rafmacalaba/datause-encoder-data huggingface dataset:
This paper uses a synthesis of economic theory to challenge the prevailing policy regimes that focus on high-powered incentives to deliver basic health services and offers new ideas that have the potential of working more effectively to improve public health. First, the paper draws upon Khemani (2019) and World Bank (2016) to summarize the insights from a synthesis of economic theory of principal-agent relationships. Economic theory argues that optimal contracts in complex organizations that are tasked with providing public goods could use low-powered incentives (steady wages, job security), and rely on recruitment of intrinsically motivated workers, as well as professional norms among peers to achieve high performance or productivity.

Second, the paper provides evidence from a rich survey in Bihar on current health policies, management practices, and incentives, beliefs and norms of health workers in the public sector. It shows how current management practices and the environment in which health workers perform their tasks are the opposite of what economic theory would recommend.

Third, the paper discusses how this difference between economic theory and policy practice can be rationalized and explained. We use available research on institutions to argue that low trust and perverse expectations of behavior in the public sector, for rent-seeking rather than service delivery, characterize institutions in poor places like Bihar. Because of these institutions, reform leaders are reluctant to provide steady wages to new cadres of public health workers because they do not trust that the additional fiscal burden will yield significant results that are valued by the citizens who vote them into, or remove them from, office.

Fourth, the paper shows how local politics in Bihar (as in other poor places of the world, examined in World Bank, 2016) exhibit potential for change that policymakers could harness to transition to institutions of greater trust, and stronger norms of performance in the public health sector.

The paper concludes with outlining a set of policy ideas for reform leaders and international partners to immediately put to use as they respond to the COVID-19 pandemic. Over time, these ideas can contribute to addressing weaknesses in public health systems that has bedeviled national and global policymakers. Fixing difficult problems like public health requires a 3
inequality traps (Piketty 1995; World Bank 2005; Bourguignon et al. 2006).<sup>1</sup> The estimates presented in this paper appear to suggest that: (i) lack of connections is indeed associated with expectations of a lower position on the future social ladder; and (ii) when informal connections are unavailable, it matters for your future expectations whether you perceive connections to be vital or not. There is also some evidence that in the European Union countries, where formal institutions are stronger, individuals are less likely to resort to informal institutions such as personal connections, even when these are available. Finally, there is also some evidence that perceptions of unequal access to opportunities are linked with inter-generational mobility, and with stronger redistributive preferences.

The rest of the paper is structured as follows. Section 2 reviews the literature on the determinants of future mobility expectations, and of experienced mobility. Section 3 provides a description of the key variables used in the paper as well as key descriptive statistics. Section 4 outlines the empirical setup and the key hypotheses to be tested. Key results are presented in section 5. Some alternative specifications are considered in Section 6 as part of robustness analysis. Section 7 discusses some extensions of the analysis, namely unequal access to opportunities in the intergenerational context and with respect to individual preferences for redistribution. Section 8 provides some concluding remarks.

# **2. Inequality of opportunity and mobility: Theory and evidence**

The _American Dream_ holds that everyone in the United States can achieve success and prosperity through hard work. The expectation that everyone can “make it” – – with hard effort an enduring national ethos does not necessarily correspond with – observed reality the degree of socio-economic mobility in the United States today is relatively low, indeed lower than in some Nordic countries (OECD, 2010; Alesina,

> 1This paper is also related, more generally, to studies that highlight the link between deprivation and under-investment in human capital (Appadurai, 2004; Alsop et al. 2006; Hoff and Pandey, 2006).

4
seven allocation scenarios. Figures 4 compares the actual and the ideal formula based allocation to alternative allocations based on our two measures, (a) aggregate misallocation and (b) the fiscal resources gap.<sup>21</sup>

**Figure 4: Mis-targeting in Comparative Allocation Scenarios**

**Measure 1: % of DAU**

**Measure 2: Fiscal Resource Gap**

<!-- Start of picture text --> Pure Formula<br>Pure Formula 0.050<br>35% 0.040 Pure (old)<br>Population 25% Pure (old) Population 0.030 Formula<br>15% Formula 0.020<br>5% 0.010<br>0.000<br>Pure Formula w -5% Multiplicative ure Formula w Multiplicative<br>HH Cost HH Cost<br>Wage Proportion Poverty Wage Proportion Poverty<br><!-- End of picture text --> The smallest differentials are evident between the actual and the fiscal gap formula that uses district wage proportions as the expenditure needs indicator (Table 4, Figure 4). This highlights that wages are already a significant driver of the overall 2006 DAU allocation. But misallocations become more pronounced when comparing to a pure formula allocation with hold-harmless or an allocation using the vintage 2005 formula. Differences are very large if one believes that population, poverty and a multiplicative cost specifications should be the main drivers for DAU allocations (i.e., the northwest

> 21 The present measure focuses on the “mis-targeting” of the DAU amount only, although the discussion could also be cast in terms of a district’s overall expenditure needs and respective revenues received.  The overall resource envelope of a locality can be viewed as presumed own-source revenue capacity (OSR), plus shared revenues (SDA/TAX), plus the DAU.  The notional expenditure needs of an individual district (i) can be calculated by allocating the overall pool of revenues available for districts nationally (i.e., OSR+SDA/TAX+DAU) by some relative of expenditure needs.  For example, if only population is used, then allocations ENi for each district would be identical on a per capita basis.  The measure of overall under or over-resourcing for a district would then be (TOTREVi/ENi), where TOTREVi are

> OSRi+SDA/TAXi+DAUi.  In the case of OSRi this assumes the notional own revenue capacity (e.g., from regression analysis), and other transfers include actual derivation and block transfers.  For reference, this variation was adopted in previous versions for the 2004 DAU and Arze (2005) for 2005.

21
and among them, a little more than half were between 16 and 30 years old and almost 80 percent had not completed upper secondary education.

For young Mexicans, especially those in the northern states, the increase in violence coincided with a reduction in legal employment opportunities. The international financial crisis of 2008-2009 contracted Mexico’s GDP by more than 6 percent. After the crisis started in the United States, the transmission mechanism into the Mexican economy was driven, to a large extent, by a reduction in demand for exports. Thus, the manufacturing industry, using large volumes of relatively unskilled labor and concentrated in northern cities, was hit disproportionally. According to Bussolo et al. (2015), youth held six out of every 10 jobs destroyed in Mexico during the crisis. Thus for young men in Mexico, particularly those in the border states, the years 2008 to 2010 became a period of significant reductions in job opportunities in the legal market combined with an important increase in labor demand by organized crime.

The dramatic increase in violence, the age-crime profile, the growing number of youth, and the perceived inability to provide them with the necessary education and job opportunities have fueled a debate over Mexico’s so-called _ninis—_ the term, meaning youth who are neither working nor in school, comes from the Spanish phrase “ _ni estudia ni trabaja_ .” Almost one in five youth in Mexico—totaling more than 4.11 million people aged 15 to 24 in the second trimester of 2015 according to ENOE survey—is classified as a nini. The population’s total share of ninis, both male and female, has remained constant during the last 15 years, but the male share has increased persistently: it grew from 660,000 in 2000 to 1.12 million in 2011.

Many ninis might take part in criminal activities that do enormous harm not only to themselves but to society at large. Thus the objective of this study is to contribute to the growing literature explaining the causes and contexts of the escalation of violence in Mexico, with a focus on the potential linkages between ninis and homicide rates. The study’s empirical analysis relies on annual state-level panel data covering the period 1995-2013. This allows us to test whether the relationship between ninis and violence is different before and after the start of the war on drugs (2007-2013), and whether it differs between the states 3
and all other variables are defined as in Equation (1). First, we define more advanced countries to be high-income and upper-middle income countries according to the World Bank’s income group classification and report the results in column (1) of Table 8.<sup>31</sup> Second, we define more advanced countries to be countries whose scores in the Global Competitiveness Report’s general country ranking are above the median score and report the results in column (2) of Table 8.<sup>32</sup> The estimates show that increased import competition from less advanced countries is the strongest stimulant for product quality upgrading by Chilean plants. The F-tests show that the difference in the effects across country groups is statistically significant for both classifications. These findings suggest that tougher competition from low-wage countries (including China and India) serves as an incentive for quality upgrading by Chilean plants and thus can be viewed as an advantageous type of competition. The products exported by more advanced countries to Chile may be too sophisticated for local plants to be able to ‘beat’ through quality upgrading. This finding provides support to the existence of a costly-to-overcome ‘technology gap’ that Cimoli and Correa (2002) argue has been responsible for lower growth benefits from trade liberalization in Latin America. Our evidence also supports the hypothesis that the high cost of catching-up with more advanced economies in order to upgrade product quality may constitute a barrier to economic growth (Parente and Prescott, 1994).

> 31 We use the World Bank country classification as of April 2007 which establishes four income groups: low-income, lower-middle-income, upper-middle-income and high-income and covers all countries included in our transport cost dataset. The classification is based on gross national income per capita using the World Bank Atlas method. Uppermiddle-income and high-income countries have an income level similar to or above that of Chile. We also estimate Equation (4) defining more advanced countries to be high-income countries and obtain qualitatively similar results. 32 The World Global Competitiveness Report (World Economic Forum, 2007) ranks 131 countries’ performance based on a broad range of factors affecting a country’s business climate: institutions, infrastructure, macroeconomic stability, health and primary education, higher education and training, goods market efficiency, labor market efficiency, financial market, sophistication, technological readiness, market size, business sophistication, and innovation. Countries are ranked and given a performance score. We use the median score to divide our sample into above-median and below-median performers.

25
positively are located in Latin America. This may reflect the valuable efforts that many countries in this region have undertaken in recent years to improve their remittance data.

<!-- Start of picture text --> Chart 1. Payment of Remittances -<br>Market Share Among Different Service Providers<br>Central Banks that Know Market Market Share Among<br>Share Among Service Providers Different Service Providers<br>100%<br>1. Bolivia<br>2. Brazil<br>80%<br>3. Colombia<br>Yes<br>4. Ecuador<br>No 28% 5. El Salvador 60%<br>72% 6. Guyana<br>7. Honduras 40%<br>8. Mexico<br>9. Nicaragua 20%<br>10. Peru<br>11. Venezuela 0%<br>Money Transfer Firms Banks Other<br>Bolivia Brazil ColombiaEcuadorEl SalvadorGuyanaHondurasMexicoNicaragua PeruVenezuela<br><!-- End of picture text --> As shown in chart 1, in six of the eleven developing countries that had this type of information available, money transfer companies dominate the market for delivery of remittances with more than a 50% market share (Bolivia, Ecuador, Guyana, Honduras, Nicaragua and Peru). In two countries, Brazil and Venezuela, commercial banks account for 100% and 87% of the market for remittance payments, respectively,<sup>5</sup> while in Colombia, exchange bureaus dominate 90% for the market of remittance payments. Because at this time no information is available on the market share among services providers in other developing countries, it is difficult to estimate the amount of remittances inflows that is not reflected in balance of payments statistics.

There are particular reasons that explain why many of the surveyed countries fail to cover in their statistics the remittance transactions conducted by money transfer companies. First, in those countries money transfer companies are neither regulated nor supervised by the central bank or any other financial sector authority, as explained in Section 3. In fact, in those countries, money transfer companies are not established as “financial institutions”, but as commercial firms. As a result, they are not subject to regulatory or oversight requirements applicable to financial institutions. Although in some countries, due to recent anti-money laundering legislation, money transfer companies have been required to register themselves

> 5 According to authorities in Brazil, money transfer companies can only pay remittances through commercial banks. In Venezuela, money transfer companies are allowed to pay remittances, but most of them do it through local commercial banks, which act as agents of these companies.

8
Turning now to the control variables in the regressions, country/year dummy variables control for any possible omitted effect that operates at the country level, such as macroeconomic shocks, the systemic component of the shock to bank equity prices, the policy response to the crisis, differences in accounting and regulatory definition of capital across countries and so on. In other words, what our model seeks to explain is just the cross-sectional, within-country dispersion in stock returns in each quarter.

To isolate the effect of capital on this dispersion, we control for other bank-specific characteristics that may affect stock returns. Specifically, we control for bank liquidity using liquid assets/assets; the bank’s reliance on deposits for funding (deposits/total assets), asset quality (loans loss provisions), the banks’ business model (net loans/assets), and the bank size (log of total assets). Also, following standard asset pricing models, we include in the regression the stock’s beta (computed as the five-year covariance between the bank’s monthly stock return and the country stock market return) and the market-to-book value of equity.<sup>8</sup> The price-earnings ratio (PE) measure possible mispricing of bank equity during the boom. Summary statistics for the explanatory variables are in the Appendix, Table A2.

Explanatory variables computed from bank balance sheet information, including the variables measuring bank capital, are available on a yearly basis rather than a quarterly basis, while our dependent variable is quarterly. For these variables, we use the last available (but not contemporaneous) observation. For example, stock returns during each of the four quarters of 2007 are regressed on the capital/asset ratio at the end of 2006.

In Table 3 we report correlations among stock returns, the various (lagged) capital ratios, and the other explanatory variables. Interestingly, there is a strong negative correlation between capital and bank size, particularly the Tier 1 leverage ratio. The regulatory ratio (RWR) and the leverage ratio (LR) have a correlation of 63 percent in the full sample and of only 31 percent in the large bank sample. In general, correlations among the various notions of capital tend to be lower for the sample of larger banks.

# **III.   THE RESULTS**

# **_Results from the baseline model_**

Table 4 contains the estimation results for the baseline model for the full sample and the sample of larger banks. The model allows the coefficient of all explanatory variables to differ among the pre-crisis and the crisis period, and the table also reports tests for the equality of the crisis and pre-crisis coefficients.

Before the crisis, several of the explanatory variables appeared to significantly affect stock returns: banks with lower loan loss provisions, a higher market-to-book ratio, and a lower P/E 8 For a discussion of why it is desirable to include these variables directly in the regressions as firm characteristics rather than going through a factor model, see Tong and Wei (forthcoming) and Whited and Wu (2006).

7
<!-- Start of picture text --> Pollcy Resarch<br>Transltlon and Macro-Adjustment<br>WPS 1001<br><!-- End of picture text -->

- This paper a oroduct of theTransition and Macro-Adjustment Division,<sup>Country Economics Depart-</sup> ment, and the Economics and Finance Division, Africa<sup>Technical Department-is part of ajoint research</sup> effort by CECTM and AFIEF on 'The Effectiveness<sup>of Adjustment Lending in Sub-Saharan Africa."</sup> Copies of the paper are available free from the World Bank,<sup>1818 H Street NW, Washington, DC 20433.</sup> Please contact Ana Maranon, room Nl1-025, extension<sup>39074 (October 1992, 60 pages).</sup>

Using a methodology that allows for endogenizing decisions to participate in World Bank adjustment lending programs, and for testing the validity of assumptions about program participation, Elbadawi studies the effectiveness of these programs in Sub-Saharan Africa.

He shows that adjustir ent programs in SubSaharan Africa had no statistically significant effect on growth in the second half of the 1980s, compared with the first half, but they have had a significant and deleterious effect on investment.

The perceived increases in export competitiveness and in the efficiency of investment (stipposed to be generated by refonn programs) has not been sufficient to counterbalance the ensuing decline in investment and hence to restore economic growth in Sub-Saharan Africa.

These findings must be qualified: the methodology for classifying countries as adjusting or nonadjusting does not allow for different degrees - of implementation so, strictly speaking, the findings reflect an assessment of the effectiveness of a proxy (adjustment lending) for the adjustment programs.

Adjustment lending has significantly improved export performance in Sub-Saharan Africa, at least compared with nonadjusting African countries.

TPhe Policy Research Working Paper Series disseminate the fndings<sup>of work under way in the Bank. An objective of the series</sup> is to get these findings out quickly, even if presentations are less than<sup>fully polished. T'he findings, interpretations, and</sup> conclusions in these papers do not necessarily represent official Bank<sup>policy.</sup> Produced by the Policy Research Dissemination Center
**MONGOLIA'S INFORMAL SECTOR -**

**Appendix B: Methodologies and Descrptive Statistics**

**43**

# Kiosk Survey Instrument

||Name of Researcher:|
|---|---|
|a. Distric|t surveyed<br>t<br>b. Genders<br>M_<br>F-<br>**[21**c. How many people present?<br>**[31**|
|d. Day s|urveyed<br>0 Z ( (v)<br>®<br>X)<br>©**®[4]**e. Ages<br>**[51**f. Education:<br>)<br>3<br>**[6**|
|g. Hour|<br> <br><br>surveyed<br>_<br>_<br>h. Married?<br>Y N**_[8]_**<br>primary secondary vocational higher|
|l.|In an average week, how many hours is this_kiosk_open?<br>**[91**|
|2.|Which days of the week is your_kiosk_usually open? (0=Monday)<br>(<br>(<br>(<br>i<br>©<br>X<br>[10]|
|3.|On average when does this_kiosk_open and close?<br>Open<br>Close<br>|
||Weekday<br>**[11]**<br>**[121**|
||<br> <br><br>Saturday<br>**[131**<br>**[14]**<br>Sunday<br>**[15]**<br>**[116**|
|4.|Is this_kiosk_registered with the duureg?<br>Y<br>N<br>**[17]**|
|5.|How many person-hours each week are spent occupying the_kiosk?_<br>(E.g. if two people working together spend 10 hours, then the total person-hours is 20)<br>**[18]**|
|6.|How many person-hours each week are spent procuring inventory, including time<br>spent in transportation?<br>(E.g. if two people working together spend 10 hours, then the total person-hours is 20)<br>**[19]**|
|7.|Do you have another job or source of income in addition to operating this_kiosk?_<br>Y<br>N<br>**2**|
||lf ves, is the other job in<br>0<br>**[21]**<br> <br>|
||(O<br>state|
||<br>0<br>state enterprise, private company, cooperative, partnership, or public organization<br>another form of self- or family-employment|
||If yes, is the job mentioned above from (D active work, a pension, 0 leave ?<br>(D0) )<br>**[221**|
||If yes, what is your average monthly income from your other job?<br>**[23]**|
||If yes, how many hours per week do you work at your other job?<br>**[24]**|
|8.|When the duureg administration asks if you are employed, how do you answer?<br>Y<br>N<br>**[25]**|
||Ifyourespond that you are not emploved, what do you give as the reason?<br>GD<br>0<br>**_)[26_**|
|(X) illnes|s, or watching a<br>|<br>because of<br>|<br>not finding<br>T fired, or the organization<br>|<br>other|
|child si|k or old erson<br>seasonal work<br>rofessional work<br>doesn't work an more<br>l|
|,|p<br> <br>p<br>y<br><br>If you respond that you are not employed, have you actively looked for a job in<br>thepast month?<br>Y<br>N<br>**[27]**|
||If you respond that you are not employed, which of the following is correct?<br>0<br>0<br>**[26]**|