rafmacalaba/datause-encoder-data huggingface dataset:
# **Intergenerational Social Mobility Based on the Investments in Human Capital: Evidence of the Long-Term Results of PROSPERA in Health**<sup>**1**</sup>

Juan Pablo Gutiérrez<sup>a</sup> Teresa Shamah-Levy<sup>b</sup> Stefano M. Bertozzi<sup>c</sup> Juan A. Rivera-Dommarco<sup>d</sup>

**Keywords:** intergenerational social mobility, health, height, conditional cash transfers **JEL** : I14, I15, I38, J62

> 1 This paper is part of the research project “Studies of PROSPERA’s long-term results” which was funded by the Government of Mexico through the National Coordination of PROSPERA (CNP) as part of a technical cooperation agreement with the World Bank. The World Bank team is grateful to the Government of Mexico, including the Ministry of Social Development (now Ministry of Welfare), Ministry of Education, and Ministry of Health for the close collaboration in this research project, including the provision of the main datasets. The World Bank team would also like to thank the National Council for the Evaluation of Social Development Policy (CONEVAL), the National Institute of Public Health, development partners such as the Inter-American Development Bank, and the national and international researchers that contributed in the Advisory Evaluation Group. This group was established to ensure technical quality and methodological rigor of the “Studies of PROSPERA’s long-term results.” These studies were coordinated by Maria Concepción Steta, WB Senior Social Protection Specialist; Clemente Ávila, WB Social Protection Economist; and Mónica Orozco, WB Consultant.

We gratefully acknowledge the comments received from the Advisory Evaluation Group, particularly from Lynnette Neufeld and Graciela Freyermuth. Furthermore, we would like to express our thanks for the extensive review performed by the World Bank and coordinated by María Concepción Steta and Clemente Ávila. It greatly strengthened the logic and structure of this paper. We particularly appreciate the revision and excellent methodological and substantive suggestions made by Mónica Orozco, World Bank consultant. Additionally, we would like to thank Javier Franco and Ariadnna C. Menchaca who supported the statistical analyses and management of the databases. Finally, we gratefully acknowledge the comments we received in the two discussion sessions hosted by the Advisory Evaluation Group, PROSPERA, the Ministry of Social Development (SEDESOL, now Ministry of Welfare) and the National Council for the Evaluation of Social Development Policy (CONEVAL).

a Center for Policy, Population & Health Research, School of Medicine, National Autonomous University of Mexico (UNAM) b Mexico’s National Institute of Public Health c School of Public Health, University of California - Berkeley d Mexico’s National Institute of Public Health
**HOW CAN CHINA PROVIDE INCOME SECURITY FOR ITS RAPIDLY AGING POPULATION?**

# **I. Introduction and Summary**

As in many developing countries, China's population is aging rapidly, due to fertility policies that have depressed the number of children combined with rapid medical improvements that have prolonged life for the old (Table 1). By 2030 over one quarter of the world's old people will live in China. While those over 60 accounted for 9% of China's population in 1990, they will be 22% in 2030 and 26% in 2050. It will take China only three decades to reach the 1990 OECD level of 18%, while in most OECD countries it took nearly a century for the proportion over 60 to double from 9% to 18%. In 2030, China's proportion will be 50% higher than many of its likely competitors in Asia, such as India, Indonesia, Malaysia or Vietnam.

This aging process will have far-reaching consequences. Socially, the family will be strained, perhaps more than in other countries, because of China's "one child policy". Economically, escalating costs associated with the aging population could put China at a competitive disadvantage relative to its neighbors. If current systems are unchanged, rising payroll tax rates needed to cover sharply increased medical and pension costs could dampen China's growth in wages and employment, induce evasion and escape to the informal sector, and will inevitably mean large intergenerational redistributions. China is considering a number of reforms to prevent this from happening.

This paper focuses on the pension system, discusses key policy choices, and evaluates their impact on costs and benefits as China's population ages. Part I describes the current pay-as-you-go (PAYG) system and the problems it faces in the short and long run; such a system is very sensitive to changing old age dependency rates. In a PAYG system, the pensions of old people today are financed by contributions of young workers today, rather than by their own savings in the past. Parts II and III simulate several policy options for solving the problems that will be faced by this system in the near future. Simple design changes involve reducing the generous benefit rate, moving toward price rather than wage indexation of pensions and raising the retirement age. The trade-offs among these options are explored in Part II. The central question asked here is: Will these 1
expectation that the level of individual income will influence radicalization disproportionately more in low-income countries. The coefficients on the other regressors are changed only slightly.

**Table 2: Endogeneity tests, Non-OECD sample (Only SAR equations are reported)**

||(1)|(2)|(3)|
|---|---|---|---|
|Income (#)||||
|Income (#) (interacted)||-0.013 [4.27e-03]***||
|Second 20%|||-0.078 [0.031]**|
|Middle 20%|||-0.125 [0.040]***|
|Fourth 20%|||-0.117 [0.046]**|
|Richest 20%|||-0.274 [0.081]***|
|Age (#)|1.022 [0.326]***|1.059 [0.315]***|1.044 [0.308]***|
|Age (#), squared|-0.146 [0.048]***|-0.151 [0.046]***|-0.148 [0.045]***|
|Female|-0.024 [0.032]|-0.024 [0.032]|-0.023 [0.032]|
|Single, never married|-3.52e-03 [0.027]|1.43e-03 [0.027]|1.49e-03 [0.027]|
|Employment status||||
|Employed part time want full time|-0.022 [0.059]|-0.021 [0.058]|-0.023 [0.058]|
|Employed part time don’t want full|-0.101 [0.026]***|-0.098 [0.027]***|-0.099 [0.027]***|
|<br>Employed full time for self-empl.|-0.137 [0.040]***|-0.126 [0.038]***|-0.127 [0.038]***|
|Employed full time for an empl.|-0.102 [0.025]***|-0.088 [0.024]***|-0.089 [0.023]***|
|Highest education level||||
|Secondary to 3 year of tertiary|-0.161 [0.044]***|-0.126 [0.037]***|-0.125 [0.036]***|
|<br>4 years of tertiary and beyond|-0.369 [0.059]***|-0.302 [0.055]***|-0.296 [0.055]***|
|Instruments for SAR||||
|Religion is important|-0.187 [0.074]**|-0.184 [0.074]***|-0.184 [0.073]**|
|Sacrificing one’slifeforbeliefs|1.322[0.107]***|1.317 [0.111]***|1.317 [0.110]***|
|Country fixed effects|Yes|Yes|Yes|
|Locality fixed effects|Yes|Yes|Yes|
|<br>Survey wavesfixed effects|Yes|Yes|Yes|
|Observations|30787|30787|30787|
|Log Pseudolikelihood|-85783.329|-85066.37|-85087.194|
|Wald test of indp.Eqns. (p-value)|0.030|0.034|0.031|

Notes: The reference categories for the employment status is “Out of workforce or unemployed”, for the highest education level is “Completed elementary education or less”, and for the locality fixed effects is “Rural area or on a farm”. X(#) and X(@)denote Ln(X) and Ln(X+1), respectively. We use Ln(X) for income to exclude individuals with zero income and Ln(X+1) for the number of children under 15 in the household to not exclude households with zero values on this variable. Standard errors are clustered by country/wave and robust to heteroscedasticity. Asterisks denote significance levels as follows: *** p<0.01, ** p<0.05, * p<0.1. The country-level income used in interaction with individual income is weighted using sampling weights. Columns 1, 2, and 3 report the results excluding income, interacting income with country-level income, and using income quintiles.

Finally, the results presented in column 3 of Table 2 (detailed results are in Table A18) confirm the robustness our main results. As mentioned above, the within country income distribution is likely to be a more exogeneous proxy for income and allows us to see how the likelihood of 32
Argentina’s trade policy in the past decades has swung from episodes of open trade to episodes of strong protectionism. Periods of protectionism were marked by an anti-export bias and import substitution policies, which have their roots in distribution conflicts that favored industry over agriculture in a country with a fundamental comparative advantage in agriculture (Brambilla, Galiani and Porto 2018 and Galiani and Somaini 2018). The anti-export bias hampered the productivity growth of the agriculture sector, while the import substitution strategy was unsuccessful in promoting industrialization. As a result, Argentina never developed its trade potential.

This paper examines the characteristics and dynamics of exporters in Argentina between 2007 and 2017, during most of which the country adhered to a protectionist policy stance. Employing highly disaggregated firm-level trade information, we benchmark firm-level export performance in Argentina during the period of 2007 to 2017 with respect to similar countries using the World Bank Exporter Dynamics Database, a catalog of comparable indicators for 70 developing economies (Fernandes, Freund, and Pierola 2016). We also investigate the role of imported inputs in the export performance of firms in Argentina during this period. We estimate premiums for exporting firms that are also direct importers of intermediate and capital goods relative to those that are not direct importers for a wide range of firm-level export outcomes (export value, export growth, number of destinations, unit values) controlling for unobserved firm heterogeneity and temporal cyclical effects. We conduct the analysis for the overall export sector and for individual economic sectors. Our work is complementary to Albornoz, Lembergman, and Juarez (2018), who study the relationship between the real exchange rates and firm-level performance of Argentina’s exports between 2003 and 2011, a period of high export growth.

The contribution of our work is threefold. First, to the best of our knowledge, we provide for the first time a systematic benchmarking of firm-level export dynamics in Argentina. Second, we document the important role of intermediate imported inputs in determining export outcomes in Argentina. Third, we contribute to the public good of the World Bank’s Exporter Dynamics 2
to be comparable with a treatment which is applied randomly across firms. Some (eventually unobservable) firm characteristics tend to affect both the probability of being state-owned and their economic performance, so sample selection is a potential statistical problem in our empirical application. For our study, we draw on existing theoretical contributions related to matching estimators for average treatment effects by Abadie and Imbens (2006) and implement the corresponding estimators as discussed in Abadie, Drukker, Herr and Imbens (2004).<sup>16</sup> Matching estimators are well established in the so-called program evaluation literature, which intends to identify the economic impact of certain policy measures. In our case, the policy variable (treatment) of interest is state-ownership, measured with the information on firms reporting to the Ministry of Industry, and our aim is to assess whether labor hoarding and/or unproductive investments are present in these firms. The two most commonly used estimators for treatment effects of policy intervention are the _average treatment effect_ (ATE) and the _average treatment effect on the treated_ (ATT). The former estimates the average effect of the policy program for a randomly drawn firm from the whole sample while the latter only considers the sub-sample of state-owned firms. Formally, these two estimators are given by (see, e.g., Wooldridge 2010) where _w_ = 1 if a firm is state-owned and _w_ = 0 otherwise.<sup>_y_</sup> 1<sup>and</sup><sup>_y_</sup> 0<sup>denote the outcome</sup> under treatment and non-treatment, respectively. The problem in this setting is that for each firm only one outcome is observable. If a firm is state-owned, we only observe<sup>_y_</sup> 1<sup>; while for</sup> non-state-owned firms only<sup>_y_</sup> 0<sup>is available. The matching approach (among other methods)</sup> intends to identify the counterfactual unobservable outcome for each firm. Here, the basic idea is that the most similar firms with regard to the outcome-relevant characteristics in the control group constitute the best estimate for the unobservable counterfactual outcome. If for example

> 16Literature reviews on matching estimators are provided by Cobb-Clark and Crossley (2003), Imbens (2004), Blundell and Costa Dias (2009) and Imbens and Wooldridge (2009).

25
products has not been forthcoming as expected. Thus, access to finance may still be a major hurdle for microenterprise growth. Using household survey data over a period of 10 years (2000-2010), this paper addresses a number of issues critical for understanding the sources and potential of microenterprise growth, such as whether and how better access to finance matters in the profitability and further growth of microenterprise in Bangladesh.

Our approach in this paper is related to a broader literature on access to finance and its role in microenterprise growth. A large literature has documented that access to better finance (in terms of better terms and conditions of loans as well as a reliable source of finance) is an essential predictor of improved productivity and growth in any economy. For example, Butler and Cornaggia (2009) find that in countries with better access to finance, corn producers increased productivity when there was an exogenous increase in ethanol demand. Thus, financial sector development is a robust determinant of growth (e.g., Johnson et al., 2002, Levine et al., 2000, McMillan and Woodruff, 2002, and Cull and Xu, 2005). While other obstacles are also important, lack of access to finance consistently emerges as one of the most important and robust underlying factors constraining firm growth. Better access to finance fosters greater firm innovation and dynamism, entrepreneurship, more efficient asset allocation and the ability to exploit growth opportunities, and growth of incumbent firms to a larger size (Beck, Demirguc-Kunt and Honohan, 2006; Beck, Demirguc-Kunt and Maksimovic, 2005).

Studies in varied settings show that access to finance also promotes a firm’s entry into the market and thus contributes to growth. For example, Mukherjee and Zhang (2007, 2005) explore the factors that affect the share of rural non-farm employment in total employment over time in both China and India, finding that the RNF sector has followed different paths in China and India over the past two decades simply because of differential access to institutional finance. Similarly, Sawada and Zhang (2012) find that nonfarm entrepreneurs perceive access to finance to be a 4
# TABLE OF CONTENTS.

|1. INTRODUCTION<br>........................<br>.<br>.|.<br>1|
|---|---|
|2. BACxGROUND. MAN MACROECONOMIC<br>DEVELOPMENTS<br>IN HuNGARY|.<br>3|
|3. A SMPLE MODEL OF INFLATION<br>..|11|
|4. ECONOMETRC ESTiMATEs.................................|.........<br>16|
|5. PoLICY OpONS FOR DISIFATION. A SIMULATION<br>ANALYsIS .<br>.22||
|6.CONCLUDING<br>REMARS..|31|
|REFERENCES ....<br>.................|33|
|FIOuI.E.1..|7|
|FIGuRE2 ....|16|
|FiGuRE 3 .....|29|
|FiouRE4.............................................|30|
|APPENDIX...............................................|.........<br>35|

# TABLES

TABLE I .............................................. .... _5_ TABLE 2 ...... . ................. 20 _TlABI._ 3 .............................................. 21 TABL.DE 4 ....................... 27 TABLE 5 ............................................. 28 i
There are other effective ways to achieve these objectives,<sup>such asspecial committeesinwhich</sup> senior policy makers from different agencies regularly<sup>meetand share relevantinformation.</sup> Moreover, effective collaboration among agencies can<sup>also be achievedthroughMemorandaof</sup> Understanding (MOUs) which formalize arrangements<sup>forthe exchangeofinformationand</sup> policy coordination among entities. Furthermore, in<sup>certaincases, acountry'slegalframework</sup> may allow a supervisory agency to become the "lead"<sup>supervisorin keyissues involvingtwoor</sup> more agencies, such as the resolution of a failing financial<sup>conglomerate.</sup> Finally, in some cases the economies of scale supposedly<sup>associatedwith the establishmentof a</sup> unified agency may not be sufficient enough to justify<sup>the merger of the institutions.8</sup>

# **1.3 Forces Driving the Establishment of Integrated**<sup>**Supervisory Agencies**</sup>

What are the main factors leading an increasing number<sup>of developedanddevelopingcountries</sup> to adopt integrated supervision? As shown in Table<sup>3,the15countriesthat participatedinthis</sup> survey indicated several reasons for adopting integrated<sup>supervision, including,byorderof</sup> importance, the following:

**Table 3. Main Reasons for Adopting Integrated Supervision (% of Agencies that Indicated any of the Following**<sup>**Reasons)**</sup>

|**Reasons**<br>|**N.**<br>**ofagencies**<br>|**As %ofall**<br>**agencies**<br>|
|---|---|---|
|**1.**Need tobettersuperviseafinancialsystemmoving<br>towardsuniversalbanking.<br>|14<br>|93%<br>|
|2.Maximizeeconomiesofscaleandscope.<br> <br> <br> <br> <br>|12<br>|80%<br>|
|3.Need<br>to<br>solve<br>problems<br>resulting<br>from<br>poor<br>communication and alackofcooperationamongexisting<br>supervisoryagencies.<br>|4<br>|27%<br>|
|4.Minimizegapsintheregulationandsupervisionof<br>financial intermediariesbyestablishingasingleauthority<br>accountableforthesupervisionofallfinancialinstitutions.<br>|3<br>|20%<br>|
|_5._Needforoperationalrestructuringofregulatoryagencies<br>(inparticular,afterafinancial crisis).<br>|3<br>|20%<br>|
|6.Overcomeotherweaknessesintheoverallquality<sup>of</sup><br>financial regulation andsupervision.<br>|2<br>|13%<br>|

Countries included: Australia, Canada, Denmark, Estonia,<sup>Hungary,Iceland, Korea,Latvia, Luxembourg,Malta,</sup> Mexico, Norway, Singapore, Sweden and the United Kingdom Almost all countries covered in the survey indicated<sup>havingadoptedintegratedsupervisionin</sup> response to the growing importance of financial conglomerates<sup>and blurringdistinctionsamong</sup>

**8** There is still not much evidence that the operating<sup>costs of new unified agenciesarelower thanthe sum</sup> of its individual predecessor regulatory bodies. So<sup>far,few agencies,suchastheFSA intheUK,have</sup> confimned that their operating costs are lower. See:<sup>CliveBriault(2001):Building aSingleFinancial</sup> Regulator. Paper prepared for the Conference on "Challenges<sup>for Unified FinancialSupervision",2-3 July</sup> 2001, Tallinn, Estonia.

9
WPS3930-IE <mark>I</mark> MPACT <mark>E</mark> VALUATION <mark>S</mark> ERIES <mark>N</mark> O <mark>. 3</mark>

# **Cash transfers, conditions, school enrollment, and child work:**

# **Evidence from a randomized experiment in Ecuador**<sup>♣</sup>

Norbert Schady Maria Caridad Araujo The World Bank

# World Bank Policy Research Working Paper 3930, June 2006

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._

> ♣ This research would not have been possible without the collaboration of our colleagues in the Secretaría Técnica del Frente Social in Ecuador.  We also thank Orazio Attanasio, Pedro Carneiro, Quy-Toan Do, Daniel Dulitzky, Francisco Ferreira, Deon Filmer, Jed Friedman, Sebastián Galiani, Paul Gertler, Mauricio León, David McKenzie, Christina Paxson, Juan Ponce, Carolina Sánchez-Páramo and participants at seminars at the Inter-American Development Bank and the World Bank for many useful comments.
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Third, there is an incentive dimension: policies that help<sup>the poor need</sup> to avoid the creation of a class of 'welfare dependents';<sup>the incentive</sup> structure must be such that, at the margin, it always<sup>benefits thepoor to</sup> work and earn additional income. This point is illustrated<sup>in figure 492,</sup> where the horizontal axis plots the income of individuals<sup>before any</sup> government program (or 'original income'), while the vertical<sup>axes measures</sup> income levels after the government program (or 'final income');<sup>as before, z</sup> denotes the poverty line. Assuming the government<sup>can measure everybody's</sup> income, it can potentially increase each individual's income<sup>bv the difference</sup> between z and the **450** line. This eliminates poverty (with<sup>the total cost of</sup> the program given by the triangle OAz). The program is financed<sup>by taxes on</sup> individuals with incomes above z, so that their final income lies<sup>below the</sup> **450** line. Note now that when original income increases along the range<sup>Oz,</sup> individual's final income stays constant. Differently put,<sup>along the range Oz</sup> poor individuals face a marginal tax rate of lOOX. In this<sup>type of transfer</sup> scheme every additional peso earned by the poor is matched<sup>by one peso of</sup> transfers taken away. The problem with this scheme is that the transfer depends on individuals' income. Under these circumstances<sup>it is natural to</sup> expect that individuals will modify their behavior to take advantage<sup>ofthis</sup> scheme (or any other so-called 'means-tested' scheme where benefits depend on characteristics that are under the beneficiaries's control). Clearly,<sup>schemes</sup> that simply transfer income to the poor give no incentive<sup>for them to work93.</sup> Thus, incentive considerations argue strongly against direct<sup>income transfers.</sup> Finally, there is a dimension of bounded rationality and administrative capability: there is a limit to the number of policies and programs<sup>that the</sup> governme:.t can run in a cost-effective and efficient way. A large number of programs opens more possibilities for waste and duplication<sup>(cf. section</sup> VII.1, below). In addition, programs that create multiple<sup>prices for the same</sup> commodity open possibilities for graft. A desideratum<sup>is for the government</sup> to concentrate on a few programs, but to implement<sup>them well. As in any other</sup> area of intervention, minimizing the possibilities<sup>for government failure is</sup> also important.

92This discussion is based on Besley and Kanbur (1990).

> 93Note also that if as a result of this transfer scheme the poor fail to work, the cost of the program doubles to the square OzAz,<sup>requiring higher</sup> marginal tax rates on the non-poor population<sup>to finance it, i.e., the solid</sup> line beyond z is flattened, generating work dis-incentives<sup>for the non-poor.</sup>