rafmacalaba/datause-encoder-data huggingface dataset:
# **_II. Income, urbanization, and Urban Health: The View from Stylized Facts_**

The purpose of this paper is to present a number of stylized facts and an assessment of the relationship between economic growth, human capital, and urban health based on cross-country empirical evidence, on the analysis of country level time series data, and on a selective review of the literature on factors influencing urban health and health equity. From these, it is possible to arrive at a menu of policies that strikes a balance between the complementary objectives of rapid income growth and improvements in the health, especially of the poorer segments of expanding urban communities.

# **Population and Economic Growth will be urban centered**

In 2006, more than half of the world’s population was living in urban areas and the trend is definitely upwards, and heading towards 60% by 2030 ("60% of World" 2004). China with an urbanization rate of 43 percent in 2006 is likely to experience a shift of as many as 200 million people to urban areas within the next 20 years and by 2020, India’s urban population, currently less than 29 percent, should be approaching 50 percent. While the majority of the rural inhabitants will migrate to small and middle sized cities, a large percentage will very likely end up in mega cities. The number of such cities will increase to 22 by 2015 and it is expected that the populations of individual cities such as Karachi and Jakarta will rise significantly (see Table 1).<sup>7</sup> From this follows the first, and widely accepted, stylized fact which is that, _The worldwide trend towards urbanization is irreversible and could accelerate. The urban sector currently accounts for more than 55 percent of GDP in the low income countries and 85 percent in the industrialized countries (de Haan 2000). It will generate the bulk of future economic growth and most of the new employment. For these reasons, the urban sector will be the focus of economic policies encompassing growth and health_ .<sup>8</sup>

> 7 The pace at which urban populations are expanding has accelerated.  London took 130 years to grow from one million residents to 8 million.  Bangkok took 45 years, Dhaka 37 years, and Seoul 25 years ("60% of World" 2004).

> 8 See also Galea, Freudenberg, and Vlahov (2005).

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aspects of their lives had improved over the preceding 10 years. (The questions in this module were asked in 2005.) These involved a long list of aspects of well-being and in each case the respondent was asked whether this item had improved or not over the last 10 years, on a 10 point scale (from “extremely worse off” to “extremely better off”).<sup>13</sup> (The sample was restricted to adults who were at least 28 years of age at the time of the interview.) Our idea here is to see whether such a rapid appraisal tool — which does not require any prior surveys, including a baseline — gives similar results to our more costly longitudinal survey-based method.

Over 1996-2005, the attrition rate was 12% (6% over 2000-05). Using a probit model for attrition over 1996-2005 we found a number of significant predictors, including age of head, share of children, landholding and some geographic variables.<sup>14</sup> (Being an SWP village was not a significant predictor of attrition.) NBS survey teams were instructed to find replacement households as similar as possible to those that dropped out. We also tested how well this replacement worked, using a regression for the probability of being a replacement household estimated on the pooled sample of replacement and “drop-out” households.<sup>15</sup> Among the same set of covariates for attrition, no regressor was a significant predictor for replacement and the regression has very low overall explanatory power. It appears that the sample with replacements can be considered representative of the population.

We checked the robustness of our results to several potential data problems. One problem concerns the aggregation of total living expenditures. It appears that in processing the 2004/05 survey data, living expenditure in one county may have failed to include in-kind consumption.<sup>16</sup> The data for three other households whose in-kind income was more than six times larger than their total living expenditure seem to have a similar problem. We re-estimated the impacts on consumption and income, dropping this

> 13 The Chinese and local language versions of the module were refined over time on the basis of field tests in poor villages in a number of locations.

> 14 A statistical addendum is available from the authors giving full details.

> 15 Note that we have baseline data for the “drop-outs” and the current year’s data for the replacements. To deal with the time difference we did a pro-rata adjustment of the data on dropouts to 2004 values according to the ratios of the means over time for each variable, based on the balanced panel.  In caculating the ratios, we also weighted by the attrition probability.

> 16 We suspect there is a problem because the total living expenditure of 68% the sample in that county is equal to cash expenditure, whereas net in-kind income is about half of overall total.

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implies that growth rates in the 1980s are poor predictors of growth rates in the 1990s, thereby also making the temporal lag of **W** _y_ a poor predictor of **W** _y_ . Not only did the instruments that we experimented with prove to be unsatisfactory on account of their weakness, but also because they sometimes led to theoretically implausible estimates of  , the co-efficient on **W** _y_ . In particular, a spatially stable growth process requires |  | < 1. However, values of  ˆ  1 were obtained for some of the specifications when using IV estimation.<sup>37</sup>

Notwithstanding the fact the above results are based on OLS, we are reasonably confident that our main conclusions are not driven by endogeneity of **W** _y_ . This is so for several reasons. Firstly, as Behar (2008, p 7) argues, **W** _y_ is only endogenous under the hypothesis of growth spillovers. Therefore, tests of the rejection of the null of no spillovers based on OLS estimation retain some validity. Second, as noted above, when entered in our specifications by itself without any interaction effects, the coefficient on **W** _y_ is not significantly different from zero (specification 2a, table 5). It is only when **W** _y_ enters in more subtle forms that we detect significant spillover effects. Third, and finally, when we re-estimate the simple spillover specification, 2b, with no interaction effects using a ML estimator which does explicitly take into account the simultaneity of _y_ and **W** _y_ , we find that the estimated spillover coefficient is actually larger than that reported in

> 37 Our estimates of  _i_ for Luxembourg, Switzerland, Austria and Hungary implied by specification 4b in table 5 also fall outside of this range (see figure 3). However, this does not necessarily imply a spatially unstable growth process for these countries because they have amongst their neighbors non-landlocked countries for which |<sup></sup> ˆ _i_ |  1 . Thus, while faster growth of these countries’ neighbors is amplified when spilling-over to Luxembourg, Switzerland, Austria and Hungary, the reverse feedback to the neighbors is then damped. This leads to the possibility of a spatially convergent growth process, even if it appears locally unstable in places.

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# **1 Introduction**

Extreme levels of household debt are prevalent throughout the developing world. This is especially true in rural economies, where households face highly volatile incomes, but often lack access to basic financial instruments that could mitigate the impact of recurring income shocks (Townsend 2006, Karlan and Morduch 2009).<sup>1</sup> The potentially far-reaching aggregate implications of extreme household indebtedness have motivated a range of large debt relief initiatives for low-income households in developing countries. Some recent examples include a US$ 2.9 billion bailout for farmers in Thailand, and the rescheduling of more than US$ 10 billion of agricultural debt in Brazil.<sup>2</sup> While the benefit of debt relief to individual households can be substantial, the merit of unconditional bailouts as a tool to improve household welfare and productivity remains highly controversial.<sup>3</sup> Proponents of debt relief argue that extreme levels of indebtedness distort investment and production decisions, so that debt relief holds the promise of improving the productivity of recipient households. Critics of debt relief, on the other hand, worry that it is difficult to “write off loans without writing off a culture of prudent borrowing and repayment” (The Economist).<sup>4</sup> They question that one-time settlements can have a lasting impact, and argue that such bailouts may instead aggravate credit rationing by inducing moral hazard and stigmatizing borrowers in default.<sup>5</sup> While both views can appeal to a foundation in economic theory, there currently exists little empirical evidence on how indebtedness, and hence debt relief, affect economic decisions at the household level.

This paper provides direct evidence on the impact of debt relief on the economic decisions of recipient households, using a natural experiment among Indian households that benefited from a large nationwide debt relief program. In particular, I test whether –similar to a bankruptcy settlement– the bailout acted to restore access to formal credit among beneficiary households and led to economically meaningful changes in investment and productivity. My analysis takes advantage of an experiment

> 1For evidence on barriers to household risk-management see also Cole et al. (2012) and Gin ́e et al. (2008)

> 2USDA Economic Research Service Briefings, http://www.ers.usda.gov

> 3See also Robert (2012) who uses the natural experiment and survey data underlying this study to test the impact of wealth on subjective well-being.

> 4“Waiving, not drowning: India writes off farm loans. Has it also written off the rural credit culture?” _The Economist_ , July 3, 2008.

> 5For the theory of moral hazard and credit rationing see Stiglitz and Weiss (1981) and Jaffee and Russell (1976). Karlan and Zinman (2009) provide empirical evidence on moral hazard and adverse selection in an emerging credit market.

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- Jensen, R. (2012). Do Labor Market Opportunities Affect Young Women’s Work and Family Decisions? Experimental Evidence from India. _The Quarterly Journal of Economics_ , 127(2):753– 792.

- Kabeer, N., Mahmud, S., and Tasneem, S. (2018). The Contested Relationship between Paid Work and Women’s Empowerment: Empirical Analysis from Bangladesh. _European Journal of Development Research_ , 30(2):235–251.

- La Ferrara, E. (2016). Mass Media and Social Change: Can we Use Television to Fight Poverty? _Journal of the European Economic Association_ , 14(August):791–827.

- La Ferrara, E., Chong, A., and Duryea, S. (2012). Soap Operas and Fertility: Evidence from Brazil. _American Economic Journal: Applied Economics_ , 4(4):1–31.

- Labowitz, S. and Baumann-Pauly, D. (2015). Bangladesh’s Forgotten Apparel Workers. _Technical Report_ .

- Lee, D. S. (2009). Training, wages, and sample selection: Estimating sharp bounds on treatment effects. _Review of Economic Studies_ , 76(3):1071–1102.

- Luke, N. and Munshi, K. (2011). Women as Agents of Change: Female Income and Mobility in India. _Journal of Development Economics_ , 94(1):1–17.

- Lundberg, S. and Pollak, R. A. (1993). Separate Spheres Bargaining and the Marriage Market. _Journal of Political Economy_ , 101(6):988–1010.

- Macchiavello, R., Menzel, A., Rabbani, A., and Woodruff, C. (2020). Challenges of Change: An Experiment Promoting Women to Managerial Roles in the Bangladeshi Garment Sector. _NBER Working Paper No. 27606_ .

- Majlesi, K. (2016). Labor Market Opportunities and Women’s Decision Making Power within Households. _Journal of Development Economics_ , 119:34–47.

- Marinova, J., Plantenga, J., and Remery, C. (2016). Gender Diversity and Firm Performance: Evidence from Dutch and Danish Boardrooms. _The International Journal of Human Resource Management_ , 27(15):1777–1790.

- McKelway, M. (2019). Experimental Evidence on the Effects of Women’s Employment. _Working Paper_ .

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## **<u>Figure 3. Destinations of emigrants from source regions</u>**

<!-- Start of picture text --> EU15+EFTA<br><!-- End of picture text --> <!-- Start of picture text --> Other high<br>income<br>EU15+<br>EFTA<br><!-- End of picture text --> <!-- Start of picture text --> LAC ECA<br>Countries/regions of origin<br>Other high income LAC<br><!-- End of picture text --> <!-- Start of picture text --> AFR<br>MENA<br><!-- End of picture text --> <!-- Start of picture text --> EAP<br>EAP SAS<br><!-- End of picture text --> Interesting though table 8 and the figures are, they do not readily show whether migration patterns are biased in the sense that migrants from one source are more likely to go to one place than another. This is because it is not clear whether the importance of, say, North America as a destination for EAP arises because it is a major recipient of migrants from every region or because EAP residents have a particular propensity or ability to migrate there. To answer this question we calculate a series of migration intensity indices. These are calculated as

# (Xi,j/X.,j)/(Xi,./X.,.)

where, Xi,j is the stock of migrants from i in region j and a dot (.) denotes summation across the corresponding subscript. This expresses the share of i in j’s stock of immigrants relative to i’s share of world total immigrants. Thus an intensity index exceeding one implies that i is a more important source for j's immigrants than it is for world immigrants on average. (The intensity index also equals j’s share of i’s stock of emigrants relative to j’s share of world emigrants. Hence, a value above 1 indicates that j is a more important destination for i’s emigrants than for source countries on average.) If bilateral flows were random, so that all emigrants from all countries had an equal chance of ending up in, say, Europe, and an equal (albeit different) chance of ending up in, say, Africa, the intensity indices would be unity. If they exceed unity for a bilateral link, they indicate that there is a bias towards that link. The indices are reported in table 9.

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based on a randomized experiment or some other design in which a proper comparison group could be constructed.

_Graduate tax._ In a system with graduate taxes, students receive a grant from the government while studying and they repay this in the form a special tax rate after their graduation. Graduate taxes are often regarded as an imperfect substitute of the ideal system with income contingent loans. Graduate taxes and income contingent loans are the same in many respects, but graduate taxes have the disadvantage that they also provide funding to students who do not want it or need it, and that graduates may end up paying (much) more than they received in the form of a grant. This latter feature may provide a disincentive to study to those who would also without further study have earned a high income. (The problem is that the graduate tax is levied on the basis of total earnings rather than the earnings increment due to the extra education.) The relative appraisal of graduate taxes changes when prospective students are debt averse. In that case it makes a difference that under an income contingent loans scheme students carry debt whereas under a graduate tax scheme they do not carry debt, even though the actual “repayment” of the loan/grant are exactly the same. Theory predicts that debt-averse individuals are more willing to enroll in education when financial aid is in the form of a graduate tax then when given in the form of an income contingent loan.

Graduate taxes are not in operation anywhere in the world, and it is thus hard to evaluate this scheme. Yet, some insight about its potential can be gained from studies that inform us 22
- 12 -

<!-- Start of picture text --> GRAPH 1 LOGSS: SAPELLI<br>CAMEROON<br>250<br>900<br>180<br>160<br># i4D-140<br>100<br>80<br>40<br>1956 1960 1965 1970 1975 19BD 1985 1989<br>YEAR<br><!-- End of picture text --> <!-- Start of picture text --> GRAPH 2 LOGS: SAMBA<br>COTE D'JVUIRB<br>170 -<br>150-<br>140-<br>130<br>12D<br>110<br>1O0<br>90<br>8D<br>70<br>50<br>50<br>40<br>30<br>10-<br>1955 1950 1955 1970 1975 1980 19B5 19B9<br>YEAR<br><!-- End of picture text -->
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- It corresponds to the year 1984 for Chile and Mexico, 1983 for Argentina and 1985 for Brazil.

- 4/ Is based on the 1985 average rate of return on all US foreign investment in Latin America, which was 10 percent, after subtracting the US inflation rate . The source is: "Transational Corporations in World Development", New York, 1988, United Nations Centre on Transational Corporations.

Large trade surpluses (as a proportion of debt) in Brazil and low trade surpluses in Argentina are crucial for explaining the differential effect of DES on debt prices in the case of these countries (Table 3). The larger the trade surplus-debt ratio the more debt reduction raises the actual debt service-debt ratio and, therefore, the more debt prices rise. On the other hand, restrictions on profit remittances under the DES regime, and lower levels of foreign held equity in Chile, compared with Mexico, may explain the favorable effect of DES on debt prices in the former country and the negative effect in the latter.

# 7. CONCLUSIONS

- The main findings of this paper can be summarized as follows:

- i) The widespread view that money-financed DES feed inflation was modelled in a short-term portfolio balance framework. Casual inspection of DES and inflation data of Argentina, Brazil and Mexico support this view.

- ii) The short term end of period portfolio model also predicts that debt-equity swaps and debt conversions raise sovereign debt prices. This is due to old debt reduction and, in the case of debt-equity swaps, to short term restrictions on profit remittances (short-term restrictions on profit
Country-level regressors in Table 2 are mostly insignificant, but higher aid is associated with larger kickbacks (significant at the .10 level). Understanding the mechanism behind this association would require more detailed analysis, however. For example, aid in response to disasters may be more prone to corruption due to disbursement pressures. Or, reverse causation could be partly responsible, if donors provide more aid for governance reform where they observe more corruption.

Predicted kickbacks are significantly lower for MNA, by about 3 percentage points, than in each of the other five regions.<sup>18</sup> The time trend is positive but insignificant.

If the four components of the overall PEFA 19 OVERALL measure were highly intercorrelated, one could argue their coefficients in Tables 1 and 2 are biased upwards (in absolute value, in Table 2). Testing them all together in a single regression, on the other hand, would risk multicollinearity problems. These correlations turn out to be relatively modest. At the country level, the highest (Spearman) rank correlation is .61 (for METHODS and ACCESS), and the lowest is .29 (for LEGAL and METHODS). The average of the six inter-correlations is .39.

Table 3 summarizes results from regressions that test all four components together, recognizing the likelihood of some collinearity problems. In the table, only results for the PEFA variables are reported, but the specifications otherwise are identical to those for the baseline tests in Tables 1 and 2. When all four components are entered together in the BID regression (equation 3.1), only METHODS is significant, with a coefficient only slightly smaller than in equation 1.3 of Table 1. By contrast, the coefficient for ACCESS is less than half as large as in equation 1.4, and it is not significant when controlling for METHODS and other components of PEFA question 19. For informal payments, COMPLAINT remains significant, with a coefficient nearly identical to that in equation 2.5 of Table 2, when all four components are included together. In contrast, the METHODS coefficient is cut by half, and it is no longer significant.

The one component that is never significant in any regressions in Tables 1-3 is LEGAL, which assesses the legal and regulatory framework for procurement, with respect to its comprehensiveness, transparency, and its promotion of competition. It can be viewed primarily as a _de jure_ measure of how the procurement system is designed to function, while the other three components are _de facto_ measures of its actual functioning. It is unsurprising that bidding and kickbacks are more strongly

> 18 The mean of kickbacks for firms in the three MNA countries (Jordan, Morocco and Tunisia) is only .54%, compared to 2.35% for non-MNA countries.

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the survey results play a crucial role in computing indicators related to the low-income population, consumer price indices, and household sector accounts. Unfortunately, HBS lacks data on the specific districts where respondents reside or the urbanity status of their settlements. Instead, it only provides information on the regions where they live. As a result, our HBS analyses are limited to only 14 clusters due to this data constraint.

_Childcare provision._ There are 175 districts in Uzbekistan. We measure yearly available preschool provisions in each district using data collected from Uzbekistan Statistics Agency. Unfortunately, the Statistics Agency does not provide a direct preschool participation rate for each district, but it provides the statistics for the total number of children in different age group categories like the number of children aged 0-2, 3-5 and 6-7 living in each district. It also provides the number of children aged 3-6 who participate in preschool education for each district. We calculate the coverage rate by dividing the number of children at preschool by the total number of children aged 3-5 living in that district. Therefore, we may overestimate the participation rate as our denominator is missing the number of children aged 6 years old. Furthermore, we are also assuming that if preschool participation of children aged 3-6 increased in that district, the number of children aged 1-2 participating in preschool education also increased in a similar manner as we do not have official statistics on this age group of children. The calculated coverage rate for each district can be seen in the maps in appendix figures 1 and 2. In the HBS analyses, since the policy variable is measurable only at the region level, we obtained the direct preschool enrollment rate of the children aged 1-6 for each region over time from the Uzbekistan Statistical Agency. This data is used to assess the policy variable coverage rate for young children across different regions and time periods.

_Sample._ In our main estimation, we restrict our sample to the households in which the youngest children are aged 1-6 years old as these households are the ones potentially, affected by the policy expansion. In individual unit analyses, our sample is all working-age women aged above 15 and below 64 living in these households. In our sample, we have around 1,200 respondents in each period of time from 109 districts.

_Dependent variables._ We use several measures for our outcome variables. In the individuallevel analyses, the outcome variables are dummy variables, defined as follows:

- **Employment** : This is measured as a dummy variable, taking the value 1 if the respondent has been engaged in paid work, for profit or gain, within the past 7 days,

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