rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 8. Kernel density plots of knowledge and preparation about migration by interest in**

**migration**

Source: Authors’ calculations from DESMIGRATIF (2018) Figure 8 shows kernel density plots of the indices by groups of interest to migrate. We can see that knowledge about migration seems to be higher for those interested in migrating. The preparation to migrate plot however shows a weaker relationship with interest in migration: those who are not interested in migration have a higher density for lower scores of the index and those who are interested in migration have slightly higher densities for higher scores.

Moreover, a phone survey conducted in 2020 among 3,224 respondents of our sample provides us with information on migration for a subset of individuals. Indeed, 55 potential migrants from the sample migrated. First of all, there is a 10 percent (significant at 1 percent level) correlation between interest in migration and actual migration. This correlation level should however be interpreted with caution considering that there is a potential negative selection of migrants in the sample of the survey.

# 6. Comparing the data-driven approach and theory-driven approach

To verify the relevance of machine-learning methods, we implement a logistic regression model of interest in migration using variables identified in the literature. We draw a list of variables from Milasi (2020), which estimates the determinants for the desire to migrate among young people from 139 countries. Milasi (2020) groups the determinants of migration in the following categories: Individual Characteristics (female, age, have kids, urban, poor health, education), Wealth (lack of food or shelter, difficulty living on present income, no improvement in living standards), Network (no opportunity to meet people, no help from friends/relatives), Context (discontent with local amenities, discontent with local education system, no trust in national government, corruption in government), Labor 34
# **Figure 6**

# **Real decoupling in EM: Decoupling with G7 and coupling with China**

This figure presents different betas from 2003 to 09/2010. Betas for G7 and China were estimated from dlog(EM) vs dlog(G7) dlog(China), were EM, G7, and China are real gdp growth or industrial production growth in EM countries, the G7 and China respectively. Betas were estimated by extending the time window. The first betas were estimated with the sample going from 1993 to 2003, and for the rest of the betas we extended the sample until 09/2010. Source: IMF IFS, GEM World Bank.

<!-- Start of picture text --> Real GDP Growth<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>G7 Beta G7 Beta +- 1 SD Linear (G7 Beta)<br>Industrial Production Growth<br>0.9<br>0.85<br>0.8<br>0.75<br>0.7<br>0.65<br>G7 Beta G7 Beta +- 1 SD Linear (G7 Beta)<br><!-- End of picture text --> <!-- Start of picture text --> Real GDP Growth<br>1<br>0.9<br>0.8<br>0.7<br>0.6<br>China Beta China Beta +- 1 SD Linear (China Beta)<br><!-- End of picture text --> <!-- Start of picture text --> Industrial Production Growth<br><!-- End of picture text --> <!-- Start of picture text --> 0.5<br>0.45<br>
important role in determining which municipalities were less likely to send emigrants to the United States and which ones were more likely to absorb returnees. Internal migration flows were also responsive to changes in local exports.

Table 8 shows the impacts of exports on migration. A 10 percent rise in exports per worker raised the number of people who had migrated in the previous five years to the municipality by about 7.1 people (panel a, column 1). This represents about 4.6 percent of the number of recent migrants for the average municipality in 2005. Most of this immigration response was generated by internal migrants (panel b). Indeed, a 10 percent increase in exports per worker raised the number of immigrants from other Mexican municipalities by about 4.9 people. The corresponding estimate for external migrants was only 2.2 people (panel c). The inflows of

> 9 See “U.S. Immigration Trends,” Migration Policy Institute, Washington, DC, https://www.migrationpolicy.org/programs/data-hub/us-immigration-trends#source.

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safeguard policy is generally thought to result in MFN protection through nondiscriminatory treatment of imports, irrespective of the source country.<sup>2</sup> The purpose of this paper is to investigate empirically one element concerning the relative substitutability of the antidumping and safeguards TTB policy instruments. We provide an econometric examination of the differential trade effects that were associated with the discriminatory treatment across export sources for the steel product categories that were affected by the 2002 US safeguard and compare them to trade effects that were associated with steel imports that were affected by US antidumping policy. We construct a panel of bilateral, product-level US steel import data that include products that were affected and unaffected by these policies, covering the period 1989-2003.<sup>3</sup> Our specific empirical approach is to match the disaggregated import data to detailed information on TTB policies that is now available in the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2012). For the 2002 steel safeguard, however, we also need to match import data to a number of different forms of policy _exclusions_ that arose both at the trading partner level and at the level of specific products that were tied to particular foreign firms. The information on this last set of policy exclusions has been compiled subsequently into a unique data set that derives directly from firm-specific petitions that were filed with the US Department of Commerce.

Our econometric results confirm that application of the 2002 steel safeguard policy is associated with differential trade impacts across the sources of US imports. Furthermore, we show how this impact on trade is similar to the differential trade effects of the more explicitly discriminatory protection that the US steel industry received under antidumping in earlier data that date to the late 1980s. Our evidence on antidumping is consistent with results from Prusa (1997, 2001), who uses a related approach to document

> 2 This is not the only potentially important distinction between SG and AD/CVD. In addition to the issue of “fair” versus “unfair” trade, the US
areas that are disconnected from core urban areas and suffer from poorer living conditions.

The wealthier status of those residents in the preferable neighborhoods allows them to purchase better access to key public services, which residents in the non-preferable neighborhoods may not be able to afford. While households living in the city core or non-precarious neighborhoods enjoy better access to services, their spending on these services is accordingly much larger than those households living in the peri-urban or precarious neighborhoods. For instance, the amount that residents in non-precarious neighborhoods spend on bills for electricity and stocking water (for own consumption) are 40 percent and 54 percent higher, respectively, than residents in precarious neighborhoods.

> 9 The consumption expenditures are based on the 2018 Kinshasa Survey with adjusted sampling weights. See Batana et al. (2021) for details.

11
respondents and a set of covariates ( _X_ , including some suitable non-linear function of income) and postulates a regression model of the form:

> where the error term,  , is uncorrelated with _X_ . If using ordinal data from a ladder question then an ordered response estimator is typically used, such as the ordered probit, in which case (9) is the underlying continuous variable generating the ordinal responses relative to thresholds that are taken to be fixed. If instead one is using the MIQ, then<sup>_y_</sup> _i_ min is the dependent variable.

This section provides an overview of some of the lessons to date in estimating such a model. The next section will discuss a number of issues in estimating such a regression.

Recall that one of the conditions for existence of a SSPL is that subjective welfare is an increasing function of income. This is invariably indicated in cross-sectional studies; indeed, I have not seen an exception in the literature though some studies find stronger income effects than others.<sup>18</sup> Stevenson and Wolfers (2008) argue that the income effect on subjective wellbeing is robust across countries, within countries, and over time.

There have been some estimates of SSPLs.<sup>19</sup> Interestingly, the estimates to date suggest that the overall poverty rate based on the SSPL is roughly similar to that implied by objective poverty lines.<sup>20</sup> It may well be that the choice of parameters in the ―objective‖ absolute lines already approximated the expected SSPL in the specific context.

However, the structure of the poverty profile has turned out to be different in some respects. While objective poverty lines often imply that larger households are poorer, this is not typically the case in cross-sectional studies using the subjective approach, which tends to suggest greater economies of scale in consumption than normally assumed, at least in developing countries. For example, in using the ELQ to test the welfare consistency of prevailing
project demonstrates that empirical research, including RCTs, can be conducted in challenging, FCV settings through appropriate rapid training of local researchers and NSPs. The ERKF project provided a unique setting for researchers to test the hypothesis that financial assistance, coupled with the 5‐week PM+E training intervention, can be more effective at reducing psychological stressors of SME entrepreneurs in FCV contexts, such as symptoms of depression and anxiety — the most common manifestations of psychosocial distress.

The results of this study are encouraging for a few reasons.

First, it confirms that even non‐clinical populations suffer from clinically significant levels of mental health issues such as depression and anxiety. The baseline data indicates that 1 of every 3 to 4 SME entrepreneurs in KPK experiences symptoms of depression and anxiety. This is new data and provides a sense of the gravity of the situation. It would help to make a case for mental health services to be made available for not only clinical populations, but also for populations at‐ risk, as per the Lancet Commission recommendations.<sup>11</sup> As such, it can also help to prevent burgeoning healthcare costs in the future, ensuring a decent quality of life for all.

> 11 According to the Lancet Commission report on Global Mental Health (2018), policy makers and practitioners need to “broaden the global mental health agenda from a focus on reducing the treatment gap for people affected by mental disorders to the improvement of mental health for whole populations.”

22
= 143-122122 in row 10, 124-98-98 in row 11). In developing countries, returns are higher for more educated individuals (2.1*** vs. 1.7*** in col. (1)). However, we lose about 20-25 countries because we do not have at least ten individuals for each experience-education bin. To remedy that issue, we use country population circa 2017 as weights (rows 1213; N = 143-122-122 in row 12, 124-98-98 in row 13), making compositional changes due to smaller countries less consequential. Results are unchanged.<sup>23</sup>

**Main Returns.** In our simulations we use population-weighted education-specific

> 21Web Appx. Table A.9 shows that local returns are higher for lower-experience bins whereas Web Appx. Table A.12 shows that results hold for different combinations of local returns.

> 22Lagakos et al. (2018b) exclude: (i) female and part-time workers because potential experience is a better proxy of actual experience for male and full-time workers; (ii) self-employed individuals because their wages may also include the returns to the capital investments they make. In our case, I2D2 calculates their wage as the amount of salary taken from the business; and (iii) public sector workers because they receive nonwage compensation and their wages may not reflect the full payment for their labor.

> 23Results hold if we use 12 or 14 years or information about high school graduation (Web Appx. Table A.13).
with both each other and with the formal sector. An ideal data set would provide details on sourcing and selling patterns by sector, geography, and formality status. In the absence of such data and given the strong correlation of size and formality status, our default approach will be to assume that informal firms exhibit linking preferences _p_ ( _θ, θ_<sup>_′_</sup> ) similar to those of small formal firms, conditional on sector and geography.

This approach is particularly attractive given its straightforward implications for the sectoral and geographic composition of linking patterns. It further allows for informal and small formal firms to experience different wedges in link formation, provided these wedges generate aggregate linking patterns that are still comparable to those of small formal firms at the sector-countysize level. Consider, for instance, a small formal retailer and an informal retailer both seeking to purchase soap. While neither can source directly from manufacturers in Nairobi, the small formal retailer might purchase from a large formal wholesaler locally, whereas the informal retailer—potentially excluded due to their tax status—might source from an alternative local wholesaler. Despite sourcing from different individual suppliers, both retailers exhibit the same patterns once their sourcing is aggregated at the sectoral and geographic levels. The similarity assumption is also motivated by findings in the administrative data, where we document that small formal firms tend to source more locally and from intermediaries (see Table B1).

Nevertheless, this assumption may not fully capture the implications of challenges specific to informal firms for their linking patterns. Therefore, we subsequently introduce modifications to our approach, drawing on stylized facts from the existing literature, to assess the sensitivity of our results to alternative assumptions about the linking patterns of informal firms.

# **5.3 Characteristics of the Revised Network**

How does the revised network that accounts for informal firms compare to the baseline network? First, we find that firm types in sectors and counties with a high incidence of informality are predicted to have a relatively larger increase in
|AVERAGECOLLECTIONCOST|
|---|---|
|Peru|1.9% (1996-1998)|
|Mexico|1.7% (1995, 1997-1998)|
|Venezuela|2.0% (1995-1998)|
|Kenya|1.2% (1995-2000)|
|South Africa|1.1% (1998-2001)|
|Uganda|3.6% (1991-2001)|
|_Average_|_1.9%_|

_Source: Author’s calculations from ARA and MOF data, and Taliercio (2000)._ 22
of the population would ultimately either recover from infection or die in an unmitigated epidemic scenario in lower- and middle-income countries. The unmitigated epidemiological model in Walker et al. (2020) assumes no behavioral response to the pandemic. For consistency, we assume that households continue to supply labor and consume at the same levels as in the pre-pandemic steady state in this stage of the calibration exercise. This assumption is relaxed in the following steps of the calibration.

The other two moments used for the calibration of the transmission parameters are the shares of infections occurring through work- and consumption-related activities. As most of the world’s population lives in urban areas, we chose to use data reported by JohnstoneRobertson et al. (2011) on locations of contacts in a South African township community.<sup>14</sup> The authors define close contacts as those involving physical contact or a two-way conversation with three or more words. They find that 6.2 percent of close contacts occur in workplaces, while 3.5 percent occur in shops or local bars and therefore can be thought of as related to consumption. Another 8.9 percent of close contacts take place during transport and could theoretically be linked to either labor or consumption. We assume that half of the transport contacts is related to labor and half to consumption. This implies that 10.6 percent of close contacts are related to work and 8 percent to consumption. For high-income countries, we use the rates employed by Eichenbaum et al. (2021) for the US. Based on an analysis of the Bureau of Labor and Statistics 2018 Time Use Survey data and contact patterns reported in Ferguson et al. (2006) and Lee et al. (2010), they conclude that 16 percent of transmissions are related to consumption and 17 percent to work.

# **3.2 Reference Lockdown and Community Transmission Parameter**

Conditional on the transmission parameters calibrated above, we calibrate the reference lockdown policy, and the parameter determining the relationship between
our estimation sample. Table 1, column (1), shows summary statistics for all contacted customers. The average customer in our sample is male, 33 years old, has an income of Rs 40,000 (US$ 528), and a loan of Rs 11,810 Rs (US$162) with a monthly payment of Rs 1,450 (US$ 21) outstanding. On average, borrowers in the sample had taken out 0.38 prior loans recorded in credit bureau data. However, a substantial fraction of borrowers (67 percent) has no credit record. The credit scores of borrowers for whom a record was available are relatively high at 731 out of 800. Table 1 columns (2) to (5) report summary statistics for the same baseline borrower and loan characteristics for each treatment condition separately, and Table 1, column (6) reports a test of equality of means across treatment groups, which confirms that the randomization was successful.

# **3.4.2 Measurement of repayment outcomes and borrower beliefs**

The main outcomes of interest in the debt forbearance experiment are loan repayment, as observed in the lender’s administrative data and credit bureau records for all borrowers in the sample, and beliefs about credit enforcement the likelihood of future forbearance. In this section, we describe the measurement of each of these outcomes in turn.

> 15 The Indian credit bureau, TransUnion CIBIL, was founded in 2000 and currently covers approximately 63 percent of adults in India (World Bank, 2022). Consumer credit scores in India range from 300 to 900, with scores between 700-749 labeled ‘good’ and scores above 750 labeled ‘excellent’.

12
less so for agriculture.

Figure 5 shows an additional dimension of heterogeneity, considering the self-employed vs employees. This variable is mostly recorded in the LAC and ECA regions only. It appears that stopping work is somewhat more common among the self-employed (46%, taking a simple average across countries) than among employees (39%).

# **4.3 Measures of Income Loss**

In addition to stopping wage work and payments for wage work, the HFPS data allow us to examine broader measures of household income. Because these indicators are measured at the household level, they are less influenced by the non-representative nature of the sample of respondents in some countries. We examine four income categories: total household income, farm income, non-farm income, and wage income. Results are reported in Figure 6.

Panel (a) suggests that total income loss was most prevalent in some SSA countries (Ghana, Nigeria, and Malawi), as well as some countries in the LAC region (Peru and Ecuador were the most affected). It appears that labor market disruptions have translated into widespread income loss in all countries, including high-income countries with stronger social security systems and public assistance programs. It is notable that in many countries, particularly in the EAP region, many more respondents report income loss than stopping work.

Panel (b) suggests significant drops in farm income across a broad set of LAC and SSA countries, as well as some countries in the EAP region. Drops in income from family farming suggest that the economic impacts of COVID-19 go beyond formal labor markets and the formal economy, including in many countries where these less formalized sectors are a significant share of economic activity. Based on Panel (c), other types of family businesses were hit even harder, especially in the LAC and SSA regions. This is consistent with the sectoral heterogeneity in stopping work observed in Section 4.2 where we found that agriculture, industry, and services were all hit significantly, though the latter two sectors fared
The main set of policies and institutions that might have an impact on the survival and growth of recently created firms is the **government interference** in the day-today operations of business. That includes a wide range of factors, going from business regulations to corruption. They can be grouped for operational purposes in four categories: legal system, market regulations, size of government and corruption level. The **tax system** , both the tax burden and tax regulation, is also consistently reported as hampering firms’ survival and growth.

# **_The impact of the investment climate on labor market outcomes_**

It has been estimated using data for a large group of ECA countries from the early 1990s and multivariate regression analysis that the **most important components** of business environment affecting employment outcomes **are the access to finance, market regulations and the start-up costs** . The tax burden is especially important in the new EU members. The legal system and corruption appear to be important in those countries less advanced in their transition towards a market economy.

The analysis presented in this paper suggests that reforms aimed at making it easier and cheaper for new firms to access the required finance to start and expand a business might help job creation, through firm entry, in the new private sector of the ECA economies. Lowering the administrative burdens to start a business and deregulating the credit and labor markets have also a positive impact on employment.

The paper is organized as follows. The next section introduces briefly the institutions shaping the investment climate of a country. A preliminary analysis, using bivariate correlations and graphs, of the impact of the investment climate on the labor market is carried out in section II. The graphical analysis is followed by a multivariate analysis that allows a more precise estimation of the effect of each institution after controlling for a number of variables. Lastly the business environment within the ECA regions is analyzed. The objective of this analysis is to facilitate the identification of idiosyncrasies of each region, in
# **Are Firm Capabilities Holding Back Firms in Mozambique?**

Gemechu Aga<sup>1</sup> , Francisco Campos<sup>1</sup> , Adriana Conconi<sup>2</sup> , Elwyn Davies<sup>1</sup> , Carolin Geginat<sup>1*</sup>

## **1World Bank**

## **2Harvard University**

_Keywords:_ enterprises; firm capabilities; skills; management practices. _JEL codes:_ O12; L26; M20; O17; M53.

> * We would like to thank – with no implication – our World Bank management and donors for financing the data collection without which this paper would not have been possible. Further we would like to thank Arti Grover, Alvaro Gonzalez, Asif Islam, Mark Lundell, Denis Medvedev, Douglas Pearce, Jorge Rodriguez Meza, Peter Holland for helpful discussions. Funding from the Swedish International Development Cooperation Agency (SIDA), World Bank Group Enterprise Surveys; World Bank’s Finance, Competitiveness and Innovation (FCI) Global Practice; Macro, Trade and Investment (MTI) Global Practice; and the Mozambique Women Entrepreneurship Finance Initiative (WeFI) is gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. The authors may be contacted at: <u>gayanaaga@worldbank.org; fcampos@worldbank.org; adrianaconconi@gmail.com; edavies@worldbank.org; cgeginat@worldbank.org</u>