rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Assumptions Table

||2012|2015|2020|2025|2030|2035|2040|2045|2050|
|---|---|---|---|---|---|---|---|---|---|
|||||(|Percen|t)||||
|Baseline TFP Growth Rate|0.0|2.0|1.3|1.2|1.2|1.2|1.2|1.2|1.2|
|Global Interest Rate (Before Tax)|4.5|4.5|4.5|4.5|4.5|4.5|4.5|4.5|4.5|
|Interest Rate Dierential|2.5|2.5|2.3|2.2|1.7|1.2|0.6|0.3|0.1|
|Unemployment Rate|12.4|11.1|9.1|7.1<br>(Per|5.8<br>cent of|5.3<br>GDP)|5.0|5.0|5.0|
|Direct Tax Revenues|5.4|5.5|5.9|6.1|6.3|6.4|6.4|6.5|6.5|
|Indirect Tax Revenues|17.2|16.4|16.2|16.2|16.2|16.2|16.2|16.2|16.2|
|Non-Tax Revenues|4.8|3.8|3.6|3.6|3.6|3.6|3.6|3.6|3.6|
|Grants|3.1|3.8|3.4|3.3|3.2|3.1|3.1|3.0|3.0|
|Contribution to EU Budget|1.0|1.1|1.1|1.1|1.1|1.1|1.1|1.1|1.1|
|Other Expenditures|18.6|19.5|19.4|19.2|19.1|19.1|19.0|19.0|19.0|

# 3.1 Baseline Projections

In this section, we present and discuss the baseline simulation results using the assumptions summarized above. Figure 6 displays the main characteristics of medium and long-term GDP growth. As TFP growth gradually slows down toward its long-term level, so does GDP growth. On average, the latter is expected to reach to about 2.6 percent in 2016, and then gradually decrease to about 1 percent in early 2040's, and 0.7 percent towards the end of the projection horizon. A Monte Carlo experiment shows that, in the long term, the growth rates are expected to remain within the band of 2.2 percent and 0.2 percent with a 90 percent degree of condence.<sup>3</sup> As a result, the GDP level is projected to grow from about US$ 77 billion in 2012, to US$ 126 billion in 2050, in real terms.

Panel (c) in Figure 6 decomposes GDP per capita growth into its main determinants. There are two interesting observations here. First, as the economy recovers from the current downturn, contributions from TFP growth also become positive and increase until reaching the long term level. Second, while the labor force diminishes during the entire projection period because of demographic transition, the falling employment ratio does not always reduce the growth rate in our projections. This is mainly driven by our unemployment rate assumptions. In the medium term, there are two opposing eects determining the net impact of the employment ratio on the GDP per capita growth rate. The reductions in labor force reduce the number of employed, whereas reductions in the unemployment rate increase it. In the medium term the latter eect dominates the former one. Therefore, employment ratio

> 3These simulations characterize TFP growth shocks that are identically and independently distributed with zero mean, and 0.75 percent standard deviations

16
Source: Authors’ calculations from TRAINS (2013), BACI (2013), Kee et al. (2008, 2009) and WDI (2013) data. APEC list has 54 products and WTO list has 411 products. Income groups are simple average values. Applied MFN tariffs and imports are mean values for 2010-11. Estimates for “other goods” using the WTO list are not reported as they follow a similar pattern than with the APEC list of 54 products. Trade data is missing for Chinese Taipei in the BACI database hence the Davos group list is reduced to 13 countries. Furthermore, as data on NTBs are scarce for many countries, individual Davos countries figures displayed in columns 7 to 10 must be considered with extreme caution as the lines with missing NTB estimates are eliminated from the estimates in col. 9. For example, China only has NTB data for 5 products on the APEC list so the average estimate in column is over 5 products, which explains why that total protection estimate in column 7 is lower than the tariff estimate in column 1.

Notes: The groups are (abbreviation and 2011 GNI per capita, yp, cut-offs in $ in parenthesis): high-income (HIC, yp>12,476$), upper-middle Income (UMIC yp>4,036$ and <12,475$), lower-middle income (LMIC, yp>1,026$ and <4,035$,) and low-income countries (LIC, yp<1,025$).

To reduce the weight of extreme elasticity estimates, for each income group estimates outside the 1<sup>st</sup> and 9<sup>th</sup> deciles were fixed at the respective decile cutoffs.

The TRI is the uniform tariff that, if applied to imports instead of the current structure of protection, would leave welfare at its current level. Melo and Vijil (2014) also report estimates of the uniform tariff (the OTRI) that, if applied on home imports instead of the current structure of protection, would leave aggregate imports at their current level. The formula captures the fact that the welfare costs of tariffs rises more than proportionately with the tariff and that it is positively related to the variance in tariffs. The formula for the TRI for country c is: _mfnn_ , _c_ = applied MFN tariffs and _ntbn_ , _c_ = ad-valorem equivalent of NTBs from Kee et al. (2009), for product _n_ at the HS 6-digit level in country _c_ ; _m n_ , _c_

- = import value (thousand US$) and

- ε _n_ , _c_ = price import demand elasticities for product _n_ in country _c_ taken from Kee et al. (2008).

12
sets for Africa with comparable inequality estimates for a substantial number of observations; see Emwanu et al. (2004), for details. A summary of the welfare estimates used in this paper is presented in Table 1. The table confirms that on average poverty fell over the 1990s but that the decrease in poverty was not distributed uniformly. The decline in poverty was lowest in the North as was the growth rate. Initial expenditure inequality was highest in the West region, whereas initial inequality in the three other regions is almost the same.

|**Table 1**<br>Region|Expenditure|Expenditure|Poverty ra|tio|Poverty change|
|---|---|---|---|---|---|
||Growth 92-99|Inequality 92|92|99|92-99|
|Central|0.057|0.302|0.543|0.245|-0.297|
|East|0.058|0.299|0.661|0.362|-0.299|
|North|0.019|0.300|0.768|0.678|-0.091|
|West|0.040|0.327|0.557|0.326|-0.230|
|National<br>**Note:**colu|0.043<br>mn entries are (un|0.308<br>weighted) region|0.633<br>means of sub|0.403<br>-county estima|-0.229<br>tes.|

# **3. An empirical inequality – growth model**

We estimate, at the sub-county level, yearly per capita expenditure growth over the period 1992 – 1999 as a function of 1992 per capita expenditure, expenditure inequality, human capital inequality, male and female human capital and household demographics. The model can be represented as 8
has a positive effect on inequality.<sup>8</sup> The effect is significantly different from zero at the 1 percent level. The Kleibergen Paap F-statistics are well above the critical values tabulated in Stock and Yogo (2005) for instruments to be declared weak.<sup>9</sup> According to those tabulations one can reject at the 5 percent significance level the hypothesis that the IV size distortion is larger than 10 percent.

Table 2 reports estimates of a model that is estimated in first differences.<sup>10</sup> The estimated coefficient on the change in the Gini is positive. The null that this estimated coefficient is equal to zero can be rejected at the 1 percent significance level. This is the case for the market Gini and the net Gini. The estimated coefficient on the change in the market Gini is around 1.6; for the net Gini it is around 1.5. Instrumental variables estimates of a model specified in first differences thus yield a contemporaneous effect of inequality on GDP per capita that is similar in size as a model specified in levels.

Table 3 shows estimates of a model that does not include the lagged dependent variable on the right-hand side of the estimating equation. Instrumental variables estimation of the static panel model yields coefficients on the Gini that are positive and significantly different from zero at the 1 percent significance level. The estimated coefficients on the Gini are around 4. The estimated coefficients on the Gini are of the same sign as in Table 1; and larger in size. The larger size is expected because of positive serial correlation in GDP per capita.

Table S2 in the online appendix shows estimates of a model that includes lags of inequality on the right-hand side of the equation. In the instrumental variables regression of column (1) inequality in periods t, t-1 and t-2 is instrumented with residual inequality in periods t, t-1, and t-2. The IV coefficients on inequality in periods t and t-2 are positive and significantly different from zero at the 5 percent level; the IV coefficient on period t-1 inequality is positive but not significantly

> 8 Figure S1 in the online appendix plots the bivariate relationship between inequality and residual inequality for the different Ginis used in the estimates shown in Table 1.

> 9 As noted in Bazzi and Clements (2013) the Stock and Yogo tabulations were developed in a pure cross-sectional context and some caution is warranted when applying them to the panel context.

> 10 First-differencing eliminates information contained in the level of the series; first differencing also implies that the country fixed effects drop out.

12
**Figure 12. Gini indices (left) and international poverty rates (right) in Moldova and peers by income levels, percent**

<!-- Start of picture text --> 60 50<br>45<br>50 40<br>35<br>40<br>30<br>30 25<br>20<br>20 15<br>10<br>10<br>5<br>0 0<br>Belarus Moldova Romania Russia Ukraine Belarus Moldova Romania Russia Ukraine<br>2015 2017 2016 2014 2016 2015 2017 2016 2014 2016<br>market market + pensions disposable consumable final market market + pensions disposable consumable<br>Note: The international poverty line: Belarus – 10 USD/day in 2005 PPP, Moldova – 5.5 USD/day in 2011 PPP, Romania – 5.5 USD/day<br>in 2011 PPP, Russia – 4 USD/day in 2005 PPP, Ukraine – 5.5 USD/day in 2011 PPP.<br>Source: authors’ calculations using HBS-2017 data for Moldova and respective CEQ analysis for other countries: Belarus – Bornukova et<br>al., 2017; Romania - Inchauste et al., 2018; Russia – Popova et al., 2018, Ukraine - Bornukova et al., 2019.<br><!-- End of picture text --> <mark>As in other countries, among the various fiscal interventions, pensions in Moldova are the main contributor to inequality reduction (F</mark> igure 1 <mark>3). Moreover, this effect is the highest among the five countries. While progressivity of pensions is in line with other countries for which data are available, the highest contribution is explained by highest relative size of the pensions in Moldova comparing to other countries. Social contributions also have the highest equalizing effect in Moldova and direct transfers are the second highest after Ukraine (</mark> Figure 1 <mark>3). Indirect taxes have the highest negative marginal contributions to inequality reduction (they actually increase inequality in Moldova more than in other countries (F</mark> igure 1 <mark>3). In terms of progressivity, most interventions are similar in Moldova, Belarus, Romania, Russia and Ukraine, however the indirect taxes are most regressive in Moldova (F</mark> igure 14).

**Figure 13. Marginal contributions to inequality reduction in Moldova and peers, percentage points.**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>SSC Direct Taxes Pensions Direct Transfers Indirect Taxes Indirect In-kind Health In-kind<br>Subsidies Education<br>Belarus Moldova Romania Russia Ukraine<br><!-- End of picture text --> Note: Some numbers on this chart are estimated using backward calculations from the original source and might be not fully precise. Source: authors’ calculations using HLCS-2016 data for Ukraine and respective CEQ analysis for other countries: Belarus – Bornukova et al., 2017; Poland - <mark>Goraus and Inchauste, 2016; Russia – Popova et al., 2018.</mark> 27
The interest rate to the PFI's was based on a formula (also part of the original design), which took the interest rate on 180-day deposits minus 2%. The rate was adjusted eve,y six months. The problem we faced was that the line of credit was not being used because the interest rate thrown up by the formula was too high to induce PFI's to come in and borrow in order to onlend. This was only partly becaus - twice yearly adjustments were too infrequent in a period of rapidly declining interest rates (as was the case at that time). So, what formula ought to be used?

The professional literature on credit markets, for example the special issue (September 1990) of the W'orld Bnk Economic Review on this topic, helped in thinking th:ough the issues invol ved. It should be stated, however, that I did not question the basic objectives of the project, which were to ensure that credit got into the hands of those engaged in the agricultural sector, although the credit markets literature would help in addressing this question as well.

One possibility that the recent IJterature alerts us to is that of rationing in credit markcis. Even if there is excess demand for credit at the going interest rate, price adjustment may not take plAace because at a higher interest rate the composition of loan applicants may change adversely. Such adverse selection can explain non-market clearing equilibria. In such a setting, it seenis clear that making an additional line of credit available to financial intermediaries, at the same cost as the current marginal cost of funds to them, will alleviate the credit rationing and achieve the objective of getting credit into the hands of those who did not previously get any. The right formula for the interest rate to the PFI's seems to be one which approximates the PFI's marginal cost of funds.

However, suppose that the market is characterized not by rationing because the interest rate is being used as an indirect screening mechanism (to use the terminology of Hoff and Stiglitz, 1990), 2
depositors and lenders rise with duration;<sup>21</sup> c) informal commercial paper markets;<sup>22</sup> d) the curb market proper;23 and e) semi-regulated financial institutions such as mutual savings and loan funds, popular funds, and private finance companies. While these "curb markets" developed in the 1960s and gained strength ir, the 1980s, they were severely repressed from 1972 to 1978, when the govermnent tried to channel all funds through the more highly-controlled banking system.

More difficult to distinguish from collective sources are those cases in which SME loans come from commercial banks but are subject to compulsory credit allocations, or cases involving commercial bill discounting and other means of financing SMEs that are supported by central bank guarantees and incentives. In both cases, tlese methods are treated as private rather than collective financial support in the present analysis.

Collective: Three sources of financial support are considered 'collective" in this study. They are: 1) specialized financial institutions and specialized funds witiin non-specialized financial institutions, catering to SMEs; 2) government-supported venture capital companies that finance technology-based SMEs; and 3) credit guarantee facilities.

# B. Main Agencies Providing Fmancial Support

21 According to Cole and Park (1983), a 1971 national survey showed that 71 percent of Korean households participated in such schemes, though the rate may have declined somewhat since then.

nt These markets, in existence since 1972, are operated by chaebol-owned finance companies and regulated by the Bank of Korea, although less so than commercial bank credit. They have their own credit rating system which, by relating interest rates to credit ratings, encourages timely repayment.

23 The curb market is a large-scale informal credit market with its own collection agency, brokers, and real estate agents. The latter serve both as a source of fuuds and as providers of expertise in assessing the value of real estate offered as collateral.

32
It is not clear why the quantile of any fixed percentile identifies a plausible comparison income. Why would incomes above or below this quantile not get a positive weight? The US is an interesting case. The new “Supplementary Poverty Measure” (SPM) produced by the US Census Bureau acknowledges past concerns that the US official poverty line has not been updated in real terms (Short, 2012). The SPM determines the poverty line by the quantile of the 33<sup>rd</sup> percentile of the distribution of a subset of consumption spending deemed to be “essential” (comprising food, clothing, shelter and utilities).<sup>27</sup> Thus the SPM sets _k_ =1.2 and<sup>_p_</sup> _z_  .033 in equation (1). However, it remains unclear why _y_ (<sup>_p_</sup> _z_ ) is a plausible comparison income for any fixed _p z_ (whether 0.5 or 0.33). In the case of the SPM it is also unclear why relative comparisons would only apply to “essential” goods. One can surely expect feelings of relative deprivation to respond as much to a lack of “non-essential” goods.

A further concern arises when the poverty line is set at a constant proportion of the mean or median, namely that the resulting poverty measure depends solely on the distribution of relative incomes in the population. If all income levels grow (or contract) at the same rate then the poverty measure will remain unchanged when the poverty line is set at a constant proportion of the mean or median.<sup>28</sup> Seemingly perverse poverty comparisons have been found using strongly relative measures.<sup>29</sup> The relevance of strongly relative measures to developing countries is especially questionable. Ravallion (2012) points out that if one uses a strongly relative line set at half the mean then its average value for the poorest 15 countries is a very low $0.64 a day, which is somewhere around a survival level (Lindgren, 2015; Ravallion, 2016b). The value for the country with the lowest mean would be only $0.38 per day, which is almost certainly not enough for survival beyond a short time. Similarly, the Garroway and de Laiglesia (2012) relative line, set at 50% of the current median in each country, gives lines that are well below the lines typical of even low-income countries and even below likely biological minima (Ravallion, 2016b).

In short, strongly relative measures almost certainly understate the social inclusion needs of globally poor people and have a seemingly perverse implication for how these measures

> 27  This follows the recommendation of a National Academy of Sciences Commission (Citro and Michael, 1995). 28  Note that this property does not depend on whether the line is anchored to the mean or the median, given that the ratio of the median to the mean is constant in an inequality-neutral growth process. However, the choice between the mean and median can matter in other respects and objections to the use of the median have been identified by de Mesnard (2007) and Kampke (2010). We do not discuss these issues here.

> 29  See, for example, the UNDP (2005, Box 3) and Easton (2002).

14
<!-- Start of picture text --> 4.90<br>4.70<br>4.50<br>4.30<br>4.10<br>3.90<br>3.70<br>3.50<br>2030 2035 2040<br>BaU Net Zero Crowding out No lab. mar. frictions Renewable Subsidies<br>percentage y-oy growth<br><!-- End of picture text -->

**Figure 7: GDP growth rates**

The transition would cause a shift in the structure of Türkiye’s economy towards the services sectors. In our scenarios, the contribution of manufacturing, especially emission intensive manufacturing and non-power energy sectors, to the GDP would decline while that of services would grow in the short and medium term and stay slightly above the baseline levels by 2040. One reason is that the CGE production system has no option to fully decarbonize the industrial sector. This finding hints at a clear policy suggestion to focus on non-emission intensive high-tech sectors to boost industrialization in the country while focusing on emission saving technological change in other sectors. This result suggests that an important next step in the analysis would be to produce an additional sectoral scenario for the industrial sector, including a more granular representation of possible shifts toward low-carbon technologies based on green hydrogen, ammonia, or CCS. The impact of decarbonization on industrial production also depends on trade relationships, since the country competes with others in the production of carbon-intensive products (such as steal or fertilizers).

<!-- Start of picture text --> 0.80 0.72<br>0.57<br>0.60<br>0.40<br>0.20 0.07 0.07<br>0.03<br>0.00<br>-0.01 0.00 -0.03<br>-0.20 -0.08<br>-0.14<br>-0.40<br>-0.35<br>-0.41<br>-0.60 -0.50<br>-0.54<br>-0.59<br>-0.80<br>-0.74<br>-1.00<br>2030 2040<br>Agriculture Manufacturing Energy<br>Electricity Waste Other Manufacturing<br>Transport Services<br><!-- End of picture text -->

**Figure 8: Change in contribution of sectors to GDP (pp difference from baseline scenario)**

17
<u>Imports: This variable could have multiple and conflicting effects. It could simply</u> reflect the degree of integration of a state with external economies and hence proxy as well for exports. On the other hand, if it is seen as representing competition for import substituting firms, the short run labor market impact could be negative and hence it could conceivably lead to more migration. The data were provided by Bancomex.

None of the variables are ideal, but they are complementary in the sense of being strong whether the other is weak. Together, we may get a reliable picture of the impact of trade/investment variables.

# **IV. Results**

In preliminary regressions, we estimated a multinomial logit model for aggregate data. Though the results were plausible, the Fry and Harris test (1998) suggests that the data violate the Independence of Irrelevant Alternatives (IIA). Therefore, we estimate a multinomial probit and follow Gourieroux’s (2000) weighted least square procedure.

Columns 1a and lb (Table 2), present the standard regression including the labor market variables. Across the next columns, the various FDI and trade variables are added. Overall, the specifications are very satisfactory with the coefficients on the core variables generally statistically significant and of predicted sign. The costs of movement measures –distance and network- are significant in all specifications and, in the former case are well within the usual range found in the literature (Greenwood 1997, see also Gabriel et al 1996, Fields 1982, and Schultz, 1982). The population of the destination enters significantly and of positive sign in all specifications consistent with either a “connection point” interpretation, a residuals amenity effect, or perhaps congestion externalities. The coefficient on the origin is unstable across specifications.

9
<!-- Start of picture text --> Public sector as a share of overall employment (%)<br>East Asia end PciI<br>Latin America and Caribbeaneo<br>South Asia=_____<br>Sub Saharan AceTT<br>Western Europe and North AmericaSc teLU<br>aSs |<br>0 10 20 30 40 50<br>Total employment<br>GB Paid employment<br>(1 Formal employment<br><!-- End of picture text -->