rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Data Manipulation.

||ln(GDP)<br>(1)|ln(GDP)<br>(2)|ln(GDP)<br>(3)|ln(GDP)<br>(4)|ln(GDP)<br>(5)|ln(GDP)<br>(6)|
|---|---|---|---|---|---|---|
|ln(Lights)|0.168***<br>(0.046)|0.152***<br>(0.047)|0.046**<br>(0.019)|0.046**<br>(0.019)|||
|ln(NO2)|||||0.062*|0.061*|
||||||(0.033)|(0.034)|
|FiW|0.015|0.015|0.036|0.039|-0.601|-0.622|
||(0.021)|(0.029)|(0.034)|(0.034)|(0.411)|(0.416)|
|FiW<sup>2</sup>|-0.002|-0.003|-0.008|-0.008|-0.007|-0.007|
||(0.004)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|
|Manipulation: Lights|||||||
|ln(Lights)_×_FiW|0.008*<br>(0.004)|-0.000<br>(0.005)|-0.004**<br>(0.002)||||
|ln(Lights)_×_FiW: No Crisis||||-0.004**<br>(0.002)|||
|ln(Lights)_×_FiW: Crisis||||-0.005**<br>(0.002)|||
|Manipulation: NO2|||||||
|ln(NO2)_×_FiW|||||0.018<br>(0.012)||
|ln(NO2)_×_FiW: No Crisis||||||0.019<br>(0.012)|
|ln(NO2)_×_FiW: Crisis||||||0.022**<br>(0.011)|
|Observations|2,855|3,703|2,622|2,622|2,622|2,622|
|Countries|170|170|170|170|170|170|
|Years|92-08|92-13|05-20|05-20|05-20|05-20|
|Within R-Squared|0.744|0.732|0.807|0.821|0.821|0.822|
||Robust stan<br>*** p_<_0|dard errors<br>.01, ** p_<_0|in parenth<br>.05, * p_<_0.|eses<br>1|||

_Notes._ All specifications include country and year fixed effects, as well as a country-specific time trend. Sensor fixed effects included for lights when sample goes beyond 2013. Robust standard errors, clustered by country, are reported in parentheses. Column 1 replicates Martinez (2019), August 2019 version, Table 2, Column 6. Eight countries included in the previous 178 country baseline have no Freedom in the World index available in this time sample: Aruba, Anguilla, Cabo Verde, Cayman Islands, Montserrat, Serbia, Sint Maarten, Turks and Caicos Islands. Columns 4 and 6 include a dummy interaction for crisis years, which are designated to be 2009 and 2020. In both, an interaction of this dummy with FiW and FiW<sup>2</sup> is also included as an unreported control.

20
The set of explanatory variables are the same as in the previous regressions. Given the form of the dependent variable, the estimated coefficients will be interpreted as an effect on the odds of a direct flight service connecting cities _i_ and _j_ . As an example, based on the model in equation (3), a one unit increase in the bilateral _ALI_ leads to a change in the odds of a direct flight equal to _exp(γ)_ .

# **V. Results and Implications**

# _V.1     Country Level Estimates_

We start by estimating equation (1) using ordinary least squares and bilateral country-level data on the volume of on-flight air passenger traffic. The results are reported in Table 2. Column 1 considers a parsimonious specification with the ALI variable entering on its own. The coefficient of interest suggests that air services liberalization has a positive and significant effect on bilateral air traffic. A 1-unit increase in ALI leads to a 2.7 percent increase in air passengers.

All the control variables considered in the baseline model enter the regression with the expected sign and are generally highly significant. Larger distances between the two countries increase the demand for air traffic reflecting fewer alternative modes of transport. However the effect is increasing at a decreasing rate, with too large distances discouraging air travel because of the increasing travel costs. The economic size of each of the two countries, captured by their population and GDP levels, has a positive effect on air passenger travel, as does the volume of bilateral trade. Sharing a national border, having a common official language and common colonial ties also affects positively air travel between countries. This indicates that cultural, social and institutional similarities reduce travel costs, encouraging the crossborder mobility of people. Finally, the regression results suggest that a country’s land area reduces 17
- 60 -

# APPENDIX TABLE 3 (Continued)

# BAHAMAS TRANSPORT COST PROFILk FOR EXPORTS To THE UNITED STATES IN 1986

|||U.S.|BAHAMAS N|OMINAL FREI|GHT RATE|BAHAMAS<br>TARIFF|-OTHERS|$,NOMINAL FR|<br>EIGHT RATE|"OTHERS"<br>TARIFF|SHARE IN<br>BAHAMAS'|
|---|---|---|---|---|---|---|---|---|---|---|---|
|OES|TSUSA|<br>IMPORTS(S)|TOTAL|VESSEL|AIR|RATE|TOTAL|VESSEL|AIR|RATE|EXPORTS|
|79|6853277|<br>136,518|<br>0.20|0.00|0.20|6.00|3.12|2,07|3.37|5.57|0.031|
|s0|6859038|<br>10,574|<br>0.37|0.00|0.37|0.00|3.79|2.55|4.68|4.62|0.002|
|el|6859058|<br>27,779|<br>2.30|0.00|2,30|5.70|3.95|0.98|4.18|5.01|0.006|
|82|6923295|<br>948.494|<br>1.77|1.77|0.00|3.20|6.68|4.73|19.70|3.09|0.216|
|83|6944120|<br>110,000|<br>0.91|0.00|0.00|0.00|0.43|4.04|3.61|0.01|0.025|
|84|6946200|<br>28,229|<br>0.26|0.00|0.26|0.00|2.02|2.31|1.96|0.00|0.006|
|85|6961058|<br>160,000|<br>0.78|0.78|.00|1.90|9.07|9.90|0.00|1.97|0.036|
|86|7003530|<br>31,347|<br>2.79|2.79|J.00|8.50|4.83|4.37|6.35|8.49|0.007|
|87|7004544|<br>177,358|<br>3.15|3,15|0.00|10.00|5.70|4.24|7.85|10.01|0.040|
|88|7125200|<br>22,000|<br>0.82|0.00|0.82|0.00|0.70|0.64|0.71|0.00|0.005|
|89|7209400|<br>63,191|66.70|66.70|0.00|7.60|4.84|2.84|9.55|7.85|0.014|
|90|7650300|<br>24,824|4.44|0.00|4.44|0.00|0.89|4.27|0.85|0.00|0.006|
|91|7721500|21,736|14.35|14.35|0.00|5.30|11.31|<br>10.90|33.79|1.92|0.005|
|92|7728000|13,120|2.58|2.58|0.00|0.00|7.38|4.27|18,44|4.81|0.003|
|93|7912700|11.835|<br>6.67|0.00|6.67|0.00|4.89|2.70|5.82|0.19|0.003|
|94|8000035|7,168,416|<br>2.09|2.15|2.46|0.00|2.53|4.66|1.97|0.00|1.629|
|95|**9999500**|158,408|<br>0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.036|

OE1S ESCRiPTION

- 79 Special radio combinations

- 80 Electrical SwitChes. nspf wIth out an automatic overload or tripping mechanism

- 81 Printeo circuit boards, plastic Impregnated glass base, with less than 3 layers of conducting materials

- 82 Parts nspt for motor vehicles

- 83 Airplanes used or rebuilt,civil aircraft

84

- Aircraft and spacecraft parts,neccerrtIfled for use in civil aircraft (see headnote 3 In tsus)

- 85 Yachts or Pleasure boats, valued over 515,000 each, sail propelled, auxiliary powered, over 31 feet<sup>In length</sup> **86** For men, soled leatnher moccasins

- 87 Women's leather cement footwear nspf, valued over $2.50 per pair **88** Electrical measuring, checking, analyzing or automatically-controliling instruments and apparatus, and parts thereof, use I

- 89 Clock parts nspf

- 90 Paintings, pastels, drawings and sketchings, executed wholly by hand, original or not

- 91 Household articles of rubber or plastic 92 Handles and knobs,of rubber or plastics

- 93 Leather, other then patent leather, cut or shaped for converslon Into uppDrs

- 94 U.S. products returned after **export** without being advanced In value or improved In condition abroad,<sup>nes</sup>

- 95 Under S251 formal and Info^mal **ntries estimated**
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characteristics related to the probability of working from home have the same weight, it exploits more information than a binary approach.

We use four groups of tasks to assess jobs’ amenability to WFH. First, we use measures of physical intensity and manual work to capture tasks that are more likely to be location-specific—because they require handling large items or use specific equipment, for example—and cannot be done at home. Second, we use measures of F2F-intensive tasks such as those that involve supervision or contact with the public. Third, we create an index of ICT use at work, to reflect the fact that while some jobs may require substantial F2F intensity, some of such tasks can be carried out using ICT and do not necessarily have to be done in-person. Finally, and in contrast to existing studies, we also exploit information on having an internet connection at home as an important factor to determine the likelihood of a remote setup. This is important since workers in developing countries who may use ICT and have internet connectivity at the workplace, do not necessarily have access to the same resources at home. Another reason why we estimate the amenability—and not the fraction—of jobs that can be performed remotely for such a large set of countries is that several of the surveys were collected circa 2012, when internet connectivity was significantly lower than today. However, under the assumption that relative connectivity across countries or types of workers remained stable, our estimates can still be used to compare the WFH measures across these categories.<sup>3</sup> We find that the social distancing measures associated with COVID-19 may exacerbate the jobs divide that preceded the crisis. The jobs intensive in tasks that are amenable to WFH are more prevalent in richer countries, and among workers with high levels of education, in salaried employment and with access to social insurance. Low-income, self-employed, and informal workers are not only less likely to do their jobs when WFH is the only option, but also less likely to have access to income protection schemes if these are channeled through the existing social security infrastructure.

Our findings highlight the importance of social protection programs to safeguard the most vulnerable during the crisis. Efforts to reach informal workers are crucial since they are less likely to have access to existing social insurance benefits, and also to additional programs launched in response to the crisis through the social security or tax administration infrastructures. Our results also show the importance of fostering technology adoption to protect jobs while respecting social distancing measures. To a large extent, the between and within country divide in WFH amenability is driven by unequal access to ICT. These benefits of digital technologies should be considered by governments in developing countries when investing in broadband infrastructure.

> 3 In fact, the coefficient of correlation between the share of internet users by country in 2012 vs 2017 is 0.94 (own estimates based on data from World Development Indicators, WDI).

4
20 are in terms of the frequency of borrowing. Inconsistency would be a source of concern if the experience variables were measured employing

# amounts borrowed.

1"We have also estimated equation (4) with a sample that is constrained to include borrowers that have at least borrowed 10 times during 1968-81. The results are quite similar to the ones presented in Table 1. For example, for Xl, y is estimated to be 0.29 with a "t" value of 2.19. 13See Devlin (1977) for a discussion of the evolution of the market

# structure.

14The number of banks is obtained from the Banker (November 1983).
# **_5 Findings_**

# _5.1 Sample Characteristics_

The characteristics of the sample are described in Table 1 along with tests for the equality of means across rounds. The rural communes in the sample have a poverty rate in 2004 (based on poverty mapping) between 27 percent and 32 percent. This rate, which is the forcing variable, is 29.6 percent on average for all the communes. For the INDH-treated communes, the average is 30.9 percent, compared to 28.6 percent for the control communes. The mean difference between the areas is significant and since the poverty rate estimate is based on the 2004 poverty map, it remains the same for all three waves.

The average household size rose from 6.0 in 2008 to 6.5 in 2011, and then fell to 5.8 in 2013. Treated communes had higher household sizes in all rounds, with significant differences in 2011 and 2013. This result is driven in part by more children (individuals under 18 years of age) per household in the target communes, with significant differences in 2011 and 2013. The number of adults is also higher in treatment communes in all rounds, and increasingly so, with significant differences in 2013. Due to the differences in household size, we present all our outcomes in per capita terms. The age of the head of the household tends to rise slightly over time, with a significantly lower figure in INDH communes in 2011 and 2013. Women-headed households represent 6–9 percent of the sample and there is no difference by treatment status. In terms of the marital status of the head of the household, almost 9 out of 10 are married, with no significant differences by treatment. Nearly 68 percent of heads of household in the sample are illiterate. There are a few significant differences between the treated and the nontreated communes with regard to educational level of the head of the household, specifically a slightly greater share of heads with less than complete primary in control regions. Since we are using a p < 0.10 level of significance for testing, the few significant differences across gender, marital status, and education could be due to chance.

9
27

**Table 5**

Maximum likelihood cointegration rank test statistics _fl_

|Null hypothesis|Likelihood ratio|95% Critical value bl|
|---|---|---|
|R=O|128.4|127.9|
|R•1|86.9|92.3|
|Rs2|56.9|62.2|
|Rs3|29.6|37.1|
|Rs4|7.5|11.0|

- Notes: **a/** Derived from a vector autoregression with two lags of each variable, and including also a linear trend in the cointegration space.

_hb_ Adjusted for small-sample bias, following Cheung and Lai (1993).
flow systems after they are transferred to the farmers, probably because they afford their users a greater degree of control and can respond to irrigation demands much better than storage or surface diversions schemes.

Thus, several design features of JCLISs suggest that these systems may continue to operate successfully under farmer management. Their long-term success, however, requires all water user groups to create a system of regular irrigation fee collection. Increasing the capacity utilization of these capital-intensive assets and evolving fair norms to manage water scarcity in the summer season are other key steps needed to improve the performance and the sustainability of JOHAR irrigation systems.

# **7. The way forward**

The government of Jharkhand set an ambitious goal of increasing the gross irrigated area in the state from 0.5 million ha to 0.8 million ha by 2021. Solar powered community irrigation systems created by JOHAR are a capital intensive but environmentally sustainable way to expand the area irrigated. Despite a sharp decline in the cost of solar panels over the last few decades (Chawla, Aggarwal, and Dutt, 2020), solar pumps are still quite expensive, especially if they are installed under a government program like JOHAR (Kishore, Shah, and Tiwari, 2014 **)** . However, even with high capital outlays, the amortized cost of irrigation from solar pumps is significantly lower than diesel pumps (Kolhe et al 2002; Odeh et al 2006; Meah, et al., 2008; GIZ, 2013). The sharp increase in the price of diesel in recent years tilts the balance further in favor of solar pumps.

The ownership and use of grid connected electric pumps is rising in Jharkhand and electric pumps are cheaper than solar pumps even if farmers were to pay the full cost of electricity. Most farmers in our sample paid highly subsidized tariffs or no tariffs for electricity used for irrigation.<sup>5</sup> The improvement in grid power supply in rural Jharkhand poses both an opportunity and a challenge for expanding access to affordable irrigation in the state.

If the power supply becomes more reliable, then the government can install electric pumps to power the community lift irrigation systems. The capital cost/ha of the command area created will be much lower for electric pumps. Charging a reasonable price for electricity will also create a significant variable cost of irrigation and incentivize more sustainable use of water. On the other

> 5 Many connections were illegal. Farmers often used their domestic power supply to irrigate their crops.

29
Krueget,yA.B. and M. Lindahl. 1998. "Education for Growth: Why and for Whom?" (mimeo).

- Lambropoulos, H. and E. Karadjia. 1999. "The Financing and the Economics of Higher Education in Egypt." (mimeo)

- Lassibille, G. and Navarro Gomez, L. 1998. "The Evolution of Returns to Education in Spain 1980-1991." _Education Economics_ 6(1).

- Liu, Z. 1998. "Earning, Education and Economic Returns to Urban China." _Economic Development and Cultural Change_ 46(4): 697-725.

- Lucas, R.E. 1988. "On the Mechanics of Economic Development." _Journal of Monetary Economics_ 22: 3-22.

- Maani, S.A. 1996. "Private and Social Rates of Return to Secondary and Higher Education in New Zealand: Evidence from the 1991 Census." _Australian Economic Review_ 1:82-100.

- Mason, A. and S. Khandker. 1997. "Household Schooling Decisions in Tanzania." World Bank (mimeo).

- Magoula, T. and G. Psacharopoulos. 1999; "Schooling and monetary rewards in Greece: an Overeducation False Alarm." _Applied Economics_ 31: 1589-1597.

- Maluccio, J. 1998. "Endogeneity of Schooling in the Wage Function: Evidence from the Rural Philippines." FCND Discussion Paper No. 54, Washington, DC.

- Menon, M. 1995. "Factors influencing the demand for higher education in Cyprus." Doctoral Dissertation, University of London.

- Miller, P., C. Mulvey and N. Martin. 1995. "What do Twins Studies Reveal About the Economic Returns to Education? A Comparison of Australian and U.S. Findings." _American Economic Review_ 85(3): 586-99.

- Mincer, J. 1974. _Schooling, Experience, and Earnings._ New York: National Bureau of Economic Research.

- Mokitimi, N.R. and W.L. Nieuwoudt. 1995. "Off-farm wage returns to education: Evidence from Lesotho." _Development Southern Africa_ 12(6): 839-849.

- Moock, P., H. Patrinos and M. Venkataraman. 1998. "Education and Earnings in a Transition Economy (Vietnam)." World Bank Policy Research Paper 1920.

- Mora, J-G. 1999. "Socioeconomic Background, Schooling, and Monetary Rewards in Spain." University of Valencia (mimeo).

9
3 an economy-wide mandated minimum wage. In many developing countries, unionization and government intervention are responsible for greater distortions. This intervention normally affects wage setting only in the formal sector of the economy, because the extreme atomization of firms makes unionization and enforcemer'. of government regulation very difficult in the informal sector.

In a world in which unions and the government are partly responsive to market conditions, the size of the wage distortion that results in the formal sector (the premium between the actual and the notional or market clearing wage) may be endogenous and dependent on variables such as relative prices, real income, and the like. The premium increases with increasing unions' market power, but it does not necessarily become less responsive to changes in market conditions. Whether this premium becomes Aess sensitive in response to changes in market conditions as labor markets become more distorted is an important question. The effectiveness of a nominal devaluation as an instrument for inducing a real devaluation decreases as distortions in the labor market increase if, as a result, the wage premium becomes less responsive to market conditions.

In considering the consequences of labor market distortions for the real exchange rate, this paper explores two hypotheses: (1) an increase in labor market distortions leads to an appreciation of the real exchange rate, and (2) labor market distortions diminish the elasticity of response of the real exchange rate to a nominal devaluation. We evaluate the quantitative importance of the first hypothesis and parametrically test the second hypothesis for the case of four Latin American countries, namely, Argentina, Chile, Colombia, and Uruguay. In doing this we exploit what is largely an original data set for Labor market variables constructed from primary country data sources.

We first present the conceptual model, and then conduct a comparative static analysis to help interpret the empirical results. The estimating model
specification except that we omit the school fixed-effects and cluster the standard errors at the family level.

Finally, we use one last specification to estimate what the attendance and enrollment rates of students who received the treatment would have been without the treatment.<sup>10</sup> We estimate these counterfactuals by modeling the behavior or students in our control groups using only the available baseline demographic characteristics. For treated students, we use their baseline characteristics and the coefficients from our regressions on the control group to project what these students’ would have done had they not been treated.

Specifically, we estimate the following model using only the registered children that did not receive the treatment: The model is estimated using ordinary least squares, and the coefficients and variables are the same as in Equation 2. The only exception, of course, is the omission of the treatment dummies. This equation highlights the fact that this proxy measure is only a linear combination of demographic variables, and it contains no new information.

# **IV. External validity, baseline balance and attrition**

We proceed as follows in this section. First, we use the available data from the SISBEN survey to compare the individuals that registered for the program to those who did not and to check comparability between the two localities. Second, for those individuals found at baseline, we compare the students assigned to each research group to ensure that the research groups are balanced at baseline. To make sure that the groups did not become unbalanced due to attrition, we then compare the distribution of students who failed to provide a follow-up survey in each research group. Once we have verified that the groups are indeed still balanced, we then estimate the results of the treatments on the various outcome variables in Section 5.

> 10 Ideally, we would have collected attendance rates of children prior to the randomization.  However, we could not have collected this information ourselves because, until the registration process was complete, we had no way of knowing which of the 515,885 eligible students would register.  We tried to collect historical attendance rates through the teachers’ records, but these records were too often incomplete and when complete, inconsistently kept.

- 16 -