rafmacalaba/datause-encoder-data huggingface dataset:
where δ and β are the corresponding share parameters in the CET export transformation and CES import aggregation functions.<sup>9</sup> The CET function describes the production transformation frontier between D and E for a fixed level of X̅ or real GDP (since there are no intermediate inputs). The assumption that X̅ is fixed is equivalent to assuming full employment of all primary factors. The composite good price P<sup>x</sup> corresponds to the GDP deflator. The composite price P<sup>q</sup> of Q corresponds to the consumer price index. Following the numerical implementation in Devarajan et al. (1997), GDP not sold to the rest of the world (i.e., E, exports of goods and services) defines the domestic good D. Given price indices, P<sup>x</sup> and P<sup>e</sup> , the implicit price for the domestic good, P<sup>d</sup> , can be derived from the GDP identities: P<sup>x</sup> X̅ = P<sup>q</sup> Q+ P<sup>e</sup> E−P<sup>m</sup> M= P<sup>d</sup> D+ P<sup>e</sup> E where Q is aggregate demand. The model can therefore be implemented using national data for macro aggregates (see Devarajan, Go, Lewis, Robinson, and Sinko 1997).

# _Estimating Equation_

The log-linear transformation of the supply and demand equations (3) and (4) provides a convenient way to estimate the elasticities: Note that equations 5 and 6 extend beyond the 1-2-3 model. As mentioned in the literature review, the import equation derived from the CES function is also known as the Armington import demand in the trade literature. Exports are also often expressed as Armington import demand from the rest of the world. However, in our case, the CET function and the estimation of Ω complete the country-specific model.

## _Data_

Data are from the World Bank’s World Development Indicators (WDI)<sup>10</sup> and the United Nations National Accounts database.<sup>11</sup> WDI is used where available. The two sources are combined to extend series or fill in missing observations. We retain the WDI levels and base year for constant prices where the two

> 9 Note that the CES and CET functions are not defined for elasticities that equal 1 (a Cobb-Douglas formulation can be used). However, the export supply and import demand equations are well defined for unitary elasticities and the estimation procedure will work in that case.

> 10 <u>https://databank.worldbank.org/source/world-development-indicators.</u>

> 11 <u>https://unstats.un.org/unsd/snaama/Index.</u>

8
Taken together, the evidence reported in Figure 7 suggests that there is considerable scope for future productivity gains from further factor reallocation across ownership forms, given the productivity advantages of the private sector and the relatively small share of labour and capital currently allocated to it. Adding this to reasonable estimates of pure productivity growth in the 1 to 2 percent range, suggests to us that scenarios in which China's productivity level gains on the world average over the next 20 years are reasonable ones to consider.

# _Scenarios for China's Current Account and NFA Position_

We now put all the pieces of the analysis together to come up with estimates of China's net foreign asset position in 2025, and of the pattern of current account surpluses or deficits required to reach this position over the next 20 years. We consider three scenarios. Scenario 1 is basically a continuation of recent trends. China’s savings rate has reached 25 percent of PPP GDP; the economy has been growing at 6 percent per capita according to the Penn World Tables; and the rest of the world’s productivity advantage over China declined from a ratio of 1.2 at the beginning of reform to about 1.0 today. So, one possible scenario is a continuation of these trends (Scenario 1). We think a more likely scenario is that there is some slowing of China’s growth rate, for example to 5 percent per annum. If reform continues, there will be ongoing productivity improvements as resources shift out of agriculture to services and industry, and out of the state sector toward the private. So, in Scenario 2 we have somewhat slower aggregate growth as result of diminishing returns, but a continuation of the historical trend in aggregate TFP and a reduction in the savings rate to about 20% of PPP GDP. As discussed above, some of the current high savings reflects distortions and institutional weaknesses, so that continued reform would result in lower overall savings. In our final Scenario 3, we consider a stagnation in reform: savings rate remains very high at 25% of PPP GDP, whereas productivity growth stagnates relative to the rest of the world and the overall growth rate declines to 4 percent per capita because of diminishing returns. If China’s reform trend continues, then we think that Scenario 2 would be the most likely outcome.

28
primary and secondary schools. Finally, we use administrative data on the monthly amounts transferred to beneficiary households over the evaluation period.

# _Estimation strategy_

We exploit the cluster-randomized treatment assignment to identify the impact of the cash transfer program on both children's education and work. In our preferred specification, presented below, we estimate the intent-to-treat (ITT) effect of the program by regressing the outcome of interest on the indicator variable for treatment while controlling for municipality, which is the stratification variable, and age dummies where appropriate: Here _Yiv_ is the outcome of interest (e.g. school or work) for child _i_ in village _v_ at follow-up, _Tv_ is the indicator variable taking the value 1 for treatment villages, **_Xb_** is a vector of stratification variables measured at baseline, denoted _b_ , **_Xivb_** is a vector of age dummies in regressions at the child level, and _εiv_ is the error term. The coefficient _β1_ estimates the intent-to-treat effect of the program using OLS. In a series of appendices, we examine whether the precision of our estimates improves when we include control variables and whether results are robust to using the following alternative models: Probit, Logit, and panel regressions with individual fixed effects (treating the 2007, 2008, and 2009 recall data as baseline measurements).<sup>11</sup> All standard errors are clustered at the village-level.

> 11 We use the following fixed effect specification: _Yivt = β0 + β1*Tvt + di+ d2008 + d2009 + d2011 + εivt_ . Here, _Yivt_ is the outcome variable for individual _i_ from village _v_ at time _t_ (i.e. 2007, 2008, 2009, or 2011), _Tvt_ is the treatment variable (1 for treatment villages in 2011, 0 otherwise), _di_ is an individual fixed effect, and _d2008_ , _d2009_ , and _d2011_ are time fixed effects. We do not have recall data on schooling and duration worked, so we cannot establish the robustness of those estimates using fixed effects.

11
**Table 5: Econometric Analysis for expenditures per Arrival Determinants:** 2000-2016

|**VARIABLES**|**FE**|**Sys-GMM**|**FE**|**Sys-GMM**|**FE**|**Sys-GMM**|
|---|---|---|---|---|---|---|
|**Dep.var.: LN_EAt-1**|||||||
|**LN_EAt-1**|0.712***|0.807***|0.715***|0.807***|0.714***|0.807***|
||(0.0334)|(0.0196)|(0.0334)|(0.0198)|(0.0334)|(0.0198)|
|**LN_GDPt-1**|-0.298***|0.00196|-0.274***|0.00217|-0.281***|0.00206|
||(0.0871)|(0.0259)|(0.0859)|(0.0259)|(0.0864)|(0.0259)|
|**LN_POPt-1**|0.282|-0.0254|0.275|-0.0255|0.276|-0.0253|
||(0.278)|(0.0172)|(0.279)|(0.0171)|(0.279)|(0.0171)|
|**LN_Xt-1**|0.0560|-0.0238|0.0530|-0.0238|0.0549|-0.0237|
||(0.0547)|(0.0219)|(0.0549)|(0.0218)|(0.0548)|(0.0218)|
|**LN_SEATSt-1**|-0.0626|0.0489***|-0.0658|0.0488***|-0.0659|0.0485***|
||(0.0407)|(0.0184)|(0.0407)|(0.0186)|(0.0406)|(0.0185)|
|**RER_EUROt**|-0.309***|-0.131**|||||
||(0.108)|(0.0617)|||||
|**RER_USAt**|||-0.226**|-0.109*|||
||||(0.0906)|(0.0592)|||
|**RER_POUNDt**|||||-0.227***|-0.103*|
||||||(0.0875)|(0.0540)|
|**Observations**|530|530|530|530|530|530|
|**R-squared**|0.664||0.662||0.663||
|**Number of id**|35|35|35|35|35|35|
|**Long-run effect**|-1.074|-0.678|-0.793|-0.567|-0.796|-0.533|
|**p-value =0**|0.00555|0.0296|0.0152|0.0549|0.0116|0.0489|
|**p-value =1**|0.849|0.301|0.527|0.143|0.517|0.0846|
|**Arellano-Bond AR(2) stat**||1.407||1.406||1.409|
|**AR(2) p-value**||0.160||0.160||0.159|
|**Hansen stat**||11.29||11.32||11.30|
|**Hansenp-value**||0.587||0.584||0.586|
|**# IV**||37||37||37|

Source and notes: Author ́s calculations. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All specifications include yearly dummies, not reported. FE: fixed-effects model. Sys-GMM: Blundell and Bond (1998) System GMM model using Roodman (2009) collapsing method.

30
19 Table 11, col. (1), shows that for households that use natural gas for space heating, the full gas bill accounts for a large portion—more than half—of the total energy and fuel bills. Col. (2) presents the energy and utility costs (after the HUS) as shares of net income for the subset of these households that did not receive HUS assistance, and col. (3) presents the same information for those that do receive the HUS.<sup>20</sup> While the shares were similar across the two groups in 2014, prior to the tariff hike, they were 5 percentage points apart thereafter, although by 2018 the difference seems to have shrunk somewhat.

Shares of energy costs (natural gas, electricity and other fuels) between 6 and 11% of net income are fairly large when compared with other expenditure categories. Between 2014 and 2018, Ukrainian households spent on average 42%-45% of net income (54%-57% of total consumption expenditures) on food, and about 4%-5% of net income (5.7%-7% of consumption expenditures) on their own health. It is noteworthy that in 2016-2018 the average share for nonHUS households is stable at 11%. In other words, the average household that does not receive HUS assistance would still be classified as energy poor by conventional standards.

Table 12 presents the average electricity, gas and fuel shares of net income among households that use natural gas for space heating, separately for households that do and do not receive the HUS, and by income-gas consumption quartile, showing that the shares are considerably lower among HUS recipients, especially for families in the first and second income quartiles. The contrast between panels A and B is especially stark for heavy gas users.

Table 13, panel A, shows the percentages of the households in each income-gas consumption quartile cell that are classified as “fuel poor” (with electricity and domestic fuels bills in excess of 10% of income), or would be, had they not received the HUS. Clearly, the

> 20 Net income is household income minus the HUS (which Ukrstat adds to the other sources of income to compute total household income).
during the Mexican crisis. Thomas et al. (2004) observe a similar pattern in Indonesia after the East Asian Crisis. They show that poor households reduced education expenditure for younger children but protected their education investments for older children. Using survey data from 100 villages around Indonesia collected prior to the crisis in May 1997 and then three times after the onset of the crisis – in August and December 1998 and May 1999 – Cameron (2000) finds that school attendance dropped slightly during the crisis, but rebounded after the crisis to levels higher than prior to the crisis. King (2009) shows that per capita education expenditures declined more steadily in post-crisis Indonesia while dropout rates did not rise. This, King argues, is because of the sticky nature of enrollment, and because parents want to protect past education investments.

The conflicting findings are explained by aggregate shocks giving rise to opposing income and substitution effects. Decreases in income (adverse income effect) lead households to withdraw children from school, on one hand, while decreases in wages or poor labor market conditions during the crisis lowers the opportunity cost of schooling leading households to keep their children in school on the other hand. As a result, educational investments depend on whether the income effect or the substitution effect dominates.

Ferreira and Schady (2008) show that the impact of economic shocks on human capital investments and human development outcomes depends on the wealth of the country. They show that in richer countries, child health and education outcomes are counter-cyclical. In poorer countries, the outcomes are pro-cyclical — where infant mortality rises, and school enrollment and nutrition fall during recessions. In middle-income countries, the pattern is mixed – where health outcomes are generally pro-cyclical, and education outcomes are generally counter-cyclical. The authors suggest that richer countries, with deeper and better-functioning credit markets, are more likely to see improvements in both health and education during down-turns. School enrollments increase because the substitution effect dominates the income effect for households with greater access to credit markets.

While there is a substantial literature on linking income shocks with education investments, the literature on the impact of income shocks on health investments, however, is relatively sparse. The majority of studies link income shocks with longer-term health effects, such as, maternal health or 5
62

# Table 111.9

# Household Income, Location, and Source of Contraceptives for Jamaica and Thailand

||Goverment<br>(Percent)|)w0<br> <br>(Percent)|Private<br> <br>(Percent)|Other<br> <br>(Percent)|
|---|---|---|---|---|
|Monthly Income|||||
|Urban-Thailand|||||
|<$130|63.2|1.5|33.8|1.5|
|$130-174|67.6|1.2|30.6|0.6|
|$174-261|65.0|0.6|32.5|1.9|
|$261-522|57.4|2.1|38.3|2.1|
|>$522|41.9|2.3|46.5|9.3|
|weekly Income|||||
|Urban-Jamaica|||||
|<$12.25|75.0|-|25.0|0.0|
|$12.26-18.36|68.4|-|15.8|15.8|
|$18.37-24.50|78.6|-|14.3|7.1|
|$24-50-40.82|79.1|-|14.0|7.0|
|>$40.82|66.7|-|33.3|0.0|
|Rural-<br>Thailand|||||
|<$130|74.3|0.0|21.6|4.1|
|$130-174|88.9|0.0|11.1|0.0|
|$175-261|76.8|2.3|20.9|0.0|
|$262-522|75.0|0.0|18.8|6.3|

# Source: Akin (1984).

a. 1984 exchange rate for Thailand was: U.S. $1 - 23 Baht.

- b. 1984 exchange rate for Jamaica was: U.S. $1 J $4.9
# **Figure 8 Evolution of core damage frequency and large release frequency for existing (Generation I**

# **and II) and for future reactor types (Generation III/III+)**

<!-- Start of picture text -->
1.0E-02<br>CDF design values LRF design values<br>Generation I CDF generation average LRF generation average<br>1.0E-03<br>Generation II<br>1.0E-04<br>1.0E-05<br>1.0E-06<br>1.0E-07<br>1.0E-08<br>Generation III<br>1.0E-09<br>Frequency of core damage and large release (1 per year)<br><!-- End of picture text -->

_Source_ : OECD (2010).

market, utilities have been demanding higher levels of reactor safety along with greater operating efficiency and reliability. A variety of probabilistic safety assessments (PSAs) confirm the trend towards improved levels of predicted safety performance. The design values of the core damage frequency (CDF) and large release frequency (LRF) for Gen III/III+ systems are substantially lower than those of Gen I/II plants (figure 8). For example, the frequency of a large release of radioactivity from a severe accident in a Gen III/III+ system is estimated to be 1600 times lower than that for conventional Gen I plants (OECD, 2010).

It is generally agreed that the Gen III/III+ systems that incorporate simplified and passive approaches to reactor design fix some of the obvious safety problems that are inherent in today‘s nuclear plants. And that these new advanced reactor designs far exceed the minimum safety standards set by U.S.NRC and other regulatory authorities around the world. However, skeptics point out that there is still not sufficient operating experience to validate the assumptions of the probabilistic risk assessments underlying Gen III/III+ plants. In fact, some experts have suggested that in view of the events at Fukushima, the CDFs for these rectors need to be reanalyzed because they might underestimate the chances of a nuclear plant accident (Goldberg and Rosner, 2011).

24
26 the change in infant deaths (D) with a change in a determining variable (X) is<sup>14</sup> The two components are (1) the response attributable to the reduction in risk of infant death and (2) the response attributable to a decline in births and thus, for a given IMR, a corresponding decl.ine in total infant d---s. **vI.eti-Ag** this expression, the cost effectiveness of programs affecting infant deaths can be calculated as Again using the reduced form elasticities, which incorporate the interaction of fertility and mortality, the cost per infant death averted by the non hospital health programs is US$1920 per death avertedl5. Without the interaction effect, that is, using only the direct response elasticities, the estimated cost per infant death averted would be US$2500, almost 30% more than the actual co3t per death averted given the interaction of mortality and fertility. If we consider only the deaths averted through the reduction in risk, that is only the first component identified above, and 14 The numbcr of infant deaths is D - IMR . B.

1<sup>5</sup> Expressing births and non hospital health expenditures in per capita terms, <!-- Start of picture text --> REXNP 1272 Rp<br>CE = =<br>D,REXNP B (.108)(.03372)( .29)<br>(IMR--)(n + n )<br>P IMR,REXNP TFR,REXNP<br><!-- End of picture text -->

**=** 1200000 Rp or US$1920.
communist economies is not associated with a reduction in employment. On the contrary, private owners tend to keep employment at higher levels than state-owned firms, _ceteris paribus_ . Finally, macro studies are consistent with micro analyses in that they suggest that privatization, especially when accompanied by complementary reforms, may have a positive effect on the level of aggregate output or economic growth. An important issue that remains unresolved is whether speed of privatization, and the accompanying dispersed versus more concentrated ownership, has a positive or negative effect on aggregate output and growth.

In view of the above results, the question naturally arises as to why the effect of privatization in CEE and CIS has been smaller in the case of domestic than foreign private owners. Discussions with managers, policy makers and analysts suggest three leading explanations. The finding may reflect in part the limited skills and access to world markets on the part of the local managers. Domestically owned privatized firms are also the ones where performance-reducing activities such as looting, tunneling and defrauding of minority shareholders have been most frequent. Finally, in a number of countries the nature of the privatization process initially prevented large domestic private owners from obtaining 100% ownership stakes and insiders or the state often owned sizeable holdings (see Kočenda and Hanousek, 2008). It frequently took these large shareholders several years to squeeze out minority shareholders and in the process the large shareholders sometimes artificially decreased the performance of their newly acquired firms in order to squeeze out the minority shareholders at low share prices.

The results highlight the importance of good management and corporate governance, access to world markets, and the presence of a functioning legal and institutional framework. For the former state-owned firms, restructuring is most easily and effectively achieved by foreign ownership. Foreign firms routinely bring in capable expatriate managers and invest heavily in training local managers. They sell products through their global distributional networks, introduce a relatively advanced system of corporate governance and stress the importance of business ethics. Corporate governance of foreign firms hence compensates to a considerable extent for the underdeveloped legal and institutional system in many transition economies. While some domestic firms have also developed good corporate governance, the underdeveloped legal system has allowed 29
60% of all public charging stations, this implies the total cost of ¥13.2 billion for all public charging stations in the sample cities. The total induced sales by the availability of the charging infrastructure are estimated to be 501,313 during our data period based on results in Table 4. These results imply an average government cost of ¥26,350 to induce consumers to buy one extra EV through subsidizing/investing charging stations. This cost estimate is an under-estimation of the effectiveness of the charging infrastructure since these charging stations will continue to contribute to future sales of EVs that we do not include in our calculation.

The estimates above suggest that investing in charging station is nearly four times as effective as subsidizing consumer purchases in promoting EV sales from the perspective of government expenditure. The better cost-effectiveness of charging infrastructure in promoting EVs is consistent with the finding in other countries. Both Li et al. (2017) and Springel (2019) show that subsidizing charging stations is more than twice as effective as subsidizing consumer purchases on a per dollar basis in the US and Norway, respectively. Both studies also find that the effect of subsidies on charging stations tapers off as the charging network gets larger. The former uses aggregate sales data by city from 2011 to 2013 while the latter uses individual registration record from 2011 to 2015. Our finding of the relative cost-effectiveness is even more striking given that our data is from 2015 to 2018 and the fact that Chinese consumers appear to be more price sensitive than those two countries. The stronger cost-effectiveness of subsidizing charging station in China than in US and Norway could be driven by the following features of housing and urban structure in China: the vast majority of city residents in China live in apartment complexes and do not have space to build private charging; and cities are denser, which allows public charging stations to serve more drivers.

# **6 Conclusion**

This study provides to our knowledge the first empirical analysis on the underlying driving factors behind the rapid growth of the world’s largest EV market, China. The analysis is based on the most comprehensive data on China’s EV market including EV sales, charging infrastructure, and various central and local policies during 2015-2018. There are three key findings. First, generous consumer subsidies from both central and local government played a crucial role and explained at least half of the EV sales during our data period. Second, while the green plate policy has a low program cost, its impact is substantial and highlights the important psychological and social dimensions of the diffusion of environmentally friendly technologies. Third, the availability of 28