rafmacalaba/datause-encoder-data huggingface dataset:
Berge, Lars, Kjetil Bjorvatn, Simon Galle, Edward Miguel, Daniel Posner, Bertil Tungodden and Kelly Zhang (2020). “Ethnically biased? Experimental evidence from Kenya.” _Journal of the European Economic Association_ 18(1), 134-164.

- Betts, Alecander, Maria Stierna, Naohiko Omata and Olivier Sterck (2021). “Social Cohesion and Refugee-Host Interactions: Evidence from East Africa.” _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group.

- Bertinelli, Luisito, Rana Comperpay and Jean-François Maystadt, J. (2021). “Refugee, Diversity and Conflict in Sub-Saharan Africa.” _Unpublished Working Paper._ Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts” Series. Washington, DC: World Bank Group.

- Blair, Christopher, Guy Grossman and Jeremy Weinstein (2021). Liberal Displacement Policies Attract Forced Migrants in the Global South. _American Political Science Review,_ 1-8. doi:10.1017/S0003055421000848

- Blattman, Christopher and Jeannie Annan (2016). “Can Employment Reduce Lawlessness and Rebellion? A Field Experiment with High-Risk Men in a Fragile State.” _American Political Science Review_ 110(1), 1–17.

- Blattman, Christopher and Laura Ralston (2015). “Generating Employment in Poor and Fragile States: Evidence from Labor Market and Entrepreneurship programs.” Available at SSRN: <u>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2622220</u>

- Blattman, Christopher, Nathan Fiala and Sebastian Martinez (2014). “Generating Skilled SelfEmployment in Developing Countries: Evidence from Uganda.” _Quarterly Journal of Economics_ 129(2), 697 - 752.

- Brück, Tilman, Neil Ferguson, Valeria Izzi and Wolfgang Stojetz (2021). “Can Jobs Build Peace?” _World Bank Research Observer_ 36(2): 234-259.

- Burger, Jerry, Alison Ehrlichman, Neda Raymond, Janet Ishikawa and Joanna Sandoval (2006). “Reciprocal Favor Exchange and Compliance.” _Social Influence_ 1: 169–184.

- Craig, Maureen and Jennifer Richeson (2014). “On the Precipice of a “Majority-Minority” America: Perceived Status Threat From the Racial Demographic Shift Affects White Americans’ Political Ideology.” _Psychological Science_ 25(6), 1189–1197.

- <mark>Darvas, Peter and Robert Palmer (2014). “D</mark> emand and Supply of Skills in Ghana: How Can Training Programs Improve Employment and Productivity <mark>?” World Bank Publications. Washington DC: World Bank Group.</mark>

- Dasgupta, Aditya, Kishore Gawande and Devesh Kapur (2017). “(When) Do Antipoverty Programs Reduce Violence? India's Rural Employment Guarantee and Maoist Conflict.” _International Organization_ 71(3), 605-632.

26
- Number of household members, the adult equivalent, number of children, number of pensioners, number of female members, number of unemployed members, number of selfemployed members, number of members working in the primary sector, number of members in part-time employment, number of members with primary, secondary, and tertiary education, number of members attending an educational facility, number of foreign citizens in the household

**When combining the harmonized variables of both datasets, some significant differences between**

**both surveys become evident.** Table 3 compares the harmonized variables of both datasets to each other. The table reveals that there are some slight deviations in most of the variables and some more important ones in the case of several of the household characteristics, which is expected given that the underlying sampling designs differ from each other.<sup>19</sup> While both datasets should be representative of the underlying population in Bulgaria by design, the table demonstrates how difficult it is to achieve this in practical applications. The differences in the harmonized variables could also be related to the fact that a full harmonization between both datasets is challenging, given that some variables rely on different underlying classifications, such as in the case of the educational variable, for example. We also compare the similarity in the distribution of potential matching variables across surveys. To this end, we plot histograms and compare them to each other.<sup>20</sup> Appendix 3 presents the resulting graphs, which confirm that there are some important differences. The systematic differences in household characteristics between both datasets are an important caveat in the analysis to come.

19 First, the EU-SILC consists of a significantly larger sample than the HBS. Moreover, the time dimension of both surveys differs. In addition, the survey design of the HBS consists of strata and primary sampling units while the one of the EU-SILC consists of primary and secondary sampling units.

20 While some researchers propose additional methods, such as calculating the Hellinger Distance (HD), there is no fixed rule regarding what degree of similarity would be suitable in the context of statistical matching, which is why we abstract from these methods (Serafino and Tonkin, 2017).

21
# **1 Introduction**

Existing evidence suggests that air pollution has negative effects on human health, especially for children and elderly adults.<sup>1</sup> A recent literature has also started to document negative effects among healthy adults in the form of reduction of labor productivity (Graff-Zivin and Neidell, 2012; Adhvaryu et al., 2014; Chang et al., 2014; Li et al., 2015) and hours worked (Hanna and Oliva, 2015).<sup>2</sup> These findings, from the U.S., India, and Mexico, point to changes in labor outcomes as relevant pollution externalities.

In this paper we focus on the causal relationship between air pollution and hours worked. The empirical analysis of this relationship is complicated by two empirical challenges. First, there could be omitted variables that affect both air pollution exposure and the outcome of interest; which may bias the estimated response to air pollution. Second, households may move in response to air pollution according to their specific vulnerability introducing sorting bias.

These challenges have been addressed in a previous study with the help of an exogenous and sudden change in air pollution in Mexico City in the early nineties (Hanna and Oliva, 2015). This research design, however, limits the extent to which one can explore non-linearities in relationship between air pollution and hours worked as well as the mechanisms at play. This paper takes advantage of panel data on hours worked from Lima, Peru, to address the empirical challenges described above while preserving enough exogenous variation in pollution exposure to uncover the non-linear relationship between pollution and hours worked.

By comparing the shape of the dose-response function across different household compositions, we shed light on how age-specific vulnerabilities to air pollution can result in different labor supply responses to air pollution across households. We also explore whether intra-household substitution in labor supply can dampen the effects at the household level. Finally, the research design also allows us to focus on a somewhat newly monitored pollutant, fine particulate matter (PM2 _._ 5), which has been shown to have some of the most adverse health effects in the medical literature (U.S. EPA, 2009), but for which there is relatively little economic research.<sup>3</sup>

> 1For a review of this literature see Currie et al. (2014) or Graff-Zivin and Neidell (2013). There is also evidence that pollution can affect human capital. For instance, several studies link pollution to poorer school and cognitive outcomes (Almond et al., 2009; Lavy et al., forthcoming), and to increases in school absenteeism (Ransom and Pope, 1992; Gilliland et al., 2001; Park et al., 2002; Currie et al., 2009a).

> 2Previous work using data from U.S. cities also finds a positive and significant correlation between air particulates and work loss (Ostro, 1983; Hausman et al., 1984).

> 3Most of the economic literature focuses on total suspended particulates (Chay and Greenstone, 2003; Sanders, 2012), carbon monoxide (Currie and Neidell, 2005; Currie et al., 2009a; Currie et al., 2009b), and ozone (Lleras-

2
-28-

# **REFERENCES:**

Agenor, Pierre-Richard and P. Montiel (1996) Development Macroeconomics, Princeton University Press, Princeton, New Jersey.

Bacha, Edmar L. " A Three-Gap Model of Foreign Transfers andthe GDP Growth Rate in Developing Countries", paper presented at an IMF Seminar, 1989.

Blanchard, Olivier (1990), 'Suggestions for a new set of fiscal indicators', OECD, Department of Statistics Working Paper, No. 79, April.

Blejer, Mario and Cheasty, Andreana, Eds. How to Measure the Fiscal Deficit. Washington, D.C.: The International Monetary Fund, 1993.

- Boskin, Michael. "Taxation, Saving and the Rate of Interest." Journal of Political Economy, vol. 86, April 1978.

- Buiter, Willem H. "A Guide to Public Sector Debt and Deficits." Economic Policy, November 1985, pp. 14-79.

Buiter, Willem H. (1990) Principles of Budgetary and Financial Policies, MIT Press, Cambridge, Maassachusetts.

- Bulow, Jeremy and Rogoff, Kenneth. "Sovereign Debt: Is to Forgive to Forget?" American Economic Review, 1989, p. 79.

- Cline, William R. International Debt: Systemic Risk and Policy Response. Washington, D.C.: Institute for International Economics, July 1984.

- Cohen, Daniel. "How to Evaluate the Solvency of an Indebted Nation." Economic Policy, November 1985, pp. 140-167.

- Cohen, Daniel. Monnaie, Richesse, et Dette des Nations. Paris, France: Centre National de la Recherche Scientifique, 1986.

- Diamond, Peter. "National Debt in a Neoclassical Growth Model." American Economic Review, vol. 55, no. , 1965.

Dinh, Hinh T. (1995), 'Fiscal solvency and sustainability in creditworthiness analysis', World Bank, Country Creditworthiness Division, Ma;y.

- Drazen, Allan and Helpman, Elhanan. "Stabilization With Exchange Rate Management." Quarterly Journal of Economics, 1987, p. 52.

- Easterly. William and Rebelo, Sergio. Fiscal Policy and Economic Growth: An Empirical Investigation. NBER Working Paper no. 4499, National Bureau of Economic Research, October 1993.
Deaton, Angus, and Salman Zaidi. 2002. _Guidelines for constructing consumption aggregates for welfare analysis._ The World Bank., Washington, D.C: Living standards measurement study (LSMS) working paper; no. LSM 135.

- Deb, Saswata, Saswata Deb, Peter C. Austin, Peter C. Austin, Jack V. Tu, Jack V. Tu, Dennis T. Ko, et al. 2016. "A Review of Propensity-Score Methods and Their Use in Cardiovascular Research." _Canadian Journal of Cardiology_ 32 (2): 259-265. Accessed 9 2, 2019. https://ncbi.nlm.nih.gov/pubmed/26315351.

Dehejia, Rajeev H, and Sadek Wahba. 1999. "Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs." _Journal of the American Statistical Association_ 94 (448): 1053-1062.

Dehejia, Rajeev. 2005. "Practical propensity score matching: A reply to Smith and Todd." _Journal of Econometrics_ 125 (1): 355-364. Accessed 9 2, 2019. https://sciencedirect.com/science/article/pii/s0304407604000831.

Dehejia, Rajeev, and Sadek Wahba. 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies." _The Review of Economics and Statistics_ 84 (1): 151-161. Accessed 9 2, 2019. http://mitpressjournals.org/doi/pdf/10.1162/003465302317331982.

Djamaluddin, Sartika. 2015. _How to Ease the Burden of Poor Household? The Role of Raskin Program._ Working Papers in Economics and Business, Faculty of Economics and Business, University of Indonesia. https://ideas.repec.org/p/lpe/wpecbs/201501.html.

Gentilini, Ugo. 2007. _Cash and food transfers: A primer._ Rome: World Food Programme.

Gentilini, Ugo. 2016. "Revisiting the 'cash versus food' debate: New evidence for an old." _The World Bank Research Observer_ 31 (1): 135–167.

Gupta, Prachi, and Bihong Huang. 2018. _In-kind transfer and child development: Evidence from subsidized rice program in Indonesia._ ADBI Working Paper Series, Tokyo: Asian Development Bank Institute (ADBI). http://hdl.handle.net/10419/190247.

Heckman, James J, Robert J Lalonde, and Jeffrey A Smith. 1999. "The Economics and Econometrics of Active Labor Market Programs." Edited by Orley C Ashenfelter and David Card. _Handbook of Labor Economics._ Amsterdam: Elsevier. 1865-2097.

Heckman, James J., and Petra E. Todd. 2009. "A Note on Adapting Propensity Score Matching and Selection Models to Choice Based Samples." _Econometrics Journal_ 12 (1): 230-234. Accessed 9 2, 2019. https://nber.org/papers/w15179.pdf.

Heckman, James J., Hidehiko Ichimura, and Petra E. Todd. 1998. "Matching as an Econometric Evaluation Estimator." _The Review of Economic Studies_ 65 (2): 261-294. Accessed 9 2, 2019. http://uh.edu/~adkugler/heckmanetal.pdf.

Heckman, James J., Hidehiko Ichimura, Jeffrey A. Smith, and Petra E. Todd. 1996. "Sources of selection bias in evaluating social programs: An interpretation of conventional measures and evidence on the effectiveness of matching as a program evaluation method." _Proceedings of the National_ 20
||Ov|erall|Co|ntrol||HWWS|||TSSM|||HWWS+TSS|M|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|N|Mean|N|Mean|N|Mean|p-value|N|Mean|p-value|N|Mean|p-value|
|_HH Income Sources at Baseline_||||||||||||||
|Most Important income: Employment (Paid Employee)|3619|0.03|919|0.03|900|0.03|0.57|880|0.04|0.15|920|0.02|0.32|
|Most Important income: Employment (Self-Empoyment with<br>employees)|3619|0.06|919|0.06|900|0.06|0.91|880|0.05|0.55|920|0.06|0.85|
|Most Important income: Not-employed (Remmitances)|3619|0.00|919|0.00|900|0.00|0.55|880|0.00|0.98|920|0.00|0.31|
|Most Important income: Self Employed Agricultural|3619|0.74|919|0.74|900|0.72|0.68|880|0.75|0.72|920|0.76|0.64|
|_Latrine Type at Baseline_||||||||||||||
|Flush/pour latrine|3618|0.03|919|0.02|900|0.05|0.05|879|0.02|0.82|920|0.04|0.34|
|No toilet facilities (open defecation)|3618|0.12|919|0.17|900|0.13|0.48|879|0.06|**0.01**|920|0.09|0.09|
|Pit latrine with slab or VIP|3618|0.84|919|0.80|900|0.81|0.84|879|0.91|**0.01**|920|0.86|0.23|
|_Axcces to Water_||||||||||||||
|Main source of drinking water comes from piped water|3619|0.14|919|0.08|900|0.22|**0.01**|880|0.12|0.23|920|0.14|0.12|
|Main source of drinking water comes from a well or borehole|3619|0.32|919|0.37|900|0.26|0.06|880|0.31|0.39|920|0.34|0.62|
|Main source of drinking water comes from surface water|3619|0.40|919|0.42|900|0.38|0.54|880|0.38|0.54|920|0.42|0.98|
|Is the water source covered|3127|0.22|849|0.27|706|0.25|0.88|777|0.19|0.18|795|0.16|0.08|
|Number of person trips HH makes to fetch water per day|3477|2.77|886|2.75|838|2.66|0.65|857|2.85|0.59|896|2.81|0.73|
|Number of times out of the last 10 attempts water has not been<br>available|3617|0.30|919|0.25|899|0.33|0.39|880|0.27|0.81|919|0.35|0.28|
|HH stores water|3619|0.93|919|0.91|900|0.94|0.18|880|0.95|0.12|920|0.93|0.55|
|HH treats their water|3619|0.40|919|0.37|900|0.39|0.80|880|0.41|0.46|920|0.44|0.22|
|_Assets owned by HH at Baseline_||||||||||||||
|Wealth index at Baseline|3619|0.00|919|0.09|900|0.13|0.13|880|-0.34|0.35|920|0.10|0.60|
|Household listens to the radio regularly|3619|0.61|919|0.55|900|0.60|0.30|880|0.64|0.07|920|0.64|**0.04**|
|Total number of assets|3619|44.18|919|44.44|900|43.95|**0.03**|880|44.09|0.17|920|44.22|0.35|
|Another House|3615|0.15|918|0.14|900|0.16|0.36|878|0.17|0.19|919|0.14|0.86|
|Radio/CD/Cassette Player|3616|0.68|919|0.65|900|0.67|0.55|878|0.71|0.07|919|0.67|0.51|
|TV|3617|0.03|919|0.03|900|0.04|0.49|878|0.03|0.99|920|0.02|0.46|
|Bicycle|3617|0.63|919|0.62|900|0.60|0.59|878|0.62|0.87|920|0.67|0.24|
|Motorcycle|3616|0.05|919|0.04|900|0.07|0.13|878|0.04|0.94|919|0.06|0.25|

39
Table 2: Calibrated Parameter Values from Status-quo Approach

|Simulation|_gA_|_gh_|_gN_|_γ_0|_SW,_0|_SE,_0|PPP Real Income (2011 USD)|
|---|---|---|---|---|---|---|---|
|Pred<br>~~7~~0|0.8%|2.2%|2.6%|0.27|0.54|0.56|1,466 (1960s)|
|Pred<br>~~8~~0|1.2%|1.9%|1.7%|0.37|0.61|0.59|3,844 (1970s)|
|Pred<br>~~9~~0|3.7%|1.7%|1.2%|0.35|0.68|0.61|7,688 (1980s)|

Note. ” _gAL_ ”: Annual growth rate of productivity of labor-augmenting technology, ” _gh_ ”: Annual growth rate of human capital per worker, ” _gN_ ”: Annual growth rate of population, ” _γ_ 0”: Investment rate, ” _SW,_ 0”: Working-age population share, ” _SE,_ 0”: Labor force participation rate.

when the investment rate was 0.27, working-age population share was 0.54 and the labor force participation rate was 0.56.

Figure 9 compares the predicted paths of GDP per capita of the three simulations (similarly labeled as in Table 2), overlaid with the actual path (labeled as ”Actual”). This comparison illuminates important features of the LTGM as a simulated prediction device as follows.

First, notice that the ”Pred ~~7~~ 0” simulation _under-predicts_ the GDP per capita as shown in Figure 9. It fits only the very beginning-of-period data, i.e., for the 1971-1973 period. The prediction diverges away below the actual one afterwards. This result is not a surprise, reflecting Figures 3, 6, and 7, which show that the investment rate, working-age population share, and labor force participation rate all increased during the 1960s, hence the 5-year average values underestimate the future values. Furthermore, the investment rate and the working-age population share further increased in the 1970s compared to the 1960s values. The investment rate got stabilized after the early 1980s, and the increase of the working-age population share also slowed down after the 1990s. The labor force participation rate continues to show increasing trend except the substantial dip during the 1977-1986 period. Furthermore, Korea’s population growth rate has fallen monotonically during the entire sample period from 3% in the 1960 to 0.4% in 2014. All these changes have increasing effects of GDP per capita, which are not captured by the current calibration method. Figure 4 illustrated the declining growth rate of human capital, particularly after the 1990s. Thus, current calibration method tends to over-estimate data, hence is different from our GDP per capita measure which is calculated from the ”rgdpna” in PWT 9.0. In Table 2, we use the ”rgdpe” measure to facilitate the cross-country comparison of development level.

23
_Table 3. Product harmonization across countries, health care-related items_

|**Country**|**Classification**<br>**scheme**|**Level of**<br>**observation**|**Total number of**<br>**categories in country**<br>**data set (at level of**<br>**obs)**|**Total number of**<br>**categories**<br>**overlapping with**<br>**master file (Ecuador)**|
|---|---|---|---|---|
|Ecuador|CPC - Clasificación<br>Central de<br>Productos|<br>Level 5 - CPC Level<br>5|222|222|
|Paraguay|UNSPSC - United<br>Nations Standard<br>Products and<br>Services Code|Level 4 - Descripción<br>ítem nivel4<br>(Commodity)|5122|217|
|Panama|UNSPSC - United<br>Nations Standard<br>Products and<br>Services Code|Level 4 - Nombre<br>Rubro (Commodity)|5122|217|
|Uruguay|SICE - Sistema de<br>Información de<br>Compras y<br>Contrataciones del<br>Estado|Level 5 - Descripción<br>Artículo|1097|166|
|Peru|CPC - Clasificación<br>Central de<br>Productos|Level 4 - Commodity|1763|203|
|Brazil<br>(federal)|Catálogo de<br>Materiais e Serviços|<br>Level 3 - Padrao|2445|181|
|Amazonas<br>(Brazil)|Catálogo de<br>Materiais e Serviços|<br>Level 3 -<br>Classification<br>Commodity|2211|195|
|Santa<br>Catarina<br>(Brazil)|Catálogo de<br>Materiais e Serviços|<br>Level 3 - Item<br>Classification<br>Description|5996|205|
|Costa Rica|UNSPSC - United<br>Nations Standard<br>Products and<br>Services Code|Level 5 (free text) -<br>DescProducto|692|92|

14
charged by the school (the higher the fee, the lower the subsidy per pupil) and not ability to pay.

At the secondary level, the government sponsors students to attend secondary schools. There could be several criteria for selecting students for sponsorship; it seems that family income is not the major consideration, allowing a substantial number of children of well to do families to be sponsored.

Table 8: Percent of family education expenses per child covered by subsidy (subsidy/expenses ratio), <u>by total family expenditure quintile and level of education (public schools only)</u>

|Quintile|Primary|Secondary|Tertiary|All levels|
|---|---|---|---|---|
|1st|98|103*|76|98|
|2nd|57|76|210*|67|
|3rd|89|56|120*|83|
|4th|45|60|118*|60|
|5th|56|36|37|42|
|All|54|43|58|51|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages

* In these cases the reported subsidy amount received exceeds the reported amount spent by the family on children’s education; furthermore some of the tertiary education quintiles the cell size is less than 5

Table 9: Percent of family education expenses per child covered by subsidy (subsidy/expenses ratio), <u>by total family expenditure quintile and level of education (private schools only)</u>

|Quintile|Primary|Secondary|Tertiary|All levels|
|---|---|---|---|---|
|1st|80|80|37|81|
|2nd|69|112*|63|82|
|3rd|47|29|-|37|
|4th|27|60|53|34|
|5th|24|20|19|22|
|All|36|40|25|36|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages

* In theses cases, the reported subsidy amount received exceeds the reported amount spent by the family on children’s education; furthermore some of the tertiary education quintiles the cell size is less than 5

17
Formally, a TVEM is defined as: where 0 1 are unknown dynamic coefficient functions that are assumed to be smooth over time ; 1 pp are the set of selected variables according to the shrinking dimension ββ (tt), ββ (tt), ... , ββ (tt) reduction technique. pp tt XX , ... XX The inference of the TVEM follows a two-step procedure. First, approximate the function of each coefficient over a small sub-interval with low-order polynomials. Therefore, within each subinterval, the functions are estimated with combinations of spline basis,<sup>6</sup> and estimation of ∙) is made by 2 + 1 quadratic penalized-spline basis functions, _P_ − _splines_ : ββ( KK+ Where are the specified inner knots. The coefficients of these truncated power functions represent the number of jumps in the estimation of jj jj ∙), which are relevant for the KK aa , bb (jj= 0,1, ... , KK+ 2) smoothness of the estimated functions. In other words, estimation of is ββ( transferred to the estimation of , respectively. Second, fit a TVEM by approximating the linear ii ββ (tt)(ii= 0,1, ... , pp) combination of these functions. jj jj aa , bb

# **B. Data**

In this paper, we use information from a database previously assembled by Iregui-Bohórquez, MeloBecerra, Ramírez-Giraldo, and Tribín-Uribe (2021), who examine the long-term transformation of the role of women in areas such as employment, education, fertility, and political participation in Colombia from 1905 to 2018. Since our main interest relies on female labor participation, we had to

> 6 A spline is a segmented function consisting of a polynomial function joined together so that the whole curve and its first and second derivatives are continuous.

6