rafmacalaba/datause-encoder-data huggingface dataset:
takes three days at most in the case of the lira.<sup>27</sup> Interestingly, our estimates suggest that shocks to volatility are less persistent when the central bank intervenes.

The conditional variance of the peso reacts differently to equal magnitude negative and positive innovations.<sup>28</sup> From the standpoint of the foreign investor, the response of the conditional variance would be greater to bad news (depreciations) than to good news (appreciations) of the same magnitude.

To examine the effect of central bank intervention on the sensitivity of the conditional variance of both currencies, we use the news impact curve (NIC) for the restricted EGARCH model (continuous line) introduced in section four.<sup>29</sup> As can be seen from Figure 7, the conditional variance of the peso reacts more to past negative shocks than to positive innovations of equal size. Moreover, the response is greater, the bigger the size of the shock. For the Turkish lira, such response is fully symmetric since the leverage effect turned out to be statistically insignificant.

The doted and discontinuous lines in Figure 7 also show the NIC for the extended models—i.e., considering intervention in frequencies and magnitudes respectively. They present the actual variance responses once the exogenous variables are taken into consideration. In general, the sensitivity of the conditional variance is greater than the one suggested by the restricted E-GARCH model with no exogenous influences (continuous line).<sup>30</sup>

> 27 This is the so-called half-life statistic indicating the number of days in which a shock to the variance reaches half its initial size. Here we calculate this as log(0.5)/log(β).

> 28 The leverage effect (γ) in Turkey was not significantly different from zero. Hence, in all the estimations for this country, we restrict such coefficient to zero in which case the responses of the conditional variance, as it is graphically shown, are fully symmetric.

> 29 To keep comparability, we standardized all NIC curves by setting A=1.

> 30 See also AIC and BIC in Tables 5 and 6.

25
<!-- Start of picture text --> : (Sees sy<br>2 } —<br>_ 0.40<br>=’<br>v<br>v<br>a|<br>0.00<br>Ausina Greece Netherlands ~~ Fenland Unded Aggregate Germany = Portugal Spain Sweden Denmark Poland France<br>Kingdom<br>"2004 M2005 2006 ©2007<br><!-- End of picture text -->
_Table 4: Regression results for restricted education equation (control group = age 13-24 years in 1974)_

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**_Region fixed effects_**|||||
|Sumatera|1.5005|1.6727|1.7209|1.7287|
||(0.2477)**|(0.2577)**|(0.2730)**|(0.2724)**|
|Java|0.3285|0.3738|0.7512|0.7531|
||(0.2316)|(0.2328)|(0.2420)**|(0.2426)**|
|Jakarta|2.4647|2.4187|2.2673|0.0000|
||(0.2914)**|(0.2917)**|(0.2992)**|(0.0000)|
|Bali|-0.1511|-0.5104|0.1959|0.1968|
||(0.3440)|(0.3468)|(0.3592)|(0.3594)|
|Nusa|-1.2114|-1.0971|-0.2188|-0.2163|
||(0.3131)**|(0.3183)**|(0.3295)|(0.3295)|
|Kalimantan|-0.1668|0.0007|0.3014|0.3171|
||(0.3525)|(0.3566)|(0.3656)|(0.3646)|
|**_Cohort of birth fixed effects_**<sup>**_(a)_**</sup>|||||
|Cohort 1: 1-6yrs in 1974|2.7996|4.6724|4.1468|4.4485|
||(0.2154)**|(1.5415)**|(1.4808)**|(1.5222)**|
|Cohort 2: 7-12 yrs in 1974|1.2512|4.6576|4.0299|4.3832|
||(0.2576)**|(1.7317)**|(1.6561)*|(1.7207)*|
|**_Programme intensity x Age in_**|**_1974 (interaction_**|**_terms)_**|||
|Age=1|-0.0648|-0.2221|-0.2941|-0.3033|
||(0.1351)|(0.1376)|(0.1291)*|(0.1512)*|
|Age=2|-0.1534|-0.3064|-0.4501|-0.4605|
||(0.1246)|(0.1239)*|(0.1503)**|(0.1608)**|
|Age=3|0.1208|-0.0162|-0.1061|-0.1229|
||(0.1324)|(0.1360)|(0.1294)|(0.1504)|
|Age=4|0.0097|-0.1606|-0.2812|-0.2951|
||(0.1123)|(0.1239)|(0.1174)*|(0.1460)*|
|Age=5|0.1549|0.0365|-0.1097|-0.1227|
||(0.1243)|(0.1262)|(0.1248)|(0.1471)|
|Age=6|-0.4876|-0.6406|-0.674|-0.6747|
||(0.1480)**|(0.1508)**|(0.1456)**|(0.1678)**|
|Age=7|0.2518|0.1939|0.1191|0.1085|
||(0.1421)|(0.1427)|(0.1486)|(0.1685)|
|Age=8|<br>0.4379|<br>0.3832|<br>0.3359|<br>0.3259|
||(0.1482)**|(0.1525)*|(0.1538)*|(0.1752)|
|Age=9|<br>-0.1957|<br>-0.3481|<br>-0.3511|<br>-0.3518|
||(0.1499)|(0.1428)*|(0.1417)*|(0.1595)*|
|Age=10|0.196|0.1008|-0.064|-0.0898|
||(0.1567)|(0.1598)|(0.1882)|(0.1996)|
|Age=11|-0.5769|-0.622|-0.6398|-0.6443|

37
Substituting the value of ���� from equation (11) in equation (10) and dividing both sides by �� yields the relationship between investment, national savings and the external sector in terms of share of GDP: The above equation captures the fact that investment can be boosted via three channels – increase in national savings, increase in foreign direct investment and increase in external debt.

The time paths of national savings, foreign direct investment and external debt are taken from the 7<sup>th</sup> Five Year Plan targets which are summarized below:

- Starting from 29.0 percent in 2015, savings to GDP rises to 31.9 percent by 2020

- Starting from 0.9 percent in 2015, FDI to GDP rises to 3.0 percent by 2020

- Starting from 25.1 percent in 2015, external debt to GDP rises to 37.4 percent by 2020

All the above variables are assumed to remain at the 2020 level from that year onward. Like in the previous section, four alternative growth scenarios are considered – Baseline, Participation and High Growth Scenarios I and II.

The following figures below show the GDP growth paths under different scenarios when investment responds to the external sector. The behavior of growth paths under various scenarios is very similar to the behavior seen earlier in section 3.1. An aspect of difference is the sudden jump in growth rate in 2017 which was absent in the previous case. This happens because the GDP growth during 2016 is a function of investment made in the previous year which is the same across the two exercises. Investment becomes responsive to the external sector beginning 2016 which has a one period delayed impact on growth which shows as the initial bump.

Under the Baseline Scenario, the GDP growth rate crosses the 6 percent mark in two intermediate Plan Years and averages 5.7 percent during the entire Plan Period. The growth rate starts declining past 2021 and remains just above 4 percent by 2030. As expected, the growth rate under Participation Scenario is higher compared to the Baseline Scenario and exceeds it by around 1 percentage point during each of the Plan Years. The growth rate declines by about 80 basis points in 2021 as growth in participation rate comes to an end. The growth rate continues to decline gradually and is just 10 basis points above the growth rate under Baseline Scenario in 2030. With an annual TFP growth rate of 1 percent, the GDP growth rate breaches the 8 percent mark in 2017 and averages just below 8 percent during 2016 – 2020. The GDP growth rate declines past 2020 as female labor force participation rate stabilizes. Yet, the growth rate remains above 6.5 percent for many periods. The implied growth rate is above the plan target when TFP grows by 1.5 percent per year. The average annual growth rate under High Growth II Scenario during the plan period outpaces the average annual plan growth rate 21
# **Figure 6: Impact of corporate leverage (debt to assets) on the comovement between local and US stock market returns**

Figure shows result from regressing the comovement between local and US stock returns on the corporate debt to asset ratios across countries.

<!-- Start of picture text --> 2.5<br>2<br>CZE<br>KOR<br>GRC<br>POL<br>1.5 ZAFFIN IND QATEGY<br>TUR AUT<br>RUS DEU IRL<br>HUN SWE FRA ITA<br>SAU BELHKGMEX AUS<br>NLD LUXSGP TWN BRA<br>ESP<br>NOR<br>NZL<br>CAN IDN<br>1 GBR DNK<br>COL ISR<br>JPN<br>PRT<br>CHE KWTCHN<br>ARG THA<br>LTU MYS<br>PER EST<br>JOR SVN<br>0.5 PHL<br>LKA<br>BHR MAR CHL ARE<br>SVK VEN<br>PAK<br>NGA<br>0 BMU<br>0 5 10 15 20 25 30 35 40 45 50<br>-0.5<br>Corporate debt to assets ratio (%)<br>Comovement between local and US stock market returns (Period pre-Lehman)<br><!-- End of picture text --> 39
( _N_ =18), i.e., 17 alternative destination<sup>40</sup> choices (if moved) and a choice to stay in the source province. Suppose that the utility level of choosing area _j_ for the _i_ th individual is ' _Vij_ = _zij_ γ + _eij_ where _zij_ is a vector of destination-specific attributes including expected income defined below (W�ıȷ), land availability, the likelihood of obtaining a job in the foreign sector, the prospect of obtaining a job in the state sector, and distance between origin and potential destination areas. The conditional logit model estimates the probability that an individual will choose a certain destination, conditional on choice characteristics.<sup>41</sup> Unlike the multinomial logit model, in the conditional logit model, _zij_ varies across potential destinations whereas the parameter _γ_ is constant across choices.

Since the outcome variables are not available for alternative locations except for the location where the migration actually took place, the differentials in economic opportunities are constructed based on migrants’ expectations. The expected incomes for migrants across different locations are likely to depend on both individual- and location-specific characteristics (see Table 2). Thus, predicted earning equations (Lucas, 1985)<sup>42</sup> are estimated using the data from the VHLSS 2004. The expected monthly wages for 18 alternative locations for each migrant are

> 40 Whereas the migrants who moved to HCMC, Hai Phong, and Hai Duong were interviewed only in urban areas, those who went to Dac Lac and Dac Nong were surveyed only in rural areas. This results in 17 alternative destinations.

> 42 Lucas (1985) explores empirically Todaro (1969) and Harris-Todaro (1970) hypotheses adopting micro data at the individual level in the context of Botswana. Developing prediction equations for wages and likelihood of employment, his results reveal that the higher the chance of finding employment in town and the higher the person’s expected earnings in town, the more likely it is that a person will migrate to town from his or her home village. Conversely, the greater the chance of finding employment in the home village and the higher the wage for a person at home, the lesser the propensity to migrate.
31 in the US market, we focus on the Survey of Current Business series. We thank Paul Zareth of the US Bureau of the Census for clarifying these distinctions.

7. Faced with domestic competitors who are more vertically integrated, the Big Three have begun to copy the Japanese practice of greater vertical integration among parts suppliers, suggesting some efficiency enhancing properties of the practice in the auto industry. See Lawrence (1990).

8. Our approach is followed by Krugman (1987) and Krishna, Hogan and Swagel (1989). Devarajan and Rodrik (1989) follow the approach of Dixit. Calibration procedures which differ substantively from ours are Smith and Venables (1988), who adjust elasticities to be consistent with Cournot conjectures, and the suggestion of Saloner (1989) that marginal cost data should be adjusted.

9. Dixit calibrated N=13.9 for 1983. If we choose 0d=1, we obtain N-4.2. Since the industry is more competitive the higher the value of N, our calibration yields a less competitive auto industry than represented by Dixit.

10. Of course, perfectly competitive conjectures are not possible in the presence of fixed costs and increasing returns to scale because firms will incur losses. Novshek (1980) notes that simply adding more firms drives Cournot output to zero in the limit. He shows, however, that if the minimum efficient scale becomes small in relation to demand as the number of firms goes to infinity, then Cournot equilibria with free entry exist and they approach perfect competition.

11. In addition to the estimate of table 1, The Quarterly Financial Reports indicates that profits in motor vehicles and equipment were $10.8 billion in 1984 and the US International Trade Commission indicates that the industry's profits were $10.4 billion in that year. We assume any excess of profits over $9.4 billion were normal.

12. Because of interindustry linkages, this step involves solving simultaneously for the entire vector of prices that satisfies firms, budget constraints in all sectors.

13. As shown in de Nelo and Tarr (199Oc), endogenous aggregate labor supply has a very small impact on the welfare costs of protection in our formulation.

14. Unlike previous elasticity estimates, Levinsohn clusters autos into groups that take into account characteristics, prior to econometric estimation of the price elasticity of demand. Hence, the price elasticities of demand in this study are likely to be
14 domestic economy achieves additional output from the use of foreign capital, but it loses the rentals.

The remaining features of the model are standard to computable general equilibrium (CGE) models. The model includes two factors, capital and labor, mobile between sectors. Labor is always in fixed supply.13 Domestic demand includes two components, final and intermediates. The government sector's role is limited to lump-sum redistributions to and from the representative consumer. In the simulations reported below, the auto sector is embedded in the static ten-sector model described in de Melo and Tarr (199Ob). In the calibration to 1984, tariffs are set at their levels in 1984 (an economy wide average of 3.5 percent) and the quotas in textiles and apparel resulting in a premium rate of 40.5 percent are also maintained at their base year level.

Table 2 describes the structure of the U.S. auto industry in 1984. Imports represented 26 percent of domestic output, the bulk of which went to final demand. As mentioned above, exports were negligible. The measure of scale economies captures the degree of multiproduct scale economies at the level of output achieved by General Motors, Ford and Chrysler in 1983. Price elasticities of demand are taken from Levinsobn (1988).14

# 4. Welfare Cost Estimates under CRTS

# 4.1 Benchmark Estimates

We begin with the benchmark estimate of the auto VER under CRTS in table 3. Welfare is evaluated by the Hicksian equivalent variation measure. We decompose the total costs of the VER into two components: rent costs (reported in column 2) and distortion costs (reported in column 3).15 We evaluate these components by estimating the total costs of the VER and
Table 7: System GMM

|equation no.|(1)|(2)|
|---|---|---|
|log of number of cumulative loans|.194<br>(.091)<sup>_∗∗_</sup>|.|
|log of number of cumulative conditions|.|.090|
|||(.043)<sup>_∗∗_</sup>|
|log GDP per capita (PPP)|.221|.219|
||(.063)<sup>_∗∗∗_</sup>|(.062)<sup>_∗∗∗_</sup>|
|aid over GDP|-1.089|-.947|
||(.528)<sup>_∗∗_</sup>|(.572)<sup>_∗_</sup>|
|Political Rights|-.104|-.106|
||(.027)<sup>_∗∗∗_</sup>|(.027)<sup>_∗∗∗_</sup>|
|_country fxed efects_|_yes_|_yes_|
|_year fxed efects_|_yes_|_yes_|
|Observations|1761|1748|
|Countries|139|138|
|Number of instruments|44|44|
|Wald statistic|159.88|153.02|
|_p-value_|0.0001|0.0001|
|Hansen J-test|23.10|25.01|
|_p-value_|0.627|0.518|
|Dif-in-Hansen test|13.99|13.89|
|_p-value_|0.45|0.451|

Note: Dependent variable: CPIA cluster A and B average. For equation (1), the log of the number of cumulative loans is the variable of interest. For equation (2), the log of the number of cumulative conditions is the variable of interest. Cluster-robust standard errors are reported. Coefficients estimated with forward orthogonal deviations and level equations for IV style instruments. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample when the number of conditions is used as variable of interest.

28
<!-- Start of picture text --> 100,000 a<br>80,000<br>ee2 60,000<br>eS°o<br>a<br>=|Z%3 40,000<br>20,000<br>° =<br>Jan. Feb. Mar. Apr. May. Jun Jul. Aug. Sep. Oct. Nov. Dec.<br>> Credit Program: All Firms @ Credit Program: Non-SMEs<br><!-- End of picture text -->

<!-- Start of picture text --> 80,000<br>60,000<br>g<br>a)<br>%ga40,000.9|<br>33 j<br>20,000<br>0 =<br>Jan. Feb. Mar. Apr. May. Jun. Jul. Avg. Sep. Oct. Nov. Dec.<br>> Employment Program: All Firms © Employment Program: Non-SMEs<br><!-- End of picture text -->
- 27 _

**new** areas of activities while improving factor mobility and the supply response to the change in the incentive **system,** hence stimulating private investment.

Data The definition and sources of the data used in the estimates are discussed below while the data is shown in Table 3.

Real Money and Quasi-Honey. It is defined **as** the aggregate of money and quasi-money of the consolidated banking system deflated by the GDP deflator. The source was the monetary survey in IFS, IMF.

Net Foreign Assets. It is defined **as** net international reserves of the consolidated banking system less long-term foreign liabilities and deflated by the GDP deflator. The source was the monetary survey in IFS, IF, Yearbook, 1987 and August 1988.

Capital Stock The data was obtained from a survey done by the Bank of Mexico for the period 1960-1985. Villalpando Hernandez. L.H. and Fernandez Moran, J., "La Rncuerta de Acervos, Depreciacion y Formacion de Capital del Banco de Mexico 1975-1985, Subdireccion de Investigaciones Economica, Banco de Hexico, October 1986. The capital stock for 1986 and 1987 was obtained by adding net investment from the National Accounts, INEGI, to the previous years' capital stock figures.

The U.S. Real Interest Rate. Is the ex-post real interest rate calculated as the ratio between one plus the Federal Funds rate and one plus the U.S. producer price inflation rate. The source was IFS, IM.
— Table 4: Robustness Checks Alternative Functional Forms

|||_Dep_|_endent vari_|_able:_||
|---|---|---|---|---|---|
|||curr|ent employ|ment||
||(1)|(2)|(3)|(4)|(5)|
|Alternative Cutofs<br>:||||||
|SPEI<-1|-0.0172**<br>(0.00773)|||||
|SPEI>1|0.00742<br>(0.0142)|||||
|SPEI<-1.5||-0.0251*<br>(0.0135)||||
|SPEI>1.5||0.0426<br>(0.0266)||||
|SPEI<-2|||-0.0693*<br>(0.0385)|-0.0708*<br>(0.0387)||
|-2<SPEI<-1||||-0.0145*<br>(0.00780)||
|1<SPEI<2||||0.00604<br>(0.0147)||
|SPEI>2|||0.0235<br>(0.0394)|0.0200<br>(0.0397)||
|Continuous measure of SPEI<br>:||||||
|SPEI|||||0.00801*<br>(0.00434)|
|Observations|572,907|572,907|572,907|572,907|572,907|
|R<sup>2</sup>|0.234|0.234|0.234|0.234|0.234|

**_Note:_** _∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01. This table reports a set of estimates obtained using our preferred specification in column 5 of Table 3 and varying the functional form of the the SPEI, our main regressor of interest. Our preferred specification controls for slope, individual age and years of education and for interaction between administration subdivision and year fixed effect. Standard errors clustered at the grid cell-month-year level are reported in parenthesis.

31
<!-- Start of picture text --> Other Food Electronic Equipment<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>CHN 20012011 CHN 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 10 20 30 40 50 0 20 40 60<br>Total LVAX share (%) Total LVAX share (%)<br>Direct LVAX Indirect LVAX Direct LVAX Indirect LVAX<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

**Figure 19: Direct and indirect labor content of exports in GVC-intensive sectors 2001 vs. 2011**

<!-- Start of picture text --> Wearing Apparel Motor Vehicles<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 1,000 2,000 3,000 4,000 5,000 0 2,000 4,000 6,000 8,000<br>US$ mil. US$ mil.<br>Direct LVAX Indirect LVAX Direct LVAX Indirect LVAX<br>Other Food Electronic Equipment<br>BRA 20012011 BRA 20012011<br>CHL 20012011 CHL 20012011<br>COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 1,000 2,000 3,000 0 1,000 2,000 3,000 4,000<br>US$ mil. US$ mil.<br>Direct LVAX Indirect LVAX Direct LVAX Indirect LVAX<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

The increasing importance of indirect wages that accompanied South Africa’s GVC integration, coupled with differences in the skills-intensity across sectors, appears to have had implications on skills demand. Figure 20 and Figure 21 replicate the above graphs but with the skilled-unskilled instead of the directindirect split. The share of skilled wages is in general higher in South Africa than in most other comparators 30
<u>2022). We use questions about trust in government and beliefs in fairness as proxies for this</u> parameter. Several factors might affect the perceived intensity of climate change effect ( ), including age, gender, level of education, and family composition. The past exposure to adverse climate change tt κκ shocks could also influence . Older individuals may perceive higher risks of future climate change impacts despite shorter future exposure, as they have lived longer and, therefore, have a tt κκ higher probability of having been exposed to adverse climate shocks in the past. More nuanced relations could work through differences in the capacity to adapt to climate change. On the one hand, younger people are more mobile, can tolerate temperature fluctuations, and can more easily escape from disaster-affected areas. On the other hand, as wealth and savings tend to peak around retirement age (Modigliani 1976), older people are able to insure against the impacts of climate change and deal with the financial shocks of natural disasters.<sup>8</sup> We use health self-assessment and information about past exposure to climatic shocks to proxy the effect of this parameter. In the theoretical model, strong discounting of the future (higher ) increases the asymmetry between old and young individuals and may lead to higher optimal investment in climate−change rr action for old individuals. Older individuals experience the intensification of the impacts of climate change sooner and so do not discount them as much as young individuals do. For a sufficiently high , young people are not too concerned about the higher intensity of climate change impacts in their old age. We approximate the future discussion rate with responses to a question that asks rr respondents to select between less money now and more money in a month. rr Willingness to support climate change actions can be constrained by either a lack of awareness of climate change impacts, which may recede with age, or ideological beliefs that may be correlated with age. Political conservatism, which has been associated with climate change skepticism, is more prevalent among older cohorts. But older adults may have greater concerns about future generations and express stronger pro-social generational orientation compared to younger adults <u>(Maxfield et al. 2014).</u>

> 8 In the proposed theoretical framework, individuals can freely borrow against their future income; in reality, uncollateralized borrowing against future income is limited, so savings matter for determining capacity to deal with climate change shocks.

9