rafmacalaba/datause-encoder-data huggingface dataset:
_β_ 3 will be biased upward. Behrman and Birdsall (1983) actually find only a small bias in their empirical estimates, however.<sup>11</sup> Finally, and contrary to the fairly benign results presented so far, using data from the 1988/89 Ghana Living Standards Survey, Glewwe (1996) finds significant selection bias in the estimation of equation (1) because it typically is estimated only on wage-employed workers, ignoring the large share of the labor force that is self-employed in Ghana. He also uses a unique set of test scores collected in the survey to control for ability. He finds that controlling for ability and selection into wage employment reduces the coefficient on years of schooling from 8.5 percent to between 3.6 and 6.1 percent depending on the age of the worker and whether he or she works in the public or private sector.

Given the weight of the evidence and Card’s general observation that the bias is unsigned, and lacking any data on ability or schooling quality, we proceed with the straightforward equation (2).<sup>12</sup> In all our estimates, we are unable to distinguish between wage-employed workers who attended public versus private school, so we must assume that the returns to public and private schooling are similar. In appendix A, we show that between 25 and 38 percent of primary school students are enrolled in private schools, but this share falls to 10 to 15 percent for secondary school and university students. As we show below, a significant part of the return to primary schooling is the option to continue on to higher levels where labor earnings increase more sharply with additional education and where the vast majority of students attend public schools.

# _Calculating monetary benefits of an additional year of schooling_

One difference between our paper and much of the literature is that our aim is to estimate the benefits, in Ghanaian cedis, of an additional year of schooling rather than a rate of return because we want to compare those benefits to the cost of providing them. In the basic model, the additional benefits of attending year _j_ of school are calculated as the difference between the present discounted value of future labor earnings having attended _j_ years of schooling less the labor earnings having attended _j_ –1 years, as in Patrinos and Psacharopoulos (2020):

> 11 The original study reported a large bias, but Eaton (1985) points out a calculation error, acknowledged by the authors in a reply.

> 12 We have also estimated equation (1), with results similar to others found in the literature for Ghana and other lowincome countries.

15
-57-

preparation and conservation, disposal of wastes and hygiene, information on inoculations for children, usage of safe water, birth control methods to reduce unwanted pregnancies, and the like.

Differently put, the presence of strong complementarities in the needs of the extremely-poor has important implications for policy design. Programs for the extremely-poor that only provide one of these components may fail to achieve the goals of improving nutritional and health status and changing fertility behavior, even if they adequately reach the target population. Therefore, there is a strong cass to aim for simultaneous provision of a basic package of food-health-education that exploits the complementarities between these needs and has the specific objectives of: (i) reducing infant mortality, (ii) improving the nutritional and health status of extremely-poor households, and (iii) reducing fertility.

The specific policy that I propose to reach these objectives is to create Help Centers<sup>99</sup> in charge of all directly targeted benefits for the extremelypoor. PASSPA should have four characteristics: first, location in areas with the highest poverty indices as computed by PK[(a-l),X]. Second, provision of nutritionally adequate foods, perhaps complemented by vitamins. Third, provision of free minimum quality but effective levels of preventive medicine and related education. Fourth, sustained provision of benefits; in particular, fiscal resources for PASSPA should be isolated from fluctuations associated with macroeconomic instability. I now discuss in more detail the elements of this proposal.

First, the argument to centralize the essential benefits for the extremely-poor under a single program derives from the complementarities discussed above. To reiterate, more food, by itself, may only have a transitory impact on the welfare of the extremely-poor, and will probably not eliminate extreme-poverty. Centralization of benefits also helps to minimize 99Perhaps labelled Centros de Atencion a la Pobreza Extrema, or CAPEs. The proposal made here does not call for the creation of new institutions and organizations. A program with characteristics similar to the ones described here is already in operation. This is the Programa de Atencion de Servicios de Salud para la Poblacion Abierta, PASSPA (see World Bank, 1990c). Unfortunately, this program is at present very small. As I argue more fully in section VII.1, with some modifications PASSPA should be made the centerpiece of the fight against extreme-poverty.
The results of the alternative decomposition (equation 4) are presented in Annex Table 3, and the overall results are presented in Table 6. The results are in line with the results that we got from the more traditional decomposition.

**<u>Table 6: Alternative Decomposition - Determinants of PISA Differentials</u>**

|PISA Scores|_as % of total test score diff_|
|---|---|
||_b2006(X2006-X2003)_<br>_b2006(X2006-X2003)/(T2006-T2003)_|
|Difference|30.8|
|T2006-T2003||
|Time|19.5<br>0.6|
|Institutions|0.2<br>0.0|
|Schools|-1.6<br>-0.1|
|Family|-0.2<br>0.0|
|<br>Student|1.0<br>0.0|

_Sources_ : PISA 2003 and 2006; authors' calculations The main explanation behind the change in test scores between 2006 and 2003 is a fixed-time effect that yielded a 19.5 incremental increase in the score. The observable characteristics contributed only marginally to the change in test scores between 2003 and 2006. It is noteworthy that the characteristics of institutions and students made a positive contribution to the positive change in test scores, whereas schools and family played a negative role.

15
(2003) estimates of ad-valorem equivalents of speci fi c tariffs. We also need information on tariffs in the rest-of-the-world to estimate the impact that a potential 33% reduction in bound tariffs in Doha will have on world prices.<sup>34</sup> Import and export data comes from the UN’s Comtrade. We take the average between 2001 and 2003 to smooth any year speci fi c shock. If data is missing for a particular country, then we use data for 2000 to calculate the average. For the few countries for which there is no data available between 2000 and 2003, we mirror the data using partners’ trade data. The partial equilibrium model also requires estimates of import demand and export supply elasticities at the tariff line level for every country in the sample. These are taken from Kee, Nicita and Olarreaga (2004a, 2004b).<sup>35</sup> Table A3 in the Appendix provides some summary statistics. The export-weighted tariff faced by bene fi ciaries countries in the European Union is zero, whereas it can be non-zero in Japan and the United States. This is because the European Union has a very generous LDC speci fi c preference program (Everything But Arms), whereas Japan and the United States preferences to LDC generally have exemptions. Notably, Bangladesh bene fi ts from almost no preference on its $ 2.6 billion of exports to the United States, whereas it faces an export weighted tariff of almost 11 percent. Similarly Vanuatu faces an export-weighted average tariff of almost 18 percent on its 17 million dollar of exports to the United States. These LDCs represent a negligible share of total TRIAD imports, as assumed in the model. Bangladesh’s share, the largest, is only 0.17 percent of total TRIAD imports.

# 3.3 Empirical Results

Table 1 provides estimates of the subsidy that the TRIAD would pay in the absence of tariff reductions, which, as we previously noted, is equal to the tariff revenue currently forgone due to preferences given to LDCs. The total value is $763 million, of which the largest share is the E.U.’s because of three factors. First, the E.U. imports larger quantities from LDCs than Japan and the U.S. Second, European preferences tend to be more generous under the EBA than under the LDC regimes of Japan and the U.S. (including AGOA in the case of the U.S.), as we can see from Table A3. Third, the MFN tariffs

> 34 We have an additional 130 countries in the data set. The tariff data come from different sources and we employ the most recent year for which data is available between 1999 and 2004. Sources are UNCTAD’s TRAINS, WTO noti fi cations and OECD (2003) CD-ROM, as well as the earlier 2001 version.

> 35 When elasticities were not estimated for particular countries in those studies we took the trade-weighted average across all countries for those products. In the case of export supply elasticities, where for some tariff lines they were not able to estimate export supplies at the six-digit level, we took the country speci fi c average across all tariff lines within the four digit level.

16
more precisely the effects of financial liberalization over time, which is attractive since financial reform is often a process over a longer period.

We focus on listed firms, since most firms in the World Scope sample are listed, and because the quality of the accounting data is expected to be higher for listed firms. Focusing on listed firms has the additional advantage that we can compare the performance of the two different measures of marginal profitability of capital, that is Tobin's q versus the sales-to-capital ratio. For each company we need a certain minimum coverage of the data to assess the changes in the financing structure of the firm. We set this coverage to three years and therefore delete firms with less than three consecutive years of observations. It is, however, necessary to delete more firms, because of outliers in the data. Such outliers can be explained by revaluation of assets, divestments, acquisitions, or simply poor data. We impose a number of outlier rules. First of all, we delete observations with negative fixed capital or investment. Such observations might be due to divestments or revaluations of capital. Secondly, we restrict investment ratios from taking high values. Such values might be due to acquisitions or revaluations of capital. Furthermore, we restrict variables to take extreme values in terms of leverage, marginal profitability or cash flow. We also delete firrns in transition economies, because soft budget constraints that have been inherited from the socialistic regime may distort the analysis. Table 6 gives the details of the deletion criteria. After deleting firms according to these criteria we have data on 394 listed firms in 13 countries.<sup>21</sup> Obviously, our sample of firms is non-random. Listed firms, for example, tend to be large in most countries. This non-randomness can be partly controlled for by allowing fixed effects.

For this set of firm-level data we generate the necessary variables to estimate equation (13). We assume that flow variables (such as investment and depreciation) during period t are decided upon at the beginning of period t. Since accounting data only provides end-of-period data, we use end-of-period t-1 figures to construct variables at the beginning of period t.

To test for a difference in financing constraints between firms of different size, we split our sample according to firm size. As measure of firm size we use net sales, reported in US dollars for comparability across countries. We construct a small size dummy, 20
**Table 1. Employment Type, Gender, and Mean Age**

|_Employment Type_|Females (%)|Males (%)|Mean Age|
|---|---|---|---|
|_Permanent_|6 (2.5%)|229(97.5%)|35.5|
|_Temporary_|70 (36.8%)|120 (63.2%)|25.5|
|_All_|76 (17.9%)|349 (82.1%)|31.1|

Most of the females in our sample are temporary workers. As can be seen in Table 1, permanent employees tend to be somewhat older than temporary workers.

# **_Clinical Results_**

Clinical examinations of our sample indicated 96 (22.6%) to be severe-OSD and 157 were (36.9%) classified as non-OSD. The remaining 172 (40.5%) were classified as intermediateOSD.12 The mean age by employment type and severity of infection is reported in Table 2.

**Table 2. Mean Age by Employment Type and Severity of OSD**

|_Employment Type_|Severity of Infection|Number|MeanAge|
|---|---|---|---|
|_Permanent_|Severe-OSD|63|36.5|
||Intennediate|114|35.6|
||Non-OSD|58|34.5|
|_Temporary_|Severe-OSD|33|27.1|
||Intermediate|58|26.1|
||Non-OSD|99|24.6|

As the severity of OSD increases, we see an increase in the mean age of study participants. This may be indicative of the cumulative nature of the infection. Also, it alerts us to the importance of controlling for age in the subsequent analysis of the economic impact of OSD.

# _Economic Impact_

In this subsection, we report on the economic impact of OSD. Data on economic variables from plantation records such as gross income and days worked were collected for the latest 12 months available. First, we report on differences of mean gross monthly income, mean days worked per month, and mean daily wage between those with severe-OSD and those classified as non-OSD. Data on income and wages is in the local currency: birr.<sup>13</sup> Subsequently, we conduct a regression

- 12 Clinical classifications of the severity of OSD were based on the criteria in Murdoch _et al._ (1993).

- 13 In 1996, US$1 = 6.33 birr.

_5_
the average scores are 42.08 and 50.99, the results imply improvements of 2.4 percent and 4 percent, respectively.

Table 4. Direct impact of concessions: effects on test scores (mathematics and reading)

||Direct impact o|n math tests|Direct impact on|reading tests||
|---|---|---|---|---|---|
|Probit estimates<br>Dependent variable||||||
|C|oeff.<br>|Std. Err.|Coeff.<br>S|td. Err.||
|Total number of students|-0.018|0.001<br>*|-0.018|0.001|*|
|Dispersion in the test|-0.351|0.033<br>*|-0.197|0.029|*|
|Localization 5|-0.441|0.079<br>*|-0.542|0.080|*|
|Localization 7|0.149|0.063<br>*|0.140|0.064|*|
|Localization 8|0.073|0.075|0.079|0.076||
|Localization 9|-0.150|0.071<br>*|-0.181|0.072|*|
|Evening sche.|0.102|0.072|0.068|0.073||
|Ration teacher / stud.|-0.071|0.005<br>*|-0.072|0.005|*|
|Average year of educ. teac.|-0.089|0.012<br>*|-0.095|0.011|*|
|% of students repeating|-2.508|0.183<br>*|-2.575|0.179|*|
|% of students in overage|19.165|0.779<br>*|18.789|0.774|*|
|Constant|4.683|0.300<br>*|4.474|0.330|*|
|Log likelihood|2152.87||-2846.556|||
|Pseudo R2|0.2768||0.268|||
|Number of obs.|18629||18630|||
|Matching|Impact variable|: Mathematics|Impact variabl|e: reading||
|Variable: Test scores||||||
|Difference Treatment-Control||||||
|Unmatched|-0.2099||0.6234|||
|ATT|0.9732||1.9364|||
|Bootstrap statistics||||||
|Repetitions|100||100|||
|Standard error|0.522||0.748|||
|Bias-corrected conf. interv.|1.2684|1.4655|1.5501|2.8472||

25
Rogoff (2004) and updated by Ilzetzky, Reinhart and Rogoff (2009). This index goes from 0 to 14 and higher values signal a more flexible exchange rate arrangement. Healthier external and fiscal positions are approximated by the current account balance and the central government budget surplus. Both variables are expressed as percentage of GDP and the data are obtained from WDI and IFS. Measures of the health of the financial sector are also included as determinants of sudden stops. Among them we have bank credit, bank deposits, bank assets and the credit to deposit ratio. All these variables are expressed as percentage of GDP except for the credit to deposit ratio which is taken from Beck, Demirguc-Kunt and Levine (2000) and Beck and Demirguc-Kunt (2009).<sup>10</sup> A country’s vulnerability to sudden stops is heightened by its exposure to international good and asset markets. In this respect we include indicators of trade and financial openness. Our measure of trade openness includes the ratio of real exports plus imports to GDP and natural resource abundance. The latter is measured by the net exports per capita of agricultural raw materials, food, fuel, metals and mineral ores. The data is compiled from WDI. Openness to the world capital markets is measured from Kose, Prasad and Terrones (2006), as the ratio of net financial flows to GDP ( _i.e._ net capital flows). To test the consistency of the financial openness indicators we break down this measure into net equity vis-à-vis net debt flows, and its decomposition into FDI, portfolio investment and other investment. Then we distinguish between local and foreign residents’ decisions by including gross inflows and outflows instead of net inflows. The data on net and gross flows is obtained from the IMF’s Balance of Payments Statistics. Finally, external shocks are added to the regression as controls. We proxy terms of trade shocks as the annual percentage change in the terms of trade index, and the data is collected from WDI. The world real interest rate is proxied by the money market rate of the reference country as in Di Giovanni and Shambaugh (2008) and this indicator is gathered from IFS.

# **4. Empirical Evidence**

This section aims to set up the empirical assessment on the determinants of sudden stops and presents the evidence obtained from our regression analysis. We use an _effective_ sample of 82

> 10 The health of the financial system can be captured more accurately by another comprehensive set of indicators such as ratio of liquid assets to deposits and short-term funding, bank capital to assets, non-performing loans (NPLs) to total gross loans, the ratio of provision to NPLs relative to total gross loans, and regulatory capital to riskweighted assets among others. However, there are serious constraints on time series availability for these series (e.g. since 2000 at best) that prevents us for using them in our regression analysis.

12
- 26 -

On the other hand, the impact of actual road improvement is found to be positive, but the observed impact seems to be relatively weak: The marginal impact is 0.273, and it is only significant at the 10 percent level. All the indications are that although OPRCs or road improvements in general are conducive to increasing crop production, people still may not benefit from possible market opportunities. Their market participation is limited. This is generally consistent with a view that transport services may not be available or too expensive, even though roads are improved. The coefficient of transport spending, of which the available data are limited to less than 90 households at each round of the surveys, is negative and significant, meaning that high transport service prices are constraining farmers’ market sales.

In addition, feeder roads should form one road network, but there are still many roads that are in poor condition in the study area. Finally, other complementary facilities and services, such as warehouses and processing mills, may also be needed to facilitate people’s market participation.

**<u>Table 11. Instrumental variable estimation of total crop sales</u>**

||Battese's<br>|specificatio<br>|n|Battese's s<br>|pecificatio<br>|n|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||
|_OPRC_*_t_|-2.545|(1.834)|||||
|_OPRC_|1.895|(0.647)|***||||
|_WORK_*_t_||||2.087|(1.111)|*|
|_WORK_||||0.100|(0.474)||
|_t_|0.465|(1.019)||-2.000|(0.606)|***|
|ln_POT_|0.254|(0.118)|**|0.292|(0.124)|**|
|ln_L_|0.190|(0.226)||0.204|(0.201)||
|ln_H_|0.823|(0.310)|***|0.868|(0.322)|***|
|ln_S_|-0.734|(0.629)||-0.901|(0.619)||
|ln_F_|0.129|(0.072)|*|0.136|(0.068)|**|
|ln_P_|0.222|(0.188)||0.332|(0.182)|*|
|ln_TR_|-0.027|(0.045)||-0.092|(0.039)|**|
|d (_S_)|-54.199|(60.434)||-55.077|(58.760)||
|d (_F_)|0.994|(0.408)|**|1.092|(0.392)|***|
|d (_P_)|2.080|(2.089)||2.852|(2.001)||
|d (_TR_)|-0.834|(0.406)|**|-1.172|(0.336)|***|
|ln SIZE|0.092|(0.142)||0.222|(0.131)|*|
|d (_MALE_)|-0.237|(0.333)||-0.230|(0.313)||
|ln_AGE_|0.126|(0.162)||0.259|(0.162)|*|
# TABLE 2

The demand for tasks and exports (log employment) OLS-FE

|||export i|ntensity||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|A) Skilled and Unskilled Labor|||||
|log highly-skilled|0.33***|0.19***|0.19**|0.19***|
||(0.087)|(0.073)|(0.073)|(0.073)|
|log skilled|0.31***|0.16***|0.16***|0.16***|
||(0.074)|(0.058)|(0.058)|(0.058)|
|log unskilled|0.11|-0.13|-0.13|-0.13|
||(0.101)|(0.082)|(0.082)|(0.082)|
|B) Production and Non-Production Labor|||||
|log production|0.27***|0.00|0.009|0.009|
||(0.073)|(0.015)|(0.015)|(0.015)|
|log non-production|0.14**|0.03|0.03|0.03|
||(0.056)|(0.041)|(0.041)|(0.041)|
|C) Tasks|||||
|log managers|0.09|0.02|0.01|0.01|
||(0.073)|(0.067)|(0.067)|(0.067)|
|log engineers|0.37***|0.22**|0.22**|0.22**|
||(0.103)|(0.089)|(0.090)|(0.090)|
|log services|0.29***|0.16**|0.15**|0.15**|
||(0.083)|(0.070)|(0.070)|(0.069)|
|log blue-collar|0.14|–0.11|–0.11|–0.11|
||(0.111)|(0.093)|(0.093)|(0.093)|
|log maintenance|–0.15*|–0.20***|–0.20***|–0.20***|
||(0.080)|(0.078)|(0.077)|(0.077)|
|_N_|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|

NOTES: OLS-FE regressions of (log) employment on export intensity (exports/sales). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

16
**Table 10: Propensity-Score Reweighted Interactions with Private Courses**

|**_Treatment Interaction with:_**|Employed<br>20+ hours|Aggregate<br>Employment<br>Index<br>Differential I|Ever<br>formal<br>byJan 12<br>mpact on:|Currently<br>formal<br>Aug13|
|---|---|---|---|---|
|_Course offered by private provider_|||||
|Unweighted (as reported in Table 8)|0.044*|0.117***|0.062**|0.025|
||(0.023)|(0.043)|(0.028)|(0.025)|
|Propensity-score Reweighted|0.075**|0.171**|0.070*|0.059|
||(0.035)|(0.066)|(0.036)|(0.042)|
|Controlling for interaction of treatment with propensity score|0.069**|0.150***|0.069**|0.034|
||(0.031)|(0.057)|(0.033)|(0.035)|

Notes: robust standard errors in parentheses, clustered at the course level.

*, **, and *** indicate significance at the 10, 5 and 1 percent levels respectively.

Propensity score calculated as a function of personal, course and labor market characteristics reported in Appendix Table 1 Each row shows the treatment interaction from a regression which includes controls for course*age*gender lotteries, and for the level effect of the course being offered by a private provider 42