rafmacalaba/datause-encoder-data huggingface dataset:
Table 22 presents the average growth rates of real per capita national income. The results suggest that a large number of republics suffered decreases in real per capita income between 1989 and 1990. As a matter of fact, the growth rate was negative in 14 out of 15 republics. Ukraine's per capita income grew quite strongly until 1989 but then the growth rate becane negative in the subsequent year. Despite the fact that Ukaine's growth rate was negative, its ranldng on the basis of its growth rate in 1990 was as high as 13. There was a large number of republics which suffered larger deterioration in their growth rates.

The poverty estimates of republics for the year 1990 are presented in Table 23. These estimates were obtained using the Ukaainian poverty lines discussed earlier. Again a general intepolation was used to compute these estimates. According to these results, _11.55_ and 5.49 percent of people were poor and ultra poor in Ukraine, respectively. Ukraine's poverty raning is 5th which means that there are only four other republics which had lower poverty than Ukaine. Among these four republics, three are Baltic republics viz, Estonia, Latvia and Lithuania. Belarus has the 4th ranking.

ITe republics which have the most severe poverty are Tajikistn, Uzbekistan, Azerbaijan and Turkmenistan. The percentage of the poor in these countries is more an 49. In Tajikistan, this percentage is as high as 67.97. Even the percentage of ultra poor is more than _52._ This may be regarded as an extremely high level of poverty in a country which until recently was a part of the USSR, a leading power after World War H.

The conclusions about poverty in the republics must, however, be qualified. In our computations we have made the assumption that the price levels in diffemnt republics are the same. It is reasonable to assume that some differences in the price levels do exist but we still believe that our overall conclusion of extreme disparities in standard of living in different republics does hold.

Tables 24 and 25 indicate that the wage gap between rural workers (which include state and collective farmers) and blue and white collar workers has been continuously narrowing in the 1980s. This trend is evident in all the Soviet republics. Since the rural workers generally had the low average wages in the early 1980s, it means that income inequality in the republics must have significandy declined during the 1980s.

44
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|Price<br>(6)<br>0.008<br>(0.006)<br>0.943<br>731,255<br>0.008<br>(0.006)<br>0.945<br>731,255<br>0.020<br>(0.016)<br>731,255<br>**op.)**<br>0.392***<br>(0.019)<br>458.81<br>731,255<br>Yes<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>ternational Standard Industry<br>ks by illegal armed groups in<br>o health, education, and other<br>e in agriculture, services, and<br>in parentheses. _∗∗∗_signifcant|
|---|
|minal Input<br>(5)<br>0.004<br>(0.009)<br>0.944<br>731,503<br>0.011<br>(0.007)<br>0.944<br>731,503<br>0.013<br>(0.036)<br>731,513<br>**rking Age P**<br>0.252***<br>(0.019)<br>187.35<br>731,513<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>cation of the In<br>i) armed attac<br>ent transfers t<br>000 GDP shar<br>el are reported|
|No<br>(4)<br>0.005<br>(0.005)<br>0.944<br>731,502<br>0.009<br>(0.006)<br>0.944<br>731,502<br>0.014<br>(0.016)<br>731,509<br>**DP (% Wo**<br>0.315***<br>(0.017)<br>330.51<br>731,509<br>Yes<br>Yes<br>Yes<br>No<br>No<br>No<br>No<br>-digit classif<br>dummies and<br>ntral governm<br>n 1995, viii) 2<br>t the frm lev|
|Price<br>(3)<br>-0.002<br>(0.004)<br>0.445<br>391,289<br>0.003<br>(0.005)<br>0.445<br>391,289<br>-0.008<br>(0.013)<br>391,289<br> **Share of I**<br>0.293***<br>(0.016)<br>371.78<br>391,289<br>Yes<br>Yes<br>Yes<br>No<br>No<br>No<br>Yes<br>ond to the four<br>ctions of year<br>in 1995, v) ce<br>ection offces i<br>andard errors a|
|inal Output<br>(2)<br>-0.012**<br>(0.006)<br>0.406<br>799,737<br>-0.002<br>(0.005)<br>0.406<br>799,737<br>-0.047**<br>(0.023)<br>799,737<br>**p. Variable:**<br>0.247***<br>(0.018)<br>178.56<br>799,737<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>No<br>effects corresp<br>include intera<br>c expenditures<br>ber of tax coll<br>5. Clustered st|
|**Dependent Variables (in logs)**<br>Nom<br>(1)<br>**Panel A. Reduced Form**<br>Predicted Infows<br>-0.006*<br>(0.004)<br>R-squared<br>0.395<br>Observations<br>799,738<br>**Panel B. OLS**<br>Share of IDP (% Working Age Pop.)<br>-0.002<br>(0.003)<br>R-squared<br>0.395<br>Observations<br>799,738<br>**Panel C. 2SLS**<br>Share of IDP (% Working Age Pop.)<br>-0.020*<br>(0.011)<br>Observations<br>799,741<br>**Panel D. First Stage**<br>**De**<br>Predicted Infows<br>0.314***<br>(0.017)<br>First Stage F-statistic<br>335.28<br>Observations<br>799,741<br>**Controls (for all panels)**<br>Year FE<br>Yes<br>Firm FE<br>Yes<br>Product FE<br>Yes<br>Year_×_Department FE<br>No<br>Confict Controls<br>No<br>Homicides Rates<br>No<br>Additional Controls<br>No<br>_Notes:_ Each coeffcient corresponds to a separate regression. Product fxed<br>Classifcation, which account for 113 four-digit codes. Additional controls<br>1995, ii) fatal victims of attacks in 1995, iii) homicide rates 1995, iv) publi<br>expenditures in 1995, vi) number of fnancial institutions in 1995, vii) num<br>industry, ix) municipal tax income in 1995, and x) night light density in 199<br>at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%.|

34
may be a desirable effect even if some of the microenterprises created due to the program are informal. Maloney (2004) reports that, according to the Brazilian National Household Sample Survey (PNAD), most informally self-employed in Brazil prefer their jobs over being a formal sector employee.

This paper proceeds as follows. Section 2 summarizes the short-term impact evaluation design and its results. Section 3 discusses the rationale for a long-term impact evaluation, as well as the longterm sample, data sources, and timeline. Section 4 presents the long-term impact evaluation results. Section 5 concludes.

# **2. The Short-Term Impact Evaluation (2010-2011)**

# **_2.a. Context and Research Design_**

The original study of the impact of financial education in Brazil focused on youth in the 11<sup>th</sup> and 12<sup>th</sup> grades of high school. This focus on youth was attractive and relevant for several reasons. First, good financial habits formed at an early age are likely to benefit schooling, employment, and standards of living throughout adulthood. Second, the focus on youth leverages their learning capacity, as students are primed to absorb, recall, and apply learning on a regular basis, in contrast to adults, who are typically less engaged in this learning routine. Finally, well-informed students can modify not only their own financial choices, but also act as agents of change in their households’ financial decisions.

In Bruhn et al. (2016), we tested short-term answers to these questions through a randomized controlled trial of a comprehensive financial education program for public high school students in Brazil. The program was developed and implemented as part of the National Strategy for Financial Education (ENEF). It spanned a 17-month period across the 2010 and 2011 academic years, and was integrated into classroom curricula of Mathematics, Science, History, and Portuguese. The instruction used new textbooks with interactive and repeated classroom exercises on financial education themes, take-home exercises such as creating household budgets with parents, and roleplay assignments. The textbooks covered nine themes: everyday family life, social life, personal property, work, entrepreneurship, large expenditures, public goods, the country’s economy, and the world economy. Appendix 1 in Bruhn et al. (2016) provides a detailed list of topics within each theme.

The curriculum was complemented by teacher training, web learning tools, and instructor handbooks. Furthermore, the learning was continuous throughout the school year, which stands in contrast to typical workshop-based financial education programs that are delivered in one shot and vary in length from 90 minutes to a few hours. As such, the intensity of treatment of this program was much stronger than typical one-off financial education workshops. To date, this study is the 5
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# **Export Shocks and the Volatility of Returns to Schooling:**

# **Evidence from Twelve Latin American Economies**

Daniel Lederman, World Bank<sup>1</sup> Diego Rojas, World Bank _Key Words: Pseudo-Panel, Returns to Education, Business Cycle, Income Volatility, Trade and Labor Market Interactions._ _JEL: C23, I21, E32, J6, F16_

> 1 Lederman is Deputy Chief Economist for Latin America and the Caribbean, World Bank. Rojas is a Consultant with the Office of the Chief Economist for Latin America and the Caribbean, World Bank. The authors gratefully acknowledge funding from the Multi-Donor Trust Fund on Trade and the Regional Studies Program of the Office of the Chief Economist for Latin America and the Caribbean. The authors are also grateful for insightful comments provided by William F. Maloney, Julian Messina, and Erhan Artuc. Numerous participants at the OECD’s Regional Workshop of the International Collaborative Initiative on Trade and Employment (ICITE) held in Santiago, Chile in May 2011 also provided helpful comments on preliminary results.
# 1 Introduction

The rural areas are home to the vast majority of the world’s poor. As most of the rural poor are engaged in agriculture, diversi fi cation and commercialization of agriculture are often regarded as essential pre-conditions for rural income growth and poverty reduction (Johnson, 2000). The importance of access to well functioning markets for agricultural specialization has been highlighted in both traditional and recent development literature. Not surprisingly, investment in rural infrastructure including market facilities has featured as an important development priority in most developing countries. While the issue of investment in market facilities has been discussed frequently in various policy fora, there is, to our knowledge, no empirical evidence on the importance of market facilities for agricultural commercialization. Similarly, little is known about the distribution of bene fi ts from such investments. This paper utilizes a unique dataset on agricultural markets in India to examine how facilities available at the market in fl uence farmers’ choice of place of sales. By relating farmers’ place of sales to their level of sales, this paper also estimates the distributional consequence of an improvement in the facilities at the market.

The issue of agricultural commercialization and specialization has long been a staple of development literature (Bardhan, 1989; de Janvry, Fafchamps and Sadoulet, 1991; Goetz, 1992; Key, Sadoulet and de Janvry, 2000; Bellemare and Barrett, 2005; and Holloway, Barrett and Ehui, 2005). A common theme of this literature is that the presence of ‘transaction costs’ a ff ects farmers’ production and sales decisions. The empirical literature, however, focuses almost exclusively on one component of the transaction costs, namely transportation cost. Taking distance to nearest market as an indicator of transportation cost, a number of recent studies have shown that access to market in fl uences a farmer’s decision to sell (Jacoby, 2000; Fafchamps and Shilpi, 2003), and to sell at the market (Fafchamps and Hill, 2005). A 1
As in many countries, the provision of input subsidies is a highly political and very sensitive issue, particularly as it is linked to the long stated goal of the Government of Indonesia (GoI) of achieving selfsufficiency in rice production. The government continues to allocate a significant share of resources to subsidization in agriculture. The 2008 budget channeled close to 0.4 percent of GDP and Rp 29.4 trillion to subsidies for rice, fertilizers, seeds, and agricultural credit, of which the first two absorbed the bulk of the resources.

In this paper we seek to analyze the fertilizer subsidies from two different angles, both important for policy makers in the country. Firstly, we analyze who is benefiting from the current system of fertilizer subsidies, and secondly we estimate the impact that a removal of the fertilizer subsidies would have on rice production. Agriculture continues to play a very important role on the country’s economy, but more importantly the production and price of rice is a key variable for Indonesian policy makers. As such, any analysis in this area has to look beyond efficiency and include equity considerations as well as the longterm goal of self-sufficiency in rice.

In the second section of this paper, we provide a short snapshot of the fertilizer system in Indonesia, as well as provide some data on fertilizer production and distribution. We focus on urea, since this fertilizer represents the bulk of the fertilizer used by rice farmers and it captures most of the subsidies being provided to the industry. In the third section, we conduct a benefit incidence analysis to show that the fertilizer subsidy is relatively regressive, with the 40 percent largest farmers capturing 60 percent of the total subsidy. In the forth section we estimate the impact of fertilizer use on rice yields, showing that the relationship is positive up to a point, beyond which the impact of additional use of fertilizer on yields is negative. We also show the relative inefficiency of the subsidy in increasing rice production, since fertilizer subsidies are significantly higher than the value of the increase in production achieved through the fertilizer subsidy. In the fifth section we discuss the rationale for subsidizing agricultural inputs and review some of the international experience to date. Finally, section six concludes and provides some policy recommendations.

For this research we use two different surveys by the National Statistics Office (BPS), the Agricultural Census 2003 and the Rice Household Survey 2008. The first comprises a random sample of over 46,000 rice farmers in 29 provinces, while the latter uses a smaller sample of over 11,000 rice farmers from the 15 largest rice-producing provinces in Indonesia. The results of the benefit incidence analysis is very similar using both surveys; there is little targeting in the subsidy, with the largest farmers capturing a disproportionate share of the subsidy.

**3**
_higher the reduction in aggregate TFP generated by the tax. If the inequalities in_ (12) _are not jointly verified, then the impact of the tax, λ, on aggregate TFP is ambiguous and depends on the magnitude of the tax, λ, relative to the energy prices, zf and zx, the output elasticities, βx and βf , and the correlation between the energy wedges, {τfi, τxi}, and {ai, τki, τyi} ._ Corollary 3 outlines a sufficient condition for a homogeneous carbon tax to negatively affect aggregate TFP in presence of distortions. Intuitively, when more productive firms face lower energy distortions, i.e. when _Corr_ ( _τji, ai_ ) _<_ 0, we can expect the tax to decrease aggregate TFP in equilibrium. This is because when _Corr_ ( _τji, ai_ ) _<_ 0, then firms that are more productive have cheaper access to energy markets, which implies that distortions tend to favour more productive firms over less productive ones. Introducing the tax would increase such distortions for more productive firms relative less productive firms in the economy. Vice versa, when _Corr_ ( _τji, ai_ ) _>_ 0, then we should expect that the effect of the tax on aggregate TFP is at least ambiguous, implying that the tax, _λ_ , has the potential to increase it. This is because the tax alleviates market distortions in favor of more productive firms in the economy. Lastly, the higher the output elasticities of energy, the stronger the positive/negative impact of the tax on aggregate TFP.

# **3 Construction of the Dataset**

The empirical exercises carried out in this paper combine firm-level data and an energy balance matrix to estimate emissions at the firm level in the Dominican Republic. The first source of firm-level information is the tax forms from the Direccion General de Impuestos Internos (DGII), which compiles tax declarations for the universe of formal firms required to file taxes in the Dominican Republic between 2007 and 2021. The number of firms each year averages around 90,000, ranging from 55,000 in 2007 to close to 129,000 in 2021. Through these tax declarations, we have the necessary information to track and compute several firm performance variables, including sales, production, value added, and productivity. Also key to our analysis is firms’ expenses and liabilities, as this will be the conduit through which a CO2 tax affects firms. The database also includes relevant firm characteristic variables such as the economic sector of activity and geographical location. We complement this information with data from the National Energy Information System (SIEN). SIEN reports energy consumption and energy generation by type of source and economic activity, as well as CO2 emissions by aggregate sectors. SIEN also details how energy is used in the main areas of consumption. This is complemented by data on supply flows, including supply, transformation centers, and final energy consumption. With this information, it is possible to estimate total emissions 9
household head is endogenous (and thus not a circumstance).<sup>33</sup> We therefore work with five circumstances (race, father’s and mother’s education, father’s occupation, and birth place). Income results are reported for all six countries, but consumption data was not available in the PNAD data (for Brazil). Table 8 contains estimates of inequality of opportunity for consumption for Colombia, Ecuador, Guatemala, Panama, and Peru. Tables 9 and 10 report the OLS coefficients of the reduced-form equation (7), for income and consumption expenditures respectively (analogously to Table 6).<sup>34</sup> In our samples, overall household income inequality is higher than earnings inequality in Brazil and Panama (by all measures) and in Colombia (by E(1) and E(2), but not by E(0)). It is lower than earnings inequality in Ecuador, Guatemala, and Peru (by all measures), and in Colombia by E(0). In all five countries for which consumption data is available, consumption inequality is considerably lower than either income or earnings inequality. This is consistent with the widespread view that income (and earnings, when these include agricultural and informal sector earnings) is measured with greater error than consumption expenditures, as well as with the expectation that consumption is likely to be closer than current income to permanent income (provided households have access to some consumption-smoothing mechanisms).<sup>35</sup> Focusing once again on the path-independent measure E(0), non-parametric estimates of inequality of opportunity for household incomes range from 25% (in Colombia) to 37% (in Guatemala). As for earnings, the parametric estimates are slightly lower: from 23% in Colombia to 35% in Guatemala. For both estimation procedures, the indices are higher than the corresponding estimates for earnings in all countries except for Brazil, where the difference is quite small: 34% for earnings versus 32% for income per capita (for the parametric estimates). In addition to earnings capacity, pre-determined circumstances affect another three important household income determinants: other incomes (such as capital incomes or transfers); the choice of one’s partner; and the composition of the rest of the household (including, most importantly, the number of

> 33 Endogeneity arises both because in some countries reported headship is an interviewee choice, and because household formation (e.g. whether or not one marries) is endogenous. 34 All coefficients in these reduced-form regressions have the expected signs, and most are significant at the 1% level. Coefficient sizes are consistent with a reduced-form specification.

> 35 See, e.g. Deaton, 1997, on both of these reasons to prefer consumption to income data in assessing the distribution of welfare in developing countries.

21