rafmacalaba/datause-encoder-data huggingface dataset:
# **Why Do Indian States Differ in Their Infrastructure Development?**

# **1. Introduction**

The Indian economy has sustained an average growth rate of around 7 percent over the last three decades, until the start of COVID-19 in 2020, since economic reforms launched in 1991. However, economic growth varies substantially across the states (Rao et al., 1999; Mishra and Mishra, 2018). As evidenced by earlier studies, some of the major factors for the differential growth rates across states include initial levels of development, existing economic structure, and differential development of infrastructure (Rao et al., 1999; Ghosh and De, 2005; Majumder, 2012; Bandyopadhyay, 2012; Mishra and Mishra, 2018). The different levels of development in physical infrastructure (transportation, energy, information), social infrastructure (education, health), and financial infrastructure (financial institutions) help explain the variations in economic growth across the Indian states.

The contributions of infrastructure development to the overall competitiveness of the economy by improving factor productivities and reducing trade and transaction costs are well documented (World Bank, 1994; Roller and Waverman, 2001; Caldern and Serven, 2004; Sahoo and Dash, 2009; 2012). Apart from contributing to economic growth through increased productivity, infrastructure development also provides less developed regions and disadvantaged sections/individuals better access to economic and social opportunities, thereby reducing inter-spatial divergence in inequality and poverty (Estache and Fay, 1995; Calderon and Serven, 2014; Bajar and Rajeev, 2016).

While the existing literature focuses on establishing the relationship between infrastructure development and economic growth (Roller and Waverman, 2001; Caldern and Serven, 2004; Sahoo and Dash, 2009; 2012), little emphasis has been paid to determining factors responsible for the variations in the development of infrastructure itself. In the Indian context, some studies empirically examine the contribution of infrastructure development to economic growth and performance of the industrial sector (Ghosh and De, 2005; Nayyar 2008; Majumder, 2012; Bandyopadhyay, 2012; Mitra _et al_ . 2002; Mitra _et al_ ., 2016; Ghosh, 2017). However, a very few studies have looked at the factors contributing to the disparities in infrastructure development which could be responsible for different levels of economic growth across the Indian states. Existing studies, such as De (2010), find that Asian countries with higher income, stronger institutions, better governance, and more open economies have higher 2
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55
13 Our rermis estimates suggest that the median post-conflict country entered peace with a mildly undervalued exchange rate observed during the 4 _PrePeace_ years, reflected by a rermis of -1%. However after peace is started, the median post-conflict country experiences a moderate RER overvaluation of 11% during _PeaceOnset_ and 10% during _PostConf1_ . RER overvaluation is substantially reduced – to 4% – during _PostConf2_ .

Foreign aid does not appear to have contributed directly to RER overvaluation during the conflict cycle. The reason is straightforward. While ODA increased during the postconflict period, peaking during _PeaceOnset_ in the median country (as documented above), the deviation of post-conflict ODA from permanent ODA flows was not very large. However, ODA is likely to have contributed to the large error-correction component, which comes to about 11% during _PeaceOnset and PostConf1_ and 6% during _PostConf2_ .

Now we turn to rermis and its decomposition in each of the seven individual post-conflict countries that we focused on in the preceding section. The lower part of Table 4 reports estimates of average RER misalignment and its breakdown, separately for each country and conflict sub-period, comparable to those reported above for the median post-conflict country. The pattern of rermis varies strongly from country to country. Burundi and Uganda experienced RER over-appreciation during most of the pre and post-peace subperiods. By contrast, Ethiopia was substantially overvalued after the war ended but achieved a major correction in the _PostConfl1_ period and brought the RER close to equilibrium in the _PostConf2_ period. The remaining countries – El Salvador, Mozambique, Nicaragua, and Rwanda – shifted from significant RER undervaluation in PrePeace to significant RER overvaluation at subsequent periods. Among the latter countries, RER overvaluation ranges from 6% in Nicaragua to a very large 41% in El Salvador during PostConf2.

High aid flows did not contribute substantially to rermis in any of the seven individual countries. (The exception is aid in Mozambique during _PeaceOnset_ , but during the two subsequent sub-periods the country experienced substantial undervaluation). This finding is broadly consistent with the evidence of a miniscule contribution of large aid flows in the median post-conflict country during the post-conflict cycle.

Summing up, there is evidence of moderate RER overvaluation observed in the median post-conflict experience, particularly during _PeaceOnset_ and _PostConf1_ sub-periods. Aid, however, does not contribute significantly and directly to RER overvaluation in the average aid-receiving country and in the seven country cases that we reviewed in more detail. The reason is that aid, while peaking during _PeaceOnset_ , does not deviate massively from its permanent values during post-conflict periods.

# **4. Catch-up Growth in Post-Conflict**

A general result from the recent cross-country econometric research on aid effectiveness is that aid does not generally influence economic performance, most notably economic
|Current students|15.34|14.51|16.42|15.64|0.42|
|---|---|---|---|---|---|
|Admission requirements||||||
|General or specific knowledge test|0.83|0.37|0.90|0.30|**0.02**|
|Interview|0.23|0.43|0.18|0.39|0.12|
|Min. scores in HS GPA or national entry test|0.87|0.33|0.88|0.33|0.84|
|**Panel B. Student body, program, field of study, and institution characteristics**||||||
|_Student body characteristics_||||||
|Percent of students that are full-time|18.74|27.64|14.11|22.90|**0.03**|
|Academic deficiencies||||||
|Mathematics is lacking in incoming students|0.79|0.41|0.83|0.37|0.15|
|Reading is lacking in incoming students|0.64|0.48|0.61|0.49|0.47|
|Writing is lacking in incoming students|0.67|0.47|0.69|0.46|0.57|
|Oral expression is lacking in incoming students|0.50|0.50|0.47|0.50|0.58|
|_Program characteristics_||||||
|Program duration (semesters)|4.81|0.96|4.81|0.91|0.99|
|Program age (years)|8.71|5.19|10.55|5.41|**0.00**|
|Total number of students in the program last year|151.55|275.94|144.11|163.48|0.69|
|Program has high quality accreditation|0.23|0.42|0.21|0.41|0.65|
|_Institution characteristics_||||||
|HEI is for profit|0.48|0.50|0.45|0.50|0.45|
|HEI is a university|0.29|0.45|0.33|0.47|0.30|
|Number of programs in the HEI|36.60|75.98|9.86|7.87|**0.00**|
|HEI is public|0.16|0.37|0.19|0.39|0.44|
|_Field of study_||||||
|Education, Arts, Humanities, and Social|0.14|0.34|0.07|0.25|**0.01**|
|Economics, Management and Accounting|0.55|0.50|0.64|0.48|**0.04**|
|Health, Agronomy, Vet, Engineering and Architecture|0.31|0.46|0.29|0.45|0.60|
|**Panel C. Noise controls**||||||
|Number of attempts to complete the survey|9.17|2.91|9.33|2.66|0.51|
|Survey completed by phone|0.45|0.50|0.42|0.49|0.47|

75
**Table 3.4e. Liabilities to Banks, Consolidated, Percent of GDP**

||Country|2002|2007|2009|2010|
|---|---|---|---|---|---|
|New Member States|Bulgaria<br>|13<br>|55<br>|67<br>|60<br>|
||Czech Republic|19|26|21|19|
||Estonia|74|111|120|115|
||Hungary|35|69|80|74|
||Latvia|20|90|105|100|
||Lithuania|19|53|72|62|
||Poland|16|25|31|31|
||Romania|10|54|49|46|
||Slovakia|21|38|20|16|
||Slovenia|24|57|44|42|
|Candidates and Potential Candidates|Albania|4|19|27|28|
||Bosnia and Herzegovina|10|40|29|27|
||Croatia|44|103|79|86|
||Macedonia, FYR|||||
||Serbia|||||
||Turkey|14|18|18|15|
|Eastern Partnership Countries|Armenia|1|3|5|6|
||Georgia|6|4|8|11|
||Moldova|6|6|13|10|
||Ukraine|2|22|19|17|

_Source_ : BIS. Note: This table reports liabilities to BIS banks, consolidated total.

**Table 3.4f. Liabilities to Banks, Locational, Percent of GDP**

||Country|2002|2007|2009|2010|
|---|---|---|---|---|---|
|New Member States|Bulgaria<br>|12<br>|40<br>|49<br>|46<br>|
||Czech Republic|18|28|24|23|
||Estonia|27|85|92|81|
||Hungary|28|56|72|69|
||Latvia|13|71|77|71|
||Lithuania|11|49|54|46|
||Poland|13|26|28|29|
||Romania|7|33|39|36|
||Slovakia|15|29|21|23|
||Slovenia|23|69|65|64|
|Candidates and Potential Candidates|Albania|4|6|11|11|
||Bosnia and Herzegovina|8|29|29|24|
||Croatia|28|73|72|67|
||Macedonia, FYR|||||
||Serbia|||||
||Turkey|16|21|22|20|
|Eastern Partnership Countries|Armenia|1|2|4|3|
||Georgia|5|4|4|5|
||Moldova|4|11|10|8|
||Ukraine|2|20|22|17|

_Source_ : BIS. Note: This table reports liabilities to BIS banks, locational total.

Finally, Table 3.4g shows that the importance of official multilateral lending has grown since the onset of the crisis. The biggest growth during the crisis has been concentrated on Latvia, Hungary, Romania and the Ukraine.

Tables 3.4h to 3.4m provide analogous data to Tables 3.4a to 3.4g but on an interregional comparative basis. Table 3.4h shows that international banks are significant crossborder creditors of the Euro Periphery, the new member states and the group of candidates and potential candidates, but that these bank liabilities are much smaller for the other emerging regions. Table 3.4i reinforces the point that the domestic banking system is the main local 15
# Patterns of Labor Market Adjustment to Trade Shocks with Imperfect Capital Mobility<sup>*</sup>

Erhan Artuc<sup></sup> Irene Brambilla<sup></sup> Guido Porto<sup></sup> The World Bank Dept. of Economics Dept. of Economics DECRG UNLP UNLP

## JEL Codes: F1; J21; J62; E22.

## Keywords: Trade shocks, capital adjustment, labor mobility, labor market dynamics.

> *The authors thank seminar participants at Carlos III, Di Tella, FREIT, LACEA-TIGN, La Plata, Penn State, Princeton, Pompeu Fabra, San Andres, Syracuse, Temple, Illinois-UC, and the World Bank. The authors have beneted from discussions with N. Bloom, R. Dix-Carneiro, J. Eaton, B. Eyigungor, B. Kovak, J. McLaren, B. Rijkers and J. Rodrigue. The Editor and the referees provided excellent comments to improve the paper. The views in this paper are the authors' and not those of the World Bank and its aliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or any other institution. Research for this paper has been supported in part by the Knowledge for Change Program, the Multidonor Trust Fund for Trade and Development, and the Strategic Research Program of the World Bank. Porto also gratefully acknowledges support from the r4d program on Employment funded by Swiss National Science Foundation and the Swiss Development Cooperation.

> Development Economics Research Group, The World Bank. email: eartuc@worldbank.org

> Universidad Nacional de La Plata, Departamento de Economia, Calle 6 e/ 47 y 48, 1900 La Plata, Argentina. email: irene.brambilla@econo.unlp.edu.ar

> Universidad Nacional de La Plata, Departamento de Economia, Calle 6 e/ 47 y 48, 1900 La Plata, Argentina. email: guido.porto@depeco.econo.unlp.edu.ar
# **Tables**

## **Table 1: Main revenues and expenditures in Central Government’s budget, Mongolia (2016)**

||**tugriks (millions)**|**share of budget (%) shar**|**e of GDP (%) inc**|**luded in analysis?**|<br>**total in survey**<br>**(millions)**|**ratio of survey total to**<br>**adminsitrative data**|
|---|---|---|---|---|---|---|
|**Revenue component**|5,852,075.9||24.5%||||
|Direct taxes|||||||
|personal income tax|550,423.5|9.4%|2.3%|yes|625,000|114%|
|propertytax<br>|104,623.3<br>|1.8%<br>|0.4%<br>|no<br>|||
|corporateprofits tax|510,582.4|8.7%|2.1%|no|||
|net refunds|(39,995.3)|‐0.7%|‐0.2%|no|||
|Indirect taxes|||||||
|value added tax|1,143,132.7|19.5%|4.8%|yes|654,000|57%|
|import/export duties|328,461.1|5.6%|1.4%|no|||
|excise tax<br>alcohol and tobacco|626,904.8<br>1768780|10.7%<br>30%|2.6%|es|22800|13%|
|<br>others|,.<br>450,026.8|.<br>7.7%||y<br>no|,||
|Social securitycontributions|1,175,932.3|20.1%|4.9%|yes|451,600|38%|
|Other taxes|||||||
|royalties on natural resources|288,714.1|4.9%|1.2%|no|||
|other|297,287.6|5.1%|1.2%|no|||
|specialpurposes|10,767.4|0.2%|0.0%|no|||
|Non‐tax revenues|855,242.0|14.6%|3.6%|no|||
|**Spending component**|9,519,906.1||39.9%||||
|Direct transfers|21040006||||1717000|82%|
|<br>Contributorybenefits<sup>(1)</sup>|,,.<br>1,614,242.4|17.0%|6.8%||,,<br>1,420,820|88%|
|pensions<sup>(2)</sup><br>||||yes|1,100,000||
|Unemployemnt benefits||||no|1,481||
|social insurance benefits<br>|||||||
|maternity||||yes|33,200||
|incapacityand others||||yes|286,139||
|Non‐contributorybenefits(3)<br>childprogram|489,758.2|5.1%|2.1%|yes|291,050<br>233000|59%|
|<br>HDF||||yes|,<br>24,910||
|meritprograms<br>tit||||yes<br>|11,840<br>21300||
|maerny programs<br>food‐stampsprogram||||yes<br>no|,<br>‐||
|In‐kind transfers<br>Education<sup>(4)</sup>|1,338,660.5|14.1%|5.6%||||
|pre‐school|268,296.1|2.8%|1.1%|yes|100,300|37%|
|primaryand secondary|563,609.3|5.9%|2.4%|yes|203,500|36%|
|tertiary|443,910.6|4.7%|1.9%|yes|119,100|27%|
|other|62,844.5|0.7%|0.3%|no|||
|Health care|662,987.1|7.0%|2.8%|yes|241,200|36%|
|Other expenditures|5,414,257.9|56.9%|22.7%|no|||
|Indirect transfers|||||||
|Off‐budget expenditures|||||||
|Mortgage Subsidy||||yes|279,600||
|ElectricitySubsidy||||yes|32,800||
|HeatingSubsidy||||yes|1,140||
|Sources: National Statistics Office of Mongolia(2017)._Mongolia Statistical Yearb_<br>Notes:|_ook 2016, _and tabula|ted data from MinistryEd|ucation,Culture|and Science and M|inistryof Health||
|(1) Total transfers accruingto social securityco<br>|ntributions and one t<br>|ime contributions for retir<br>|ement(from 20<br>|16 Yearbook,table<br>|15.6.1)<br>||
|(2) We include both contributoryand non‐cotr<br>(3)Total transfers accruingto social welfare co|ibutory pensions here<br>ntributions(from 201|,because the Hosuehold<br>6 Yearbooktable 15.6.1)|Surveyfor 2016|does not separate|this two.||
|<br>(4) Total education expenditure and distributio|<br>n byeducation level b|, <br> yWorld Bank staff using|data from Minist|ryof Finance.(tot|al is slightlydiffere|nt to data from Op.Cit.|

27 MONGOLIA Distributional impact of taxes and transfers / November 2018
# **Appendix Table 2: Financial Rules of Thumb - IV Specification**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Financial Rules of<br>Thumb Composite<br>Score|Q1: Productive<br>Loans are Better<br>than<br>Consumption<br>Loans|Q2: Separate<br>Business and<br>Household<br>Accounts|Q3: Never<br>Borrow Beyond<br>Means|
|Takeup in Autonomous Trainer Selection (T1)|0.046**|0.024*<br>|0.076|0.038|
||(0.021)|(0.013)<br>|(0.048)|(0.036)|
|Takeup in Fixed Trainer Selection (T2)|-0.004|0.011<br>|-0.025|0.002|
||(0.021)|(0.014)<br>|(0.054)|(0.031)|
|R-squared|0.043|0.965<br>|0.003|0.722|
|N|7885|7885<br>|7885|7885|
|Dependent Variable Mean in Control Group|<br>0.606|0.502<br>|0.889|0.427|
|F-test(p-value): T1 = T2|0.032|0.322|0.08|0.317|

This table presents instrumental variable analysis for financial rules of thumb using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
<!-- Start of picture text --> 10h. Pakistan 14 10i. Sri Lanka 40<br>14 40<br>12 35<br>12 35<br>30<br>30 10<br>10 25<br>25 8<br>8 20<br>20 6<br>6 15<br>15 4<br>4 10<br>10<br>2 5<br>2 5<br>0 0<br>0 0<br><!-- End of picture text --> Source: Authors’ estimates using CPAT and GTAP-10 data (see also Section 3.2).

# **4.5 Comprehensive energy price reform**

The previous results look solely at the effects of a carbon tax. However, Figure 3 makes it evident that to achieve the optimal price for all fuels, governments will need to remove both explicit and implicit subsidies (the latter achieved mostly through the introduction of the carbon tax). It would defeat the purpose of rationalizing prices if governments introduced a carbon tax, on the one hand, while spending on fossil fuel subsidies on the other. Only the complete removal of fossil fuel subsidies would provide clear price signals, given their distortionary nature. We, therefore, show a ‘comprehensive energy price reform’ (CEPR) scenario, under which all fossil fuel subsidies, price controls and exemptions are gradually phased out along with the introduction of the US$25/ton CO2e carbon tax by 2030. Section 5 below discusses how to overcome some policy implementation challenges.

In terms of net monetized welfare co-benefits, the results for South Asia of implementing the CEPR are still resoundingly positive (Figure 11.a). The larger portion of these net welfare co-benefits comes from the carbon tax, with just phasing out fossil fuel subsidies coming in at a non-negligible 0.31 percent of GDP in 2030. The welfare co-benefits (net of efficiency costs) of the CEPR amount to 1.4 percent of GDP in 2030.<sup>39</sup> In terms of government revenue gains from the CEPR package, South Asia could receive an additional 2.0 percentage points of GDP on average, split about equally between the carbon tax revenues and the forgone expenditure on subsidies (Figure 11.b). However, there are important differences in composition across economies. In the case of Nepal, which does not have explicit, direct fossil fuel subsidies, most (if not all) of the revenue would come from the carbon tax. In the case of Bhutan, which depends on subsidies on LPG from India but consumes very little carbon, the revenue would indirectly come from the fossil fuel subsidy phaseout.<sup>40</sup> The Maldives and Pakistan (both of which are considering

> 39 In other words, similar to the net welfare benefits of the US$25/ton CO2e carbon tax in 2030 (Figure 4). This is because the combined introduction of a carbon tax and phaseout of fossil fuel subsidies, price controls and exemptions under the CEPR disproportionately increases efficiency costs (e.g., due to larger consumer surplus losses from lower fossil fuel consumption). However, said efficiency cost increases are more than compensated by the positive gross welfare cobenefits to yield a positive net benefit estimate under this scenario as well.

> 40 Bhutan imports LPG from India at subsidized prices. Removing the LPG subsidy would thus accrue to the Indian (as opposed to the Bhutanese) government.

22
growth and development in the region (as it is argued for China by Wang, 2005). At the same time, poor economic performance, political conflicts and corruption in the 90s (and for many countries up until today) in Central and Eastern European countries keep the scores the governments in this regions extremely low.

# 6 Concluding remarks

This paper has provided a conceptual framework for measuring governance quality using citizens’ evaluations consistently across countries and over time. It further provided empirical illustration - using data from the World Values Survey Association - of the usefulness of the methodology by developing governance quality rankings for 120 countries. These rankings significantly differ from those provided by available indicators that mostly capture foreigners’ (mostly interest groups) or arm-chair experts’ opinions.

The surveys of the WVS project are certainly subject to important limitations. They are not conducted in the same year for all countries, and the questionnaires may slightly differ from country to country, which may produce significant departures from objective estimation. It is also possible that in spite of the claims to the contrary by the survey organization, the survey may not be based on stratified random sampling for some countries due to practical difficulties (for instance, WVS for Vietnam).

Notwithstanding these limitations, the dataset constructed by us has important merits. The governance-related questions and answers are reported on the level of individual respondents in our dataset, which gives researchers a great flexibility in composing the rankings. In particular, it is possible to compose rankings among the people with higher education, different genders, income, etc. Most importantly and contrary to many other indicators, the data used in our estimation are freely accessible, and can be easily used by other researchers to replicate or modify our estimation procedure.

Ideally, our theoretical framework should be implemented using a world poll with stratified random sampling employing a uniform questionnaire across countries and over time. The World Gallup Poll or a similar instrument might offer such an opportunity in the near future.

23