rafmacalaba/datause-encoder-data huggingface dataset:
3 transparent and competitive contracts.<sup>8</sup> SHI agencies may be more prone to “capture” by provider interest groups who may secure better terms (including higher wages) for providers. One suspects that SHI systems are also likely to be administratively expensive, often requiring revenue-collection efforts separate from those of the tax authorities, a contracting apparatus, and where multiple SHI schemes exist alongside one another a loss of monopsony power. It is not implausible that SHI systems spend less and are less successful at translating money into better health. But even on these counterarguments, the evidence is at best anecdotal.

It is not just with respect to the health sector that there is a lack of evidence on the merits and demerits of SHI and tax-financed health systems. The same is true of their consequences for labor markets. Some of the “old” SHI countries—notably France, Germany and the Netherlands—have recently been reducing or are trying to reduce their reliance on payroll financing<sup>9</sup> , in part out of a belief that it has contributed to unemployment and informalization of the labor market. These issues have also been debated in Latin America.<sup>10</sup> Yet there is really very little direct evidence on this issue, most taking the form of indirect evidence from studies of the employment effects of payroll taxes.<sup>11</sup> This indirect evidence, however, refers to all payroll taxes which is problematic as the impacts on labor supply may be different between, say,

> 8  A World Bank report in the late 1990s on health insurance in Argentina (World Bank 1997), commenting on the purchasing capacity of health insurers, argued that “it is acknowledged by Argentines that personal connections and corrupt practices, instead of quality and economy, weigh heavily in the award of capitated contracts and other payments to medical providers and suppliers, and this adds substantially to the inefficiency and high cost of health care in Argentina” (p.7).

> 9  France widened the tax base from earnings to include nonwage income. Germany is contemplating reducing the emphasis on the payroll, while the Netherlands in 2005 introduced a reform where insurers receive only half their income from payroll revenues (albeit channeled through a central fund), the rest coming from flat-rate direct contributions from members (with offsetting income supplements for low income groups) (Gottret and Schieber 2006; International Network on Health Policy & Reform 2006).

> 10  See Baeza and Packard (2006) on this.

> 11  Kugler and Kugler (2003) have estimated the impact of payroll tax increases in Colombia that were implemented in parallel with the health financing reforms. According to their estimates, the health financing reforms, which raised the payroll tax rate by 5 percentage points, would have reduced wages by between 0.7% and 1.1%, and employment by between 2% and 2.5%. By contrast, Bauer and Riphahn (2002) find that Germany’s payroll tax has had very limited employment effects.
# _Initial Results_

Table 4 presents the results of this exercise for several Latin American countries plus the U.S. We emphasize that these results are only suggestive, since they are affected by the measurement error intrinsic to international databases such as those of Barro and Lee (2000) and the Penn World Table 6.1. Although useful to generate broad international stylized facts, such databases are too noisy to be reliable in undertaking a countryspecific analysis. Moreover, although the calibrated model is a good approximation for broad international patterns, it may be way off for particular countries. A serious analysis about a particular country necessarily entails obtaining better data and adjusting the model for country idiosyncrasies. We illustrate the relevance of this for the case of Chile below.

Columns 1-3 of Table 4 come from Barro-Lee data on human capital and the Penn World Tables, using α = 1/ 3, γ = .0085 , and a procedure to construct capital stocks as described in Klenow and Rodríguez-Clare (2004). Column 4 calculates the income tax τ implied by equation (2) above assuming that all countries have the same interest rate as in the U.S., calibrated above as _r_ = 6.8% . The country with the lowest implied income tax is Mexico, which has a physical capital-output ratio equal to that of the U.S. in spite of having a relative price of capital that is twice as high. The only way for this to be an equilibrium is to have an income tax much smaller than that of the U.S.

Column 5 presents the composite capital-output ratio _k_ ≡ _h_ ( _K_ / _Y_ ) α /(1 −α ) as a ratio of the U.S. level. Column 6 uses equation (3) to calculate the value of _a assuming that all countries had the same implicit tax on R&D as the U.S. (i.e.,_ φ = − 2.0 ), and presents it also as a ratio of the U.S. level. Column 7 shows the associated R&D investment rate, using equation (1). Column 8 shows the product of relative _k_ and relative _a_ , which yields labor productivity relative to the U.S. Thus, for example, if Chile had φ = − 2.0 , given its levels of human capital, the relative price of investment, and the (real) physical capital17
indicating that the B40 could grow at lower speeds than the higher percentiles; see the cases for the resource-output oriented, low-output and high-output growth countries. Notably, there is an extensive range of uncertainty in all simulated shared prosperity gaps. The larger the distance between the bottom 40 and upper-income percentiles, the more positive the magnitude of the standard deviation of the simulated outcomes; these results hold across the studied country categories.

In comparison with point predictions and perfect-foresight methods, our proposed approach considers both the outcome precision of a multiplicity of scenarios and the uncertainty— standard deviation of simulated outcomes—embedded in the predictive fan chart generated for each situation. This multiplicity of scenario results and the predictive fan chart and associated uncertainty provide strong incentives to hedge risks in poverty and income inequality declining via the constant evaluation and revamping of income re-distributive policies; the potential new plans should be flexible and easy to adjust to rapidly changing economic conditions.

# **REFERENCES**

- Aghion, P., E. Caroli, and C. Garcia-Penalosa. 1999. "Inequality and Economic Growth: The Perspective of the New Growth Theories." _Journal of Economic Literature_ 37 (4): 1615-1660.

- Anand, S., and P. Segal. 2015. "The Global Distribution of Income." In _Handbook of Income Distribution_ edited by A. B. Atkinson, and F. Bourguignon, 937-979. Amsterdam, The Netherlands: Elsevier.

- Benabou, R. 1996. "Inequality and Growth." _NBER Macroeconomics Annual_ 11: 11-74.

- Bulte, E., and R. Damania. 2008. "Resources for Sale: Corruption, Democracy and the Natural Resource Curse." _The BE Journal of Economic Analysis & Policy_ 8 (1).

- Campos-Vazquez, R. M., E. Chavez, and G. Esquivel. 2017. "Growth Is (Really) Good for the (Really) Rich." _The World Economy_ 40 (12): 2639-2675.

- Chotikapanich, D. 2008. _Modeling Income Distributions and Lorenz Curves_ . New York: Springer Science & Business Media.

- Cowell, F. A., and E. Flachaire. 2015. "Statistical Methods for Distributional Analysis." In _Handbook of Income Distribution Volume 2A_ edited by A. B. Atkinson, and F. Bourguignon. Amsterdam, The Netherlands: North-Holland.

- Crafts, N., and K. H. O’Rourke. 2014. "Twentieth Century Growth." In _Handbook of Economic Growth_ edited by P. Aghion, and S. N. Durlauf, 263-346. Amsterdam Elsevier.

- Cruz, M., J. E. Foster, B. Quillin, and P. Schellekens. 2015. "Ending Extreme Poverty and Sharing Prosperity: Progress and Policies." Policy Research Note, W. Bank, Washington, DC.

- Devarajan, S., Y. Dissou, D. S. Go, and S. Robinson. 2015. "Budget Rules and Resource Booms and Busts: A Dynamic Stochastic General Equilibrium Analysis." _The World Bank Economic Review_ 31 (1): 7196.

- Dollar, D., T. Kleineberg, and A. Kraay. 2014. "Growth, Inequality, and Social Welfare: Cross-Country Evidence." _World Bank Policy Research Working Paper_ (6842).

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**Table 6. Regression Results**

|||||_Specification_|_Specification_|
|---|---|---|---|---|---|
|_Item_|_Spec_|_ification 1_|_Specification 2_|_3a_|_3b_|
|_Core variable_||||||
|Schooling||0.0953**|0.0954**|0.0956**|0.0956**|
|Schooling squared||0.0020**|0.0020**|0.0019**|0.0019**|
|Experience||0.0192**|0.0185**|0.0185**|0.0185**|
|Experience squared||–0.0000**|–0.0000**|–0.0000**|–0.0000**|
|Urban dummy||0.1630**|0.1632**|0.1635**|0.1635**|
|_Maquila and women co_|_ntrol_|||||
|Women dummy (<br>1<br>δ )||–0.2710**|-0.2871**|-0.3684**|—|
|_Maquila_dummy (<br>2<br>δ )||0.3139**|0.2157**|0.2276**|—|
|o<br>n *_maquila_<br>W me||||||
|(<br>3<br>δ )|||0.1664**|0.2578**|—|
|_D_<br>_am_<br>_yn_<br>_ic effects_<br>_Women_||||||
|1991||||—|-0.3684**|
|1995||||0.04<br>47|-0.3237**|
|2001||||0.<br>0727**|-0.2957**|
|2006||||0.1650**|-0.2034**|
|_la_<br>_Maqui_||||||
|1991||||—|0.2276**|
|1995||||–0.181<br>5*|0.0461|
|2001||||0.0117|0.<br>2393**|
|2006||||0.0367|0.2643**|
|n *_maquila_<br>Wome||||||
|1991||||—|0.2578**|
|1995||||0.07<br>99|0.3377**|
|2001||||–<br>0.0742|0.1836**|
|2006||||–0<br>.2280**|0.0298|
|ear controls<br>Y|Yes||Yes|Yes|Yes|
|ls<br>Industry contro|Yes||Yes|Yes|Yes|
|Constant||6.4083**|6.4260**|6.5318**|6.1634**|
|_R_-squared||0.47|0.47|0.47|0.47|

* Significant at 5 percent; ** significant at 1 percent. _Note_ : The dependent variable is the log of wages. Schooling is measured as the years of formal education. Industry controls include dummy-variables for each of the nine industries at the one-digit level of industrial aggregation; the agricultural sector was chosen as the base category. Specifications 3a and 3b are two different ways of presenting the same equation. Sample size is 43,268.

The second specification includes three dummy variables capturing the gender wage gap, a _maquila_ -specific wage premium, and a premium associated with women in the _maquila_ sector, respectively. Recall that the interactive term measures the difference in the gender wage gap inside and outside the _maquila_ sector. Between 1991 and 2006, women earned average wages that were 28.7 percent lower than those of men. The average wage of workers in the _maquila_ sector was more than 20 percent higher than those of workers outside the sector. Controlling for observable characteristics, female workers in the _maquila_ sector earned 9.5 percent (≈ –28.7 + 12
Learning from Developing Country Power Market Experiences: The Case of Colombia Table 6-3 Evolution of Colombian pool price and its volatility, and energy traded in the pool.

|**Year**|**Minimum**<br>**pool price**<br>**(COP/kWh)**|**Average**<br>**pool price**<br>**(COP/kWh)**|**Maximum**<br>**pool price**<br>**(COP/kWh)**|**Energy traded**<br>**in the pool**<br>**(GWh)**|**Average pool**<br>**price**<br>**(USD/MWh)**|
|---|---|---|---|---|---|
|**2005**|28|74|187|17,747|32|
|**2006**|27|74|281|17,529|31|
|**2007**|30|84|155|16,692|40|
|**2008**|29|88|439|16,469|45|
|**2009**|33|139|321|17,934|64|
|**2010**|32|129|361|18,247|68|
|**2011**|33|75|404|16,786|41|
|**2012**|35|116|308|17,017|64|
|**2013**|40|177|451|14,949|95|
|**2014**|39|225|481|15,507|112|
|**2015**|47|377|2,822|16,905|137|
|**2016**|61|301|885|20,143|100|
|**2017**|60|108|383|19,254|37|

Source: Own elaboration based on data collected by the World Bank.

<!-- Start of picture text --> COP/kWh<br>3,000<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Maximum price Minimum price Average price Yearly average price<br><!-- End of picture text --> Figure 6-5 Evolution of monthly pool price (hourly minimum, average and maximum) in Colombia.

Source: Analysis based on data collected by the World Bank.

40
**Figure 1: Regulatory Quality indicator:1996-2010**

<!-- Start of picture text --> 2.5<br>Bulgaria<br>Czech Republic<br>2 Denmark<br>Estonia<br>France<br>1.5 Germany<br>Hungary<br>Italy<br>Latvia<br>1<br>Lithuania<br>Netherlands<br>Poland<br>0.5 Romania<br>Slovakia<br>Slovenia<br>0 Spain<br>Sweden<br>United Kingdom<br>‐0.5<br><!-- End of picture text --> _Source: WGI dataset available on_ _<u>http://info.worldbank.org/governance/wgi/index.asp</u>_ humanKit−1 is included to control for a possible (direct) influence (not reflected in the productivity gap term) of the quality of human capital on the ability of the economy to respond to shocks in the productivity frontier. It is defined as the proportion of the workforce with secondary education, with data drawn from the World Development Indicators (WDI) dataset.

Open_tradeit−1 measures trade openness accounting for the effect of competition from global trade on the country’s ability to converge to the productivity frontier. Two different measures are tested: i) the country’s openness to _global_ trade, defined as the sum of country i’s global exports and imports divided by country i’s GDP; and ii) the openness to trade _within the EU region_ , defined as the sum of country i’s exports to and imports from the EU region divided by country i’s GDP. Both trade and GDP data are from UNCTAD.

_Country_ i is a vector of country dummies included to account for unobserved time-invariant factors that might affect productivity growth in a particular country, while _time_ t is a vector of time dummies included to control for possible productivity shocks that might occur in a given year.

**The model is estimated over the 1995-2010 period for a set of 30 countries** : EU countries, including Romania, plus Iceland , Norway and Switzerland)<sup>**17**</sup> . Tables 1 and 2 below provide summary statistics for the variables listed in Eq(1) while the estimation results are presented in Table 3.

> 17 See Table I, annex, for the set of countries covered by the current analysis.

5
For the estimates of quality improvements, we have used experimental evidence, but only for the link between any particular intervention and outcomes on standardized tests. To obtain _monetary_ benefits, we must again rely on nonexperimental evidence linking test scores to future labor earnings, or test scores to additional years of schooling to future labor earnings. In both instances, we rely on Evans and Yuan (2019) who, fortunately for our work, include Ghana as one of their case studies. Those estimates are, presumably, subject to most of the potential biases of Mincer regressions. In addition, while all the tests used to evaluate the effectiveness of the various interventions are similar, they are not identical, nor are they identical to the tests used in Evans and Yuan (2019). So, in effect, we must assume that a standard-deviation improvement in scores in each of these tests measures the same thing. While it might seem desirable to standardize such tests in all trials, that probably is not possible even within a country, let alone internationally. Instead, a better sense of how a one-standard-deviation improvement in scores in each test relates to all other tests would be helpful.

All studies of the impact of schooling on future labor earnings face the problem of the long time lag between schooling and labor earnings. Even if one were to run a careful experiment and follow the children into their labor-earning years, there is no guarantee of external validity for children in the future once the study’s results are known.

Given these problems, it might seem better to eschew benefit-cost analysis for costeffectiveness analysis, as in Kremer et al. (2013). What we gain from the extra analysis needed to establish benefit-cost ratios is the ability to compare spending in other areas that is not meant to improve test scores. For policy makers who must determine budget priorities across many fields, not just with respect to the education sector and its outcomes, this is useful information.

38
# **5 Tables and Figures**

Table 1: Impacts on Earnings and Work

||(1)<br>Earned<br>Last|(2)<br>Income<br>7 Days|(3)<br>Main Job<br>Last 7 D|(4)<br>Earnings<br>ays (USD)|(5)<br>Main Job H<br>Last|(6)<br>ours Worked<br>7 days|
|---|---|---|---|---|---|---|
|Offered Training|0.06<sup>_∗∗∗_</sup>|0.13<sup>_∗∗∗_</sup>|5.61<sup>_∗∗_</sup>|9.84<sup>_∗∗∗_</sup>|3.41<sup>_∗∗∗_</sup>|3.54<sup>_∗∗_</sup>|
||(0.02)|(0.03)|(2.83)|(3.76)|(1.04)|(1.47)|
|Offered * Round 2||-0.08<sup>_∗∗_</sup>||-3.37||-0.85|
|||(0.04)||(4.81)||(1.96)|
|Offered * Round 3||-0.15<sup>_∗∗∗_</sup>||-9.36<sup>_∗_</sup>||0.44|
|||(0.04)||(4.79)||(1.93)|
|Control Mean|0.69||34.90||27.65||
|Control Mean R1||0.61||29.44||23.90|
|Control Mean R2||0.69||35.96||29.67|
|Control Mean R3||0.75||39.38||29.44|
|Observations|2,597|2,597|2,597|2,597|2,597|2,597|
|p-value Round 2||0.07||0.12||0.09|
|p-value Round 3||0.53||0.90||0.01|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of follow-up data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level. Earnings are top winsorized at the ninety-ninth percentile; we replace earnings with 0 for participants who do not work.

Table 2: Impacts on Job Quality

||(1)<br>Ar<br>Stu|(2)<br>ea<br>dies|(3)<br>Cont<br>Writ|(4)<br>ract<br>ten|(5)<br>Cont<br>Perm|(6)<br>ract<br>anent|(7)<br>Jo<br>Ben|(8)<br>b<br>efts|(9)<br>Regis<br>Fi|(10)<br>tered<br>rm|
|---|---|---|---|---|---|---|---|---|---|---|
|Offered Training|0.01|0.01|0.04<sup>_∗_</sup>|0.04|0.03<sup>_∗∗_</sup>|-0.00|0.01|-0.00|0.06<sup>_∗∗_</sup>|0.07<sup>_∗∗_</sup>|
||(0.02)|(0.03)|(0.02)|(0.03)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|(0.03)|
|Offered * Round 2||0.02||-0.00||0.05<sup>_∗∗_</sup>||0.03||-0.03|
|||(0.04)||(0.04)||(0.03)||(0.03)||(0.04)|
|Offered * Round 3||-0.01||0.02||0.05<sup>_∗_</sup>||0.00||-0.01|
|||(0.04)||(0.04)||(0.03)||(0.03)||(0.04)|
|Control Mean|0.44||0.29||0.08||0.15||0.41||
|Control Mean R1||0.39||0.23||0.09||0.07||0.35|
|Control Mean R2||0.45||0.33||0.07||0.16||0.44|
|Control Mean R3||0.48||0.33||0.10||0.21||0.43|
|Observations|2,597|2,597|2,597|2,597|2,597|2,597|2,597|2,597|2,597|2,597|
|p-value Round 2||0.32||0.31||0.01||0.37||0.21|
|p-value Round 3||0.98||0.11||0.02||0.95||0.05|

Notes: Offered Training is the coefficient from a pooled OLS regression of the outcome (including three rounds of followup data, 4, 10, and 15 months after the end of the program) on an indicator for being offered the training. **Job Benefits** is a dummy variable equal to one for employees that receive contributions to social insurance, health insurance and paid holidays. We control for the baseline value of the dependent variable, stratification dummies and survey wave dummies. Standard errors are clustered at the respondent level.

19
We observe a maximum decrease of about -0.8 p.p. in the agricultural employment rate after four years (significant at 10 percent) and an increase in the employment rate of 0.75 p.p. after six years (significant at 1 percent), while the employment rate in other sectors reaches a maximum increase of about 0.04 p.p. in the fourth year. Overall, the analysis of drought shocks of the same magnitude but shorter duration confirms the findings we obtained using a 12-month SPEI, though with less significant impacts.

**_Reshuffling of shocks_** – An additional check we present is a falsification test that alters the combinations of shocks across years. We therefore run our preferred regression specification on a set of randomized shock years, maintaining the same composition of treated and control provinces that were used in the original sample. Since drought occurs in specific years, by altering the time of onset one would expect to find non statistically significant effects. In Figure 8 we report event study results for the three outcomes considered, from which we observe the absence of significant effects after the shock occurs. In addition, estimates of aggregate _ATT_ s, reported in Table A2, confirm that the placebo results using both 3- and 12month SPEIs, do not statistically differ from zero. Altogether, these tests provide evidence that our main estimates are not driven by some underlying systematic trend in the data, and that the validity of our identification strategy is warranted.

**_Conditional parallel trend estimates_** – Our estimates in Section 4 assume that the parallel trend assumption holds unconditionally. We therefore present conditional parallel trend parameters, controlling for a set of observed provincial characteristics of the working age population, that is age, female population, educational level and urban population. Comparing those _ATT_ s with the parameters in Table 3, we observe no differences in the _ATT_ of employment in the agriculture sector, while slightly less evidence is detectable for the unemployment rate. To strengthen the causal interpretation of our conditional estimates, Table A3 shows a balancing test of the covariates adopted in the conditional estimates, in which we regress each control variable on the treatment; that is, the drought shocks measured

17
# **Exposure to Volatility**

43. Even the most creditworthy among the MICs are subject to disruption of capital flows and financial crisis from **Figure 15. Crises and Private Flow Reversal** time to time. The 1980s were marked by a series of debt crises. **29 26** *Pre-crisis The 1990s saw a series of I 8 8 *Post-crisis - currency and financial crises: Mexico in 1994/95, East Asia _D_ (Indonesia, Korea, Malaysia and * Thailand) in 1997/98, Russia in **-20 - -21 -20** 1998, and Brazil in 1999. Private **24** non-foreign-direct-investment <!-- Start of picture text --> 29 26 *Pre-crisis<br>I 8 8 *Post-crisis<br>-<br>D<br>*<br>-20 - -21 -20<br>24<br>FY93-95 FY97-99 FY96-98 FY96-98 FY97-99<br>Note Private flows include portfolio flows (bonds and equity) and bank<br>lending. All flows are net of outflows, as used in the IMF Balance<br>of Payments tables<br>Source International Financial Statistics, various issues.<br><!-- End of picture text --> (FDI) flows to these countries suffered sharp reversals during the crises (Figure 15).

_44._ The volatility of capital flows varies depending on the composition of capital flows. FDI tends to be more stable than portfolio flows. Indeed, in the aftermath of the crisis, FDI flows increased in the East Asia crisis countries and Brazil as depressed asset prices encouraged mergers and acquisitions. Non-FDI flows, on the other hand, declined sharply. Within non-FDI flows, the reversal of flows mirrored the initial composition of the flows. Prior to their crises, Mexico and Brazil received comparatively larger amounts of portfolio (bond and equity) flows than East Asia which received larger amounts of bank lending. Following the crisis, Mexico and Brazil suffered a collapse of portfolio flows, whereas the East Asian crisis countries suffered an acute reversal of bank lending, especially short-term debt. Russia seems to have suffered on all fronts including FDI, portfolio flows and bank lending.

45. Private flows tend to respond pro-cyclically to economic shocks. Thus, an adverse shock (e.g., a decline in commodity prices) can lead to a reversal of private flows and exacerbate the impact of the shock. Short-term debt is potentially the most reversible component of capital flows. It is not only pro-cyclical to economic shocks, but it also tends to respond more to negative shocks than to positive shocks. Because of such procyclical behavior, reversal of short-term debt exacerbated the Asian crisis.

46. Volatility in capital flows can have severe economic and social consequences for the affected countries, as illustrated by the East Asian crisis. The East Asian experience also showed that, given the increasing global financial integration, crisis in one emerging economy can quickly spread to others and threaten international financial stability.

**16**