rafmacalaba/datause-encoder-data huggingface dataset:
instead lobbied for protection from cheaper iron imports.<sup>28</sup> The critical innovation for the development of the native steel industry was the foundation in 1879 of the _Escola de Minas_ (Mining School) at Ouro Preto, Minas Gerais, which, as the _Escuela de Minas de Antioquia_ was a major diffuser of new practices and led to the establishment of the first new blast furnace since the failures at the beginning of the century. On the other hand, the textile industry for which we have entrepreneurship data above was less fortunate. As (Birchal, 1999, p. 183) notes “the existing informal and spontaneous technological innovative system was not developed enough to take the process of technological assimilation farther in the direction of a profound modification of existing foreign technologies, or to create a more complex indigenous technological alternative” again, much as Wright argues for the American South.

This raises the larger question of, given the sheer disparity of our innovative capacity numbers, if it was so hard for the American South to start competitive industries with its innovative capacity captured by 60 engineers per 100,000 individuals, how did Latin America’s industry expand so quickly at the turn of the 20th century with an average of 10? Much also may lie in the Potemkin nature of much of Latin American industrialization that, as in the American South, was dependent on imported capital goods and innovated little. Across the region, blocks in the input-output table were rapidly filled in but, as Haber (2005, 2006) notes, these industries were not modern in the sense of being at the technological frontier or being able to export to other countries. Much as Wright notes in the American South, Latin America relied heavily on foreign technology imports, and was sluggish in developing an indigenous local machine and capital goods industry, a result attributed to low innovative capacity.<sup>29</sup> The fact that Latin America evolved the highest levels of tariffs in

> 28Of the thirty ironworks in the headwater region of the Rio Doce in 1879, only seven used Italian forging methods, while the rest used the old African cadinho (crucible) technique. Graduates of the _Escola de Engenharia do Exercito_ (Military Engineering School) established in 1930 led the steel industry as it developed through the 1960s.

> 29“Since... capital goods industries now required well-developed scientific and engineering capabilities, Mexico had little choice but to import its capital equipment. The blast furnaces and rolling mills came from the United States, the high-speed cigarette machinery from France, the paper-making machinery from

30
## Summary Statistics

||1|982|1|987|1|992|
|---|---|---|---|---|---|---|
|Variable|Mean|Std Dev|Mean|Std Dev|Mean|Std Dev|
|DEP|:07|:02|:08|:02|:08|:02|
|REN|:03|:02|:03|:02|:04|:03|
|RES<br>|:11|:11|:10|:07|:09|:05|
|A<br>K|:09|:11|:08|:10|:06|:09|
|V(s)<br>|13:71|23:63|13:11|21:77|11:37|15:84|
|C4�<sup>4</sup><br>N|37:86|18:39|37:27|19:03|37:03|18:06|

## Spearman Correlation

|Correlation between|1982 and 1987|1987 and 1992|1982 and 1992|
|---|---|---|---|
|DEP|:66|:59|:50|
|REN|:89|:90|:84|
|RES<br>|:22|:43|:25|
|A<br>K|:90|:56|:57|
|V(s)<br>|:93|:94|:88|
|C4�<sup>4</sup><br>N|:93|:95|:90|

Number of observations=400 in 1982, 430 in 1987, 367 in 1992 (for<sup>A274in1982,320in1987,283in1992)</sup> K<sup>:</sup>

# References

- [1] Aslan, A. (2008). Testing Gibrat’s law: empirical evidence from panel unit root tests of Turkish ...rms. International Research Journal of Finance and Economics, 16, 137-142.

- [2] Baumol, W. (1982). Contestable markets: an uprising in the theory of industry structure. American Economic Review, 72 (1), 1-15.

- [3] Baumol, W., & Fischer, D. (1978). Cost-minimizing number of ...rms and determination of industry structure. Quarterly Journal of Economics, 92, 439-467.

- [4] Baumol, W., Panzar, J., & Willig, R. (1982). Contestable markets and the theory of industry structure. San Diego: Harcourt Brace Jovanovich.

22
# **6.4 Partner choice**

As Tables 5 and 7 demonstrate, allowing participants to choose their own partners has a strong, _negative_ effect on risk-taking. Table 11 begins to address the issue by examining the determinants of matching among players by running social network regressions. For each communication game (where partners are either elected or assigned), we create all possible dyads or pairs among players: if a game has twenty players, there are 190 possible pairs or combinations of two players. However, only ten of those pairs are matches, either randomly assigned by the computer or elected by the players themselves.<sup>10</sup> The goal is to determine the relevant variables used by players in choosing their partners. We use the information on where players were sitting during the games, whether they had ever been guests or hosts,<sup>11</sup> the social network survey, and the census survey.

Columns (2), (3) and (4) report the results for all elected partner games, broken down into one-shot and dynamic treatments. For comparison purposes, column (1) reports the results of the network regression using the randomly assigned matches from the other communication treatment (without endogenous partner choice) as dependent variable. By comparing the R-squared of the elected partner games to that of the assigned partner manipulations, it is clear individuals match on information that is observable to the econometrician – there is a much better fit when players can choose their partner.

The coefficient on “sitting next to your partner” is only significant (taking a negative sign) when the partner is assigned. This reflects the fact that the random assignment process avoided pairing individuals who were sitting next to each other (so that they could not share information and infer they were matched). In contrast, having

> 10 Dyadic observations are typically not independent due to the presence of individual-specific factors common to all observations involving the individual.  Thus, OLS or probit methods will yield inconsistent standard errors.  We use game and player fixed effects to correct for this problem.  Another method used in the literature, especially among sociologists, is the Quadratic Assignment Procedure (QAP).  This method, similar to bootstrap, computes the p-value directly.  We compared both fixed effects estimates and QAP estimates and found them very similar.  Thus, we only report fixed effects estimates.

> 11 On certain days, participation was restricted to individuals that had already played (hosts) who were allowed to attend only if they invited someone that had never played (a guest).

23
**rage** a

followed the timing of key license renewals. The emphasis the companies and the different governments have traditionally given to the text of the licenses represents the recognibon that Jamaican governments have had labitude in interpreting laws. Thus, regulation of public utilities through legislation may provide too much discretion to the party in power. On the other hand, a change in a license cannot be done unilaterally by the government as requires the consent of the company. Thus, a violation of a clear license stipulation could be, and has been, seen by the Courts as a breach of contract.

# _l. The Parties end Their Constituencies._

12. Essentially, ail academicians will agree that since 1962 (and to some extent since 1944, the date when Jamaica was granted universal suffrage) Jamaica has had three periods: a period of rapid growth. fueled by import substitution, bauxite exports, and tourism, that ended in 1972. a period of stagnation, socialconflict and attempted socialization through democratic means from 1972 till 1980, and a period of restructuring following the old, pre-1972Z private sector oriented policies, from 1980 till today.

13. These three periods also reflect the electoral politics of Jamaica. Since 1944, Jamaica's politics have been dominated - and affected - by the fierce competition between its two main parties. the Jamaican Labor Party JLP, and the People's National Party PNP. Both parties have been altemating in power every other election, with an electoral cycle, in principle, of five years.<sup>5</sup> Thus, since 1944, the JLP was in power from 1944 to 1955, from 1962 to 1972. and from 1980 to 1988. The PNP has been in power from 1955 tc 1962. from 1972 to 1980 and from 1988 Ill today. Thus, power is highly contested between these two parties, and following an electoral defeat, each has serious expectatons of retuming to power in iess than a decade.

' **The loalty ofthe** elecoral **cycle** seems **to** have been challenged by the call for new elections by the **JLP** In **1983, which** alkwed **it** to continued in power **uni** 1988. The PNP **daired** that the **call** for **new** elections was illegal, as **it was** based **on** the **old vwbe** registry. and hence **it** boycotted the **elecdion** This **gave** the **JLP** a total **contrai ol** the **lower** house **until** the 19B8 **eledios.**
# Uncertainty and Global lWarming

## An Option-Pricing Approach to Policy'

_Andrea Baranzini' Marc Chesneyb Jacques Morisset'_

- _a. University of Geneva, Department of Economics, 102 bd Carl-Vogt, CH-1211 Genstve 4, Switzerland_

- _b. Department of Finance, HEC-ISA Centre, F-78350Jouy-en-Josas, France_

- _c. World Bank, 1818 H Street NW, Washington, DC_ 20433, _USA_

1. We would like to thank Antonio Estache and Danny Leipziger for helpful comments and William Cline for providing us data on the costs and benefits of global warming.
serious fiscal crises in both Colombia and Argentina can be traced to excessive expenditure increases during the booms -that is to the incapacity to achieve or maintain surpluses in good times. If that is the case, expansionary policies during bad times can be expected to lead to an intertemporally unsustainable outcome -a deficit bias- and can be rightly interpreted as leading to an increase in default risk.

**Table 2**

# **Cyclical Response of Fiscal Aggregates Latin American and the OECD**

||**ImpactofG **|**DPGrowth**|
|---|---|---|
||**OECD**|**Latin**<br>**America**|
|Totalsurplus(%ofGDP)<br>|0.25|0.08|
|Primarysurplus(%ofGDP)|0.22|0.05|
|TotalRevenue(%change)|0.84<br>|1.32|
|TotalExpenditure(%change)|**0.09**|0.61|

**Note:** Numbers give the typical impact of a one percentage point increase in real GDP on the budget category. A positive number for the surplus means that it moves in a stabilizing direction.

**Source:** Gavin, et al. (1996)

In these conditions the proper response of responsible creditors is not to finance an increased deficit during busts, or to do it at significantly higher spreads. In summary, the core of the problem would thus appear to be the incapacity to maintain surpluses in good times. As Gavin et al. put it: **"It is during booms that the seeds of crisis often are sown, although the crisis becomes evident only when the boom subsides."** But why is fiscal policy procyclical in booms? The main reasons lie in the political economy of fiscal policy and the lack of strong budgetary institutions. It is hard enough for a responsible Minister of Finance to avoid a deficit -specially in periods in which financing is readily available, as is usually the case in booms. It is much harder to maintain a too visible surplus through discretionary budgetary decisions. As Allen Schick's paper<sup>14</sup> in this Conference shows, there are strong pressures in any normal discretionary budget process to increase expenditures. Political incentives are aligned with spending out any potential surplus in a boom.

Such political pressures may also inhibit a responsible Minister of Finance to indulge in an explicit discretionary counter cyclical policy (or just to permit automatic stabilizers to operate) during downturns, as once the Pandora box (of a

> 14 Schick, A. (2002):"Budget rules vs. Political Will," Brookins Institution, mimeo.

6
**A7** Russia

**Table A7: Regional disparity** trends in **Russia**

|Year|-<br>Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|-<br>Weighted<br>coefficient<br>of variation<br>(CVW!<br>|Relative<br>mean<br>Deviation<br>!}**(Rw)**|Unweighte<br>gini index<br>(Gu)|d Weighted<br>gini index<br>(Gw)|<br>Theil<br>index<br>**(T)**|
|---|---|---|---|---|---|---|---|
|1994|14.065|0.479|0.447|0.313|0.247|0.221|0.094|
|1995|17.739|0.539|0.524|0.345|0.263|0.243|0.111|
|1996|20.778|0.629|0.635|0.377|0.278|0.268|0.144|
|1997|21.307|0.625|0.645|0.387|0.283|0.280|0.153|

**Figure A7: Reg!onal disparity trends in Russia**

<!-- Start of picture text --> 0.700<br>0.600<br>0.500<br>f] Gnsl - ,~~~~~. . _ , .~ . ........<br>2~~~~~~~~~~~~~~~~~2<br>0.00 '<br>1994 1995 1996 1997<br>Year<br>Simple coefficient of varation (CVu) -, Weighted coefficient of variation (cVw) Relative mean Deviation (Rw)<br>+ Unweighted gini index (Gu) + Weighted gini index (Gw) -Theil index (T)<br><!-- End of picture text --> 41
Table B13: Mechanism at play, the role of uncertainty on Mergers and Acquisitions

|VARIABLES|(1)<br>CR5|(2)<br>CR5|(3)<br>CR5|(4)<br>CR5|(5)<br>HHI|
|---|---|---|---|---|---|
|Uncertainty (standardized)|-0.379|-1.381|-1.442|-1.327|-0.048|
||(0.508)|(0.965)|(0.960)|(0.888)|(0.040)|
|Uncertainty _×_ Developing||1.715|1.784*|1.997*|0.103**|
|||(1.051)|(1.048)|(1.029)|(0.049)|
|Observations|1,370|1,370|1,352|1,149|1,149|
|R-squared|0.794|0.795|0.793|0.802|0.695|
|Controls|No|No|Yes|Yes|Yes|
|Period|2003-2022|2003-2022|2003-2022|2003-2019|2003-2019|

Notes: The unit of observation is the destination-year level, _it_ . ‘CR5” stands for the concentration ratio of the top 5 investing firms; countries with fewer than 5 foreign investors on average are excluded. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts words from EIU reports for 143 countries since the 1950s. A higher “T3” value indicates greater uncertainty. Uncertainty in year is approximated as the logarithm of the average word count in EIU reports over the three years preceding the investment. The _ExchangeRateV olatility_ is calculated from the monthly exchange rate of the local currency against the US dollar, normalized by the whole-period mean and standard deviation. For a given destination-year, _it_ , _ExchangeRateV olatility_ is proxied as the average score in the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). All specifications include world-region by time fixed effects, and countries are grouped into 7 regions based on World Bank classifications; country fixed effects. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

38
# **2 Composition Space and Revealed Comparative Advantage: A Literature Review**

Following the tradition of most empirical work, this paper analyzes export data. Indeed, export data are available across countries at a detailed sector level, more reliably than production data. Furthermore, trade data may be more relevant for assessing cross-country competitiveness since, by definition, this notion reflects competition between exporters from different countries.

However, the focus on export data may omit important features of production and trade. For instance, the ability to participate in global value chains and production sharing drives much of developing countries’ diversification towards more complex production. An analysis based solely on export data, although already quite complex, may not capture fully the consequences of the cross-border nature of value chains or the importance of international backward and forward linkages (Baldwin, 2010).

# **_Composition Space and Revealed Comparative Advantage_**

**_— —_** At an abstract level, changes or rather relative changes in factor endowments will correspond to trajectories of countries in the composition space of exports, where coordinates are the shares of exports by product. Composition space has many dimensions; as many as distinct product **_—_** lines in the data. A change of composition in exports resulting from a change over time (or **_—_** across countries) of factors endowment is naturally represented by a trajectory in a space with as many dimensions as products, where the coordinates are the relative export composition by country. The concept transposes to represent the position of cross-sections of countries with different endowments in factors of production. The composition coordinates are export shares:

Therefore, the composition space is the hyper-plane of co-dimension one (Fig 1) where the “movement” takes place: and A perspective going back at least to Balassa (Balassa, 1979, 1986) is that the position of the problem can be reverted. Trajectories in composition space can "reveal" information about the importance or relative importance of factors of production for (export) competitiveness.

Balassa proposed the revealed comparative advantage (RCA) as a more meaningful representation of the position of countries in Composition Space than the composition vector. The RCA is the ratio of the country’s export share of a particular product against the global

> 3 The paper refers to a dot subscript as sums by countries or products (Einstein’s convention).

4
economies. These new employment opportunities attract labor from agriculture, reducing agricultural employment.

The estimates capture general equilibrium effects of U.S. market access on Vietnam’s local labor markets. We explore three potentially important channels: demand-led, production-led and real estate-led. There is evidence for all three channels although the demand channel is the most quantitatively important. Furthermore, we examine the impact of U.S. market access on income. Despite a decline in agricultural employment, agricultural income per capita does not fall after the implementation of the bilateral trade agreement. This suggests that the agricultural sector becomes more productive in response to labor reallocation to non-farm sectors. At the same time, non-farm income per capita significantly increases, leading to overall household income growth.

Overall, U.S. market access provides a “kick” to Vietnamese provinces exposed to U.S. tariff cuts to Vietnamese export products. Employment in manufacturing industries grows and positively propagates to employment in services, mostly via the demand channel. New job opportunities in manufacturing and services attract excess labor from agriculture. Nevertheless, agricultural income does not fall. This, combined with an increase in non-farm income, boosts households’ overall income in these provinces.

To document this effect, we use data from the two Vietnam Households Living Standards Surveys (VHLSS) in 2002 and 2004. Both surveys recall activities in the past 12 months. In other words, we compare employment between 2001-2002 and 2003-2004. VHLSSs provide information on household income, expenditures, occupation and various other household and individual characteristics. While the unit of the identification variable (i.e. province’s tariff cuts) is at the province level, the unit of the outcome variables is at the household level. This is to increase the sample size. In addition, we would also be able to control for time-invariant factors at 3
Table 4: Main treatment effects on unconditional support

||Reduce|Eliminate|Cost|Index|
|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|
|Any treatment|0.017*|0.029***|0.026***|0.051***|
||(0.01)|(0.01)|(0.01)|(0.01)|
||0.076|0.001|0.003|0.002|
|Efciency treatment|0.019**|0.025**|0.027**|0.051**|
||(0.01)|(0.01)|(0.01)|(0.02)|
||0.027|0.019|0.030|0.015|
|Equity treatment|0.012|0.023***|0.019**|0.039***|
||(0.01)|(0.00)|(0.01)|(0.01)|
||0.180|0.000|0.013|0.000|
|Environment treatment|0.020|0.038**|0.030**|0.062**|
||(0.02)|(0.01)|(0.01)|(0.02)|
||0.220|0.012|0.011|0.024|
|p-value (Ef vs Equ)|0.345|0.811|0.529|0.470|
|p-value (Ef vs Env)|0.975|0.284|0.741|0.598|
|p-value (Equ vs Env)|0.545|0.307|0.389|0.350|
|Control Mean|0.443|0.270|0.313||
|Observations|37036|37036|37036||

Note: This table shows the overall impact of the treatments on respondents’ unconditional support for subsidy reform. The regression analysis is based on Equations (1) and (2). Robust standard errors are in parenthesis and p-values of each treatment coefficient are presented below the standard errors. In addition, the table includes p-values comparing differences between the coefficients of the treatments. _Reduce:_ See Q21, which asks about support for a reduction in energy subsidies. The variable takes on the value of 1 if the respondent selects strongly support or support, 0 otherwise. _Eliminate:_ See Q22, which asks about support for an elimination of energy subsidies. The variable takes on the value of 1 if the respondent selects strongly support or support, 0 otherwise. _Cost:_ See Q24, which asks about support for a reduction in energy subsidies if this led to a price increase. The variable takes on the value of 1 if the respondent selects strongly support or support, 0 otherwise. _Index:_ An unweighted average of the z-scores of the unconditional support outcome variables, oriented so that a higher index means more support.
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<mark>WPS3563</mark>

# Health Insurance Impacts on Health and Nonmedical Consumption in a Developing Country

## Adam Wagstaff

Development Research Group and East Asia Human Development Unit The World Bank, Washington DC, USA

## Menno Pradhan

Indonesia Resident Mission The World Bank, Jakarta, Indonesia World Bank Policy Research Working Paper 3563, April 2005 The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

**Corresponding author and contact details:** Adam Wagstaff, The World Bank, 1818 H Street NW, Washington, D.C. 20433, USA.  Tel. (202) 473-0566. Fax (202)-522-1153. Email: <u>awagstaff@worldbank.org.</u>

**Acknowledgements:** We are grateful for comments on an earlier version to Martin Ravallion and to participants at the International Health Economics Association Meeting in San Francisco in 2003, and faculty and students at Tinbergen University.