In sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs.

> 68 However, in many conflict-affected countries, governments lack the basic capacity to maintain Civil Registration and Vital Statistics (CRVS) systems including the registration of births and deaths in non-displacement situations, let alone the registration of IDPs displaced due to natural disasters or conflict.

> 69 IOM has introduced biometric registration systems in South Sudan, Sudan, DRC and Nigeria to circumvent these problems.
Indicators. Other variables controlling for household demographics and assets are constructed from the data sets.

The commune surveys collect information about the occurrence of emergencies in the last 3 years listed by type and month in addition to information on population, economic conditions, agriculture and land. These data allow identifying communes exposed to weather events, such as storms, floods and droughts.

Although the data allow estimating nationally‐representative consumption and income estimates and their changes over time, several shortcomings for the purpose of this study are to be noted. First, the study relies on observations from only three years within a five year time period and only a subsample of the included households is observed in several years. This short‐term panel does not allow to understand time‐variant household factors or structural changes over longer time horizons. Second, little information is available about labor allocation, production inputs and returns, which are important to explain income differences over time. The lack of such information can limit the explanatory power of the regression models. Yet many of these variables could be determined by weather conditions and thus create potential endogeneity biases.

# **2.2 Weather data**

In addition to these self‐reported weather events in the commune surveys, this study uses weather data from the CRU of the University of East Anglia to control for rainfall and temperature conditions. From the global CRU TS3.21 data set, monthly time series of rainfall, minimum, mean and maximum temperature from 1961 to 2014 is available at 0.5 x 0.5‐degree grid.<sup>4</sup> This balanced panel of weather data was produced using statistical interpolation based data from 4,000 individual weather stations (Harris et al. 2014).

These data are merged at the commune level to construct current and long‐term weather variables for each month. For each commune, the household interview dates are specified and the current weather variables are defined for the 12 months before that interview date (e.g
was built by applying the same rent-imputation method in both years.

Except for Bangladesh, where including rents does not affect shared prosperity, in all other countries the housing component has a sizable and statistically significant effect. In the case of Iraq and Albania, including rents, obtained via the log linear model, reduces shared prosperity. The log-linear model has instead a positive effect on shared prosperity in the case of Peru. However, it is worth noticing that in the case of Albania selfassessment was recorded only for 3 percent of the total non-market-tenants in the 2008 survey. Therefore, the large increase in shared prosperity observed in the analysis (4.7 percent) is due to the missing information in 2008: for 97 percent of the non-market-tenants, a vector of zeros was added in 2008 as self-assessment value.

Our results suggest that, although size and magnitude of the effect of including housing in the consumption aggregate are not predictable and might vary within country according to the imputation method selected, shared prosperity does change as a result and the change can be substantial and might imply churning in country-rankings (see the case of Peru and Iraq), or altering our estimates of a country’s performance over time.

# **4.4. Non-anonymous Distributional Implications**

Gini index, poverty indices and shared prosperity all satisfy anonymity: a permutation of the underlying welfare vector does not affect the distribution as long as welfare levels do not change. Nevertheless, rent imputation may determine a proportional shift in the welfare vector with no distributional consequences (e.g. on the Gini index, which is invariant to scale), but at the same time it may imply a churning of individuals along the distribution. In an extreme scenario, it may be possible that after rent is included in the welfare aggregate all individuals below the poverty line become non-poor and the same number of initially nonpoor individuals become poor. In this case, poverty indices may remain the same, leading us to conclude that rent imputation has no effect on poverty
> the average annual growth rate of real GDP per capita of countries with an EFW rating in the first tier was 3.4 percent, as compared to 0.4 percent for countries whose rating was in the third tier. When developed countries are omitted from the analysis, the differential growth rates between the persistently free and the persistently unfree developing economies are even greater. The persistently free least-developed countries (LDCs) grew at an annual rate of 5.2 percent during the two decades, compared to 0.6 percent for the least free group. The ten freest economies out of those with low incomes in 1980 grew more than four times the average of the other countries. Countries with more economic freedom also tend to achieve far better social outcomes. They have substantially higher per capita incomes, including for the poorest 10 percent of the population,<sup>10</sup> have longer life expectancy, higher adult literacy, lower infant mortality, lower incidence of child labor, better access to improved (treated) water sources, and greater overall “human development” achievement as measured by the UN. Economic freedom is also generally

> 7 The Fraser Institute’s Economic Freedom of the World Index, Freedom House’s Economic Freedom Indicators, and the Heritage Foundation’s Indices of Economic Freedom.

> 8 The Economic Freedom of the World (EFW) index is perhaps the most ambitious attempt to quantify economic freedom and its impact on investment and economic growth. The index, currently available for 141 countries, measures the consistency of a nation’s policies and institutions with economic freedom, in particular the extent to which various countries rely on open markets to allocate goods and resources.

> 9 After adjustment for differences in initial income level, tropical location, share of population near an ocean, and human capital.

> 10 Interestingly, the share of the income earned by the poorest 10 percent of the population was found to be unrelated to the degree of economic freedom in a nation.

6
- ILO (2016) Technical paper. A just transition to climate-resilient economies and societies: Issues and perspectives for the world of work. Geneva: ILO.

- ___. (2019) Skills for a greener future: A global view based on 32 country studies. Geneva: ILO.

- ___. (2021) Global framework on core skills for life and work in the 21st century. Switzerland: ILO

Janser, Markus (2018a) The greening of jobs in Germany: First evidence from a text mining based index and employment register data, IAB-Discussion Paper, No. 14/2018, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg

- ___. (2018b) The greening of jobs in Germany: Online Appendix ‘text Mining & Descriptives’ Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg

- Lewis, P., Morris, J., and Gregory, C. (2022) Green Topics: Identifying Linkages to Occupations and Education Programs using a Linguistic Approach. O*NET working paper. June 2022

- OECD, Eurostat (1999). The environmental goods and services industry: manual for data collection and analysis.

- O*NET (2010) O*NET® Green Task Development Project. The National Center for O*NET Development.

- ___. (2013) Greening of the World of Work: O*NET® Project’s Book of References. The National Center for O*NET Development.

- Oyunbileg, D., Stoevska, V. (2017) Employment in the environmental sector and green jobs in Mongolia. Pilot Study.  International Labour Organization (ILO)

- Stoevska, V. Hunter, D. (2012) Proposals for the statistical definition and measurement of green jobs. Discussion paper prepared for informal consultation, November 2012. International Labour Office. Geneva.

- Stoevska, V., Elezi, P., and Muraku, E. (2014) Report on the pilot project towards developing statistical tools for measuring employment in the environmental
2 The Data Revolution in Justice 24 their inappropriate use may present. Recent research examines the potential of machine learning techniques to “improve human decision”, in specific by evaluating the potential of machine learning to decide whether defendants should await trial at home or in jail in different US urban cities (Kleinberg et al., 2017). The authors focus on bail decisions, in which judges have to assess whether the defendant will flee or commit a new crime if released, and trade off these risks against the cost of incarceration. In brief, the judges have to decide, based on their prediction, what a defendant would do if released. This specific prediction task makes an algorithm particularly promising for this job.

The authors use a large data set of cases heard in New York City from 2008 to 2013 to build an algorithm that assesses the risk of fleeing or recidivism. The algorithm then makes a prediction based only on data available to the judges at the time of the bail hearing. After comparing results, the authors find that the algorithm improves judicial decisions, and has the potential to reduce crime as much as 25% without changing the numbers of people waiting in jail. By evaluating the results, they realize that judges release many defendants the algorithm ex ante identifies as very high risk, and that stricter judges do not jail the riskiest defendants first. These results are not restricted to New York City, as the authors report similar findings in a national data set as well. Moreover, the authors show the potential of machine learning tools to diagnose why judges mispredict.

The results from the aforementioned study suggest a promising avenue for future research on the application of machine learning to predict the risk of recidivism and improve the precision in judicial decisions. According to the United Nations (2018), the proportion of prisoners held in detention without being sentenced for a crime has remained almost constant between 2003–2005 and 2014–2016, and still affects almost one third of all prisoners. Thus, the importance of potential
# **Question 4: Is There a Positive Association Between Skilled and Unskilled Migration?**

Efforts by migrant-sending countries to halt or reduce levels of skilled migration typically assume that high skilled migration is a phenomenon largely unrelated to less-skilled migration – the same countries concerned about outmigration of skilled workers typically welcome the remittances and job opportunities that migration provide for less-skilled workers. Likewise, immigrant-receiving countries may wish to reduce unskilled immigration while increasing skilled immigration. But the empirical evidence suggests that levels of skilled and unskilled migration actually have a strong positive association—and thus that attempts to limit one form of migration are likely to affect the other.

Both skilled and unskilled migrants are likely to determine their migration decisions in part on the basis of the institutional characteristics of their home country, and the presence of common links with potential destination countries. One specific avenue for a positive association between high and low skill migrants is family reunification. In the U.S. New Immigrant Survey, for example, 45.8 percent of spouses who are brought into the country on a family visa by a university-educated migrant do not have a university degree – evidence of a channel through which an increase in the number of high skill migrants may also result in the increase in the number of low skill migrants.

We analyze stocks of migrants to examine how the stocks of both high skill and low skill migrants evolve over time. We use two data sources: the database of Docquier and Marfouk (2005), which provides bilateral stocks of high and low skill migrants aged 25 and over from 198 source countries in 31 destination OECD countries in 1990 and 2000, and the U.S. Integrated Public Use Microdata Series (IPUMS) Census data, which provides similar stocks over the period 1960 to 2000 for the United States (Ruggles et al., 2010).

Figure 1 presents the relationship between the stocks of low skill and high skill migrants, with levels on the left and changes on the right. Each observation is a destination-source country pair in
and global studies. After the validation exercises, the authors implemented the LASSO-PMM method in 43 countries worldwide.

> 6 The donor survey is the one that contains the actual income data from the first-round survey. The receiver is the second-round survey where the income is imputed.

> the second-round survey where the income is imputed.

> 7 To simplify notation, in this document i will be used to refer to the logarithm of household per capita income. yy

5
do not pay bribes. Of course, this positive relationship between bribe payment and the severity of access to finance as an obstacle may be muted in the data because the more financially constrained firms are less able and therefore less likely to pay a bribe. Despite this, we do find evidence of a strong positive correlation between the two. Figure 2 illustrates the point graphically.

The present paper contributes to the literature discussed above in several ways. First, we explore the impact of corruption on job creation, a hitherto neglected issue as far as the effects of corruption are concerned. Second, we examine the role of financial constraints in determining whether corruption hurts or benefits the firms, and by how much. Thus, we bring together two contrasting models of corruption - “grease the wheels” and “sands the wheels” – together under one unified model. Third, as Fan et al. (2009) and Svensson (2003) note, most previous studies have used perceived corruption indices that rely on the aggregated perceptions of businesspersons or country experts, many of whom may have formed impressions—perhaps subconsciously— based on common press depictions of countries or conventional notions about what institutions or cultures are conducive to corruption. Thus, the use of these indices raises concerns about perception biases. The _Manual on Corruption Surveys_ prepared by the UN (UNDOC, UNDP and UNODC-INEGI 2018) argues that corruption measures that are based on the actual experience of individuals with corruption (obtained from survey data) are far superior to the corruption measures based on opinions and perceptions of the experts. Thus, we depart from the literature by using firms’ experience with corruption instead. We do so by employing measures of the overall corruption and petty corruption.

Fourth, we pay due attention to endogeneity concerns. We do so in several ways. For corruption faced by the firm, we proxy this by the average level of corruption experienced by all other firms (that is, excluding own firm) in the country-industry cell to reduce endogeneity 6
transportation network.<sup>4</sup> This geographical information serves as the basis for our analysis of the projects’ welfare impact.

Projects are included in our database if they lie physically along Belt and Road routes defined by two official Chinese sources (Office of the Leading Group for the Belt and Road Initiative, 2017; Government of China, 2017) and if they are mentioned in one of the following sources as being part of BRI: (i) a document issued by a government or its press agency, (ii) an article in a major academic journal or global news source, or (iii) a quote by a government official in a global or leading national news source. Sources reviewed include joint declarations by China and Belt and Road 4 Four other databases of Belt and Road projects exist. The Reconnecting Asia database by the Center for Strategic and International Studies (CSIS), the China Global Investment Tracker by the American Enterprise Institute (AEI), the MERICS Belt and Road Tracker, and the Hong Kong Trade Development Council (HKDTC) database. The CSIS database is a broad database covering 152 BRI-related transportation projects in Eastern Europe and Asia and overlaps substantially with ours, though does not include detailed GIS files that overlay these projects onto the transportation network. The database is notable for including some (but not complete) information on project costs. The AEI database emphasizes capital flows associated with certain projects, covering 50 investments related to road, rail or shipping infrastructure. The MERICS Belt and Road Tracker includes ample discussion of policy related to the BRI but does not offer a specific database of projects accessible to researchers. The database by HKDTC has a very broad scope that extends beyond transport links. It does list 106 logistics-related projects, most of which are not directly related to transport infrastructure.

8
agricultural activities and report having cultivated at least one plot in a given season (for countries with two cropping seasons, i.e., Malawi and Uganda, we focus our analysis on the main season). Starting with 36,014 household-wave observations in the original dataset, dropping 9,359 observations that report not having engaged in agricultural activities, and then dropping 1,381 observations without cultivated plots, leaves us with a maximum of 25,274 household-wave observations, comprising 11,154 households and 50,478 plot-wave observations. In all of the main regression analyses we work with household fixed effects, which requires two rounds of data. After dropping 748 observations with missing data on the use of agricultural advice, 162 observations with missing data on shocks, and 755 observations with missing data on one of the key household level control variables, the subsample of households that appear in the data for at least two rounds consists of 20,233 household-wave observations.

The rest of this section describes the construction of the key variables used to test the model’s predictions. Additional information on the construction of variables and differences in survey designs across countries can be found in Appendix B, together with a complete list of all variables and basic summary statistics (see Table A1). **Extension services.** In all three countries, the LSMS-ISA surveys collect detailed information on whether households received advice related to farming activities, as well as the number of extension contacts and the sources of advice (e.g., governmental extension service, NGOs, or other farmers). The dummy variable _Advice_ indicates whether a household has received advice related to farming at least once over the past 12 months. The dummy variable _Extension_ considers only advice that was obtained through sources other than media (i.e., sources such as TV, radio, or flyers). The variable _Contacts_ contains the number of extension contacts, which includes visits to the household as well as visits by the household to the source.<