rafmacalaba/datause-encoder-data huggingface dataset:
_5_

# Box 1: Model functions and variable definitions

<!-- Start of picture text --> Aggregate Production<br>(1) Q = QCES Y, M m , t}<br>(2) y' = CD 1, L',Iti1<br>|(3) J JcEs. {Kd,. Kr, t-<br>Derived factor demands (cost minimization)<br>(4) Mkt,- MInt MY. PY, PtM&E(1+tMnX t}<br>(5) Kd. -= 14, {Y, PmMcaE(l +tidc, PQ, w, t}<br>(6) Kf -- K fY, PmQ4,E(l+t), P-v w t-<br>Cost: finctions (mark-up prcing)<br>(7) PQ ~PQ {PE(1.+t 1 ilin)- FY<br>(8); Py= Py-WJ{P,t};<br>O0ther-imjoris<br>-<br>(9> -M.' M, MF -{Y,- PMOE(I+t PQ<br>Exot upy and demnd. :-0.--.-. -<br>: (102) Xs {Q'P. Px-E<br>t (Ii) X- X I . P- E -<br>efinitions<br>.(13) P1 PJ{F0,MCaDE(l+MCP)t<br>Functions and depreciation parameter . price id of .MTat<br>CES Const Elasticity of Substitution. -PMW1 price inodexfM. ($)<br>-CD Cobb-Douglas price:mdexof Y (Sue-;<br>e-- depreciation rate (12.5%)- PQ price index of Qad oKbf Suc)-<br>-:P,- -:xport price: index ($)<br>Variables -EC Pw p rice index<br>* E nom. exchange rate, Suuc. per $ -Q -domesticoutput (coust. Sue)<br>EB exchange rate, base year value' t time index (tech progress)<br>j aggr. capital stock (const. Suc.) TB trade balance: ($)- M.<br>-Kd dom. capial stock (const. Suc.) , implicit import x on-<br>Kf imp. capital stock (const Suc.) tca implicit import tax on<br>L labor tMoth- implicit. import tax on mbi-<br>Mcp imp. capital goods (const. Suc.) - w wage rate index (Suc.)<br>mint imp. intermediates (const. Suc.) Xs- export supply (const Suc).<br>Mo other imports (const. Suc.) XD -export demand (const Suc.,<br>MV total import value ($) XV export value ($)<br>PJ price of efficiency capital stock Y dom. GDP (constL Suc..)<br>PMCao price of Mc ,p and of Kf ($) Yw OECD: GDP (consL SuCe.)-<br><!-- End of picture text -->
Ehrmann, M., and M. Fratzscher. 2009. "Global Financial Transmission of Monetary Policy Shocks." _Oxford Bulletin of Economics and Statistics_ 71 (6): 739-759.

Ehrmann, M., M. Fratzscher, and R. Rigobon. 2011. "Stocks, Bonds, Money Markets and Exchange Rates: Measuring International Financial Transmission." Journal of Applied Econometrics 26 (6): 948-974.

Eickmeier, S. 2007. "Business Cycle Transmission from the US to Germany—A Structural Factor Approach." _European Economic Review_ 51 (3): 521-551.

Enders, Z., G. J. Müller, and A. Scholl. 2011. "How do Fiscal and Technology Shocks affect Real Exchange Rates? New Evidence for the United States." _Journal of International Economics_ 83: 53–69.

Erceg, C., L. Guerrieri, and C. Gust. 2006. "SIGMA: A New Open Economy Model for Policy Analysis." International Finance Discussion Papers 835, Board of Governors of the Federal Reserve System, Washington, DC.

Fischer, S. 2015. "The Transmission of Exchange Rate Changes to Output and Inflation." Speech, Monetary Policy Implementation and Transmission in the Post-Crisis Period conference, Board of Governors of the Federal Reserve System, November 12, Washington, DC.

Forni, M., and L. Gambetti. 2016. "Government Spending Shocks in Open Economy VARs." _Journal of International Economics_ 99: 68–84.

Francis, N., M. T. Owyang, and D. Soques. 2015. "Does the United States Lead Foreign Business Cycles?" _Federal Reserve Bank of St. Louis Review_ 97 (2): 133-158.

Frankel, J., and G. Saravelos. 2012. "Can Leading Indicators Assess Country Vulnerability? Evidence from the 2008-09 Global Financial Crisis." _Journal of International Economics_ 87 (2): 216-231.

Frazer, G., and J. Van Biesebroeck. 2010. "Trade Growth under the African Growth and Opportunity Act." _The Review of Economics and Statistics_ 92 (1): 128-144.

Gale, W. and A. Samwick. 2016. "Effects of Income Tax Changes on Economic Growth." Economic Studies at Brookings, Brookings Institute, Washington: DC.

Georgiadis, G. 2015. "Determinants of Global Spillovers from US Monetary Policy." _Journal of International Money and Finance_ 67: 41-61.

Glick, R., and S. Leduc. 2013. "The Effects of Unconventional and Conventional U.S. Monetary Policy on the Dollar." Working Paper 2013-11, Federal Reserve Bank of San Francisco.

Goldberg, L. S. and C. Tille. 2008. "Vehicle Currency Use in International Trade." _Journal of International Economics_ 76 (2): 177-192.

29
<!-- Start of picture text --> 6.0 fe)% of GDP<br>4.0<br>2.0<br>0.0<br>-2.0<br>-4.0<br>-6.0<br>-8.0<br>-10.0<br>2005 2007 2009 2011 2013 2015 2017<br>—— Overall budget balance<br>—— Reference budget balance<br>CEMAC target<br><!-- End of picture text --> <!-- Start of picture text --> 44<br>3 3<br>22<br>11<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>4 4<br>-5 -5<br>18 20 22 24 ~.2 28 = 30 18 20 22 24 26 28 = 30<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>1<br>0<br>-1<br>-2<br>-3<br>4<br>-5<br>18 20 22 24 26 28 = 30<br><!-- End of picture text -->
estimates than, those found in Paglialunga _et al._ (2022), which show a one percent temperature increase to be associated with 0.5 percentage point increase in the Gini index.

To investigate the potential long-run effects, we estimate Equation (2) and present the results in Columns (2) and (4) of Table 2. We document strong and statistically significant effects of hotter temperature on income inequality for both country-level analysis and subnational-level analysis and both measures of inequality. Again, we also find the effects at the subnational level to be stronger than those at the country level. Specifically, higher temperature by 1<sup>◦</sup> C is found to increase the Gini index and the Theil index by 0.35 (1 percent) and 0.59 (2.3 percent) percentage points (Panel B, Columns 2 and 4). Comparing the panel FE and long differences models, the magnitudes of effects appear larger for the latter. This may suggest inequality could accumulate over the longer term (i.e., intensification of negative effect). But we also note that the t-tests for the differences with the two models are only statistically significant for the Theil index with the subnational analysis, but not for the Gini index (and the country-level analysis).

To put our estimates into perspective, within our sample, Finland has one of the lowest Gini indexes (i.e., less inequality), with a value of 26.5, while the Central African Republic has one of the highest Gini indexes (i.e., more inequality), at 53.7. Given this, our estimated increase in the Gini index of 0.349 percentage points (Column (2) of Panel B) suggests that 1°C increase in temperature widens the gap in inequality between these two countries at the two ends of the inequality spectrum by 1.3 percent. This shift is substantial, especially when considering the cumulative and intensified impacts over time and across different countries. Another way to contextualize the impacts of temperature on inequality is by examining the progression of inequality. In our sample, the average Gini index has decreased from 37.7 in 2010 to 34.7 in 2019, showing a movement towards decreased inequality. However, our estimates suggest that a one-degree increase in temperature could counteract this positive trend, effectively offsetting 14
oil going from $30 to maybe $50. I think the other $30 is all speculative demand feeding on it—speculators and herding behavior.” Similar views were echoed by others as well, including Medlock and Jaffe (2009) and Wray (2008).

The empirical evidence on the subject has been mixed. Two IMF studies (2006, 2008) found no evidence that speculation had systematically influenced commodity prices. A similar conclusion was reached by a series of studies under‐ taken by the US Commodities Futures Trading Commission, the agency that re‐ gulates US futures exchanges.<sup>**9**</sup> Sanders, Irwin, and Merrin (2008) expressed skep‐ ticism about the assertion that speculation has led to bubbles in agricultural fu‐ tures prices.

Other authors share somewhat different views. Robles and others (2009) identified speculative activity in the futures market as a source of the 2007/08 agricultural commodity price increases. Plastina (2008) concluded that between January 2006 and February 2008, investment fund activity might have pushed cotton prices 14 percent higher than they would otherwise have been. In the non‐ ferrous metals market, Gilbert (2007) found no direct evidence of the impact of investor activity on the prices of metals, but found strong evidence that the fu‐ tures positions of index providers had affected the prices of soybeans (though not of maize) in the US futures exchanges. Perhaps, the strongest evidence is a subsequent study by Gilbert (2010: 420) who concluded that “By investing across the entire range of commodity futures, index‐based investors appear to have in‐ flated food commodity prices.” Why is the empirical evidence mixed? Often it is argued that despite the fact that investment fund activity remained steady or even increased between mid‐ and end‐2008, prices declined sharply during that period. Hence, the argu‐ ment goes, investment funds did not affect commodity prices. However, that is based on the logic that if investment fund activity continued to increase, then prices would increase forever (or, at least they would not decline), an unlikely outcome. Supply and demand fundamentals will prevail, eventually. Thus, from an empirical perspective, the key question should be whether investment fund activity contributed to the recent boom (or any boom, for that matter), not whether it affects commodity prices in general. Consequently, examining the role of investment funds in commodity markets requires first, the identification of the boom period and second, either analyze that period in isolation–perhaps within a univariate modeling framework–or use of earlier periods (or some other prior) as counterfactuals.

Identifying the “suspect” period will contain a large element of subjectivi‐ ty and hence will inevitably lead to differing opinions. However, there are at least three reasons why investment fund activity may have influenced commodi‐ ty prices. First, investment in commodities is a relatively new phenomenon, and

**_Page 8_**
_Table 7: Eastern Corridor savings potential in US$ million – mainly related to improved utilization of rail and improved port system_

|**Strategic**<br>**objective**|**Intervention**|**Description**|**Dedicated**<br>**freight**<br>**corridor**<br>**(US$**<br>**millions)**|**Logistics hu**<br>**dedicated li**<br>**(US$**<br>**millions)**|**b and**<br>**nk**<br>**%**|
|---|---|---|---|---|---|
|Rail corridor<br>solution|Modal shift within<br>corridor|Rail‐friendly freight on road shifts to<br>rail (origin and destination are<br>within the corridor)|**1,647**||**44%**|
|Kolkata as<br>gateway for<br>|Hinterland port shift of<br>rail‐friendlyfreight|Freight that can use rail will shift to<br>the closerport of Kolkata||**496**|**13%**|
|Uttar Pradesh,<br>Jharkhand and<br>Bihar|Hinterland port shift of<br>freight on road|Freight that cannot use rail (due to<br>location of load points), but will shift<br>exports away from Western ports<br>due to the improved link and<br>shorter distance||**868**|**23%**|
|Improve access<br>and reduce<br>congestion|Corridor‐city link|Freight shifts from road to rail<br>because it can more easily reach the<br>port on rail||**549**|**15%**|
||Kolkata city logistics<br>improvement|The hub and link will also have a<br>concomitant positive alleviation<br>effect on inner‐citycongestion||**146**|**4%**|
||Terminal‐port link|Reduce current ExIm costs due to<br>reduced congestion||**39**|**4%**|
|**Total**|||**1,647**|**2,097**||

# **‐ ‐** **<u>Applications of the FDM Examples of Cost Benefit analysis on the Eastern Corridor</u>**

The six interventions discussed above all relate to modal shifts, attracting freight to the port, and reducing congestion in and around Kolkata. The fact is that, despite these interventions most freight remains on road and exactly because of these interventions the road traffic on the Eastern Corridor will increase as more ExIm freight uses the corridor. Therefore, the systemic issues that plague road transport in India in general need also to be addressed. The versatility and detail of the India FDM allow the assessment of what the potential benefit could be on the Eastern Corridor resulting from road efficiency interventions. Although this study is only a prefeasibility analysis, it does not detail the cost/investment side of the cost‐benefit analysis. The results, however, are real and illustrate how the model can be used.

In total, US$ 1.7 billion per annum could be saved through six systemic interventions listed in Table 8 Many of these interventions require industry‐wide collaboration and/or costly infrastructure development – the cost and practicality of which also need to be taken into account.

38
# **Table 7. Key Financial Ratios by Size and Leverage Groups**

||Leverage Gr<br>|oups<br>||
|---|---|---|---|
||Low|Medium|High|
|Nominal Sa|les Growth|||
|Small|-0.22|-4.24|-10.67|
|Medium|2.59|3.76|-0.33|
|Large|9.26|4.65|5.78|
|Operating I|ncome over To|tal Assets||
|Small|0.03|0.03|0.01|
|Medium|0.09|0.07|0.04|
|Large|0.09|0.12|0.06|
|Interest Cov|erage Ratios|||
|Small|5.51|2.08|1.11|
|Medium|10.55|2.45|1.38|
|Large|16.78|5.03|1.83|
|Tobin's Q||||
|Small|0.91|0.97|0.97|
|Medium|0.90|0.99|1.00|
|Large|0.94|1.20|1.07|

Note: Reported are medians. Firms are split into nine groups—intersection of three size groups and three leverage groups.
1999-2001 wave. We see that per capita income and preference for equality are positively correlated with richer countries showing a greater preference for equality, and that the relationship is possibly stronger among countries of Central and Eastern Europe and the former Soviet Union than in the sample as a whole. The positive correlation may be for several reasons. The preference for equality may be something of a luxury, which becomes more affordable as countries grow richer. Richer countries may also have greater means to redistribute and this may affect preferences. In Figure 2, we present a similar plot of the mean response by country against the Gini coefficient (averaged over the 1990s through the early 2000s, due to limited data availability). Preferences for equality are somewhat negatively related to income inequality in the sample as a whole. As in Figure 1, this may have several drivers and the line of causality is not clear: On one hand, a more equal distribution of income may serve to promote a greater collective preference for equality. On the other hand, greater preferences for equality may lead to policy choices that promote a more egalitarian distribution of income. It is interesting that for the countries which are the subject of this paper, higher inequality in the distribution of income is associated with a lower preference for moving towards greater equality.

Figure 1. Equality Preference and Per Capita Income, 2000 <!-- Start of picture text --> 9.0<br>8.0<br>UKR<br>RUS<br>7.0<br>EST<br>MDA<br>6.0 ALB BIHBGR POL<br>CZE<br>BLR<br>5.0<br>LTU<br>4.0 HRV SVN<br>ROM<br>3.0<br>6.0 7.0 8.0 9.0 10.0 11.0 12.0<br>Log of Per Capita Income<br>Source: World Bank (2005) WDI database; WVS 1999-2001; and authors' calculations.<br>Note: Per capita income is log of 2000 income in 2000 PPP terms. Lower values of preference for<br>equality indicate greater preference for income equality.<br>Equality Preference<br><!-- End of picture text --> 7
<!-- Start of picture text --> 5 250<br>Direct losses<br>0<br>Indirect losses<br>200<br>−5 Value added losses in<br> construction sector<br>−10<br>150 Value added losses in<br> non−construction sectors<br>−15<br>−20 100<br>−25<br>10%<br>50<br>−30 50%<br>100%<br>−35<br>125% 0<br>−40 150%<br>−45 −50<br>0 2 4 6 8 10 20 40 60 80 100 120 140 160 180<br>Time (years) Direct losses (billion US$)<br>Change in VA (%)<br>Indirect losses (billion US$)<br><!-- End of picture text --> Fig. 6. Simulations for different amounts of direct losses (left hand side panel) and VA changes (total, in the construction sector, in the other sectors) as a function of the total amount of direct losses (right hand side panel).

and the total indirect losses (in blue) (as a function of direct losses). The nonlinearity identified in H08 is still present with this new model version. For direct losses amounting to $95 billion, the model estimates output losses about $80 billion, to be compared with only $11 billion output losses for $63 billion of direct losses (i.e. output losses multiplied by 7 for direct losses increase by 50%). For disaster causing more than $100 billion, output losses also exceed $100 billion.

To help interpret the figure, value added changes in the construction sector are separated (in light blue) from the other sectors (in purple). It can be seen that output losses in all sectors but construction increases non-linearly with direct losses. In the construction sector, there are gains (i.e. increase in value added) for small-enough disasters (up to about $110 billion of direct losses), and losses for larger disasters. This result suggests that, for small disasters, the construction sector benefits from higher demand and can increase production; for large disasters, the construction sector cannot benefit from the higher demand, because cross-sectoral ripple-effects constraint its production. This difference may explain — at least partly — why different econometric studies found positive and negative impacts of disasters on growth.

36
been floated as of May 2003. Finally, different export promotion measures were put in place with the aim to diversify exports, improve quality, encourage higher value added, and develop industries through backward linkages. However, there is considerable debate over whether these measures are consistent with other trade liberalisation measures undertaken in the economy.

# **2. Methodology**

To assess the effects of trade policies on trade, production, factor markets and poverty in Bangladesh we use a general equilibrium framework. We build a dynamic CGE model and calibrate it with a social accounting matrix for the year 2000. We follow the representative household approach and use the 2000 Bangladeshi Household Income and Expenditure Survey (HIES) to subsequently estimate poverty effects of different trade policy shocks. In the following sections we briefly describe the model and the data used.

## **2.1 Model Features**

Much current debate focuses on the role of growth in alleviating poverty. However, the majority of CGE models used in poverty and inequality analysis are static in nature. The inability of this kind of model to account for growth effects makes them inadequate for long run analysis of the poverty impacts of economic policies. They exclude accumulation effects and do not allow the study of the transition path of the economy where short run policy impacts are likely to be different from those of the long run. To overcome this limitation we use a sequential dynamic CGE model. This kind of dynamics is not the result of intertemporal optimization by economic agents. Instead, these agents have myopic behavior. It is basically a series of static CGE models that are linked between periods by updating procedures for exogenous and endogenous variables. Capital stock is updated endogenously with a capital accumulation equation, whereas population 5
<!-- Start of picture text --> Treatment Status and Average School Performance<br>g|<br>g I I<br>.&II |<br>:<br>&%- BASELINE | INTERVENTION || POST INTERVENTION<br>S28 I I —— =<br>— I<br>2; | ===<br>a s ! L_ ~<br>®Oscr} SSS r _<br>=o io<br>g* 1<br>te | I<br>gII<br>I<br><!-- End of picture text -->

<!-- Start of picture text --> § I I<br>Fs I I<br>3 I |<br>2005 2006 2007 2008 2009 2010 2011 2012<br>Year<br>——--—-—- Control —————_ Traetment Schools<br><!-- End of picture text -->
**Table 10: Informal sector employment by** **<u>gender and age - % .</u>**

|||Men|200|7|Women|||Men|200|9|Women||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||AGR|INF|FOR|AGR|INF|FOR|AGR|INF|FOR|AGR|INF|FOR|
|15-24|52.5|24.1|23.5|49.5|21.2|29.4|48.6|21.6|29.8|47.4|16.0|36.6|
|25-34|40.1|30.4|29.5|45.4|27.7|26.9|35.7|25.2|39.2|41.5|24.6|34.0|
|35-44|43.8|32.2|24.0|50.2|30.8|19.0|41.4|31.0|27.6|45.9|28.9|25.3|
|45-54|48.9|27.8|23.3|56.3|27.7|16.1|48.0|25.3|26.7|56.8|23.5|19.8|
|55-64|61.7|22.7|15.6|68.5|24.8|6.7|61.9|19.2|19.0|67.4|23.2|9.4|
|65+|73.9|19.0|7.1|72.9|25.0|2.1|79.2|14.3|6.6|73.5|22.9|3.6|

AGR = Agricultural employment; INF = Informal sector employment; FOR = Formal sector employment

# **3. The quality of employment**

Defining and measuring the quality of employment is not straightforward, as the former depends on the perspective that one has and the latter depends on data availability. In particular, there are trade-offs between the good of greater quality of employment (in terms of benefits and wages for example) for workers and the downside for firms of greater labor costs and labor regulations that governments may choose to use to improve the quality of employment. We look in this section into four main dimensions of job quality: type of employment, benefits, hours worked, and earnings.

# _Small share of “secure” employment_

The structure of employment in Vietnam is such that a small share of workers is in jobs that are generally considered secure. Wage employment is typically the type of employment that offers the highest job security. It is also the most common type of employment in developed countries (for example, Western European countries have rates of wage employment of over 80 percent). Although the share of wage employment in total employment has significantly increased in Vietnam over the recent period (20072009) from 30.5 percent to 33.4 percent, it remains relatively low by comparison with similar countries (Table 11).

**Table 11: Structure of employment in Vietnam and comparator countries in 2008 (%)**

||Indonesia|Philippines|Thailand|Vietnam*|
|---|---|---|---|---|
|Wage employment|32.6|52.4|43.2|33.4|
|Employers|2.9|4.2|2.6|4.8|
|Own-account workers|47.5|31.3|31.8|44.7|
|Unpaid family members|16.9|12.2|22.3|16.8|
|Other|-|-|-|0.3|

_Sources: Laborsta (ILO); Vietnam (LFS, 2009)_ 15