rafmacalaba/datause-encoder-data huggingface dataset:
economic level see (Mohnen and Roller, 2005). At the macro economic level, as we move from the frontier, many things move concomitantly, and it becomes even more difficult. That said, as an example figures 3 and 4 plot the perceived quality of scientific research institutions, and the degree of University-Industry collaboration against distance to the technological frontier and suggest that such factors are plausibly relevant (World Economic Forum et al., 2012). The former is consistent with more general arguments about human capital above. The lack of collaboration could result from the perceived low quality of research, or could reflect the lack of sophistication of the private sector- it does not recognize such alliances as productivity enhancing. Both the effective output of R&D per dollar spent, and the likelihood that it will be communicated to the private sector fall among poorer countries suggesting an important offset to the Schumpeterian benefit to lagging.

In this paper, we estimate the pattern of returns to R&D across the development process as suggestive evidence that such offsetting effects may be important. We construct a global panel of R&D expenditure across 40 years for 75 countries that allows us to treat a broader sample of rich and poor countries than has heretofore been done. To date, the literature taking a global view on returns to R&D is relatively thin and we first estimate a large and statistically significant rate of return from our sample comparable to those found in earlier studies.<sup>6</sup> Experimenting with internal and external instruments to control for the possible endogeneity of R&D still leaves lower, although still high rates of return.

We then employ recent advances in Instrumental Variable Varying Coefficient (IVVC) estimators to allow returns across all factors to vary semi-parametrically across the development space. There is increasing dissatisfaction in the growth literature conceptually about the assumption of a constancy of parameters across countries (see Pack, 1994; Brock and

> 6Lichtenberg (1993) works with panel of 57 countries with between 1 and 15 annual observations and finds that the social return to privately funded R&D is on the order of seven times as high as the return to fixed investment. He does not, however, attempt to identify a difference in rates of return between the developing countries and advanced countries in his sample. Coe et al. (1997) and a sub-sequent literature (Keller, 2002) estimate the impact of foreign R&D on manufacturing TFP growth in developing countries, abstracting from local R&D efforts which are assumed minor.

4
Germany, France, and the UK. The results remain essentially unchanged compared to baseline. An additional concern be that the results are driven the mass of may by elections over the past decade, and in particular following the Global Financial Crisis (GFC). In Column 7, we drop all elections after 2007 and test our main specification again. Even though this reduces our sample, the results remain robust and highly significant. The main findings are not driven by excessive volatility in international oil prices over the last decade. Nevertheless, we also find a significant negative relationship between oil price shocks and incumbency in the years since the GFC (available on request).

The main result is also robust and quantitatively similar to using the lagged change of the import commodity index, consisting of 45 import commodity prices (Column 8). A 1% increase in import commodity index in the previous year is associated with about 11 percentage point increase in the likelihood that the incumbent party will lose the election.

Finally, in Column 9 we show that a crude oil shock is not only more likely to cause electoral turnover, but it may also cause a reversal in political leaning. In particular, we use an indicator for different types of ideological transitions. A transition is defined as any instance where the incumbent and election winners have a different ideology (i.e. left-wing versus right-wing). The result in Column 9 shows that oil shocks are associated with a reversal in political orientation. A 1% increase in oil price shock leads to a six percentage points increase in the likelihood that the winning party belongs to the other end of the political spectrum. In other words, following an oil price increase, a left-leaning incumbent party is more likely to be replaced by a right-leaning party and vice versa. It is as if voters punish the incumbent party and would like a wholesale change in political orientations.

Oil price shocks reduce consumption growth, suggesting one potential mechanism through which oil shocks affect voting behavior. Columns (1)-(2) in Table 3 shows the results data on household and regression using per capita final consumption by non-profit institutions serving households. The results suggest that oil shocks in the previous period have a significant negative effect on lagged private consumption growth. This reduction could provide an explanation for why voters react so strongly against incumbents in upcoming elections. Meanwhile, Columns (3)-(4) show that there is no corresponding effect of oil shocks on lagged GDP growth, suggesting that the main mechanism works through the consumption channel, not output.

9
Table D11: Ethnicity randomization checks, before 2011, 4

||(1)|(2)|(3)|
|---|---|---|---|
||Judge plur. Pokot|Judge plur. Somali|Judge plur. Turkana|
|Pla. plur. Pokot|-0.0102<br>(0.00724)|||
|Def. plur. Pokot|0.0236<br>(0.0701)|||
|Pla. plur. Somali||0.0142<br>(0.0211)||
|Def. plur. Somali||-0.0308<br>(0.0226)||
|Pla. plur. Turkana|||0<br>(.)|
|Def. plur. Turkana|||0<br>(.)|
|DV mean|0.0109|0.0174|0|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies||||
|Other controls|Yes|Yes|Yes|
|Observations|3106|3106|3106|

The regressions test whether female plaintiffs/defendants are more likely to be matched with judges of their own ethnicity than judges of other ethnicities.Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 2. Sample is restricted to the years 1976-2010. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiffs, def. = defendants, plur. = plurality, maj. = majority.

47
(Table 2). Only 12.1 percent of MENA firms have a loan or line of credit, compared to over 40 percent in the Eastern Europe and Central Asia and Latin America and Caribbean regions respectively.

Figure 3. GCI Financial Markets Indicator (2005-2009), MENA vs Non-MENA <!-- Start of picture text --> ISR<br>BHR<br>MLT<br>QAT<br>KWT ARE<br>JOR OMN<br>SAU<br>TUN<br>MAR<br>EGY<br>SYR<br>DZA<br>7 8 9 10 11<br>Log GDP per capita (2005,USD)<br>Source: GCI, WDI<br>6<br>5<br>gci8finmkt<br>4<br>3<br><!-- End of picture text --> Table 2. Finance <u>Characteristics, By Region</u>

||% Firms with a checking<br>orsavings account|% Firms with a bank<br>loan/line ofcredit|Proportion of loans<br>requiring collateral(%)|
|---|---|---|---|
|MENA|59.3|12.1|75.2|
|EAP|87.7|37.6|76.6|
|ECA|90.2|40.1|80.3|
|LAC|92.9|47.6|72.4|
|SAR|78.9|34.8|84.3|
|AFR|88.1|23.8|79.7|

_Notes: Surveys were implemented following the GLOBAL sampling methodology. However, the MENA average includes nonGLOBAL surveys since region has not had a complete survey roll-out. MENA average includes Algeria 2007, Egypt 2008, Iraq 2011, Jordan 2006, Lebanon 2009, Morocco 2007, Syria 2009, West Bank and Gaza 2013, and Yemen 2010. Source: Enterprise Surveys_

# **2.3. GOVERNANCE**

Good governance has been shown to have a causal link to growth at the micro and macro levels.<sup>13</sup> Governance has also been significantly linked with an array of other economic outcomes including GDP per capita,<sup>14</sup> FDI, private investment, and openness to trade. The definition of governance

> 13 See La Porta et al 1999; Kaufmann, Kraay, and Ziodo-Lobaton, 2002; etc.

> 14 See Acemoglu, Johnson, and Robinson, 2001; Halls and Jones, 1999; Easterly and Levine, 2002; Rodrik, Subramanian, and Trebbi, 2002

8
factor explaining the adoption of broadband internet, it was considered a suitable candidate for an instrument.

In this paper, I proposed and estimated total factor productivity under the assumption that it evolves according to a process which depends on past broadband adoption. I estimated the effect of broadband adoption on labor demand for different worker types. I find positive correlations between broadband adoption and labor demand of most worker types of about 2%. The instrumental variable estimation showed a positive but statistically insignificant effect of broadband use on total labor demand. The fact that I used two‐way clustered standard errors could have affected the statistical significance of the test. Alternatively, lack of statistical significance could come from the fact that broadband should only affect labor demand through its direct effect on TFP growth. In Table 8, I showed a positive relationship between broadband use and total factor productivity, which suggests a positive complementarity between broadband use and productivity. To further asses the possible skill complementarity of broadband, I interacted total factor productivity and broadband adoption. Table 17 in the Appendix shows that labor demand for unskilled labor is negatively correlated to productivity increases even further for firms that use broadband, regardless of the method used to estimate TFP. Table 18 shows that for exporting firms although unskilled labor demand is negatively related to TFP growth, the value of production and output per worker are positively related to TFP growth.

The paper contributes by providing evidence about the effect of ICT adoption which is associated with productivity growth on labor demand. The lack of significance in the instrumental variable estimations suggests that other identification strategies are needed to capture the causal effect of broadband use on labor demand. Particularly, since the instrument appears to be highly sensitive to entry and exit dynamics. Although several possible instruments were available, download speed appears to be a strong predictor of broadband adoption to a greater extent than the number of subscriptions or upload speed, but more work should be conducted in determining the appropriate instruments to pin down the causal effects of broadband adoption on labor demand and productivity.

# References

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Ackerberg, D. A., Caves, K., & Frazer, G. (2015). Identification Properties of Recent Production Function Estimators. _Econometrica_ , _83_ (6), 2411–2451. https://doi.org/10.3982/ECTA13408 Ackerberg, D., Caves, K., & Frazer, G. (2006). Structural Identification of Production Functions. _MPRA Paper No. 38349_ , _12_ (38349), 1–35. Retrieved from http://folk.uio.no/rnymoen/Ackerberg_Caves_Frazer.pdf Akerman, A., Gaarder, I., & Mogstad, M. (2015). The skill complementarity of broadband internet. _Quarterly Journal of Economics_ , _130_ (4), 1781–1824. https://doi.org/10.1093/qje/qjv028 18
The population in Pakistan in year , is an exogenous variable derived from UN population projections, while pollution concentration, , (expressed as <u>μE</u> ~~)~~ is derived endogenously as a PPVVKK,tt SS PPCCpp PPMM2.5 function of economic activity (see below). The theoretical minimum risk exposure level, , for PM 2.5 PPVVKK,tt CC ss<sup>3</sup> 5.9+2.4 is assumed to be uniformly distributed between 2.4 and 5.9 so that = 4.15 (GBD 2017 2 SSmmrrppll Risk Factor Collaborators 2018, Supplementary Appendix, page 73). We use <u>μE</u> p SSmmrrppll= as a parameter and calibrated to equal based on data from the year 2017. ddppS hss p rr ss3 rr p rrssCCmm The parameter value is calculated as follows. First Estimates of urban and rural deaths from air pollution . 80XX−06 for Pakistan were extracted from (World Bank 2020b) as = 56,205 and = 34,078 . These were combined with WHO (1987) estimates for average rrssrrbbEE pollution concentrations in urban ( = 56 ) and rural areas ( SSCCS ll AAPP ddppS hss = 52 ) . These were ssrrb CC SSCCS ll AAPP ddppS hss then combined with population data to derive deaths per unit of PM2.5 as ssrrb CC rrssrrbbEE PPMM2.5 PPMM2.5 <u>m3 m3</u> p ~~,~~<sup><u>yy</u></sup> uurruuSSmm ddppS hss p rr<sup><u>μ</u></sup> + rruurrSSll ddppS hss p rr<sup><u>μ</u></sup> The economic value of life is not calculated in the model. Instead we only report deaths related to ddppS hss p rr mm3<sup>μμyy</sup> rr p rrssCCmm= PPCCppPPVVKK,2017 The economic value of life is not calculated in the model. Instead we only report deaths related to pollution.

# 3.8.3.1 Health expenditure attributable to pollution

Health expenditure due to pollution was estimated following the methodologies described in Preker _et al._ (2016); WHO (2020) and elaborated in World Bank (2020). Note that in the model medical costs are not a deadweight loss as the correspond to a redistribution of resources from households to the health care sector.

The first step uses WHO (2020) estimates of total health care expenditure as share of GDP in 2017 (for which data is available), which was 2.9% ( ) or 762.8m rupees. Figure 8: Health care spending by payee as a percent of GDP. TTDDXXPPVVKK,2017 <!-- Start of picture text --> Health Care Expenditure<br>3.50<br>3.00<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>Government Voluntary Out-of Pocket<br>% GDP<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: WHO (2020) 44
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- Jamali, D (2009). Constraints and opportunities facing women entrepreneurs in developing countries: A relational perspective. Gender in Management: An international Journal, 24(4), 232–251.

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- Khandelwal, P., and Sehgal, A. (2018). Exploring work-family interface for Indian women entrepreneurs. _Gender in Management_ , 33(3), 203–216. https://doi.org/10.1108/GM- 04- <u>2016-0075.</u>

- Kristiansen, S., Furuholf, B., and Walid, F. (2003). Internet café entrepreneurs: Pioneers in information dissemination in Idonesia. _The International Journal of Entrepreneurship and Innovation_ , 4(4), 251-263.

- Laajaj, Rachid and Karen Macours. 2017. Measuring Skills in Developing Countries. Policy Research Working Paper 8000, World Bank, Washington, DC.

- Larsen, K. S., and Long, E. (1988). Attitudes toward sex-roles: traditional or egalitarian? _Sex Roles,_ 19, 1–12. doi: 10.1007/BF00292459

- Littunen, H., and Virtanen, M. (2006). Differentiating growing ventures from non-growth firms. _International Journal of Entrepreneurship and Management_ , 2(1), 93–109 Springer.

- Madichie, N.O. and Gallant, M. (2012). Broken silence: a commentary on women’s entrepreneurship in the United Arab Emirates. _The International Journal of Entrepreneurship and Innovation_ , 13(2), 81-92.

- Martins, P. S. (2021). Employee Training and Firm Performance: Evidence from ESF Grant Applications. IZA Discussion Paper, No. 14153

- McKenzie, D. (2017). Identifying and spurring high-growth entrepreneurship: Experimental evidence from a business plan competition. _American Economic Review,_ 107 (8), 2278– 2307.

- Mead, D.C. and Liedholm, C. (1998) The Dynamics of Micro and Small Enterprises in Developing Countries. _World Development_ , 26(1), 61-74.

- Mitra, J., and Basit, A. (2019). Personal networks and growth aspirations: a case study of second-generation, Muslim, fe- male entrepreneurs. _Small Business Economics_ , 1–23. https://doi.org/10.1007/s11187-019-00211-3.

- Newburry, W., Belkin, L. Y., and Ansari, P. (2008). Perceived career opportunities from globalization: globalization capa- bilities and attitudes towards women in Iran and the US. _Journal of International Business Studies_ , 39(5), 814–832.

- Okafor, G. (2017). The Determinants of Firm Performance and Bribery: Evidence from Manufacturing Firms in Nigeria. _International Economic Journal_ , 31(4), 647-669.

23
<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>Of<br>BO<br><!-- End of picture text --> <!-- Start of picture text --> IP<br><!-- End of picture text --> gw Unsatisfactory IEG Qutcomerating Bg Satisfactory IEG Outcome rating gTotal
education. Thus, they are considered as _not exposed to the parallel system or as having limited_ _exposure to the parallel system._ This defines _Cohort 1._ _Cohort 2_ is defined as those individuals who faced the decision to enroll in or continue in secondary education under the parallel regime. An individual aged 16 years in 1990 (or aged 26 years in 2000) was either entering the first or second year of secondary education. At this time, those students (or their families) were making the decision to participate in the parallel system for most of their secondary education. Thus, those individuals aged 16 years of age or less in 1990 (or 26 years or less in 2000) were _fully or substantially exposed to the parallel system._ (see Box below).

_Cohorts affected by the Parallel System_ **_in_** _Secondary Education_ <!-- Start of picture text --> Political i -<br>and other O<br>events<br>0~~~~~~~~~~~~~<br>Dates ~~~~~July, Sept. 19<br>Dates. 1989 1990 1990 1999<br>Education Unified Parallel<br>system ____________________<br>Cohorts Cohort 1 Cohort 2<br>No exposure Significant or fill exposure<br>Exposure (aged 17 or more in 1990) (aged 16 or less in September 1990)<br>Age<br>groups in 27-35 16-26<br>2000 ___<br>Note: The Kosovo Living Standards and Measurement Survey was carried out during the Fall<br>(September-November) 2000.<br><!-- End of picture text --> 8
Appendix 9: Linear probability model with need under stressed conditions Table 52

|Dependent variable:|Need of fis|cal adjustm|ent based|on Escolano|et al.(201|4)|||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure|0.028***|0.022***|0.030***|0.022***|0.031***|0.013|0.007|0.024***|
|**Economic factors**<br>(% of GDP)|(0.005)|(0.005)|(0.006)|(0.005)|(0.010)|(0.017)|(0.008)|(0.008)|
|GDP growth (t-1)||-1.412***||-1.474***|-1.385***|-1.499***|-1.424***|-1.466***|
|||(0.275)||(0.276)|(0.265)|(0.278)|(0.276)|(0.273)|
|Inflation (t-1)||-0.122***||-0.144*|-0.145*|-0.144*|-0.146|-0.144*|
|||(0.035)||(0.085)|(0.086)|(0.085)|(0.090)|(0.086)|
|U.S. Interest rate||-0.043***<br>(0.012)||-0.058*** <br>(0.013)|-0.060*** <br>(0.014)|-0.057*** <br>(0.013)|-0.061***<br>(0.014)|-0.058***<br>(0.013)|
|**Political factors**|||||||||
|Gini|||-0.001|-0.002|-0.002|-0.007|-0.001|-0.001|
||||(0.006)|(0.005)|(0.005)|(0.011)|(0.005)|(0.005)|
|Rule of law|||0.013|0.068**|0.062**|0.070**|-0.020|0.068**|
||||(0.025)|(0.029)|(0.029)|(0.029)|(0.051)|(0.029)|
|Margin of Majority|||-0.054|-0.102|-0.108|-0.103|-0.107|-0.031|
||||(0.096)|(0.088)|(0.086)|(0.088)|(0.088)|(0.205)|
|Election's year|||0.000|0.002|0.003|0.001|0.002|0.001|
||||(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|**Interactions**|||||||||
|EMEs * Rigidity|||||-0.014<br>(0.012)||||
|Gini * Rigidity||||||0.000<br>(0.000)|||
|Rule of law * Rigidity|||||||0.004*<br>(0.002)||
|Majority * Rigidity||||||||-0.004<br>(0.010)|
|Constant|-0.350***|0.026|-0.408|-0.064|-0.072|0.132|0.276|-0.107|
||(0.124)|(0.139)|(0.316)|(0.315)|(0.313)|(0.504)|(0.333)|(0.322)|
|Observations|1,825|1,772|1,597|1,555|1,555|1,555|1,555|1,555|
|R-squared|0.064|0.127|0.074|0.158|0.162|0.159|0.163|0.158|
|Number of ifscode|84|84|75|75|75|75|75|75|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard erro|rs clustered|by countr|y in parent|heses|||||
|***p<0.01,**p<0.0|5,*p<0.1||||||||

- 81 -
- c. **Government-Owned Banks:** the fraction of the banking system's assets that are 50% or more government owned.

10. _Outcomes._ To measure bank development, performance and fragility we use the following indicators: 12

- a. **Bank Development:** equals claims on the private sector by deposit money banks and as a share of GDP and is the average value over the 1997-99 period.

- b. **Net Interest Margin:** equals net interest income divided by total assets, 1997.

- c. **Overhead Costs:** equals total bank overhead costs as a share of total banks assets, 1997.

- d. **Nonperforming Loans:** nonperforming loans as a share of total assets, 1999.

- e. **Crisis:** whether a country suffered a major banking crisis according to Caprio-Kiingebiel (1999) during the 1990s or late 1980s.

# **III.D. Indexes**

Most of these indexes of the bank regulatory and supervisory regime incorporate the answers to many questions from the survey that we conducted. We list the specific questions in Table 1. We used two methods for constructing indexes from the underlying questions. First, many of the questions can be specified as simple zero/one variables as documented in Table 1. Thus, our first method for constructing the indexes simply involves summing the individual zero/one answers. This first method means that we give equal weight to each of the questions in constructing the index.

The second method that we use for constructing indexes involves the construction of the first principal component of the underlying questions. In constructing the first principal component, the factor analytic procedure a principal component with mean zero and standard deviation one. One advantage of the principal component method is that we do not specify equal weights on the individual questions. One disadvantage is that it is less transparent how a change in the response to one question will change the index.

In the text and tables, we discuss and report the results using the principal component indexes. We have also conducted all of the analyses using the first method for constructing indexes. The 12 The sources of the outcome variables are as follows. For bank development, we extend the Levine, Loayza, and Beck, (2000) database by using more recent version of the Intemational Monetary Fund's IFS statistics. The net interest margin and overhead cost variables are from the Beck, Demirguic-Kunt, and Levine (2001 a) database on financial structure. Nonperforming loans are from this paper's underlying survey of bank regulation and supervision.

20