rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Crop Yields and Land Under LDO Restrictions**

||||**Panel A**|**: Potential yi**|**eld**||
|---|---|---|---|---|---|---|
||Rice|Maize|Millet|Sorghum|Sweet Potato|Cassava|
|% Area Under LDO|3,841.460|5,391.475|6,538.496|5,093.550|7,234.107|6,852.160|
||(3.88)***|(3.34)***|(2.66)**|(2.85)***|(3.98)***|(3.92)***|
|Travel Time to Large City|2.268|2.100|-0.727|1.352|3.800|3.420|
||(2.54)**|(1.50)|(0.30)|(0.78)|(2.41)**|(2.23)**|
|Constant|2,406.270|2,649.598|321.399|-36.057|4,058.798|4,375.430|
||(3.57)***|(3.42)***|(0.18)|(0.04)|(3.21)***|(2.44)**|
|District Fixed Effect|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|124|82|46|32|107|118|
|R-squared|0.49|0.83|0.38|0.88|0.62|0.31|
||Banana|Soybean|Bean|Otherpulse|Ground Nut|Other Oilseeds|
|% Area Under LDO|3,456.569|1,982.288|2,254.279|1,710.232|1,632.559|1,491.524|
||(2.28)**|(2.94)***|(3.09)***|(3.86)***|(3.06)***|(1.63)|
|Travel Time to Large City|-1.801|0.708|0.784|1.003|0.820|-3.123|
||(1.55)|(1.14)|(1.17)|(2.50)**|(1.66)|(3.76)***|
|Constant|5,653.358|6.995|8.051|689.853|454.588|5,542.838|
||(6.25)***|(0.02)|(0.02)|(2.02)**|(0.89)|(5.57)***|
|District Fixed Effect|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|101|63|63|132|85|130|
|R-squared|029|038|038|033|040|039|
||.|.|.<br>**Panel**|.<br>**B: Actual Yie**|.<br>**ld**|.|
||Rice|Maize|Millet|Sorghum|Sweet Potato|Cassava|
|% Area Under LDO|2,372.102|1,042.140|614.788|-70.490|1,043.829|2,970.653|
||(3.02)***|(1.11)|(1.91)*|(0.18)|(0.25)|(1.32)|
|Travel Time to Large City|0.800|0.271|-0.328|-0.226|-2.533|3.551|
||(1.07)|(0.33)|(1.03)|(0.59)|(0.70)|(1.80)*|
|Constant|1,392.330|6.511|65.248|183.046|1,424.177|9,483.472|
||(1.64)|(0.01)|(0.28)|(0.89)|(0.49)|(4.11)***|
|District Fixed Effect|158|82|46|32|107|118|
|Observations|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.39|0.83|0.38|0.76|0.62|0.31|
||Banana|Soybean|Bean|Otherpulse|Ground Nut|Other Oilseeds|
|% Area Under LDO|7,774.127|945.411|940.833|725.325|508.961|2,598.704|
||(2.25)**|(2.78)***|(2.86)***|(3.42)***|(2.14)**|(2.14)**|
|Travel Time to Large City|-4.171|0.352|0.339|0.387|0.015|-3.257|
||(1.57)|(1.12)|(1.12)|(2.01)**|(0.07)|(2.96)***|
|Constant|11,597.539|22.398|21.880|924.925|215.110|5,843.857|
||(5.61)***|(0.11)|(0.11)|(5.65)***|(0.94)|(4.42)***|
|District Fixed Effect|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|101|63|63|132|85|130|
|R-squared|0.29|0.38|0.38|0.33|0.40|0.39|

Absolute value of t statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
Musgrove (eds.), _Disease Control Priorities in Developing Countries_ . New York: Oxford University Press, 771-792.

Canning, David. 2006. "The Economics of HIV/AIDS in Low-Income Countries: The Case for Prevention." _Journal of Economic Perspectives_ 20(3): 121-142.

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Caulfield, Laura E., Stephanie A. Richard, Juan Rivera, Philip Musgrove, and Robert E. Black. 2006. "Stunting, Wasting, and Micronutrient Deficiency Disorders," in Jamison, Dean T., Joel G. Breman, Anthony R. Measham, George Alleyne, Mariam Claeson, David B. Evans, Prabhat Jha, Anne Mills, and Philip Musgrove (eds.), _Disease Control Priorities in Developing Countries_ . New York: Oxford University Press, 551-567.

Chinn, Menzie D. and Robert W. Fairlie. 2006. "ICT Use in the Developing World: An Analysis of Differences in Computer and Internet Penetration." IZA Discussion Paper 2206. Bonn: Institute for the Study of Labor.

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Cowell, Alexander J. 2006. "The Relationship Between Education and Health Behavior: Some Empirical Evidence." _Health Economics_ 15(2): 125-146.

"Curbing the Obesity Epidemic" 2006. _The Lancet._ May 13.

Cutler, David, Angus Deaton, and Adriana Lleras-Muney. 2006. "The Determinants of Mortality." _Journal of Economic Perspectives_ 20(3): 97-120.

44
solutions to (standardized) exams, a practice that clearly undermines the education system.<sup>9</sup> Furthermore, monetary transfers that are imposed on a per capita basis might also affect poor households disproportionately since they have to pay a higher share of their income (Emran <u>et al. 2013).</u> The form of corruption in schools that is locally perceived as most problematic is direct bribing with the purpose of increasing the attention or grades a teacher gives to an individual student at the expense of others. Furthermore, bribes can be necessary to gain access to the best public high schools and to universities.<sup>10</sup> In sum, while payments to teachers are in part motivated by grade-buying or seeking better treatment for the child, a larger share seems to operate as a per capita tax. In the latter case, the extent and magnitude of informal payments is more likely to be determined by local norms, the preferences and the bargaining power of teachers, parents, and school officials, and less by the pursuit of inflated grades or preferential treatment for the child. Both kinds of petty corruption, however, can be expected to affect incentives negatively, increase the socioeconomic gradient in educational outcomes, and contribute to a social climate where corruption is an everyday experience.

In this section, we discuss the data and present key descriptive statistics of our sample.

# _Data_

We use data from a nationally representative household survey conducted in Moldova in 2011– 12 (henceforth abbreviated CELB 2012) which was specifically designed to investigate the effects of migration on children and elderly left behind. The survey includes 3,568 households with 12,333 individuals, of which 2,501 are children of age 6–18.<sup>11</sup> In addition to socioeconomic characteristics of household members, detailed information on private financial and non-

> 9. This problem was so widespread that, sometime after our survey took place, the education minister introduced video surveillance during the final high school exam, a move that lead to a spike in failure rates. Something similar has recently been studied in Romania; see <u>Borcan et al.</u> (2017).

> 10. Heyneman et al. <u>(2008), for example, discuss survey data that indicate that about 80% of university students</u> in Moldova, Bulgaria, and Serbia were aware of payments of illegal bribes in university admission. 11. The response rate was above 80%. For detailed information on the survey, see <u>Böhme and Stöhr</u> (2014) and Böhme et al. <u>(2015).</u>

7
Policy Research Working Paper 8142

# **Abstract**

Across Latin America, school dropout is a growing concern, because of its negative social and economic consequences. Although a wide range of interventions hold potential to reduce dropout rates, policy makers in many countries must first address the basic question of how to target limited resources effectively for such interventions. Identifying who is most likely to drop out and, therefore, who should be prioritized for targeting, is a prediction problem that has been addressed in a rich set of research in countries with strong education system data. This paper makes use of newly established administrative data systems in Guatemala and Honduras, to estimate some of the first dropout prediction models for lower-middle-income countries. These models can correctly identify 80 percent of sixth grade students who will drop out in the transition to lower secondary school, performing as well as models used in the United States and providing more accurate results than other commonly used targeting approaches.

This paper is a product of the Education Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at madelman@ worldbank.org and fhaimovich@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
location choice since microfinance branching decisions are not made based on profitability. Hence these results suggest significant welfare effects of being able to access finance.

The degree to which the financial system allows families to manage risk affects their entrepreneurial opportunities. For example, Rosenzweig and Wolpin (1993) and Rosenzweig and Binswanger (1993) find that low wealth families without access to risk amelioration services choose lower-return, low-risk production process relative to families without these constraints. Hausman (1979) shows that in the absence of well-developed financial markets that allow poor entrepreneurs to diversify risk, the higher discount rates of the poor will stymie entrepreneurial behavior. This work suggests that financial developments that make it easier for entrepreneurs to diversify, manage, and pool risks might materially expand the economic opportunities faced by many individuals and thereby affect the evolution of dynastic income differences. Clearly, more work is needed on these themes.

Finally, research has focused on the impact of wealth and liquidity constraints on selfemployment decisions and entrepreneurial survival. Specifically, researchers have looked into the determinants of the switch into self-employment and its subsequent success (Paulson and Townsend 2004; Demirguc-Kunt et al. 2008). Their results suggest wealthier households are more likely to start new businesses. Furthermore, the results suggest that chance of survival for new entrepreneurs is higher if there is an existing prior relationship with a financial institution (Demirguc-Kunt et al., 2008). Earlier studies using U.S. data similarly emphasize the importance of wealth and liquidity constraints in starting businesses (see for example, Evans and Leighton, 1989; Evans and Jovanovic, 1989; Holtz-Eakin et al. 1994.)<sup>11</sup> Thus, financial development expands the economic opportunities of individuals that are unable to tap into the dynastic wealth of their families to fund their entrepreneurial dreams.

# **3.1.5 Summary**

Overall, empirical research suggests that households benefit significantly from financial development, though considerable work remains. General equilibrium models and natural policy

> 11 There is also an extensive literature that examines the role of other factors, such as institutions, socioeconomic factors, individual characteristics and psychological factors in identifying the determinants of the decision to start entrepreneurial activity. See for example, Blanchflower and Oswald (2007) and Djankov et al. (2005 and 2006) among others.

30
# **Table of Contents**

|||page|
|---|---|---|
|1.|Introduction .........................................|<br>1|
|2.|Some Stylized Facts on the Informal Sector ....<br>....|...<br>4|
||2.1<br>Magnitudes and other Characteristics ....<br>....|.... 4|
||2.2<br>The Role of Women ..............................|.<br>8|
|3.|Model Formulation and Concepts .......................|.<br>12|
||3.1<br>Assumptions ....................................|.<br>13|
||3.2<br>Specific Aspects of the Model ..................|.<br>15|
||3.3<br>The Revenue Function of the Firm ....<br>.......|..... 18|
||3.4<br>Marginal Revenue Products and Profit||
||Maximization ...................................|.<br>19|
|4.|Description of the Data and Variables ....<br>........|.....<br>22|
|5.|The Empirical Model ..................................|.<br>28|
||5.1<br>Initial Specification ..........................|.<br>29|
||5.2<br>Preliminary Tests for Aggregation and Pooling ...|.<br>29|
||5.3<br>Diagnostic Analysis ............................|.<br>31|
||5.4<br>Analysis of Nonlinearity .......................|.<br>34|
||5.5<br>Distribution of the Errors .....................|.<br>37|
|6.|Empirical Findings and Interpretation ....<br>........|.....<br>38|
||6.1<br>Factor Productivities ..........................|.<br>42|
||6.2<br>Nonparametric Evidence .........................|.<br>59|
||6.3<br>Simulation Experiments .........................|.<br>67|
|7.|Policy Implications ..................................|.<br>70|
||7.1<br>Enterprise-Specific Policies ....................|<br>71|
||7.2<br>Women-Specific Policies .72||
|Appe|ndix A: Nonparametric Analysis .74||
||Nonparametric Modeling .74||
||Defining Conditional Means and Their Properties|75|
||<br>Estimating Conditional Means and Derivatives .76||
|Appe|ndix B: The Rural Model .78||
|Bibl|iography .84||

**J. Barry Smith is a member of the Department of Economics, York University, Toronto, Ontario and Morton Stelcner is in the Department of Economics, Concordia University, Montreal, Quebec. The authors thank the foLlowing individuals: Jorge Castillo, Shahid Khandker, Valerie Kozel, and Marcia Schafgans of the World Bank; Richard Laferriere, Department of Economics, Concordia University; Georges Monette, Department of Mathematics, York University; Jeffrey Racine, Department of Economics, York University; Gwenn B. Hughes, Policy and Strategic Analysis Branch, Labour Canada, Ottawa, and Gail A. Spence, Canadian Pacific Forest Products Limited, Montreal. The authors also benefited from the opportunity to present an earlier version of the paper at a seminar sponsored by the Women in Development Division. Jeffrey Racine performed the nonparametric analysis and wrote Appendix A. The research was funded by the Women in Development Division, the World Bank.**
26 generated much of the dynamism during the course of the 1980s, and foreign enterprises were an important growth factor in the 1990s. Another important example of the gradualisms is that firms and households were exposed only step-bystep to competitive markets and new price relations.<sup>36</sup> The delay of the contraction and privatization of SOEs was also an important element of the gradualism.

It is often (realistically) argued that the gradualism mitigated tendencies to mass unemployment, as occurred in the former Soviet Union and Eastern Europe during their transitions. In other words, it is likely that the gradualist approach contributed to improve the synchronization of job destruction and job creation during a transition period.<sup>37</sup> An obvious disadvantage was, of course, that the expansion of private (and often more efficient) firms was retarded as a result, and that reforms of financial markets were delayed.<sup>38</sup> We cannot be sure, however, that gradualism will be equally successful in the future. First, successful reforms of factor markets, not least financial markets, often require a large number of comprehensive and complementary policy measures, including the buildup of market-supporting institutions of various types. Second, there is always a risk that a gradualist reform process will come to a stop, since gradualism gives various interest groups time to build up resistance to further changes (“veto points”). By way of comparison, recent attempts in Western Europe to gradually deregulate product and factor markets have encountered such problems.

It remains to explain _why_ China chose such a pronouncedly gradualist approach to begin with. One reason might be that the country had recent experiences of a number of

> 36 See, for instance, Lau, Qian and Roland (1998). In particular, “shock effects” of the price reforms were mitigated by a dual price system during a transition period, in the sense that economic agents could count on previously established prices for production volumes that had already been planned and contracted. These arrangements, however, also opened the gates for price arbitrage and corruption. 37 Zhang (2004) reports that the average annual ratio of job destruction in SOEs recently has exceeded the rate of job creation only by a small margin, yielding a net employment reduction of no more than 1.4 percent per year between 1995 and 2000 and by 2.0 percent between 1999 and 2002 – according to a large sampling study.

> 38 By emphasizing the mitigation of unemployment rather than the promotion of microeconomic efficiency, some observers, such as Godoy and Stiglitz (2006), have argued that the delay of privatization was conducive to China’s transition to a market economy.

By way of comparison, developed countries have occasionally used the same technique to avoid an abrupt rise in unemployment at the time of rapid structural change, such as in connection with the contraction of coal, steel and shipyards industries in Western Europe in the 1960s and 1970s.
- Muralidharan, K. & Niehaus, P. (2017). Experimentation at scale. _Journal of Economic Perspectives_ , 31(4), 103–24.

- Muralidharan, K., Romero, M., & Wüthrich, K. (2019). Factorial designs, model selection, and (incorrect) inference in randomized experiments. (26562).

- Pennings, S. (2021). Cross-region transfer multipliers in a monetary union: Evidence from social security and stimulus payments. _American Economic Review_ , 111(5), 1689–1719.

- Ralston, L., Andrews, C., & Hsiao, A. (2017). The impacts of safety nets in africa: what are we learning?

- Rosenzweig, M. R. & Udry, C. (2019). External Validity in a Stochastic World: Evidence from Low-Income Countries. _The Review of Economic Studies_ , 87(1), 343–381.

- Vivalt, E. (2020). How Much Can We Generalize From Impact Evaluations? _Journal of the European Economic Association_ . jvaa019.

50
all distributions of out-of-pocket health payments as a share of total expenditure, the mean substantially exceed the median and the coefficients of variation are large, in particular in Montenegro.

**Table 9 Out-of-pocket payments for health care (as % of total expenditure)**

||Mean|Median|Coeff. of variation*|
|---|---|---|---|
|Albania|6%|3%|1.44|
|Bosnia and Herzegovina|2%|0%|2.16|
|Kosovo|8%|4%|1.33|
|Montenegro|1%|0%|2.84|
|Serbia|4%|1%|1.96|

*Coefficient of variation is equal to the standard deviation divided by the mean

# _5.3 Effect of out-of-pocket payments on poverty measures_

In Table 10, we use another approach to assess the poverty impact of health care payments. It consists of comparing the poverty measures before and after health care spending is taken into consideration. Given data availability, we use the PA Poverty Lines, calculated in local currency (LCU), by the World Bank Poverty Assessment team as national poverty lines. A comparison of poverty headcounts and poverty gaps before and after health care spending provides a sense of the impoverishing effect of health expenditure, in terms of the additional number of people classified as poor or the deepening poverty among the poor<sup>16</sup> .

Table 10 shows that health payments increase the number of poor Albanian households from 13% to 16% of the total population, i.e. poverty headcount increases by 20 percent. The relative impact on the measured poverty gap is even larger (34 percent). Looking at differences across countries, overall the impact of health expenditure on poverty headcount is not negligible: health payments increase the incidence of poverty by 15% in Kosovo, 13% in Serbia, 10% in Bosnia and Herzegovina and 6% in Montenegro. Also the after-health-payment poverty gap increases by 28% in Kosovo, 20% in Serbia, 11% in Bosnia and Herzegovina and 1% in Montenegro. Where the poverty gap after accounting for out-of-pocket payments is typically larger than adjustments to the poverty headcount (e.g. in Albania, Bosnia and Herzegovina and Serbia), then health care payments not only raise the prevalence of poverty but also its intensity. For purposes of comparison, Table A4 in the Appendix provides estimates using the single international poverty line.

The magnitude of these results should be treated with some caution because of potential bias. If poor people are less likely to seek care, the after-healthcare-payment headcount may be downward biased; on the other hand, if rich people are more likely to be insured, the measure will be upward biased.

While no causal relationship can be inferred from above results, it is undeniable that taking into account health care payments notably raises the incidence and intensity of poverty in the Western Balkans. The greatest differences are found in Albania and Kosovo, followed by Serbia. Montenegro is notable for the degree of financial protection its healthcare system appears to provide.

> 16 See World Bank _Quantitative Techniques for Health Equity Analysis—Technical Note # 19._

19
# Social and Economic Impacts of Rural Road Improvements in the State of Tocantins, Brazil

Atsushi Iimi Eric R. Lancelot Isabela Manelici Satoshi Ogita The World Bank Group<sup>1</sup> JEL Classification: O120: Microeconomic Analyses of Economic Development I320: Measurement and Analysis of Poverty Q130 Agricultural Markets and Marketing; Cooperatives; Agribusiness J430 Agricultural Labor Markets Keywords: Impact Evaluation Results and Methods, Rural Roads Infrastructure and Transport, Access to Markets, Education and Jobs for Rural Communities and Farmers, Agriculture,

> 1 The authors thank Luis A. Andres, Lead Economist (GWADR), and Kirsten Hommann, Senior Economist (GSURR), as well as from the World Bank’s Brazil Country Office for their constructive feedback. The execution of this study would not have been possible without the kind financial support of a number of initiatives, including a DEC research grant, an LCR Sustainable Department (LCSSD) fund, and a Poverty and Social Impact Analysis (PSIA) grant.
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