rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5405

# **Abstract**

According to T.W. Schultz, the returns to human capital are highest in economic environments experiencing unexpected price, productivity, and technology shocks that create “disequilibria.” In such environments, the ability of firms and individuals to adapt their resource allocations to shocks becomes most valuable. In the case of negative shocks, government policies that mitigate the impact of the shock will also limit the returns to the skills of managing risk or adapting resources to changing market forces. In the case of positive shocks, government policies may restrict access to credit, labor, or financial markets in ways that limit reallocation of resources toward newly emerging profitable sectors. This paper tests the hypothesis that the returns to skills are highest in countries that allow individuals to respond to shocks. Using estimated returns to schooling and work experience from 122 household surveys in 86 developing countries, this paper demonstrates a strong positive correlation between the returns to human capital and economic freedom, an effect that is observed throughout the wage distribution. Economic freedom benefits those workers who have attained the most schooling as well as those who have accumulated the most work experience.

This paper—a joint product of the Poverty and Inequality Team, Development Research Group; and the Education Group, Human Development Network—is part of a larger effort in these departments to understand how investments in human development, such as education, improves people’s productivity and their ability to move out of poverty and have a better life. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cmontenegro@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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._ Produced by the Research Support Team
Table 4: Information requirements for regulators under alternative guarantee mechanisms

|||Target Functions|
|---|---|---|
||Guarantee an<br>Exchange Rate (5.1)|Guarantee a Return<br>(5.2)<br>Guarantee<br>Equal<br>Return<br>Among<br>Partners (5.3)|
|Notional<br>(or proxy)<br>Values<br>(for I, q,<br>and<br>c<br>fxed and<br>known in<br>advance)|For the three mechan<br>lators is directly obser<br>the market at minima<br>plied by the regulated<br>and (bi-lateral) oppor<br>values would only req<br>regulator and investor<br>quantity.|isms, all information required by regu-<br>vable, contractually set or gleaned from<br>l cost; conversely, none needs to be sup-<br> entity, which minimizes renegotiations<br>tunistic behavior.<br>The use of notional<br>uire an underlying model verifed by both<br>that relates investment, costs, tarif and|
|Real Val-<br>ues (forI,<br>q andc)|In<br>order<br>to<br>calcu-<br>late<br>excess<br>beneft<br>caused by the ex-<br>change rate guaran-<br>tee and therefore de-<br>termine<br>the<br>exten-<br>sion of T, the reg-<br>ulator would require<br>real values for I, q<br>and c for each pe-<br>riod.|The regulator must be able to calculate<br>the real I and real net Beneft B for each<br>period.<br>The government does not need<br>to observe individual q and c values di-<br>rectly in every period, even though doing<br>so may reduce opportunistic behavior by<br>the incumbent. If the regulator faces in-<br>formation restrictions, it can audit the in-<br>cumbent on a random basis.|

# **7 Simulating alternative guarantee mechanisms in a real life highway concession**

In this section, we investigate the behavior of the proposed mechanisms using data from CCR NovaDutra, a highway franchise taken place in Brazil from 1996 to 2020. We test target functions from Subsections 5.2 and 5.3 with profit functions constructed under a broad array of different exchange rate scenarios.<sup>34</sup> For that, we suppose the same project have taken in eight developing countries characterized by limited currency market interventions between 1996 and 2020: Brazil, Mexico, Indonesia, Mozambique, Chile, South Africa, Peru and the Philippines. Table 5 compares selected indicators among these countries and shows they are diverse in terms of income, export composition, population and private investment in infrastructure.

> 34 For detailed information about the project, see Appendix B. Due to lack of data regarding prices and demand, it was not possible to simulate the target function discussed in Subsection 5.1 nor propose a test function based on notional values that would be consistent with the project’s cost structure.

34
MIALE SURVIVORS

|Characteristics|Net<br>Ben|Net<br>Tax|
|---|---|---|
|Age of death|82.7|73.7|
|Ever lone parent(%)|1.6|1.4|
|Ever married (%)|82.5|89.3|
|Ever divorced|34.5|36.6|
|Years divorced|6.3|4.8|
|Years tertiary educ.|0.73|1.4|
|Years of unemployment|6.9|4.8|
|Years of employment|35.3|38.5|

# FEMALE SURVIVORS

|Characteristics|Net<br>Ben|Net<br>Tax|
|---|---|---|
|Age of death|82.8|71.5|
|Ever lone parent(%)|38.3|35.6|
|Ever married (%)|93.0|92.2|
|Ever divorced|36.9|39.0|
|Years divorced|9.8|8.2|
|Years tertiary educ.|1.0|1.5|
|Years of unemployment|2.5|1.9|
|Years of employment|29.1|32.4|

From the first panel of Table 2 it is clear that longevity, lone parenthood, a low level of tertiary education and a history of relatively low participation in the labour force are all characteristics of net beneficiaries from the PAYG system. When the data is divided by sex, in the second and third panels, it becomes clear that lone parenthood is not a significant factor within each gender group, and that that although men are more likely to be net taxpayers if married (cross-sectional surveys show married men 18
10 combinations of elasticity ranges of from 0.5 to 1 for raw cashmere and 1 to 3 for garment production.<sup>9</sup> The results appear in Table 2. Of particular note is the result that the export controls on raw cashmere appear to reduce total export earnings.10 Also noteworthy is the magnitude of the transfers. Because a large amount of raw cashmere is exported, the impact of even the modest 16.6 percent price differental between the world market and domestic price imposes large losses on herdsmen and allows large gains to those able to export. The estimated losses of _$5.5_ to $7 million dollars would represent **_$5_** to $7 per person to the approximately 1 million people in the countryside. The rents generated from exporting are about $4 million.

As of March, 1993, Romania imposed export prohibitions or quotas on a relatively large number of raw material and intermediate inputs. These included wood products that are inputs into the furniture industry, stardng with logs, timber and firewood, up through lumber, plywood, particle board, and veneer.<sup>11</sup> Data on quantities and values of production and exports of aU the various products were not available, but the 1990 Romanian input-output table separately identifies the woodworking and furniture industries. Estimates of the impact of the export controls were based on the input-output table, under the assumption that the woodworking sector represented production of inputs used in the furniture sector. Equations 7 through 12 were used to calculate estimates of the magnitude of the transfers and net costs associated with these export restrictions. The values for the variables in those equations, and an explanation of the sources and reasoning behind them, appear in Table 3. The resulting estimated impacts of the wood products export controls appear in Table 4.

Romania currentiy imposes restrictions on cutting of timber to protect the environment and allow rebuilding of the timber stock. These restrictions can be interpreted as implying a zero elasticity of supply of timber (and therefore wood products) in the short run. However, in the long
# Motivating Bureaucrats through Social Recognition: Evidence from Simultaneous Field Experiments

Varun Gauri, Julian C. Jamison, Nina Mazar, Owen Ozier, Shomikho Raha, and Karima Saleh<sup>_1_</sup> Keywords: RCT, external validity, bureaucracy, behavioral insights, nudges, healthcare JEL Codes: C93, D73, D91, I18, L38, O35

> 1 Gauri, Jamison: Development Economics Vice Presidency, The World Bank, vgauri@worldbank.org and jjamison@worldbank.org. Mazar: Boston University, nmazar@bu.edu. Ozier: Human Development Team, Development Research Group, The World Bank, oozier@worldbank.org. Raha: Governance Global Practice, The World Bank, sraha@worldbank.org. Saleh: Health, Nutrition, and Population Global Practice, The World Bank, <u>ksaleh@worldbank.org. The authors gratefully acknowledge the contributions of Obert Pimhidzai, for leading early</u> data collection; and Ghazia Aslam, Iman Hafiz, Temilade Sesan, and Egon Tripodi for subsequent data and analytic work. We also thank David Evans, Anne Fitzpatrick, Rachel Glennerster, Pamela Jakiela, Oyebola Okunogbe, and audiences at UC Berkeley/CEGA and Oxford/CSAE for helpful comments on earlier drafts. This field trial of social recognition was part of a larger study undertaken by Saleh, from the HNP, World Bank, described in Gauthier, Pimhidzai and Saleh. 2018. "Resource Tracking in Primary Health Care in Nigeria: Case Study from Niger and Ekiti states,” Vol I and II, World Bank, Washington, D.C. This study is registered with the AEA RCT registry (socialscienceregistry.org), number AEARCTR-0002763. The findings and views expressed in this paper do not necessarily represent those of the World Bank, its Executive Directors, or the governments of the countries they represent.
Haiti’s per capita GDP performed very poorly in the period 1980-2003, relative to both the country’s own historical experience and to other countries in the Latin American and Caribbean region. In 1980 Haiti’s per capita GDP stood at $632, and by 2003 it had fallen by about half to $332 (Figure 2.3). In the same period, Jamaica’s per capita income increased by around 17 percent, and the Dominican Republic’s by 57 percent.

<!-- Start of picture text --> Figure 2.3 : GDP Per Capita in Selected Countries and Haiti, 1980-2003<br>4,500.00<br>Costa Rica Dominican Republic<br>4,000.00<br>Haiti Jamaica<br>Mexico<br>3,500.00<br>3,000.00<br>2,500.00<br>2,000.00<br>1,500.00<br>1,000.00<br>500.00<br>0.00<br>Source: WBI 2003.<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br>Constant 1995 USD<br><!-- End of picture text --> Fundamental to lack of growth in Haiti is the country’s long history of political instability, lack of governance, distortions at the macroeconomic level, and inadequate levels of private investment (see Sections 3 and 4). Macroeconomic stability and a lessening of distortions, so as to encourage private sector investment are essential to increased productivity.

# **Sectorial GDP**

In recent years Haiti’s agricultural sector has done less well than services and manufacturing sectors. During 1996-2002, the share of agriculture in total GDP fell while the share of services and industry increased (Table 2.7). In 2002, Haiti remained very much a dual economy where, on one hand, agriculture contributes 27.1 percent of GDP, accounting for around 50 percent of employment, and industry on the other hand, contributes 16.3 percent of GDP, but only around 10 percent of jobs.

Agricultural output has suffered from a growing population farming a finite area of land. The result has been the division of cultivated land into smaller and smaller plots, so that by the 1990s, 78 percent of Haiti's farms had an average size of less than two 11
53

**Methodology** and the data

Nelson and Schwert (1982), and Geweke, Meese and Dent (1983) argue that the OLS version of the Granger test is more powerful than most of the other procedures, for example, Sims (1972). The Granger procedure can be described by the following system, Where, X is the real export growth defined as the first difference of the natural logarithm of the real exports measured in domestic currency. Inflation, P is defined as the first difference of the natural logarithm of the GNP deflator. R is the real exhange rate which is the ratio of traded goods price level to non-traded goods price level. This variable is measured by S.Pf/P as defined earlier. And AX,, A&, and AP, are stationary time series and e1, v,, and ut are uncorrelated error terms that retain the classical regression assumptions. Unidirectional causality from AR to AX or from AX to AR requires that at least some bj's and gi's to be significandy different from zero. The same condition is required for causality from AP to AX (some c1's •0) or from AX to AP (some l,'s• O), from AP to AR (some Ig's•0 0) and from AR to AP (some rn;'s•; O). Bidirectional causality in the order above implies that b 1•0, _g 1 0, X4•0,_ I;•0, h;•0, and mn;dO for at least some i's. Of course, if AR, AX, and AP are causally independent, all the coefficients of AR in equations (31) and (33) and of AX in equations (32) and (33) and of AP in equations (31) and (32) should be statistically insignificant.

The time series X, R, and P are nonstationary. These series are tested by the Augmented Dickey-Fuller test (1984).47/ All series are first differenced and checked for unit _47/_ Test statistics are not reported. For all variables, we tested the null hypothesis that p=O in the following regression equation: A R1, = Bo + **pR,. 1**<sup>+Di-jai A RF; + A* R is X, R, and P respectively. Critical values are in Dickey-</sup> Fuller (1979).
our estimation strategy and results are presented in section 3.4. We conclude with a discussion of our findings in section 3.6.

# **2 Data**

In this section, we examine youth employment outcomes using the most recent year of available data, before documenting trends between 1990 and 2010. We then describe the evolution of cohort size for the same time period.

## 2.1 Labor Market Data

The labor market outcome data comes from the International Income Distribution Survey (I2D2). The I2D2 is a harmonized collection of household and labor surveys from 19902010 constructed and maintained by the research group of the World Bank.<sup>9</sup> Our data set includes repeated cross-section data from 450 surveys at the individual level in 83 lower, middle and upper middle income countries. The available years and frequency of waves differ among countries as shown in Table 5 in the Appendix.

When combining different micro surveys in one data set, it is important to ensure that questions are asked consistently over time. Although the I2D2 has been harmonized, there can be large changes in variables when the underlying survey changes, due to changes in survey design driven by differences in the formulation of questions or the target group. This will introduce measurement error into our measures of labor market outcomes, which could potentially be correlated with changes in cohort size and bias the estimates. We therefore identify potential structural breaks, using the outlier detection tool “bacon” in Stata. We first identified annualized changes in labor market outcomes amounting to more than four percentage points, which we considered to be outliers. We then used bacon on the full set of observations to identify outliers conditional on changes in GDP. These outliers were excluded from the main analysis. We include all outliers as a robustness check and find little difference to our main results.

We use four labor market outcome variables: (1) Primary activity, (2) Sector of work and (3) Employment status. Primary activity, in turn, is divided into at least three categories: Employed, unemployed, and inactive. For young people between the age of 15 and 24, we further divide inactive workers into two categories, depending on whether they attend school or not.<sup>10</sup> As we do not have reliable information on how many hours people work, all young people who have a job and have worked at least 1 hour during the past seven days are classified as employed. This mean that youth who both attend school

> 9 An earlier version of the data set is described in detail in Montenegro and Hirn (2009).

> 10 In some surveys in the I2D2, school attendance is unavailable and therefore proxied for by school enrollment.

5
- Deaton, A., and V. Kozel. 2005. “Data and dogma: The great indian poverty debate.” _World Bank Research Observer_ .

- Deaton, A., and J. Muellbauer. 1986. “On measuring child costs: With applications to poor countries.” _Journal of Political Economy_ 94(4): 720– 744.

- Deaton, A., and C. Paxson. 1998. “Economies of scale, household size, and the demand for food.” _Journal of Political Economy_ 106(5): 897–930.

- Du, S., B. Lu, F. Zhai, and B. Popkin. 2002. “A new stage of the nutrition transition in china.” _Public Health Nutrition_ 5(1A): 169–174.

- Duh, J., and D. Spears. 2017. “Can improving the disease environment explain india’s calories consumption puzzle?.” _Economic Journal_ 127: 2378– 2409.

- Engel, E. 1895. “Die lebenskosten belgischer arbeiter-familien fruher and jetzt.” _International Statistical Institute Bulletin,_ 9: 1–74.

- FAO/WHO/UNU Expert Consultation. 2001. “Human energy requirements.” _Food and Nutrition Technical Report Series_ .

- Federation of Hotel and Restaurant Associations of India. 2004. “Restaurant industry in india: Trends and opportunities.” _Research Study_ .

- Ferro-Luzzi, A., C. Petracchi, R. Kuriyan, and A. V. Kurpad. 1997. “Basal metabolism of weight-stable chronically undernourished men and women: lack of metabolic adaptation and ethnic differences.” _American Journal of Clinical Nutrition_ 66: 1086–1093.

- Fiedler, J. L., and S. Yadav. 2017. “How can we better capture food away from home? lessons from india linking person-level meal and householdlevel food data.” _Food Policy_ 72: 81–93.

- Gibson, J. 2002. “Why does the engel method work? food demand, economies of size and household survey methods.” _Oxford Bulletin of Economics and Statistics_ 64(4): 341–359.

- Gibson, J., and B. Kim. 2007. “Measurement error in recall surveys and the relationship between household size and food demand.” _American Journal of Agricultural Economics_ 89(2): 473–489.

25
||(0.524)|(0.525)|(0.525)|(0.523)|(0.755)|(0.782)|(0.760)|(0.742)|(0.934)|(0.940)|(0.937)|(0.929)|(0.517)|(0.525)|(0.527)|(0.523)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Ever temporarily closed<br>during the COVID-19<br>outbreak|0.070|0.074|0.064|0.066|-0.032|0.079|0.050|0.000|-0.531|-0.518|-0.386|-0.542|-0.063|-0.050|-0.021|-0.060|
||(0.078)|(0.088)|(0.083)|(0.083)|(0.656)|(0.660)|(0.642)|(0.630)|(0.619)|(0.615)|(0.589)|(0.612)|(0.582)|(0.590)|(0.548)|(0.585)|
|Adjusting or converting their<br>production or services|-0.058|-0.019|-0.027|-0.027|2.038***|2.122***|2.038***|1.993***|0.872|0.892|0.821|0.948|0.536|0.589|0.558|0.583|
||(0.073)|(0.071)|(0.076)|(0.073)|(0.585)|(0.602)|(0.579)|(0.577)|(0.680)|(0.666)|(0.606)|(0.658)|(0.575)|(0.580)|(0.591)|(0.575)|
|Started or increased online<br>business activity|-0.300***|-0.302***|-0.286***|-0.274***|-1.009*|-1.202**|-1.045*|-1.075*|-0.916***|-0.820**|-0.995***|-0.956**|-1.262*|-1.136|-1.122|-1.149|
||(0.092)|(0.098)|(0.097)|(0.091)|(0.561)|(0.556)|(0.572)|(0.593)|(0.342)|(0.376)|(0.383)|(0.396)|(0.741)|(0.731)|(0.729)|(0.730)|
|Started or increased delivery<br>of goods, services or carryout|0.167**|0.147**|0.120|0.147*|0.503|0.625|0.586|0.554|0.733|0.740|0.490|0.857*|0.230|0.272|0.259|0.288|
||(0.071)|(0.066)|(0.086)|(0.074)|(0.477)|(0.460)|(0.457)|(0.476)|(0.460)|(0.485)|(0.420)|(0.494)|(0.645)|(0.672)|(0.664)|(0.670)|
|Started or increased remote<br>work|0.025|-0.275|0.003|0.005|-1.328*|-1.333*|-1.252*|-1.279*|0.010|-0.052|-0.195|-0.047|0.144|0.015|-0.012|0.021|
||(0.099)|(0.219)|(0.094)|(0.099)|(0.753)|(0.755)|(0.729)|(0.737)|(0.642)|(0.621)|(0.585)|(0.611)|(0.437)|(0.445)|(0.440)|(0.443)|
|Exporting directly (at least<br>10% of sales)|-0.132|-0.089|-0.088|-0.092|0.207|-0.028|-0.103|-0.020|-0.804|-0.552|-0.205|-0.556|1.487**|1.603**|1.655***|1.585**|
||(0.096)|(0.102)|(0.102)|(0.104)|(0.781)|(0.760)|(0.694)|(0.767)|(0.763)|(0.784)|(0.695)|(0.801)|(0.697)|(0.624)|(0.621)|(0.630)|
|Constant|0.144|0.338|0.136|0.148|-2.597|-2.776|-2.719*|-2.377|-0.104|-0.115|0.033|0.102|-2.501***|-2.502***|-2.465***|-2.397**|
||(0.258)|(0.327)|(0.262)|(0.265)|(1.653)|(1.689)|(1.640)|(1.625)|(0.951)|(0.953)|(0.856)|(0.915)|(0.965)|(0.948)|(0.931)|(0.940)|
|Number of observations|262|262|262|262|223|223|223|221|264|264|264|264|261|261|261|258|
|r2_w|0.398|0.405|0.377|0.378|||||||||||||
|r2_b|0.008|0.001|0.008|0.008|||||||||||||
|r2_o|0.023|0.010|0.026|0.025|||||||||||||
|rho|0.850|0.884|0.833|0.835|||||||||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Sector information used as the interaction terms is taken from the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

62