rafmacalaba/datause-encoder-data huggingface dataset:
for infrastructure. For example, Calderón and Servén (2003, 2008) employ demographic variables as instruments -- alone or in combination with internal instruments -- in a generalized method of moments (GMM) panel framework. Roller and Waverman (2001) follow a similar approach.

Note also that in a time-series context the issue of simultaneity is arguably more problematic for those studies using investment flows to measure infrastructure services than for those using physical asset stocks. Decision lags and time-to-build suggest that physical assets are likely to be predetermined variables relative to output or productivity, and this may help address identification issues. However, time series data also pose the problem of spurious correlation, which if untreated will result in upward-biased estimates of infrastructure effects on output, particularly in the production-function approach mentioned earlier. Output (or productivity) and infrastructure stocks typically display stochastic trends, and failing to account for them can lead to the spurious finding of a positive and significant association between both variables where in reality there is none. Indeed, this upward bias was largely responsible for Aschauer’s early findings of a very large impact of infrastructure on output using time series data. In a panel context, recent theoretical research shows that spurious regression is much less of an issue provided the cross-section dimension of the data is sufficiently large (Philips and Moon 1999). As for pure time-series models, recent studies often seek to avoid this problem by following cointegration methods to estimate a long-run relation between infrastructure, aggregate output or productivity, and other production inputs. This, however, is typically done in a single-equation context, and therefore it is subject to the same identification problems just discussed, unless the researcher can somehow establish the existence of a single long-run relation among these variables that can be interpreted as ‘the output equation’.

Finally, heterogeneity is a pervasive problem too. The contribution of infrastructure, as summarized by the standard measures described earlier, to output or its growth rate may well vary across countries and time periods, for various reasons. Physical infrastructure stocks, for example, are rarely homogeneous in terms of quality or productivity, which should be reflected in their output or growth impact; yet few empirical studies using physical stock data control for the quality of stocks – which is 9
THE REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Looking ahead, the country will need to reduce the large gap in access between rural and urban areas. Some of the recent success stories in Africa have resulted from the combination of reforms in the aforementioned areas.

Despite relatively high levels of access, the country’s urban water utility, SNDE (Société Nationale de Distribution d’Eau), remains highly inefficient. The utility’s inefficiencies create significant hidden costs for the utility equivalent to over 150 percent of revenue. SDNE barely captures 60 percent of the revenue that it needs to operate effectively. The main source of inefficiency is underpricing (figure 9). Water tariffs are $0.49 per cubic meter, substantially lower than tariffs in other African countries with scarce water resources and make it possible to recover just about 52 percent of the utility’s costs. Unaccounted losses and under collection of bills also contribute to the costs of inefficiencies but in much lesser extent. Distribution losses are relatively high—45 percent compared with 33.4 percent in other African resource-rich countries. The utility’s collection ratio is also comparatively good at 88 percent, compared with 69.4 percent for other resource rich countries. Addressing inefficiencies will help to place the utility on a firmer financial footing, and thus place them in a better position to invest further in network expansion.

**Figure 9. Hidden costs of Congo’s water utilities are high**

<!-- Start of picture text --> Nigeria<br>Ghana<br>Ethiopia<br>Congo, Rep. of<br>Tanzania<br>Congo, Dem. Rep. of<br>South Africa<br>Benin<br>0 50 100 150 200 250 300 350<br>% of revenues<br>unaccounted losses underpricing collection inefficiencies<br><!-- End of picture text --> Source: Briceño-Garmendia and others 2008.

# Financing the Republic of Congo’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, the Republic of Congo needs to expand its infrastructure assets in key areas (table 7). The targets outlined below are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

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- 19 -

Table 2: Proportion of Less-Developed Countries Offering Instruction in Selected Subjects of the Primary School Curriculum by Per Capita Income Level of Country

|Low<br>Income<br>Counties|Lower<br>Middle-<br>Income<br>Countries|Upper<br>Middle-<br>Income<br>Countries|High<br>Income<br>Countries|
|---|---|---|---|
|(N=26)|(N=38)|(N=25)|(N=5)|
|Comb.Lang.<br>100.0|100.0|100.0|100.0|
|<br>Nat.Lang.<br>65.4|68.4|92.0|100.0|
|<br>Loc.Lang.<br>11.5|7.9|8.0|0.0|
|For.Lang.<br>19.2|10.5|36.0|80.0|
|<br>Off.Lang.<br>61.5|50.0|28.0|0.0|
|Mathematics<br>100.0|100.0|100.0|100.0|
|Science<br>96.1|100.0|100.0|100.0|
|Comb.Social Sci.<br>100.0|94.7|100.0|100.0|
|Social Studies<br>46.2|81.6|64.0|80.0|
|History<br>50.0|31.6|52.0|20.0|
|<br>Geography<br>50.0|31.6|48.0|20.0|
|Civics<br>38.5|31.6|28.0|0.0|
|Comb.Moral<br>84.6|78.9|72.0|100.0|
|Religion<br>57.7|47.4|48.0|100.0|
|Moral Ed.<br>34.6|29.0|4.0|0.0|
|Aesthetic Ed.<br>100.0|89.5|100.0|80.0|
|Music<br>84.6|76.3|84.0|40.0|
|Art<br>92.3|81.6|96.0|80.0|
|Physical Ed.<br>100.0|92.1|92.0|100.0|
|Hygiene<br>34.6|44.6|40.0|60.0|
|Comb.Voc.Ed.<br>80.8|81.6|52.0|80.0|
|Manual<br>53.9|42.1|40.0|60.0|
|Agriculture<br>38.5|39.5|12.0|0.0|
|Domestic Science 38.5|42.1|8.0|60.0|
|Vocational Ed.<br>0.0|7.9|4.0|0.0|
|Business<br>3.9|0.0|4.0|20.0|
|Other<br>34.6|39.5|44.0|20.0|
# **Supplementary Information**

## **A Data Appendix**

We construct the estimation sample using data from five major sources: 1) EV sales and heavyduty EV sales purchased from EV-volume; 2) vehicle attributes purchased from IHS/Polk and Taobao; 3) financial and non-financial incentives collected by the authors; 4) number of charging ports from the IEA Global EV Outlook; 5) unit labor cost index in construction sector from OECD; and 6) socio-economic data from the World Bank.

The financial incentives are collected and calculated at the country, year, model level. For consistency across countries, we only consider central/ federal EV incentives or subsidies. The financial incentive can be offered in a variety of forms, including direct consumer subsidy, acquisition tax credit or ownership tax credit (Figure 7b). For China, we use the range-based calculation method in Table 2 of Li et al. (2021). The range-based subsidy is year-specific from 2013 to 2018. Starting 2019, central subsidy depends on both driving range and battery capacity. Further, the central subsidy is canceled for models with price above 300,000 RMB in 2020. For the US, consumers receive a federal income tax credit calculated based on battery capacity. Japan’s incentive is provided as direct consumer subsidies at the model-year level. Canada does not have central subsidies to our knowledge, instead we use population weighted average of provincial direct subsidies offered by British Columbia, Quebec, and Ontario.

For European countries, the financial incentives are collected primarily from the European Automobile Manufacturers Association’s (ACEA) guide on purchase and tax incentives for electric vehicles. The financial incentives consists of consumer subsidy and/or acquisition or ownership tax deduction. Switzerland does not have central subsidies to our knowledge, instead we use the population weighted average of metropolitan area tax credits offered by Zuric, Lausanne, Basel, Bern, and Geneva. Each province has its own tax credit determined by cylinder capacity or weight. For the rest of European countries in the sample, acquisition or ownership tax for vehicles are typically _CO_ 2-dependent and full or partial deduction of these taxes applies to electric vehicles. In addition to _CO_ 2, vehicle taxes sometimes also varies by PHEV/BEV type, curb weight, and engine power. We refer to the ACEA tax guide to calculate the vehicle tax and deductions for each country in each year.

S1
modest increases thereafter become clearer in the next rows which set to zero each of the key parameters and calculate the resulting change in mobility. Notice, first, that 1-year mobility falls by a full 90 percent if we set ση<sup>2,whichrepresentstransitoryshocksandmeasurement</sup> error to zero. As we have noted earlier, our analysis proceeds under the understanding that individuals can largely smooth such transitory shocks through own savings and these shocks are therefore limited welfare impact. By contrast, “bad mobility” σǫ<sup>2due to risk and “good”</sup> mobility due to convergence, ρ) account for roughly 1 percent each across 1 year.<sup>21</sup> The relative impact of these parameters clearly changes as we increase the span over which we are measuring mobility. At 25 years, setting transitory shocks to zero reduces mobility by a still large, but much reduced by 23 percent (as transitory shocks are, by definition, transitory and mobility over this duration is driven to a greater extent by the cumulative effect of persistent shocks experienced by individuals over this period). By contrast, mobility due to persistent risk accounts for 7.4 percent and due to convergence, to 8.6 percent. Having identified which parameters have the largest influence on measured mobility, we now turn to their relative contribution to welfare.

# VI. Welfare Analysis

The voluminous literature on consumption and saving with individual income risk and incomplete insurance markets has generated a number of insights.<sup>22</sup> One important insight is that workers can effectively self-insure against transitory income shocks through borrowing or own saving, and that the effect of these shocks on equilibrium prices and quantities are relatively small.<sup>23</sup> A second important insight of this literature is that very persistent or fully permanent income shocks have substantial effects on consumption and welfare even if individual households have own savings, but no or only limited access to insurance markets. Indeed, when labor income is the main source of income and labor income shocks are highly persistent, we would expect that consumption responds (almost) one-for-one to labor income shocks. This point has been made more formally by Constantinides and Duffie (1996) and

> 21Note that since mobility is highly non-linear in its underlying parameters, measured mobility does not decompose additively into its component parts.

> 22See, for example, Heathcote, Storesletten, and Yaron (2009) for a recent survey.

> 23See, for example Aiyagari (1994) and Heaton and Lucas (1996) for quantitative work and Levine and Zame (2002) for a theoretical argument. Kubler and Schmedders (2002) show that welfare cost of “transitory” labor income shocks are non-negligible, but the labor income process they consider has ρ = 0.5.

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# **X. TABLES**

**<u>Table 1 Colombia: Safeguard and Antidumping Investigations 1990–June 2004</u>**

|Category|Number<br>completed|Res<br>Number|triction<br>Percentage<br>of number<br>completed|
|---|---|---|---|
|Antidumping|37|23|62|
|Safeguard|34|25|74|
|WTO|11|10|91|
|Special|10|5|50|
|Andean|13|10|77|

**Source: Tabulated from date in Table 5.2 of WPS3615**

44
the only government units to depend on extrabudgetary resources in China (Wong 1998, Fan 1998). The role of extrabudgetary resources in rural public finance will be discussed further in Section 3.

# **2.4. Aggregate Trends in Rural Public Finance**

Throughout the transition period, governments at all levels have faced enormous upward pressure on public spending, while laboring under a steep fiscal decline that was reversed only in the mid-1990s. Until the TSS reform introduced a new set of tax instruments and tax administration, the relaxation of state control over the economy had led to a rapid erosion of the government’s capacity to tax (World Bank 2002; Wong 2005). As the budget shrank from more than 30 percent of GDP to less than 12 percent during 1980-1995, all levels of government felt the squeeze. At the same time, rapid economic growth and urbanization raised demands for new investments in basic infrastructure and improved services. In education, enrollment rates have risen at all levels, and especially in higher education. Health care spending has risen rapidly. Social safety net expenditures have also increased enormously as a result of reforms in state-owned enterprises and the aging population. As explained in the section earlier, most of these demands fell on local budgets. In addition, wages have risen steeply during the transition, driving up the cost of labor-intensive public services – again hitting subnational budgets harder.<sup>22</sup> Figure C shows that, as a share of GDP, subnational expenditures fell from 18 percent in 1978 to 8.3 percent at the nadir in 1996 despite growing demands and the rising costs of local services. Under a transfers system that had been decimated by fiscal decline, cutbacks in public spending fell disproportionately on poor, rural localities.

> 22 This was true starting in the 1980s (Wong 1991).

19
**Figure 3B  China's trade balance in industrial goods with major trading partners, 2001 and 2009**

<!-- Start of picture text --> billions of US<br>40<br>dollars 2001<br>30 2009<br>20<br>10<br>0<br>-10 Japan South India Russia Mexico Canada ASEAN-10 Turkey<br>Korea<br>-20<br>-30<br>-40<br><!-- End of picture text --> Source: Mattoo, Ng and Subramanian (2011) A corollary of our analysis is that unless Chinese currency policy changes significantly, and unless there can be credible checks on the use of such policies in the future, the perception we outlined above will remain. There are some signs that China has slowly but surely embarked on a process of internationalizing its currency that will over time eliminate the undervaluation of the renminbi. The horizon for renminbi internationalization is as yet unclear but it is unlikely to happen over the next year or two.

# **III. The Dominance to Come**

As shown in Table 1 and discussed in Subramanian (2011), within the next twenty years China is likely to be economically dominant: by 2030, its market-based GDP is projected to equal that of the US, its PPP-based GDP by then to be twice that of the US, China‘s trade in goods to be nearly two times that of the US and Europe, and the renminbi stands a good chance of nipping at the heels of the dollar, if not eclipsing it, as the main reserve currency. These projections do not require China to grow at anything close to the torrid rates of 11 percent that China has posted in the last fifteen years. They require China to grow at just under 7 percent a year over the next two decades. Nor do these projections require China to maintain its current trajectory of steeply rising openness (measured as the ratio of trade-to-GDP).

9
foreign presence lowers the average dispersion of a sector's productivity, but they also observe that the effect is more significant in sectors with simpler technology. This is interpreted to mean that foreign presence forces local firms to become more productive in sectors where best practice technology lies within their capability, but that there are no significant transfers of modern technology. Furthermore, they find no significant effects of foreign presence on the rate of productivity growth of local firms, and interpret this as additional support to the conclusion that technology spillovers do not occur.

Aitken and Harrison (1991) use plant-level data for Venezuelan manufacturing between 1976 and 1989 to test the impact of foreign presence on total factor productivity growth. They conclude that domestic firms exhibited higher productivity in sectors with a larger foreign share, but argue that it may be wrong to conclude that spillovers have taken place if MNC affiliates systematically locate in the more productive sectors. In addition, they are also able to perform some more detailed tests of regional differences in spillovers. Examining the geographical dispersion of foreign investment, they suggest that the positive impact of FDI accrued mainly to the domestic firms located close to the MNC affiliates. However, effects seem to vary between industries. Aitken and Harrison (1991) is also one of the few studies, apart from Katz (1969), where inter-industry spillovers from foreign investment are discussed explicitly. As noted earlier, they assert that forward linkages generally brought positive spillover effects, but that backward linkages appeared to be less beneficial because of the foreign firms' high import propensities (although there were differences between industrial sectors).

Cantwell (1989), who investigates the responses of local firms to the increase in competition caused by the entry of US multinationals into European markets between 1955 and 1975, also argues that positive technology spillovers did not occur in all industries. His analysis differs notably from the other studies discussed in this section - he does not focus on productivity, but rather on changes in the market shares of foreign and local firms - but his conclusions are interesting. He asserts that "the technological capacity of indigenous firms ... was the major factor in determining the success of the European corporate response" (p. 86) to the US challenge, and that the size of the national market was an additional determinant. More 22
30

# **References**

Angrist, J. D., & Lavy, V. (2001). Does teacher training affect pupil learning? Evidence from matched comparisons in Jerusalem public schools. _Journal of Labor Economics, 19_ (2), 343-369. Baker, S., & Smith, S. (1999). Starting off on the right foot: The influence of four principles of professional development in improving literacy instruction in two kindergarten programs. _Learning Disabilities Research and Practice_ , _14_ (4), 239-253. Doi:10.1207/sldrp1404_5 Banerjee, A., Cole, S., Duflo, E., & Linden, L. L. (2007). Remedying education: Evidence from two randomized experiments in India. _The Quarterly Journal of Economics, 122_ (3), 1235-1264. Doi:10.1162/qjec.122.3.1235 Borenstein, M., Hedges, L.V., Higgins, J.P.T., & Rothstein, H.R. (2009). _Introduction to meta-analysis._ Chichester, United Kingdom: John Wiley & Sons.

Borko, H. (2004). Professional development and teacher learning: mapping the terrain. _Educational Researcher, 33_ (8), 3-15.

Bourgeois, E., & Nizet, J. (1997). _Aprendizaje y formación de personas adultas._ Paris, France: Presses Universite de France.

Cardemil, C. (2001). Procesos y condiciones en el aprendizaje de adultos. _Jornada Nacional de_ _Supervisores. Supervisión para aprendizajes de calidad y oportunidades para todos. Educación Rural._ Santiago: Ministerio de Educación. Retrieved from http://biblioteca.uahurtado.cl/ujah/Reduc/pdf/pdf/mfn253.pdf. Accessed December 16<sup>th</sup> , 2016. Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: Teacher valueadded and student outcomes in adulthood. _The American Economic Review_ , _104_ (9), 2633-2679. Doi:10.1257/aer.104.9.2633 Danielson, C. (2013). _The Framework for Teaching: Evaluation Instrument._ The Danielson Group. Darling-Hammond, L. (2005). Teaching as a profession: Lessons in teacher preparation and professional development. _Phi delta kappan_ , _87_ (3), 237. Doi:10.1177/003172170508700318 Darling-Hammond, L. Wei, R. C. & Andree, A. (2010). _How high-achieving countries develop great_
14 <!-- Start of picture text --> PEE PEU PEN<br>P = PUE~ PUU PUN<br>-PNE PNU PNN<br><!-- End of picture text --> where e.g. PEU denotes the transition probability of an individual who has been employed at the beginning of period t-1 and who is unemployed at the beginning of period t. Such matrices give a richer picture of the labour market situation of specific groups than the computed inflows into and outflows from unemployment ( (PEU + PNU) and (PUE + PUN) respectively ) and the reported unemployment stocks.

# 2. Data availability and quality

The assessment of ALMPs in transition economies is still in its infancy. Data availability and quality are quite limited which renders evaluation difficult. Across the countries of interest different data sets are available to analyse the effectiveness of ALMPs.

In Hungary there have been some surveys which allow to identify "problem groups" and perform impact analysis on the retraining and public employment measures using micro data. The unemployment flow data from the available administrative data, on the other hand cannot be used for aggreqate impact analysis using "transition methodology" as the data only include benefit recipients.

In Poland these administrative data cover all individuals who register as unemployed, stocks and inflows of ALMP participants and the flow of unemployed hired for "regular", i.e. non-subsidised jobs as well as stock and flows of notified vacancies. The estimation of augmented matching functions is, therefore, possible in the Polish case. The Polish LFS data, on the other hand, did not contain any data on labour market policy participation in the past. To allow for the analysis of Polish ALMPs using micro data a supplementary questionnaire was added to the August 1994 survey containing mainly questions related to labour market policy (cf. G6ra and Sztanderska (1995)). The data resulting from this supplemeni have now become available and should enable researchers to evaluate ALMPs in a transition economy at a technical level comparable to Western evaluation studies. For this paper the impact analysis of Polish ALMPs is, however, confined to the above mentioned estimation of augmented matching functions and to the presentation of some raw data from the supplementary questionnaire.