rafmacalaba/datause-encoder-data huggingface dataset:
When deciding on investing, entrepreneurs look at a host of factors from cost of inputs, to reliability of infrastructure, to quality of institutions. Given its broad nature, it is impossible to meaningfully portray the investment climate in any country by examining a handful of indicators. Consequently we study the investment climate in India by constructing a composite index. This approach will enable us to summarize and compare in one indicator the many different features of the investment climate in 16 Indian states. Furthermore this methodology will allow us to gauge what specific features of the investment climate best describe a more business friendly environment in India.

# **1. Methodology**

In building such index we start from the assumption that entrepreneurs look at and compare a wide range of features of the business climate in each state when deciding on investing. More specifically we assume that investors take into account a host of factors closely related to the production process such as quality and reliability of infrastructure services, availability of finance, level of corruption, etc.

We use the Investment Climate Survey data collected by the World Bank in India in 2005. This micro data set is based on face-to-face interviews of a representative sample of over 4000 entrepreneurs in both manufacturing and retail establishments in 16 Indian states. The data therefore represent the views of ‘in state’ firms and existing investors. Nevertheless thanks to the standardized methodology used to collect such information comparison across states is possible and meaningful.<sup>1</sup> We identified 46 variables descriptive of the business environment in the 16 states. For simplicity we grouped them in three categories: inputs, infrastructure and institutions (Table 1).<sup>2</sup> Within each of these three categories we then distinguish two dimensions:

> 1 Before applying principal component the firm level data is converted in state level averages, after outliers have been dropped. Furthermore since not all variables of table 1 were available for all states, in a limited number of cases (and never to exceed 20% of the variables used) missing values were imputed with the sample average.

> 2 As suggested by one reviewer, four variables classified in Table 1 as Input Perceptions (Share of short term, share of long term, trade finance, and loan duration) were reclassified as Inputs Cost

2
_Table 5. Divides in uses of DTs across gender and age of owner_

|Owners|use 2G/2.5G<br>mobile phone<br>USE DTs w|communicate<br>w customers<br> <br>ith 2-2.5G PHO|advertise<br>w SMS<br>NES|use<br>smartphone|use<br>computer<br>USE DTs|use<br>accounting<br>software<br>with COMP|use inventory<br>control/POS<br>software<br>UTERS or INTERN|use internet<br>to find<br>suppliers<br>ET-ENABLED 3|use internet<br>to understand<br>customers<br>-4G PHONES|<br>use internet<br>for online<br>banking|
|---|---|---|---|---|---|---|---|---|---|---|
|ALL FIRMS|73%|53%|15%|7%|5%|4%|4%|3%|6%|2%|
|women|70.0%|37.1%|8.9%|3.3%|2.4%|1.5%|1.4%|1.5%|3.5%|1.4%|
|men|74.1%|52.0%|12.8%|7.5%|7.9%|3.5%|3.2%|3.6%|7.1%|2.7%|
|old|73.9%|51.0%|13.2%|5.8%|5.4%|4.0%|3.7%|2.5%|4.8%|2.1%|
|young|69.9%|56.1%|18.9%|9.4%|4.9%|4.9%|3.8%|3.0%|8.1%|2.2%|

Note: Data cover 2174 enterprises that responded to owner gender and age (youth=30 years and younger) questions across Ghana, Kenya, Mozambique, Nigeria, Senegal, South Africa, and Tanzania.

Figure 1 presents a complementary breakdown of divides in uses of DTs by owner gender-age subgroups. Young men-owned enterprises are consistently the largest users of internet-enabled DTs. While 13 percent of younger men-owned firms use a smartphone, only 3 percent of older womenowned firms use one. The digital divide is even larger with computer use, with less than 2 percent of young women-owned firms using one and more than 4 times more younger men-owned firms using one (8 percent).

_Figure 1. Divides in uses of DTs by owner gender-age subgroups_ <!-- Start of picture text --> 80%<br>76%<br>71%71%<br>69%<br>70%<br>62%<br>60% 58%<br>49%<br>50%<br>42%<br>40%<br>30%<br>21%<br>20%<br>17%<br>15%<br>13%<br>11% 11%<br>10% 8% 8%7% 7%<br>6% 5% 5%4% 5% 5% 6%<br>3% 2%3% 3%3% 3%2% 1%2% 3% 3% 1%2%3%3%<br>0%<br>use 2G/2.5G use mobile touse mobile to use use computer use use inventory use internet use internet use internet<br>mobile communicate advertise w smartphone accounting control/POS to find to for online<br>phone w customers SMS software s/w suppliers understand banking<br>customers<br>USE DTs with 2G-2.5G PHONES USE DTs with COMPUTERS or INTERNET-ENABLED 3G-4G PHONES<br>younger women older women younger men older men owners<br><!-- End of picture text --> Note: Data cover 2174 enterprises that responded to owner gender and age (youth=30 years and younger) questions across Ghana, Kenya, Mozambique, Nigeria, Senegal, South Africa, and Tanzania.

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Table 5. OLS Estimation Results for Components<sup>of Private Investment</sup>

|Dependent variable<br>is Real Priv. Investment/GDP in:|Buildi|ngs|Equip|ment|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Constant|-4.080|-3.826|-2.639|-2.637|
|Flow of real credit to private<br>sector as a ratio to GDP|-0.014<br>(-0.72)||0.106*<br>(3.90)|* 0.105**<br>(3.76)|
|Foreign exchange reserves as<br>a ratio to GDP|0.319"'<br>|0.308"'<br>|0.326*<br>|* 0.325**<br>|
|(2-year sum, lagged 1 period)|(6.75)|(6.94)|(4.67)|(4.46)|
|Real exchange rate|0.458"'<br>|0.456"'<br>|0.519*<br>|0.518"<br>|
|(trade weighted, 1985-<br>100)|(3.11)|(3.14)|(2.36)|(2.28)|
|Public infrastructure<br>capital<br>stock as a ratio to GDP|0.825'<br>(2.01)|0.979<br>(2.80)|1.422*<br>(2.33)|1.426*<br>(2.25)|
|Lagged growth of GDP|1.872'<br>(1.88)|1.74<br>(1.80)||0.149<br>(0.09)|
|R-BAR SQUARED<br>F STATISTIC|0.92|0.92|0.91|0.90|
|DURBIN-WATSON|2.27|2.24|1.96|0.97|
|N|20|23|20|20|

Notes: 1. All variables are in natural logs and the<sup>interest rate</sup> variable is dropped as it is insignificant<sup>at 10%.</sup> 2. The sample includes annual data from<sup>1969 to 1988.</sup> 21
4

# II. **COUNTING INFLUENCE THROUGH BIBLIOMETRICS**

Publication counts capture the volume of output of an author, institution, journal or country. They do not necessarily capture the _influence_ of that author, institution or journal on the research community. Citations are a commonly used measure of scholarly influence, the assumption being that a heavily cited paper is more influential than a less heavily cited paper, and that an individual who has authored several highly cited papers has been more influential among his or her peers than one who has authored a similar number of less cited papers.

The _h_ -index (Hirsch, 2005), which we refer to below as _I_<sup>1</sup> , is the most widely used citation-based summary measure of scholarly influence, reflecting both the number of publications and the number of times they have been cited. An _h_ -index of 20 means that an author (or institution or journal) has 20 publications to its name each of which has been cited at least 20 times. Figure 1 illustrates. It shows the citation curves for two publication portfolios (e.g. for two individuals). The horizontal axis plots the cumulative number of articles up to the total _N_ , ranked in descending order of citations. The y-axis plots the number of times the _n_ th article has been cited. The _h_ -index is found by drawing a 45-degree line on the chart, and reading off the x-axis or the y-axis. Hirsch argues that this index is a robust and relevant measure of ―...the importance, significance and broad impact of a scientist‘s cumulative research contributions‖ and the index is widely used. It is reported, for example, in citation software such as Publish or Perish (Harzing, 2010), SCOPUS and the Web of Science (which implemented the _h_ - index within two years of the publication of Hirsch‘s paper).<sup>2</sup> The _h_ -index has shortcomings. Both A and B in Figure 1 have the same _h_ -index even though A‘s first few articles are cited far more often. In Figure 2 D has a higher _h_ -index than C though C‘s citation curve might reasonably be argued to be the more impressive one on balance. Alternatives to the _h_ -index have been proposed, but Ravallion and Wagstaff (2011) have argued they have unattractive properties. They proposed instead an axiomatic approach to measuring scholarly influence based on an influence function where influence is strictly increasing in the number of citations. If a publication portfolio has a citation curve that lies everywhere above that of another publication portfolio, the influence function approach will say the former has greater

> 2 Alonso et al. (2009) provide a useful review of the literature on the _h_ -index—a review that covers some 90 papers—in just four years since Hirsch (2005).
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**Table** 5 Reallocation of Earmarked Funds Under **Law** 55/85, 1986-89 (millions of Colombian current pesos)

||19|86|19|87|1988|1989|
|---|---|---|---|---|---|---|
|Donor|Budget|Actual|Budget|Actual|Budget|Budget|
|PROEXPO|6,651.0|5,408.0|10,503.0|20,744.0|31,120.0|42,000.0|
|ICBF|3,100.0|2,635.0|3,300.0|1,145.0|0.0|19,315.0a|
|SENA|2,286.0|2,449.0|2,980.0|2,984.0|0.0|19,500o0a|
|FAN|990.0|500.0|1,000.0|332.0|696.0|850.0|
|Supernotariado Y Registro|910.0|677.0|1,510.0|1,315.0|1,750.0|1,750.0|
|Fondo Rotatorio De Aduanas|0.0|56.0|359.0|70.0|350.0|570.0|
|Corturismo|278.0|242.0|643.0|643.0|404.0|582.8|
|Focine||35.0|146.0|32.0|164.0|185.1|
|Fondo Nacional Del Carbon|556.0|139.0|775.0|297.0|649.0|926.2|
|Gasolina ACPHb|9,683.0|9,937.0|17,556.0|17,880.0|25,492.0|40,324.0|
|Reallocations among various<br>agencies (LEY 55/85)C|3,453.0|3,164.0|1,245.0|1,157.0|1,555.0|2,969.0|
|Ecopetrol (Decree 399/86)|5,120.0|5,120.0|5,500.0|5,500.0|5,500.0||
|Instituto De Fomento|||||||
|Industrial||||||970.0|
|TOTAL|33,027.0|30,362.0|45,517.0|52,099.0|67,680.0|129,942.1|

- a. ICBF will reallocate this amount within its budget to a program that is a government priority (Children's Welfare Homes). SENA will reallocate this within its budget to special government programs.

- b. Estimates based on Law 55/85 percentages.

## c. Several agencies included.

Source: Contraloria General de la Republica for actual figures and Departmento Nacional de Planeacion for budget figures.

# C. Consideration of Social Security and Public Enterprise Profits

12. Focussing on the traditional sources of earmarking--as in Tables 2 and 3 above--understates the true extent of the earmarking of public funds in Colombia.<sup>5</sup> Public enterprise profits and the payroll tax for social security and SENA, ICBF, etc. are the major reasons for th- understatement. Net profits of public enterprises (i.e. gross receipts net of operating

- 5/ Qu!2"e apart f.om the fact that some relatively minor funds are not included in our data.
the twelve holding companies represented on the list of top 100 largest Slovak companies owned significant stakes in 146 manufacturing firms (Trend, 1996).

**Figure 2: Ownership Concentration after Mass Privatization**

<!-- Start of picture text --> Omnia Romo-Omnia Danubla<br>Omnia Roll Domotechnika Omnia Odbyt wholesale of Invest export of Omnia Bmo<br>export and Import wholesale of Domestic retail of refrigerators and doIestic exports to Czech<br>of bearings household bearings smelting products sppiences market<br>appliances pp<br>Distribution<br>Omnia Holding<br>Company<br>Production<br>Strojame NIS 2000 TO ZL ZL ziZVL ZVLZKL ZL KF.V<br>1% 36% Vyroba Obt Sala 4% 61 64 0 45<br>38% 66 3%<br>Source: Omnia Annual Report, 1995<br><!-- End of picture text --> All direct sales were done through auctions. The National Property Fund (NPF) favored bidders with developed long-term strategies (Interview, 1996). In eight cases management won over outside bidders while in four cases management lost. Frequently, management did not participate in the bidding process but was consulted by all bidding parties. Direct sales were highly leveraged (Figure 3). The new owners were required to put a (at most) 10% downpayment on the book value of the company. The rest would be paid in equal installments over a period of two to seven years. The resources of the privatized firm (retained earnings or debt) could also be used to finance subsequent 12
system. Head-teachers criticized the standard of teaching, teacher absenteeism, and the lack of parental support at home. Teachers blamed low performance on the lack of adequate facilities and physical resources. Learning coordinators focused on the poor physical resources, teacher absenteeism, poor school administration and the lack of cooperation between teachers and the community (Arif and others 1999; Haque and others 2000, The Bureau of Curriculum Development and Extension Services, NWFP 1999; and the Bureau of Curriculum and Extension Centre, Balochistan 2000). One study at least, _did_ find that teachers matter—the Multi-Donor Support Unit for the Social Action Program in 1995 collected information on 11,563 students in 527 schools throughout the country and reported that teacher performance, as measured through test scores was positively correlated with student scores. This effect was particularly pronounced in rural areas where trained teachers accounted for a 12-percent increase in student scores. Finally, private schools were doing better. Both in the Multi-Donor Support Unit test and a later test conducted by Action Aid, children in private schools performed significantly better than those in public schools, and in all subjects.

Despite the somewhat bewildering array of tests and outcomes, the lack of coordination between different testing bodies severely limits the ability to draw meaningful inferences. Consequently, over the past 15 years we do not know whether performance has improved, whether provincial differences in achievement have declined (Khan 1995), or whether increasing enrollments have had an impact on learning. As Table 1 shows, the available documentation is simply inadequate.<sup>3</sup> Even an educated guess at the answer to these pressing questions is impossible. Similarly, while some of the tests document gender differences (with differing results for different subjects), there has been no attempt to examine the variation in test scores along other dimensions—do children in rich families perform better than in poor families? What about in poor and rich villages? Or in families with literate/illiterate mothers? These gaps, which are the mainstay of educational policy in OECD countries, remain understudied.

The current state of knowledge regarding learning achievement in India is similarly hampered by a lack of coordination and systematization of testing tools, sampling, and reporting protocols. A

> 3 In Assessing Learning Achievement, the UNESCO (2001) specifies that a “good survey” should include at the very minimal, careful documentation of the sampling methodology (with standard errors), as well as instrument construction and validation. Although we are currently in the process of trying to contact each individual author to update this review, in a number of cases, instruments have been discarded and published documents remain the only source of information. These documents provide little more than the information summarized in Table 1.

- 7 -
manufacturing. In simulating alternative development strategies, we again solve the model as a nonlinear program, maximizing the utility of the representative consumer subject to all the model equations and solving for the optimum levels of the three subsidy instruments. The three policy instruments are not independent and, as in the small model, one could choose alternative instruments that would achieve the same optimum (e.g., domestic production taxes or subsidies).

In modelling both import and export externalities, we are postulating a link but are not specifying the mechanism by which the externality works. For example, the acquisition of technological mastery through imports of capital goods invo -rs learning, requiring both time and resources. Likewise, on the export side, developing e!ftcient institutional support for exporting involves investment. In the model, these links are costless and, in comparative statics experiments, timeless as well. The elasticities in the various externality equations should be interpreted as implicitly incorporating these costs.

Table 4 reports the structure of the model economy in the base year. The data are drawn from Chenery, Robinson, and Syrquin (1986) and represent an archetype semiindustrial country with per capita income of $600 (in 1970 dollars). This economy is in the early to middle phase of the industrial transformation described by Chenery and Syrquin (1986). We seek to capture the transition to the next stage, which involves roughly doubling per capita income and lowering agriculture's share of GDP from 20 percent to about 15 percent.

During this transition, resources move out of agriculture, mostly into light manufacturing. Vernon (1989) describes technical change in the production of such goods as characterized by process rather than product innovation. In Vernon's view, the .21-
|Country|_R −sq._|Lag|Polynomial|Feb. Actual|Mar. proj.|Apr. proj.|
|---|---|---|---|---|---|---|
|Brazil|0.25|3|3|-0.009|-0.069|-0.034|
|Chile|0.21|3|3|-0.018|-0.010|-0.020|
|Colombia|0.18|5|3|-0.004|-0.004|-0.018|
|Mexico|0.20|5|3|-0.007|-0.047|-0.059|

Table 1. MIDAS results for nowcasting industrial production growth rate (m/m). The _R − sq._ reflects the one-step ahead projection residuals for the period 2019-Feb until 2020-Feb, while Lag and Polynomial represent the optimal number of lags and polynomial degree in Equation 7.

to 5 in Colombia and Mexico. In addition, the one-step-ahead predicted values are plotted for each economy in Figures 5 to 8

# **5.1 Comparison with other methods**

The predictive test contrasts the MIDAS approach to an autoregressive method of first and second orders in Table 2. The results reveal a significant forecast improvement when incorporating high-frequency information from Google indicators in all cases except Colombia. We use the root-mean-square error ( _RMSE_ ) for model comparison. The _RMSE_ represents the quadratic mean of the differences between the one-step ahead predicted values and the observed data. Therefore, the lower the _RMSE_ the better model performance. Table 2 shows that _RMSE_ is almost three times higher when compared to _AR_ specifications in Brazil, 26 percent higher in the case of Chile, while 14 percent higher in case of Mexico. Overall, the results provide strong evidence in favor of using a MIDAS regression combining high-frequency indicators gathered from Google mobility in comparison to standard methods.

13
The high living costs in LAC is primarily caused by the difference in GDP per capita. The absolute living costs in LAC (US$2,000) are smaller than costs in the high-income countries (US$ 5,600) (Table 17 of the Annex).

The results indicate living costs are a substantial part of the costs of tertiary education in particular in Latin America (29 percent) as compared to high-income countries (19 percent). Hence, even with free (public) university education, living costs will amount to a substantial financial burden for students from low-income families.

<u>Total Costs</u> is the sum of education and living costs (Figure 3).

**Figure 3. Total Costs**

<!-- Start of picture text --> Belgium (Flemish)<br>Ireland<br>Belgium (French)<br>Finland<br>Netherlands<br>Germany<br>Sweden<br>Mexico<br>Italy<br>Austria<br>France<br>Canada<br>High Income Average<br>Australia<br>Average<br>United States<br>United Kingdom<br>Japan<br>New Zealand<br>Colombia<br>LAC Average<br>Brazil<br>Peru<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Total Cost/GDP per capita (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries Note: Findings for Brazil and Peru are based upon tuition fees of a relatively small sample of higher education institutions. The findings for these countries should therefore be considered preliminary.

The average total cost of tertiary education for the selected LAC countries is 64 percent compared to 30 percent for high-income countries. These averages hide some important differences. In LAC, especially Brazil, Colombia and Peru tend to have the least affordable total costs. The Anglo-Saxon countries (Australia, Canada, New Zealand, UK, and US) and Japan have high total costs, 40 percent and 51 percent, respectively. Continental and Northern Europe have more affordable total costs of tertiary education, 23 percent and 22 percent, respectively.

<u>Grants is measured by the average amount of grant a student receives per year from</u> public programs relative to a country’s GDP per capita (Figure 4) These grants can cover, at least partially, tuition, housing or food expenditures.

15
17% of all bank branches.<sup>9</sup> In addition to _Ej,[p,G,]_ and _Ej[Gjj_ (section 3.2), we include population density; more dense areas will presumably generate higher demand for GB loans per unit area, and may also entail a higher marginal cost to the bank.

Regressing GB density on these three variables we obtained the results in Table 6. The impact of the poverty-weighted marginal benefit on the spatial allocation of GB branches is highly significant (a t-ratio of 4.14, which is significant at the 0.1% level). The other two factors hypothesized to influence local demand for GB loans are also significant.

Are similar factors at work in determining the geographic distribution of other banks? The third column of Table 6 also gives the results obtained on regressing the density of other "non-Grameen" banks on the same three variables. While population density continues to have a positive impact on bank density, the other two variables have reversed their signs; as expected, higher values of the unweighted gain from sector switching attract other banks, consistently with our interpretation of the result for GB density in Table 6. However, the other banks are clearly attracted by gains to the non-poor, as indicated by the (significant) negative coefficient on poverty-weighted gain, controlling for the unweighted gain.

It is notable that, comparing the regressions in Table 6, the coefficients on povertyweighted gains and unweighted gains are roughly equal, but with opposite signs, for the two types of banks. The restrictions that the coefficients on the weighted and unweighted mean gain sum to zero is accepted for both regressions (a t-test gives a value of 0.615 for GB branches and 0.789 for other banks). Columns 2 and 4 of Table 6 give the regressions when these restrictions

**9** For fuller discussions of how Grameen Bank works see Hossain (1984), Khandker, Khalily, and Khan (1995), and Khandker (1996).

18