rafmacalaba/datause-encoder-data huggingface dataset:
# **Bibiliography**

- BANSEFI y World Bank. . “Diagnostico de la situación financiera, equipamiento tecnológico y censo de las entidades de ahorro y crédito popular. Junio de 2002.

- Beck, Thorsten, Asli Demirguc-Kunt, and Ross Levine “Finance, Inequality and Poverty: Cross-Country Evidence”, World Bank Policy Research Working Paper 3338, July 2004

- Beck, Thorsten, Asli Demirguc-Kunt, and Maria Soledad Martinez Peria, “Reaching out: Access to and use of banking services across countries” World Bank Policy Research Working Paper draft April, 2005

- Braunstein, Sandra and Carolyn Welch. “Financial Literacy: An Overview of Practice, Research, and Policy,” _Federal Reserve Bulletin_ , November 2002.

- Caprio, G. and Honohan, P., _Finance for Growth: Policy Choices in a Volatile World,_ World Bank, Oxford University Press, New York, 2001

- Caskey, John P. “Lower Income Americans, Higher Cost Financial Services.” A monograph published by the Filene Research Institute, 1997.

- ____________.  “The Economics of Payday Lending.” A monograph published by the Filene Research Institute, 2002.

- Caskey, John P. and Andrew Peterson.  “Who Has a Bank Account and Who Doesn’t: 1997 and 1989,” _Eastern Economic Journal_ , Vol. 20, No. 1, Winter 1994.

- Christen, Robert, “Commercialization and Misson Drift: The Transformation of Microfinance in Latin America. World Bank Consultative Group to Assist the Poorest (CGAP) Occasional Paper, Washington, 2000.

De Soto, Hernando, _The Mystery of Capita_ l, Basic Books, New York, 2000 Frumkin, Samuel, William Reeves, and Barry Wides, “Payroll Cards: An Innovative Product for Reaching the Unbanked and Underbanked,” A report issued by the Office of the Comptroller of the Currency, October 2003.

- Goldsmith, Raymond W., “Financial Structure and Development”, Yale University Press, New Haven, 1969

- Hilgert, Marianne and Jeanne Hogarth. “Household Financial Management: The Connection between Knowledge and Behavior,” _Federal Reserve Bulletin_ , July 2003.

58
# **Table D3 (continued)**

# **Summary Statistics of 21 Cottonseed Processing Companies in Burkina Faso**

||**_XII_**|**_XIII_**|**_XIV_**|**_XV_**|**_XVI_**|**_XVII_**|**_XVIII_**|**_XIX_**|**_XX_**|**_XXI_**|
|---|---|---|---|---|---|---|---|---|---|---|
|**I. Nature of operation**|||||||||||
|**Number of employees**|58|24|14|112|32|39|73|18|65|30|
|**Capacity (tons/day)**|36|20|12|70|50|40|60|10|40|20|
|**Year operations began**|2007|2009|2007|2005|2008|2006|2001|2008|2007|2009|
|**Wage rate (CFAf/month)**|31,000|40,000|30,000|50,000|25,000|35,000|30,000|32,000|40,000|27,000|
|**II. Seed statistics**|||||||||||
|**Crushings in 2008 (tons)**|1,440|—|700|2,000|1,000|2,360|4,944|850|1,200|—|
|**Crushings in 2009 (tons)**|2,800|500|1,000|2,000|3,500|1,640|2,593|675|2,500|1,000|
|**Seed price in 2008 (CFAf/ton)**|91,368|—|92,545|90,270|90,270|75,000|75,000|75,000|90,270|—|
|**Seed price in 2009 (CFAf/ton)**|91,368|90,250|92,545|90,270|90,270|75,000|75,000|75,000|90,270|75,000|
|**III. Oil and meal statistics**|||||||||||
|**Oil price in 2008 (CFAf/jc)**|—|—|11,500|14,000|13,730|10,000|13,500|12,500|14,000|—|
|**Oil price in 2009 (CFAf/jc)**|—|11,000|9,500|10,000|9,180|8,300|11,000|12,000|12,500|8,300|
|**Meal sold in 2008**|1,224|—|595|—|870|1,780|3,955|256|1,200|—|
|**Meal sold in 2009**|2,380|360|850|—|3,000|1,360|2,075|263|2,500|832|
|**Meal price in 2008 (CFAf/ton)**|111,223|—|95,000|97,500|95,000|75,775|80,000|90,000|95,000|—|
|**Mealprice in 2009 (CFAf/ton)**|97,058|80,000|95,000|97,500|80,000|74,775|80,000|90,000|100,000|78,929|

**Notes:** ‘—‘ implies data not reported or the answer was not very clear. Wage rate is for unskilled laborers; whenever specified in days or hours, the conversion to monthly wage rate was made by assuming 8 hours per day and 25 days per month. Similarly, when the oil price was reported in tons, it was divided by 55 [≈1,000/18] to convert it to 20‐liter can (jc) equivalent. **Source:** Zanfongnon (2009) from company statistics.

**_Page_ 51**
Table 6: Change in the absolute and relative valuations of equity and debt and the PEPP The dependent variable in regression 1 is the change in the market value of equity on March 19, 2020 divided by the book value of assets at the end of 2019. The dependent variable in regression 2 is the change in the market value of debt on March 19, 2020 divided by the book value of assets at the end of 2019. The dependent variable in regression 3 is the change in the market value of equity on March 19, 2020 divided by the sums of the changes in the market values of equity and debt on March 19, 2020, conditional on both of these being positive. The dependent variable in regression 4 is the change in the market value of equity on March 19, 2020 divided by the sums of the changes in the market values of equity and debt on March 19, 2020, conditional on the sum of these being positive. Investment grade is a dummy variable indicating a long term, domestic or foreign, issuer credit rating of at least (lower than) BBB-/Baa3 issued by either Moody’s, S&P or Fitch. Leverage is liabilities divided by assets at the end of 2019. Log of assets is the natural logarithm of assets at the end of 2019. ROA is net income before extraordinary items divided by assets at the end of 2019. Book-to-market is the book value of equity divided by the market value of equity at the end of 2019. Beta is the coefficient of a regression of the daily log stock return on the daily log return of the MSCI World index. Regressions include country fixed effects. Robust standard errors clustered at the country level are in parentheses. *, ** and *** denote significance at 10%, 5%, and 1%, respectively.

||(1)<br>dMVE/Book<br>assets|(2)<br>dMVD/Book<br>assets|(3)<br>dMVE/(dMVE<br>+ dMVD)<br>dMVE > 0,<br>dMVD > 0|(4)<br>dMVE/(dMVE<br>+ dMVD)<br>dMVE +<br>dMVD > 0|
|---|---|---|---|---|
|Investment grade|0.0219***|0.00121**|-0.146***|-0.213**|
||(0.00718)|(0.000527)|(0.0241)|(0.0742)|
|Leverage|-0.0661**|0.00214***|-0.540***|-0.990**|
||(0.0247)|(0.000579)|(0.136)|(0.330)|
|Log assets|-0.00286|-0.000122|0.0213|-0.00812|
||(0.00203)|(0.0000896)|(0.0151)|(0.0336)|
|ROA|-0.0805***|-0.000760|-0.120|1.491|
||(0.0143)|(0.00164)|(0.251)|(1.115)|
|Book-to-market|-0.0239***|0.00109***|-0.243***|-0.303***|
||(0.00503)|(0.000267)|(0.0641)|(0.0695)|
|Beta|0.0141**|-0.000324|-0.0757|-0.122|
||(0.00563)|(0.000319)|(0.0503)|(0.112)|
|Observations|2211|111|63|84|
|Adjusted R-squared|0.094|0.113|0.383|-0.015|
|CountryFE|Yes|Yes|Yes|Yes|

43
## **Papers Cited on Returns to Education in India and China in Tables T4.I and T4.C in the main Text**

## **Papers Cited on Returns to Education in China**

Jamison and Van der Gaag (1987), Xie and Hannum (1996), Johnson and Chow (1997), Liu (1998), Meng and Kidd (1997), Maurer-Fazio (1999), Li (2003), Bishop et al. (2005), Zhang et al. (2005), Asadullah and Xiao (2020), Hauser and Xie (2005), Ren and Miller (2012), Bargain et al. (2009), Wang (2012), Gustafsson and Li (2000).

## **Papers Cited on Returns to Education in India**

Duraisamy (2002), Bargain et al. (2009), Kingdon (1998), Kanjilal-Bhaduri and Pastore (2018).

# **References**

- Asadullah, M. N. and Xiao, S. (2020). The changing pattern of wage returns to education in post-reform china. _Structural Change and Economic Dynamics_ , 53:137–148.

- Bargain, O., Bhaumik, S. K., Chakrabarty, M., and Zhao, Z. (2009). Earnings Differences Between Chinese And Indian Wage Earners, 19872004. _Review of Income and Wealth_ , 55:562–587.

- Bishop, J. A., Luo, F., and Wang, F. (2005). Economic transition, gender bias, and the distribution of earnings in china. _Economics of Transition_ , 13(2):239–259.

- Duraisamy, P. (2002). Changes in returns to education in India, 1983-94: by gender, agecohort and location. _Economics of Education Review_ , 21(6):609–622.

- Gustafsson, B. and Li, S. (2000). Economic transformation and the gender earnings gap in urban china. _Journal of Population Economics_ , 13(2):305–329.

- Hauser, S. M. and Xie, Y. (2005). Temporal and regional variation in earnings inequality: Urban china in transition between 1988 and 1995. _Social Science Research_ , 34(1):44–79.

- Jamison, D. T. and Van der Gaag, J. (1987). Education and earnings in the people’s republic of china. _Economics of Education Review_ , 6(2):161–166.

- Johnson, E. N. and Chow, G. C. (1997). Rates of return to schooling in china. _Pacific Economic Review_ , 2(2):101–113.

- Kanjilal-Bhaduri, S. and Pastore, F. (2018). Returns to Education and Female Participation Nexus: Evidence from India. _The Indian Journal of Labour Economics_ , 61(3):515–536.

- Kingdon, G. G. (1998). Does the labour market explain lower female schooling in India? _Journal of Development Studies_ , 35(1):39–65.

- Li, H. (2003). Economic transition and returns to education in china. _Economics of education review_ , 22(3):317–328.

- Liu, Z. (1998). Earnings, education, and economic reforms in urban china. _Economic Development and Cultural Change_ , 46(4):697–725.

- Maurer-Fazio, M. (1999). Earnings and education in china’s transition to a market economy survey evidence from 1989 and 1992. _China Economic Review_ , 10(1):17–40.
- 6 -

to explore further the implications of education on savings through two additional channels.

The first channel, extensively debated in the recent literature, is based on the positive long-run relationship between the stock of human capital and the expected level of income.<sup>7</sup> A simple specification between education and expected income (E(yi)) would be the following:

# (6) E(yi) = peD<sup>t</sup> ,

# p.t>Oand X > 1

The positive effects of education on expected income is captured by the parameters p and x, which represent the technology level and externalities associated with education respectively. The linear technology, which is a common feature of a class of endogenous growth models (see Romer (1986)), ensures that the rate of return to human capital does not decline as the stock of capital increases. Externalities ensure that the income growth rate is higher than the education growth rate following the argument that educated people are more productive, invest more, or are better complement to physical capital in a well-educated environment (see Lucas (1988)).

Little attention has been paid, however, to the second channel, which consists in the effect of education on income volatility. More educated people are less likely to be unemployed or, if unemployed, they are generally covered by an unemployment insurance, therefore reducing their income uncertainty. Similarly, insurance is usually thought less available in countries with lower educational attainments, although the extended family may substitute for various forms of insurance. Empirically, Psacharopoulos (1985), for example, has found that the higher the degree of education, the lower the vulnerability of earnings with cyclical fluctuations in the economy. To account for this effect, the following relationship can be written: (7) **(52(yl) =** ?,(eo), ?I < 0

> 7 This positive relationship has been empirically confirmed by several cross-country studies (e.g. Barro and Lee (I994) and Benhabib and Spiegel (1994)) or households' surveys (Caroll and Weil (1994). Other studies (e.g. Schultz (1975)) have also emphasized the positive correlation between education and labor income because qualified people are more mobile and have a greater wage bargaining power than less educated people
2004, the BDH budget was approximately 0.7 percent of GDP. Unlike most other cash transfer programs in Latin America, the BDH program did not explicitly make transfers conditional on changes in household behavior. That being said, because the original intent was to model the BDH on PROGRESA, program administrators stressed the importance of school enrollment when signing up households for transfers. For a brief period, BDH television spots that explicitly discussed how parents were responsible for the schooling and health status of their children were aired on national television. As a result, some households believed that there was an enrollment requirement associated with the program, even though no requirement was ever monitored or enforced in Ecuador. We exploit this quirk in the administration of the BDH program to assess the importance of conditions attached to cash transfers.

The analysis is based on an experimental design. At the outset of the study, a lottery was used to assign 1,391 households, including 3,072 school-aged children, into a treatment group that would be eligible for transfers and a control group. None of these households had previously received transfers from the BDH. A household survey was collected prior to the intervention and a follow-up survey approximately one-and-a-half years later. The randomized study design makes it possible to convincingly measure the causal impact of the BDH program on enrollment and child work.

The main results in the paper are two. First, the BDH program had a large, positive impact on school enrollment, about 10 percentage points, and a large, negative impact on child work, about 17 percentage points. Second, the fact that some households believed that there was a school enrollment requirement attached to the transfers helps explain the magnitude of program effects.

# **2. Background and study design**

# _A. Country and program background_

Ecuador is a lower-middle income country. In 2004, its per capita GDP was 1,435 in constant 2000 US dollars, 3,595 in PPP-adjusted 2000 constant US dollars, about half the population-weighted Latin American average. Inequality is high (the Gini coefficient is 0.44), although not especially so by Latin American standards. Poverty is widespread. An estimated 18 percent of the population lives on less than a dollar per person per day, and more than 40 percent live on less than two dollars per day (World Bank 2004).

3
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Unsurprisingly, the rankings of countries are different for the two indexes. A particularly interesting case is El Salvador, a lower-middle-income country in Latin America.<sup>27</sup> This country ranks as number 5 of 145 countries based on the SCI scores. This would place this country in a surprisingly higher position than many richer countries, including its regional upper-middleincome neighbor—Colombia—which is ranked at number 10. Yet, according to a recent evaluation for 44 indicators concerning children in the 2030 SDG agenda by UNICEF, El Salvador has inadequate data for tracking progress on more than half (i.e., 27) of these indicators. The same figure for Colombia is much lower at just 14 indicators (UNICEF, 2018). The SPI ranks El Salvador as 48 while the ranking of Colombia changes to 12.

Another example is Botswana, an upper-middle-income country in Africa. This country has an SCI rank of 122, near the bottom of the SCI scores. This places Botswana lower than Madagascar (number 89) and Zimbabwe (number 105), both low-income countries in Sub-Saharan Africa. Indeed, Botswana’s GDP per capita was $15,807 in 2011 PPP, which was respectively 11 times and 8 times higher than that of Madagascar and Zimbabwe (World Bank, 2018b). Furthermore, a recent study suggests that Botswana has accomplished both upward consumption mobility and pro-poor growth in the past decade, which reduced its headcount poverty rate to 18 percent (using the international poverty line of $1.9/ day in 2011 PPP dollars). The opposite story held Madagascar, where the country has undergone much downward mobility, which drove its poverty rate up to as high as 82 percent (Dang and Dabalen, in press). Zimbabwe, on the other hand, has a higher income level than Madagascar, but is observed to have implemented at most one household consumption survey during the period 1990-2012; the corresponding figure for Botswana is two

> is that, since the SPI is the average of a larger number of indicators than the SCI, it is more likely to have a normal distribution by the central limit theorem (see, e.g., Casella and Berger (2002)).

> 27 Unless otherwise noted, we use the World Bank’s (2018) income classification for all countries.

32
estimation procedure developed in Section 2 above allows for serial correlation of ...rst-stage errors.

# 4 Results

Before estimating equation (20), we establish that our instruments are signi...cantly related to the migrant share of the village labor force. We estimate the relationship as a quadratic, cubic, and quartic function of the years since IDs were issued each interacted with period t�2 land per capita. These results are reported in columns (1)–(3) and columns (4)–(6) of Table 2 for odd years from 1989-2001.<sup>16</sup> We ...nd a strong relationship between our instruments and the size of the migrant network for each speci...cation. For the remainder of our estimation we favor the quartic function interacted with t � 2 land per capita for two reasons: First, the e¤ects of ID card distribution on the migration network can be determined more ‡exibly when we use the quartic speci...cation. Secondly, the partial R<sup>2</sup> increases slightly from the quadratic to the quartic for the both samples we consider. After controlling for the household characteristics, the instruments have jointly signi...cant e¤ects on the share of migrants with an F-statistic of 44.62 for the 1989 to 2001 sample.

We next proceed to estimate model (20), but ...rst treat migration as exogenous and show results for both linear probability and probit implementations in Table 3. In all four speci...cations, we observe a positive association between migrant share of the village and probability that a household is below the poverty line, and this re‡ects the response of households to short-term shocks and the simultaneity between short-term migration and consumption decisions. Given the descriptive evidence shown in Figure 2, it is unsurprising to ...nd that short-term increases in the poverty headcount will be correlated with year to year changes in the share of migrants from the village. The positive relationship suggests that migration is truly endogenous and suggests the need for an estimation strategy that allows for identi...cation in a dynamic binary response model where there are endogenous regressors. When we introduce the years-since IDs instrument, which is shown elsewhere to

> 16Since the RCRE survey was not conducted in 1992 and 1994, we estimate the dynamic model with two-year spacing from 1989 to 2001.

24
Table 6 reports the results of the OLS regressions for each dependent variable.

**Table 6. OLS Estimates of SOE Performance in the Republic of Korea**

||**Performance**<br>**Evaluation**|**Customer**<br>**Satisfaction**|**Debt**<br>**Ratio**|**ROA**|
|---|---|---|---|---|
|CEO duality|<br>-0.451|<br>0.057|<br>-7.653|-165.573**|
||(0.415)|(2.434)|(27.713)|(90.108)|
|Board size|0.075**|-0.087|-2.895**|3.148|
||(0.035)|(0.123)|(1.283)|(4.172)|
|Board|||||
|independence|-0.285|-6.489**|29.989|-25.602|
||(0.722)|(2.598)|(26.674)|(86.694)|
|Corporatization|-0.010**|0.029*|-0.284*|0.129|
||(0.005)|(0.017)|(0.167)|(0.547)|
|Disclosure|0.010**|0.009|0.264|1.892**|
||(0.005)|(0.024)|(0.242)|(0.790)|
|Assets|0.070|0.002|6.570***|3.167|
||(0.043)|(0.199)|(2.128)|(6.965)|
|Operation period|0.003|0.050**|-0.258|-1.167|
||(0.005)|(0.023)|(0.253)|(0.826)|
|Publicly traded|-0.570|-1.525|2.162|97.845|
|company|(0.588)|(2.894)|(26.177)|(85.105)|
|Public|||||
|corporation|0.687|3.607*|-6.512|0.324|
||(0.436)|(2.059)|(24.492)|(79.617)|
|Quasi-<br>governmental||0.387|-3.052|-8.719|
|<br>organization||(1.115)|(10.986)|(35.936)|
|Constant|3.001***|90.494***|-31.393|63.599|
||(1.030)|(3.702)|(37.160)|(121.267)|
|Adj R-squared|0.1048|0.2136|0.0241|0.041|
|Observations|113|169|226|225|

_Notes:_ *** Significant at .01; ** Significant at .05; * Significant at .10 a/ Standard errors are in parentheses.

While some of the results are consistent with the predictions of hypotheses H2 (board size) and H5 (transparency), those significant relationships are not found for all performance measures. For instance, on board size, the results indicate that it is positively associated with performance measurements such as the performance evaluation grade and the debt ratio, but no significant 13
## WPS 3546

# Incentives to Learn

Michael Kremer<sup>*</sup> Edward Miguel<sup>**</sup> Rebecca Thornton<sup>***</sup> Owen Ozier<sup>****</sup> World Bank Policy Research Working Paper 3546, March 2005 _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_ _<u>http://econ.worldbank.org</u>_

* Department of Economics, Harvard University, The Brookings Institution, and NBER. Department of Economics, Littauer 207, Harvard University, Cambridge, MA 02138, USA; 1 (617) 495-9145, <u>mkremer@fas.harvard.edu.</u>

** Department of Economics, University of California, Berkeley and NBER. Department of Economics, 549 Evans Hall #3880, University of California, Berkeley, CA 94720-3880, USA; 1 (510) 642-7162, <u>emiguel@econ.berkeley.edu.</u>

*** Department of Economics, Harvard University, Littauer 207, Cambridge, MA 02138, USA; <u>rlthornt@fas.harvard.edu.</u>

**** ICS Africa, Box 599, Busia, Kenya; <u>owen@ozier.com.</u>

The authors thank ICS Africa and the Kenya Ministry of Education for their cooperation in all stages of the project, and would especially like to acknowledge the contributions of Elizabeth Beasley, Pascaline Dupas, James Habyarimana, Sylvie Moulin, Robert Namunyu, Petia Topolova, Peter Wafula Nasokho, Maureen Wechuli, and the PSDP field staff and data group, without whom the project would not have been possible. The project benefited greatly from a collaborative process between ICS Africa, the researchers, the World Bank team in Nairobi, and PREM researchers. Gratitude is also extended to the teachers and school children of Busia for participating in the study. George Akerlof, David Card, Brian Jacob, Victor Lavy, Antonio Rangel, Joel Sobel, and Doug Staiger have provided valuable comments. We are grateful for financial support from the World Bank, the Young Green Foundation, and the MacArthur Foundation. All errors are our own.