rafmacalaba/datause-encoder-data huggingface dataset:
schools with even a small share of public financing: their achievement score per dollar is only a third as high. More importantly, the cost savings that produce this greater cost effectiveness come disproportionately from personnel whose cost per student is 52 percent lower, with much smaller cost savings for other inputs (Table 5).

|Table 5:<br>**Relativ**<br>**non-loc**|**e cost effectiveness and expenditures comp**<br>**ally financed schools**<br>Proportion of funds from local sources:|**osition of locally versus**|
|---|---|---|
||<5%<br>> 25%|Percentage difference|
|Overall score|42.46<br>48.25|13.6%|
|Total expenditures|675<br>265|-60.7%|
|Achievement score per<br>peso|0.06<br>0.18|189.1%|
|Cost per student (Philipp|iespesos)<br>.||
|Personnel|469<br>226|-51.8%|
|Maintenance and<br>operating<br>_<br>|42<br>35<br>|-16.5%|
|Textbooks<br>|1<br>7<br>**5**|-26.6%|

Source: Adapted from Jimenez and Paqueo (1996), table 1.

Similarly, James, King and Suryadi (1996) find that even controlling for whether schools are public or private, a greater share of funds that are locally raised compared to centrally allocated increases significantly educational output (in their model, decreases cost for a given output)<sup>2 2</sup> .

Many countries have adopted locally run community schools as a mechanism to accommodate the demand for schooling in remote and rural areas that are not reached by the 22 Both of these papers use econometric estimation of the dual, that is, cost per student controlling for quality, rather than estimate a production function. However, their results on local financing indicate that the primal problem is not maximization of output subject to a budget constraint, but rather some more complicated objective function involving teacher utility of the type we explore here.

30
within industries does not appear to be directly associated with earnings. While the results from the Table 5 do indicate some compositional changes favoring females and low-skilled workers, these changes are not strong enough to establish income-level correlations.

Table 4: Relationship between the green goods trade and earning across gender and skill levels (without industry fixed effects)

||All|Male|Female|High skilled|Low skilled|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|**Annual Earnings**||||||
|Green Goods Export Share|0.00149|0.00399|-0.00233|0.000207|0.00388|
||(0.64)|(1.66)|(-0.79)|(0.07)|(1.24)|
|Green Goods Import Share|0.0105***|0.00875***|0.0121***|0.00484**|0.0166***|
||(4.72)|(4.04)|(3.79)|(2.07)|(5.28)|
|Industry FE|No|No|No|No|No|
|Workers control|Yes|Yes|Yes|Yes|Yes|
|N|77544|52868|24676|15708|61836|
|adj. R-sq|0.365|0.360|0.388|0.036|0.123|

_Note: Results are based on repeated cross-sectional labor force survey data that are merged with industry level trade data. Industry fixed effects are at two-digit ISIC Rev 4 level. Worker’s control includes age, age squared, gender, marital status and 8 groups of educations in column (1). In column (2) and (3) gender is excluded while in column (4) and (5) education is not included as these regression are gender and skill based, respectively. Standard errors clustered at industry level. t-statistics in parentheses. p < .10, ** p < .05, *** p < .01_ 29
Table A.10: Attrition of contact farmers and other farmers

|Variables|Treate|d|Contro|l|Dierence|
|---|---|---|---|---|---|
||Mean|SD|Mean|SD|in mean|
|CFs attrited from Midline|0.109|0.313|0.048|0.216|0.062|
|Number of Observations|137||42||179|
|Household attrited from Midline|0.090|0.372|0.087|0.374|0.003|
|Number of Observations|2750||935||3685|

Sources: Household survey, 2012, 2013; Contact farmer survey, 2012, 2013. Notes: CF=contact farmer. T-test inferences are based on standard errors clustered at the community level. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level.

53
The economic literature on sudden stops was motivated by the 1994 Mexican crisis, with episodes of sudden-stops subsequently identified for many emerging market economies going back to the 1980s, the period of substantial cross-border private capital flow liberalization. The link between sudden stops and output loss has also been excessively investigated in recent years. In particular, consistent with short-run dynamics between financial contractions and drops in real sector value added growth rates identified in this paper, Hutchison and Noy (2006) find that sudden-stop crises have a large but short-lived negative effect on output over and above that of currency crises. Calvo and Reinhart (2000) attribute the link between sudden-stops and output collapses to the credit channel, whereby abrupt stops in foreign capital inflows cause local credit markets to dry up, thus reducing investment and domestic demand. A related channel, emphasized by Mendoza (2001), concerns the combined effect of sudden-stops and currency crises, whereby the deterioration of collateral in the financial sector causes debt deflation followed by real contraction. Against this theoretical backdrop, our regression results identify industries most susceptible to credit contractions and deteriorations in financial collateral values induced by a sudden-stop. The construction sector appears the most sensitive, followed by transportation (which also includes the highly credit-based shipping industry), wholesale and retail trade, and manufacturing.

Given the complex non-linear associations between financial contractions and real sector growth rates, we repeat the exercise of interacting financial contraction dummies with financial openness and foreign exchange reserves focusing only on the right-hand side of the distribution of financial contraction episodes, i.e., really big contractions. As Table 10 shows, the size of the contraction varies greatly by for each episode. Latin American economies experienced some of the largest drops in financial sector growth: with Venezuela experiencing a 35.9 percent drop in 1960, Costa Rica experiencing a 55.0 percent drop in 1959, and Chile experiencing a whopping 83.5 percent contraction in real financial sector value added in 1982. Several Asian economies also underwent large financial contractions, with Indonesia experiencing a 28.4 percent drop in 1997 during the Asian Financial Crisis. On the other hand, financial contractions in other, especially European economies, tended to be much smaller in magnitude. For instance, in 1951 Britain’s financial sector growth rate simply declined to zero, while Sweden experienced mild contractions of 0.6 and 2.8 percent in 1975 and 1992 respectively.

15
_Academy of Sciences of the United States of America_ 93 (23): 13416-13420. Accessed 9 2, 2019. https://ncbi.nlm.nih.gov/pmc/articles/pmc24108.

- Himanshu, Abhijit Sen. 2013. "In-Kind Food Transfers — II: Impact on Nutrition and Implications for Food Security and Its Costs." _Economic and Political Weekly_ 48 (47): 60–73.

- Hoddinott, John, Daniel Gilligan, Melissa Hidrobo, Amy Margolies, Shalini Roy, Susanna Sandstrom, Benjamin Schwab, and Joanna Upton. 2013. _Enhancing WFP’s capacity and experience to design, implement, monitor, and evaluate vouchers and cash transfer programmes: Study summary._ Washington, DC: International Food Policy Research Institute.

- Hoddinott, John, Susanna Sandstrom, and Joanna Upton. 2018. "The impact of cash and food transfers: Evidence from a randomized intervention in Niger." _American Journal of Agricultural Economics_ 100 (4): 1032--1049.

- Ianchovichina, Elena, and Maros Ivanic. 2014. _Economic effects of the Syrian war and the spread of the Islamic state on the Levant (English)._ Washington DC: World Bank Policy Research Working Paper 7135.

- International Monetary Fund. 2017. _Staff Report for the 2017 Article IV Consultation._ Washington DC: International Monetary Fund.

- IOM. 2019. _DTM Baseline Dashboard._ October 31. Accessed January 16, 2020. http://iraqdtm.iom.int/default.aspx.

- James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. _An Introduction to Statistical Learning._ Springer.

- Kaul, Tara. 2018. "Household Responses to Food Subsidies: Evidence from India." _Economic Development and Cultural Change_ 67 (1): 95-129. Accessed 10 16, 2019.

- http://appam.org/assets/1/7/household_responses_to_food_subsidies_evidence_from_india.p df.

- Kaushal, Neeraj, and Felix M. Muchomba. 2015. "How Consumer Price Subsidies affect Nutrition." _World Development_ 74: 25-42. Accessed 10 16, 2019.

https://sciencedirect.com/science/article/pii/s0305750x1500090x.

- Kochar, Anjin. 2005. "Can Targeted Food Programs Improve Nutrition? An Empirical Analysis of India’s Public Distribution System." _Economic Development and Cultural Change_ 54 (1): 203-235. doi:https://doi.org/10.1086/431260.

- Krishnamurthy, Prasad, Vikram Pathania, and Sharad Tandon. 2017. "Food Price Subsidies and Nutrition: Evidence from State Reforms to India's Public Distribution System." _Economic Development and Cultural Change_ 66 (1): 55-90. Accessed 10 16, 2019.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2345675.

- Krishnan, Nandini, and Sergio Olivieri. 2016. _Losing the Gains of the Past._ Washington DC: World Bank Policy Research Working Paper 7567.

21
Valuation of land sometimes presents another challenge. Beyond the inconsistencies noted above, several of the surveys from Africa ask whether or not the land can be sold and then ask the value of the land, but only if it can be sold. This poses challenges for valuing land that is owned, but cannot be sold. In these cases, collecting data on the potential rental value of land allows land values to be estimated indirectly.

Another challenge is differentiating ownership rights between the land and house. In some places, particularly in urban areas, the house and the land on which it sits may be owned separately. The surveys for Guyana (1999), Haiti (2001) and Jamaica (2001) ask about ownership of the house and housing plot separately, although most surveys do not.

4.2. Best Practices in Collecting Asset Ownership Data at the Individual Level In this section we examine the incidence of questions on individual asset ownership with attention to how this information is elicited. We complement the review of the LSMS questionnaires with a review of household surveys undertaken by the International Food Policy Research Institute (IFPRI), the International Center for Research on Women (ICRW), ORCMacro, and other institutions. Data on the LSMS questionnaires is summarized in Table 4 below and Appendix Tables A2-A3. Appendix Table A4 presents data on the IFPRI surveys, Table A5 presents data on ICRW surveys, and Table A6 presents data on surveys implemented by other institutions. This discussion is organized by asset category, followed by a review of other variables that are important for a gender analysis of asset ownership and control.

While most LSMS questionnaires collect information on at least some household assets, as Table 4 shows far fewer collect information on asset ownership at the individual level.<sup>22</sup> Nonetheless some progress has been made, particularly in Latin America, with respect to soliciting data at the individual level on land and housing ownership. These cases demonstrate that at least it is possible to collect individual level information.

21
Kucuker H. "Occupational fatalities among coal mine workers in Zonguldak, Turkey, 1994–2003". Occup Med (Lond) 2006 Mar;56(2):144-6. PMID 16490795 Terazawa K, Takatori T, Tomii S, Nakano K. Methane asphyxia. Coal mine accident investigation of distribution of gas. Am J Forensic Med Pathol. 1985 Sep;6(3):211-4. PMID 3870672 Retzer, John. "Ten Worst Mining Disasters". Michigan, USA: Bogey Media. Archived from the original on 18 April 2010. Retrieved 17 May 2014.

"The world's worst coal mining disasters". Retrieved 16 August 2015.

"Chinazhaoge Blog" (in Chinese). sohu.com. Retrieved 7 August 2010.

Pittsburgh Post Gazette July 3,1960 page 3 section 1 History.com. "Mine explodes in Oklahoma". History.com. A+E Networks. Retrieved 10 February 2016.

http://www.dmm.org.uk/uknames/u1893-03.htm McAteer, Davitt (December 6, 2007). Monongah: The Tragic Story of the 1907 Monongah Mine Disaster, the Worst Industrial Accident in US History. West Virginia University Press. p. 332. ISBN 1-933202-29-7 35
Policy Research Working Paper 10013

# **Abstract**

This paper contributes to the debate on ways to improve the calculation of inequality measures in developing countries experiencing severe budget constraints. Linear regression-based survey-to-survey imputation techniques are most frequently discussed in the literature. These are effective at estimating predictions of poverty indicators but are much less accurate with inequality indicators. To demonstrate this limited accuracy, the first part of the paper discusses several simulations using Moroccan Household Budget Surveys and Labor Force Surveys. The paper proposes a method for overcoming these limitations based on an algorithm that minimizes the sum of the squared difference between a certain number of direct estimates of an index and its empirical version obtained from the predicted values. Indeed, when comparing the estimated results with those directly estimated from the original sample, the bias is negligible. Furthermore, the inequality indices for the years for which there are only model estimates, rather than direct information on expenditures, seem to be consistent with Moroccan economic trends.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at vmolini@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
billion people living outside the rich world.<sup>5</sup> So, both of Rawls’s assumptions (or desiderata) seem to hold.<sup>6</sup> But we shall, for the sake of exposition, modify the Rawlsian metaphor in so much as we shall let the global assembly (i) be the one of all individuals in the world, and not of peoples’ representatives, and (ii) not be designed so that the individuals reach a contractarian arrangement. As is customary (from _Theory of Justice_ ), individuals meet behind the veil of ignorance. In our original position, each of them is allocated two characteristics that will determine his fate: county and income class within that country.<sup>7</sup> As we have just seen, assignment to country is “fate” since there is no inter-country movement of people. Things are a bit more complicated regarding assignment to income class. It can also be seen as “fate” if there is no social mobility within countries. At the other extreme, with perfect social mobility, assignment to income class would not matter as each individual would, through his own exertion and luck, find his “merited” position in society.

We know that differences between mean country incomes are large: about threequarters of global inequality between individuals is due to between-national income differences.<sup>8</sup> Consequently, to what nation one gets allocated is indeed of significant import for own life chances. By being allocated to a country, the individual receives two “public” goods that are unalterable by his own effort and that are basically fixed during the largest part of his life: mean income of the country (relative to the rest of the world) and national level of inequality (which determines what share of total income will be

> 5  See <u>http://www.oecd.org/dataoecd/61/37/34607274.pdf, accessed February 9, 2007. The poor and rich</u> countries are defined here conventionally: the rich as the European Economic Area (inclusive of Switzerland but without Greece), United States, Canada, Australia and Japan; the poor as everybody else.

6 The total stock of people living outside a country of their birth is estimated at 3 percent of world population (see Ozden and Schiff, 2006).

7 If there are N countries, the probability of being assigned a given country is 1/N. In other words, the probability does not depend on the country’s population. One could of course envisage a different “lottery” where the probability of being assigned a country would be proportional to the country’s population, or to its share of people born in a given year.

> 8 See Milanovic (2002, p.78 and 2005, p. 112), Sutcliffe (2004), Bourguignon and Morrisson (2002, p. 734), Berry and Serieux (2007, p. 84).

3
US or Indian data for example) should go some way toward solving this problem, but at the same time, because a large part of infrastructure is both of local and national use (for example roads that go through and connect different regions), and because there might be inter-regional spillover benefits from the infrastructure stock, it obviously raises other difficult questions with respect to allocation of costs and benefits of infrastructure to one local area rather than another.

Second, the results in Cadot et al. (2006) show that in a world of limited resources and non-perfectly benevolent governments, assessing the optimality of past and present infrastructure investment decisions (including the choice between new investments and maintenance of existing ones) would best be based on a positive theory of these decisions. Similar work in the context of developing countries would therefore be extremely useful. We discuss in the last section of this paper how the political economy literature could provide part of the needed ingredients for such an approach.

# **3.1.5. Other Issues**

Having looked at the different problems likely to bias estimates of the effects of infrastructure and at how they have been addressed in the literature, we cannot reject the possibility that results may simply differ according to the geographical areas and time periods analyzed. While the level of aggregation should matter little as long as suitable controls are used (Holtz-Eakin, 1994), different pictures tend to emerge for groups of developing countries vs. developed one, or for a given country in the 1950s or 1960s vs. the 1990s. Several theoretical arguments are relevant here. First, as was already mentioned above, as initial investments to establish specific road or communication infrastructure for example may only have a one-time effect on productivity. In particular this should imply that developing countries with acute infrastructure shortages be characterized by high returns to these investments. Alternatively, network effects are often mentioned in the literature as a motive for why specific infrastructure investments may produce higher returns once some minimum critical threshold in term of coverage is 29
# **References**

- Abrevaya, J. (2009): “Are There Missing Girls in the United States? Evidence from Birth Data,” _American Economic Journal: Applied Economics_ , 1, 1–34.

- Ahlawat, N. (2009): “Missing Brides in Rural Haryana: A Study of Adverse Sex Ratio, Poverty and Addiction,” _Social Change_ , 39, 46–63.

- Almond, D. and J. Currie (2011): “Killing Me Softly: The Fetal Origins Hypothesis,” _Journal of Economic Perspectives_ , 25, 153–72.

- Almond, D. and L. Edlund (2007): “Trivers-Willard at Birth and One Year: Evidence from US Natality Data 1983-2001,” _Proceedings of the Royal Society B_ , 274, 2491–2496.

- ——— (2008): “Son Biased Sex Ratios in the US 2000 Census,” _Proceedings of the National Academy of Sciences of the United States of America_ , 105, 5681–5682.

- Almond, D., H. Li, and L. Meng (2010): “Son Preference and Early Childhood Investments in China,” _Working Paper_ .

- Amaral, S. and S. Bhalotra (2017): “Population Sex Ratios and Crime Against Women: Long Run Effects of Sex Selection in India,” Mimeo, University of Essex.

- Ananat, E. O., J. Gruber, P. B. Levine, and D. Staiger (2009): “Abortion and Selection,” _Review of Economics and Statistics_ , 91, 124–136.

- Ananat, E. O. and D. M. Hungerman (2012): “The Power of the Pill for the Next Generation: Oral Contraception’s Effects on Fertility, Abortion, and Maternal and Child Characteristics,” _Review of Economics and Statistics_ , 94, 37–51.

- Anderson, R. G. and A. Kichkha (2017): “Replication, Meta-analysis, and Research Synthesis in Economics,” _American Economic Review_ , 107, 56–59.

- Anderson, S. and D. Ray (2010): “Missing Women: Age and Disease,” _Review of Economic Studies_ , 77, 1262–1300.

- Anukriti, S., S. Kwon, and N. Prakash (2016): “Dowry: Household Responses to Expected Marriage Payments,” _mimeo_ .

- Arnold, F., S. Kishor, and T. Roy (2002): “Sex-selective Abortions in India,” _Population and Development Review_ , 28, 759–785.

- Ashraf, Q. H., D. N. Weil, and J. Wilde (2013): “The Effect of Fertility Reduction on Economic Growth,” _Population and development review_ , 39, 97–130.

- Bailey, M. (2006): “More Power to the Pill: The Impact of Contraceptive Freedom on Women’s Life Cycle Labor Supply,” _Quarterly Journal of Economics_ , 121, 289–320.

- Bailey, M. J., O. Malkova, and Z. M. McLaren (2017): “Does Parents’ Access to Family Planning Increase Children’s Opportunities? Evidence from the War on Poverty and the Early Years of Title X Does Parents’ Access to Family Planning Increase Children’s Opportunities? Evidence from the War on Poverty and the Early Years of Title X,” _NBER Working Paper 23971_ .

- Becker, G. S. and H. G. Lewis (1974): “The Interaction between Quantity and Quality of Children,” _Economics of the Family: Marriage, Children, and Human Capital_ , 81–90.

- Bhalotra, S. (2010): “Fatal Fluctuations: Cyclicality in Infant Mortality in India,” _Journal of Development Economics_ , 93, 7–19.

- Bhalotra, S., R. Brule, and S. Roy (2018a): “Women’s Inheritance Rights Reform and the Preference for Sons in India,” _Journal of Development Economics_ , Forthcoming.

29