rafmacalaba/datause-encoder-data huggingface dataset:
Finally, the DCGE model links to a micro-simulation model, which allows for the endogenous estimation of growth impact on poverty reduction. All HBS sample households are included in the micro-simulation model, and their total expenditures and expenditures on each commodity or commodity group are linked to each of the six representative households included in the DCGE model. The endogenous changes derived from the DCGE model for the six representative households are used to recalculate consumption expenditure of their corresponding households in the survey dataset. New levels of total consumption expenditures are recalculated based on individual household budgets; and the new poverty rates for each region, rural and urban, and the national total are obtained by comparing expenditure levels (in real terms) with the official poverty line defined for HBS.

Table 6. Scenario Assumptions

||**Government transfers**|**Subsidy**|**TFP**|
|---|---|---|---|
|**Reform 1 (accelerated)**||||
|1A: Use all savings for budget<br>consolidation|as base|100%<br>decrease of<br>subsidy in<br>2011|as base|
|1B: Reduce fiscal deficit by<br>50% and use remainder for<br>direct transfers to households|increase transfers in<br>2011 between 40% and<br>380% depending on<br>initial size of transfers<br>and population shares|100%<br>decrease of<br>subsidy in<br>2011|as base|
|1C: Reduce fiscal deficit by<br>50%, compensate only the<br>poorest of the poor, and use<br>remainder for productivity-<br>enhancing investments|increase transfers in<br>2011 between 22% and<br>155% depending on<br>initial size of transfers<br>and population shares|100%<br>decrease of<br>subsidy in<br>2011|22% in construction,<br>electricity, water, trade<br>transport in 2011; from<br>2013, 1 percent TFP<br>growth in all sectors|
|**Reform 2(gradual)**||||
|2A: Use all savings for budget<br>consolidation|as base|33%, 50%,<br>100%<br>reduction<br>from 2011 to<br>2013|as base|
|2B: Reduce fiscal deficit by<br>50% and use remainder for<br>direct transfers to households|increase transfers from<br>30%–100% in 2011 to<br>15%–50% in 2013|33%, 50%,<br>100%<br>reduction<br>from 2011 to<br>2013|as base|
|2C: Reduce fiscal deficit by<br>50%, compensate only the<br>poorest of the poor, and use<br>remainder for productivity-<br>enhancing investments|increase transfers from<br>20%–74% in 2011 to<br>20% in 2013|33%, 50%,<br>100%<br>reduction<br>from 2011 to<br>2013|7% in construction,<br>electricity, water, trade<br>and transport 2011–<br>2013; from 2014, 1% TFP<br>growth in all sectors|

Source: DCGE model results.

With the CGE and micro-simulation models, we quantify the impact of the accelerated (Reform Option 1) and the gradual (Reform Option 2) reform scenarios; within each of these scenarios, we analyze the 11
is worth noting the relatively strong growth of most services sectors compared to manufacturing, which is also reflected in bank commercial credit patterns (see figures 18 and 19). As a result, bank loans to services sectors accounted for more than 60 percent of the performing loan portfolio as of 2005Q3, up from 42 percent in 2000.

Figure 18: Cumulative Real Growth by Economic Sector (2000-2005) and GDP Contribution by Sector in 2005 <!-- Start of picture text --> 25% 35%<br>30%<br>30%<br>24%<br>20%<br>Cumulative Real GDP 25%<br>Growth 2000 - 2005<br>10.4% 20%<br>15%<br>15%<br>11%<br>10%<br>9% 10%<br>9% 9% 9%<br>5%<br>5% 3%<br>0%<br>-0.7%<br>0% -5%<br>Trade & Manufacturing Other Fin Servs & Transport & Agribusiness Construction Utilities Mining<br>Leisure Services Real Estate Telecoms<br>% of GDP (2005) Real Growth<br>% of GDP (bars)<br>Cumulative Real growth 2000-2005 (lines)<br><!-- End of picture text --> Source: INEGI.

Note: The GDP of each year is calculated as the average of GDP over the four quarters, and it is deflated by the retail price index in order to derive the cumulative real growth rate for 2000-2005.

Figure 19: Cumulative Real Growth in Bank Commercial Credit for 2000-2005 by Sector <!-- Start of picture text --> 200% 191% 30%<br>25%<br>150%<br>114% 20%<br>95%<br>100% 15%<br>10%<br>50% 21% 27% 27% 31% 33% 40%<br>5%<br>0% 0%<br>-5%<br>-50%<br>-50% -10%<br>-66% -39%<br>-100% -93%-84% -13% -11% -7% -15%<br>Other ManufMiningTextilesPaperMetal WoodReal estateMachineryAgricultureChemicals ConstructionFood TradeLeisureTelecomsCementOther serv.<br>(bars)<br>2005 (line)<br>Cumulative Real Growth (%) for 2000-2005 Share (%) of the banks' portfolio at end of<br><!-- End of picture text --> Source: BOM and SHCP.

Note: Only bank performing loans to the non-financial private sector are included in the analysis. Economic sectors are based on CNBV’s classifications. Some numbers might differ from other Figures because they are deflated using the retail price index as opposed to the GDP deflator.

19
Sector-level variables include information on trade policies, namely a composite index of the average applied tariffs and its standard deviation. The index was estimated as the first principal components derived from factor analysis. The other trade policy indicator measure at the industry level is the share of tariff lines within each industry that faces one of the so-called “core” non-tariff barriers. These data were taken from Nicita and Olarreaga (2006).

Country-level explanatory variables capture various aspects of the investment climate, besides trade policies. They include an index of infrastructure coverage (from the _World Development Indicators_ (WDI) database), institutional quality (from Kaufmann et al. 2005), and real manufacturing GDP growth (also from WDI). In some models, we also include the level of development (GDP per capita from the Penn World Tables). An important explanatory variable for our analysis is a regulatory index capturing the ease of entry, which was calculated from data from the World Bank’s _Doing Business_ database. We use principal components analysis to calculate a composite index on infrastructure coverage (including paved roads per square kilometer and telephone lines per capita), the institutional index (including corruption, political stability, and rule of law), and the regulatory index (including Difficulty of Firing Index, Difficulty of Hiring Index and Days for Starting a Business). We also use patent-counts data from Lederman and Saenz (2005), namely the sum (stock) of utility patents granted to researchers in each country during 1963-2000 by the United States Patent and Trademark Office (USPTO) per person. The latter provides a measure of the density of innovative ideas available to firms operating in each country.

Some studies, such as Criscuolo et al. (2005), also treat explanatory variables measured at a higher level of aggregation than the firm level as exogenous factors, but these are measured with data from the firm surveys themselves. Our approach is different in this regard, as we use objective data from other sources. As discussed elsewhere in this volume, the use of aggregate variables derived from the same dataset as the firm data can be assumed to be exogenous only under certain conditions, namely firms’ deviations from the average must be orthogonal to the average and normally distributed with expected value of zero. We do not have to make any assumptions in this regard, because our data are objectively measured at the country level from data from other sources. The disadvantage of our approach is that we have fewer degrees of 8
spending by the Federal government had a significant poverty-reducing distributional effect. Recall that the regressions in Table 4 include state-specific trends (state fixed effects in the changes in log poverty measures). We also estimated the same regressions without the statespecific trends. The results were quite similar to those in Table 4.

Because we reject the pooling hypothesis across states for the coefficient on industrial output growth in both regressions, it is somewhat difficult to gauge the magnitude of the 1994 break in the sector-specific coefficients in Brazil from Table 4. By aggregating the GDP-share _J J_ adjusted elasticity ( β _i_<sup>_s_</sup> _it_ − 1 ) across states with the appropriate weights, Figures 3a and 3b help visualize the evolution of the poverty responses to growth in each sector over time. Although each 1% growth in the service sector still reduces both H and PG by much more than the same growth rate in the other two sectors, the difference has been slightly reduced over time, and this reduction is more pronounced for PG than for H.

The temporal pattern can be seen in the growth incidence curves (GIC). The GIC gives the rate of growth at each percentile of the distribution (ranked by income per person).<sup>26</sup> Figure 4 gives the GIC for the whole period (1985-2004), and for each sub-period.<sup>27</sup> Not only was average annual growth clearly higher in 1993-2004 than during 1985-1993, but its incidence was quite different. The incomes of the poor fell faster than those of the rich in the first sub-period (causing inequality to increase, and reducing the growth elasticity of poverty), and rose faster in the second sub-period, causing inequality to fall and increasing the growth elasticity of poverty.

Figure 4 makes it clear that the pattern of poverty reduction in Brazil was very different between the periods 1985-1993 and 1993-2004. The results reported in Table 4 suggest four candidate explanations for these differences: First, growth was somewhat higher in the second period than in the first (when it had actually been negative). Second, after 1994 growth became more effective in reducing poverty across all three sectors. (Although this may have been offset by a “composition” effect: growth became slower in services, which has the highest growth elasticity of poverty reduction.). Third, inflation was poverty-increasing in both periods, but we know that the rate of inflation was much lower in the second sub-period. Fourth, social security

> 26 The GIC is obtained by calculating growth rates on the date-specific quantile functions (obtained by inverting the cumulative distribution function); see Ravallion and Chen (2003). If the GIC is normalized by the growth rate i the n mean then one obtains a curve giving the (total) elasticity to growth at each level of income; for further discussion see Essama-Nssah and Lambert (2006). 27 Since the PNAD survey was not carried out in 1994, we use 1993 as the watershed year in this figure.

18
<!-- Start of picture text --> Projected decrease in<br>diarrhea rate (p.p)<br>HM -25--19<br>HM -1.9--18<br>(i -1.8--1.7<br>& (i -17--13<br>*% | (4) -1.3--1.0<br>y a [_]-1.0--05<br>‘ [__] NA<br><!-- End of picture text -->

<!-- Start of picture text --> Projected decrease in<br>diarrhea rate (p.p)<br>[___] -0.70- -0.63<br>[9 -0.63 - -0.57<br>( -0.57 --0.51<br>a GE 0.51 - -0.45<br>* .d; 3 GEHB -0.45-0.32 -- -0.32 -0.05<br>[J NA<br><!-- End of picture text -->
|Other manufactured tobacco and manufactured tobacco|4 400 ALL/kg|
|---|---|
|substitutes;|From January 2016 – 5 100 lek/kg<br>From January 2017–5 800 lek/kg|
|Light oils (gasoline and benzene)|37 ALL /liter|
|Fireworks|200 ALL /kg|
|Primary cells and primary batteries|200 ALL /kg|

Source: Ministry of Finance (2016)<sup>46</sup>

# _Social Insurance Contribution_

Employed persons and their employers shall be liable to pay social and health care insurance contribution. The contribution cannot be awarded under a minimum and over a maximum monthly wage. The employee’s contribution is 9.5% while the employer pays 15.8%. Health insurance is levied at 1.7% for both employee and employer. The self-employed pay 23% for social security and 7% for health insurance. The types of insurance and the respective rates are presented in the following table.

|**No.**|**Type of contribution**|**Total**|**Employer**|**Employee**|**Employer**<br>**Share**|**Employee**<br>**Share**|
|---|---|---|---|---|---|---|
||**Total of Social Insurance**||||||
|A|<br>**Contribution**|**24.50**|**15.00**|**9.50**|**0.61**|**0.39**|
|**1**|Sickness insurance<br>contribution|0.30|0.18|0.12|**0.61**|**0.39**|
|**2**|Maternity insurance<br>contribution|1.40|0.86|0.54|**0.61**|**0.39**|
|**3**|Pension insurance<br>contribution|21.60|13.22|8.38|**0.61**|**0.39**|
|**4**|Accident insurance<br>contribution|0.30|0.18|0.12|**0.61**|**0.39**|
|**5**|Unemployment insurance<br>contribution|0.90|0.55|0.35|**0.61**|**0.39**|
|B|**Health Care Insurance**<br>**Contribution**|**3.40**|**1.70**|**1.70**|**0.50**|**0.50**|
||**Total**|**27.90**|**16.70**|**11.20**|**0.60**|**0.40**|

# **_Liability to contributions:_**

1. Compulsory social insurance and the payment of respective contributions are mandatory for all economically active persons with permanent residence in Albania, as for the persons employed with an

> 46See Table on

> <u>al/Legjislacioni/COUNCIL_DECISIONS/Legjislacioni%20Tatimor/Akcizat/Ligji%20nr.%208976%20dt.12.12%202002,P%C3 %ABr%20akcizat%20n%C3%AB%20Republik%C3%ABn%20e%20Shqip%C3%ABris%C3%AB,%20i%20ndryshuar.pdf and http://www.financa.gov.al/files/userfiles/Legjislacioni/Update-of-Albanian-fiscal-laws-for-2014.pdf</u>

- <u>http://www.tatime.gov.al/sq</u>

42
LINE # ENTRIES SOURCE OR FORMULA

1. Total domestic debt outstanding and disbursed

2. Domestic Debt held by Monetary Authorities

- IMF _Government Finance Statistics_ (GFS) and Country Source A GFS and Country Source B

3. Interest payments on # 1:

- a) Total from GFS

- b) Domestic

- c) Foreign

- d) Total of b) and c)

4. Effective Domestic Interest Rate 5. Total Central Government Libor-based External Debt (US$ ,000) 6. Marginal Foreign Central Government Interest Rate on #5 7. Average Effective Foreign Central Government Interest Rate 8. Exchange Rate, Average Annual _LC/$_ 9. Percent change in # 8

- a) GFS

- b) Country Source C

- c) Country Source C

- d) # 3a and # 3b

- # 3a divided by (#1(t) + #1(t1))/2

World Bank Debtor Reporting System (DRS) Table 2 Creditor types 2,6, & 7

- World Bank DRS

- Interest payments from DRS Table 2 plus change in reported interest arrears (from DRS intermediate spreadsheets) divided by annual average of # 5

- _International Financial Statistics_ (IFS) - - -

- (#8(t) #8(t 1))/#8(t 1)

22
CHAPTER 6

# CONCLUSION

The case study of the Indian cotton export policy has suggested that negative consequences of commodity export restriction are more serious than generally thought. This paper first identified three economic problems: lack of incentive for farmers, resource misallocation, and slow administration of the govemment sector. As shown in the Indian case, the incentive problem, where a price disincentive factor for farmers can reduce output, could cause an input availability problem for downstream industry in the long-run. The resource misallocation can be serious not only between different sectors, such as the agricultural sector and the manufacturing sector (as taught by basic economic theory), but also within the agricultural sector. Every commodity has different varieties, such as long-staple cotton and short-staple cotton. Since the govemment can hardly forecast changes in demand for and supply of each variety in a flexible way, a uniform export quota tends to cause a mismatch of varieties in the domestic market. This problem can be called intraindustry resource misallocation, in contrast to the more familiar inter-industry resource misallocation. The slow and inefficient government administration is also serious. There is a season of harvest for each commodity, and ideal timing to make export contracts with overseas buyers is very important in today's competitive world commodity market. Since the procedure to decide export quotas tends to be time consuming, traders often fail to capture exporting opportunities. Efficiency of public exporters is another concem. Since export quotas are accompanied by economic rents, governments tend to assign public traders to monopolize exports. However, exporting is easier said than done; some of the public exporters cannot find suitable overseas buyers. Hence some quotas go unfilled, and the economic rent is not fully captured.

This paper then revealed that political problems of export quota can be even more serious than these economic problems. One of the reasons that many governments tend to prefer export quotas to export taxation is that a quota level is easier to change than export tax rates, should market conditions change. However, the flexibility of the control actually facilitates rent-seeking activities. Since quota is more frequently revised than tax rates, interest groups are more involved in lobbying activities. Assigning export entitlements to public traders does not solve the rent-seeking problem. It was suggested from the Indian case that public rent-seeking is sometimes even more difficult to resist for the government. Manipulation of crop statistics is another serious problem. Since the levels of export quotas are usually based on an official meeting of concerned groups, each interest group tries to influence the level of national export quota by padding crop estimations. Consequently, the whole official crop statistics tend to be unreliable. In the end, 49
- IPCC (2007), _Climate Change 2007: Mitigation of Climate Change. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change_ , Metz, Bert, Ogunlade Davidson, Peter Bosch, Rutu Dave and Leo Meyer, editors, Cambridge University Press, Cambridge, UK and New York, NY.

- Hertel, Thomas W., editor (1997), “ _Global Trade Analysis: Modeling and Applications_ ,” Cambridge University Press, New York.

Hope, Chris (2006), “The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern,” _The Integrated Assessment Journal_ , 6(1), pp. 19–56.

Houser, Trevor, Rob Bradley, Britts Childs, Jacod Werksman, and Robert Heilmayr (2008), _“Leveling the Carbon Playing Field,”_ Peterson Institute for International Economics, Washington DC.

- Hufbauer, Gary Clyde, Steve Charnovitz and Jisun Kim (2009), “ _Global Warming and the World Trading System, Peterson Institute for International Economics_ ,” Washington, DC.

- Kehoe, T.J., T.N. Srinivasan, and J. Whalley, (2005), _Frontiers in Applied general Equilibrium Modeling,_ Cambridge University Press, Cambridge.

Linnemann, Hans, Pitou Van Dijck, and Harmen Verbruggen (1987), “Export-Oriented Industrialization in Developing Countries,” ISBN 9971-69-112-4, Manila.

- Lockwood, Ben and John Whalley, (2008), “Carbon Motivated Border Tax Adjustments: Old Wine in Green Bottles?” NBER Working Paper No. 14025.

Manne, Alan, Robert Mendelsohn and Richard Richels (1995), “MERGE: A model for evaluating regional and global effects of GHG reduction policies,” _Energy Policy_ , 23(1), pp. 17-34.

McKay, Michael D., Conover, William J., and Beckman, Richard J. (1979), “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code,” _Technometrics_ , 21(2), pp. 239-245.

Morris, Harvey, Fiona Harvey, and Geoff Dyer (2009), “Beijing to Spur Energy Efficiency,” Available at www.ft.com (accessed September 23, 2009).

- Nordhaus, William, (2007), “The Challenge of Global Warming: Economic Models and Environmental Policy in the DICE-2007 Model,” manuscript, May 2007.

- Panagariya, Arvind, (2009), “Climate Change and India: Implications and Policy Options,” mimeo, University of Columbia.

- Pauwelyn, Joost (2009), “Testimony Before the Subcommittee on Trade of the House Committee on Ways and Means,” March 24, 2009; available at http:// waysandmeans.house.gov/media/ pdf/111/pauw.pdf

- Peterson, Everett B. and Joachim Schleich (2007), “Economic and Environmental Effects of Border Tax Adjustments,” Working Paper Sustainability and Innovation No. S 1/2007. Fraunhofer Institute

22
1. True poverty indicators _τa_ , using the “census”.

2. Direct estimators _τ_ ˆ _a_<sup>_DIR_</sup> using the “survey”, defined as the sample versions of _τc_ .

3. Census EB estimators _τ_ ˆ _a_<sup>_CEBac_</sup> presented in Marhuenda et al. (2017) based on a twofold nested error regression model, and obtained using a Monte Carlo approximation with _M_ = 50 replicates. Note that, for this estimator, the fitted model agrees with the true data generating process (3).

4. Census EB estimators _τ_ ˆ _a_<sup>_CEBa_</sup> presented in CMN (2020) based on a nested error model with only **area** random effects obtained using a Monte Carlo approximation with _M_ = 50 replicates.

5. Census EB estimators _τ_ ˆ _a_<sup>_CEBc_</sup> presented in CMN (2020) based on a nested error model with only **cluster** random effects and including the aggregate cluster and area means of the considered auxiliary variables, where _M_ = 50.

6. Traditional ELL estimators _τ_ ˆ _a_<sup>_ELLc_</sup> , based on a nested error model with only **cluster** random effects and including the aggregate cluster and area means of the considered auxiliary variables, where _M_ = 50.

7. Unit-context Census EB estimators _τ_ ˆ _a_<sup>_UC−CEBa_</sup> based on a nested error model with random effects at the **area level** . This estimator follows the approach from Masaaki et al. (2020) that is a modified version of that of Nguyen (2012), which uses only area means for some of the right hand side variables.<sup>13</sup> Nguyen (2012) proposes this solution for the case when only a dated census and a recent survey are available.

8. Unit-context **two-fold nested error** Census EB estimators _τ_ ˆ _a_<sup>_UC−CEBac_</sup> based on a twofold nested error model with random location effects at the **area and cluster level** . This estimator follows the approach from Masaaki et al. (2020) and Nguyen (2012), where only area means for some of the right hand side variables are used.<sup>14</sup> Model bias and MSE are approximated empirically as in MR (2010), as the averages across the _L_ = ˆ 10 _,_ 000 simulations of the prediction errors in each simulation ( _l_ ), _τa_<sup>_j_(</sup><sup>_l_)</sup> _− τa_<sup>(</sup><sup>_l_)</sup> and of the squared prediction errors respectively, where _j_ stands for one of the methods: _DIR, CEBac, CEBa, CEBc, ELLc, UC− CEBa, UC − CEBac_ .<sup>15</sup> Model bias and root MSE for a given area’s estimate are computed at the area level as follows:

> 13The covariates used in this model are: _x_  ̄1 _ac_ , _x_  ̄3 _a_ , _x_  ̄4 _ac_ , _x_  ̄5 _a_ , and _x_  ̄7 _ac_ .

> 14The covariates used in this model are: _x_  ̄1 _ac_ , _x_  ̄3 _a_ , _x_  ̄4 _ac_ , _x_  ̄5 _a_ , and _x_  ̄7 _ac_ .

> 15 _E_<sup>�</sup> _τ_ ˆ _c_<sup>_j−τ_</sup> _c_ � for the bias and _E_<sup>�</sup> _τ_ ˆ _c_<sup>_j−τ_</sup> _c_ �2 for the MSE, where _E_ ( _._ )denotes expectation under model 2.

9