rafmacalaba/data-use-mentions-extended huggingface dataset:
supplemental module was conducted within RLMS which asked additional questions on oral contractual agreements, employer’s social security contribution, whether business is registered, and whether labor laws and work agreements are respected. While very useful, the supplementary data are outdated at this point and unfortunately, no follow-up has been conducted since.

Using the LFS, informal workers can be identified as follows: i) workers working but not at an enterprise; ii) workers without a written contract; and iii) self-employed without registration. The official Rosstat definition of informally employed uses the first criterion, with the difference being that it includes workers’ primary as well as secondary jobs. We define a broader measure that applies all three criteria but only to the primary job (henceforth referred to as “LFS (broad)”). While these differences yield slightly different estimates in levels of informal employment (in fact,

> 8 - The Life in Transition Survey data and documentation are publicly available on line at: <u>https://www.ebrd.com/what we-do/economic-research-and-data/data/lits.html</u> 9 The European Social Survey data and documentation are publicly available at: <u>https://www.europeansocialsurvey.org/data/round-index.html</u> 10 Those with a secondary job, by definition have a primary job, so we assume that the main characteristics of their employment are defined by the primary job. There is an important share of workers who have no primary job but do have an irregular job (around 9 percent for the period 2010-2016),  but we decided not to include these because of two reasons: first, irregular workers’ attachment to the labor market is not very clear and, second, several questions about welfare and other job characteristics - necessary for the <u>analyses done in the rest of the paper- are not included for this group in the RLMS.</u>

9/67 Informal Employment and Worker’
are roughly cut in half. Results are similar for repayment by the due date (columns 1-3), with the exception of the regression for “Balance, Sept. 30” where the linear interaction term and the interaction term with quartile 1 of predicted repayment remain statistically significant at the 5% and 10% levels, respectively. Even in this latter cases, however, the coefficient magnitudes are reduced substantially vis-à-vis the corresponding estimates in Online Appendix Table 4.

All told, we view these results as providing no strong support for the idea that a reduction in ex-post moral hazard – increases in repayment even conditional on amount borrowed and agricultural profits – is also an important contributor to the increased repayment we observe among fingerprinted farmers in the lowest predicted-repayment quintile.

# **6.3 Test of the Positive Correlation Property**

Following several recent articles that use data from insurance markets to test for the presence of asymmetric information (Chiappori and Salanié, 2003; Chiappori, Jullien, Salanié and Salanié, 2006), the predictions of the theoretical model of Section 3 can be used to perform a similar test. In the insurance market context, many models of adverse selection and possibly moral hazard that assume competitive insurance markets predict a positive correlation between coverage and the probability of the event insured, conditioning on the information available to the insurer. In our context, the test involves a positive correlation between loan size and default.

In order to test this prediction, multiple loan contracts must coexist in equilibrium, but according to the model (see Appendix Figure 3), all agents should borrow the high amount _bH_ when dynamic incentives cannot be used, and so there should be no correlation. With dynamic incentives however, both high and low loan sizes ( _bL_ and _bH_ ) will be taken and so the correlation can be tested. Using data on the loan size and default at maturity date, we find, as expected, no correlation for borrowers in the control group (t-stat = 1.13), but find a strong
243|0.99|
|Aggregate Score for Practices Not Covered in Handbook|0.18|0.13|0.18|0.17|0.17|0.18|0.19|0.254|0.445|0.681|0.757|

# **Chi-Square Test of Joint Orthoganality from Multinomial Logit (P-value):** 0.857

**Notes:** This table presents summary statistics for the baseline survey data. Columns 1 and 2 present mean and standard deviations for the full sample. Column 3 provides average values for the control group and columns 4-7 present average values by treatment status. Columns 8-11 present p-values for equality of means tests between a particular treatment group and the control group.
and administrative data for poverty mapping in off census years. Fourth, we expand the validations from Corral et al. (2022) and illustrate how Fay-Herriot models are still the preferred approach for off-census years among traditional small area estimation methods. Finally, we present evidence on how, despite different performance across models, all models are beneficial for targeting. While some models may outperform others in their potential poverty reduction when relying on estimates for targeting, even the worst performing models achieve decent poverty reduction, but may come at a considerable cost.

The results found here should be caveated by the fact that they are dependent on the Mexican Intercensal data used. Results and model performance may differ considerably in a different country or using different data. It is quite possible that in less-developed economies the geo-referenced covariates perform better than what is observed here, but there is no guarantee this will be the case. Validating the usefulness of a particular type of data is not straight forward as it may not work as well everywhere, it is for this reason that comparisons here are made in a more holistic manner. When comparing the performance of geo-referenced covariates to other type of data one should use the same method, as is done here. On the one hand, we show how using census aggregates at the municipality level relying on gradient boosting comes very close to the best performing method’s estimates. On the other hand, using the same method with GIS covariates we show how it falls short of competing with a model where only census aggregates are used.

The paper opens considerable avenues for future research. One avenue is related to methodology. Specifically, how the noise of machine learning methods can be accurately estimated since the noise measures presented here rely on having the true poverty rates which is not possible in the vast majority of contexts. Second, the results suggest that most methods would do a solid job for spatial targeting type exercises. However, a key question is whether or not this will hold elsewhere and under what circumstances will one method
We measure the impact of urbanization on rural poverty in surrounding rural areas, distinguishing between the _location_ and the _economic linkage_ effects, using a large sample of Indian districts for the 1983–1999 period. During this period, the country urbanized at a relatively slow rate: the urban population was 23.3 percent of the total in 1981 and 27.8 percent of the total in 2001 (Government of India 2001). However, given the sheer size of the Indian population, this moderate increase translates into a massive rise in the absolute number of urban dwellers (126 million). This number represents an increase of almost 80 percent in the urban population over this period. These figures mask large variability in urbanization patterns at the subnational level; in particular, districts have urbanized at very different rates (figure S3.1a in the supplemental appendix, available at http://wber.oxfordjournals.org/). For instance, a district such as Idukki in Kerala increased its urban population by 13,000 (+29 percent) between 1981 and 2001, whereas the urban population increased by 1.6 million (+416 percent) in Rangareddi (Andhra Pradesh) and by 2.4 million (+130 percent) in Pune (Maharashtra) over the same period. In the subsequent analysis, we attempt to exploit this variability to identify the impact of urbanization on rural poverty.

India also provides an interesting case in terms of the policy environment and economic performance given the structural changes in economic policy, the rate of growth, and the poverty levels experienced by the country during this period. Despite disagreements regarding the extent to which economic growth increased the welfare of India’s poor, poverty in India declined steadily in the 1990s, particularly in rural areas (Kijima and Lanjouw 2003). The geography of the decrease in the share of the poor, however, is extremely varied (figure S3.1b in the supplemental appendix, available at http://wber.oxfordjournals.org/
14 workers also face high severance pay costs if they lay workers off. Finally, firms with revenues exceeding 500,000 LKR per quarter or 1.8 million LKR per year must also register for VAT. The VAT tax rate is 20% of value-added for most goods, with producers of some goods paying a lower 10% rate and others exempt entirely.

# **2.1 Formality Levels in Practice**

Figure 1 summarizes the percentage of firms which reported being registered with each of the four government entities according to the number of paid employees in the firm. The data come from the baseline of the Sri Lanka Longitudinal Survey of Enterprises (SLLSE), collected by the authors between January and May 2008. The survey contains 2,865 enterprises, and is representative of enterprises in the 31 largest cities and towns (outside the Northern province which was inaccessible due to civil conflict). A door-to-door listing exercise of households was carried out, to ensure the survey was able to detect both formal and informal firms. The vast majority of enterprises have zero paid workers, and we see that only 23 percent of these non-employers are registered at the local (P.S. / UCMC) level and only 20 percent are registered at the DS Division level. Less than 1 percent of the non-employers report being registered for VAT. Registration at all four levels rises quickly with firm size, so that 75 percent of those with 5 paid employees are registered with the P.S. or UCMC and 68 percent with the DS. However, the percent registered then appears to plateau, with approximately 70 to 80 percent registered with the DS for firms with 6 to 20 employees. There are fewer firms in the SLLSE sample as the number of paid workers grows, making the point estimates more variable at larger firm sizes. Registration of at least some of the workers of the firm with EPF/ETF is less common in firms with fewer than 5 workers, but also increases rapidly with firm size so that 87
is not as consistent in predicting a positive role of social capital. For example, Olson’s (1982) economic analysis of interest groups and Banfield’s (1958) sociological analysis of “amoral familism” find a negative relationship between social capital and economic growth.

Putnam (2000) has in fact recognized that social capital may display positive or negative externalities and proposed the distinction between bonding and bridging social capital in order to study these divergent effects. Bonding social capital emerges when trust and cooperation are restricted within groups, so that negative externalities may ensue for other people. Bridging social capital emerges when new linkages between groups arise, so that cooperation increases. Recent exploratory findings in the economic literature seem to confirm that bonding and bridging social capital can be considered as two distinct variables, and that the former has a negative effect on economic growth and the latter has a positive effect (Knudsen et al. 2010; Beugelsdijk and Smulders 2009).

Moreover, some studies show that family ties, usually included in bonding social capital, causally reduce trust in people, which is the basic indicator for bridging social capital (Alesina and Giuliano 2009; Ermish and Gambetta 2008) while other studies show that both family ties and trust in people are positively correlated with people’s life satisfaction (Alesina and Giuliano 2007, Helliwell and Wang 2010).

Two main problems thus clearly emerge. First, how to distinguish between the bonding and bridging dimensions starting from a variety of proxies for social capital, and how the bonding dimension is related to the bridging dimension. Second, how these two dimensions of social capital are related to individual life satisfaction: in particular, whether they relate positively in any case, thus departing from the mixed results on the relationships of bonding and bridging social capital with economic growth.

In addressing these two problems, and by using a large world dataset, this paper obtains two main surprising results. First, proxies for bonding and bridging social capital appear to describe two sides of the same latent variable,
# SUMMARY

This paper uses a variety of sources from the current literature to review some of the more salient features of labor markets and employment in Ghana during economic decline and changes before and after the Economic Reform Program. We begin in Section 1 with an outline of the basic structure of labor markets in Ghana, identifying the critical segments, and the interrelationships between them. We discuss _inter alia_ the aspects of government policy which specifically impinge on theses segments. It is argued that in Ghana, as in most developing countries, the narrow view of labor market policy is inappropriate, since it focuses specifically on the formal sector which is a small part of the labor market. A more helpful view of labor market issues is obtained by including the self-employed, the wage earners in the informal sector as well as the formal wage employees in the scope of the discussion. However, the data on earnings and employment in the formal sector tend to be more comprehensive in these economies, and in any case the formal sector is a large part of the monetary or non-subsistence economy. Thus, after a brief review in Section 2 of the essential aspects of Ghana's macroeconomic evolution and policies in recent years, the discussion turns to the formal sector in Section 3. It focuses on the employment and wage implications of the phenomena decline and partial recovery of the Ghanaian economy, and pays particular importance to the relative performance of the private and the public sectors and to the changes in the differential in wage levels between them. We turn in Section 4 to the topic of the difference in wages between small and large firms within the manufacturing sector of Ghana. It is interesting to see that, even after a prolonged period of wage decline, which might be expected to lead to a squeezing of wage differentials of all kinds, the scale related wage differential continues to be as important in Ghana as in other developing countries. Some detailed data exist for the manufacturing sector in Ghana due to special enterprise surveys conducted by the Research Program of Enterprise Development
> <sup>35</sup>

where they have occurred for fewer. Besley and Burgess find that greater circulation of vernacular newspapers leads governments to make larger transfers to citizens affected by food shortages.<sup>17</sup> Adserá, et al. (2003) and others also find that in cross-country comparisons, _newspaper circulation per 1000 population_ , from _World Development Indicators_ , is significantly associated with lower corruption. Taken together, the literature provides support for the notion that voter information – rather than political credibility – might explain the policy performance of young democracies.

Newspaper circulation appears to be highly vulnerable to government policy intervention. Using data from Djankov, et al. (2003), one finds that a 40 percent increase (one standard deviation) in the market share of state-owned newspapers as a fraction of the market share of the top five newspapers is associated with a reduction in newspaper circulation by 0.4 standard deviations. It is likely that newspaper circulation may itself be a proxy for both unobserved political credibility and unobserved citizen information, making it difficult to interpret the estimated effects of newspaper circulation on policy.

Despite this ambiguity, the lower set of regressions in Table 4 adds newspaper circulation to the basic regression specification of Table 2, across all policy areas but the market share of government newspapers. The elections variable remains significant in all but two cases when newspaper circulation is included, despite the likely association of newspaper circulation and the credibility of political actors. Further supporting the argument that newspaper circulation is related to credibility, newspaper circulation is itself associated with lower corruption, higher public good provision (rule of law, bureaucratic

> 17 Strömberg considers a similar information problem, but assumes that credible pre-electoral promises can be made, and shows that households with radios were more likely to receive transfers from a particular US government program during the Great Depression.
FMSD administrative data. FMSD also provide us with the loan’s repayment status over time. We see exactly when the lender classifies a loan as delinquent and subsequently “cancels” the loan. The initial administrative data did not report payments after the loan was “canceled.” The bank offered these delinquent borrowers an opportunity to restructure their remaining debt after their loan got “canceled.” These borrowers had the opportunity to continue paying outstanding principal with reduced interest and fees. Since these payments occur after the bank cancels the loan, we do not observe whether delinquent borrowers continue paying their loans. Furthermore, we do not observe the reduction in interest and fees that the bank offers to customers as a part of the re-structuring. In order to properly record payments that delinquent borrowers make, we obtain lender administrative data on every payment that our sample borrowers make from 2015 to 2019. By merging the payment records with the rest of the administrative data, we observe payments that delinquent borrowers make after the bank canceled their loan and reaches out with a restructured proposal. We subtract the payments that borrowers make after their loan gets canceled from their last outstanding principal before the bank “canceled” their loan. The payment data do not distinguish between principal, interest, and fees. We only observe the payment that each borrower makes in a given month. Since our outcome variable is outstanding principal, subtracting a payment that includes interest and fees gives us an incorrect calculation of a borrower’s outstanding principal. This means that the 238 borrowers who repay after the borrower cancels their loan have an incorrect measure of outstanding principal for the time after their loan gets canceled.
**Figure 7: Total cost and tariff revenue per kWh billed in 2014 U.S. dollars**

<!-- Start of picture text --> $0.70<br>$0.60<br>$0.50<br>$0.40<br>$0.30<br>$0.20<br>$0.10<br>$0.00<br>Total cost Tariff<br>Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Comoros Congo, Rep. Côte d'Ivoire Ethiopia Gabon Gambia, The Ghana Guinea Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Niger Nigeria Rwanda São Tomé and Príncipe Senegal Seychelles Sierra Leone South Africa Sudan Swaziland Tanzania Togo Uganda Zambia Zimbabwe<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on utility data. _Note_ : Tariff revenue excludes rebated taxes, such as VAT.

A breakdown of average tariff data was available for a subset of 16 countries, presented in 32
can be defined as the “provision of assistance in the form of cash to the poor or those who face a probable risk, in the absence of the transfer, of falling into poverty” (Tabor, 2002)<sup>5</sup> .

# **3.1    The Context of Malawi**

Malawi is one of the poorest countries in the world, ranking 160 out of 188 countries in the 2007 Human Development Index (UNDP 2007). Eighty-five percent of the population million lives in rural areas (entire population =14.2 million). As an agricultural society, most Malawians are dependent on smallholder farming and fishing. In 2008, gross domestic product (GDP) was USD 800 per capita, with the percentage share of household income in the poorest 10% of households at 2.9% compared to 32% in the wealthiest 10%. According to the World Bank's Malawi Social Protection Status Report (2007) the national poverty line of Malawi is set at MK16,165/year corresponding to MK44.3 or USD 0.50 per person per day, significantly below the standard USD l per day per person. The average poor household subsists on an income of around MK36.4 - 17.8 percent below the MK44.3 daily poverty line while the ultra poor subsist on MK26.40, on average.

According to the 2005 Malawian Integrated Household Survey (IHS), 52% of households fall below the poverty line, and of these, 22% fall below the ultra poverty line, so that approximately 7 million people living in an estimated 1.3 million households are absolutely poor and 3 million people living in 550,000 households are ultra poor (NSO 2005).

Vulnerability is defined as inability of households to deal with shocks to their livelihoods. Rising vulnerability implies both an increasing likelihood of shocks taking place and a declining ability to overcome shocks without experiencing livelihood collapse. Vulnerabilities affecting Malawi include (Malawi Government, 2007):
Second, perceptions do not seem fixed, confirming the original intuition of Zaller (1992). In fact, for some countries the shifts in perceptions are quite remarkable. For example, Poland and Portugal.<sup>19</sup> Finally, there seems to be some correlation between the evolution of objective inequality and subjective perceptions (more on this below).

The ISSP surveys of 1992, 1999, and 2009 also provide data on the evolution of demand for redistribution. ‘Net’ demand for redistribution is defined as the difference between the share of individuals who strongly agree with the statement: “it is the responsibility of the government to reduce income differences between people with high incomes and those with low incomes” and those who strongly disagree with it. A negative value indicates that more individuals disagree with the statement than agreeing with it, while a positive value indicates the opposite.

Figure 3 plots the evolution of demand for redistribution over time and across countries. As in the case of perceptions, some clear differences between countries are highlighted: European countries, both in the East and the West, have a stronger demand for redistribution than the rest of the world, particularly when compared to the United States, which is the only country in the sample that has a negative net demand for redistribution. This is not surprising given the differences in preferences between European and U.S. citizens well documented in the literature. Within Europe, Eastern European countries show a higher demand than most Western and Southern countries. Over time, demand for redistribution has also moved differently in the various countries, increasing from 1999 to 2009 in some countries (e.g., Hungary, Poland, and France), and decreasing in others (e.g., Bulgaria, Portugal, and Spain).

> 19 Note that we do not use panel data, so the shift in share may simply be due to a cohort effect, i.e. people from younger cohorts may have different opinions and that may