rafmacalaba/datause-encoder-data huggingface dataset:
simple production function with log output _Y_ of each establishment _i_ in a district _d_ and industry _j_ as the dependent variable. The specification takes the form: Our core regressors are the specialization and diversity measures for each plant’s district. For these estimations, we use a form of indices that measures specialization and diversity on a tenpoint scale ranging from zero to nine based upon the decile of a district in the overall distribution for India. This transformation makes the _β_ and _δ_ parameters easier to interpret as described below. This transformation also implicitly suppresses some of the variation in specialization index value vis-à-vis the diversity index.

We include a vector _Xi_ of plant inputs into the production function: log employees, log book values of capital, and log costs of materials. We exclude plants with missing values for these metrics, which increases the relative weight of organized-sector facilities in the estimations. We also control for other district traits described below with a vector of districtlevel controls _Zd_ . Regressions include three-digit industry fixed effects γ _j_ to capture regular differences in production techniques and spatial locations across industries. We use establishment weights from the surveys and cluster standard errors by district.

Column 1 provides a baseline estimation of the plant-level production function before district-level conditions are incorporated. These underlying parameters for the production function, emphasizing employees and materials, are very stable across estimations. Columns 2 and 3 show that simple connections do not exist for the production function to specialization or diversity in isolation. Interestingly, Column 4 finds, however, that both specialization and diversity correlate with greater productivity when jointly modeled. The 0.015 coefficients can be interpreted as quantifying that each increase in decile of specialization or diversity (e.g., moving from the 8<sup>th</sup> to the 9<sup>th</sup> decile) is associated with a 1.5% increase in the conditional output of the plant.

While we have yet to come to terms with why both features of the district’s industrial base need to be modeled, we do know that this pairing is not simply reflecting other aspects of the district landscape. Column 5, for example, shows very similar results when controlling for log manufacturing employment in district per square kilometer, log manufacturing employment 18
actions, although doing so would have only increased their payout (with the exception of unnecessary referrals). Fifth, as highlighted in Table 1, participants identified both unnecessary actions that were paid for (mimicking real-life overuse) as well as actions that were neither listed nor incentivized (for example, measuring vital signs), suggesting they did not simply respond by naming the listed or paid actions. Sixth, we find that participants in the incentive arms spend more time on the interview than those in the information arm ( Table A.3), which is consistent with increased effort. In other settings, increased time spent is associated with improved performance (Das et al., 2012; Rivkin and Schiman, 2015; Lavy, 2016; Cattaneo et al., 2017).<sup>7</sup> Seventh, our estimated price elasticities are relatively low and consistent with estimated wage elasticities in LMICs (Goldberg, 2016). In the context of hypothetical tasks with limited effort costs, if anything, one might expect artificially high price elasticities; instead our elasticity estimates are comparable to “real world” estimates. Finally, we find that our results are robust to controlling for participants’ knowledge and costly effort as measured with the antenatal care vignette and direct observations, respectively. This suggests that participants took the experiments seriously and that our task induced real attention and

# **5 Empirical estimation**

We conduct our analysis in five steps. First, we estimate overall performance across all actions for each of the three treatment arms. Then, we examine the effect of incentives by comparing performance on the subset of five incentivized actions across arms. Third, we assess incentive spillovers by comparing performance across arms on listed and unlisted actions. Fourth, we examine the effects of the two potential confounders on overall performance: whether the facility was assigned to PFP, DFF, or control in the larger trial and whether the participant had a higher than median score on the knowledge test of maternity care protocol. Finally, we benchmark the effect of information

> 7Some interviews were not completed within the day that they were started and the survey enumerator returned to the facility when the health worker was next on shift to complete the interview. There may also have been instances in which an enumerator failed to promptly record the interview as complete. In these instances, the interview length cannot be calculated correctly from time stamps because the end date was several days after the start, and the interviews were not “paused” in the interim. We omit these observations from the analysis of time-on-task. This leaves us with a sample of 1,178 observations.

18
Finally, as a control variable we use an indicator for COVID-19 medical products from the World Trade Organisation at the HS4 product level.<sup>4</sup> Summary statistics are provided in Appendix Table 1.

# **3. Empirical approach**

Our empirical specification to test the hypotheses discussed in Section 2 consists in a differencein-differences specification that estimates the within effect of the COVID-19 shock on bilateral exports to each major market mediated by proxies for production vulnerability or resilience as in: e e = ∑ c c e v + ∑ c c e ∗ νν νν νν −nn νν,ee νν −nn YY v ββ cc_cciic ccc ∗ccveiiv ccvvccv e eφφ e cc_cciice e cc (1) νν,e −nn ee ee where ccv is the logarithm of the value of exports by country iiv ccvvccv + ∑αα c cc_cciic cc ∗XX + kk _e_ of HS4 product + θθ + ππ + εε _p_ to destination market _i_ in month-year YY _t_ and is an independent and identically distributed error term. The coefficients of interest ( ) are those on the interactions between each of the measures of εε v at νν νν νν,ee the product level (unskilled labor intensity and complexity) or ββ , φφ v ccv iivat the product-ccvvccv νν,e exporting country level (reliance on imported inputs, export concentration of inputs, and China ccv iiv ccvvccv export share in inputs) and lagged COVID-19 incidence c c is the number of reported eenniieeiie −nn COVID-19 deaths per capita per month in each exporting country using the lags defined in Section cc 2. By including exporting country-importing country-HS4 product fixed effects !"#, Equation (1) identifies the coefficients based on the within (time-series) variation in bilateral export flows kk at the product level as the COVID-19 shock unfolds. Exporting country-month-year fixed effects !$ account for changes in economic conditions in the exporting country related or unrelated to θθ

> 4 http://www.wcoomd.org/-/media/wco/public/global/pdf/topics/nomenclature/covid_19/hs-classification-reference_2_124_4_20_en.pdf?la=en.

13
<!-- Start of picture text --> 0 −.05 −.1 −.15 −.2 −.25 −.3 −.35 −.4 −.45 −.5 −.55 −.6 −.65 −.7 −.75 −.8 −.85 −.9 −.95 −1<br>.8<br>.6<br>.4<br>Expenditure share (alpha)<br>Panel B: Two price changes<br>.2<br>0<br>0 −.05 −.1 −.15 −.2 −.25 −.3 −.35 −.4 −.45 −.5 −.55 −.6 −.65 −.7 −.75 −.8 −.85 −.9 −.95 −1<br>1<br>.8<br>.6<br>Contour Map of the GAP Estimations by Price Changes and Expenditure Shares<br>.4<br>Figure 14: Expenditure share (alpha)<br>Panel A: One price change<br>.2<br>0<br>(LV−PV)/m<br>500 400 300 200 100 0<br>Price change (in %)<br>(LV−PV)/m<br>500 400 300 200 100 0<br>Price change (in %)<br><!-- End of picture text --> 40
|PPR<br>Titie|Working Papor Series<br>Author|Date|Contact|
|---|---|---|---|
|WPS184 Women and Forestry: Operational Issues|Augusta Molnar|||
|WPS18S Uniform Trade Taxes, Devaluation,<br>and the Real Exchange**Rate:**<br>A Theoretical<br>Analysis|Stephen A. O'Connell|||
|WPS186 The Uruguay Negotiations<br>on Subsidies<br>and Countervailing<br>Measures: Past<br>and Future Constraints|Patrick A. Messerlin|April 1989|S. Torrijos|
||||33709|
|WPS18? The Flexibility<br>of Wages When the<br>Output Price Changes: An Empirical<br>Study of 13 Industrial<br>Countries*|M4nahes Prywes|April 1989|J. Israel<br>31285|
|WPS188 International<br>COmparlsoOs<br>of Wage<br>and Non-Wage Costs of Labor|Luis A. Riveros|||
|WPS189 The Treatment of Companies under Cash<br>Flow Taxes:Some Adeinistrative,<br>Transitional,<br>and International<br>Issues|Emil M. Sunley|||
|WPS190 Macro Performance Under Adjustment||||
|<br>Lending|Riccardo Faini<br>Jaime de Melo<br>Abdel Senhadji-Semiall<br>Julie Stanton|Aprl'<br>1989|M. Ameal<br>61466|
|WPS191 Openness, Outward Orientation,<br>Trade<br>Liberalization<br>and Economic Perform-||||
|ance In Developing<br>Countries|Sebastian Edwards|||
|WPS192 Inflation,<br>Price Controls and<br>Fiscal Adjustment: The Case of||||
|Zimbabwe|Ajay Chhlbber<br>Joaquln Cottani<br>Reza Firuzabadi<br>Michael Walton|||
|WPS193 Bank Lending to Developing<br>Countries<br>In the 1980s: An Empirical Test of<br>Major Hypotheses on Voluntary and<br>Involuntary<br>Lending|Peter Nunnenkamp|||
|WPS194 Efficient Debt Reduction|Jeffrey Sachs|||
|WPS195 Commercial<br>Bank Claims on Developing<br>Countries: How Have Banks Been<br>Affected?|Harry Hulzinga|||
# **_1. Introduction_**

In 2018, the World Bank launched the Human Capital Project (HCP), an initiative aimed at raising awareness among policy makers about the importance of investing in human capital. The HCP includes an advocacy component which features the Human Capital Index (HCI), a measure of the human capital that a child born today can expect to attain by age 18, given the risks of poor health and poor education that prevail in the country where she lives (Kraay, 2019).

The HCI quantifies the trajectory from birth to adulthood in terms of the consequences for productivity by means of three components: (1) a measure of whether children survive from birth to school age (age 5); (2) a measure of expected years of basic education (primary and secondary), adjusted for quality; and (3) two broad measures of health: child stunting rates and adult survival from age 15 to age 60. The index is constructed so that a value of 1 represents the productivity in adulthood of a child born today if he or she enjoyed complete education and full health until age 18. Countries are measured with respect to this benchmark; the value of the index can thus be interpreted as a percentage of that productivity level.

While useful in a global context, the original version of the HCI may not adequately reflect the education and health challenges that are relevant for specific regions of the world. Countries in Europe and Central Asia provide their citizens relatively good basic education and health services; the region’s citizens begin their productive life in a much better position than their peers in other regions of the world. But job markets today demand higher levels of human capital than in the past. Good basic education will not be enough; higher education institutions must prepare students for the challenges the future of work may hold. Health care systems will need to ensure that citizens remain healthy throughout their adult life, as, more and more, learning and skill acquisition will take place along an individual’s life cycle, not just in the initial years of life. This is also more important as the region’s workforce is becoming, on average, older, and therefore improving adult health will be needed to ensure a productive aging for the region’s population.

This paper outlines an extension of the HCI which addresses the relevant education and health challenges of Europe and Central Asia, namely by including higher education in the education component of the index and by looking at three crucial adult health risk factors -obesity, heavy alcohol consumption and tobacco smoking- in the health component. This extension could also be useful for assessing the state of human capital in middle income countries in general, particularly for those where basic education attainment and child health are less of a concern but where significant challenges remain as young people transition into the labor market.

2
Table 10: Matched and Unmatched Moments at the gmm estimates

||Description|Model|Data|Discrepancy (%)|
|---|---|---|---|---|
|1.|Prob. of formal entrep (%)|2.78|2.31|20.1|
|2.|Prob. of formal entrep,_z < zm_ (%)|0.65|0.55|18.2|
|3.|Prob. of informal entrep (%)|18.5|17.71|10.2|
|4.|Prob. of informal entrep,_z < zm_ (%)|12.8|11.70|9.40|
|5.|Income of formal entrep|179.8|189.12|-10.4|
|6.|Income of formal entrep,_z < zm_|116.8|106.13|10.1|
|7.|Income of informal entrep|80.7|85.60|-5.72|
|8.|Income of informal entrep,_z < zm_|59.8|66.93|-10.6|
|9.|Income of non-entrep|104.2|104.2|0.00|

_Notes._ The table reports averages. Income is in thousands of fcfa ($1 _∼_ 500 fcfa) _zm_ is the median initial wealth in the data nously fixed interest rate, and agents make their choices based on these factors. Our focus is on the impact of factors such as infrastructure and borrowing constraints. We provide graphical representations that focus on how various policy options lead to differences within cities or industries.

# **5.1 Impact of Infrastructure: Paved road density**

This counterfactual experiment consists in examining how the equilibrium would change should there be a change in the country infrastructure level. Our measure of infrastructure index is the paved road density. This policy could be a project of road construction, a bridge construction or the paving of an existing road. We assume a change in the infrastructure index _Rij_ defined by where _η_ represents the increment in infrastructure quality implied by the policy.

## **Impact on Industries**

The impact of this infrastructure policy with the Cameroon data within the context of the model is quantified for a range of relative quantity increments _η_ , starting from _η_ = 0, the current state as produced by the structural estimates, to _η_ = 5 _R_<sup> ̄</sup> , where _R_<sup> ̄</sup> is 33
policy study of training for vocationally-specific skills in developing countries conducted by the World Bank. **The** objective of the broader study is to provide guidance to educational policy makers in developing countries and to Bank staff in the s*electiGa, design and development of appropriate training models. The present paper contributes to this objective by exploring the linkage between broad economic policies and the development of skilled manpower for economic and social development. A better understanding of this linkage is important to those concerned with the selection of appropriate economic development strategies as well as those responsible for the development of effective training programs.

5. Benson (1987) offers a taxonomy identifying conditions mnder which formal modes of VET **are** expected to **be an** efficient institutional source of skills acquisition. This taxonomy considers the capacity of these modes for realizing economies of scale in training, the complementary linkages with general education, and the ability to focus on social alongside private interests in training. In addition, while many educational planners and labor market analysts have come to recognize the benefits of moving training closer to the enterprise in terms of added efficiency, this does not translate directly into the dismantling of existing VET systems (Squire, 1981, p.204). The large investment of most industrialized and developing countries in formal modes of VET suggests instead that less costly alternatives will be explored to improve linkages between schools and enterprises, including the restructuring of VEr financing (Dougherty and Tan, 1988).

6. Against this background, tha paper begins with4 a social systems model to trace the impact of the economic environment and policies on the 3
The findings of these serious studies are all well and good, and they suggest that further investments in PMGSY may well be socially profitable, but they do not establish as much. For what must be demonstrated is that spending yet more billions on PMGSY will yield a greater improvement in welfare than like spending on the next best alternative.

The plan of the paper is as follows. Section 2 deals with three key shadow prices (SPs), namely, the social discount rate, the premium on public income and the conversion factor for road-building. Section 3 describes the planned extension of the network and estimates the cost, at SPs, of a representative feeder road over an assumed 30-year lifecycle. Estimating the benefits is altogether much harder. In keeping with the three broad domains of commerce, education and health, Section 4 is split into three corresponding subsections. Various methods and sources are used, notably a survey of 30 villages in Orissa’s poor uplands (hereinafter, the Orissa Survey). Those forms of benefits that have defied my attempts to measure them are indicated and briefly discussed. The costs and measured benefits are compared in Section 5. If the former exceed the latter, the difference is the smallest contribution required of all non-measured benefits such that the project be socially profitable. In other words, the difference then measures the leap of faith involved in asserting such profitability. Section 6 takes up the program’s external effects on the wider economy, and I argue that, on balance, these will be beneficial. Alternative approaches to the measurement of benefits are briefly discussed in Section 7. The main conclusions concerning social profitability and the complementary policies needed to ensure it are drawn together in Section 8.

# **2 Shadow Prices**

Market prices depart from shadow prices for various reasons: market failure, externalities and, especially important in the present setting, the presence of distortionary taxes and subsidies as well as a social desire for greater equity than that yielded by market forces. There is no certainty whatever that replacing market prices with shadow prices will make a project socially profitable; but given the considerations involved, doing the accounting at SPs is strongly desirable. The practical difficulty is that estimating a complete set of SPs is very demanding of time and resources, and no such set is available off the shelf. Some short-cuts are, therefore, unavoidable. Three central ‘prices’ demand attention, namely, the social discount rate, the premium on public income relative to private income, and the cost at shadow prices of building and maintaining 6
|**Table 5. Share of Sector Value-Added b**|**y Federal Okr**|**ug of Russi**|**a**|||||Sector|
|---|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|total|
|Railway transportation|10.5|28.0|8.8|19.6|15.2|13.5|4.5|3.1|
|Truck transportation|9.1|30.3|9.4|17.9|17.5|11.5|4.3|1.7|
|Pipelines transportation|7.1|14.4|8.2|26.9|30.1|10.2|3.1|0.3|
|Maritime transportation|8.8|19.9|8.4|20.0|24.0|15.2|3.8|0.6|
|Air transportation|7.7|38.2|10.0|13.6|14.7|12.0|3.9|0.6|
|Other transportation|8.9|31.0|9.5|18.5|17.2|11.0|4.0|0.8|
|Telecommunications|8.9|34.2|9.1|17.1|15.1|11.3|4.4|1.2|
|Science & science servicing|9.2|29.4|7.9|20.3|17.5|10.6|5.1|1.4|
|Financial services|9.1|34.1|8.1|16.8|15.6|11.5|4.7|7.5|
|Ferrous metallurgy|16.7|26.0|4.6|9.4|31.8|10.9|0.6|1.5|
|Non-ferrous metallurgy|7.0|5.0|2.4|6.0|16.9|44.0|18.7|2.9|
|Chemical & oil-chemical industry|8.9|28.2|8.1|39.6|2.9|11.5|0.7|1.6|
|Mechanical engineering & metal-working|9.8|33.3|5.3|32.9|8.2|6.2|4.3|5.1|
|Timber & woodworking & pulp & paper industry|34.8|21.9|3.3|16.3|3.4|15.5|4.8|1.3|
|Construction materials industry|7.9|40.0|11.2|19.2|9.9|9.2|2.6|0.9|
|Food industry|16.3|36.5|12.2|14.8|4.5|8.3|7.3|3.8|
|Other industries|9.5|53.1|7.5|20.1|2.7|5.6|1.5|0.8|
|Public services, culture and arts|9.5|35.4|8.3|17.2|13.6|11.1|4.9|5.6|
|Agriculture & forestry|6.1|22.8|20.1|26.3|6.2|15.0|3.5|7.1|
|Coalmining|7.3|0.3|3.8|0.1|2.6|75.0|11.0|0.5|
|Housing and communal services|9.3|37.6|9.3|16.8|11.2|11.1|4.7|2.8|
|Construction|7.8|35.5|15.7|22.3|8.5|8.5|1.8|7.4|
|Electric industry|9.7|28.9|8.3|20.8|14.6|13.0|4.7|2.9|
|Gas|0.7||2.5|4.9|90.8|0.5|0.6|0.5|
|Crude oil extraction|4.0||7.9|22.1|62.8|2.1|1.1|4.8|
|Oil refining and processing|8.7|16.5|9.3|39.7|3.9|17.3|4.7|1.0|
|Other goods-producing sectors|11.7|22.3|6.4|22.8|19.6|12.0|5.2|0.8|
|Post|9.2|33.1|9.0|17.7|15.2|11.1|4.5|0.3|
|Wholesale and retail trade|9.0|27.5|8.4|17.8|21.6|11.4|4.4|30.5|
|Textiles and apparel|9.4|39.6|9.5|16.1|9.9|10.8|4.6|0.5|
|Market total|9.2|27.7|9.4|19.6|17.6|12.0|4.4||

39
college experience, college degree, and graduate degrees) in the total number of workers with at least a high school diploma. This statistic indicates the composition of skilled labor in each country. The differences across countries are again very sharp, thus implying that the composition of the skilled labor force varies across countries. Countries with high shares of highly-skilled workers in the skilled group (41 to 56 percent) are Colombia, Peru, Mexico and Nicaragua. Notice, for instance, that because Nicaragua has the lowest skill share, the relatively few workers with degrees tend to reach a high educational attainment. Countries with low shares of highly-skilled workers are El Salvador, Paraguay, Argentina and Chile (19 to 23 percent). The participation of highly-skilled workers in the total labor force can be obtained by multiplying column 5 by column 2.

To estimate the returns to skill in each country, we pool data from all years and estimate Mincer-type regressions with the log hourly wage of each worker explained by individual worker characteristics. The main variable of interest is a binary variable that indicates whether the worker is skilled or unskilled. The equation takes the following standard form: Subscript _i_ denotes individuals, _j_ the industry of employment, and _t_ denotes years. There is a separate equation for each country (country subscripts are dropped). The hourly wage is given by _w_ . It is computed as the reported weekly wage divided by the number of hours worked per week.<sup>4</sup> We define skilled workers as those with a high school diploma or more. Thus, the binary variable _Sk_ is equal to one if the individual has at least a high school diploma. The coefficient _γ_ measures the skill premium, that is, the percentage difference in wages of skilled workers relative to unskilled workers. We control for individual characteristics in the vector **x** and for industry and year effects in the indicator variables _δt_ and _δj_ . The controls included in **x** are gender, age and age squared, marital status, whether the individual works full-time or part-time, a dummy for individuals in rural areas, and regional dummies. The estimates from these equations are correlations from cross-sections

> 4In several surveys these data refer to the total wages received and number of hours worked during the week prior to the survey.

5