rafmacalaba/datause-encoder-data huggingface dataset:
altitude of his or her area of residence using standard adjustments (Centers for Disease Control and Prevention, 1998).

We measured child cognitive development using two different indicators. First, for children aged from 12 to 30 months, we applied the Spanish version of the MacArthur Communicative Development Inventory (CDI, Cognitive Development Laboratory at San Diego State University). This instrument appraises language and communication skills in infants and young children through parental reporting. In the field, we used the short infant form ("Versión Breve del Inventario del Desarrollo de Habilidades ComunicativasInventario I"), which includes a 105-word vocabulary checklist with separate columns to report comprehension and verbalization of each word. This short form is recommended when parental literacy is low, as is the case in our study.

Second, for children age 36 to 71 months, we applied the Spanish version of the Picture Peabody Vocabulary Test (PPVT) (“Test de Vocabulario en Imágenes Peabody (TVIP)”) (Dunn, 1965). Based on the PPVT, the TVIP contains 125 translated items to assess the vocabulary of Spanish-speaking and bilingual children. Items have been carefully selected based on a rigorous item analysis for their universality and appropriateness for Spanish-speaking communities. The TVIP is frequently used to evaluate the language development of Spanish-speaking preschool children, as well as older students (Muñoz et al., 1989; Umbel et al., 1992). We report the results in terms of the percentile distribution of Latin American outcomes.

Finally, we measured happiness of the mother using a rich set of indicators. First, we asked about their satisfaction with floor quality, overall housing quality and quality of life. The possible answers were: (i) _very satisfied_ ; (ii) _satisfied_ ; (iii) _regular_ ; and (iv) _unsatisfied_ . In the empirical analysis reported in this paper, we convert these responses into a binary variable that equals one if the answer is in categories (i) or (ii) and zero otherwise.

Second, we collected measures of depression and perceived stress for mothers of children younger than six years of age. In order to measure stress, we implemented the Perceived Stress Scale (PSS) developed by Cohen, Kamarck and Mermelstein (1983). We used a ten-item version of the PSS designed to capture the degree to which mothers found their lives to be unpredictable, uncontrollable and overloaded during the month 11
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appear higher in Asia than in other regions, though the focus on primary and secondary education may partially explain the relative success of Asia (see Table 8). The social rates of return of primary and secondary education are generally higher than those associated with tertiary education. The Asia's traditional focus on primary education is evidenced by high subsidies and low private enrollment, in sharp contrast with the situation in Latin America where university education is privileged. A somewhat better measure of school quality is the performance of children on tests of cognitive skills, standardized across economies. In the relatively few international comparisons available from such tests, East-Asian children tend to perform better than children from other developing countries. 17 The second interesting result is the inverse sequencing associated with the impact of education on the saving rate in Latin America. The initial impact appears positive, while the lagged impact is negative and generally higher than the initial one. Although it can be argued that these empirical results are due to the absence of explanatory variables in the regression or spurious correlation, the model provides some tentative explanations that are briefly explored below. The contemporaneous positive effect of education on the savings rate can be explained by the relatively high stock of education in Latin America which magnifies the externalities associated with education (see equation (9)). The stock of overall education was indeed about 55 percent higher in Latin America than in Asia in 1960, though the difference declined to only 8 percent in 1987 ( Table 8 ). The model also suggests that the positive effect may also be the consequence of the low cost of education in Latin America, but the empirical evidence remains inconclusive on that issue in part because household survey rather aggregated data would be needed for a meaningful analysis. On the one hand, people seems to spent proportionally less on education in Latin America than in Asia because of lower population pressures, including slower population growth and a lower proportion of young people, and public policy. As a result, the stock of education grew by 4.2 percent in Asia against only 2.0 percent in Latin America over

> 17. A recent study by Wolff (1993) found that the average quality of Latin America's primary education was very poor relative to Asia. For example, a 1992 study on science and mathematics achievement for thirteen years old found out that Brazilian students from Sao Paulo and Fortaleza were outscored by students from Korea, Taiwan, and China as well as by other students from every developed country in the sample.
58 Table 2: BANK3 RESERVES AGAINST DEVELOPING COUNTRY LOANS, 1986 (in percent and by country) <!-- Start of picture text --> Belgium 15<br>Canada 10-15<br>France 33-45<br>Germany 35-70 (40)*<br>Japan 5<br>Netherlands 24-26<br>Spain 7-68 (1O)*<br>Sweden 35-80 (50)*<br>Switzerland 30-60 (40)*<br>United Kingdom . 6-10<br>United States 5<br><!-- End of picture text --> F figures in brackets given an approximate average where there Source: International Bank Credit Analysis Group (IBCA), London.
# Table 6: Correlation Between Manager Quality and Protective Investments

||(1)<br>Poll Equipment|(2)<br>Own Protect|(3)<br>Own Protect|(4)<br>Late Commute|(5)<br>Late Commute|(6)<br>Flex Commute|(7)<br>Flex Commute|(8)<br>Managers Careful|(9)<br>Managers Careful|
|---|---|---|---|---|---|---|---|---|---|
|Median Road Size/Cell|0.00207|0.0125|0.0133|0.0130<sup>_∗_</sup>|0.0133<sup>_∗_</sup>|0.0135|0.0139|0.0151|0.0139|
||(0.00603)|(0.0146)|(0.0145)|(0.00773)|(0.00748)|(0.0136)|(0.0134)|(0.0118)|(0.0119)|
|Man. Score|0.0194<sup>_∗∗∗_</sup>|0.0456<sup>_∗∗_</sup>|0.0450<sup>_∗∗_</sup>|0.0284<sup>_∗∗∗_</sup>|0.0249<sup>_∗∗∗_</sup>|0.0556<sup>_∗∗∗_</sup>|0.0499<sup>_∗∗∗_</sup>|0.0654<sup>_∗∗∗_</sup>|0.0601<sup>_∗∗∗_</sup>|
||(0.00687)|(0.0180)|(0.0184)|(0.00890)|(0.00869)|(0.0148)|(0.0142)|(0.0151)|(0.0153)|
|N|1000|2045|2045|2020|2020|2002|2002|1959|1959|
|R2|0.105|0.197|0.210|0.0836|0.100|0.173|0.194|0.140|0.148|
|Fixed Efects (1)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Fixed Efects (2)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Level of Observation|Firm|Employee|Employee|Employee|Employee|Employee|Employee|Employee|Employee|
|SE clustering|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|Grid Cell|
|Employee Controls||No|Yes|No|Yes|No|Yes|No|Yes|
|Mean(dependent var)|.047|.523|.523|.056|.056|.132|.132|.21|.21|
|Answer scale|Dummy|Dummy|Dummy|Dummy|Dummy|Dummy|Dummy|Dummy|Dummy|

Notes: Standard errors are displayed in parentheses (* p _<_ 0.10, ** p _<_ 0.5, *** p _<_ 0.01). Man. Score is a standardized index of managerial ability constructed using our survey (see Appendix A.1 for details). We control for log distance to the main city in the region and whether the grid cell contains any road. We also control for whether the grid cell is incomplete (i.e., _<_ 500m x 500m), its area, as well as a dummy for whether it is in our main surveyed area. Employee controls include education, age, age squared, vocational training (dummy), cognitive ability, employee tenure and log wage. We control for missing managerial score (dummy) and missing employee controls (dummy). Road size goes from 1 (Trail/Track) to 5 (Highway). The dummy dependent variables are defined as follows: Poll Equipment is equal to 1 if any anti-pollution excludable technology or equipment (e.g., masks) is provided at the firm; Own Protect is equal to 1 if the employee reports doing anything to protect herself against air pollution ; Late Commute is equal to 1 if the employee reports that avoiding pollution on the commuting route is an important reason why she may arrive (leave) late (early) at work ; Flex Commute is equal to 1 if the employee reports that her manager allows her to come in or leave early or late to avoid pollution on the commuting route ; Managers Careful is equal to 1 if the employee thinks that her employer / manager is careful with trying to avoid exposing her to pollution.
al. (2018), who estimated youth preferences and WTP for different job attributes and support services in Kenya.� The traditional approach to estimate WTP for job attributes is to apply hedonic methods to observable data on monetary and non- monetary job attributes. A key advantage of this revealed preference approach is that it is based on observable market transactions. However, it has been difficult to obtain reliable estimates using this approach, because the tradeoffs that individuals face in the labor market are not randomly assigned, but likely depend on the individuals’ labor market productivity and on the characteristics of the firms to which they can apply, neither of which are fully observable. For instance, high productivity workers are likely to receive offers with higher wages as well as more desirable non-wage job attributes such as greater flexibility in terms of working hours (Hwang et al. 1992). Suppose the true underlying tradeoff workers make is to accept lower wages in exchange for more flexible hours. If we cannot fully control for individual productivity, we will observe a positive correlation between earnings and flexibility of hours across individuals, which does not reflect this true tradeoff. In addition, in the presence of search frictions in the labor market, more cost-efficient firms will offer jobs that not only pay higher wages, but also offer better non-wage attributes, because they face greater opportunity costs when they have job vacancies that go unfilled (Hwang et al. 1998). Thus, if it is not possible to fully control for differences in cost-efficiency across firms, the WTP estimate from hedonic wage regressions will be biased.<sup>ii</sup> An important advantage of using a choice experiment – a stated preference method in which respondents are asked to choose between two hypothetical options, each with different levels of attributes – is that the attributes can be randomized across individuals, 4
2 Looking at regional trends (Chart 1), decreasing vocational schooling ratios (though not absolute numbers) Is the prevalent pattem, except in Eastem Europe (not shown) and in Latin America, where the ratio is also more than double that in other developing country reglons.

## Chart **1**

# Vocational Enrollment Ratio **by Region**

<!-- Start of picture text --> Ratio<br>0.30 1<br>0220 - '<br>;-U- Lati Amrican Lotin Amrrict WeeEure *nd<br>0.16 Afra Crabbebn theh<br>WesAerin EurioMdleEaten<br>3.06 North Afric<br>19r0 19o e 1970 1971 1980 1984<br>Year<br>Af rica Asia Middle East and<br>-G Latin America and -Western Europe North Africa<br>the Caribbean<br><!-- End of picture text --> Trends over the period 1960-1984 diffler markedly in low and middle Income countries (Chart 2 and 3)2. In low income countries, as total secondary school enrollment (as a proportion of the age population of secondary school age) rose from three to about 20 percent, the vocational schooling **ratio** declined from 19 percent leveling off to 8 percent. Thus the albeit slow growth In secondary schooling in low Income countries has taken place primarily in the academic/general streams.

2 We use World Bank categories: low income countries are those with a GNP per capita below $1,810 in 1986: middle income countries have a GNP per capita in the range $1,810 - $7,410.
or R&D in a vacuum, but must be aware of the _complementarities_ with a variety of other factors.. That is, it is not the case that a country that, due to business climate variables, invests very little in physical capital should still assume high returns to investing in innovation. A lack of physical or human capital, or firms to take ideas to market will keep returns to innovation low. Viewed differently, as countries get farther from the frontier, the lack of complementary factors- firm capabilities, functioning NIS, human and physical capital- offsets the Schumpeterian effect. And, in fact, Goñi and Maloney find an inverted U in returns where Griffith et al.’s (2000) relationship eventually turns over and returns start to fall and become negative for very poor countries.

Fourth, the specific tax on innovation,  and general barriers to _all types of accumulation_ , τ, lower the return and hence investment in knowledge. This is, again, reflected in the center panel of figure 2a/b. That is, if a firm is thinking of adopting a new technology, it will not only be affected by innovation issues, but it will be affected by access to credit markets to finance the related necessary machinery, ability to import that machinery, the overall predictability of the investment climate, the reliability of the contracting environment, the ability to diversify risk,<sup>17</sup> etc. An innovative startup will care as much about bankruptcy laws and barriers to entry and exit as any other firm. That is, a low rate of innovation, e.g., measured by R&D/GDP, would not provide any information as to whether the linkages among universities, firms, etc. are healthy or reflect garden variety accumulation (investment) barriers as are commonly discussed in World Bank Doing Business-type analyses. Aghion (2016), for example, has made the case for the necessity of complementing innovation and structural reforms with reference to Europe, arguing the Netherlands, Canada, Australia, and Sweden that invest in knowledge economy _and_ structural reforms have performed best. Hence, though we cannot say _ex ante_ which factors are most binding, the circle around the NIS needs to err on the ample side and incorporate all the elements grouped together in figure 2b as  .

Fifth, standard appropriability market failures impinge on knowledge capital accumulation as well. If a firm’s knowledge investments can be costlessly appropriated by others, μ=1, the return to that investment will fall considerably.

_Implications for our conception of the NIS_

> 17 See Michelacci, C., & Schivardi, F. (2013), Krishna et al. 2016).

16
may be two ways of creating it. The first could be to train unskilled personnel in house. The second is to give them better general education so that they can be trained up quickly. Incumbent firms may have an advantage if much of the training has to be done in-house, while new entrants are better off if there is a wide pool of well-educated labor that can be brought up to speed quickly without prolonged on-the-job training. This implies that entry should be higher in industries that require high skills in countries with a better-educated work force.

As our country variable, we use Edu, which measures the average schooling years in the total population of age 25 and above, and captures the regulations that promote education. We use data for the year 1995 from Barro and Lee (2000), which is an update of the data and methodology developed in Barro and Lee (1996).

As our industry variable, we include a measure of hourly US wage rates as a proxy for the required skill level in the industry. Wage is obtained from the Occupational Employment Statistics (OES) Survey, Bureau of Labor Statistics, U.S. Department of Labor. This survey covers over 130 million employees in the U.S., and therefore includes employees at both small and large firms. The wage rates are collected at the 3-digit SIC level and we convert them at the 2-digit NACE code level. In Table 10 column (vi), we find that entry is indeed higher in high wage industries in countries that have a better educated work force, although the effect is not statistically significant.

Finally, we have presented interactions one at a time thus far. Some of the interaction variables are strongly correlated (see Table 9) so it is hard to estimate their effect independently. Nevertheless, in Table 10 column (vii), we present a regression with all the interactions included (when we have used two proxies for an effect, we include the one that has the most data for this regression). We find that all variables retain their predicted effect and statistical significance except the financial sector interaction.

_3.6.5 Eurostat_ 30
participants learn more about the impacts. Consumption gains should become evident in due course, consistent with the project’s underlying impact on permanent income.

There is another explanation for the high savings rate from the short-term income gains. This postulates that the SWP systematically alters the returns-to-saving in the participating villages. By this view, the project provided local public goods that increased the marginal product of private capital, and so stimulated higher savings to support the desired private investment, which would yield longer-term income gains beyond the life of the project.<sup>9</sup> This assumes that there are capital-market imperfections, which entail that investment depends on own-savings and that the marginal products of private capital are not equalized across locations. With the poor facing severe constraints on access to credit and yet having higher marginal products of capital in their own (farm and non-farm) enterprises (given low capital stocks and concave production functions) one might expect to see a sizeable (and pro-poor) investment response. Clearly, this explanation offers a more positive view of the prospects of a sustained impact on poverty from the SWP, in that it suggests that income gains will persist well beyond the disbursement period (as the returns to investment start to be realized) and that sustainable consumption gains would emerge.

By re-surveying in 2004/05 the same sample studied by Ravallion and Chen (2005) we hope to throw light on which of these explanations is most plausible.

# **3. Data**

The original plan for the impact evaluation of SWP was to do a baseline survey in 1995 and to only do follow-up surveys during the Bank’s disbursement period, up to 2000. However, we decided to re-survey the original sampled households in 2004/05, to try to resolve the issues about longer-term impact raised by Ravallion and Chen (2005) and discussed in the previous section.

All surveys were implemented by the Rural Household Survey (RHS) team of the government’s National Bureau of Statistics (NBS). The surveys covered 2,000 randomly-sampled households in 200 villages, with roughly half not participating in the

> 9 Jalan and Ravallion (2002) provide a micro model of growth with imperfect capital markets that is consistent with this property, and find supportive evidence in the same region of rural China.

8
# Gold Mining and Proto–Urbanization: Recent Evidence from Ghana<sup>_∗_</sup>

Marcel Fafchamps Stanford University, BREAD and NBER Michael Koelle Forhad Shilpi Oxford University The World Bank Keywords: Urbanization, natural resources JEL Code: R12

> _∗_ We would like to express our sincere thanks to Dr. Phylomena Nyarko of Ghana Statistical Services for making the census data (10 percent sample) available to us, and to Mrs. Jacqueline Anum and Harold Coulombe for their generous help with the data. We would also like to thank Ryan Engstrom and Richard Hinton from George Washington University, and Brian Blankespoor from World Bank for their assistance with various GIS data. This research was funded by the Knowledge for Change Program of the World Bank.
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used then the presence of the log of quantity term rules out the use of includLng that fuel ln the model (as ln a cross sectLon where some firms or countrLes do use the fuel _Ln_ question).

_ThLs_ brief review of the varlous econometric models uoed for analyzlng fuel shares and fuel substitution highllghts several practical issues:

- (i) Although most models appear to fit well on superfLcial criterla (e.g. goodness of fit), more extensive testlng for concavLty and the stabliLty of prlce elasticitLes at varLous data poLnts can lead to unsatisfactory performance.

- (LL) No one model appears to be best Ln all cases. Different models may be needed for different circumstances.

- (liL) Care needs to be taken over the definltlon and measurement of the competing fuels in specific lndustrles. For example, ln power generation the fuel used is heavy fuel oil, so that the price used should not be that of petroleum products as a whole (the price of gasoline can move qulte dlfferently from that of HFO).

- (iv) All the models proposed require estimation by full lnformatlon maximum likelLhood technlques. _This_ can be handled by standard software packages (such as TSP) but the performance of such technLques wLth very small sets may not be very reliable.

- (v) The use of these econometric models appears to be largely limlted to hlghly developed countries (e.g. the U.S., Canada, Australia). In principle they are applicable ln any situatLon but data lLmLtatLons are an important consideration. At an industry level it is necessary to have fuel prices and shares in total energy expendLture for a number of years disaggregated by industry type (in order to avoid the effects of the shlft ln the composltlon of industry).

- (vi) All the models described provlde estimates of own and cross prlce elastLcLties for the varLous fuels. These are very important for pollcy purposes since they indicate the potential response to changes in fuel costs which could be brought about by state interventlon. In the light of this it is important to find a model which gives stable values for these elasticities. Merely checklng the elasticltles at the mean values of hLitorLc energy shares is an insufflcient check - they need to be evaluated at all data points. The work of Considlne is partlcularly helpful ln suggesting an alternative to the translog model which is well known for produclng highly variable elasticities.

- (viL) Very lLttle work has been undertaken on dynamlc models. Since adjustment to fuel price changes may take several years this is an important issue. Cross-section studies are one attempt to pLck up the effects of dlfferent relative prlces wlthout the need to model short term dynamlcs, but these raise problems of homogenelty (particularly for the lndustrial sector). The theoretical problems of bulldlng a fully specLfied dynamic model have been discussed by Epstein (19811 and Nakamura (19861. one major problem would be the requirement for dLsaggregated Lnformation on the capital stock by energy type -whlch is rarely avallable. As an alternative Mountaln and Rsaio suggest using autoregressive