rafmacalaba/datause-encoder-data huggingface dataset:
|**Table A7.**<br> **Individual and H**|Business<br>**ousehold Outc**|Income and<br>**omes with g**|<br>CO<br>**ender inter**|Outlook on<br>**actions**|Decision-|
|---|---|---|---|---|---|
||Knowledge<br>(1)|Assets<br>(2)|Cohesion<br>(3)|Life<br>(4)|Making<br>(5)|
|Business Training (1=Yes)|0.088<br>(0.071)|0.099*<br>(0.055)|0.178***<br>(0.066)|0.130*<br>(0.070)|0.337<br>(0.249)|
|BT x Female|-0.006<br>(0.061)|-0.063<br>(0.049)|-0.028<br>(0.055)|0.097<br>(0.059)|-0.221<br>(0.202)|
|Lottery Winner (1=Yes)|-0.070|0.001|0.040|0.071|-0.004|
||(0.094)|(0.062)|(0.081)|(0.096)|(0.400)|
|LW x Female|0.015<br>(0.075)|-0.042<br>(0.055)|-0.006<br>(0.064)|0.025<br>(0.073)|-0.144<br>(0.276)|
|BT and LW|-0.050|0.103|0.206**|0.110|-0.328|
||(0.090)|(0.075)|(0.098)|(0.095)|(0.323)|
|BT and LW x Female|0.134*|-0.171***|-0.123*|0.004|0.235|
||(0.073)|(0.060)|(0.073)|(0.074)|(0.256)|
|R-Squared|0.09|0.35|0.02|0.11|0.03|
|P-value of t - test of ...||||||
|BT = BT and LW|0.13|0.96|0.79|0.84|0.04|
|LW = BT and LW|0.85|0.21|0.14|0.72|0.48|
|BT = LW|0.09|0.13|0.13|0.53|0.41|
|BT + BT x Female = 0|0.17|0.46|0.00|0.00|0.56|
|LW + LW x Female = 0|0.52|0.50|0.65|0.24|0.65|
|BT and LW+BT and LW x Female=0|0.23|0.27|0.19|0.17|0.73|
|Mean of dependent variable among controls|-0.02|0.05|-0.06|0.02|-0.10|
|N. Observations|3494|3494|3494|3494|3494|

Note: The reported mean of the dependent variable is computed using CO members not offered business training nor assigned to be lottery winners.The dependent variables are aggregates of standardized z-scores. See Appendix B for a definition of the aggregates. All regressions are estimated using OLS methods and include as covariates the stratification variables (eligibility for loan lottery, business ownership at baseline and gender), risk aversion, education, landholdings, digit span recall and all interactions of these with treatment dummies. Standard errors reported in parentheses are clustered at the CO level. The following symbols *, * * and ** * denote significance at the 10 , 5, and 1 percent level, respectively.
are less likely to pay bribes and spend less manager time dealing with officials. In Bangladesh, growing firms report lower constraints from anti-competitive behavior by other firms and labor regulation.

There are also some interesting cross-country differences in reported constraints for the firm characteristics, which may be relevant for policy.

The most striking difference among the SAR countries concerns the **location** characteristic. Earlier we found that in contrast to the rest of the region, job-creation was uncorrelated with location in **India** once we controlled for other firm characteristics. Elsewhere, employment creation was more prevalent in big city locations. Turning to the constraints regression analysis, we find once again that India does not reflect the dualism pattern found elsewhere in low income countries. The typical pattern is that it is firms in urban (large city) locations that report higher costs of constraints than do firms in rural areas including small cities. Indeed for electricity, Indian firms in more rural locations report higher constraints than do firms in urban areas. In India, large-city firms report higher constraints only in relation to transport, courts, labor regulation, and customs administration. However, for the _majority of constraints_ , there is no significant large city – small city gap in the reported cost of constraints in India.

The standard dualism pattern for low-income countries (found in the large multiregion dataset) is characteristic of most SAR countries apart from India. It is especially pronounced in Bangladesh and Pakistan (red cells). This suggests that in terms of the reported constraints on growth, India is a more integrated economy between rural, small city and large city areas than are the other countries in this sample.

The positive **firm size** effects across a range of constraints are weaker in **India** than elsewhere. The difference between **services** and manufacturing firms is also different in India, where it is services firms that complain more about electricity. Unlike the case elsewhere, they are also more likely to have a generator and have more frequent power outages than do manufacturing firms. In Pakistan, services firms complain much less about electricity than do manufacturing firms and are more likely to have a 23
creditors. A alternative measure of liquidity is the quick ratio, QUICKRAT, which is measured as current assets _less inventory_ divided by current liabilities. This may more accurately reflect the inability of firms to liquidate their inventory during periods of distress. In addition, we include the market value of leverage, LEVERAGE. We expect that firms with secured accounts receivable will exhibit greater liquidity risk.

# _C. Turnover Variables_

Short-term risk may also depend on a borrower's trade cycle, which is measured by the borrower's turnover ratio of current assets into cash. A slow conversion cycle indicates a greater risk of default. We use two turnover ratios: The accounts receivable turnover ratio, ACCPAYTURN, measured as accounts receivable divided by sales, suggests how long it takes a company to collect from its customers. The inventory turnover ratio, INVENTURN, measured as inventory divided by the cost of goods sold, indicates how quickly a company sells its inventory. We expect that firns with secured L/Cs should have relatively strong accounts receivable - since these firms need highquality accounts receivable in order to receive financing, we expect a self-imposed discipline on the part of the borrower to restrict trade credit to high-quality customers. In contrast, we expect that firms using secured L/Cs should have greater business risk and slower inventory turnover.

# _D. Future Growth Variables_

In previous literature, Opler et. al (1997) find that a firm's corporate financing decisions depend on various measures of the borrower's future growth opportunities, measured as the market-to-book ratio. Barclay and Smith (1995) use Compustat's breakdown of _long-term_ debt priority classes to explain variation in priority structure.

23
WPS3715

# **Estimating the Returns to Education in Argentina: 1992-2002**

**Paula Inés Giovagnoli**<sup>*</sup>

Center for Distribution, Labor and Social Studies (CEDLAS), Universidad Nacional de la Plata

**Ariel Fiszbein**<sup>*</sup> World Bank

**Harry Anthony Patrinos**<sup>∗</sup> World Bank

**<u>Abstract</u>** <u>.  Returns to schooling in urban Argentina for a ten-year period are estimated.  In addition to</u> comparable earnings functions, we also estimate the returns using quantile regression analysis to detect differences in the returns across the distribution.  Over time, men in higher quantiles have higher returns to schooling compared to those in the lower quantiles.  For women returns are highest at the lowest quantile.  The returns to education increased during the last decade.  We do not rule out that increased demand for skills is driving the increasing returns over the decade.

## JEL Classification Codes: I21, J31

Keywords: returns to schooling, wages, quantile regressions

## World Bank Policy Research Working Paper 3715, September 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 http://econ.worldbank.org._

> ∗ The authors thank Mariana Marchionni, George Psacharopoulos and Emiliana Vegas, as well as participants at seminars at the Universidad de las Americas, Puebla, Mexico and Universidad Nacional de La Plata, Argentina, for their useful comments.
23 of natural monopoly services as intermediate<sup>inputs plays a role</sup> in determining which elasticity price-increase<sup>combinations are</sup> likely. The elasticity of demand for intermediate<sup>input demand</sup> is higher because industrial users have greater<sup>substitution</sup> possibilities than private residential users,<sup>including</sup> generating their own power. This clearly<sup>constrains monopolies'</sup> ability to raise prices significantly, and<sup>therefore probably</sup> rules out in practice the larger percentage<sup>price increases in</sup> the upper part of Tables 2 and 3.

For at least some reasonable elasticity values in<sup>Tables 2</sup> and 3, monopoly pricing app^ars to produce<sup>non-negligible costs,</sup> even if the private monopoly produces<sup>more efficiently than the</sup> public enterprise it replaces. However,<sup>just as our simulation</sup> assumptio a might cause us to underestimate<sup>the social costs of</sup> private unregulated monopoly, they<sup>can also cause us to</sup> overestimate them. Two of our assumptions<sup>have this effect.</sup> We have assumed in the above analysis that<sup>the public</sup> enterprises price their products at marginal<sup>cost. To the extent</sup> that this is not the case, the welfare loss<sup>estimates are lower.</sup> If the privatized public enterprise was pricing<sup>its product below</sup> marginal cost, the price was actually being<sup>set inefficiently</sup> low, and the allocative efficiency losses are<sup>not as large as a</sup> straightforward application of the Harberger formula<sup>would</sup> suggest. However, it is much more likely<sup>that the public</sup> enterprise was setting its price above marginal<sup>cost. In this</sup> case, it was already acting to some degree<sup>like a monopolist, and</sup>
<!-- Start of picture text --> Policy Research<br><!-- End of picture text -->

**Country Operations** WPS 843

This paper -a product of the Country Operations Division, Eastern Africa Department, Africa Regional Office and initiated whilc the author was in Division 11, Country Policy, Industry and Finance, Operations Evaluation Department- is part of a review of cross-country experience with long-termn growth. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Afsar Nokhostin, room J10-285, extension 34150 (30 pages). January 1992.

Empirical studies on new growth theory have tended to ignore financial policy's role in development. Ghani provides evidence that the initial level of financial development is positively associated with a country's later GDP growth rate, after controlling for the effect of the starting value of human capital and the investment rate.

A country that starts with a more developed financial system tends to grow faster because it can make more efficient use of resources. It can do so through several channels, including better evaluation and monitoring of firns, lower transaction costs for financial intermediation, and externalities generated from information collected and processed in financial markets.

Policy reform that fosters financial development also has a significant positive effect on the growth rate of real GDP. The empirical evidence Ghani presents for 50 developing countrics tends to reinforce a classical theme of development economics: the importance of human capital and financial markets.

> The PolicyRcscarch Working PaperSeriesdisseminates Lhc findingsofwork underway in thc Bank. Anobjectiveof thc serics is to get these rmdings out quickly, even if presentations are less than fully polished. The findings, interpretations, and ct aclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
Figure 5.3

**Outstanding Corporate Bonds Issued**<sup>**(Percentof TotalExternalFinance):**1990-1999</sup> **Time Series**

<!-- Start of picture text --> 30.0<br>25.0 , .<br>20.0<br>tS O _ _ | I =~~~~~~~~~~~~~~~~~a Malays Ia<br>_ S | ik ~~~~~~~~~~~~~~~~~~~~~~~~~~Thtafnaengd<br>t5.0 __ * doneai<br>5.0<br>0.0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>25D _0<br>30.0 ~~~~~~~~~~~~~~~~~~~~--llpla---<br>Slr9apore<br>1990 991 _ 992 gm3 994 9 99 6 _~~~17 997 98 W99<br><!-- End of picture text -->

Source: Shirai (2001b: Chart 2d, 16) The Asian Policy Forum (under the ADB Institute)<sup>labels thefinancialsystem in</sup> Asia an "intermnediate financial market structure,,"1<sup>7 2whichis betweena bankc-centered</sup> system and a full-fledged capital market; commercial<sup>banks not onlyprovide traditional</sup> banking services but also are active in the<sup>corporatebondmarket asmajor issuers,</sup> investors, underwriters, dealers/brokers<sup>andguarantors.' 7</sup> A somewhat different approach to the assessment<sup>of the fmnancialstructureand</sup> development of East Asian economies is presented<sup>in arecent study by DemirgidiyKunt</sup> and Levine (200 la)'<sup>7</sup> , where selected economies<sup>are classifiedaccording totheirstatus</sup> 71
Policy Research Working Paper 10651

# **Abstract**

Does it matter whether poverty reduction programs target the female or male spouse? A randomized controllled trial in Ethiopia is used to study the differential impacts of easing information and financial constraints on agricultural productivity and household welfare, using data from 1,214 households in two regions of Ethiopia. The program targeted the husband, the wife, or both in a married household. The results indicate that the targeted spouse determines the type and channel of impacts. Targeting both spouses increased agricultural productivity in the short run and the monetary value of small ruminants and poultry in the long run, with a marginal positive impact on nonfood expenditure. Targeting only the female spouse resulted in increased business income from businesses with female involvement. This consequently increased household use of formal savings devices. This is in line with female preferences outside agriculture and for off-farm activities, and it results in little impact on agricultural productivity, despite an increase in women’s access to extension services. Targeting only the male spouse has no impact on household savings or expenditure even though it increases men’s wage income. The results suggest that the sharing of knowledge about the intervention changed household decisions. This would explain the different outcomes when both spouses were targeted, rather than only one.

This paper is a product of the Gender Innovation Lab, Africa Region. 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 tketema@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
|**Economy**|**Cumulative**<br>**Methane(mil ton)**|**Population**<br>**2019**<br>**(millions)**|**Cumulative**<br>**methane per**<br>**capita**|**Deviation**<br>**from Global**<br>**Average**|**Payments or**<br>**Receipts ($**<br>**Billions) Valuing**<br>**excess stock at**<br>**$0.3per ton**|**Payments or**<br>**Receipts ($**<br>**Billions) Valuing**<br>**excess stock at**<br>**$10per ton**|
|---|---|---|---|---|---|---|
|Antigua and Barbuda|4.77|0.10|49.12|22.15|0.00|0.02|
|Samoa|6.99|0.20|35.47|8.50|0.00|0.02|
|Kuwait|114.51|4.21|27.22|0.25|0.00|0.01|
|Saint Kitts and Nevis|2.02|0.05|38.23|11.27|0.00|0.01|
|Fiji|23.99|0.89|26.96|-0.01|0.00|0.00|
|Palau|0.45|0.02|25.00|-1.97|0.00|0.00|
|Tuvalu|0.27|0.01|23.17|-3.80|0.00|0.00|
|Tonga|2.71|0.10|25.93|-1.03|0.00|0.00|
|Slovak Republic|146.99|5.46|26.94|-0.03|0.00|0.00|
|Cook Islands|0.30|0.02|17.10|-9.87|0.00|0.00|
|Dominica|1.55|0.07|21.59|-5.38|0.00|0.00|
|Liechtenstein|0.54|0.04|14.20|-12.76|0.00|0.00|
|Seychelles|2.04|0.10|20.87|-6.09|0.00|-0.01|
|Moldova|108.34|4.04|26.80|-0.17|0.00|-0.01|
|Marshall Islands|0.75|0.06|12.76|-14.21|0.00|-0.01|
|Andorra|1.03|0.08|13.35|-13.61|0.00|-0.01|
|Saint Vincent and the<br>Grenadines|1.89|0.11|17.09|-9.87|0.00|-0.01|
|Luxembourg|15.39|0.62|24.99|-1.97|0.00|-0.01|
|Kiribati|0.48|0.12|4.08|-22.88|0.00|-0.03|
|Bahamas|7.29|0.39|18.72|-8.25|0.00|-0.03|
|Austria|237.05|8.96|26.47|-0.49|0.00|-0.04|
|São Tomé and Príncipe|0.84|0.22|3.91|-23.06|0.00|-0.05|
|Malta|5.60|0.44|12.72|-14.25|0.00|-0.06|
|Gambia, The|55.11|2.35|23.47|-3.49|0.00|-0.08|
|Djibouti|16.47|0.97|16.92|-10.05|0.00|-0.10|
|Solomon Islands|8.27|0.67|12.35|-14.62|0.00|-0.10|
|Trinidad and Tobago|26.09|1.39|18.70|-8.26|0.00|-0.12|
|Cabo Verde|2.91|0.55|5.29|-21.67|0.00|-0.12|
|Maldives|2.01|0.53|3.79|-23.18|0.00|-0.12|
|Lesotho|44.21|2.13|20.80|-6.16|0.00|-0.13|
|Croatia|98.21|4.13|23.78|-3.19|0.00|-0.13|
|Armenia|65.39|2.96|22.11|-4.86|0.00|-0.14|
|Cyprus|17.37|1.20|14.49|-12.47|0.00|-0.15|
|Belgium|294.70|11.54|25.54|-1.43|0.00|-0.16|
|Gabon|41.76|2.17|19.22|-7.74|-0.01|-0.17|
|Comoros|5.62|0.85|6.60|-20.36|-0.01|-0.17|
|Eritrea|76.76|3.50|21.95|-5.01|-0.01|-0.18|
|Guinea-Bissau|33.17|1.92|17.27|-9.70|-0.01|-0.19|
|Costa Rica|115.15|5.05|22.81|-4.15|-0.01|-0.21|
|Hungary|235.59|9.68|24.33|-2.64|-0.01|-0.26|
|Oman|104.60|4.97|21.03|-5.94|-0.01|-0.30|
|Liberia<br>Guinea|100.32<br>303.48|4.94<br>12.77|20.32<br>23.76|-6.65<br>-3.20|-0.01<br>-0.01|-0.33<br>-0.41|

38
Latin American countries, Spanish speakers are classified as the reference group. In the two African countries in our sample, the reference group includes individuals who are native speakers of either the colonial language(s) or the most common local language.<sup>22</sup> These are Wolof in Senegal and English and Mende in Sierra Leone, where the minority groups include those who identify a language other than these languages as their “primary language” and “mother tongue,” respectively.

**Table 1. Languages used for identifying the reference and minority groups in each country**

||Reference|groups|Minority groups||
|---|---|---|---|---|
|Country (Year)|_Languages_|_% in_<br>_population_|_Languages_|_% in_<br>_population_|
|Bolivia (2001)|Spanish|59.7|Mother Tongue Quechua, Aymara<br>or Other|39.2|
|Mexico (2010)|Spanish|78.7|Speak Any Indigenous Language|21.0|
|Peru (2007)|Spanish|83.3|Mother Tongue Quechua|13.8|
|Senegal (2002)|Wolof|51.0|Primary Language Pulaar, Serer or<br>Other|47.3|
|Sierra Leone (2004)|English, Mende|32.1|Mother Tongue Temne, Krio or<br>Other|67.6|

Note: The wording of the language question is different in each census. The reference and minority groups are mutually exclusive. The “Other” language category pertains to other local languages; foreign languages are excluded from the analysis. Population figures are for ages 12 and above.

Source: IPUMS-I.

Together, the reference and minority groups encompass 90 to 100 percent of the population aged 12 and above in all countries included in the sample (Table 1). Interestingly, a significant share of the respondents classified as the “minority group” live in rural areas of their respective countries, ranging from 54 percent in Peru to 76 percent in Mexico (Figure 1). In

> small fraction of the overall category, and, if anything, to upwardly bias our results (i.e., decrease the magnitude of the respective coefficients).

> 22 In all cases, the data used only offers one mother tongue/primary language for each individual. Of the five countries studied here, only the Senegal census asks for one’s primary language and secondary language, where roughly 40 percent of individuals note a secondary language and 22 percent cite Wolof.

11
to achieve identification of all four parameters. Notice that (4’) requires, on the left hand side, estimates of the cross-sectional variance of residual income for each age group z, while (6’) requires that we use the panel dimension of our data set to estimate the covariances in individuals’ residual incomes viz over time. Thus, our estimation strategy exploits both the panel dimension and the repeated cross sections available in the data set. As in Caroll and Samwick (1997), we use residual income data at the individual level to obtain unbiased estimators of the terms on the left hand side of (4’) and (6’). Specifically, viz<sup>2andvizvi,z+1</sup> serve as as individual level ”observations” of the variance and covariance terms on the left hand sides of (4’) and (6’) respectively. We estimate our system of two equations ((4’) and (6’)) using a simultaneous, non-linear, seemingly unrelated regressions model (NLSUR) (as described in Gallant, 1975 and Amemiya, 1983). This permits the estimation of the two non-linear equations, with the cross-equation restrictions implied by the common parameters, simultaneously and achieves additional estimation efficiency by combining information from both equations (Davidson & MacKinnon, 2004).<sup>15</sup>

# IV. Data

Using the estimation methodology described in the preceding section, we estimate income mobility parameters using individual income data from Mexico. Specifically, the individual income data are taken from the Encuesta Nacional de Empleo Urbano (ENEU, Mexican National Urban Employment Survey) which was conducted by the Instituto Nacional de Estadistica, Geografia e Informatica (INEGI, National Institute of Statistics, Geography and Information), the primary statistical agency in Mexico, and the Secretaria del Trabajo y Prevision Social (STPS, Secretariat of Labor and Social Security), Mexico’s Labor Ministry.

Until recently, the ENEU was the primary survey instrument for collecting earnings and employment data in Mexico. The survey is sampled to be representative geographically and by social strata (see INEGI 2000). The basic sampling unit is the dwelling. Demographic information is collected on the household (households) occupying each dwelling. Subsequently, an employment questionnaire is administered for each individual aged 12 and above in the household on position in the household, level of education (years of schooling), age and sex 15See also Davidson and MacKinnon (2004) for a through discussion of the asymptotic equivalence between estimates obtained using a non-linear-least-squares methodology and the generalized method of moments.

9