rafmacalaba/datause-encoder-data huggingface dataset:
long term impact of policy-induced changes in households’ saving rates and in the price of investment on the steady-state capital stock. In the long run, we also allow some substitutability between intermediate consumption and value added (an elasticity of substitution of 1, against 0 in the short and medium term), so as to reflect greater opportunities for changes in technologies.

The chosen yardstick for welfare is the assessment of equivalent variation, which is the sum of two terms. The first one measures the gain (or the loss) of disposable income caused by the reform (producers surplus), and the second one measures the income needed after the reform to obtain the same level of utility as before the reform (consumers surplus).

The model is calibrated using data (a Social Accounting Matrix, SAM) for the year 2005<sup>14</sup> , the year before food prices started to surge on international markets. The SAM for 2005 updates the SAM built for the year 2001 by Thurlow and Wobst (2003). In particular, it accounts for (i) the large increase in public consumption over GDP, (ii) the large increase in the value of imported petroleum products, and (iii) changes in output and exports of most important agricultural commodities between 2001 and 2005. The SAM contains 12 different households (including 2 rural poor and 2 urban poor, 4 rural non poor, 4 urban non poor) of different population sizes, 9 types of labor (distinguished by gender and school levels, plus child labor), physical capital & land, and 43 sectors of activities (including 21 agricultural sectors and 4 food processing sectors). See the annex for the full list of the various dimensions of the model.

# **III. The Welfare Impact of Commodity Inflation in Tanzania**

We use the CGE model to measure ex-ante the impact of commodity inflation on households’ welfare. The exogenous shock consists in raising commodities’ world prices, as denominated in Tanzanian shillings, by the relative change observed between January 2006 and June 2008. Such a change is computed econometrically on monthly data, and discounted by the overall CPI in Tanzania to capture relative prices changes on the domestic market. The Figure 1 reports the computed changes, for the most important

> 14 The estimated SAM for 2005 is available upon request from the author.

7
they faced no frictions. We turn to the National Study of the Changing Workforce which, in addition to data on incomes and hours worked, contains data on preferred hours of work. We use this measure of preferred hours worked to recover productivities and preferences ( _n, α_ ) for each individual as discussed in Section 3.4. While labor market frictions are modest (optimal hours worked differ from observed hours worked by about 10% on average), they do not appear to be an overly large driver of income inequality relative to productivity and preference differences. We discuss our findings with frictions in more detail in Appendix C.2.

# **6 Application: Optimal Income Taxation**

In this section we analyze how labor supply elasticities impact the optimal extent of redistribution via the implied degree of income inequality due to heterogeneity in productivities vs. heterogeneity in preferences. We calculate optimal income tax schedules using the distribution of productivities and preferences recovered under the various assumptions on the magnitudes of labor supply elasticities in Section 5. We contrast these optimal schedules to the optimal schedules calculated assuming that all inequality is driven by productivity heterogeneity (as in Mirrlees, 1971 or Saez, 2001).<sup>33</sup> We find that (1) optimal tax rates are slightly higher than Mirrleesian optimal rates under our baseline elasticity estimates and (2) larger effort elasticities and larger income effects lead to lower optimal rates relative to the Mirrleesian case.

## **6.1 Optimal Tax Problem**

The optimal tax problem is to maximize social welfare, subject to a budget constraint and incentive compatibility constraints that individuals maximize utility conditional on the given tax schedule. Let us denote _c_<sup>_∗_</sup> ( _n, α_ ), _z_<sup>_∗_</sup> ( _n, α_ ) and _u_<sup>_∗_</sup> ( _n, α_ ) as the optimal consumption, income, and utility levels for individual ( _n, α_ ) under a given tax schedule. For some welfare weights _μ_ ( _n, α_ ), the government maximizes: The budget constraint is given by ( _E_ denotes government expenditures): 33 We assume the government can observe the distribution of incomes and hours worked so as to back out the distribution of productivites and preferences, but cannot condition the tax schedule on hours worked. If the government were to condition taxes on hours worked, then individuals would misreport their hours (as the government cannot feasibly monitor every person’s hours worked).

31
Since the power sector is one of the primary sources of GHG emissions in MENA countries, the model estimates the GHG emissions from fuel combustion in power plants. The model can fix GHG emissions at the level specified by policy makers and calculate the shadow price of carbon mitigation. Instead of capping GHG emissions from the power sector, emission price or carbon tax can be introduced as an alternative approach to reduce power sector GHG emissions.

The model produces the following outputs:

- The marginal cost of electricity for each time block considered. It is also the electricity price with a competitive market assumption. The difference in the marginal costs of electricity is the main driver of cross-border electricity trade. A country with higher marginal costs of electricity production tends to import electricity from the one with lower marginal costs of electricity production.

- Optimal generation capacity additions, and optimal mix of electricity generation in each country for both domestic consumption and exports; including the level of exploitation of renewable energy resources for electricity generation.

- Cost savings due to the regional electricity trading.

- Volume and value of regional electricity trade.

- Amounts of GHG mitigation from the power sector.

# **4. Input Data and Assumptions**

The main input data used in the model are: projections of peak load (load forecasts) and energy demand profiles; existing and planned generation capacities and cross-border transmission interconnections; costs (capital costs, fixed and variable O&M costs); projections of fuel prices, profiles of generation resources, particularly, of renewable energy resources (e.g., resource availability profiles measured in terms of capacity factors for each time block in each jurisdiction). Electricity generation technologies considered in the study are: open cycle gas turbine (GT), 9
# **Table 1: Estimates of Crypto-assets Adoption**

|Panel A: Statista Global<br>Used or Owned Crypto-a<br>%<br>**Country**|Country Survey<br>ssets (2020)<br>**Share(%)**|: Share of Respondents wh<br>**Country**|o Indicated That They<br>**Share(%)**|
|---|---|---|---|
|Nigeria|31.9|Lithuania|8.7|
|Vietnam|21.1|Egypt,Arab Rep.|8.3|
|Philippines|19.8|Norway|8.1|
|South Africa|17.8|Portugal|8.1|
|Thailand|17.6|Australia|7.8|
|Peru|16.1|Korea,Rep.|7.6|
|Turkey|16.1|Serbia|7.5|
|Colombia|15.3|Russian Federation|7.3|
|Argentina|14.4|Austria|7.2|
|Indonesia|13|Poland|7.2|
|Brazil|12.5|China|6.9|
|Malaysia|12.3|Hungary|6.4|
|Chile|11.7|Romania|6.4|
|Saudi Arabia|11.4|Belgium|6.3|
|Switzerland|11.1|United States|6.2|
|Greece|11.1|France|5.6|
|Kenya|10.5|Pakistan|5.6|
|Dominican Republic|10.3|Canada|5.2|
|Netherlands|10|Germany|5.2|
|United Arab Emirates|10|Finland|5.1|
|Mexico|9.7|New Zealand|5.1|
|Ireland|9.6|Israel|4.9|
|Singapore|9.6|United Kingdom|4.7|
|Spain|9.4|Italy|4.7|
|Morocco|9.3|Denmark|4.4|
|Czechia|9.2|Sweden|4.3|
|India|8.8|Japan|3.7|

Source: Statista Global Consumer Survey. Note: Statista reported that the survey contains between 1,000-4,000 respondents per country and that the samples are representative of the online population. These figures should be interpreted with caution as data gaps remain significant.

9
species from georeferenced data provided by the Global Biodiversity Information Facility (GBIF). In this paper, we have combined those maps with extinction threat factors to produce composite global indicators of extinction threat that provide new insights into the spatial domain for conservation planning. The paper develops threat factors for terrestrial species that include their species occurrence region sizes, their sensitivity to human encroachment, the density of neighboring populations, and the degree of formal protection. We also develop threat factors for marine species that include species occurrence region size, degree of formal protection, degree of coverage by EEZs (Exclusive Economic Zones), the impact of coastal population, and two measures of commercial fishing activity. The paper explores three approaches to composite threat indicator construction. The first comes from econometric estimates of the relationship between our threat indicators and expert-based Red List ratings from IUCN. Our approach extends numerous pilot exercises in the published database. The model uses our threat indicators to estimate the probability that a species is assigned a threatened status (NT, VU, EN, CR) by IUCN. We find a close statistical association between the threat probabilities predicted by our model and IUCN threat status assignments. We also find a close spatial association between the two variables. We use our econometric results to predict threat probabilities for mapped GBIF species that have not been rated by IUCN. We add these to the comparative mapping exercise and find that radical expansion of the species We construct a second composite indicator that assigns higher conservation priority to species based threat indicator, but modifies it to accommodate grouping by habitat scale. A spatial are more widely distributed than threatened species generally. By implication, assignment of warrant formal protection.

|medians and means of the four factors that have significant effects on IUCN threat ratings for<br>second-order differences at global scale.<br>We compare their spatial pattern with the pattern<br>concentration of high-threat areas in the latter case, although the overall spatial pattern of<br>indicators.<br>Thistrendcontinuesforthemodel-based<br>indicator,whichshowsevenmore|
|---|
> rate.  Let _Wi_ /<sup>_W_</sup> _i EGS_ be the relative wage in state _I_ , where _Wi_ is the wage rate in the (non-PW) casual labor market and _Wi EGS_ is the MGNREGS wage rate. The correlation coefficient between ln( _Wi_ /<sup>_W_</sup> _i EGS_ ) and un-met demand ( _EDi_ ) is –0.558 which is significant at the 1% level.<sup>24</sup>

However, there are two reasons to question whether this really reflects greater tightening of the casual labor market in states where there is less un-met demand for work on the scheme. First, the implied relative wage rate at zero rationing is too high to be believed. We postulate that the relative wage depends on the excess demand as follows:<sup>25</sup> The expected value of the log relative wage rate when all demand for work on MGNEGS has been satisfied is then given by  . Using the data in Table 1 and 5, the estimated value of  is 0.398 (st. error=0.105) and the estimate of  is –1.777 (st. error=0.519; R<sup>2</sup> =0.311.) This implies that, when all demand for work is satisfied, the market wage rate would be 50% higher than the MGNREGS wage rate.<sup>26</sup> Yet the work is very similar, and there is no obvious reason why such a differential would exist in equilibrium.

Second, the excess demand variable could well be picking up some other factor correlated with it. Following our discussion in section 2, poverty is a plausible candidate. Adding the poverty rate to equation (5), we find that the effect of excess demand becomes insignificant (prob.=0.19) while the poverty rate is significant (a coefficient of 0.828, with st. error of 0.333). A higher poverty rate may be associated with greater landlessness and hence a larger supply of casual labor, bringing down the wage rate.

# **5. Rationing and the participation of women**

Nationally, almost half (48%) of the employment as registered in the administrative data for 2009/10 goes to women.<sup>27</sup> This is very high for a country where a minority of women

> 24 The fit is slightly better using the log relative wage; using _Wi_ /<sup>_W_</sup> _i EGS_ instead the correlation coefficient is -0.520.

> 25 Using _Wi_ /<sup>_W_</sup> _i EGS_ as the dependent variable the estimated intercept is 1.466 (s.e.=0.132). We also tried a quadratic function of _ED_ but this did not improve the fit.

> 26  Note that exp(0.398)=1.489 is the implied ratio of the levels of wages.

> 27  While the administrative data are clearly inadequate for measuring aggregate demand for work, there is no

15
61

- Gale, Douglas and Hellwig, Martin. "Incentive-Compatible Debt Contracts: The One-Period Problem," Review of Economics Studies, 1985, a, pp. 647-63.

- Gelb, Alan H. "Financial Policies, Growth, and Efficiency," World Bank PPR Working Paper No. 202, June 1989.

- Gerschenkron, Alexander. Continuity in History and other Essays, Cambridge, Massachusetts, Harvard University Press, 1968.

- Gerschenkron, Alexander. Economic Backwardness in Historical Perspective - A Book of Essays. Cambridge: Harvard University Press, 1962.

- Gertler, Mark. "Financial Structure and Aggregate Economic Activity: An Overview," Journal of Money. Credit. Banking, August 1988, 2(3, Pt. 2), pp. 559-88.

- Gertler, Mark and Gilchrist, Simon. "Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms," Quarterly Journal of Economics, May 1994, 1Q9(2), pp. 309-40.

- Gertler, Mark and Rose, Andrew. "Finance, Public Policy and Growth," in Gerard Caprio Jr. et al. 1994, pp. 13-45.

- Giovannini, Alberto and De Melo, Martha. "Government Revenue from Financial Repression," American Economic Review., September 1993, 83(4), pp. 953-63.

- Goldsmith, Raymond, W. Financial Structure and Development. New Haven, CT: Yale University Press, 1969.

- Gorton, Gary and Pennacchi, George. "Financial Intermediaries and Liquidity Creation," Joumal of Finance, March 1990, 45(1), pp. 49-71.

- Greenwood, Jeremy and Jovanovic, Boyan. "Financial Development, Growth, and the Distribution of Income," Journal of Political Economy, October 1990, 9B(5,Pt.1), pp. 1076-1107.

- Greenwood, Jeremy and Smith, Bruce. "Financial Markets in Development, and the Development of Financial Markets," Journal of Economic Dynamics and Control, 1996, forthcoming.

- Grossman, Gene and Helpman, Elhanan. "Quality Ladders in the Theory of Economic Growth," Review of Economic Studies, January 1991, 58(1), pp. 43-61.
**Introduction.** In 1989 the first partially free elections in Poland’s post-World War II history took place, and Poland became the first country in Central and Eastern Europe to reestablish democracy and embark on an economic and social transition to a market economy. A "shock therapy" program implemented during the early 1990s enabled the country to transform its economy into one of the most dynamic and robust in the region. Today, Poland’s economy is by far the largest among the new EU-member states. It accounts for over 50 percent of the cumulative population of the 10 new member states<sup>1</sup> and also about 50 percent of their cumulative GDP.

The beginning of Poland’s transition in 1990 was marked by exceptionally difficult macroeconomic conditions, which included high inflation, a large legacy of external debt, and a high black market foreign exchange premium. Saddled with a large part of the enterprise sector that was considered “value subtracting,” Polish policymakers took huge risks by making the zloty convertible, fixing the exchange rate, and lowering import barriers. With privatization lagging behind, many predicted a crisis based on the notion that enterprises would not be able to cope with market conditions, which would lead to politically unacceptable mass bankruptcy and social upheaval. This did not happen. On the contrary, Poland turned out to be unique among the large front-running European transition countries in having an unbroken growth record once growth resumed after the initial output collapse.

This paper attempts to analyze the linkages between macroeconomic and economic growth variables, their movement over time, and their impact on poverty and income distribution. Poland, a middle-income country, is of particular interest not only because its data source permit a relatively detailed analysis of such developments, and also because the macroeconomic environment and economic growth variables show a sizable degree of variance. In addition, Poland has struggled in the last few years to reduce poverty while still experiencing positive but subdued economic growth.

# **A. A SUCCESSFUL MACROECONOMIC PERFORMANCE**

Economic growth has not been uniform over the last decade. After years of growth above 5 percent or above in the mid 1990s, growth declined substantially to reach a meager 1 percent in 2001, only to increase again in 2003 with good prospects for the coming years (see Figure 1 and Table 1). The slowdown in growth was accompanied by an increase in poverty and unemployment as is evident from Figure 1.

This section provides a brief discussion of recent economic developments in Poland. The Polish Central Statistical Office’s annual Household Budget Survey (HBS) micro-data sets (1994-2002), as well as publicly available real sector and monetary macro data, have been used to analyze linkages between the macroeconomic environment on the one hand and poverty, inequality, and consumption distribution trends on the other.

> 1 The 10 new member states that joined the EU on May 1 2004 are the Czech Republic, Cyprus, Estonia, Hungary, Latvia, Lithuania, Malta, the Slovak Republic, Slovenia, and Poland.

2
# **3. Price Rising Effects**

## **a. Price Data: The Trade Unit Value Database**

Trade unit values from the French Research Center in International Economics (CEPII) are used to approximate trade prices.<sup>3</sup> These data provide reliable and comparable unit values across countries. Following Cadot and Gourdon (2012), we use Cost of Insurance and Freight (CIF) trade unit values (reported by the importer country) to estimate any potential price-raising effects of NTMs. The CIF price was deemed most appropriate for the analysis as it includes the Freight on Board (FOB) price of the product along with the cost of compliance with SPS and TBT regulations, and any other expenses from pre-shipment inspection, licensing, or any other sort of NTMs.

## **b. Methodology**

The estimation below can be thought of as a simple treatment-effect approach where prices are “treated” by NTMs. We assume that prices are determined by Equation 3. Here, i indexes countries, k indexes products, p∗k is the world price of product k, pik is its price in country i, tik the tariff imposed by country i on product k, and λi a cost-of-living adjustment factor for country i depending on its level of income and a number of other characteristics such as landlocked status, remoteness, the quality of the infrastructure, and the regulatory environment. δijk is a dummy variable that equals one if country i imposes an NTM of type j on product k, and zero otherwise. Let aj be the cost of complying with NTM of type j. We assume that this cost is the same across countries and products. Assuming that tariffs and NTM compliance costs are fully passed through to domestic prices, the basic price determination equation is: Logging Equation 3 yields: lnpik = lnpk∗ + ln(1 + λi) + ln(1 + tik)+ ∑lnj �1 + ajδijk� **[4]** Due to our assumption of perfect tariff pass-through, we can redefine our dependent variable as: Also note that: so δ . ln�1 + aj ijk�= δijkln�1 + aj�

> 3 This database provides a world-level dataset reporting reliable unit values at a high level of disaggregation. One shortcoming is important to note: despite the treatment of missing unit values in the database, there is still a relatively large share of missing data. Berthou and Emlinger (2011) estimate that the database covered around 80% of world total trade values in 2008.

17
Figure 6 (a): China Skill Premiums – basic controls <!-- Start of picture text --> China: Skill Premium and Skilled Workforce - Basic controls<br>only<br>0.7<br>Skilled-1: At least<br>0.6<br>Upper Secondary<br>0.5<br>Skilled-2: Tertiary and<br>0.4 above<br>0.3 Skill Premium-1<br>0.2<br>Skill Premium-2<br>0.1<br>0<br>1999 2005<br>Year<br>of skilled workers<br>Skill premium and proportion<br><!-- End of picture text --> Figure 6 (b): China Skill Premiums – industry controls <!-- Start of picture text --> China: Skill Premium and Skilled Workforce - With Industry<br>controls<br>0.7<br>Skilled-1: At least<br>0.6<br>Upper Secondary<br>0.5<br>Skilled-2: Tertiary and<br>0.4 above<br>0.3 Skill Premium-1<br>0.2<br>Skill Premium-2<br>0.1<br>0<br>1999 2005<br>Year<br>of skilled workers<br>Skill premium and proportion<br><!-- End of picture text --> 27
**Table 1: Tourist arrivals and contribution to GDP in selected countries** , latest year available

|**Country**|**Tourist Arrivals**|**Tourist Arrivals**<br>**(% ofpopulation)**|**Direct Contribution to GDP**<br>**(% of GDP)**|
|---|---|---|---|
|**Maldives**|1,286,000|300.6%|39.6|
|**Seychelles**|303,000|320.0%|26.4|
|**The Bahamas**|1,482,000|378.8%|19.0|
|**Vanuatu**|95,100|35.2%|18.2|
|**Cabo Verde**|598,000|110.8%|17.8|
|**St. Lucia**|348,000|195.5%|15.0|
|**Belize**|386,000|105.2%|15.0|
|**Fiji**|792,000|88.1%|14.4|
|**Antigua & Barbuda**|265,000|262.5%|13.0|
|**Barbados**|632,000|221.8%|13.0|
|**Dominica**|78,000|106.1%|12.4|
|**São Tomé and Príncipe**|**29,000**|**14.5%**|**10.8**|
|**Jamaica**|2,182,000|75.7%|10.3|
|**Iceland**|1,792,000|534.2%|8.5|
|**Mauritius**|1,275,000|100.9%|7.4|

Source: UNWTO and WTTC 26
2 OF <!-- Start of picture text --> IN U.S. I<br><!-- End of picture text -->

# ON IN COUNTRIES IN MULTIMOD

(TAYLOR, LIVERPOOL) AND ON LARGE INDUSTRIALIZED

# OTH-IND

<!-- Start of picture text --> 01O -O-<br>0<br>-03~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-.<br>04<br>1985 19S6 1987 1988 1989 1990<br>GNP<br><!-- End of picture text --> <!-- Start of picture text --> OTH-(ND<br><!-- End of picture text --> <!-- Start of picture text -->
-0.<br>- -0<br>1955  1986  1987  198  1989  1990<br>S  tR<br><!-- End of picture text -->

OTH-IND OTH-IND <!-- Start of picture text --> 52<br>5<br>48<br>44<br>42<br>3 a ~~~~~~~~~~~~~~~~~~~~-0<br>56--004<br>S --<br>32-<br>3-<br>28-<br>2 6<br>2 4-<br>1989 1988 1981 1988 1989 1990<br>LICHA<br><!-- End of picture text --> <!-- Start of picture text --> 0.02<br>~~~~~~~~~~~~~~~~~~~~~~~001<br>-0.02<br>03<br>005<br>-0.06<br>-0 08<br>-0 09<br>-0l W<br>~~~~~~~~~~~~~~~~~~~~~~~~~~-0 II<br>1989 1986 3987 38 9919<br>S-I INTEREST RATE<br><!-- End of picture text -->