rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9937

# **Abstract**

This paper studies the distribution of credit during crisis times and its impact on firm indebtedness and macroeconomic risk. Whereas policies can help firms in need of financing, they can lead to adverse selection from riskier firms and higher default risk. The paper analyzes a largescale program of public credit guarantees in Chile during the COVID-19 pandemic using unique transaction-level data on the demand and supply of credit, matched with administrative tax data, for the universe of banks and firms.

Credit demand channels loans toward riskier firms, distributing 4.6 percent of gross domestic product and increasing firm leverage. Despite increased lending to riskier firms, macroeconomic risks remain small. Several factors mitigate aggregate risk: the small weight of riskier firms, the exclusion of the riskiest firms, bank screening, contained expected defaults, and the government absorption of tail risk. The empirical findings are confirmed with a model of heterogeneous firms and endogenous default.

This paper is a product of the Development Research 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 fhuneeus@bcentral.cl, jkaboski@nd.edu, mauricio.larrain@uc.cl, sschmukler@worldbank.org, and mavera@cmfchile.cl.

_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
# WPS4040

## ESTIMATING THE RETURNS TO EDUCATION:

## ACCOUNTING FOR HETEROGENEITY IN ABILITY

Harry Anthony Patrinos Cris Ridao-Cano

### Chris Sakellariou<sup>∗</sup>

_Abstract_ : Typically estimates of the benefits of education investments show average private rates of return for the average individual. The average may not be useful for policy. An examination of the distribution of the returns across individuals is needed. The few studies that have examined these patterns focus on high-income countries, showing investments to be more profitable at the top of the income distribution. The implication is that investments may increase inequality. Extending the analysis to 16 East Asian and Latin American countries we observe mixed evidence in middle-income countries and decreasing returns in low-income countries. Such differences between countries could be due to more job mobility in developed countries, scarcity of skills, or differential exposure to market forces.

JEL Classification: I21, J31

### Keywords: Returns to education, quantile regression

### World Bank Policy Research Working Paper 4040, October 2006

_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._

> ∗ Patrinos <u>(hpatrinos@worldbank.org) and Ridao-Cano (cridaocano@worldbank.org) are with the World Bank;</u> Sakellariou (acsake@ntu.edu.sg) is in the Department of Economics, Humanities and Social Sciences, Nanyang Technological University, Singapore.

1
14 The second paper expands upon the theme of remittances and investment in entrepreneurial activities by using a 1988 survey of 1526 Egyptians migrants who had worked abroad and then returned home (McCormick and Wahba, 2001). Since the survey includes data on the pre- and post-migration employment histories of migrants, the paper is able to examine how international migration and remittances affect the probability that a migrant will become an entrepreneur -- employer, self-employed person or business owner – upon return from working abroad.<sup>15</sup> The paper finds that two factors – time spent working abroad and total amount of money saved abroad – have a positive and significant effect on the likelihood of a return migrant becoming an entrepreneur. However, these two factors work differently for literate as opposed to illiterate migrants. For the 70 percent of return migrants in the Egyptian data set who are literate, the primary factor affecting the probability of becoming an entrepreneur is the amount of time spent working abroad. By contrast, for the 30 percent of return migrants in the Egyptian data set who are illiterate, the total amount of money saved abroad is the most important factor. According to the paper, illiterate Egyptian migrants may not learn many new skills working abroad, and this is the reason that savings accumulated abroad – rather than time spent abroad – is the critical factor affecting the likelihood of becoming an entrepreneur.

The third paper continues on the theme of remittances and entrepreneurship by examining how international remittances affect the level of capital invested in 6044 small enterprises in urban Mexico (Woodruff and Zenteno, 2004). Most of the enterprises included in this 1998 survey are, in fact, micro-firms: 60 percent of them hire no employees and the median cost of invested capital in all firms is less than $1500. Since it is important to separate the supply of wealth/credit from the demand for investment capital, the paper identifies an instrumental variable -- the historic rate of Mexico-to-US migration – which is correlated with access to wealth but not with demand for credit. Using this instrumental variable, and controlling for various factors, the paper finds that international remittances (principally from the United States) are responsible for more than 25 percent of all capital invested in small and micro-enterprises in Mexico. Within those regions of Mexico with the highest rates of migration to the United States, the paper estimates that remittances are responsible for 40 percent of all capital invested in these
We therefore take an instrumental variables approach to address this problem by instrumenting for Δln(miht1-) with ln(miht-2 ) .<sup>22</sup>

# **3.3 Comparison to the literature**

With a number of important caveats, this estimation approach has similarities to the method that is employed by Prusa (1997, 2001), which examines the trade effects of US antidumping actions that were implemented between 1980 and 1994.<sup>23</sup> Prusa focuses only on AD cases for the 1980-1994 time period but does examine all (i.e., steel and non-steel) products that were targeted in those cases. In contrast, our focus is on steel products only, so that our lack of US industry-level covariates that would be needed to control for product-level import variation across industries is less of a concern. Furthermore, our sample covers the 1989-2003 time period for which we have a consistent time series of 10-digit HTS import data; and we also consider the impact of the imposition of SG and CVD cases in addition to AD, as well as the removal of such policies on product-level imports.

A second important contribution made by our approach is that we have constructed a dynamic panel of all steel products that were imported by the US over the time period - regardless of whether a particular product was targeted by a TTB. By contrast, Prusa's estimation sample considers only US imports in product categories that were hit with an AD measure, and he uses separate samples to examine the impact of TTBs on products that were exported by targeted and non-targeted suppliers. Our approach goes one step further. A dynamic panel of all US steel imports allows us to take advantage of the substantial variation in the trade data of the products that were targeted and not-targeted by US TTBs in addition to the exporters that were targeted and not-targeted by US TTBs within categories of targeted products.

# **3.4 Data**

# **3.4.1  Trade data**

To estimate the model, we use product-level data on US imports of steel at the 10-digit Harmonized Tariff Schedule (HTS) level, where most steel products are allocated to chapter 72 or 73. Annual import data for

> 22 Because of the dynamic panel structure of our data, two potential problems with the IV estimator used in equation (2) are bias that is associated with the use of a weak instrument and bias that is associated with correlation in measurement error.

> 23 Konings, Vandenbussche, and Springael (2001) apply Prusa's approach to AD cases in the European Union.

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**Table 7: School Construction and Parenting: Difference-in-differences estimates.**

||1966-1970|Maternal Cohort|Differen|ce in Diff|erences|
|---|---|---|---|---|---|
||High INPRES|High-Low INPRES||||
||Mean|Gap|[1]|[2]|[3]|
|Parenting warmth|-0.03|-0.20|0.002|0.005|0.008|
||||(0.062)|(0.063)|(0.063)|
||||[0.976]|[0.896]|[0.896]|
|spaceParenting in bottom quartile of age-specifc warmth scale (%)|35.1|9.8|-0.895|-0.963|-1.181|
||||(3.055)|(3.126)|(3.060)|
||||[0.828]|[0.728]|[0.764]|
|Parenting consistency|-0.13|-0.23|-0.042|-0.045|-0.046|
||||(0.104)|(0.104)|(0.104)|
||||[0.704]|[0.712]|[0.692]|
|spaceParenting in bottom quartile of age-specifc consistency scale (%)|33.9|9.0|1.193|1.089|1.056|
||||(2.940)|(2.932)|(2.930)|
||||[0.704]|[0.696]|[0.704]|
|Parenting hostility (reversed)|0.05|0.05|0.102|0.107|0.107|
||||(0.109)|(0.110)|(0.110)|
||||[0.428]|[0.360]|[0.344]|
|spaceParenting in bottom quartile of age-specifc reversed hostility scale (%)|34.4|1.6|-8.999|-9.548|-9.303|
||||(3.886)|(4.338)|(3.929)|
||||[0.064]|[0.080]|[0.056]|
|_Controls (D-in-D’s)_||||||
|Caregiver district and year of birth FE’s|||YES|YES|YES|
|Treated cohort interaction with # school-aged children in 1973 (ln scale)|||NO|YES|YES|
|Child gender|||YES|YES|YES|
|Child-age FE’s|||YES|YES|NO|
|Child’s age(linearly,in months)|||NO|NO|YES|

Notes: Sample is 2,118 children. Standard-errors in parentheses are clustered at the district of maternal birth. Wild-bootstrap significance p-values in brackets.
Figure 5: Fitted values of the fiscal rules <!-- Start of picture text --> 140% 60%<br>120% 50%<br>100% 40%<br>80% Capital Capital_Fitted 30% GNFS GNFS_Fitted<br>60% 20%<br>40% 10%<br>20% 0%<br>1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br>0% -10%<br>1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br>-20% -20%<br>-40% -30%<br>45%<br>40%<br>35%<br>30% Transfers Transfers_Fitted<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>% growth (y/y) % growth (y/y)<br>% growth (y/y)<br><!-- End of picture text --> <!-- Start of picture text --> Source: Own calculations<br><!-- End of picture text --> The user has the option of turning the rule on, useful ensure fiscal sustainability, or to turn off the rule to allow the fiscal consequences under unchanged policies to be examined.

Historically purchases of goods and services and compensation of employees make up the largest share of total expenditure with a large component dedicated to servicing debt (see Figure 6).

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- 21 -

45. Under-investment in housing exists when the user cost of capital in housing'is significantly higher than the user cost of capital in the rest of the economy. This would indicate that policies are preventing a balanced flow of resources across sectors. The working hypothesis is that there are two main factors raising the user cost of capital in the housing sector above the opportunity cost of capital<sup>in</sup> the economy. First, Lhe interest rate on private financing reflected<sup>by</sup> curb or unregulated market interest rates (UMM) which<sup>more than</sup> compensates for the high rate of net capital gains. Second, the relative price of housing whose value has been raised by<sup>financial</sup> policies, land policies as well as high transaction costs which include large transfer taxes. So far in Korea, property taxes and other forms of user taxes are not high by international standards. Maintenance costs and rates of depreciation are not known to be especially high and tend to balance each other out.

46. What was a likely outcome when the paper was originally written has been confirmed by the recent work of Kyung-Hwan Kim (1987). He estimated that small changes in green belt boudaries for Seoul (by one kilometer) would increase developable land by about 14 percent and lead to a 2.7 percent decrease in housing prices; the elasticity of housing prices with respect to land supply being estimated at -0.2.

47. Similarly, Kim could show that policies were preventing the flow of funds into housing. Using a housing market survey for Korean cities of 1982 he found that the estimated real rate of return to housing was on the order of 8 percent for single family units and 12 percent for multi-family units. These rates considerably exceeded annual average real rate of return on corporate bonds of 5.1 percent by 3 to 8 percentage points.14/

# B. Skewed Size Distribution of New Housing Output

48. Two convergent forces lead to a skewed size distribution of output in favor of larg= units. First, in most countries,<sup>the top</sup> income deciles of households control a very high share of total assets. In the absence of financial repression and with well developed equity markets their wealth will be diversified into a variety of financial and real assets. Under financial repression they favor real estate assets and acquire the largest units possible. Second,<sup>under</sup> rapidly rising incomes the other households would like to buy adequate

- 14/ There may be questions about the accuracy with which corporate bonds reflect the exact opportunity cost of capital in the Korean economy. However, there is agreement that this is the best indicator, see Dornbush and Yung-Chul Park (1987).
The first column of Table 1 points to quite rapid increases in physical capital per worker (K/L) in both industrial and developing countries. The 5.1 percent per year growth rate for East Asia implies more than a quadrupling of capital per worker over the thirty year period of observation. The 2.4 percent a year increase in Latin America implies more than a doubling of the capital-labor ratio over the period. Even the 2.1 percent per year increase in Sub-Saharan Africa implies a near doubling of capital per worker. The stock of education per worker, measured by years of schooling completed, grew at quite high rates in most developing country regions, although it grew very slowly in the industrial countries. This was particularly the case for secondary and tertiary education stocks, which grew extremely rapidly in most developing country regions. The 9.2 percent annual growth in the stock of secondary school education in East Asia, for instance, implies a fourteen-fold increase in this stock over thirty years.

Before placing too much emphasis on the apparent increases in capital and in education per worker in developing countries as indicators of changes in factor endowments, it is important to examine the capital-output ratio (K/Q). One of Kaldor’s key stylized facts of economic growth (Branson 1979, p465) was a constant capital/output ratio and a rising capital/labor ratio. This is frequently interpreted to imply that technical change is Harrod-neutral, with capital per worker increasing in line with effective labor. If true, this would imply an absence of changes in factor endowments, implying no long run changes in factor endowment ratios, and hence no role for Rybczynski effects.

In fact, it appears from Table 1 that the physical capital-output ratio increased quite substantially over the period in both developing and industrial countries. For human capital, the education to output ratios have increased substantially in developing countries, but fallen quite rapidly in developed countries. These results have potentially important implications for our interpretation of the process of growth and structural change. Before going too far, however, it is important to check the Nehru _et al_ data against other data sets to ensure that these results are not merely artifacts of the data 19
<!-- Start of picture text --> Parts and components* (2014)<br>&<br>= ent pS -<br>86 ot agen”<br>—2 eeng t heat<br>g tos Eee<br>£=So i#+aele :Se aete ett oyi ~-<br>oS ange tee - +<br>~¢ $ + +<br>2<br>-10 0 10 20<br>Predicted imports (log)<br>All countries + Brazil (as importer)<br>« Brazil (as exporter) ———— 465 degree line<br>(*) BEC 1114+1214+21+22+42+53,<br><!-- End of picture text -->
# Table 6

Determinants of Social Status - Ordered Probit _Coefficients report effect of unit change in X on standard deviation change in y_ * _dzl in parenthesis)_

|Variable|(a)<br>OLS<br>No<br>Interactions|(b)<br>IV<br>No<br>Interactions|(c)<br>OLS<br>With<br>Interactions|(d)<br>IV<br>With<br>Interactions|
|---|---|---|---|---|
|Festival Expenditures/1000 (1992 rupees)|0.1723<br>(1.8)||0.4289<br>(3.4)||
|Festival Expenditures x Income||**---**|-0.0165<br>(3.1)||
|Festival Expenditures (Predicted)|**---**|0.3094<br>(1.0)|---|0.5753<br>(1.9)|
|FestivalExpenditures(Predicted) x Income|**---**|**---**|**---**|-0.0155<br>(3.4)|
|Beedu Village<br>;______________________________________|-0.0522<br>___<br>(0.2)|-0.0233<br>(0.1)|0.3151<br>(1.0)|0.3216<br>(0.9)|
|Ooru Village|0.0432<br>(0.2)|0.0570<br>(0.2)|0.2172<br>(0.8)|0.1708<br>(0.6)|
|Annual Income/1000 (1992 rupees)|-0.0122<br>(1.4)|-0.0157<br>(1.4)|0.0725<br>(2.6)|0.0669<br>(2.6)|
|Father's Land Holdings<br>l______________________________________|0.0084<br>____<br>(0.3)|-0.0037<br>(0.2)|0.0015<br>(0.1)|-0.0095<br>(0.4)|
|Land in Acres<br>_l_______________________________________|-0.0560<br>__________(0.5)|-0.1304<br>(1.1)|0.0129<br>(0.1)|-0.1542<br>(0.4)|
|Number of Adults<br>!|0.2121<br>~~~~~~~|0.2223<br>~~~~~~~~~~(<br>(2.2)|0.1083<br>2.1)<br>(I.l)|0.1424<br>_(1.5)_|
|_<br>Number of Kids 10-16 years|<br>0.0042<br>(0.0)|<br> <br>0.0438<br>(0.3)|<br> <br>-0.0764<br>(0.6)|-0.0371<br>(0.2)|
|Number of Kids < 10 years|0.0907<br>(0.9)|0.1241<br>(1.2)|0.0347<br>(0.4)|0.0964<br>(0.9)|
|Maximum Years of Schooling|0.0514<br>(1.8)|0.0539<br>(1.6)|0.0348<br>(1.3)|0.0324<br>(.0)|
|Age of Head|0.0114<br>(1.4)|0.0926<br>(0.0)|0.0099<br>(1.2)|0.0083<br>(1.1)|
|Pseudo R-Squared|0.09|0.10|0.13|0.14|

22
SEED-CO. (2010). “Agricultural Crop Planner: _2010 – 2011 Season_ ”. Published by SEED-CO, Box 35310 Lusaka, Zambia.

Shumaker, G., McKissick, J.C. and Smith, N. (2007). “Economics of Peanuts for Biodiesel Production”. The University of Georgia, Center for Agribusiness and Economic Development, College of Agricultural and Environmental Sciences. Center Report # CR-07-04. February 2007.

Sinkala, T. (2011a). “The Current Status and Development of Biofuels Industry in Zambia”. Presented at the Renewable Energy Forum organized by the Energy Regulations Board, Intercontinental Hotel, Lusaka, Zambia. August 24 – 25.

Sinkala, T. (2011b). “Biofuels Development in Africa: _Technology Options and Related Policy and Regulatory Issues_ ”. United Nations Economic Commission for Africa - Regional Integration, Infrastructure and Trade Division. Addis Ababa, Ethiopia.

Sinkala, T. (2011c). “Biofuels potentials, market opportunities and barriers”. Presented at the United Nations Economic Commission for Africa Conference on Strategy for Promoting Renewable Energy in Southern Africa. Birchwood Hotel, Johannesburg, South Africa. 28 - 29 November 2011.

Sinkala, T. and Chitembo, A. (2007). “An analysis of the global Jatropha industry and an in-depth case study of the value chain for jatropha in Zambia”. International Labour Organisation (ILO). Geneva, Switzerland.

Sinkala, T. (2008). “Liquid Biofuels as a Viable Alternative to Fossil Fuels”. Working paper for the Global Renewable Energy Forum, a joint activity of the United Nations Industrial Development Organization and the Brazilian Ministry of Mines and Energy, Foz do Iguaçu, Brazil. 18-21 May. For the United Nations Industrial Development Organization.

Sinkala, T. and Mulenga, C. (2008). “Report of the Tour of Liquid Biofuels Industry in Brazil”. Joint report by the Ministry of Energy and Water Development and Biofuels Association of Zambia. Lusuka.

Strauss, J. (1986). “Does better nutrition raise farm productivity?” The Journal of Political Economy 94 (2): 297 – 320.

The Independent (2012). “Ethanol fuel prices explained”. http://www.theindependent.co.zw/local/35439ethanol-fuel-prices-explained.html?

United Nations Framework Convention on Climate Change (UNFCCC) (2007) _._ “Approved baseline and monitoring methodology AM0047 _‘Production of biodiesel from waste oils and/or waste fats from biogenic origin and/or biodiesel from oil seeds grown on unutilised, or marginal lands which had uneconomical agricultural productivity (if any)’_ ”. AM0047 / Version 02 Sectoral Scopes: 01 and 05 EB 3.

17
examining smaller firms and household enterprises. Vijverberg and Haughton (2004) find larger firms less likely to die in Vietnam, whereas McPherson (1995) finds no significant relationship with firm size in Swaziland and Botswana, and even finds larger firms being more likely to die in Zimbabwe.

Figures 3b and 3c examine the relationship between firm death and firm size, considering two measures of firm size: employment, and profitability. Since the modal firm in our sample has no paid workers, and 99.4 percent have 10 workers or fewer, we compare firms with no workers to those with 1 to 10 workers in Figure 3b. There is no strong size effect apparent in this figure. Column 3 of Table 3 confirms this lack of employment size effect. Figure 3c shows firms earning more profits are slightly less likely to die than the smallest firms. Column 4 of Table 3 confirms this, with log profits being a significant negative predictor of firm death.

_Are firms in certain sectors more likely to die?_ While many studies have just used data from manufacturing firms, studies which have included other sectors have found death rates to vary with sector. However, they come to different conclusions about which sectors have higher death rates. McPherson (1995) and Mead and Liedholm (1998) find that firms in the retail sector are more likely to close than firms in the services or manufacturing sector; Klapper and Richmond (2011) find manufacturing firms to face a higher risk of closing compared to firms in the services sector and Vijverberg and Haughton (2004) find manufacturing and services firms to be significantly more likely to close than those in the retail sector. Figure 3d shows that in our data, retail firms have the highest death rates, and manufacturing firms the lowest, with services in the middle. However, column 5 of Table 3 shows that these differences are not statistically significant.

# **5.2 Univariate Individual-Level Correlates of Firm Death**

Figure 4 and columns 6 through 8 of Table 3 examine how the characteristics of the owner are correlated with firm death. These have been less studied in much of the literature, since many surveys of firms do not provide personal characteristics of the owner.

_Are firm death rates higher for younger or older entrepreneurs?_ Younger owners have had less time to accumulate skills and capital, and are at an age where job mobility is also higher even in wage work. But older entrepreneurs may close as they near retirement, or see less time to recover from shocks. The result might be that survival probabilities are highest for the middle 15