rafmacalaba/datause-encoder-data huggingface dataset:
# **I. Introduction**

Youth transition to work has important implications on youth development and the development of their communities, nations and the world. In order to assess what policies can best assist youth in this transition, we need to know how to measure youth activities in and out of the labor market; what can we learn from existing data on youth time allocations across countries; and what factors affect these time allocations. This paper provides some preliminary answers to these questions. It uses a new standardized micro data base for a large set of developing countries to (i) describe the patterns of labor market outcomes for youth and (ii) explain the contributions of supply and demand factors to youth outcomes.

There are several indicators that are relevant to describe youth outcomes. A description of the time spent in the labor market needs to take into account the important share of young people who are still in school, combine school and work, or temporary withdrawn from the labor market. The types of work youth are doing and the difficulties they are facing in their search activities are most important. To get a full picture of how well youth are faring, it is important to examine more than one dimension of the youth labor market. No one measure provides a complete picture of the health of the labor market for youth, and so multiple measures are needed to analyze youth labor markets in developing economies.

According to the level of country development and the gender and education of youth the relevant indicator could vary. We will list the various indicators and their possible caveats below, but we briefly outline some of the difficulties to keep in mind in defining youth time allocations in developing countries:

- The unemployment rate is typically viewed as a measure of the difficulty finding work, but it also reflects willingness to accept work that is offered.  In middle income countries the ratio of youth to adult unemployment rate is high and suggests greater difficulty youth face is finding work.  In low income countries, the youth unemployment rate can be very low, except that in urban areas, the more educated and better off portions of the population can have high unemployment rates.  It is doubtful that the more educated youth really are facing greater hardships in finding work than are the poorer and less educated youth in developing countries.

- The fraction of the youth who are employed does not account for school enrollment, for the quality of the jobs available, or for some types of work that are unpaid and informal.  Almost everywhere young female’s employment rate is lower than young male’s employment rate, due in part to the lack of measures for household work.  Unpaid work is a frequent employment option for youth, especially those in poor households.

- Because of the lack of measures for household work, an important share of youth, particularly young women, are considered out of school and out of work. Others have called this state idleness.  However, idleness is difficult to distinguish from productive but unpaid activities conducted in the home.

2
rates and death rates was never very large, and population growth rates rarely exceed 1% per year. The demographic transition that took place in developing countries was quantitatively quite different. Death rates declined very fast, generating population growth rates that exceeded 4% per year in some countries (implying doubling times of less than 20 years).

It is instructive to look at the history of the demographic transition for a specific country. This section will look at the case of Brazil, a country whose mortality decline and fertility decline were fairly typical of countries that have now progressed quite far through the demographic transition. Brazil has exceptionally good data with which to track demographic changes at both the macro and micro level, including detailed census data back to 1960. Figure 7 shows the brod overview of the demographic transition in Brazil, based on the United Nations estimates of birth rates, death rates and population growth rates from 1950-55 to 2000-05.<sup>1</sup> The figure shows that the demographic transition was already well underway in Brazil by 1950, with the crude death rate having fallen to 15 per 1000, while the crude birth rate was almost 45 per 1000. The rate of population growth was about 28 per 1000, or 2.8% per year, in the 1950s, a rate much higher than was ever experienced by currently high income countries when they went through the demographic transition. Although the crude birth rate was falling in the 1950s, death rates were falling faster, causing a peak population growth rate of 3.0% in the 1960-65 period. This was also the period in which world population growth rates reached their historic peak, at around 2% per year (Lam, 2005).

1 The crude birth rate is defined as the number of births per year divided by the total mid-year population, expressed as a ratio of births per 1000 population. The crude death rate is deaths per year per 1000 population. The rate of natural increase is simply the difference between the crude birth rate and the crude death rate. The rates shown are estimates of the average annual rates in each five-year period.

> <sup>9</sup>
Tabl- 2

# Elasticity Specification (Central Case)

|Sector|(1)|Colu<br>(2)|InN<br>(3)|otes<br>(4)|<br>(5)|Elasticity<br>of<br>Substitution<br>Intermdiates<br>(.)<br>(1)|Elasticity<br>of<br>Substitutio<br>Capital/Labor<br>(2)|Elasticity<br>of<br>Transformtion<br>n Dometic/Expor<br>Sales<br>(3)|Price<br>Ele<br>t of Fina<br>Dometic<br>(4)|stieiti<br>l<br>Demand<br>Imports<br>(S)|es<br>Premis<br>Rates<br>(6)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Agriculture|a|**c**|*|k|f|1.4|0.6|4.0|0.76|0.3||
|Feed|a|c|**'**|f|f|0.8|0.3|3.0|0.90|1.1||
|Mining|b|b|*|j|f|0.5|0.3|S.0|0.50|1.0||
|Iron<br>and Steel|a|d|*|I|f|8.0|1.0|8.0|1.0|1.4||
|motor Vehicles|a|c|*|h|h|2.0|0.8|3.0|1.2|1.1|22.61|
|Toxtiles**and**Apparel|i|c|*|I|f|2.6|1.0|8.0|0.4|3.9|40.6X|
|Other Manufactures|a|c|*|*|t|8.6|0.3|8.0|1.6|1.8||
|CUher<br>Conwusor|a|c||t|t|3.2|0.8|3.0|1.9|2.4||
|Traoed Services|b|c|*|g|g|2.0|0.8|0.7|0.5|0.6||
|Noe-Traded Services||b||g|||0.0||0.6|||

- (.) CES and CET functions imply that the corrsponding elasticities of subatitutIon (transformation) corr eond to comp nsated import demand (export supply) elasticities.

All price elasticities if demand defined **as** positive numbrs. For premla estimates, a text. Column notes correspond to te sources from which estImtes are Interpolated. For Interpolation detilIs **ao** Tarr (198).

**(a)** ShiellI, Deardorff and Stern (1906); (b) Dixon at ***l.** (1962); (e) Caddy (1976); (d) kman; (e) own estimates; (t) Stern, Francis and Schumacher (1976); (9) Houthakker and Taylor (1970); (h) Lovinsohn; (1) Crandall (1981); 0) Bohi and Russell (1978); (k) USDA (1984); (l) Hutbauer at el (1966).
- [3] B. S. Bernanke, A. S. Blinder, The Federal Funds Rate and the Channels of Monetary Transmission, The American Economic Review 82 (4) (1992) 901–921.

- [4] B. S. Bernanke, M. Gertler, M. Watson, Systematic Monetary Policy and the Effects of Oil Price Shocks, Brookings Papers on Economic Activity 1997 (1) (1997) 91– 157.

- [5] B. S. Bernanke, I. Mihov, Measuring Monetary Policy, NBER Working Paper Series WP 5145 (1995) 1–66.

- [6] B. S. Bernanke, I. Mihov, Measuring Monetary Policy, The Quarterly Journal of Economics 113 (3) (1998) 869–902.

- [7] G. A. Calvo, C. M. Reinhart, Fear of floating, The Quarterly Journal of Economics 117 (2) (2002) 379–408.

- [8] L. J. Christiano, M. Eichenbaum, C. L. Evans, Sticky price and limited participation models of money: A comparison , European Economic Review 41 (6) (1997) 1201–1249.

- [9] L. J. Christiano, M. Eichenbaum, C. L. Evans, Monetary policy shocks: What have we learned and to what end?, in: J. B. Taylor, M. Woodford (eds.), Handbook of Macroeconomics, vol. 1, chap. 2, Elsevier, 1999, pp. 65–148.

- [10] L. J. Christiano, M. Eichenbaum, R. Vigfusson, Assessing Structural VARs, National Bureau of Economic Research 12353 (2006) 1–55.

- [11] R. Clarida, M. Gertler, How the Bundesbank Conducts Monetary Policy, in: Reducing Inflation: Motivation and Strategy, National Bureau of Economic Research, 1997, pp. 363–412.

- [12] D. Croushore, C. Evans, Data revisions and the identification of monetary policy shocks, Journal of Monetary Economics 53 (6) (2006) 1135–1160.

- [13] D. O. Cushman, T. Zha, Identifying Monetary Policy in a Small Open Economy Under Flexible Exchange Rates, Journal of Monetary Economics 39 (3) (1997) 433–448.

- [14] K. M. Dominguez, J. A. Frankel, Does Foreign Exchange Intervention Matter? The Portfolio Effect, The American Economic Review 83 (5) (1993) 1356–69.

- [15] M. Eichenbaum, Comment on ’Interpreting the macroeconomic time series facts: The effects of monetary policy’ by C.A. Sims, European Economic Review 36 (5) (1992) 1001–1011.

- [16] M. Eichenbaum, C. Evans, Some Empirical Evidence on the Effects of Shocks to Monetary Policy on Exchange Rates, The Quarterly Journal of Economics 110 (4) (1995) 975–1009.

- [17] V. Grilli, N. Roubini, Liquidity and exchange rates: Puzzling evidence from the G-7 countries, Mimeo, Yale University, New Haven.

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financial distortion in the model though it is a feature that is also part of the social planner’s assessment of the economic environment (David et al., 2020).

In addition to isolating the effect of a firm’s age and size on its average cost of finance from other factors, the regression specifications also seek to assess productivity dependence in the distribution of financial wedges. This is motivated by the real misallocation literature, where it is a well-established feature of resource misallocation that distortions tend to tax productive firms more heavily than less productive ones. As shown in the literature, idiosyncratic distortions carry larger aggregate effects when increasing with the firms’ physical productivity.<sup>6</sup> By including _log_ ( _TFPQi,s,t/TFPQs,t_ ), in contemporaneous or lagged fashion, we seek to establish if such a relationship manifests as well in the context of financial distortions.

Table 6: Cost of Finance: The Role of Firm Age and Size

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Age of the frm|0.0114<sup>_∗∗∗_</sup>|0.0097<sup>_∗∗∗_</sup>|-0.0057<sup>_∗∗_</sup>|-0.0242<sup>_∗∗∗_</sup>|
||(0.0033)|(0.0033)|(0.0027)|(0.0083)|
|lnasset|-0.5315<sup>_∗∗∗_</sup>|-0.5384<sup>_∗∗∗_</sup>|-0.5460<sup>_∗∗∗_</sup>||
||(0.0002)|(0.0002)|(0.0002)||
|log<br>~~T~~FPQ|0.3970<sup>_∗∗∗_</sup>|0.3995<sup>_∗∗∗_</sup>|0.4160<sup>_∗∗∗_</sup>||
||(0.0001)|(0.0001)|(0.0001)||
|lnasset_t−_1||||-0.1715<sup>_∗∗∗_</sup><br>(0.0009)|
|log<br>~~T~~FPQ_t−_1||||0.0680<sup>_∗∗∗_</sup><br>(0.0004)|
|Observations|7758234|7758234|7758234|5263441|
|Time fxed efects|Y|N|N|N|
|Firm fxed efects|Y|Y|Y|Y|
|Industry-time fxed efects|N|Y|Y|Y|
|Country-time fxed efects|N|N|Y|Y|

Note: Standard errors in parentheses. This table presents the results obtained using equation 22.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

> 6See, for instance, Hsieh and Klenow (2014) and Bento and Restuccia (2017).

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2

# I. **INTRODUCTION**

According to Google‘s Books Ngram Viewer, the terms ―health economics‖ and ―Health Economics‖ started appearing in books only in the 1940s, four decades after the terms ―agricultural economics‖ and ―international economics‖ surfaced. The first two decades of health economics were slow, but in the mid-1960s use of the terms ―health economics‖ and ―Health Economics‖ increased sharply, and the growth has mostly continued since then.

In this paper, we take a bibliometric tour of the last forty years of health economics. Our bibliographic ―metadata‖ are drawn from EconLit which dates from 1969 although it includes many earlier classic papers that were reprinted as chapters in collections published after 1969, including for example Arrow (1963). Unlike other databases such as the Social Science Citation Index, SCOPUS, and Medline, EconLit allows for a relatively clean definition of health economics based on EconLit‘s inclusion criterion (―a substantial economics content‖) and a publication‘s JEL codes. We run risks of omission and commission: there are many noneconomics and multi-disciplinary journals that have published many papers by health economists, and some authors may not have chosen a health JEL code despite their paper having a substantial health content; conversely, our net will catch some items that are devoid of economics (and not written by economists), as well as items where health is not the main focus of the publication. In any event, for reasons to be explained below, we had little choice. And our pragmatic definition of the sub-discipline enables us to avoid many esoteric considerations regarding, for example, the emphasis to be placed in characterizing health economics as the topics its practitioners study (e.g. financial aspects of health services) rather than the discipline (viz. economics) characteristically applied to understanding and explaining phenomena in health and health care (Culyer, 1981; Williams, 1979).

Our EconLit metadata include author, title, journal, the year of publication, author‘s institution, country of focus, keyword, and three-digit JEL code. EconLit does not include any citation data; for 78 percent of records in our dataset we were able to obtain citation data from Google Scholar. In addition to getting at influence through citations, we also want to say something about the topics that health economists work on. The JEL sub-categories are not
# _B. Robustness Checks_

We run a number of robustness checks on our main models (not shown in tables). First, we run our regressions using different control variables from the World Bank’s Doing Business report (World Bank 2006) including cost to register property, credit information, public registry, private bureau, cost to enforce contracts and informal economy. Our main results are maintained. Second, our results are robust to excluding GDP per capita. Third, we exclude the quadratic (squared) term in our regressions to force a linear relationship between the degree of market power and the various measures of bank risk exposures. The robustness checks are generally robust with the findings of the previous sections. Banks with more market power carry riskier loan portfolios, but increased market power is also generally accompanied by greater levels of financial stability in terms of reduced overall risk. The robustness tests also confirm the finding that banks with more market power hold significantly more equity capital.

# **VI. Conclusions**

Under the traditional “competition-fragility” view, more bank competition erodes market power, decreases profit margins, and results in reduced franchise value. This encourages banking organizations to take on more risk to increase returns. Under the alternative “competitionstability” view, more market power in the loan market may result in higher bank risk as the higher interest rates charged to loan customers make it harder to repay loans, and exacerbate moral hazard and adverse selection problems. Both theories have received some degree of empirical support using different measures of bank or loan risk and the degree of competition or market power.

We argue that the two strands of the literature need not necessarily yield opposing predictions regarding the effects of competition and market power on stability in banking. Even if market power in the loan market results in riskier loan portfolios, the overall risks of the banks need not increase. If banks enjoy higher franchise value derived from their market power, they may protect this value from the higher loan risk through more equity capital, a smaller loan portfolio, or other risk-mitigating techniques. Thus, when banks charge higher rates for business loans and have a riskier loan portfolio, they may still choose a lower overall risk level.

15
Table 8: 2SLS Estimates of Labor Supply by Household Head Status

||Wage Em|ployment|Self Emp|loyment|
|---|---|---|---|---|
||(1)<br>Household Head|(2)<br>Other Members|(3)<br>Household Head|(4)<br>Other Members|
|Panel A: Labor market par|ticipation||||
|Household with migrant|-0.414***|-0.231**|0.125|0.298***|
||(0.147)|(0.100)|(0.098)|(0.091)|
|Observations|5791|11088|5791|11088|
|Wald _χ_<sup>2</sup>|906.861|1882.340|6353.722|8786.879|
|_F_-test 1stage|53.309|62.508|53.309|62.508|
|Coecient 1stage|1.633***|2.030***|1.633***|2.030***|
|Panel B: Hours supply|||||
|Household with migrant|-17.642**|-11.559*|-11.369*|-10.493**|
||(7.434)|(6.610)|(6.662)|(4.285)|
|Observations|2772|3073|4450|7068|
|Wald _χ_<sup>2</sup>|2009.570|2551.071|943.402|1107.469|
|_F_-test 1stage|24.419|30.862|50.477|57.718|
|Coecient 1stage|1.515***|2.032***|1.647***|2.036***|
|Controls|||||
|Individual characteristics|Yes|Yes|Yes|Yes|
|Household characteristics|Yes|Yes|Yes|Yes|
|VDC characteristics|Yes|Yes|Yes|Yes|
|Region Fixed Eect|Yes|Yes|Yes|Yes|

Note: 2SLS estimates are reported in the table. Instrument used for 2SLS is share of international migrants in a VDC in 2001 (IV1). Sample is working age (18 to 60) adults and standard errors are clustered at VDC level. Standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 1 ,<sup>_∗∗_</sup> _p <_ 0 _._ 05 ,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 . Individual controls are age age<sup>2</sup> , years of education, gender, household head identier, and marital status. Household controls are female HH head, share of male and female adults, share of elderly, share of children, amount of land-owned, house ownership and social caste. Similarly VDC level controls are, unemployment rate, poverty rate, illiteracy rate, inequality (Gini), and urban/rural location. Regions are Kathmandu, other urban areas, Western hills, Eastern hills, Western Terai, and Eastern Terai. Coecient 1stage is the estimated coecient on the exogenous instrument in the rst-stage. Sample in Panel B is conditioned on being employed in that particular sector. Log of hourly wage is added as an extra individual control for column (1) and (2).

34
_5._ The assessment of this and alternative pricing systems is made relative to the objective of minimizing the aggregate deadweight loss -- that is. maximizing aggregate producers' surplus -- for a given amount of revenue from taxing agricultural output. Theoretical analysis then shows (consistent with results in other areas of the ecenomics of taxation) that some transport price intervention is optimai if and only it output is taxed. For the special case of a constant elasticity of agricultural supply, the proportional rate of the optimal transport subsidy is equal to the rate of the tax on output (calculated on the export price). There is then an optimal transport subsidy (or an optimal location-specific price for cotton at the depot), that rises in an absolute amount with distance but is less than the 100 percent subsidy implicit in panterritorial pricing.

6. Combining Cote d'Ivoire data (available at the level of 56 zones) with different values of the elasticity of supply, it appears that:

- a. First, to raise the same amount of revenue that the country raises by panterritorial pricing of cotton:

- as a general proposition, the loss to aggregate producers' surplus from panterritorial rather than optimal pricing increases with the elasticity of supply. But in Cote d'lvoire where cotton is grown in a very compact area, the percentage gain from moving to optimal pricing is small: say, up to 3 percent for the higher elasticities of supply;

- but as optimal is substituted for panterritorial pricing, there results a marked redistribution of producer surplus, from farmers in remote zones to those near the port;

- and if the given revenue is raised by a pure export tax, so that farmers pay the full cost of transport to port, the effect in terms of aggregate producer surplus is superior to that under panterritorial pricing (though inferior to optimal pricing), while the redistribution of producer surplus from remote to near zones is greater than results from a move to optimal pricing. The movement from panterritorial to optimal pricing reduces producer surplus for the remotest zone by about one-quarter, while a move to full-cost pricing of transport lowers it bv one-third: and

- lastly, transport investments that reduce the cost per ton-km yield the largest return (in terms of producer surplus) under panterritorial pricing, lower under optimal pricing, lowest under a pure export tax which leaves farmers with the full cost of transport. The differences according to the pricing system (in the Cote d'Ivoire) are again not very large but rise with the elasticity of supply, and would be larger if cultivation were more dispersed over space than in this country. But note that the pricing systems that produce the highest and the lowest rate of return to transport
# <u>Upside to OJT: Productivity and wage effects</u>

What is the upside to OJT, in terms of productivity gains and wage effects? Studies find positive effects of OJT on direct measures of firm productivity and wages. For example, Konings and Vanormelingen (2015) use firm-level panel data of OJT to estimate its impact on productivity and wages in Belgium (figure 1). Treating for endogeneity of input factors and training, they find that the increase in productivity of the trained worker is higher than the increase in wages the trained worker commands because the worker participated in OJT. In numeric terms, the average sales per worker, a proxy for productivity, increased from 1.7 to 3.2 percent, while their wages only increased from 1.0 to 1.7 percent. This occurred in response to a 10 percent increase in the number of workers who benefited from training.<sup>9</sup> Another study confirms these results. Dearden, Reed, and Van Reenen (2006) study the impact of work-related training on productivity. Using panel data from the United Kingdom, they find that OJT is associated with significantly higher productivity. In response to a 1 percent increase in training, there is an increase in value-added per hour of about 0.6 percent, as well as an increase in hourly wages of about 0.3 percent. They also provide evidence of training externalities (a topic covered in greater detail in section 2.2), while controlling for firm selection and training.

Studies that look at the impact of OJT on productivity in developing countries are more limited due to data constraints. Therefore, to get a directional sense, we reviewed studies that show a link between the incidence of OJT and wage gains (which are studied widely in the developing world). Evidence from two carefully prepared country studies suggests that individuals’ earnings can substantially increase with OJT (Tan and Lopez-Acevedo 2003, for Mexico, and Rosholm, Nielsen and Debalsen 2007, for Kenya and Zambia). Rosholm, Nielsen and Debalsen (2007) find a 20 percent wage increase, and Tan and Lopez-Acevedo (2003) find an impact of 5 to 7 percent due to OJT. These studies also control for endogeneity. These studies also find that the incidence of training is different for different groups by gender, prior skill level and age (box 4).

> 9 Details on each study (as well as other papers reviewed), including data set descriptions, dependent and independent data sets, and challenges with regard to methodology (if any) are included in Annex 1. Initial data collection work is described in the internal memo (Saraf, Mariscal and Velazquez, 2016).

12
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