rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8127

# **Abstract**

This paper proposes a new interpretation of the farm size–productivity relationship. Using two rounds of the Ethiopian Rural Household Survey, and drawing on earlier work on five countries in Sub-Saharan Africa, the paper shows that the relationship between farm size and productivity is neither monotonic nor univocal. Most previous studies that tested the inverse farm size–productivity relationship used ordinary least squares estimation, therefore reporting parameter estimates at the conditional mean of productivity. By expanding these important findings to consider the entire distribution of agricultural productivity, the analysis finds sign switches across the distribution, pointing to a “direct-inverse-direct” relationship. Less productive farmers exhibit an inverted U-shape relationship between land productivity and farm size, while more productive farmers show a U-shape relationship that reverses the relationship. In both cases, the relationship points toward a threshold value of farm size; however, the threshold is a minimum for the less productive farmers and a maximum for the more productive ones. To the left of the threshold, for very small farmers, the relationship between productivity and farm size is positive; for the range of middle farm size, the relationship is negative; and to the right of the threshold, the relationship is direct (positive) again. From a policy perspective, these findings imply that efficiency-enhancing and redistributive land reform should consider farm size in the proper context of the present and potential levels of agricultural productivity. The results and their policy implications underline the relevance of the most recent efforts of the international development community to collect more reliable georeferenced data on farm size and agricultural productivity.

This paper is a product of the Global Indicators 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://econ.worldbank.org. The authors may be contacted at ssavastano@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
# **4. Fairness through the eyes of people**

In this section, we explore how individuals' characteristics relate to their views on inequality. As shown in the previous section, most of the change in perceptions over the last decade was driven by the share of the population that perceived income distribution as being very unfair, thus we focus on explaining the correlates of such measure, although we also show the results for a broader definition of unfairness.

## **4.1 DATA**

Our sample of individuals comes from pooling all LA countries from nine different waves of Latinobarómetro over the 1997-2015 period. Appendix Tables A1-A3 show basic descriptive statistics of the sample. Roughly half of respondents are women (50.9%), the average age was 39.4 (most interviewees—38%—were aged 25-40). Over half of the sample (57.3%) reported being married or in a civil union, and are adherents to Catholicism (70.7%).

About 90 percent of the sample are literate, the majority of respondents (76%) completed at least primary school, while a third of them (32.3%) had secondary education or more. Almost two-thirds of the sample (64%) were part of the labor force, and 9.9% of them were unemployed. Access to basic services among respondents is relatively high: 87.6% of individuals had access to running water inside their dwelling and over two-thirds (69.7%) reported that their dwellings had access to a flush toilet connected to a waste-removal system (i.e., sewerage). Ownership of durable goods ranges from low levels regarding cars and computers (27.3% and 29.6%, respectively) to high levels regarding fridges and mobile phones (79.2% and 76.4%).

To assess the differences between Latinobarómetro’s sample and the household surveys’ sample (SEDLAC), Appendix Table A4 compares a set of summary statistics in both data sets in 2013. To ensure comparability of the samples, we restrict the calculations to individuals aged over 18, and to countries with data available in both databases. In general, the samples are similar in observable characteristics. For instance, the average age in Latinobarómetro’s reduced sample is 40.6 years, while in SEDLAC it is 42.7 years. Similarly, the percentage of males is 48.9% in Latinobarómetro and 47.6% in SEDLAC. The main difference arises from educational attainment. On average, the SEDLAC subsample is more educated (46.1% of the population has secondary education or more, while this figure is 38.8% in Latinobarómetro).

## **4.2 ESTIMATION STRATEGY**

To formally assess the relationship between individuals’ characteristics and fairness perceptions, we run Logit regressions where the dependent variable takes the value 1 if the individual believes income distribution is very unfair and 0 otherwise. In the baseline specification, we assume that unfairness perceptions can be characterized according to the following equation: 16
Beaton, Mullis et al., 1997; Martin et al. 1997 ; Martin et al. 2000; Martin et al. 2004; Martin, Mullis & Foy 2008; Martin, Mullis et al 2012; Mullis et al. 1997; Mullis et al. 2000; Mullis et al. 2004; Mullis, Martin & Foy 2008; Mullis et al. 2012). Countries participating in TIMSS 2015 were identified through the IEA website. The variable used in the analyses was whether or not the country had participated in TIMSS during the five year period preceding the specific PISA cycle, as follows: PISA 2000 (TIMSS 1995 or TIMSS 1999), PISA 2003 (TIMSS 1999), PISA 2006 (TIMSS 2003), PISA 2009 (TIMSS 2007), PISA 2012 (TIMSS 2007 or TIMSS 2011), PISA 2015 (TIMSS 2011).

Countries that had participated in any one of three _regional large-scale assessments_ were identified from their websites. Countries participating in SACMEQ I, II and III were listed in the SACMEQ website (2015). Countries participating in PERCE, SERCE and TERCE were listed in the LLECE website (UNESCO Santiago 2015). Countries participating in PASEC 2012 were listed in the CONFEMEN website (CONFEMEN 2015). The year(s) of participation in each assessment were noted. The variable used in the analyses was whether or not the country had participated in a regional assessment prior to a specific PISA cycle.

Countries also build their own assessment capacity through _national assessments_ . The UNESCO Global Monitoring Report provided data on each country’s history of national assessments (UNESCO 2015). The variable used in the analyses was whether or not the country had conducted a national assessment in the three years prior to a specific PISA cycle.

**Role of economists.** The indicator for the role that economists play in the growth of PISA is the country’s membership in the OECD. Historical data on OECD membership was extracted from the OECD website. The variable used was whether at the time of a particular PISA cycle the country was an OECD member.

# **C. Issues with matching**

All data were matched by country and year, and the unit of analysis used in this paper is the “country-PISA cycle.” The assumption is that all countries could have participated in up to six cycles of PISA, depending upon whether or not the country existed at the time of the specific cycle.

Some issues in matching data were encountered, since both PISA and TIMSS report information on “education systems” while the World Bank reports information on “economies.” This means, for example, that PISA and TIMSS report results for various education systems in the United Kingdom (Scotland, England, Wales and Northern Ireland) whereas the World Bank 6
_s_

## **FICURE** 4d

# IDA ADJUSTMENT LENDING TO THE LICs & TOTAL NET TRANSFES

<!-- Start of picture text --> 0.07 -<br>a SENEGAL<br>0.06 - O BURUNDI<br>0.05 -<br>0.04 - 0 SOMALIA O BURKINA<br>ElC.A.R<br>II ALL NON-SALS<br>a. 0.03- a ABA<br>O 0 MADAGASCAR<br>0 OSIERA LEONE<br>0.02 -0 GHANA<br>OTANZANIA<br>ffi 0.01 a,SUDAN 31<br>WALL SALS<br>0^S ° - 0 s GUINEA<br>z O NIGER<br>.( -0.01 -<br>O 0 KENYA<br>-0.02 -<br>0 ZAIRE<br>-0.03 - 1 . 1 1 1 1 1 1 1 1<br>0 0.002 0.004 0.006 0.008 0.01 0.012<br>SAL+SECAL/GNP (AVG. 1982-1986)<br>0 1:MALAWI 2:PAKISTAN<br><!-- End of picture text --> Correlation Coefficient: .02
- Bento, P. and D. Restuccia (2017): “Misallocation, Establishment Size, and Productivity,” _American Economic Journal: Macroeconomics_ , 9, 267–303.

- ——— (2021): “On average establishment size across sectors and countries,” _Journal of Monetary Economics_ , 117, 220–242.

- Bhandari, A., T. J. M. Tobey Kass, E. R. McGrattan, and E. Schulz (2022): “On the Nature of Entrepreneurship,” _SOI Working Papers, Internal Revenue Service_ .

- Bolivia, I. (2017): “Encuesta de Hogares,” Instituto Nacional de Estad ́ıstica, Bolivia.

- Brooks, W. J., J. P. Kaboski, Y. A. Li, and W. Qian (2021): “Exploitation of labor? Classical monopsony power and labor’s share,” _Journal of Development Economics_ , 150, 102627.

- Busso, M., M. V. Fazio, and S. Levy (2012): “(In)Formal and (Un)Productive: The Productivity Costs of Excessive Informality in Mexico,” Research Department Publications 4789, Inter-American Development Bank, Research Department.

- Central Bureau of Statistics (2017-2018): “Nepal Labour Force Survey,” National Planning Commision, Central Bureau of Statistics, Government of Nepal.

- DANE (2019): “Gran Encuesta Integrada de Hogares, GEIH,” Departamento Administrativo Nacional de Estad ́ıstica, Colombia.

- de la Rep ́ublica Dominicana], B. C. (2019): “Encuesta Nacional Continua de la Fuerza de Trabajo, ENCFT,” Banco Central, Rep ́ublica Dominicana.

- De Loecker, J. and J. Eeckhout (2018): “Global Market Power,” Working Paper 24768, National Bureau of Economic Research.

- De Loecker, J., J. Eeckhout, and G. Unger (2020): “The Rise of Market Power and the Macroeconomic Implications,” _The Quarterly Journal of Economics_ , 135, 561–644.

- Department of Social Services (2019): “Household, Income and Labour Dynamics in Australia Survey, HILDA,” Department of Social Services, Government of Australia.

- EUROSTAT (2019): “European Union Statistics on Income and Living Conditions, EU-SILC,” EUROSTAT.

- Fairfield, T. (2015): _Private Wealth and Public Revenue in Latin America: Business Power and Tax Politics_ , Cambridge University Press.

- Gindling, T. H. and D. Newhouse (2014): “Self-employment in the developing world,” _World development_ , 56, 313–331.

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WPS4122

*

# **The Role of Education Quality in Economic Growth**

Eric A. Hanushek Hoover Institution Stanford University CESifo and NBER Stanford, CA 94305-6010, United States Phone: (+1) 650 / 736-0942 E-mail: hanushek@stanford.edu Internet: www.hanushek.net Ludger Wößmann University of Munich, Ifo Institute for Economic Research and CESifo Poschingerstr. 5 81679 Munich, Germany Phone: (+49) 89 / 9224-1699 E-mail: woessmann@ifo.de Internet: www.cesifo.de/woessmann

## **Abstract**

The role of improved schooling, a central part of most development strategies, has become controversial because expansion of school attainment has not guaranteed improved economic conditions. This paper reviews the role of education in promoting economic well-being, with a particular focus on the role of educational quality. It concludes that there is strong evidence that the cognitive skills of the population – rather than mere school attainment – are powerfully related to individual earnings, to the distribution of income, and to economic growth. New empirical results show the importance of both minimal and high level skills, the complementarity of skills and the quality of economic institutions, and the robustness of the relationship between skills and growth. International comparisons incorporating expanded data on cognitive skills reveal much larger skill deficits in developing countries than generally derived from just school enrollment and attainment. The magnitude of change needed makes clear that closing the economic gap with developed countries will require major structural changes in schooling institutions.

### World Bank Policy Research Working Paper 4122, February 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* This project developed through conversations with Harry Patrinos, who provided useful comments and suggestions along the way. We have also benefited from comments by Martha Ainsworth, Luis Benveniste, François Bourguignon, Deon Filmer, Paul Gertler, Manny Jimenez, Ruth Kagia, Beth King, Lant Pritchett, and Emiliana Vegas. Support has come from the World Bank, CESifo, the Program on Education Policy and Governance of Harvard University, and the Packard Humanities Institute.
three or below out of a maximum value of six.<sup>17</sup> Table 6 presents these regressions for all firms from weak shareholder protection countries and for various subsamples of firms where bonding theories predict that the effects on _q_ will be largest, i.e., firms that list in public exchanges, firms that raise capital in public markets, and firms that list in the U.S. markets.

We again find the same basic pattern. Internationalization is not associated with an enduring increase in _q_ . We also confirm the results when including the shareholder protection index directly in the regressions, or when controlling for legal origin. These are the same variables used by La Porta et al. (2002). We also included interaction terms between the internationalization dummy variable and shareholder protection to assess further whether internationalization has a different effect on firms from different legal systems. We find that these interaction terms enter insignificantly. In additional (unreported) robustness tests, we included measures of institutional quality, such as an index of the efficiency of the judicial system produced by Business International Corporation and an index of accounting standards produced by the Center for International Financial Analysis and Research, and obtained similar results. Including these controls did not affect our conclusions. Also, we included interactions between these institutional indexes and the internationalization dummies. These interactions are not significant, and our results were not affected by their inclusion.

# **4. Results: Dynamics**

The analyses in Tables 1-6 compare average valuations before internationalization with average valuations after internationalization, which is a natural test of conflicting theories of internationalization. Nevertheless, averaging over the pre- and post-internationalization periods

> 17 We combine these two sources in order to increase the coverage in terms of countries.  Results are similar to those reported if we only consider the shareholder rights index from La Porta et al. (1998).  We also estimated the regressions including those countries with a shareholder rights index of two or less and obtained similar results.

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The loans were rolled out at Wasasa’s branches in the cities of Adama and Assela, in the Oromia region of Ethiopia. The psychometric-appraised, uncollateralized loan product was branded as a “WEDP-X” loan and was advertised to women entrepreneurs participating or interested in WEDP in those cities. WEDP-X was advertised via flyers at the “One-StopShop” offices where WEDP clients register and access advice and services. One-Stop-Shop staff were trained to highlight these flyers to prospective clients. The flyers and staff script specified that if a woman wanted a loan for her business but lacked collateral, she could take a 45-minute tablet-based test to enable the MFI to know her better, and then potentially be offered an uncollateralized loan with the features mentioned above.

# **3 Experimental design**

The application and baseline survey process took place on a rolling basis from April 2018March 2020.<sup>6</sup> Women entrepreneurs presented themselves at Wasasa branches in the cities of Adama and Assela if and when they wished to apply for a loan. The full application process was explained to women before they applied for a WEDP-X loan, including that only half of those women who qualified for the loan would actually receive the loan due to the need to test a new product, and that this would be determined by computer-randomized assignment.

As soon as a woman decided to apply, she completed the psychometric test on a tablet, in private, then and there at the Wasasa branch office. A short additional baseline survey was then administered to her by MFI loan officers working at the Wasasa branch, also on a tablet. The psychometric test data was synced to a server, which calculated the psychometric credit score in real time. The score was then sent through to the branch computer and viewed by the loan officer. Applicants scoring above the cut-off were told they were potentially eligible for the loan, and invited to submit documentation and schedule a business appraisal. For

> 6The baseline survey was concluded earlier than planned due to the COVID-19 pandemic. Ethiopia saw its first confirmed COVID-19 case on March 13th. Educational institutions were closed on March 16th, and a transportation ban began on March 30th. A state of emergency was declared on April 8th.

7
cam¢ot be reabsorbed in the tradeablss **sector, since the** coublnation of **a** fixed world prlce and **flxed** nominal wages prevents **the** product wag. from falling." The role for a parity **change** or UTCS is clear: *ither pollcy can be used to reduce **the** tradeableo product wage directly and (in **the absence** of illegal trade 45 ) achieve the optimal allocation at point 2.

# Cavts

WLth flexible goods prices, perfect capital mobility and forward-lookLng consumers, a devaluation or UTCS **Is** unambiguously expansionary from an Initial posLtion of unemployment. If any of **these** assumptions fail, however, **a** devaluation **may** have contractionary effects in the short run that offset the bensfits from falling product **wages** in the tradeables sector. Figure 5 may therefore give an overly optimistic picture of the role of a permanent parity chang or UTCS as a device for alleviating transitional unemployment. 46 A Assuming homotheticity of preferences, the equilibrium must fall somewhere between points I and 4. Notice that whle one can **say** that the nominal exchange rate ib overvalued at point 3 (given trade taxes), the real **exchange** rate, q, has adjusted fully to the fall in demand for nontradeables, and **say** not **be *misaligned" at all relative** to its full employment equLlibrium level (i.e., the equilibrlum may be at point 4). **This shws** that when there Lo unemployment due to sticky wages, the familiar prlce-index **based real** exchange rates may give a misleading indication of Incentives for resource allocation.

4.5 In the presence of smuggling or customs fraud, our analysis of Soctions 3 and 4 indicates that the UTCS is second-best, since it will lead to **some** combination of a wastage of resources and an unintended commercial policy.

46 There are other reasons why Figure 4 may be overly optimistic. As Buiter (1988) points out, the impact of a devaluation on resource allocation depends a great deal on the &QQ of underlying wage (or price) rigidities. If real **wages** were sticky due to lndexation, for exmple, then the ability of the authorities to reduce the product wage in tradeables would be limited by **the** implied reduction in the real **wage** index. If workers consumed only tradeables, for example, the authorities might find It Lmpossible to ralse the price of tradeables wLthout causing an offsetting rLse in the economy-wide 42
the receiving country j’s census usually identi...es individuals on the basis of age, gender g, country of birth i, and skill level s. Our method consists in collecting (census or registers) gender-disaggregated data from a large set of receiving countries, with the highest level of detail on birth countries and three levels of educational attainment: s = h for high-skilled, s = m for medium-skilled and s = l for low-skilled. Let Mt;g;s<sup>i;j</sup> denote the stock of adults 25+ born in j, of gender g, skill s, living in country j at time t.

Table 1 describes our data sources. For countries where population registers (mainly Scandinavian countries) are used, data is based on the whole population. In countries where Census data are used, statistics are either based on the whole population (Australia, New Zealand, Belgium, etc.) or on a sample of it (e.g. 25 percent in France, etc.). In some cases, we combine comprehensive register data on the numbers of adult males and females, but use sample data to estimate the educational structure (the UK is estimated on 10 percent of the population; in Germany, the microcensus is based on 1 percent of the population). The education structure is sometimes given by region or groups of countries; we then assume a constant share within the region. In a couple of countries, we use household and labor force surveys to estimate the educational structure. Finally, we also use IPUMS International data set for Mexico, Spain and the United States.

Aggregating these numbers over destination countries j gives the stock of emigrants from country i: Mt;g;s<sup>i=</sup> Xj<sup>M i;j</sup> t;g;s<sup>.</sup> This is the method used in DM06, without gender breakdown.

By focusing on census and register data, our methodology badly captures illegal immigration for which systematic statistics by education level and country of birth are not available<sup>7</sup> , except in the USA. Demographic evidence indicates most US illegal residents are captured in the census. However, there is no accurate data about the educational structure of these illegal migrants. Hence, we probably underestimate the number of unskilled in the immigrant population, assuming that most illegal immigrants are uneducated. Nevertheless, this limitation should not signi...cantly distort our estimates of the migration rate of highly-skilled workers.

> 7Hatton and Williamson (2002) estimate that illegal immigrants residing in OECD countries represent 10 to 15 percent of the total stock.

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