rafmacalaba/datause-encoder-data huggingface dataset:
minimal and that only through transfers some Latin American countries achieve some redistribution through the fiscal system.

**Table 4 Macroeconomic results of a remittance shock and a payroll tax for Jamaica**

||Remittance<br>effect, percent<br>change from<br>initial<br>equilibrium|Tax effect, percent<br>change from<br>remittance shock|Total effect,<br>percent<br>change from<br>initial<br>equilibrium|
|---|---|---|---|
|Remittances|10.00|0.00|10.00|
|Total household income|2.04|1.16|3.22|
|Household labor income|1.43|2.20|3.67|
|CPI|0.54|-0.03|0.50|
|Real exchange rate|0.88|-0.08|0.80|
|Real GDP|-0.37|0.10|-0.26|
|Private consumption|1.50|1.19|2.71|
|Investment|-0.06|-2.58|-2.63|
|Exports|-2.70|0.05|-2.66|
|Imports|0.81|-0.06|0.75|
|Unskilled labor supply|-0.76|0.55|-0.21|
|Skilled labor supply|-0.84|0.40|-0.45|
|Unskilled wage|2.31|2.08|4.45|
|Skilled wage|2.26|1.99|4.29|
|_Memo_||||
|Absolute change in unskilled labor<br>supply|-3,822|2,761|-1,061|
|Absolute change in skilled labor<br>supply|-4,563|2,139|-2,424|

# **VI.  Conclusions**

In this paper we have reviewed the impact of workers’ remittances on the real exchange rate and concluded that surges in workers’ remittances appear to contribute to real exchange rate appreciations and in this regard our tests do not detect any differential impact for Latin America. Moreover, we have also explored whether the estimated appreciation would be consistent with the natural appreciation that one would expect in the real equilibrium exchange rate following a positive shock, or instead whether the observed changes are more likely to be driven by changes in the misalignment component. The findings of the paper would indicate that not all of the observed changes are consistent with the equilibrium changing according to the new fundamentals.

Against this background a natural question that arises is what policy makers can do about that real appreciation and therefore about the potential loss in international competitiveness that may come with large remittance flows to the region. We now discuss these issues.

21
<!-- Start of picture text --> Figure 2: Quantity and Quality of Education Data<br>Panel A: Expected Years of School By Age 18<br>Expected Years of School<br>BDI MWISLEMOZCODMDGGNBCOMTGOETHGINGMBHTIAFGZWEUGABENKIRTLSSLBYEMNPLTZASENLSOCMRVUTKENTJKBGDKHMSTPTUVKGZGHAMHLZMBCIVPNGSDNCOGPSEHNDNICPAKMDANGATONWSMINDVNMMMRLAOBOLCPVGTMGUYPHLMARJAMAGOBTNSWZSLVFJIBLZPRYARMUZBXKXUKRGEOVCTDMAJORLCAEGYTUNIDNECUBIHLKANAMCUBALBPERMNGZAFGRDNRUCOLDZAIRQLBNBRADOMMKDSRBCHNVENSURGABBRBTHAPLWCRIAZEMNEBWAMEXIRNBLRBGRMUSARGTURURYPANCHLBHSATGROUTKMRUSHRVKAZMYSLVAGRCSYCPOLVGBHUNPRTESTLTUCYPTTOSVKSVNCZEISRMLTPRINZLESPJPNITAKORABWOMNFRAFINGBRBHRCANAUSTWNBEDNKDEUSWEISAUTSAUNLDHKGUSA L SMRBMUCHEKWTIRLBRNNORARESGPMACLUX QAT<br>CAF BFARWA MRTSYR<br>NER MLI DJI<br>TCD<br>LBR SSD<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>Panel B: Harmonized Test Scores<br>Harmonized Test Scores<br>SGP<br>JPNKORTWNHKG<br>VNM CUBSRBBGRRUSHRVKAZLVAPOLHUNESTPRTLTUCYPSVKSVNCZEISRNZLESPITAFRAFINGBRCANAUSBELDNKDEUSWEISLAUTNLDUSACHEIRLNOR MACLUX<br>UKR<br>BDI KENTJKKHMKGZ MDAMMRSWZARMGEOECUBIHALBMNGCOLCHNVENGABTHAAZECRIMNEMEXIRNMUSARGTURURYCHLROUMYSGRCSYCTTOMLTOMNBHR ARE QAT<br>LBRMWISLEMOZCODMDGCOMTGOETHGINGMBHTIBFARWATCDAFGSSDZWEUGABENKIRTLSSLBYEMNPLTZASENLSOCMRVUTBGDMRTTUVZMBCIVPNGSDNSYRCOGPSEHNDNICPAKNGATONINDLAOGTMGUYPHLMARJAMAGOSLVPRYXKXJORTUNEGYIDNLKANAMPERZAFDZAIRQLBNBRAMKDDOMBWAPAN SAUKWT<br>NER MLI GHA<br>6 8 10 12<br>Log Real GDP Per Capita at PPP<br>Note: Dataset version 21 Sept 2018<br>14<br>12<br>10<br>Years<br>8<br>6<br>4<br>600<br>500<br>Test Scores<br>400<br>300<br><!-- End of picture text --> Notes: Expected years of school are calculated using repetition‐adjusted enrollment rates by school level to proxy for age‐specific enrollment rates up to age 18. Enrollment rates are taken from the UNESCO Institute for Statistics, and extensively revised/updated/expanded with estimates provided by World Bank staff. Harmonized test scores are taken from Patrinos and Angrist (2018) and are measured in TIMSS‐equivalent units, i.e. a mean of 500 and a standard deviation of 100 across students in OECD countries. Real GDP per capita adjusted for differences in purchasing power parity is taken from the Penn World Tables (Feenstra et. al. (2015)) , with missing countries filled using data from World Bank estimates of GDP at PPP. Graph shows the most recent data for all countries.

11
reservations were consistently bundled into a package of reforms implemented by states (e.g., alongside large independent investments in women’s education), the border approach will not be able to differentiate the specific role of the reservations.

We further investigate the channels through which this entrepreneurship flourishes. The heightened start of women-owned establishments is concentrated in industrial sectors in which women have traditionally owned establishments. Equally as important, the greater entrepreneurship is mostly concentrated in household-based enterprises, rather than establishments opening as independent facilities.

We then discuss the potential channels through which this effect occurs, providing both quantitative and qualitative evidence. We do not find much evidence that the entrepreneurship is linked to changes in reporting or to greater access to government-sponsored contracts and business. Likewise, improved financing conditions appear to play a modest role, as the increase in female-owned loan-holding establishments is weaker than the total effect registered and suggests that financing accompanied the increased entry rather than caused it. We then discuss how entry patterns could be consistent with increases at the local level of public goods and infrastructure favored by women (e.g., Pande 2003, Besley et al. 2004, Chattopadhyay and Duflo 2004a,b) or greater aspirations of women towards entrepreneurship (e.g., Beaman et al. 2012). We discuss how these stimuli could have produced the industry patterns observed, to the extent that women entrepreneurs built upon sectors where women have traditionally been active (e.g., Ghani et al. 2013a) and at a household-based scale of operation that was feasible to achieve.

This study contributes to three strands of academic literature. Most directly, our work connects to prior studies of the impact of political reservations for India and women’s economic outcomes. To our knowledge, we are the first in this literature to quantify the entrepreneurship effects in a panel setting that uses state-level variation in implementation timings for identification, with Beaman et al. (2010) providing related evidence using micro-data from West Bengal.<sup>1</sup> Second, our work builds upon prior studies of gender differences in entrepreneurship<sup>2</sup>

> 1 Beaman et al. (2010) show an increase in the number of self-help groups with their own bank accounts after the implementation of reservations, and that these groups had on average larger bank accounts. To the extent to which these self-help groups are associated with more entrepreneurship, this increase would be consistent with our findings. We further discuss this study below.

4
services industry, as they are the major source of jobs. In addition to using the �� variable, to facilitate the interpretation of the results we also create the dummy variable ���4 equal to 1 for sectors in the highest quartile of financial dependence.

Figure 2 illustrates the identification strategy of this paper. It shows that total employment in high and low FD sectors exhibited a very similar trend up to the year 2008, but diverged significantly since then until the year 2010. After that year, both high and low FD sectors started to converge again albeit to a lower level, possibly reflecting the fact that the initial financial shock spread out to the real economy.

We estimate the dependent variables related to the labor market using the EU Labor Force Survey (EU-LFS) for the reference years 2004–2013. The EU-LFS is the largest European household survey, with 1.8 million interviews conducted each quarter throughout the participating countries. The survey collects labor and demographic information for residents living in private households in the 28 member states of the European Union, as well as in two candidate countries and in three countries of the European Free Trade Association (Iceland, Norway, and Switzerland). The survey draws on nationally representative samples; the sampling rates vary between 0.2 and 3.3 percent across countries.

To assure comparability, the EU-LFS uses output harmonization. This means that the European Union issues standards for the output of the LFS, including a list of variables and categories, minimum sampling precision, observation period, and periodicity, while the national statistical institutes across Europe are responsible for designing and conducting the survey. They must follow the standards and forward the results to Eurostat, which processes the information centrally. While some factors may make the survey less than perfectly comparable, this process of harmonization nonetheless makes it attractive 9
50 45-kg cylinders in the country, with 13 kg being the most common cylinder size. The estimated quantity purchased per user household per month was about 8 kg, suggesting that households on average refill 13-kg cylinders twice every three months or so. On a per household basis, Brazil was the only country where the quantity of LPG purchased declined steadily with quintile level in urban areas. The actual amounts purchased (which was not reported) might not have followed the same pattern if, for example, prices paid by high-income urban households had been lower than those paid by low-income households.

Table A2.19: Number of households purchasing LPG and estimated average quantity purchased per month by purchasing households in Brazil

|**Quintile**|**Number of pu**<br>**Rural**|**rchasing househo**<br>**Urban**|**lds (percent)**<br>**Total**|**Kg purchas**<br>**Rural**|**ed per househol**<br>**Urban**|**d (per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|1,324,705 (69)|3,626,484 (90)|4,951,189 (83)|7.5 (1.8)|8.9 (2.1)|8.5 (2.0)|
|**2**|1,249,814 (83)|5,528,346 (95)|6,778,160 (92)|8.0 (2.4)|8.9 (2.5)|8.7 (2.5)|
|**3**|1,171,781 (87)|6,676,602 (95)|7,848,483 (94)|8.3 (2.8)|8.5 (2.8)|8.5 (2.8)|
|**4**|970,943 (89)|8,017,543 (93)|8,988,486 (93)|8.1 (3.3)|8.2 (3.2)|8.2 (3.2)|
|**5**|614,433 (90)|8,506,101 (79)|9,120,534 (80)|8.5 (4.2)|8.1 (3.7)|8.1 (3.7)|
|**All**|5,331,676 (81)|32,355,076 (89)|37,686,852 (88)|8.0 (2.7)|8.4 (3.0)|8.4 (2.9)|

_Source:_ Authors’ calculations. _Note_ : See notes for Table A2.3.

The average expenditure shares of LPG declined sharply with quintile level for each category of households examined (Table A2.20). For the lowest-income user households, the expenditure share was more than 6 percent.

Table A2.20: Shares of total household expenditure on LPG for all households and for purchasing households (percent) in Brazil

|**Quintile**|**All househol**<br>**Rural**|**ds (% of total e**<br>**Urban**|**xpenditure)**<br>**Total**|**Use**<br>**Rural**|**r households (**<br>**Urban**|**%)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|4.6|5.8|5.4|6.6|6.5|6.5|
|**2**|3.0|3.4|3.3|3.6|3.6|3.6|
|**3**|2.2|2.4|2.4|2.6|2.5|2.5|
|**4**|1.6|1.6|1.6|1.8|1.7|1.8|
|**5**|1.0|0.7|0.7|1.1|0.9|0.9|
|**All**|2.9|2.3|2.3|3.5|2.5|2.7|

_Source:_ Authors’ calculations.

# **Mexico (ENIGH 2008)**

The data are taken from the 2008 National Household Income and Expenditure Survey (ENIGH) conducted by the National Institute of Statistics and Geography (INEGI) in Mexico. The fieldwork for the survey was carried out between August and November 2008.

The value of all non-purchased items was about 30 percent of total purchased expenditure, while non-purchased food accounted for about 62 percent of all non-purchased items. These ratios were approximately constant across the quintile groups. Expenditures were not deflated because adjustment factors were not available.
<!-- Start of picture text --> Time period (1986-2005) - (2016-2035) / ° K\<br>Return period 50 Es rh<br><!-- End of picture text --> <!-- Start of picture text --> Time period (1986-2005) - (2016-2035) RE, a<br>Return period 50 ee ae<br><!-- End of picture text --> <!-- Start of picture text --> Lee Lee SoHE ID<br><!-- End of picture text --> <!-- Start of picture text --> Snes ETERS 5 Dy<br><!-- End of picture text --> <!-- Start of picture text --> allay Rig OO ae<br><!-- End of picture text --> <!-- Start of picture text --> CA ee os<br><!-- End of picture text --> <!-- Start of picture text --> Legend : neeew<br><!-- End of picture text --> <!-- Start of picture text --> Legend AeSiseuh ag<br><!-- End of picture text --> <!-- Start of picture text --> = WIP (decrease) a<br>MM RCP 85d aT a ee oo"<br><!-- End of picture text --> <!-- Start of picture text --> = WWTP (decrease) ps A St<br>Ml RCP4.5 deer Oth, KERMIT f<br><!-- End of picture text --> <!-- Start of picture text --> eyy<br><!-- End of picture text --> <!-- Start of picture text --> ey<br><!-- End of picture text --> <!-- Start of picture text --> Time period (1986-2005) - (2036-2055) eats A<br>Return period 50 SEO Y<br><!-- End of picture text --> <!-- Start of picture text --> Time period (1986-2005) - (2036-2055) Be -<br>Return period 50 ES x<br><!-- End of picture text --> <!-- Start of picture text --> Coad ms, a<br><!-- End of picture text --> <!-- Start of picture text --> “ + ¥ . " a<br><!-- End of picture text --> <!-- Start of picture text --> SpSe RP ee ves<br><!-- End of picture text --> <!-- Start of picture text --> Bae | ea CAWSEY<br><!-- End of picture text -->

<!-- Start of picture text --> Legend4 : Te<br>= WWIP (increase) et<br><!-- End of picture text --> <!-- Start of picture text --> Legend < SRR ad < ett<br>a WWTP (increase) eee! Nese ROS<br><!-- End of picture text --> <!-- Start of picture text --> WWTP (no change) 5<br>(JMl RCP 4.5 no change aeonsS ee 2<br>lll RCP4.5RCP4.5 decreaseincrease ion) if:<br><!-- End of picture text --> <!-- Start of picture text -->
*_WWIP (no change)(Gecroate) EaBish bokge oeSoSye ¢<br>[MMMMMMRCPRCP 8.5 8.5 decreaseincrease PNVgCUE ae a eyfis<br><!-- End of picture text -->

<!-- Start of picture text --> oy<br><!-- End of picture text --> <!-- Start of picture text --> ef<br><!-- End of picture text -->
_COTE D’IVOIRE: Volatility, Shocks and Growth_ variables, the cycles in yield seem less so, at least upon casual examination. Of course, yield shocks caused by weather and other factors may cause or contribute to cycles in output and prices.

**Figure 15: Cocoa Yields per Hectare in Cote d’Ivoire, 1961-2002**

<!-- Start of picture text --> Cocoa Yields in Cote d'Ivoire<br>(hg/ha) 1961-2002 (FAO)<br>6500<br>6000<br>5500<br>5000<br>4500<br>Cocoa Yield<br>HP Trend<br>4000<br>3500<br>3000<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text -->

# **Cycles in Coffee Prices, Output and Yields**

Figure 16 shows data on deviations from trend in coffee production, prices and yields, similar to data reviewed in section 2. Coffee, the second most important commodity in Cote d’Ivoire, shows a cyclical pattern much like cocoa, and moves together with cocoa.
Figure 4: Policy Impact on Education Outcomes <!-- Start of picture text --> Non−Experimental Schools Experimental Schools All Schools Non−Experimental Schools Experimental Schools All Schools<br>.08<br>.15<br>.06<br>.1<br>.04<br>.05 .02<br>Treat Effect on Rates of Child Literacy<br>Treat Effect on Secondary School Enrollment 0<br>0<br><!-- End of picture text --> Panel A: Secondary School Enrollment Panel B: Child Literacy _Notes_ : The bars in the figure represent the OLS estimates of the assignment to the API program during the government implementation of the _Plus_ modality on school-level secondary school enrollment rates (Panel A) and child literacy rates (Panel B). Vertical lines overlaid on each bar displays the 95 percent and 90 percent confidence intervals, respectively. Confidence intervals are based on asymptotic inference.

the assignment of the mentors under the _Plus_ modality at scale. Each bar represents the treatment effect of the policy ( _α_ 1 in equation (6)) for a different sample of schools in the state of Chiapas. For the sample of schools that did not participate in the second experiment, we find that the program increases the fraction of children who enroll in secondary schools by 4.5 percentage points ( _p_ -value = 0.039), which represents an increase of 6.5 percent with respect to the sample mean. For the schools that were part of the experiment, the impact of receiving the program during the government implementation is larger (+8.5p.p., _p_ -value = 0.031), although the two estimates are statistically similar. These effects on secondary school enrollment are in line with the experimental findings on the enrollment in seventh grade (+12.4 percentage points, see Table 2). We interpret this result as evidence that the program at scale is effective in increasing schooling opportunities despite the change created by the policy implementation. Finally, the pooled estimates of the entire population of schools in Chiapas (+5.5 percentage points, _p_ -value = 0.004) are in line with the estimated effects of each subpopulation of schools.

The results for child literacy are shown in Panel B of Figure 4. After two years of rollout of the program, we find that villages that received mentors under the _Plus_ modality at scale display a 2.3 percentage points ( _p_ -value = 0.026) increase in child literacy rates when compared to villages without mentors. The magnitude of this effect implies a reduction of illiteracy rates by 20 percent with respect to the sample average. The estimated program effect for the 37
- Using a longer time-series with higher temporal frequency, to allow for more detailed analysis by including more variables and their lags.

- Assessing the role of technology on the price-pollution channel in more detail. For example, we could look at how the relationship differs in countries with easier access to exhaust filters versus others.

- Estimating the causal effect of price changes to pinpoint and isolate the role of price signals, for example, by using instrumental variables.

Our results on a negative relationship between prices and air pollution suggest that price-based instruments may be able to tackle urban pollution, but may not be able to solve PM2.5 pollution and its health impacts on their own. In many countries within our sample, a US$1 increase in fuel prices would constitute a substantial rise in price that would incur significant loss in consumer welfare and trigger political opposition. So, governments must use price measures in tandem with other policies—such as public transit investments and technology subsidies—to ameliorate urban air pollution.

# References

Abu-Allaban, M, Gillies, J A, Gertler, A W, Clayton, R and Proffitt, D. 2007. “Motor vehicle contributions to ambient PM10 and PM2.5 at selected urban areas in the USA.” _Environmental Monitoring and Assessment_ 132: 155–163.

- Andrade, M, de Miranda, R M, Fornaro, A, Kerr, A, Oyama, B, Afonso de Andre, P and Saldiva, P. 2012. “Vehicle emissions and PM2.5 mass concentrations in six Brazilian cities.” _Air Quality, Atmosphere & Health_ 5: 79–88. https://doi.org/10.1007/s11869-010-0104-5

- Barnett AG, Knibbs LD. 2014. “Higher fuel prices are associated with lower air pollution levels.” Environ Int. 2014 May;66:88-91. doi: 10.1016/j.envint.2014.01.029. Dahl, C A. 2012. “Measuring global gasoline and diesel price and income elasticities.” _Energy Policy_ 41: 2–13. https://doi.org/10.1016/j.enpol.2010.11.055.

EEA. 2021. _Sources and emissions of air pollutants in Europe_ . European Environment Agency.

- EIA. 2022. Annual Energy Outlook 2022: Table 36. Transportation Sector Energy Use by Fuel Type Within a Mode. Energy Information Authority.

- Erdogdu, E. 2014. “Motor fuel prices in Turkey.” _Energy Policy_ 69: 143–153.

- Gendron-Carrier, N, Gonzalez-Navarro, M, Polloni, S and Turner, M A. 2022. "Subways and urban air pollution." _American Economic Journal: Applied Economics_ 14 (1): 164–96.

IEA. 2020. Energy Balances. International Energy Agency

- Jones, A M, Harrison, R M, Barratt, B and Fuller, G. 2012. “A large reduction in airborne particle number concentrations at the time of the introduction of ‘sulphur free’ diesel and the London Low Emission Zone.” _Atmospheric Environment_ 50: 129–138. https://doi.org/10.1016/j.atmosenv.2011.12.050.

- Kampa, M and Castanas, E. 2008. “Human health effects of air pollution.” _Environmental Pollution_ 151(2): 362–367.

13
cost of capital (including the reduction of profit margins and interest-rate spreads) in the domestic financial system. These indicators are discussed in the section above.

The second group of outcome indicators refers directly to international financial integration. Some of the most important indicators are, first, an increase in the participation of foreign banks and other financial institutions in the domestic market, measured for instance by the ratio of foreign to total number of banks or the market share of foreign-owned banks. _Bankscope_ is the premier data source about banks around the world. The second indicator is an expansion in the stock of domestic assets held by foreigners, and likewise, an increase in foreign asset holding by domestic residents, with the resulting rise of gross capital flows in the balance of payments. It is not clear, however, what type of asset should increase the most as result of financial globalization, whether gross foreign direct investment (FDI), portfolio investment, or debt. This should be determined by their relative scarcities in the portfolio of assets. In recent reform experiences, FDI has been the predominant form of foreign assets. The most important source for data on capital flows is _Balance of Payments Statistics_ published annually by the International Monetary Fund. For foreign asset stock data, see Lane and MilesiFerreti (1999) and Kraay et al. (2000).

Finally, measures of external vulnerability can represent outcome indicators of the government’s failure to provide for adequate supervision, to ensure macroeconomic equilibrium, or to eliminate implicit insurance or guarantees for speculative flows. Some of them are, high black-market premium on foreign exchange, substantial real exchange rate misalignment (which, particularly in the case of overvaluation, might indicate the need for a drastic adjustment), large domestic-foreign interest rate spreads (which, after accounting for risk premia, may indicate the presence of controls), and the classic indicators of high levels of external debt (particularly of short duration) relative to GDP, exports, or foreign reserves. For a discussion of leading indicators on external vulnerability, see Goldstein, Kaminsky, and Reinhart (2000), and IMF (2000).

# **C.  INTERNATIONAL TRADE**

**_1. General Principles._** There is a large empirical literature suggesting that economies that are more open to international trade have higher rates of growth, as a result of higher investment and sustained gains in factor productivity (Frenkel and

14