rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9359

# **Abstract**

To promote the development and diffusion of electric vehicles, central and local governments in many countries have adopted various incentive programs. This study examines the policy and market drivers behind the rapid development of the electric vehicle market in China, by far the largest one in the world. The analysis is based on the most comprehensive data on electric vehicle sales, local and central government incentive programs, and charging stations in 150 cities from 2015 to 2018. The study addresses the potential endogeneity of key variables, such as local policies and charging infrastructure, using the border regression design and instrumental variable method. The analysis shows that central and local subsidies accounted for over half of the electric vehicles sold during the data period. Investment in charging infrastructure is much more cost-effective than consumer purchase subsidies. In addition, the policy that merely provided electric vehicles a distinctively license plate was strikingly effective. These findings demonstrate the varying efficacy across policy instruments and highlight the critical role of government in promoting fuel-saving technologies.

This paper is a product of the Office of the Chief Economist, Infrastructure Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 11. Rates of expansion of access to safe water and improved sanitation are above the African average**

<!-- Start of picture text --> a. Water b. Sanitation<br>4.0 4.00<br>3.0 3.00<br>2.0 2.00<br>1.00<br>1.0<br>0.00<br>0.0<br>-1.00<br>-1.0<br>-2.00<br>-2.0<br>Senegal SSA Senegal SSA<br>% population getting access every year<br>% population getting access every year Piped water Stand posts Flush Toilets<br>Well/boreholes Surface water Improved latrines Traditional latrines Open defecation<br><!-- End of picture text --> _Source:_ World Health Organization (2010a and 2010b) from Demographic and Health Surveys for 1993, 1999, and 2005. _Note:_ SSA = Sub-Saharan Africa.

**Table 10. Evolution of operational indicators associated with the urban water utility SDE**

|Year|Water<br>delivered<br>(million<br>m<sup>3</sup>/year)|System losses<br>(%)|Collection ratio<br>(%)|Average cost<br>($/m<sup>3</sup>)|Average<br>effective tariff<br>($/m<sup>3</sup>)|Total hidden<br>costs<br>($ million/year)|Total hidden<br>costs<br>(% revenues )|
|---|---|---|---|---|---|---|---|
|2000|78.86|26.10|97.00|1.05|0.57|46|94.27|
|2001|84.00|21.90|97.20|1.05|0.56|44|84.66|
|2002|87.86|21.60|97.71|1.04|0.59|42|75.32|
|2003|90.90|20.10|98.17|1.20|0.74|43|57.41|
|2004|95.09|19.90|98.30|1.25|0.82|42|50.28|
|2005|99.90|19.90|97.86|1.25|0.82|44|50.45|
|2006|103.63|19.80|98.20|1.25|0.86|42|43.70|
|2007|108.71|19.70|93.73|1.25|1.03|30|25.35|
|2008|109.17|20.94|96.73|1.25|1.14|17|13.08|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

23
governments had stagnated."' These immediate drawbacks of the moratorium no doubt are real enough, but their magnitude appears negligiba compared to the massive transfer of wealth implicit in the rise in bank stock prices and the surge in the secondary market price of Brazilian debt after the moratorium had ended.

# 4. Maior non-U.S. creditor banks

Non-U.S. creditor banks hold the majority of commercial bank claims on LDC's. Japan and the Europan countries have banking systems that are highly concentrated within a few large banks, which in principle could made these banking systems more vulnerable to LDC debt than the U.S. banking system. Unfortunately, the major non-U.S. banks aren't subject to the stringent disclosure requirements that apply to U.S. banks, and thus data on, for instance, the LDC exposure of individual banks is more restricted. Exposure data for the main Canadian and British banks, however, has been fairly well publicized. The LDC portfc,lio's of German, French and Japanese banks, however, are still strictly off-the-record. Interestingly, it is the banking systems with relatively low LDC exposure that have restricted public access to LDC exposure data. Maybe this reflects the fact that low-exposure - banks wish to conceal their true exposure and the fact that LDC exposure - poses no threat to them to strengthen their b&rlaining position in the rescheduling arena.

- As exposure data for only a few top non-U.S. banks and then only to Brazil and Mexico - is available, it is not possible to estimate the discounts on LDC debt implicit in bank stock prices for the non-U.S. creditor banking systems individually. Implicit discounts can in principle

21
Table A9: Relationship between management and energy intensity (quantity), by country income group.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||High|Upper Middle|Lower Middle|High|Upper Middle|Lower Middle|
|General|0.170|0.214|0.304<sup>_∗_</sup>|0.550<sup>_∗∗∗_</sup>|0.410<sup>_∗∗_</sup>|0.243|
||(0.131)|(0.160)|(0.166)|(0.139)|(0.164)|(0.165)|
|No. Employees||||0.416<sup>_∗∗∗_</sup>|0.412<sup>_∗∗∗_</sup>|0.704<sup>_∗∗∗_</sup>|
|||||(0.114)|(0.103)|(0.105)|
|Log(Sales)||||-0.561<sup>_∗∗∗_</sup>|-0.547<sup>_∗∗∗_</sup>|-0.617<sup>_∗∗∗_</sup>|
|||||(0.084)|(0.071)|(0.068)|
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|1218|1236|1080|1217|1233|1079|
|Adjusted _R_<sup>2</sup>|0.206|0.240|0.094|0.249|0.285|0.185|
|df<br>m|81|76|67|83|78|69|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**51**
# **Table 11(b): Reduced Rank Standardized Coefficients**

||Beta Vector|Std Err|Alpha Vector|Std Err|
|---|---|---|---|---|
|rm2|1.0000|0.00000|-0.030553|0.0079880|
|rgdp|-1.2990|0.084212|-0.019096|0.0040082|
|Interestrt|1.2506|0.17615|0.00000|0.00000|
|DISC|-1.0733|0.14192|0.70952|0.13679|
|Dexrtend|8.5118|1.4804|0.00000|0.00000|
|INFL|0.13232|0.028321|0.00000|0.00000|
|Constant|0.00000|0.00000|||
|FFUNDSRT|0.00000|0.00000|||

# **Table 11(c): Hypotheses Tests for the Beta Vector**

|rm2|Zero|Chi^2(1)|5.1698 [0.0230]*|
|---|---|---|---|
|rgdp|Zero|Chi^2(1)|4.8416 [0.0278]*|
|Interestrt|Zero|Chi^2(1)|18.221 [0.0000]**|
|DISC|Zero|Chi^2(1)|25.228 [0.0000]**|
|Dexrtend|Zero|Chi^2(1)|27.786 [0.0000]**|
|INFL|Zero|Chi^2(1)|8.2673 [0.0040]**|
|Constant|Zero|Chi^2(1)|1.6581 [0.1979]|
|FFUNDSRT|Zero|Chi^2(1)|3.5538 [0.0594]|
|Joint Significance for beta|Zero|Chi^2(7)|54.023 [0.0000]** strong evidence that rm2 is<br>non stationary]|
|Joint Significance for FFUNDSRT and Constant|Zero|Chi^2(2)|4.5538 [0.1026]|

|P-values are in brackets|
|---|

*** Significance level at 1%; , ** Significance level at 5%; *  Significance level at 10%

68
Policy Research Working Paper 10739

# **Abstract**

This paper examines the role of firm dynamics in aggregate total factor productivity, job flows, and wage inequality in Ecuador. Utilizing a comprehensive employer-employee dataset, the paper documents firm dynamics and job flow patterns that are consistent with the presence of market distortions. Also, the paper identifies factor misallocation as the main contributor to Ecuador’s total factor productivity deceleration. Given these trends, the paper explores allocative inefficiency drivers through firm- and industry-level regressions. Firms in the top productivity quintile face distortive non-wage labor costs that are 3.7 times higher than the bottom quintile, after controlling for firm size and age. The findings also provide evidence of credit misallocation across firms. Additionally, industries with higher job mobility, credit access, and competition and lower nonwage labor costs, minimum wage incidence, and zombie firms demonstrate higher allocative efficiency. Moreover, worker-level regressions indicate that misallocation drivers explain up to 41 percent of wage inequality, with non-wage labor costs and product market frictions as distortions driving this inequality.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fpatinopena@worldbank.org and eferro@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Frydman, Roman, Edmund S. Phelps, Andrzej Rapaczynski and Andrei Shleifer** . 1993. “Needed Mechanisms of Corporate Governance and Finance in Eastern Europe.” _Economics of Transition_ , 1(2): 171-207.

**Frydman, Roman, and Andrzej Rapaczynski.** 1991. “Markets and Institutions in Large Scale Privatization: An Approach to Economic and Social Transformation in Eastern Europe”, In _Reforming Central and Eastern European Economics; Initial Results and Challenges_ , ed. Vittorio Corbo, Fabrizio Coricelli, and Jan Bossak. Washington D.C.: The World Bank.

**Glaeser, Edward L., and José A. Scheinkman.** 1996. “The Transition to Free Markets: Where to Begin Privatization.” _Journal of Comparative Economics_ 22(1): 23-42.

**Gorodnichenko Yuriy, and Yegor Grygorenko** . 2008. “Are Oligarchs Productive? Theory and Evidence.” _Journal of Comparative Economics_ , 36(1): 17-42.

**Gouret, Fabian.** 2007. “Privatization and output behavior during the transition: Methods matter!” _Journal of Comparative Economics_ , 35:3–34.

**Grigorian, David A.** 2000. “Ownership and Performance of Lithuanian Enterprises.” The World Bank Policy Research Working Paper Series 2343.

**Grosfeld, Irena, and Thierry Tressel.** 2002. “Competition and Ownership Structure: Substitutes or Complements? Evidence from the Warsaw Stock Exchange,” Economics of Transition, 10(3): 525-551.

**Gupta, Nandini, John Ham and Jan Svejnar.** 2008. “Priorities and Sequencing in Privatization: Theory and Evidence from the Czech Republic.” _European Economic Review_ , ... (1): ...

**Guriev, Sergei and William Megginson.** 2006. “Privatization: What Have We Learned?” in Bourguignon, Francoi and Boris Pleskovic eds. _Beyond Transition._ Proceedings of the 18<sup>th</sup> ABCDE, World Bank.

**Guriev, Sergei, and Andrei Rachinsky** . 2005. “The Role of Oligarchs in Russian Capitalism.” _Journal of Economic Perspectives_ , 19(1): 131-50.

**Gylfason, Thorvaldur** . 1998. “Privatization, Efficiency and Economic Growth.” CEPR Discussion Paper 1844.

**Hanousek, Jan, and Evžen Kočenda** . 2003. “The Impact of Czech Mass Privatization on Corporate Governance.” _Journal of Economic Studies_ , 30(3/4): 278-93.

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35
**Figure 18: Estimated contributions to the decrease in volatility: middle income EAP in 2000-05 vs. 1980s**

<!-- Start of picture text --> 8.3% 7.7%<br>5.8%<br>5.2%<br>3.9%<br>Institutional Volatility of Volatility of Trade Volatility of Volatility of<br>Quality Inflation Gov. Openness TOT Foreign<br>Spending Growth<br>-9.0%<br><!-- End of picture text --> Source: Author’s own calculations using data from WDI, IMF and Polity IV. For each variable, EAP is the average of China, Indonesia, Thailand, Malaysia and the Philippines.

# **Mitigating Upward Pressures on Volatility from the Financial Crisis**

While trade openness majorly contributed to the East Asian economic success, it created at the same time a channel through which external volatility gets transmitted (Table A1). In the face of the financial crisis, East Asian countries should act therefore on the domestic front and improve domestic factors that have proven to mitigate volatility. Our regression results suggest that improvements in the quality of institutions up to the average level of OECD countries would reduce volatility by an additional 6 percentage points in both low and middle-income countries (Figure 19). Further improvements in monetary and fiscal policies could also help: in middle-income countries, bringing inflation volatility to median OECD levels could reduce output growth volatility by a further 14.6 percent, and by bringing volatility of government spending to median OECD levels (0.99 percent, i.e. Germany for the period 2000-2005) output growth volatility could be reduced by an additional 20.7 percent (Figure 19). Interestingly, low-income countries present a similar pattern, with improvements in monetary and fiscal policies achieving the highest impact. These estimates are indicative as they represent improvements of fictitious countries with average characteristics, but suggest which areas, at the regional level, could achieve the highest reduction in volatility.

24
# **5.** **<u>Data</u>**

# (a) <u>Students’ census evaluation</u>

The experiment was designed to assess the impact of the ‘GYM package’ using results from the annual students’ census evaluation ( _Evaluación Censal de Estudiantes_ , ECE) administered by MINEDU. The strategy of asking teachers from grades seven and eight to implement the ‘GYM session’ generated two separate evaluation cohorts: the eighth-grade cohort assessed in the ECE 2015, and the seventh-grade cohort assessed in the ECE 2016. First introduced at the secondary education level in 2015, the ECE administers _Mathematics_ and _Reading Comprehension_ tests to all students enrolled in grade eight.<sup>17</sup> In 2016, the evaluation included a _History, Geography and Economics_ test. These are our key outcomes measures.

The ECE in 2015 and 2016 were administered in mid-November. All pupils were requested to sit their exams on the same dates throughout the entire Peruvian geography. Test content and marking were carried out centrally by MINEDU, while the administration of the tests was under the supervision of a bespoke team of enumerators. The ECE data set provides individual test scores for all participating students. Based on these scores, students are classified into four groups according to their level of learning: ‘Before beginning’, ‘Beginning’, ‘In process’ and ‘Satisfactory’. Only those in the ‘Satisfactory’ group have achieved a learning level consistent with their current grade. In 2015, this accounted for 9.5% in _Mathematics_ and 14.7% in _Reading Comprehension_ for all the student populations in the eighth grade.

Apart from test scores, the ECE also collects pupil background data via a self-administered questionnaire, which includes standard modules on household, parental and student socioeconomic characteristics, one question on education expectations (¿What is the highest education level you expect to achieve?) and, in the ECE 2015 only, two Likert scales to measure students’ perceptions about their ability to understand and complete different tasks related to math and reading comprehension (respectively). We test the impact of the intervention on education expectations and students’ self-perception of their own abilities in these two subjects to improve our understanding of the mechanisms.

> 17 Two other ECE studies take place annually in Peru, a census assessment of students in Year 2 and a sample assessment for Year 4s. For our study, only data from the Year 8 ECE is relevant.

12
20 <u>Table 5(contd): Robustness checks on the Africa Effect</u>

||(11)|(12)|(13)|(14)|(15)|(16)|
|---|---|---|---|---|---|---|
||employment|_employment|_employment_|employment_|employment|_employment_|
|Age2|0.216***|0.194***|0.219***|0.193***|0.222***|0.183***|
||(0.0267)|(0.0373)|(0.0275)|(0.0378)|(0.0259)|(0.0366)|
|Age3|0.372***<br>|0.366***<br>|0.384***<br>|0.365***<br>|0.387***<br>|0.360***<br>|
||(0.0296)|(0.0367)|(0.0301)|(0.0377)|(0.0288)|(0.0371)|
|Age4|0.492***|0.508***|0.507***|0.503***|0.520***|0.504***|
||(0.0346)|(0.0377)|(0.0354)|(0.0388)|(0.0340)|(0.0390)|
|Age5|0.752***<br>|0.813***<br>|0.760***<br>|0.800***<br>|0.769***<br>|0.806***<br>|
||(0.0425)|(0.0454)|(0.0423)|(0.0453)|(0.0414)|(0.0465)|
|Africa|-0.202***<br>(0.0274)|-0.0906*<br>(0.0385)|-0.249***<br>(0.0245)|-0.168***<br>(0.0394)|-0.238***<br>(0.0233)|-0.127***<br>(0.0374)|
|ln GNI|0.0692***|0.0491***|0.0659***|0.0489***|0.0691***|0.0447***|
||(0.00555)|(0.0112)|(0.00585)|(0.0115)|(0.00565)|(0.0114)|
|ln Startup Size|0.589***<br>(0.00737)|0.597***<br>(0.00953)|0.588***<br>(0.00759)|0.600***<br>(0.00969)|0.567***<br>(0.00725)|0.597***<br>(0.00949)|
|Outages||-0.00569**<br>(0.00205)||-0.0112***<br>(0.00208)||-0.00610**<br>(0.00222)|
|Finance||0.00866***||0.00717***||0.00816***|
|||(0.00144)||(0.00147)||(0.00144)|
|Land||0.00360*||0.00196||0.00373*|
|||(0.00152)||(0.00167)||(0.00164)|
|Legal Rights||0.000922||0.00984||0.0102|
|||(0.00749)||(0.00724)||(0.00726)|
|Skill Ratio||-0.0100***<br>(0.00113)||-0.00780***<br>(0.00111)||-0.00942***<br>(0.00111)|
|Foreign||0.470***||0.473***|0.641***|0.566***|
|||(0.0325)||(0.0327)|(0.0298)|(0.0389)|
|Exporter||0.605***||0.621***||0.603***|
|||(0.0375)||(0.0376)||(0.0376)|
|Terms of Trade|-0.542***<br>(0.114)|-0.774***<br>(0.193)|||||
|Insolvency Time|||-0.0417***|-0.0434***|||
||||(0.00798)|(0.0122)|||
|Africa X Foreign|||||-0.308***|-0.259***|
||||||(0.0458)|(0.0624)|
|Constant|-0.0575|0.560|0.0915|0.603*|-0.137|0.476|
||(0.133)|(0.289)|(0.142)|(0.282)|(0.137)|(0.296)|
|r2|0.454|0.507|0.452|0.506|0.466|0.507|
|N|40180|19493|39466|19493|40243|19793|
|Standard errors in pa|rentheses||||||
|<br>="*p<0.05|<br> **p<0.01||***p<0.001|"|||
24

# ,1] Ensure to the extent possible that who benefits, pays;

',2] Do so in part by making the lines of accountability (the rules, or incentives) as ransparent as possible; and (3] Proiide some enforcement mechanism to ensure that the system workcs as it should **(Box** 14,.

S . §iE ,tisicm. ; S 21 -X ,ffivffi = ' B g **%2s.** .tu **a** atc = _S._ x ' _ah_ S s~~~~* 4 g a . 4, ; E=l1 oc-al; **=** i S =~.. s= |: ua.*vI 4 (tuuaflj)ieka . EaUc~ **aXuad m** Baidom to wel It It d, ho _ 1 = **N~4** = = S~~~ .S::S::.2 t= = === **,S hive** dectst,a&t. x..l 5 _; s3 BtIOoal4alw*is=e ma 4*Z: l2~1 . . 3gmsbmz Mel$;u1 O4- BxV to ln4nce of clea3i.t loca:x:j:B ::::s::: s ydo cett .oove. capsolt ahae of t:.> dbI Alhugh prciel how these principles may~ be best saZt1*isfie d , e _of_ crourse upon (say

naur numero ynew.en, rvddthey structur governen,andman _of_ oter luocal factoers theonz

> nollowin the brif ommcznnts the strctue ofgoeir met aplcaind clanyoheeoclfatr.h
to transfer what they know to students? (4) To what extent does teacher content and pedagogical knowledge matter?

Averaging across countries, we find that students receive about two hours and fifty minutes of teaching per day—or just over half the scheduled time. This is largely because teachers, even when in school, are not teaching. Furthermore, teachers’ subject knowledge is strikingly low. Only about one in ten fourth-grade teachers master their students’ language curriculum, and about a quarter of the teachers fail simple tasks (such as subtracting two-digit numbers for math teachers, or choosing the correct pronoun or conjunction to complete a sentence for language teachers). With regard to pedagogy, few teachers are able to assess children’s abilities and evaluate their students’ progress, and few exhibit practices that are typically associated with good teaching (e.g. regularly checking for students’ understanding and giving feedback).

Exploiting the linked student-teacher data across countries, and within-teacher withinstudent variation, we find significant, and importantly large, positive effects of teacher content and pedagogical knowledge on student achievement.

Our findings have two main sets of implications: the first are for education systems and education policy reform; the second are for the experimental and quasi-experimental research agenda on ways to improve education quality. On the former, rapid expansions in school enrollment in developing countries have put substantial pressure on education systems’ ability to provide quality education—and rapid hiring of teachers to limit student–teacher ratios may have contributed to our findings of underprepared teachers. At the same time, the high rates of teacher absenteeism from school, as well as absenteeism from the classroom while at school, point to systemic governance, accountability, and management issues. The magnitude of the problem highlighted here suggests that there is an urgent need to tackle these underlying causes of poor service delivery. Failure to do so not only represents a waste of the considerable resources that countries spend on education, but more importantly, condemns generations of students to subpar education and consequently diminished opportunities in life. The findings also suggest care in further system expansions (such as at the preschool or secondary school levels) to ensure that the same patterns are not repeated.

Our findings also provide a quantitative lens through which the growing experimental and quasi-experimental literature on education can be interpreted and understood. Over the last 15 years, more than 200 randomized controlled trials have been conducted in the area of education (Evans and Popova 2016). However, the literature has yet to converge to a consensus among researchers about the most effective ways to increase the quality of primary education, as recent 3