rafmacalaba/datause-encoder-data huggingface dataset:
between the wage in any of the occupations considered and the average wage. If this gap is multiplied by one hundred, it can be interpreted as a percentage. The higher the gap, the higher is wage inequality.

According to figure 4, wage inequality across occupations does not increase with openness. If anything, it could decline. A more rigorous analysis, allowing for different levels of wage dispersion by country and year, fails to unveil any systematic impact of globalization Neither the ratio of trade to GDP, nor the openness indicator constructed by Sachs and Warner or the ratio of foreign direct investment to GDP have a statistically significant effect on the dispersion of wages by occupation (Rama, 2001b). The results do not change when industrial countries are excluded from the analysis. However, this apparent neutrality of globalization hides important changes along two dimensions: education and gender.

According to the most popular model in international trade theory, globalization should reduce the wage premium to education in developing countries. In one of its simplest versions, this model considers two regions of the world and two factors of production: skilled and unskilled labor. The two regions differ in their endowments. Skilled labor is abundant in industrial countries and scarce in developing countries. Globalization can be interpreted as a dramatic reduction in trade barriers and transportation costs. As a result of this reduction, the two regions of the world face an incentive to shift their product mix in favor of the sector in which they do have a comparative advantage. Thus, there is an increase in the demand for skilled labor in industrial countries, and an increase in the demand for unskilled labor in developing countries.

**Figure 4. Dispersion of Wages across Occupations and Openness to Trade**

<!-- Start of picture text -->
1.4  -<br>1.2  -<br>6  1. - *<br>15<br>Z  0.6<br>cjn<br>0.2<br>0<br>0  50  100  1 50  200  250  300<br>Foreign trade  in  % of GDP<br><!-- End of picture text -->

_Source:_ The dispersion of wages by occupation was calculated using data from Freeman and Oostendorp (2000). Data on the trade-to-GDP ratio are from the World Bank.

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This relationship between discoveries and development is illustrated in Figures 1 and 2, which show both a scatter plot of discoveries against GDP per capita, and the estimates of equation (10): the discovery curve.

**Figure 1: Discovery Events, HS 4-Digit          Figure 2: Discovery Events, HS 6-Digit**

<!-- Start of picture text --> 0 5000 10000 15000 20000 25000 0 5000 10000 15000 20000 25000<br>GDP Per Capita GDP Per Capita<br>30<br>150<br>20<br>100<br>Discoveries Discoveries<br>10 50<br>0 0<br><!-- End of picture text --> Source: Author’s calculations These results suggest the following pattern. The initial stages of the diversification process tend to be driven by the introduction of new products (discoveries). However in later stages of the diversification process, when discovery activity declines, productive diversification is driven by more even production among the goods the country already produces. Finally, at high levels of income, discovery activity falls, and the diversification process is reversed as production becomes more specialized.

Based on this robust relationship between discovery and development, we now further refine the stylized facts from section 3. Specifically, there is a certain level of discovery activity that we would expect to find in an economy given its level of development. However, as Figure 23
5 Estimates from the Luxembourg Income Study (LIS) previously corresponded to total health expenditures (“hcmed” in LIS terminology). These, it turns out, include nonmonetary expenditures, such as the imputed value of publicly financed health insurance in some countries, e.g. the US. In the 2019 HEFPI database, expenditures are – wherever possible – monetary expenditures only (“hmcmed” in LIS terminology).<sup>2</sup> This process led to some datapoints being no longer available from LIS. For example, in the case of the US, out-of-pocket expenditures before 2012 reflect only the imputed value of Medicare and Medicaid; actual out-of-pocket expenditures are not available. Of the 54 LIS points that were included in the 2018 HEFPI database, 15 were affected by this revision. Aside from the US, the other dramatic change was Taiwan whose catastrophic health expenditure rate felt from 30 or so percent to under 7% and is now in line with values reported elsewhere (van Doorslaer et al. 2007).

Over 30 of the surveys we use ask about out-of-pocket expenditures in both the consumption and health modules. Very often there are differences across them in the number of items and the recall period. Lu et al. (2009) find that use of the single-item question leads to a smaller estimate of out-of-pocket expenditure than the survey’s multi-item question and a 4-week recall period leads to a larger (annualized) estimate of out-of-pocket spending than a 12-month recall period. Our sense is that a single item is unrealistic and that – at least up to a point – asking more items is better. Ideally, the list should explicitly separate outpatient and inpatient care. It also seems clear that a while a 4-week recall period might be appropriate for frequently consumed items, such as over-thecounter medicines, it is too short for most items of health expenditure, and far too short for inpatient care, where a 12-month window seems more appropriate. We ended up switching modules in several surveys we used in the HEFPI 2018 database, mostly to the consumption module, which typically did 2 The total consumption variable was also altered to match and now covers only monetary consumption.
# **6. Conclusion**

This paper extends the early empirical literature on economic growth in transition economies, with particular focus on fiscal policy variables (fiscal balance as well as selected expenditure components). It utilizes a parsimonious model drawn from Fischer and Sahay (2000) of economic growth as a function of initial conditions, stabilization, liberalization and structural reform, consistent with existing empirical literature (as summarized for example in Havrylyshyn 2001). The paper expands the data used in previous analyses by some 7 to 10 years, extends the baseline model, explores potential sources of nonlinearities in the relationship between growth and public finance. The main findings are summarized as follows.

First, the fiscal balance matters for growth. This is a departure from the “first generation” studies of economic growth that found little perceptible impact of fiscal balance on growth. This finding is nonetheless fully consistent with recent, growing evidence drawn from studies of fiscal consolidation in the region that a sound fiscal position boosts economic growth. The results are also based on a larger and more representative sample relative to the early literature on economic growth in transition economies.

Second, the size of government matters for economic growth and the impact is nonlinear. The results reported in this paper shed light on the inconclusive literature on the links size of government and economic growth, while adding a new nuance: Beyond indicative expenditure thresholds (in percent of GDP), public spending has a negative impact on growth, while at levels below this indicative cutoff, there is no measurable impact. These findings, while new, are not completely surprising. As government expenditures grow, they may be associated with larger distortionary taxation and regulatory activities, potential less efficient provision of services, and new opportunities for rent-seeking and corruption.

Finally, the quality of governance matters. The previous key finding is mitigated by the relative weakness of public sector institutions. In particular, the size of government has adverse consequences on growth, but not when the government is relatively more effective or public sector institutions are stronger. This is consistent with other strands of the literature that emphasize the interactions between growth and good policies (e.g., Burnside and Dollar 2000 and 2004).

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omy are discussed (see, for example, Basel III, 2011 and the technical documentation in BCBS, 2010). To see how the credit-to-GDP ratio is related to our credit demand specification, we can combine (3) and (4) to relate the disequilibrium provision of credit to the real economy to credit velocity as: The left-hand side of (5) can be viewed as an ” _unrestricted_ ” version of the credit-to-GDP ratio, explicitly allowing the elasticities of credit to GDP and the price level, as measured by the GDP Deflator, to differ from unity. The right-hand side of (5) is a time-varying measure of disequilibrium credit provision to the real economy which captures the excess or the lack of credit supplied to the real economy that is not utilised to satisfy transaction demand. Equation (5) thus postulates that credit in excess of the transaction demand for credit, as shown on the left-hand side of (5), is provided to satisfy the speculative (or portfolio) demand for credit. It is this quantity that affects asset prices by stimulating the formation of asset price bubbles and hence persistent deviations of credit velocity from its long-run steady-state value. Prudential supervisors should therefore focus on managing large departures of credit from its transaction demand component, that is, the left hand side of (5).

The relation in (5) describes a theoretical long-run equilibrium relationship. In order to compute the right-hand side of equation (5), which captures the time-varying disequilibrium credit provision to the real economy, one only needs estimates of _β_<sup>_gdp_</sup> and _β_<sup>_de f_</sup> of the left-hand side relation of (5). Nevertheless, as for any statistical estimation problem, one needs to condition on all relevant explanatory variables that influence the dependent variable to obtain consistent estimates of _β_<sup>_gdp_</sup> and _β_<sup>_de f_</sup> . In our context, this means that we need to condition on the velocity determinants that appear on the right-hand side of equation (5) as well as on the GDP and price level measures, ie., the _gdpt_ and _de ft_ variables that appear on the left-hand side. Moreover, it is important to leave _β_<sup>_gdp_</sup> and _β_<sup>_de f_</sup> on the left-hand side of (5) unrestricted, since estimates of all other parameters in the velocity equation will be biased if the imposed restrictions are not supported by the data.

Leaving the _β_<sup>_gdp_</sup> and _β_<sup>_de f_</sup> parameters in (5) unrestricted is an important generalisation of the credit-to-GDP ratio as it allows us to view restrictions that are as a any imposed testable implication of the model on the data. In the above context, this means that the unity restriction, which is imposed on _β_<sup>_gdp_</sup> and _β_<sup>_de f_</sup> when the credit-to-GDP ratio is used to estimate equilibrium credit, can be tested statistically for validity. Given that there exists ample evidence in the empirical literature on money demand that the typical range of parameter estimates of the income elasticity (of money demand) across countries is between 0.25 _−_ 3.5 (Sriram, 2001, page 360), we also anticipate considerable heterogeneity in 8
endogeneity as household decisions to pay for schooling (and send children to school) can be endogenous to decisions about child labor. The school expenditure variable, which reflects potential household budget limitations in financing children’s schooling, indicates that higher costs associated with education appears to have a non-significant effect on labor involvement and schooling, with controls for levels of household wealth and other factors in the model.<sup>31</sup> Even though school costs account for a large share of household expenditure especially among the poorest quintile (16% for public and 30% for private school students), they do not appear therefore to be a significant factor in pushing children prematurely into the labor force to be able to offset school fees or in pulling children out of school because of a lack of funding. Yet in other countries higher school costs do have potential effects on increased child labor such as in Pakistan (Hazarika and Bedi 2003) and Bangladesh (Shafiq 2007). In terms of school distance, estimations illustrate that a distance of less than 30 minutes (compared to the reference group of over 30 minutes) appears to have a non-significant effect on working but a positive and significant effect on the prevalence of schooling by an estimated 3% (for similar results, see Deb and Rosati 2004). It was expected that potential effects of distance on working and schooling would be larger. The reported results could be because distances to school in 2005/06 remain relatively long for most children largely irrespective of various background characteristics including North/South or rural/urban residency, with descriptive data indicating that 22.5% of primary students in the country take at least one hour to get to and come home from school.

> 31 While measuring school quality is difficult with most household surveys including GLSS, Brown _et al._ (2001) and Ray (2003) use school costs at the household level as a rough proxy for school quality, as schools with higher costs would on average tend to have higher quality school infrastructure and possibly higher learning outcomes.

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and roads; less often in oil/gaa, health, power, housing and railroads; and relatively seldom in irrigation, water, education, coal and fertilizers.

57. Such cross-sectoral comparisong reveal little, however, about the type or usefulness of such studies. In the power, roads and water sectors the terms of reference seemed to be clearly defined and similar across countries. These are cases where the Bank has been concerned with pricing issues for many years, and the methodology to be used in marginal cost calculations is well known by project staff and consultants. In the telecommunications and health sectors, the Bank's focus on efficiency pricing is relatively new and study approaches appear more diverse. A recent review of pricing initiatives undertaken as part of telecommunications projects (including LRNC studies, tariff changes and setting up economic cells within the enterprises) concluded that of the 22 examples only four have been completed satisfactorily, 10 have never been carried out, six have been failures and the remaining two studies are under review.IA/ A major work pro-ram has been proposed to strengthen the supervision of tariff studies now underway and to develop guidelines for tariff analysis. A similar approach was used in the oil/gas sector where the initial set of gas utilization studies was provided extra supervision and, based on that experience, a guideline was prepared to set out the methodology for future such studies.

Table 1. Proiects with pricing studies

||N%mber of<br>SARs Reviewed|% with Pricing or<br>Costing Studies|
|---|---|---|
|Telecommunications|6|100|
|Ports|8|62|
|Rtdads|2|50|
|Oil/Gas|10|40|
|Health|11|36|
|Power|20|30|
|Housing|7|29|
|Railroads|7|29|
|Irrigation|14|14|
|Water|14|14|
|Education|20|10|
|Coal|5|0|
|Fertilizer|5|0|

58. In the ports, education and housing sectors, approaches varied widely and, for the most part, seemed geared more to financial than to economic definitions of costs. Prototype terms of reference in those sectors would be usefu'.. In irrigation, pricing studies have often been carried out by local teams who were part of the system being reviewed. This has generally produced poor results. In addition, Bank supervision of the studies has been lax and little attention given to their eventual review and implementation. Finally, it is striking that no cost or pricing studies were financed under the coal or fertilizer projects reviewed. Our sample period may have produced a fluke statistic; however, these sectors
## **Figure 11:  Interest Rate Risk on Technical Provisions – RG 04**

<!-- Start of picture text --> DKK mln. Red Yellow<br>Portfolio Provision Decrease Increase Decrease Increase<br>1. Guaranteed benefits<br>2. Bonus Potential on Paid up Premiums<br>3. Bonus Potential on Future Premiums<br>4. Assumed probability of surrenders<br>5. Guaranteed benefits<br>6. Bonus Potential on Paid up Premiums<br>7. Bonus Potential on Future Premiums<br>8. Assumed probability of surrenders<br>9. Guaranteed benefits<br>10. Bonus Potential on Paid up Premiums<br>11. Bonus Potential on Future Premiums<br>12. Assumed probability of surrenders<br>Red Yellow<br>13. Total: Guaranteed benefits<br>14. Total: Bonus Potential on PuP<br>15. Total: Bonus Potential on FP<br><!-- End of picture text -->

# **9. Basic Information and First Summary**

The total impact of the stress tests is obtained by taking into account the worst result from either an increase or decrease in stress parameters in the Red and Yellow Tests. A first summary is generated that is reported as risk report RG02.

**Figure 12:  Basic Information – RG 02**

<!-- Start of picture text --> DKr mln.<br>1. Balance<br>2. Required solvency before stress test<br>3. Capital bases before stress test<br>4. Collective bonuspotential before stress test<br>5. Contribution multiplier *<br>Part of the available bonus potential to be applied to cover losses in<br>6. - red scenario in case of an interest decrease %<br>7. - red scenario in case of an interest increase %<br>8. - yellow scenario in case of an interest decrease %<br>9. - yellow scenario in case of an interest increase %<br>10. Tax percentage 15%<br>* Contribution multiplier: determines the percentage of asset loss covered by the<br>collective bonus potential<br><!-- End of picture text -->

# **10. Concluding Report**

The above summary is used as the basis for calculating the impact of the stress tests on the excess capital base of pension institutions. This is completed after allowing for the distribution of risks (losses) between policyholders and owners in accordance with the 25
Figure 2: _Real Ex-post Wholesale Rates_ **_(IFS 60B)_** <!-- Start of picture text --> R eal M oney M arket R ate, 1975-96<br>Percentiles<br>20<br>10<br>Median<br>1 st q uartile<br>0 -- 7 7-7_-7_- , .," *3rd quartile<br>1st decile<br>-10 _ 0 -9th decile<br>-20 1 1 L I<br>75 80 85 90 95i<br><!-- End of picture text --> <!-- Start of picture text --> Real Money Market Rate, 1975-96<br>M edians<br>10<br>,Industrial<br>O 8 7+t + ~~~~~~~~~~~~~~~~~D evelopi ng<br>75 80 85 90 95<br><!-- End of picture text --> <!-- Start of picture text --> Real Money M arket Rate, 1975-96<br>Median and Dispersion<br>10 16<br>8 14<br>6<br>12<br>10 6 Median l<br>2 4 90th-l1Oth il<br>0 ~~~~~~~~~~~~~~8<br>-2 6<br>-4 4<br>75 80 85 90 95<br>32<br><!-- End of picture text -->
axis, with a positive sign indicating that the plot is on the command area side of the boundary. However, we now plot outcomes on both the sample plot and the most important plot. In this figure, substitution will manifest as decreases in input and labor use on the most important plot when the sample plot is in the command area, while input and labor use increase on the sample plot. Second, we present regression evidence in Tables 7 and 8. In the discussion below, we focus on results from the tables, but we note that these results are consistent with visual intuition from Figure 5.

Table 7: Sample plot shock causes households to substitute labor and input intensive irrigated horticulture away from most important plot

||Culti-<br>vated|Irri-<br>gated|Horti-<br>culture|Banana|HH<br>labor/<br>ha|Input<br>exp./ha|Hired<br>labor<br>exp./ha|
|---|---|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|(4)|(5)|(6)|(7)|
|RDD (Site-by-seas|on FE, Sp|ecifcatio|n 3)|||||
|CA|0.038|-0.044|-0.038|0.092|-32.2|-6.0|-1.8|
||(0.040)|(0.026)|(0.024)|(0.032)|(20.0)|(2.7)|(2.1)|
||[0.344]|[0.087]|[0.110]|[0.004]|[0.107]|[0.028]|[0.404]|
|Sample plot efect|0.005|0.162|0.137|-0.133|70.8|6.3|3.7|
|SFE (Spatial FE,|Specifcati|on 4)||||||
|CA|0.004|-0.036|-0.037|0.065|-33.2|-6.7|-0.5|
||(0.049)|(0.033)|(0.029)|(0.036)|(23.8)|(2.8)|(2.3)|
||[0.930]|[0.270]|[0.206]|[0.072]|[0.162]|[0.017]|[0.825]|
|Sample plot efect|0.022|0.171|0.156|-0.142|76.9|4.3|3.2|
|# of observations|2,179|2,179|2,179|2,179|2,166|2,169|2,169|
|# of clusters|182|182|182|182|182|182|182|
|Control mean|0.368|0.114|0.109|0.199|66.8|5.6|3.9|

_Notes:_ Regression coefficients on a command area indicator for the sample plot (“CA”) are presented above. Specifications control for distance to the command area boundary, its interaction with CA, log area of the sample plot and the most important plot, and a command area indicator for the most important plot. RDD specification includes site-by-season fixed effects, and SFE specification includes spatial fixed effects. Standard errors are in parentheses, and p-values in brackets. “Sample plot effect” estimates are from Table 4.

First, consistent with the presence of separation failures, we find households substitute labor and inputs away from their most important plot. Households decrease allocations of household labor (32 - 33 person-days/ha) and inputs (6,000 - 6,700 34
these studies, however, cannot distinguish if the increase in registered businesses is due to informal firms becoming formal or to the creation of new firms. Mexico introduced a one-stopshop (OSS) for business registration in some of the most populous and economically developed municipalities starting in 2002, reducing the number of days to register a business from 30 days to 1 day. Bruhn (2011), using individual employment status from the Mexican Labor Market Survey, found that reform has increased the number of registered businesses by 5 percent and employment by as much as 2.8 percent. In addition, by encouraging competition, the reform seems to benefit consumers at the expense of the incumbent businesses. The competition from new registered firms decreases the prices by 0.6 percent and revenues of incumbent firms by 3.2 percent. Using the administrative data from the Mexican Social Security Institute, Kaplan et al. (2011) find that the same reform increased the number of registered businesses by 5 percent and employment by 7–8 percent.

A follow-up study by Bruhn (2013) divides informal business owners into those who share characteristics with formal business owners and those who have characteristics similar to wage earners. The study finds that wage earner types are less likely to register their businesses due to the reform, but more likely to become wage workers due to the job opportunities created by the reform. On the other hand, informal business owners with the characteristics of formal business owners are more likely to register, but only in municipalities with high pre-reform constraints to formal entrepreneurship. The effects are relatively small, and most informal business owners remain informal even after the reform.<sup>8</sup> The quality of new entrants due to entry reform has a significant bearing on overall productivity growth and creative destruction driven by competition in the formal sector. Utilizing cross-counrty and cross-industry interactions in a DID setting, using a comprehensive database of European firms Klapper et al. (2006) find that costly business entry regulations hamper the creation of new firms especially in naturally high-entry industries based on a comprehensive database of European firms for 1998-1999. Costly regulations also force new entrants to be

> 8 Similar OSS reforms in Colombia have brought down the time to register a business from 55 days to less than 9 days and lowered the registration fees by 30 percent. Cardenas and Rozo (2007) use administrative data from chambers of commerce in six major cities to show that the Colombian reform led to a 5 percent increase in registered businesses. They did not analyze the job effect, however. Also, locational characteristics of business entry reforms might matter as Bruhn and Mckenzie (2013) find that this type of reform does not have any effect in less populous and more remote municipalities in Brazil.

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