rafmacalaba/datause-encoder-data huggingface dataset:
Note that irrespective of the distribution and dimension of analysis, the Spearman rank correlation statistic is positive _(pD_ >0). This is an indication of direct association, where geographical regions (socioeconomic groups) with large concentration of wealth at time period t, also have large concentration of wealth at time period t2 _._ Similarly, geographical regions (SEG) with large concentration of poor at time t, also have large concentration of poor at time _t2 ,_ suggesting tendency towards concordance in the rank ordering of geographical regions (SEGs) overtime.

**Table 7:** Rank **correlation test of overtime concordance** in the distribution of welfare **across socioeconomic groups and economic regions**

|||_Distri_<br>_in_|_bution of_<br>_come_|_Distrib_<br>_pop_|_ution ofpoor_<br>_ulation_|
|---|---|---|---|---|---|
|_Dimension of analysis_|_Number of_<br>**_groUP s_**<br>_(n)_|_ED2_|_Statistics_<br>**_p_**|_ED2_|_Statistics_<br>**_p_**|
|Socioeconomic groups|7|24|.5710|20|.6428|
|Urban areas|||[.100]***||[.069]***|
|Socioeconomic groups|7|18|.6785|16|.7143|
|<br>rural areas|||[.055]**F||[.044]**|
|Socioeconomic groups<br>nationa**l**evel|7|24|.5715<br>[.100]**|24|.5714|
|Economi'c Regions|8|26|.6905<br>[.0350]**|74|.1191<br>[.397]|

* Significant at the I percent level.

** Significant at the 5 percent level.

** Significant at the 10 percent level.

The test rejects the null hypothesis of no association between the distribution of income across economic regions over the two periods in favor of the alternative. The concordance in rank ordering is preserved for several regions, and even when there is discordance, the absolute deviation is small, suggesting that regions with large concentration of income in 1994 also have large concentration of income in 1998. These regions include "Autres Villes" and "Ouaga-Bobo". The Spearman coefficient of rank correlation is _pD_ = .6905 and the corresponding p - _values_ for the right tail testP <.05 suggesting a significant association at the 5 percent level. However, the test fails to reject the null in favor of the alternative when the comparison is based on the distribution of poor across economic regions. The sum of squared deviation is relatively large for the number of groups, reflecting the magnitude of change in the poverty maps between the two reference periods. The correlation rank statistics is relatively small **_(p)_** =.119), and the corresponding _p- value is_ large (P = .397).

20
|PPR Working Paper**Sries**|
|---|

|Tltle|Author|Date|Contact|
|---|---|---|---|
|WPS172The Effects of Peru's Push to<br>Improve<br>Education|Elizabeth M. King<br>Rosemary T. Bellew|March1989|C.Cristobal<br>33640|
|WPS173 Staffing and Training Aspects of<br>Hospital Managemnt:**Some**Issues<br>for Research|Julio Frenk<br>Enrique Ruelas|||
|WPS1741rado Restrictions<br>with lmported<br>Intermediate<br>Inputs: When Does||||
|the Trade Balance Improve?|Ramon E. Lopez<br>Danl Rodrik|March1989|M. Ameal<br>61466|
|WPS175 An Integrated<br>Model of Perennial<br>and Annual Crop Production for<br>Sub-Saharan Countries|Robert D. Weaver|||
|WPS176 Credit Rationing,<br>Tenancy,<br>Productivity,<br>and the Dynamics<br>of Inequality|Avishay Braverman<br>Joseph E. Stiglltz|||
|WPS177 Casii-Flow or Income? The Choice of<br>Base for Company Taxation|Jack M. Mlntz<br>Jesus Seade|April<br>1989|A.Bhalla<br>60359|
|WPS178Tax Holidays and Investment|Jack M. Mintz|April1989|A.Bhalla<br>60359|
|WPS179 Public Sector Pricing<br>In a Fiscal<br>Context|Christopher<br>Heady|April<br>1989|A.Bhalla<br>60359|
|WPS18OStructural<br>Changes In Metals<br>Consumption:<br>Evidence from<br>U.S. Data|Boum-Jong Choe|April<br>1989|S. Lipscomb<br>33718|
|WPS181Public Finance, Trade and Development:||||
|What**Have**We Learned?|Johannes F. Linn<br>_De_orah L. Wetzel|April 1989|M. Colinet<br>33490|
|WPS182TheExperience<br>of Latin America<br>With Export Subsidies|Julio Nogues|April1989|S.Torrivos<br>33709|
|WPS183**Private**Investment,<br>Macromanagemant||||
|and Trade Liberalization: The<br>Cassof Mexico|Alberto R. Musalem|||
# **Reference**

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Diener, E. and Lucas, R.E. (1999). Personality and subjective well-being. In Kahneman, D., Diener, E. and Schwarz, N. (Eds), _Foundations of Hedonic Psychology: Scientific Perspectives on Enjoyment and Suffering_ , Chapter 11. New York: Russell Sage Foundation.

Di Tella, R., MacCulloch, R. and Haisken-DeNew, J.P. (2005). Happiness adaptation to income and to status in an individual panel. Harvard Business School Working Paper.

Duesenberry, J.S. (1949). _Income, Saving and the Theory of Consumer Behavior_ . Cambridge, MA, Harvard University Press.

Easterlin, R. (1974). Does economic growth improve the human lot? Some empirical evidence. In David, R. and Reder, R. (Eds.), _Nations and Households in Economic Growth: Essays in Honor of Moses Abramovitz_ . New York: Academic Press.

Easterlin, R.A. (1995). Will raising the incomes of all increase the happiness of all? _Journal of Economic Behavior and Organization,_ vol. 27, pp. 35-47 Easterlin, R.A. (2001). Income and happiness: Towards a unified theory. _Economic Journal_ , vol. 111, pp. 465-484.

Ferrer-i-Carbonell, A. (2005). Income and well-being: An empirical analysis of the comparison income effect. _Journal of Public Economics,_ vol. 89, pp. 997-1019.

Frey, B.S. and Stutzer, A. (2002a). What can economists learn from happiness research? _Journal of Economic Literature_ , vol. 40, pp. 402–435.

Graham, C., Eggers, A. and Sukhtankar, S. (2004). Does happiness pay? An exploration based on panel data from Russia. _Journal of Economic Behavior & Organization_ , vol. 55, pp. 319–342.

Guriev, Sergei M. and Zhuravskaya, Ekaterina V.,(Un)Happiness in transition (November 24, 2008). Available at SSRN: <u>http://ssrn.com/abstract=1077709</u> Inglehart, R, and Rabier, J.R. (1986). Aspirations adapt to situations - But why are the Belgians so much happier than the French? A cross-cultural analysis of the subjective 24
## a summary and suggests avenues for future research.

# II. Prospects for potential growth

Factors weighing on potential growth over the last decade are likely to persist in the remainder of the current decade. Potential growth prospects are estimated for a sample of 29 advanced economies and 53 EMDEs, unless otherwise specified (table 1). Demographic trends are expected to remain unfavorable, weighing on potential growth even while trend improvements in human capital investment and female labor force participation are expected to continue. Although growth of fixed investment in advanced economies is expected to pick up slightly from its pre-pandemic rates, it is unlikely to return to the rates seen in 2000-10, and in EMDEs it is expected to remain weak. Short of possible surges in productivity growth not assumed in the projections—which could occur as a result of technological breakthroughs or the exit of unproductive firms following the disruptions of the pandemic—these trends imply an outlook of mediocre potential growth.<sup>5</sup>

## II.1 Design of the baseline projections

The baseline projections presented here apply the production function approach to assumed paths for capital, population, and education and health outcomes. Projections for population-related variables (including age and gender structures of the population, fertility, and life expectancy) are based on UN Population Projections under the assumptions of median fertility, normal mortality, and “normal” (i.e., trend) migration. Cohort effects are assumed to remain at their latest (2021) levels.<sup>6</sup> Projections assume that education and health outcomes follow their long-term average trends. For example, gender-specific secondary and tertiary enrollment rates are assumed to continue rising through the forecast period at the average rates of the past two decades. Economy-wide averages are calculated as the population-weighted averages of these gender-specific rates. Similarly, gender-specific and age-specific secondary and tertiary education completion rates are assumed to rise at the average rates over the past two decades. Again, economy-wide averages are calculated as the population-weighted averages of these gender- and age-specific rates. These trends in education and health outcomes drive the projected growth of both TFP and labor supply.

Investment growth in the forecast period, 2022-30, is assumed to match the latest (October 2022) consensus forecasts for each economy for which they are available. For economies for which consensus forecasts are unavailable, investment growth in 2022 is

> 5 Some studies for individual advanced economies have suggested that the pandemic could have raised aggregate productivity through exit of unproductive firms (Kozeniauskas, Moreira and Santos 2022 and Van den Bosch and Vanormelingen 2022).

> 6 Cohort effects refer to systematically different labor market participation rates between different cohorts of workers over their life cycles (Balleer, Gomez-Salvador, and Turunen 2014; Kudlyak 2013).
# Economic Parameters of Deforestation

|Summary**.........................................**<br>**......**|**..**i|
|---|---|
|1. Introduction<br>.........|1|
|2. Modelling Approach and Summary of Results ......|5|
|3. Policy Implications<br>........|12|
|Log Export Bans and Other Trade Restrictions<br>......<br>Agricultural Intensification,<br>Plantation Forestry and OtherIndirect<br>Policies**......**|12<br>**15**|
|Efficient Forest Protection Policies...........................|18|
|4. A Dynamic land Use Model with Exogenous Timber Prices ....<br>........|22|
|Constant Logging Effort ..................................|22|
|Variable Logging Effort ...................................|27|
|Open Access Scenario ....................................|31|
|Appendix to Section 4: Derivation of Comparative Statics Results......|33|
|5. A Land Use Simulation Model with Endogenous Timber Prices ....<br>......|37|
|The Model.......|38|
|Implementation and Simulation Results .41||
|<br>Appendix to Section 5: The Mathematica Program ................|54|
|Rderences........|58|
# **Structural Reforms and Labor Market Outcomes: International Panel Data Evidence**<sup>**1**</sup>

Claire H. Hollweg Daniel Lederman Devashish Mitra

**Keywords:** Unemployment, wages, labor market participation, economic reforms **JEL classification codes:** E24, F16, J20

> 1 The authors are, respectively: Consultant, Trade and Competitiveness Global Practice, World Bank; Deputy Chief Economist and Lead Economist, Office of the Chief Economist, Latin America and the Caribbean, World Bank; and , Professor, Department of Economics, The Maxwell School of Citizenship & Public Affairs, Syracuse University. The corresponding author can be contacted at chollweg@worldbank.org. The authors thank Markus Brückner, Jakob B. Madsen, Peter Robertson, Diego Rojas, and an anonymous referee for detailed comments. All remaining errors are our responsibility.
# **Estimation Results:**

Table 5 reports the cross section results for 3 variables from the ICRG data set. The first column presents the results of the OLS estimation for the base specification for aid (using the first instrument set)<sup>11</sup> with GDP and population. Subsequent columns show the IV estimation instrumenting for aid and the third column shows the model with a fuller specification that includes most of the above-mentioned variables. The model in pure changes is not shown but the results are similar. The regressions in Table 4 show that even the OLS regressions do not indicate any significant relationship between aid and institutional measures.

Table 6 shows similar estimations for the two indicators from the Fraser data set. In this table, column 4 shows the estimation in pure changes (differencing all variables). Table 6 shows the only dependent variables among the several tested that have a consistent relationship with aid-the variable from the Fraser data set measuring the extent of regulation. These results indicate that aid is quite robustly associated with more regulation. High values of this indicator suggest that the economy is over regulated. Aid seems to have consistently raised regulation; an alternative way to interpret this result is that aid has not been a force for market liberalisation.<sup>12</sup> Table 7 shows the results for our financial sector specific institutional variable or contract intensive money. Table 6 indicates that some specifications using this variable show a negative relationship between aid and institutions.

The OLS cross section results in Table 5, 6, and 7 indicate that it is possible to get a significant relationship between aid flows and institutions. But in all cases except one, these results are not robust across alternative specifications. In other words, any positive or negative relationship between aid and institutions is not robust to the addition of control variables or to using IV techniques, with the exception of the indicator “regulation”.

> 11 Similar results are obtained using oda and ta. 12

> Or that the “Washington Consensus” view is substantiated.

16
**Figure 4.4.  Percent of unemployed household heads by country groups**

<!-- Start of picture text --> Capital city Other Urban Rural<br>20<br>15<br>10<br>5<br>0<br>EU Accession Balkans/EE Caucasus Central Asia Slavic<br>%<br><!-- End of picture text --> Note: EU Accession - average among Hungary, Lithuania, Poland. Balkans-average among Albania, Bosnia, Bulgaria, Kosovo, Moldova, Romania, Serbia. Caucasus-average among Armenia, Azerbaijan, Georgia. Central Asia-average among Kazakhstan, Kyrgyz, Tajikistan, Turkmenistan, Uzbekistan. Slavicaverage among Belarus, Russia.

Source: see Table 2.1 Among the unemployed household heads, the relative poverty rate in Other Urban areas is slightly below that of rural areas but half again as large as that of the Capital (Figure 4.5). This suggests that the financial safety nets available to the unemployed are best in the capital cities. In the absence of an adequate public or private safety net, unemployment may be especially likely to raise poverty risk in the urban context because cash is needed for essential goods and services. The finding suggests that unemployment in the secondary cities may be of longer duration and so more likely to lead to poverty than is the case in the capitals, where a wider range of jobs is available and finding new work easier.

**Figure 4.5.  Poor unemployed household heads by settlement area** (Percentage of unemployed hh heads who are poor)

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>Capital city Other Urban Rural<br>%<br><!-- End of picture text --> Note: average among 20 ECA countries. Albania, Armenia, Azerbaijan, Belarus, Bosnia, Bulgaria, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz, Lithuania, Moldova, Poland, Romania, Russia, Serbia, Tajikistan, Turkmenistan, Uzbekistan Source: see Table 2.1 25
among East Asian countries have a larger degree of variability than those in LAC (1.7 vis‐à‐ vis 0.8 quarters), with Thailand displaying the longest contraction duration (8 quarters) while contractions in Taiwan, South Korea, and Hong Kong last only 3 quarters.<sup>7</sup> Finally, we should point out that we calculated some features of the business cycle of other emerging markets (not located in Latin America or East Asia) where we have quarterly data on GDP. More specifically, we computed the duration and amplitude of output fluctuations in India, South Africa and Turkey. We find that output contractions in South Africa last the longest in our sample of 23 emerging market economies (with an average duration of 8.3 quarters).

# **Fact 2: The duration of expansions differ substantially in mean and dispersion across country groups.**

Table 1 shows that, on average, expansion episodes among LAC countries are shorter in duration ( **16** quarters) than those of East Asian countries ( **21.3** quarters) and OECD economies (approximately **24** quarters). Phases of expansion in economic activity among LAC countries also display a higher degree of variability ( _i.e._ a standard deviation of **17.6** quarters) than those of East Asian countries and OECD economies ( **13.6** and **11** quarters, respectively).

We also find that within the LAC region there is a widespread variation across countries. The average duration of expansionary phases (in quarters) fluctuate between 5.3 (Paraguay) and 62 (Colombia). One the other hand, the range of variation of expansionary phase duration goes from 5 (Taiwan) to 44 quarters (Malaysia). Finally, we should point out that expansions are shorter for the average EE group, but with a big dispersion compared to the OECD economies.

> 7 We also collected a measure of GDP, quarterly data, for China; however we could not register an output contraction within the sample period of our data.

9
# **4.2 Data**

## **4.2.1 Bilateral trade data**

The dependent variable captures the aggregate exports between country of origin _o_ and destination country _d_ . The trade data to construct the dependent variable are taken from the _IMF Directions of Trade Statistics._ They cover the period from the first quarter of 1990 to the last quarter of 2009. We work with a set of 32 developed and developing countries. The sample selection is largely dictated by data availability, but we also aim at having a representative sample.

## **4.2.2 Uncertainty**

Similarly to Bloom (2009) we use a measure of stock market volatility as a our main proxy for uncertainty. Implied volatility such as VIX would be the preferred measure, because it better reflects the sentiments about near future. Such measure is not readily available for a large set of countries and therefore we will be using the actual volatility of the stock market in the importing country. However, Bloom (2009) demonstrated in the example of the US, that for the subsample in which both the actual and implied volatility are available the correlation exceeds 0.8. This very high correlation should make the actual volatility an acceptable proxy. An additional argument in favor of using stock market volatility is also that this measure is correlated with many alternative measures of uncertainty used in the empirical literature. Alexopoulos and Cohen (2009) show that the variance of the stock market is highly correlated with more pragmatic measures, such as the number of times a major newspaper mentions the word uncertainty within a given period. Moreover, Bloom (2009) reports statistical evidence showing that stock market volatility is strongly linked to other measures of productivity and demand uncertainty, including the variance of firm profit growth and TFP growth and the disagreement among professional forecasters in expectations about macroeconomic variables.

In our estimations, instead of using the stock market volatility explicitly, we employ dummy variables identifying periods of exceptional uncertainty. We adopt the method suggested by (Bloom, 2009), i.e. our dummy variable takes a value of one when the Hodrick-Prescott detrended stock market volatility exceeds its mean by at least 1.65 standard deviations.<sup>6</sup> Table 1 documents the episodes of exceptional uncertainty included in our sample of countries. It shows that during the last two decades there have been numerous uncertainty shocks in developing and advanced countries alike. Most of them coincide with times of financial turmoil, most notably during the crises in Asia, Russia and Mexico or with political shocks, as in 2001.

## **4.2.3 Demand**

We use the total imports of the destination country _d_ minus the imports from the exporter _o_ as an approximation for the absorption capacity of the importer. Subtracting the imports from the exporter avoids endogeneity problems, as suggested by (Bricongne et al., 2012). We prefer this measure to GDP

> 6This choice might seem somewhat arbitrary and we will be looking at different thresholds in our estimations.

13
seem to have increased for women. However, this variable is always around 20 to 21 years during this period. The dependent variable in our analysis is the log real hourly wage rate. Wages were adjusted by the Consumer Price Index in order to have a comparable series through the decade. In contrast to what happened with the variable “years of education,” women earned less money than men during the period. The average hourly real wage has declined since 1992 (see Figure 1).

<!-- Start of picture text --> Figure 1: Average Hourly Wages over Time<br>5.00<br>4.50<br>4.00<br>3.50<br>3.00<br>2.50<br>2.00<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Men Women<br><!-- End of picture text -->

# <u>Summary of the Sample</u>

In 1992 the levels of schooling were as follows: 30 percent of the labor force had a primary education, 18 percent had complete secondary and 12 percent had complete university (see means and standard deviations in Annex Table 1). Women were more heavily represented at higher levels of schooling. For example, while 23 percent of men had incomplete secondary 1 The conglomerates available for the whole period (1992-2002) and considered in our paper are La Plata, Santa Fe, Paraná, Comodoro Rivadavia, Neuquen, Jujuy, Río Gallegos, Salta, San Luis, Santa Rosa, Tierra del Fuego, Capital and Conurbano Bonaerense.

8
32 base the entire policy framework for emission reductions (Johnstone and Karousakis, 1999).

There is a rapid growth in the number of motor vehicles in much of the developing world. In most countries, the growth is taking place at more than 10% a year and the doubles within 7 years (Gakenheimer, 1999). Thus, relying on single instruments, specifically command and control instruments (CAC), may be insufficient in controlling emissions from transport sectors in developing countries. Using the ambient air pollution data collected from the busiest intersection in Delhi, India, Kathuria (2002a) empirically demonstrates that CAC measures have not led to concomitant improvement in ambient air quality in the city.

Eskeland and Jimenez (1992) argue that, although there are no rigorous studies of pollution control in developing countries, there exists convincing casual evidence that regulations to protect the environment are ineffective or unnecessarily costly. The developing countries that have heavily relied on regulatory measures, containing vehicular emission through CAC instruments might be an uphill task. With the rapid increase in the number of vehicles, unless the enforcement standards are made more stringent, a regulatory approach based on emissions standards alone is bound to result in greater pollution (Kathuria, 2001b).

As there is no certainty about where the growth of motorization in developing countries will attenuate, its rapid growth shall continue for years to come. In order to avoid high economic and social impacts costs, Gakenheimer, (1999) suggest that actions to confront costs must be high yield ones. Faiz et al. (1990) argue that the promising way to control the increase in vehicle emissions within developing countries is through traffic management, and with administratively simple policy measures. It is not very difficult to formulate and implement policy instruments geared at changing vehicle use and fuel consumption patterns.