rafmacalaba/datause-encoder-data huggingface dataset:
correlation disappears.

Table 5: Characteristics of those in a green job

|Model|OLS, y=ln(GTI)|
|---|---|
|Female|-0.082***<br>-0.060***|
||(0.014)<br>(0.014)|
|base=age 15-24||
|25-34|0.002<br>0.018|
||(0.020)<br>(0.019)|
|35-44|0.020<br>0.020|
||(0.020)<br>(0.019)|
|45-54|0.046**<br>0.046**|
||(0.022)<br>(0.020)|
|55-65|0.084***<br>0.084***|
||(0.024)<br>(0.023)|
|base=less than matric||
|Matric|-0.344***<br>-0.307***|
||(0.013)<br>(0.012)|
|Other tertiary|-0.449***<br>-0.418***|
||(0.019)<br>(0.018)|
|Degree|-0.475***<br>-0.399***|
||(0.015)<br>(0.015)|
|base=black/African||
|Coloured|-0.146***<br>-0.067***|
||(0.019)<br>(0.017)|
|Indian|-0.289***<br>-0.178***|
||(0.026)<br>(0.025)|
|White|-0.369***<br>-0.188***|
||(0.015)<br>(0.014)|
|Urban|0.155***<br>0.008|
||(0.016)<br>(0.014)|
|Constant|2.944***<br>3.390***|
||(0.040)<br>(0.041)|
|Industrydummies|No<br>Yes|
|Observations|31,638<br>31,637|
|R-squared|0.131<br>0.273|
|Robust standard errors in pare|ntheses *** p<0.01, ** p<0.05, * p<0.1|

OLS estimates, as per equation (3). The dependent variable is ln(GTI-strict). Sample includes employed individuals aged 15-65 who work in strictly green occupations. All models include survey quarter dummies and province dummies. Model 1 excludes industry dummies while model 2 is the full sample of those in strictly defined green jobs. Survey weights applied in all regressions. Source PALMS V3.3.

# 4.3 Is There a Wage Premium for Being in a Green Job?

There is a wage premium to green jobs. Table 6 presents regression results as per equation 4. Model 1, where we only include a strictly green job dummy, dummy variables for location 22
# **References**

- Alesina, A., Hohmann, S., Michalopoulos, S., & Papaioannou, E. (2021). Intergenerational Mobility in Africa. _Econometrica_ , _89_ (1), 1–35.

- Asher, S., Novosad, P., & Rafkin, C. (2021). Intergenerational Mobility in India: New Methods and Estimates Across Time, Space, and Communities. _Working Paper. Available at: http://paulnovosad.com/pdf/anr-india-mobility.pdf (accessed November 2021))_ .

- Black, S. E., & Devereux, P. J. (2011). _Recent Developments in Intergenerational Mobility_ (Vol. 4b).

- Card, D., Domnisoru, C., & Taylor, L. (2022). The Intergenerational Transmission of Human Capital: Evidence from the Golden Age of Upward Mobility. _Journal of Labor Economics_ , _40_ (S1), S39–S95.

- Celhay, P., Sanhueza, C., & Zubizarreta, J. (2010). Intergenerational Mobility of Income and Schooling: Chile 1996-2006. _Revista de Analisis Economico_ , _25_ (2), 43–63.

- Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States. _The Quarterly Journal of Economics_ , _129_ (4), 1553–1623.

- Corak, M. (2013). Income Inequality, Equality of Opportunity, and Intergenerational Mobility. _Journal of Economic Perspectives_ , _27_ (3), 79–102.

- Emran, M. S., Greene, W., & Shilpi, F. (2018). When Measure Matters: Coresidency, Truncation Bias, and Intergenerational Mobility in Developing Countries. _Journal of Human Resources_ , _53_ (3), 579–607.

- Emran, M. S., & Shilpi, F. (2021). Economic Approach to Intergenerational Mobility: Measures, Methods, and Challenges in Developing Countries. In V. Iversen, A. Krishna, & K. Sen (Eds.), _Social mobility in developing countries: Concepts, methods, and determinants._ Oxford University Press.

- Francesconi, M., & Nicoletti, C. (2006). Intergenerational Mobility and Sample Election in Short Panels. _Journal of Applied Econometrics_ , _21_ , 1265–1293.

- Haider, S., & Solon, G. (2006). Life-Cycle Variation in the Association between Current and Lifetime Earnings. _American Economic Review_ , _96_ (4), 1308–1320.

- Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S., Smith, N., & Verashchagina, A. (2007). The Inheritance of Educational Inequality: International Comparisons and Fifty-Year Trends. _The B.E. Journal of Economic Analysis Policy_ , _7_ (2).

31
|Table R.UkK1: Summary<br>St|atistic|s||
|---|---|---|---|
|E-filing<br>|1790<br>|o.20<br>|o.40<br>|
|Turnover<br>|1790)<br>|0 «6©2562.81<br>|10868.73<br>|
|Employrnent|1623|35,02|143.96|
|Productivity|1623|157.51|935.95|
|City|17390|o.99|1.28|
|Industry|1790|3.99|2.16|
|VAT|1790|0.66|o.4|
|PT|1790|0.50|0.50|
|PIT|1790|o.75|O.43|
|UT|1790|0.35|O.48|
|SFP|1790|0.90|0.30|
|LTO<br>|1790<br>|0.68<br>|0.46<br>|
|Quantity ofTaxes|17390|o.42|o.49|
|Tax Accounting Procedures|| 1790|0.34|o.47|
|Periodicity of submitting|1790|0.35|0.48|
|TOC (UAH)|1314|16015.83|24476.64|
|TAC (UAH<br>|L790<br>|16118.89<br>|233533.07<br>|
|TCC of FAS (UAH)|L790)|6SFRF.29|0| SPSL|
|TCC ofVAT {UAH}|1327|9272.55|13609.34|
|TAC (Hrs)|1790)|1392.57|1545.21|
|TCC of F&S (Hrs)|1790|252.57|263.38|
|TCCofVAT<br>(Hrs|1327|798.38|812.08|

7) are categorical variables Turnover is im thousands (1000).TOC is takal tax compliance cost, TAC is total tax accounting costs, P&S is filling and subritting tax forme and WAT is value added tax. UAH if the natianal currency 4nd Ar is time in heures.
governance, public investment may become a vehicle for dispensing political favors rather than acquiring productive assets.<sup>4</sup> In the case of infrastructure in Latin America, the accumulation of physical assets appears to track infrastructure investment fairly closely in empirical country studies of Brazil (Ferreira 2005a), Colombia (Suescún 2005), and Costa Rica (Bolaños 2005), although the quality of the fit varies across sectors.

In turn, a majority of empirical studies concludes that public capital raises output and/or growth. The literature is far from unanimous, however, and its conclusions appear to depend on the approach followed.<sup>5</sup> Studies using measures of physical infrastructure assets find significantly positive output contributions in the vast majority of instances, while those that measure public capital using cumulative investment flows tend to be less conclusive, likely for the reasons outlined in the preceding paragraph. In many cases, however, both approaches yield similar results; e.g., for Brazil, Ferreira and Araujo (2005) find significantly positive output effects using both financial and physical measures of public capital.

Moreover, even if wasteful public investment spending weakens the link between spending and outcomes, an across‐the‐board reduction in public investment will still result in cuts in productive infrastructure projects. Sacrificing such projects weakens the economy’s growth potential, and the right response is instead to protect high‐return projects from spending cuts.

To sum up, the international evidence raises the concern that fiscal discipline, as conventionally defined and enforced, excessively discourages public investment, in turn jeopardizing growth. In essence, fiscal discipline involves the application of rules targeting certain fiscal aggregates to keep them within limits prescribed by financial prudence. The question at issue is whether conventional rules and targets tend to bias fiscal discipline against public investment—or, more

> 4 Along these lines, Keefer and Knack (2006) show that, other things equal, across countries weaker governance is associated with higher public investment.

> 5 Easterly and Rebelo (1993) provide an early finding of a strong effect on growth of public spending on communications and transport. Romp and de Haan (2005) and Calderón and Servén (2007) survey the most recent empirical literature.

5
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|---|---|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|AFCC2/RI-Nile Cooperation for Results Project|Lars Anders Jagerskog|
|Tajikistan Public Sector AccountingReform Project|Hassan Aliev|
|Tajikistan Public Sector AccountingReform Project|Hassan Aliev|
|Tajikistan Public Sector AccountingReform Project|Hassan Aliev|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|Low Income CommunityHousingSupport Project|Sonya M. Sultan|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|BF-Youth Employment & Skills Development|Rebekka E. Grun|
|Mongolia: ImprovingPrimaryEducation Outcomes for the most v|ulnTungalagChuluun|
|Mongolia: ImprovingPrimaryEducation Outcomes for the most v|ulnTungalagChuluun|
|Mongolia: ImprovingPrimaryEducation Outcomes for the most v|ulnTungalagChuluun|
|Mongolia: ImprovingPrimaryEducation Outcomes for the most v|ulnTungalagChuluun|
|Mongolia: ImprovingPrimaryEducation Outcomes for the most v|ulnTungalagChuluun|
|Mongolia: ImprovingPrimaryEducation Outcomes for the most v|ulnTungalagChuluun|
|Mongolia: ImprovingPrimaryEducation Outcomes for the most v|ulnTungalagChuluun|
Last, we add to a growing literature on the role of information frictions in developing country labor markets (Abebe et al., 2021a,b; Abel et al., 2020; Bassi and Nansamba, 2021; Beam, 2016; Banerjee and Chiplunkar, 2022; Caria and Falco, 2022; Carranza et al., 2022). Specifically, we show that labor market entrants have false beliefs about formal wage growth. This is most closely related to Jensen (2010) who shows that secondary school graduates underestimate returns to education. These types of misconceptions are important since they can distort choices and reduce welfare.<sup>10</sup> While the reason for why people hold false beliefs and how to best correct them requires additional research, our results suggest that informing (young) people about events with distant benefits may not be effective if recipients heavily discount the future. In these settings, short-term incentives can offer a promising alternative to change behavior.

# **2 Background**

## **2.1 Youth unemployment in Mexico**

The Mexican labor force comprises of just over 50 million people (ENOE 2017). The unemployment rate prior to the COVID-19 pandemic was roughly 3.5% for the overall population and around 6% for youths (INEGI 2017). Among the employed population 56.5% of the overall population and 60.5% of youths work informally (INEGI 2017; IMJUVE 2017). Informal employment is the dominant entry point into the labor market for youths. Table 1 depicts transition probabilities between non-employment and formal / informal employment across quarterly panel data from the 2018 and 2019 National Labor Force Survey (ENOE) for youths aged 18 to 21. The figures estimate average transition probabilities from one quarter to the next, conditional on initial status type.

Youths without employment are almost three times as likely to be informally employed than formally employed in the following period (15.4% vs. 5.6%). At the same time, informal employment is less stable: informally employed youths are almost twice as likely to be unemployed in the following period than formally employed youth (26.4% vs. 15%). Finally, 10.6% of people transition from informal work to formal work. While this is larger than

> 10Another recent example is Alfonsi et al. (2022) who show that Ugandan job seekers hold unrealistically high wage expectations, which can be reduced by linking people to mentors with work experience. There are also examples of how misconceptions may be beneficial to workers. In a recent study, Bandiera et al. (2021) show that debiasing job seekers’ optimism about employment prospects lowers their job search and employment rates.

7
# **VI Results**

## **VI.A Original Experiment**

As described above, we begin by analyzing the effects of the early versus late treatment using the 1997 original experimental design that assigned 320 of 506 localities to receive government transfers as early as 1998 and all the way to the end of 1999. We estimate equations (1), (2), (3) and present the results in the corresponding panels A to C of _Tables 6 and 7_ . We look at durable assets, imputed non-durable (food, personal products, and clothing) expenditures, further to income and schooling, as outcomes of interest and to original and new households, respectively, as were defined in section IV. Our interest here is to analyze the durable expenditure behavior in line with the long-term perspective of the overall report and the original spirit of the program.

### **VI.A.1 Original Households**

_Table 6_ displays no significant differences for treated vs. control original households in most cases. The outcomes employed in the analysis are an aggregation (counting) of different durable goods, a different aggregation through prices for example would be problematic given the large variation in quality and vintage for durable goods. We therefore present a counting exercise on the following assets: (i) transportation, which refers to number of vehicles (cars, vans, motorcycles); (ii) entertainment, which includes TV, radio and sound equipment; (iii) kitchen supplies, which adds number of blenders, refrigerators, microwaves, gas stove or electric grills; (iv) basic housing services, which identifies having firms floor, electricity, drainage and water connection in the property; (v) IT assets, which refers to computers and cellphones; and (vi) IT and entertainment services, which include home phone, internet and pay-TV.<sup>17</sup> We note that as some of the IT and IT and Entertainment goods are not available in the baseline wave (1997) when we produce the analysis for such goods in the DiD and ANCOVA specifications we use as baseline measures the full aggregation of durables.

In Panel B of _Table 6_ we perform a testing strategy based on the excess growth of the durable goods in treated vs. control households; while in Panel C we present a standard ANCOVA specification, i.e. where we regress follow-up outcomes on treatment and control for baseline outcomes. The results suggest that in general there are no baseline differences, confirming that the initial randomization is still valid and that the random selection of households for the last follow-up or non-differential attrition rates for the most part, see the coefficients attached to the Treatment dummy. However, in some of the cases, the treatment dummy (outcome in 1997) displays significant, yet negative, differences. The differences resulting from the passing of time (2017 dummy) shows that access to the assets has greatly improved for all items, not surprisingly given that the two waves are 20 years apart, this signals a general improvement in

> 17A good-by-good analysis is also available upon request.

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# **_Households’ Access to Food and Food Markets_**

Besides the ultimate food insecurity experience, the HFPS for Ethiopia elicited responses related to households’ access to food and food markets. In particular, households were asked whether they were able to buy enough staple foods (e.g., _teff_ / _injera_ , wheat/bread, maize and cooking oil) in the previous week. These questions are important to capture households’ physical access to and affordability of food, and allow us to investigate the ways through which the conflict may have impacted food systems and livelihoods. On the one hand, the outbreak of the war has directly affected access to food by impeding the functioning of markets, especially in conflict hotspot areas. On the other hand, the war curtailed livelihood activities, leading to reductions in income and hence rendering food unaffordable. Thus, the outbreak of the war is expected to directly impact the accessibility as well as affordability of food. Table A5 shows pooled summary statistics of these measures and proxies for accessibility and affordability of food and food markets.

# **_Labor Market Participation_**

Both the 2019 face-to-face survey and the 2020–2021 phone surveys collected detailed householdlevel data on labor market participation in income-generating activities, including farming, nonfarm family businesses, and wage employment. The labor market outcomes for the 2019 LSMSISA survey were mostly collected in August and hence we refer to the baseline data as August 2019 round. The data on households’ participation in various livelihood activities are available for most rounds, but with slight differences across livelihood activities and rounds. First, the data on farming and non-farm activities are available for all rounds, but wage-related activities were dropped in some rounds (August 2020, February 2021, and May 2021) of the phone surveys. These wage-related data are available for two rounds after the outbreak of the war (December 2020 and January 2021), allowing us to assess labor market participation effects of the war in the immediate aftermath of the outbreak of the war. Second, there was a slight change in the reference period across rounds. In the August 2019 face-to-face survey collected the labor market participation information of household members in the last 7 days preceding the survey. But the reference period and framing of the questions were slightly changed during the phone surveys. Specifically, when the phone survey started in April 2020, respondents were asked to report on whether household members were able to perform farming and non-farm activities since the outbreak of the COVID14
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———. 2011b. “Argentina Extents Its Import-Export-Balance Policy to the Pharmaceutical Industry.” Global Trade Alert, London.

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1%E2%80%9D-policy-extended- refrigerators-washing -machines-cookers-and-toy.

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