rafmacalaba/datause-encoder-data huggingface dataset:
social insurance payments (see Panel 2 of Appendix Table A.1), the rate of increase was slow until 2015. Between 2015 and 2016 there was another significant increase within the RUMiC sample, which is likely driven by the information intervention that is the focus of this paper. There are several potential reasons for slow progress in increasing participation among migrants.

First, although the central government has passed laws and developed policies aimed at increasing the health insurance and pension participation rates of migrants, implementation details and the enforcement of laws and regulations are left to local governments. As local leaders are generally evaluated based on how well they meet growth targets, the prospect of rising labor costs is a frequent concern and reduces incentives to enforce laws requiring employers to make payroll contributions. Unless they are particularly law-abiding, employers have no incentive to make voluntary contributions when enforcement is not effective. Previous studies have found that formal sector firms, which are more likely to face inspections (particularly state-owned enterprises, foreign invested and joint venture firms), generally provide written contracts to their workers and make social insurance contributions to their employees (Giles et al., 2013; Li and Freeman, 2015). The majority of migrant workers, however, are not working in large, formal sector firms.

Second, the financing of social insurance schemes is also left to local governments, but decentralized administration continues to limit portability of social insurance accounts, and thus limits migrant willingness to participate. While payments made by firms and young, healthy migrants are positive contributions to local health insurance and pension funds, most local governments are reluctant to transfer funds to other regions when migrants relocate as this would have immediate budgetary consequences. The main sticking point centers on how the employer portion of accounts is handled. Although they comprise two-thirds to 75% of total health insurance and pension fund accounts, a migrant is only able to transfer 12% of employer contributions. Further, even transfering one’s own contributions when moving faces hurdles as the fund manager accepting the transfer is required to match the employer contribution if it is not transferred, and this is a stipulation that destination governments almost never accept. These obstacles to portability mean that migrants, with relatively short time-horizons in the city, have weaker incentives to participate in social insurance than local residents (Giles et al., 2013).

Third, as program details are formulated by local governments, rules vary along multiple dimensions: by type of insurance, by type of employment, and, more importantly, across – regions the program specifics that a migrant finds in a new city are very likely to be different from those learned through prior experience. Program specifics, including premium and benefit levels, are both complicated and vary considerably accross cities, and are frequently 7
policy rate in any country. The relatively low rate selected here may be understood therefore to capture some preference for the welfare of future generations, in addition to the cost of capital. Panel C provides in our assessment a reasonable, if slightly optimistic basis for evaluating benefits, assuming ρ= 0.06 and a perpetual GDP growth rate of g= 0.055, the growth rate for developing Europe and Asia in 2019 (IMF, 2020). Comparing results in panels A, B and C illustrates the substantial degrees of freedom available in an analysis of the present value of a project’s benefits.

Focusing on the results in panel C, the present value of all projects when built in complement in our baseline specification is just $87.9 billion, far less than the independently estimated costs of $321 billion. Looking at an individual corridor, the value of the China-Pakistan Economic corridor is just $3.7 billion, far less than project costs in Pakistan alone of $49 billion according to De Soyres, Mulabdic and Ruta (2019). We now review the implications of alternative specifications.

First, we investigate alternative values of the model’s three fundamental parameters. Equation (4) shows that scaling the parameter α will increase one-to-one the expected benefit of the project. Valentinyi and Herrendorf (2008) show that across nonagricultural (urban) sectors this share varies little, from a minimum of α= 0.03 in construction and equipment investment to a maximum of α= 0.06 in services. Applying the value for services would increase benefits relative to our baseline specification by 0.06/0.05 = 1.2 or a 20 percent increase in benefits, to $105.4 billion, still far less than $321 billion. While a higher share of income to the fixed factor could feasibly obtain in some extremely dense urban centers, it would be optimistic to assume it is everywhere in all cities triple the estimate for the service sector.

Setting the labor share β = 0 corresponds to the case where consumer market access does not matter, only the value of the market to firms. As expected, benefits are reduced substantially. Increasing the labor share to β = 0.8 corresponds to an upper bound estimate of the labor share in developing countries (Gollin, 2002), which leads to 28
analysis and findings. Section 6 discusses the school-level QAT score regression analysis and findings. Section 7 discusses the regression-discontinuity analysis and findings on the threat effect on mean QAT scores arising from QAT failure on marginal first-time failers and the carrot effect on mean QAT scores and test participation arising from missing teacher bonus qualification on marginal bonus nonqualifiers. Section 8 summarizes the main findings and provides some concluding remarks.

# **2. Context and program features**

The Foundation Assisted Schools (FAS) program was introduced into an education landscape characterized by three defining features. One: equitable access to schooling and attainment and achievement are acute, persistent challenges. Estimates using household sample survey data for Punjab from 2004/05, the year prior to the launch of the FAS program, indicate that the school participation rate (grade 1+) of children ages 6–15 years was 65.7%—this share drops to 61.2%, 60.9%, and 48.7% when the sample is restricted to girls, children from rural households, and children from the poorest (bottom expenditure quintile) households, respectively. Conditional on any schooling (grade 1+), while 91.8% of individuals ages 17–21 years completed primary school (grade 5), only 40.7% of them completed secondary school (grade 10), with significantly lower shares for rural children and, in particular, poor children. The National Education Assessment System (NEAS) for Pakistan finds that scaled mean scores in mathematics and language assessments for a sample of grade-4 students in public schools in Punjab in 2005 were significantly less than 500, suggesting that, on average, students, obtained less than 50% of the points in the assessments (Government of Pakistan 2006). Das et al. (2006) and Andrabi et al. (2007) also find that the learning levels of grade-3 students in a selected sample of villages in Punjab measured in 2004 were far below curriculum standards in English, Urdu, and mathematics.

Two: the public sector, which is the dominant provider of education, suffers from chronic weaknesses which impair its ability to effectively address the challenges in education access, equity, and quality. To a large extent, public sector performance is hampered by weak accountability and incentive systems (Government of Pakistan 2009; Social Policy and Development Center 2003). This state-of-affairs is exemplified by recent evidence on relative teacher performance between private and public schools in rural Punjab. Andrabi et al. (2007) 6
- Duflo, E. 2012. “Human values and the design of the fight against poverty.” _The Tanner Lectures on Human Values_ . Harvard University. Available at: https://t.ly/gMyMY

- Eble, A., and M. Escueta. 2023. “When Your Bootstraps Are Not Enough: How Demand and Supply Interact to Generate Learning in Settings of Extreme Poverty.” _NBER Working Paper_ 31388. Cambridge, MA: National Bureau of Economic Research.

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- Edmonds, E., B. Feigenberg, and J. Leight. 2021. "Advancing the Agency of Adolescent Girls." _Forthcoming at the Review of Economics and Statistics_ . August 5 version, available at: <u>https://bit.ly/3iXGzdi</u>

- Farfan, G., A. Holla, and R. Vakis. 2021. “Poor Expectations: Experimental Evidence on Teachers' Stereotypes and Student Assessment.” _World Bank Policy Research Working Paper_ 9593. Washington, DC: World Bank.

- Favara, M., G. Chang, and A. Sánchez. 2018. “No longer children: What do Young Lives children do when they grow up? Transitions to post-secondary education and the labour market.” _Research Report_ . Oxford: Young Lives. Available at: https://bit.ly/2N45uGv

- Favara, M., and A. Sánchez. 2017. "Psychosocial competencies and risky behaviors in Peru." _IZA Journal of Labor and Development_ 6:3.

- Flechtner, S. 2014. “Aspiration Traps: When Poverty Stifles Hope”. _Inequality in Focus_ 3(1). Washington, DC: World Bank.

- ———. 2017. “Inequality, (unmet) aspirations and social protest.” in H. Hanappi and S. Katsikides (eds.). _Evolutionary Political Economy in Action. A Cyprus Symposium, Part 1: Political Economy in Action_ . 109-123. Abingdon/New York: Routledge.

- Fourrier-Nicolai, E., and M. Lubrano. 2019. “The Effect of Aspirations on Inequality: Evidence from the German Reunification using Bayesian Growth Incidence Curves.” _AMSE Working Paper_ halshs-02122371f. Aix-Marseille School of Economics (AMSE).

- Fruttero, A., N. Muller, and O. Calvo-Gonzalez. 2024. “The Power and Roots of Aspirations.” _World Bank Research Observer_ lkae004.

- Gagete-Miranda, J. 2022. “An Aspiring Friend Is a Friend Indeed: On the Mechanisms Behind Peer Influences on Human Capital Accumulation.” _Manuscript_ . Milan: Bocconi University. Available at: https://bit.ly/3BUzDFs.

- Galiani, S., P. J. Gertler, and R. Undurraga. 2021. “Aspiration adaptation in resource-constrained environments.” _Journal of Urban Economics_ 123(103326).

- Garcia, A., R. Lensink, and M. Voors. 2020. “Does microcredit increase aspirational hope? Evidence from a group lending scheme in Sierra Leone.” _World Development 104861_ .

29
workers. Since the baseline and what-if counterfactual simulations contain similar underlying economic projections and parameters, their differences minimize the effects of common assumptions and isolate the effects of the critical factor of each counterfactual simulation. The simulation period is for a 20-year span, from 2011 to 2030, which makes it more comparable to the first wave. This time horizon also makes the analysis more conservative because it will not cover the full impact of the education wave beyond 2030.

Table 4: CGE baseline and counterfactual scenarios

|Scenario|Keyfeatures|Purpose|
|---|---|---|
|Baseline – education wave|Population projections from UN<br>(2015) medium fertility variant<br>scenario; economic growth<br>projections from World Bank<br>(2015); the share of skilled<br>workers grows assuming constant<br>education attainment rates.|Establish a business-as-usual or<br>reference case for comparison<br>with counterfactual scenarios.|
|No education wave|Same as baseline except for the<br>fact there are no changes in the<br>share of skilled workers.|Provide a counterfactual scenario<br>without the “education wave.”|
|Higher elasticity of<br>substitution between<br>skilled labor and capital|The baseline plus a higher<br>elasticity of substitution of 3<br>(previously 2) between skilled<br>labor and capital.|This scenario tests the sensitivity<br>of the results for changes in the<br>substitutability between skilled<br>labor and capital to mimic the<br>consequence of a biased<br>technological change|
|Alternative nesting<br>structure of labor and<br>capital|The baseline with an alternative<br>nesting structure. Skilled and<br>unskilled labor are substitutable<br>with each other, and together also<br>substitutable with capital. We use<br>elasticities of substitution of 2 for<br>the two relevant nests.|This scenario tests the sensitivity<br>of the results for technological<br>changes that may affect the<br>substitutability between skilled<br>and unskilled workers.|

Source: Authors’ elaboration.

# 4. Results

## 4.1 Results at macro level: Growth differentials and wage premia

In the simulations, the productivity factor for each country is set exogenously to replicate the evolution of economic growth to 2030 based on the World Bank projections reported in the Global Monitoring Report 2015/16 (World Bank, 2015). The catching up of productivity of developing countries to the levels of high-income countries generates faster growth rates for the developing countries. The growth differential vis-à-vis high-income countries is quite pronounced for the East Asia and the Pacific region as well as for South Asia region. These differentials bring 18
26 Tb ILa 7: AVERACE PER CAPITA REAL INCOME BY SOCIOECONO#4IC GROUP (RATIOS TO YEAR 1)

|Experisent|||**Blas**Run<br>(BR)|E-1|E-2|E-S||E-|4|E|-S|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Year||a/|5<br> a<br>7|5<br>7|7|o<br>a|7|a|7|a|7|
|Cnpital;sta|(6.3)<br>/|8.07<br>(42.7)|1.12<br>1.19|0.t40.90|0.76<br>0.90|0.77|0.91|0.72|0.61|0.76|0.94|
|Big Farwer'|(9.6)|1.08<br>(15.1)|1.11<br>1.17|0.94<br>1.08|0.94<br>0.97|0.91|O.94|0.88|0.68|1.13|0.94<br>.|
|Sol<br>Fuawro|(80.0)|0.83<br>(1.0)|1.12<br>1.21|1.00<br>1.06|1.03<br>1.08|1.00|1.00|0.97|0.94|1.24|1.00|
|Modern Worker. I/|(27.6)|1.48<br>(3.9)|1.18<br>1.21|0.96<br>1.09|0.78<br>0.91|0.66|0.82|0.67|0.73|0.62|0.92|
|Landles<br>Ag. Worke|rs(8.7)|0.80<br>(0.4)|1.17<br>1.23|1.00<br>1.10|1.07<br>1.08|<br>1.00|1.00|<br>1.00|0.93|<br>1.27|1.00|
|InformalWorkers|(17.8)|0.56<br>(0.6)|1.14<br>1.20|0.68<br>0.96|0.64<br>0.69|<br>0.59|0.68|<br>0.43|1.16|<br>0.69|0.77|
|Econony-Wide Averag<br>Real_P.r_Capita Inc|e<br>ome|0.91<br>(5.8)|1.14<br>1.22|0.9S<br>1.05|0.79<br>0.91|0.76|0.67|<br>0.67|0.66|<br>0.79|0.91|
|Thei<br>I/||22.87<br>26.86|22.07 22.2B<br>28.28 2t.66|22.67 23.91<br>31.8831.62|21.84 24.16<br>29.91 32.88|21.48<br>38.69|2S.44<br>36.02|26.85<br> 40.82|22.05<br>83.47|16.26<br> 24.09|24.29<br>38.92|
|HeadCountg/|S/|S8.78|86.04 87.79|38.15<br>87.07|56.52<br>54.87|6.40|86.11|67.67|39.68|56.67|66.19|
||_|6/<br>U3.92|S8.04<br>87.79|6E.71 64.74|66.1 65.48|68.76|67.71|70.52|69.08|65.67|66.80|
|Poverty**Gap**h/|_gi_|8.85|2.75<br>1.68|5.62<br>8.95|7.886.41|10.04|7.71|14.74|7.40|4.65|6.21|
||V|6.6|2.75<br>1.68|9.62<br>6.27|14.08 11.19|17.91|14.30|28.91|12.69|10.04|11.64|

I/ End of _year._ Year _I are_ luvel values (real wealth in parenthess); all other values are ratios to year 1.

- _hi_ Shares in total population in yoar I in parenthese.

- S/ Includes un_aloyed and government workers.

/ Theil inequality index; T - ln Cvi/wI **;** )I v* **s** are inco4 and population hare.

S/ Unemployed included * ong modern workers.

fr Uneaployed included among informl workers.

0/ Hoed-count ratio Ha E p / * **e** 0.44. Cost of living at calm i; p * population shares. y (a I i I is I i h/ Poverty gap i * E (a-i)p E y p **_;_ y** - per capital income of claos i.

Notes: i is diacentinuous becus **we** have aacoud a uniform distribution of income **among** socioeconomic groups. The value of 0 indicates the share of Ineme which ould have to be redistributed to brine thos below the poverty line up to the poverty line.
change over the electoral cycle. These patterns suggest that in countries with high political competition, the spending on social assistance and income protection appears to crowd out the spending on job protection programs over the electoral cycle. As elections approach, governments appear to reduce spending on job protection programs and increase it on social assistance and income protection policies. Once elections are behind, the spending on job protection increases, and spending on social assistance declines.

# **6. Conclusions**

In this paper, we use newly assembled data on various types of social protection spending across 154 countries during the COVID-19 pandemic in 2020 and 2021 to analyze the effect of the electoral cycle on the size and composition of the social protection pandemic stimulus budget.

Electoral cycles appear to affect the size and composition of social programs in politically competitive environments, even during systemic shocks, such as the COVID-19 pandemic. Our results demonstrate that the preferences and motivations of politicians seeking re-election should be taken into account when designing social policies. To stay in power, incumbent politicians may feel the need to show immediate results by directing resources to highly visible policies, such as social assistance, and underinvesting in other policies, such as job protection schemes, which may be particularly important in severe economic downturns. A similar argument could apply to underinvestment in policies that bring significant longer-term benefits, such as health and education. Expanding social programs at certain periods of the electoral cycle to achieve political gains may undermine the policies’ effectiveness in protecting the most vulnerable population groups <u>(Bueno 2021). Increasing the provision of public goods to gain votes could increase budget</u> deficits and repayment costs, increasing inflation and the future tax burden. Reducing taxation to secure voter support before elections may decrease the quality of social protection programs and endanger their long-term sustainability (Prichard 2018). Better governance and promotion of the rule of law could mitigate these effects and result in more effective public policies and higher welfare.

Our estimations show that the degree of political competition affects the relationship between the electoral cycle and the composition of the social protection policy mix implemented in response to the pandemic. The greater the political competitiveness, the larger the political budget cycle 17
# **Patterns of earnings inequality**

While it is quite common to find commentaries mentioning differences in the extent of inequality by gender,<sup>8</sup> race, and ethnicity<sup>9</sup> in Latin America, along with decrying the overall extent of inequality, it is far less common to see a formal analysis of to what extent the former is responsible, or in any way linked, to the latter. Psacharapoulos and Patrinos (1994) have tackled the estimation of racially and/or ethnically separate earnings equations for Latin American countries and attempted to measure the contribution of racial and ethnic differences to earnings differences. In addition, in the earnings equation decomposition literature, it is rare to see any reaggregation of the data into a measure of overall earnings inequality in order to be able to see how various counterfactual calculations might affect such a measure.

It is particularly difficult to study the differential patterns of earnings inequality by race/ethnicity across all of the Latin American and Caribbean countries because many Censuses and labor force or household surveys do not ask questions delineating race (Florenz, Medina, and Urrea 2001). We surveyed the most recent available household surveys for these countries at the time our project began in 2003, and found four in which there is both sufficient coding to be able to separate out “dominant” and “disadvantaged” groups by race/ethnicity and sufficient sample sizes to be able to estimate separate earnings regressions by gender and race/ethnicity. These are the 1999 _Encuesta Continua de Hogares_ for Bolivia, the 1996 _Pesquisa Nacional de Amostra da Domicilio_ for Brazil, the 2000 _Encuesta Nacional sobre Condiciones de Vida_ for Guatemala, and the 1999 _Survey of Living Conditions_ for Guyana. These countries all have either sizable Afrodescendant or Indigenous populations, with Brazil and Guyana having the two highest percentage Afro-descendant populations (44.7% and 42.6% respectively) and Bolivia and Guatemala having the two largest reported indigenous populations (71% and 66% respectively) in Central and South America. Guyana is also interesting in that the “dominant” population is of South Indian background (Indo-origin, in our terminology below) rather than white background (in contrast to the other three countries). So while we use the term “white” as shorthand to refer

> 8 Women still lag behind men in most indicators of well-being — wage gaps, control over resources, voice, health care (Cunningham et al, 2004) — but there are increasingly noted trends of disadvantage among men, including higher death rates, alcoholism, and suicides than women, as well as particularly in LAC countries a reduction in their rate of higher educational attainment (Jacobsen 2006).

> 9 Sociologists have theorized that racial or ethnic inequalities are a result of a history of power relations that create an unlevel playing field through setting up a situation wherein endowments, opportunities, and/or expectations differ by demographic gruop (Baiocchi 2003). These theories are becoming salient as ethnic and racial groups are

3
GLS transformation.

As described in Cogley and Sargent (2005), conditional on Γ, _λit_ and _h_<sup> ̃</sup> _it_ , the elements of _A_<sup> ̄</sup> are coefficients in linear regressions involving the residuals of the panel VAR. Therefore, their conditional posterior is standard.

Given _h_<sup> ̃</sup> _it_ , equation 3 is simply a panel VAR with fixed effects. As we employ conjugate priors for _B_<sup> ̄</sup> and _Q_ , their conditional posteriors are well known and easily sampled from. Geweke (1993) shows that the conditional posterior of _λit_ is a Gamma distribution and this set of parameters is easily drawn. We use a random walk Metropolis Hastings algorithm to draw from conditional posterior of the degrees of freedom parameters _νK,λ_ .

With a draw of Γ _, A,_<sup> ̄</sup> _B, Q_<sup> ̄</sup> and _λit_ in hand, equations (1) to (3) constitute non-linear state space model for each country. To draw from the conditional posterior of _h_<sup> ̃</sup> _it_ we use the particle Gibbs sampler of Andrieu et al. (2010a) and Lindsten et al. (2014).

We use 21,000 iterations and retain every 2<sup>_nd_</sup> draw after a burn-in period of 1,000 draws. In the technical appendix (figure A2.1) we show that the estimated inefficiency factors are low, providing evidence in favor of convergence of the algorithm.

# **4 Data**

This section, along with Appendix A3, provide the definitions, transformations, and sources of the data used in the baseline model (and extensions). Data for 54 advanced economies and EMDEs are used in the baseline model from 1980Q1 to 2022Q4 (see table A3.1). The main variables are described in table 1 and the baseline model includes government spending, tax revenue, the real interest rate, real GDP, and the GDP deflator (figure 1). Appendix A3 provides details on the definition of the real interest rate.

For government spending, to get the greatest cross section both national accounts data– that is real public consumption expenditure on a seasonally adjusted basis–and data from government financial statements–usually collected as monthly nominal government ex10