rafmacalaba/datause-encoder-data huggingface dataset:
levels of infrastructure. Some of the other factors affecting infrastructure development are economic growth, capital expenditure, and access to finance (Ari and Toivanen, 2005; Billon, 2010; Cerra et al., 2017). While studies such as Chakraborty and Guha (2009), Bajar (2014), and Nallathiga (2015) discuss the nexus between variations in infrastructure development and economic performance across the Indian states, they do not empirically examine the factors responsible for differential development of infrastructure. The existing studies on infrastructure development at the state level mainly looked at three aspects viz., determinants of per capita infrastructure expenditure (Mohanty et al. 2017); drivers of public-private partnerships (PPPs) in the infrastructure sector (Anant and Singh; 2009; <mark>Kaur & Malik, 2020);</mark> divergence in infrastructure development <mark>and economic growth (Pillai, 2008; P</mark> radhan, 2017; Rasul and Sharma, 2014; Umdor & Panda, 2007), etc. Overall, to the best of our knowledge, there is no study that empirically examined the drivers of differential development of infrastructure stocks at the state level in India. Therefore, it is imperative to understand the factors that explain the variations in infrastructure development across the Indian states. It would help understand why the different Indian states have attained different levels of economic development. The present study aims to help fill this research gap.

Our study empirically examines how economic, fiscal, demographic, social, institutional, and political factors have influenced infrastructure development across the Indian states. Unlike previous studies (Ari and Toivanen, 2005; Billon, 2010; Cerra et al., 2017; De, 2010) that have focused on a particular type of infrastructure or a relatively narrow range of infrastructure indicators, we consider a wider range of infrastructures (altogether 13 types of infrastructures). We group them into three categories – physical, social, and financial – infrastructure. Each of them is represented by an index that is built using principal component analysis (PCA) over annual stocks of relevant infrastructures. The physical infrastructure index includes road, rail, power, and telecommunication indicators. The social infrastructure index includes education and health infrastructure indicators. The financial infrastructure index covers access to finance and financial institutions. We use the latest panel cointegration and GMM systems to account for state-specific heterogeneity and endogeneity and empirically examine the potential factors determining these three types of infrastructure indices across states. The possible factors include economic factors (e.g., gross regional products or GRP and the ratio of gross capital formation to GRP), financial factors (e.g., interest rate, yields on bond markets, foreign direct investment, etc.), demographic and cultural factors (e.g., population density, cast structures, rate of urbanization, etc.), political/institutional factors (e.g., alignment 3
internet, and (iii) to perform a rich analysis of heterogeneities, as national household surveys have data on income, wages and other variables. The analysis based on national household surveys allows a more granular measure of the potential impact of the shock while also capturing asymmetries within countries.

# **4.1. Methodology and data**

School closures implied a severe interruption in the learning process. Unfortunately, it is extremely difficult to estimate the long run impact of this interruption on the human capital stock given the proximity of the shock and the data at hand. NLT provide first estimates on the long run effect of school closures based on two main simplifying assumptions: (i) the human capital stock, which is relevant as a determinant of future earnings, is well approximated by years of formal education, and (ii) the loss of human capital can be approximated by the share of the school year in which the learning process was interrupted. While the first assumption is standard in part of the literature ( _e_ . _g_ . Black and Devereux, 2011), the second follows the contributions of Abadzie _et al_ . (2009) and Adda (2016).<sup>6</sup> The human capital loss could be lower if successful compensating measures are taken in the future, but on the other hand, they could be larger if interruptions make the process of learning more difficult to resume and lead to cumulative learning losses over time.<sup>7</sup> Following NLT, the loss of education (as a share of the school year) for individual _i_ in country _c_ is defined as:

> 6 An argument against the first assumption would be that years of schooling do not capture differences in educational quality that lead to substantial differences in skill levels. Since we do not have information on cognitive abilities, we use only years of education here. As the estimates on the negative impact of the pandemic on enrollment in private education included in Section 3 suggest, not considering the quality of education makes our estimates to be a lower bound.

> 7 Monroy-Gómez-Franco _et al_ . (2022) provide an extension of the model that follows the framework of NLT and considers also cumulative learning losses. Their application to Mexican data indicates that a learning loss equivalent to one third of a school year could translate into a long-run learning loss between one and two years. Of course, there could be dynamic effects of the interruption in human capital accumulation. Disruptions earlier on in life may have longer lasting and more severe effects. This implies that younger children may be more heavily impacted than older ones.

11
# **Table 3:  Regressions Correcting for Differences in the Number of Firms Across Countries**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI)_ and _Concentration 3_ are measures of concentration. _Log firm size_ is the logarithm of the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). Results in columns (a), (c), and (d) are weighted by the inverse of the square root of the number of firms; those in column (b) are weighted by the inverse of (the square root of the number of firms  the inverse of the standard deviation of the Lerner Index). *** <u>p<0.01, ** p<0.05, * p<0.1</u>

|Variables||Acc|ess to finance||
|---|---|---|---|---|
||(a)|(b)|(c)|(d)|
|Lerner index|-0.434**|-0.387**|||
||[0.200]|[0.195]|||
|Concentration 3|||-0.000<br>[0.122]||
|Herfindahl index||||0.034|
|||||[0.138]|
|Log firm size|0.089***|0.089***|0.089***|0.089***|
||[0.003]|[0.004]|[0.003]|[0.003]|
|Manufacturing|0.019*|0.018*|0.020*|0.020**|
||[0.010]|[0.011]|[0.010]|[0.010]|
|Exporter|0.044***|0.042***|0.042***|0.044***|
||[0.008]|[0.012]|[0.008]|[0.008]|
|Foreign-owned|-0.074***|-0.086***|-0.076***|-0.075***|
||[0.012]|[0.013]|[0.012]|[0.011]|
|Government-owned|-0.198***|-0.232***|-0.197***|-0.200***|
||[0.022]|[0.037]|[0.023]|[0.022]|
|Log firm age|0.013**|0.018***|0.013**|0.013**|
||[0.005]|[0.007]|[0.006]|[0.005]|
|Financial development|0.357***|0.332***|0.352***|0.369***|
||[0.056]|[0.043]|[0.060]|[0.052]|
|Inflation rate|-0.160|-0.212*|-0.152|-0.156|
||[0.099]|[0.116]|[0.098]|[0.099]|
|Constant|0.369***|0.382***|0.341*|0.227*|
||[0.135]|[0.138]|[0.186]|[0.130]|
|Observations|68,353|68,353|67,270|68,353|
|R-squared|0.199|0.181|0.201|0.198|

22
37 Table 7: Export Values and Shares **of** World Exports **for** Selected Agricultural Commodities Elasticity in SSA Countries (US$ millions, _I%)_

|||_1980_||_1991_|
|---|---|---|---|---|
|_Product_|_Export Value_|_Percentof &ports_|_Erport Value_|_Peircent ofExports_|
|_Cocoa Beans_|2055.3|72.5|1410.0|67.2|
|_Pabn Oil_|79.B|3.9|74.0|2.7|
|_SIsal_|63.4|48.4|18.6|40.4|
|_Coffee_|2794.4|22.4|1137.0|15.0|
|_Groundnut_|||||
|_Oil_|89|23.9|92.3|31.9|
|_Tea_|281.6|14.0|385.4|15.5|
|_Coffton_|1170.6|15.0|937.8|11.1|
|_Tobacco_|339|8.9|837.3|14.6|
|_Groundnut_|79.5|15.0|22.7|2.7|

Souse: UNCTAD Ycabook. (1987 and 1993)
**Exhibit 23: Taxable Employment per Company by Sector**

<!-- Start of picture text --> 14<br> 12<br>Tea company operating<br> 10 in services and industry<br>enters the market<br> 8<br> 6<br> 4<br> 2<br> ‐<br>2008 2009 2010 2011 2012<br> Agriculture Industry Multiple sectors Services<br>employees per company<br>Average number of tax‐paying<br><!-- End of picture text --> _<u>Note:</u>_ Refers to companies registered after 2008 and active as of 2012 (dubbed for simplicity as “Relevant”). The large growth in employment in companies operating in multiple sectors is almost exclusively due to a tea company that registered two activities, one in services, and another in industry and is thus classified as operating in multiple sectors. The “Multiple Sectors” line becomes dotted when such company enters operations.

27
<mark>Assaad, R., and Krafft, C., 2013. The Egypt labor market panel survey: introducing the 2012 round.</mark> _<mark>I</mark> ZA Journal of Labor & Development_ , _2_ <mark>(1), 8.</mark> <mark>Assaad, R., and Krafft, C., 2015. The structure and evolution of employment in Egypt: 19982012.</mark> _<mark>The Egyptian Labor Market in an Era of Revolution</mark>_ <mark>, 27-51.</mark> <mark>Atasoy, H., 2013. The effects of broadband Internet expansion on labor market outcomes.</mark> _<mark>ILR Review</mark>_ <mark>,</mark> _<mark>66</mark>_ <mark>(2), pp.315-345.</mark> <mark>Autor, D.H., 2001. Wiring the labor market.</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>15</mark>_ <mark>(1), pp.2540.</mark> <mark>Campante, F. R., and Chor, D., 2012. Why was the Arab world poised for revolution? Schooling, economic opportunities, and the Arab Spring.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>26</mark>_ <mark>(2), 167-88.</mark> <mark>DiMaggio, P. and Bonikowski, B., 2008. Make money surfing the web? The impact of Internet use on the earnings of US workers.</mark> _<mark>American Sociological Review</mark>_ <mark>,</mark> _<mark>73</mark>_ <mark>(2), pp.227-250.</mark> El-Mallakh, N., Maurel, M. and Speciale, B., 2018. Arab spring protests and women's labor market outcomes: Evidence from the Egyptian revolution. _Journal of Comparative Economics_ , _46_ (2), pp.656-682.

Faberman, J. and Kudlyak, M., 2016. What does online job search tell us about the labor market?. _Economic perspectives_ , _40_ (1), pp.1-15.

<mark>Hendy, R., 2015. Women’s participation in the Egyptian labor market: 1998-2012.</mark> _<mark>T</mark> he Egyptian labor market in an era of revolution_ <mark>, 147.</mark> Hjort, J. and Poulsen, J., 2019. The arrival of fast Internet and employment in Africa. _American Economic Review_ , _109_ (3), pp.1032-79.

ITU (2019). World telecommunication/ICT indicators database.

Kroft, K. and Pope, D.G., 2014. Does online search crowd out traditional search and improve matching efficiency? Evidence from Craigslist. _Journal of Labor Economics_ , _32_ (2), pp.259-303.

Kuhn, P., 2003. The Internet and matching in labor markets. _New Economy Handbook, Academic Press, Amsterdam_ .

Kuhn, P. and Skuterud, M., 2004. Internet job search and unemployment durations. _American Economic Review_ , _94_ (1), pp.218-232.

Kuhn, P. and Mansour, H., 2014. Is Internet job search still ineffective?. _The Economic Journal_ , _124_ (581), pp.1213-1233.

17
> - 11 -

Table 1 Total Imports and Imports Exempt from Duty Payment 1960-86

|||Z Exempt|Duty|Paid|
|---|---|---|---|---|
|Year|Total<br>1000 US$|From Duty<br>Payment|<br>asZof Imports<br>Subject to Duty|<br>as 2 of Total<br>Imports|
|1960|1,249,291|<br>68|49.3|15.6|
|1961|1,460,379|<br>60|56.1|22.2|
|1962|1,345,502|<br>54|51.2|23.3|
|1963|980,677|<br>53|43.4|20.4|
|1964|1,077,163|<br>45|41.2|23.3|
|1965|1,198,361|<br>50|45.8|23.2|
|1966|1,124,306|<br>51|51.9|2!.3|
|1967|1,095,542|<br>49|38.5|19.7|
|1968|1,169,189|<br>52|38.3|18.4|
|1969|1,576,09C|<br>53|39.9|18.6|
|1970|1,684,633|<br>54|34.8|16.0|
|1971|1,809,409|<br>54|32.5|15.5|
|1972|1.904,682|<br>57|30.2|12.8|
|1973|2,229,468|<br>63|28.1|10.3|
|1974|3,634,918|<br>59|23.7|10.0|
|1975|3,946,501|<br>57|25.1|10.1|
|1976|3,033,000|<br>51|22.2|11.7|
|1977|4,161,539|<br>51|19.7|9.9|
|1978|3,853,655|<br>48|22.2|11.5|
|1979a/<br>1980b|<br>5,228,463|<br>62|23.9|9.5|
|1985|3,814,229|<br>33|22.7|15.6|
|1986|4,724,134|<br>29|28.8|20.5|

a/ First 10 months. _bi_ Data not available from 1980 to 1985. Source: INDEC reduction in the height of tariff barriers up until the end of the 1970s. The increase in the value of T in 1985 and 1986 stems from both the increase in the proportion of imports subject to duty and the introduction of a 10 percent surcharge on imports in 1985, as part of the Austral Plan.
<!-- Start of picture text --> Psychological abuse often Psychological abuse often<br>.2 .1<br>.1 |_________________________p=.355 | |_________________________p=.764 | |_________________________p=.215 | |_________________________p = 0.491 | |_________________________p = 0.334 |<br>0 0<br>|_________________________________________________|<br>p=.384<br>|__________________________________________________________|<br>|_________________________________________________________________________| p = 0.419<br>p=.135<br>Control Supply Boys No Goal Goal<br>Not Attrited Attrited Not Attrited Attrited<br>Physical abuse often Physical abuse often<br>.2 .1<br>.1 |_________________________p=.061 | |_________________________p=.616 | |_________________________p=.310 | |_________________________p = 0.023 | |_________________________p = 0.245 |<br>0 0<br>|_________________________________________________|<br>p=.303<br>|__________________________________________________________|<br>|_________________________________________________________________________| p = 0.009<br>p=.780<br>Control Supply Boys No Goal Goal<br>Not Attrited Attrited Not Attrited Attrited<br>Force sex often Force sex often<br>.2 .1<br>.1 |_________________________p=.179 | |_________________________p=.318 | |_________________________p=.175 | |_________________________p=.741 | |_________________________p=.727 |<br>0 0<br>|_________________________________________________|<br>p=.091<br>|__________________________________________________________|<br>|_________________________________________________________________________| p=.425<br>p=.930<br>Control Supply Boys No Goal Goal<br>Not Attrited Attrited Not Attrited Attrited<br><!-- End of picture text -->

**Figure A2** Baseline outcomes by attrition status: IPV

_Notes._ This figure shows the mean of each specified outcome among females who attrited and those who did not by treatment status. The left-side column presents these means for each community-level treatment arm, specified on the x-axis. Above the mean bars are _p_ -values for tests of equality between the means of attrited and non-attrited females. Below the mean bars are _p_ -values for tests that attrition differed between treatment arms, first comparing the _Supply_ arm to ELA only communities and then comparing the _Boys_ arm to the ELA only communities. The right-side column presents the outcome means by attritor and non-attritor status for females assigned to the _Goal_ treatment and those who were not, separately. Above the mean bars are _p_ -values for tests of equality of means between attritors and non-attritors within each arm. Below the mean bars is a _p_ -value from a test that attrition differed between females who were assigned to _Goal_ and those who were not.

_Source._ Female baseline data.

58
<!-- Start of picture text --> A. Total<br>40003500<br>3000<br>2500<br>2000<br>100<br>1000-<br>oO<br>2000 2001 2002 2003 2004 2005 20068 200T<br><!-- End of picture text --> <!-- Start of picture text --> B. Males<br>40003500<br>3000<br>2500<br>2000<br>100<br>1000<br>-oO<br>2000 2001 2002 2003 22004 2005 2006 200T<br><!-- End of picture text --> <!-- Start of picture text --> C. Females<br>40003500ee<br>3000Be<br>2500 BS<br>2000 — private<br>1500 tn<br>cr<br>c<br>1000Oe 7 |<br>-oO—"<br>2000 2001 2002 2005 200d 2005 2006 200T<br><!-- End of picture text -->
||Til|PPR Working Paver Series<br>Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS216|Price and Quality Effects of<br>Vec-Revisited: A Cae Study of<br>Korean Footwear Exports|Jaime de Melo<br>L Alan Winters|June 1989|M. Ameal<br>6146S|
|WPS217|Public Debt, North and South|Helmut Reisen|||
|WPS218|Public Finance Trade and<br>Divelopment: TIe Chilean Experience|Vittorio Corbo|July 1989|A. Oropesa<br>61758|
|WPS219|Rural Credit in Developing Countries|Avishay Braverman<br>J. L-uis Guasch|June 1989|C. Sproner<br>37570|
|WPS220|Capacity Building for Policy Analysis|Samuel Paul<br>David Steedman<br>Francis X Sutton|July 1989|E. Madrona<br>61712|
|WPSZ21|How Does Uncertainty About the Real<br>Exchange Rate Affect Exports?|Ricardo J. Caballero<br>Vittorio Corbo|June 1989|A. Oropesa<br>61758|
|WPS222|Why Stabilisation Policies In Zambia<br>Did N'ot Succeed|Christopher Colclough|||
|WPS223|Overvalued and Undervalued Exchange<br>Rates in An Equilibrium Optimizing<br>Model|Jose Saul Lizondo|||
|WPS224|The Economics of the Government<br>Budget Constraint|Stanley Fischer|May 1989|S. Fischer<br>33774|
|WPS225|Targeting Assistance to the Poor<br>Using Household Survey Data|Paul Giewwe<br>Oussama Kanaan|June 1989|B. Rosa<br>33751|
|WPS226|Inflation and the Costs of<br>Stabilization: Historical Cases,<br>Recent Experiences and Policy Lessons|Andres Solimano|||
|WPS227|Institutional Reforms in Sector<br>Adjustment Operations|Samuel Paul|||
|WPS228|Economic Performance of Developing<br>Countries in the 1980s|Robert Lynn<br>F. Desmond McCarthy|||
|WPS229|The Effect of Demographic Changes on Sa<br>for Life-Cyde Motives in Developing|ving|||
||<br>Countries|Steven B. Webb<br>Heidi Zia|July 1989|E. Khine<br>61765|
|WPS230|Unemployment, Migration and Wages<br>in Turkey, 1962-1985|Bent Hansen|July 1989|J. Timmins<br>39248|
|WPS231|The World Bank Revised Minimum<br>Standard Mo('l|Doug Addison|May 1989|J. Onwuemene-<br>Kocha<br>61750|
energy and GDP data and no sophisticated technical equipment exists to monitor performance. Some localities measured energy intensity per GDP or per value added in large-scale ( _guimo yi shang_ ) enterprises. This measure can be problematic because GDP data for the third sector is often not reliable, especially when it gets down to county-level data.

# _3.2 (Dis)Incentives for Local Policy Implementers_

As described in the analytical framework, besides the green planning system, the incentives provided to local actors via the cadre management system play a crucial steering function in local policy implementation. One can differentiate between political incentives, meaning political awards local bureaucrats can expect, and economic incentives, referring to the economic payoffs different actions are likely to produce for both political leaders and private actors (Kaufman, 2012).

# **Political and economic incentives in the cadre promotion and evaluation system**

Incentives play a central role in motivating local cadres to fulfill national green mandates and targets. Like other mandatory targets, binding environmental targets have been linked with the annual cadre promotion and evaluation system. Outstanding performances in the annual cadre evaluation are rewarded through promotions (in rank or position), additional wage or bonus payments, or other material benefits, including administrative benefits (e.g., free transport, entertainment, training, and travel), and other allowances for cadres (e.g., subsidized housing, health care, and opportunities for further education). If local leaders fail to meet binding targets, they can face punishments through, for example, denial of promotion and formal censure, such as redeployment to a remote region or, in rare cases, expulsion from office. _Box 3_ summarizes rewards and punishments used to encourage local cadre to not misuse the flexibility granted to them under China’s highly decentralized structure. Local leaders (e.g., the Party secretary and mayor of a province, municipality, or county) also sign individual responsibility contracts that include specific annual energy or emission reduction requirements for their locality. The signing of personal responsibility contracts helps to ensure that government officials at each layer of government administration are motivated to at least partially fulfill upper level government directives.

13
existing data. ―Important new insights‖ are claimed about (for example) the causes of poverty and how best to fight it even though there has been no net addition to the stock of data—just a repackaging of what we already had—and no sound basis is evident for attributing causation.<sup>36</sup>

**How is the index useful for development policy?** If we agreed that the index provides an adequate characterization of some development goal, and that its embodied tradeoffs are acceptable, what would we do with it?

An important role served by mashup indices can be to provide an easily administered antidote to overly narrow conceptualizations of development goals. Putting aside the straw-man argument that GDP is seen as the sole measure of welfare, the HDI has helped sensitize many people to the importance of aspects of human welfare that are not likely to be captured well by command over market goods. This can provide a useful re-balancing when policy discussions appear to put too little weight on factors such as access to public services in determining undeniably important aspects of human welfare such as health (Anand and Ravallion, 1993).

Does this translate into better development policies? It has been argued that country comparisons of a mashup index can influence public action in those countries that are ranked low. This has been claimed by proponents of both the HDI and DBI. In the context of the HDI, there is an interesting discussion of this point in Srinivasan (1994, p.241), who argues that: ―... there is no evidence that HDR's have led countries to rethink their policies, nor is there any convincing reason to expect it to happen. It was widely known, long before the first HDR in 1990, that in spite of her low per capita real income Sri Lanka's achievements in life expectancy and literacy were outstanding, in comparison not only with neighbors, but also with countries (developed and developing) with substantially higher per capita in-comes. This knowledge did not demonstrably lead other countries to learn from Sri Lanka's experience. An even more telling example is that of the Indian state of Kerala with its substantially lower rates of infant and child mortality and higher rates of literacy in comparison to other states, including Punjab with more than twice Kerala's real domestic product per capita. Yet such disparities in performance within the same country have not led to significant policy changes in the lagging regions. Surely socioeconomic-political processes, rather than low levels of income and lack of knowledge about the feasibility of achieving substantial improvements, precluded the policy changes needed to bring about improvement.‖

> 36  For example, in the press release (PR) for the MPI, one of the authors is quoted as saying that ―The MPI is like a high resolution lens which reveals a vivid spectrum of challenges facing the poorest households.‖  The PR does not point out that the MPI relies entirely on existing publicly available data. The contribution of the MPI is to mashup these data.

25