rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table A.19. The district fixed-effect RDD-DD regressions of individual-level outcomes using a poverty rate bandwidth of 7%</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1561**|0.0575***|0.0419|0.3801|0.3777*|0.3388*|0.0697|1.5792**|
||(0.0733)|(0.0171)|(0.0775)|(0.4918)|(0.1922)|(0.1796)|(0.0619)|(0.7210)|
|(Poverty rate – 50) * Post-program|0.0069|-0.0057***|-0.0038|-0.0332|-0.0582**|-0.0549**|0.0076|-0.0473|
|period|(0.0057)|(0.0018)|(0.0043)|(0.0269)|(0.0226)|(0.0219)|(0.0049)|(0.0517)|
|Program * (Poverty rate – 50) * Post-|0.0040|0.0046|0.0007|0.0239|0.0474|0.0577|-0.0297***|0.0351|
|program period|(0.0127)|(0.0037)|(0.0126)|(0.0752)|(0.0393)|(0.0352)|(0.0092)|(0.1234)|
|Age|0.0254***|0.0045***|0.0103***|0.1130***|-0.0054|-0.0055|0.1608***|0.3268***|
||(0.0015)|(0.0006)|(0.0007)|(0.0079)|(0.0049)|(0.0048)|(0.0103)|(0.0533)|
|Age squared|-0.0003***|-0.0000***|-0.0001***|-0.0015***|0.0003***|0.0002***|-0.0082***|-0.0162***|
||(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0005)|(0.0023)|
|Male|-0.0578***|-0.0084**|0.0826***|0.9369***|-0.1580***|-0.1447***|0.0295**|0.0840**|
||(0.0084)|(0.0037)|(0.0061)|(0.0602)|(0.0341)|(0.0328)|(0.0131)|(0.0388)|
|Ethnic minorities (yes=1, Kinh=0)|0.1941***|-0.1031***|-0.0340*|-0.2291|-0.2750**|-0.2706**|-0.1525***|0.8975***|
||(0.0286)|(0.0120)|(0.0193)|(0.1848)|(0.1154)|(0.1124)|(0.0256)|(0.1226)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1668***|0.0354***|-0.0770***|-0.5728***|0.7621***|0.6897***|0.2459***|-1.2109***|
||(0.0390)|(0.0113)|(0.0214)|(0.2116)|(0.0892)|(0.0867)|(0.0574)|(0.3148)|
|Observations|<br>90,314|<br>90,314|<br>90,314|<br>90,314|<br>61,659|<br>61,659|<br>34,203|<br>34,203|
|R-squared|0.190|0.067|0.174|0.153|0.097|0.091|0.197|0.295|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

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# Perceptions of Distributive Justice in Latin America during a Period of Falling Inequality<sup>*</sup>

Germán Reyes<sup>†</sup> Leonardo Gasparini<sup>‡</sup> JEL Classification: D31, D63, D83 Keywords: Inequality, Fairness, Distributive Justice, Perceptions, Latin America

> * The authors would like to thank Carolina García Domench, Giselle Del Carmen and Rebecca Deranian for their support and thoughtful comments. The findings, interpretations, and conclusions in this paper are entirely those of the authors.

> † The World Bank and Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), Facultad de Ciencias Económicas, Universidad Nacional de La Plata.

> ‡ Centro de Estudios Distributivos, Laborales y Sociales (CEDLAS), Facultad de Ciencias Económicas, Universidad Nacional de La Plata and CONICET.
The Effect of Climate and Technological Uncertainty in Crop Yields on the Optimal Path of Global Land Use<sup>_∗_</sup> Yongyang Cai Jevgenijs Steinbuks Joshua Elliott Thomas W. Hertel JEL: C61, Q15, Q23, Q26, Q40, Q54 Keywords: Climate Change, Crop Yields, Dynamic Stochastic Models, Global Land Use, Uncertainty Sectors: Agriculture, Climate Change, Forestry, Environment

> _∗_ Cai: Hoover Institution, Stanford University and Becker Friedman Institute, University of Chicago, yycai@stanford.edu. Steinbuks: Development Research Group, The World Bank, jsteinbuks@worldbank.org. Hertel: Center for Global Trade Analysis, Purdue University, hertel@purdue.edu. Elliott: Computation Institute, University of Chicago, jelliott@ci.uchicago.edu. Acknowledgements: The authors are sincerely grateful to Kenneth Judd for his invaluable contributions that greatly improved this paper. They appreciate financial support from the National Science Foundation, grant 0951576 “DMUU: Center for Robust Decision Making on Climate and Energy Policy” and hardware support of the BlueWaters and Beagle supercomputer projects.
# **Defining Natural Resource Abundance**

To classify ECA countries into ‘resource-rich’ (or abundant) and ‘resource-poor’ categories, we follow (Lederman & Maloney, 2007). Resource rich countries are considered to be those with net exports of minerals and natural resources per person being systematically positive over the period. This classification was previously introduced by (Leamer, 1984), and is dictated directly from a standard Heckscher-Ohlin model. The net exports of minerals and resources include petroleum and derivatives (SITC 33) and raw materials (SITC 27, 28, 32, 34, 35 and 68).

Figure 3 shows the evolution of net exports per capita for the countries under analysis. Two messages emerge. First, the price effect on trade flows of natural resources is evidenced by the ‘funnel-shape’— the almost symmetric increases in trade flows for net exporters and importers. Second, there is substantial variation in natural resource endowments. Azerbaijan, Kazakhstan, Russia, Tajikistan, Turkmenistan and Uzbekistan were net exporters of minerals and resources over the period and therefore will be considered to be “resource rich”. The cases of Russia, Azerbaijan and Turkmenistan stand out, with net exports of minerals during the period 2000-2011 averaging 176, 159 and 127 dollars per person. Armenia, Belarus, Kyrgyz Republic, Moldova and Ukraine were net importers, and therefore were classified into the resource-poor category. Georgia was also classified into the resource-poor category, as its net trade position in minerals and natural resources oscillated between positive and negative over the period.

Figure 3: Net exports of minerals and natural resources per capita in ECA countries <!-- Start of picture text --> 600<br>AZE ARM BLR GEO KAZ KGZ MDA RUS TJK TKM UKR UZB<br>500<br>400<br>300<br>200<br>100<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>-100<br>-200<br>-300<br>Net Exports of Minerals & Resources in Dollars Per Capita<br><!-- End of picture text --> Source: Authors’ calculations based on Comtrade Data from WITS.

# **Benchmarking Diversification Performance**

We place ECA’s export diversification performance along the product dimension in perspective by comparing it with that of other countries of similar characteristics. Specifically, we are interested in 4
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Given the difference in the estimates of fuel consumption, the estimated tax per km varies by 50 percent or more. The differences among vehicles (about O.4c for private cars vs. 1.60 for heavy trucks) can capture the difference in their disruptive effect on the flow of traffic, but not their damaging effect on the road surface.

One way to overcome the limitation of the fuel tax as a discriminatory taxing device is to supplement it with a sharply differentiated scheme of annual license fees. The Ghanaian scheme is not sufficiently differentiated, and the existing differences are almost completely offset by differences in the annual utilization rates.

Table 16 describes the annual license fee, annual utilization rates (based on GHA, 1990), and the fee per km. There is hardly any difference among the different modes, and even in the case of the most heavily taxed modes (private cars and heavy trucks), the fee per km is only a small fraction of the road user costs.

**Table 16.** The Annual Vehicle Examination and Licensing Fee, 1988

||1988<br>Cedi|average fee<br>U.S.$*|Annual<br>utiliza-<br>tion<br>C('<br>ki)|Fee<br>per km|
|---|---|---|---|---|
|Private car|3,211|14.27|25|0.06|
|Taxi|2,000|8.89|80|0.01|
|Light bus|2,400|10.67|_55_|0.02|
|Heavy bus|3,200|14.22|75|0.02|
|Light truck|3,000|13.33|70|0.02|
|Medium truck|_4,500_|20.00|80|0.03|
|Heavy truck|20,000|88.90|80|0.11|

Rate of exchange: 225 cedi per $.

These conclusions are hardly affected by use of other estimates of the rate of utilization [e.g., those by SW"P (1988b, App. 4), which are slightly lower, or those by Kocks (1984), which are only half as large].

As noted, the damaging power to paved roads of heavy trucks is 2,000 times that of the private car, the annual fee is six times as large, but the fee per km is only twice as large. This narrow differential clearly increases the risk of undercharging the heavy trucks.
## **Reference**

Ahluwalia, Isher **J.** 1996. India's Opening Up to Trade and Investment, in Policy Reform in India, Development Center Seminars, Development Center, OECD, Paris France.

Bajpai, Nirupam and Jeffrey Sachs. 1996. India's Economic Reforns: The Steps Ahead. Development Discussion papers no. 532. Harvard Institute for International Development, Havard University. Lockheed, Marlaine, and Adriaan Verspoor. 1991. Improving Primary Education in Developing Countries. New York, Oxford University Press.

Verspoor, Adriaan. 1989. Pathways to Change: Improving the Quality of Education in Developing Countries. World Bank Discussion paper no. 53. Washington, DC.

. 1997c. Primary Education in India. Development in Practice Series. The World Bank, Washington DC.

. 1997d. India: Achievements and Challenges in Reducing Poverty. A World Bank Country Study. The World Bank. Washington, DC.

. 1996. India: Five Years of Stabilization and Reforn and the Challenges Ahead. A World Bank Country Study. The World Bank, Washington DC.

. 1995a, Economic Developments in India: Achievements and Challenges. A World Bank Country Study. The World Bank. Washington DC.

1995b. India: Recent Economic Developments and Prospects, The World Bank, 1995.

. 1988. Center State Relations in Financing Indian Education, The World Bank, 1988.

. 1989. Trends in Public and Private Finances for Education in India, The World Bank, 1989.

. 1987. Education and Basic Needs in India, The World Bank, 1987.

# Korea: Major Reform Events

In retrospect, Korea's development has passed four main stages. Unlike several of the larger economies in East Asia, which evolved from protectionism, inward-looking trade regimes towarcd relatively open economies, Korea did not have a sufficiently large population to contemplate a strategy other than export-led development, which consists importing raw materials and intermediate goods for processing and export with value-added. It development experience shows that export orientation, effective public policy intervention, and financial liberalization are the center of fast economic development over the past three decades.

## **War and Construction (1950-60)**

After World War II, devastating South Korea was almost entirely dependent on US aid for Korea's reconstruction in 1953.4 Some argued that the significance of US aid have helped Korea to prepare for an industrial takeoff in 1960s.. The development efforts in the 1950s included several false starts; progress was made in reconstruction, including the restoration of transportation and communication networks. The government also completed a land reform program that has stalled before the war.

4 Aid ifinanced nearly 70% of total imports between 1953 and 1961 and 75% of total fixed capital formation (See Stephan Haggard, Byung-Kook Kim and Chung-in Moon, Nov. 1990, pp. 3) _55_
emotional maturity domains (0.3 standard deviation) of the Early Development Instrument, a finding consistent with research from high-income countries (World Bank, 2016).

Despite the national trends of girls outperforming boys across the key metrics in schools, rates vary across the country in terms of both enrollment and performance. In Sukamara Regency, Central Kalimantan, for example, the GPI for the school enrollment rate of 16- to 18-year-olds is 1.56, showing a significant disadvantage of boys; the percentage of boys enrolled is 61 percent while the percentage of girls enrolled is 95 percent. Girls also face disadvantages in different regions, for example, in Probolinggo Regency, East Java, which has a GPI of 0.62, meaning that the percentage of boys enrolled is about one and a half times the percentage of girls.<sup>7</sup>

**Figure 2. Gender Parity Index (GPI) based on School Participation Rate 16- to 18-year-olds across districts in East Java Province**

<!-- Start of picture text -->
1.40<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Source: Susenas 2018, processed.<br>Kab. Pacitan Kab. Ponorogo Kab. Trenggalek Kab. Tulungagung Kab. Blitar Kab. Kediri Kab. Malang Kab. Lumajang Kab. Jember Kab. Banyuwangi Kab. Bondowoso Kab. Situbondo Kab. Probolinggo Kab. Pasuruan Kab. Sidoarjo Kab. Mojokerto Kab. Jombang Kab. Nganjuk Kab. Madiun Kab. Magetan Kab. Ngawi Kab. Bojonegoro Kab. Tuban Kab. Lamongan Kab. Gresik Kab. Bangkalan Kab. Sampang Kab. Pamekasan Kab. Sumenep Kota Kediri Kota Blitar Kota Malang Kota Probolinggo Kota Pasuruan Kota Mojokerto Kota Madiun Kota Surabaya Kota Batu<br><!-- End of picture text -->

In response to data that show a high degree of variance in gender gaps in education in Indonesia, a team at the World Bank conducted a subnational research sample study to examine issues of gender gaps throughout the country and across different indicators, as well as issues related to students with disabilities. This study focuses primarily on the social inclusion issue of gender, while also referencing issues faced by children with disabilities—an issue that is garnering greater attention in Indonesia through the ratification of national policies, as well as international frameworks.<sup>8</sup> Findings from field visits are coupled with reviews of the literature related to school enrollment, student achievement, early marriage, literacy, labor force participation, and other studies on gender and disability in Indonesia, to determine how national trends are exhibited at local levels and what gaps in gender equality are masked by the national figures.

> 7 Calculated using Susenas 2018.

> 8 Social inclusion is defined by the World Bank as “the process of improving the terms for individuals to take part in society” and “the process of improving the ability, opportunity, and dignity of people, disadvantaged on the basis of their identity, to take part in society” (World Bank, 2013; Inclusion Matters: The Foundation for Shared Prosperity).

4
incidence of violence and women’s economic standing and access to opportunities (Eswaran and Malhotra 2011, Tauchen, Witte and Long 1991). Women’s ownership of assets and their relative share of wealth can increase their bargaining power within the household and hence can act as a deterrent to physical violence against them (Agarwal and Panda 2007; Panda and Agarwal 2005; Oduro, Deere, and Catanzarite 2015). Similarly, cash transfers provided to women and intended to reduce poverty and food insecurity have been shown to decrease physical and sexual violence (Hidrobo, Peterman and Heise 2016). Alternatively, higher bargaining power may attract more violence by threatening the existing male dominance, as suggested by the backlash theory of violence (Koenig et al. 2003, Rocca et al. 2009). The evidence suggests that the association of financial autonomy or higher bargaining power within household with protective role of violence is influenced by context specific factors (Vyas and Watts 2009).

In addition to who makes decisions and has bargaining power, whether decisions are made independently or jointly may also influence IPV. Indeed, empirical papers from demography, development studies and public health have established a relationship between women’s responses on joint decision making patterns and IPV. Coleman and Straus (1986) provide the first evidence of this relationship using data on US couples. While the authors expected that families making decisions jointly would have the highest degree of conflict, they found the opposite. Kim and Emery (2003) replicate their study in the Korean population and encountered similar results. Hindin and Adair (2002) use the Philippines Cebu Longitudinal Health and Nutrition Survey (CLHNS) and find that only 6% of women reported IPV when all household decisions were made jointly compared to 25% when no decisions were made jointly. Using the 2000 Haiti DHS, Gage (2005) finds that women who had the final say on major household purchases were almost 2.7 times more likely to experience emotional violence, and 1.7 times more likely to experience physical or sexual violence than women who made such decisions jointly with their partners.

7
The growth rate of public investment in health is taken to be equal to the growth rate of investment in infrastructure. Similarly, the shares of maintenance expenditures in health, user fees, other spending on goods and services, and wages and salaries are assumed to be equal to the corresponding shares in investment in education. Finally, the growth rate of other public investment is assumed to be equal to a constant value of 1 percent throughout the simulation period. For this component, whereas the shares of maintenance expenditures and user fees is taken to be zero, the shares of other spending on goods and services, and wages and salaries are set at 1 percent of investment.

Total public capital expenditure data are obtained by adding up the components of capital expenditures. As shown in Table 1, investment in infrastructure represent about two-thirds of total investment, investment in education about 10 percent of that total, and investment in health about 18 percent. In proportion of GDP (as shown in the bottom part of Table 2), public investment represents about 5 percent in the base period.

# **2.  Aid Requirements and the Medium-Term Budget Framework**

We now turn to a calculation of aid requirements using the methodology described earlier and the SPAHD framework for Niger described by Pinto Moreira and Bayraktar (2005).<sup>6</sup> To project exogenous variables, we follow the assumptions underlying the experiments in that paper. These involve, for instance, setting the rate of growth of export and import prices to the same value, imposing an effective interest on foreign borrowing of 0.5 percent per annum. Domestic borrowing is kept at 1 percent of GDP (the value observed in the base period) and foreign (concessional borrowing is limited to 2 percent of GDP). Tax rates that are exogenous are also kept constant initially.

> 6The paper also provides details about parameter estimates and the calibration process.

14
- (e) _Differences in risk_ : Finally, if insurance markets are missing, risk-averse individuals will underinvest, and hence have higher returns. Differences in risk aversion by gender could thus lead to differences in returns.

# **5. Empirically Investigating Why Returns Are So Low for Women**

The model in the last section suggests several possible explanations for the gender differences we observe in returns. We test each of these in turn.

# _5.1. Do Women Invest More of the Treatment on Children?_

A high shadow cost of capital within the household, whether driven by a desire to spend money on children’s education and health, or due to husbands capturing the treatments made to females could lead to less of the treatments staying in female-owned businesses. We showed in Table 3 that females appear to have invested less of the smaller treatment amount, but no less of the larger. In Table 4, we use the survey data to test whether the treatment has a greater effect on schooling, expenditures and household asset accumulation in households of female microenterprise owners than in households of male microenterprise owners. Columns 1 to 3 of Table 4 use the household surveys taken to estimate the impact of the treatment on school attendance of 5 to 12 year olds, 12 to 15 year olds, and 17 to 18 year olds respectively. School attendance information was gathered only in the April waves of the survey and the sample for these regressions is limited to those households with children of the specified age. We find that the treatment has no effect on school attendance regardless of the gender of the owner.

We find similarly insignificant differences on expenditures for groceries, health and education between households of female owners and households of male owners. (See columns 4 through 6 of Table 4). We do find significant differences in the rate at which household durable goods ownership increases, and in financial assets. Column 7 of Table 4 takes as the dependent variable the first principal component of a vector of 17 household assets, including landline and cellular telephones, television, autos, bicycles and gold jewelry. The weights in the index are derived from baseline data. Asset ownership increases generally in the sample, but the regression results reported in column 7 shows that the increase is significantly larger in households of male enterprise owners 17

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<!-- Start of picture text --> 3000000 100<br>80<br>2000000<br>60<br>1000000<br>40<br>0 20<br>01mar2019 01apr2019 01may2019 01mar2020 01apr2020 01may2020 01jun2020<br>.4<br>.2<br>.2<br>.1<br>0 0<br>-.2 -.1<br>-.4 -.2<br>01mar2019 01apr2019 01may2019 01mar2020 01apr2020 01may2020 01jun2020<br>number of travelers google serch index<br>number of travelers<br>"airport" google search index<br>growth rate: number of travelers<br>growth rate: google search for "airport"<br><!-- End of picture text --> Note: The TSA passenger throughput data cover daily arrivals for March 1-May 28, 2019 and March 1-May 28, 2020. The top panel compares raw daily passenger volumes to the Google search index for “airport”. The bottom panel shows how the daily growth rates in passenger volumes at U.S. checkpoints compare with the growth rate of Google search intensity for “airport.” Figure 3: Daily passenger volumes at U.S. TSA checkpoints and Google search data for MarchMay 2019 and 2020 We conduct a similar validation exercise for hotel and restaurant services using restaurant reservations data from OpenTable.<sup>9</sup> The dataset contains information on daily restaurant reservations at OpenTable network restaurants in seven countries (Australia, Canada, Germany, Ireland, Mexico, the United Kingdom, and the United States). The OpenTable data for 2020 are given in percentage changes relative to the same day in 2019. To make these data comparable with the Google search data, we convert the daily records into weekly averages. Figure B.1 (in Appendix B) plots daily percentage changes in Google search index for restaurant and percentage changes in actual restaurant reservations for the seven countries. The graphs in Figure B.1 (in

> 9 <u>https://www.opentable.com/state-of-industry</u>

13