rafmacalaba/datause-encoder-data huggingface dataset:
effects of the distribution of educational quality at the bottom and at the top on economic growth, as well as interactions between educational quality and the institutional infrastructure of an economy.

Our measure of the quality of education is a simple average of the mathematics and science scores over all the international tests depicted in Figure 4.1. We interpret this as a proxy for the average educational performance of the whole labor force. This measure encompasses overall cognitive skills, not just those developed in schools. Thus, whether skills are developed at home, in schools, or elsewhere, they are included in the growth analyses. As in the analyses underlying Figure 3.1, the source of the income data is version 6.1 of the Penn World Tables (cf. Heston, Summers, and Aten (2002)), and the data on years of schooling is an extended version of the Cohen and Soto (2001) data.

The basic result is reported in column (2) of Table 4.1 and depicted graphically in Figure 4.2.<sup>44</sup> After controlling for the initial level of GDP per capita and for years of schooling, the test-score measure features a statistically significant effect on the growth in real GDP per capita in 1960-2000. According to this specification, test scores that are larger by one standard deviation (measured at the student level across all OECD countries in PISA) are associated with an average annual growth rate in GDP per capita that is two percentage points higher over the whole 40-year period. Below we relate these numbers to outcomes of school reform policies.

When educational quality is added to a model that just includes initial income and years of schooling (column (1) of Table 4.1), the share of variation in economic growth explained by the model (the adjusted _R_<sup>2</sup> ) jumps from 0.25 to 0.73. As in Figure 3.1, quantity of education is statistically significantly related to economic growth in a specification that neglects educational quality, but the association between years of schooling and growth turns insignificant and is reduced to close to zero once the quality of education is included in the model (see the bottom of Figure 4.2).<sup>45</sup> In either specification, there is evidence for conditional convergence in the sense that countries with higher initial income tend to grow more slowly over the subsequent period.

> 44 For a brief technical description of added-variable plots see Section 3.2 above.

> 45 The result remains the same when the measure of years of schooling refers to the average between 1960 and 2000, rather than the initial 1960 value. The coefficient estimate on years of schooling in the 50-country sample of column (1) of Table 4.1 is somewhat smaller than the one reported for the 92-country sample reported in Figure 3.1, corresponding to the fact discussed above that the estimate tends to be smaller in high-income countries.

32
# **Table 18: Promoting savings**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|GDP per capita (log)|-0.152*|-0.143|-0.132|-0.119||||
||(0.082)|(0.089)|(0.088)|(0.088)||||
|Population density (log)|-0.037<br>(0.035)|||||||
|Expected GDP (log)|0.097**|0.091**|0.090**|0.080*|0.066|0.036||
||(0.042)|(0.039)|(0.041)|(0.041)|(0.040)|(0.027)||
|# deposit accounts/1,000 adults (log, lagged)|-0.046|-0.068|-0.076|-0.080|-0.098|-0.131***|-0.099***|
|(commercial banks)|(0.070)|(0.073)|(0.072)|(0.073)|(0.067)|(0.043)|(0.038)|
|# loans/1,000 adults (log, lagged)|0.069|0.078|0.063|0.046|-0.021|||
|(commercial banks)|(0.078)|(0.079)|(0.069)|(0.066)|(0.049)|||
|Deosit insurance|-0.111|-0.103|-0.096|||||
|p|(0.114)|(0.113)|(0.109)|||||
|Credit rank|0.001<br>(0.001)|0.001<br>(0.001)||||||
|Number of observations|58|58|58|58|58|79|80|
|Log-likelihood|-25.74|-26.05|-26.23|-26.47|-27.38|-36.69|-39.43|
|Pseudo R<sup>2</sup>|0.166|0.156|0.150|0.142|0.113|0.108|0.0755|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Dependent variable: FI Savings (effective) - equals 1 if the financial regulator oversees promoting savings and has a dedicated team, equals 0 otherwise. Table reports marginal effects of probit estimation.

44
innovation is self‐reported and becomes a highly subjective concept, particularly when innovation refers to “significant” improvements. Therefore, the distinction between innovations and mere product, process, marketing or organizational changes becomes murky.

In recent years, several authors have advocated for a focus on knowledge capital assets rather than on innovation inputs or outcomes, leading to a renewed effort to capture investments in intangible assets using data from different sources (Corrado et al., 2005, 2006, and 2011; Hulten & Hao, 2012). In this approach, three broad categories of intangibles are identified which have some overlap with what is commonly considered as innovation‐related activities (or capabilities): (1) computerized information (software and databases); (2) innovative property (R&D as well as intellectual property protection costs, architectural and engineering designs); and (3) economic competencies (brand names, firm‐specific human capital and organizational capital). While this approach offers a better and broader measure of firms’ capabilities, it poses a challenge as the information required cannot be obtained from existing innovation surveys. Moreover, it’s important to highlight that measures of innovation assets are not equivalent to innovation outcomes, as inputs can be used more or less efficiently. Therefore, there is some uncertainty about the type and extent of innovation outcomes that can be produced by firms using their knowledge assets.

# **2.2 The problems with subjective measurement**

As discussed above, one of the main challenges when measuring firm‐level innovation outcomes is the subjective nature of the definition of innovation used in the survey questionnaires. While in the case of “new” products, processes, organization practices or marketing methods, the main challenge is to check the reliability of the self‐reported outcome, an additional challenge arises when trying to capture the introduction of “significantly improved” products, processes, organization practices and marketing methods. The definition of “significantly improved” is highly subjective and can vary across individuals in the same country but also across countries.

Subjective data have strong limitations as measures of actual outcomes. In an influential study, Bertrand and Mullainathan (2001) show the likely bias when analyzing subjective data given their likely correlation with context variables. The authors conclude that while subjective variables can be used carefully as explanatory variables or to explain behavioral differences between individuals, models that use subjective data as dependent variables are likely to produce biased estimates.

The possible sources of bias when using subjective data are numerous, such as those that derive from cognitive problems as shown in Bertrand and Mullainathan (2001). A large amount of experimental 6
this paper is the first to go beyond these variables and show how different types of U.S. shocks affect government balances, expenditures, revenues, and debt, as well as extend the sample of EMDEs studied.

Finally, an extensive literature looks at the determinants of financial crises, including Kraay and Nehru (2006), Manasse and Roubini (2009), and Kose et al (2021).<sup>4</sup> The latter two papers find that increases in U.S. interest rates increase the probability of a financial crisis in EMDEs in the following year. This study is the first to examine how different types of U.S. interest rate shocks— real, inflation, and reaction—affect the likelihood of EMDE financial crises.

# **<u>2. Methodology</u>**

# **2.1 Differentiating between inflation, reaction, and real shocks** .

A key factor behind the effects of rising U.S. interest rates is the differentiation between real, inflation, and reaction shocks. Real shocks are defined as changes in interest rates that are caused by changing prospects for U.S. economic activity; they are identified as those that raise U.S. yields as well as U.S. equity prices. Inflation shocks are defined as changes in interest rates that reflect changing prospects for inflation; they are identified as those that raise U.S. yields but reduce equity prices, and within that group, those that raise inflation expectations. Finally, reaction shocks are defined as changes in interest rates due to changing market perceptions of the Fed’s reaction function. They are identified as those that, like inflation shocks, raise U.S. yields but reduce equity prices; however, unlike inflation shocks, reaction shocks are assumed to lower inflation expectations.

To decompose U.S. interest rate movements into different types of shocks, the analysis builds on the method developed in Matheson and Stavrev (2014) and employs a sign-restricted Bayesian VAR model with stochastic volatility (see appendix A2 for details). The data are monthly, from January 1982 to June 2022. The model includes four variables: 2-year and 10-year bond yields, the S&P 500 index, and inflation expectations (see table A1 for details). The inclusion of the 10year yield in addition to the 2-year yield is used to capture the persistent part of expectations of inflation and future monetary policy decisions.

The model is specified as: where is an N × 1 vector of endogenous variables, tt tt is an N × p + 1 vector of lagged dependent tt YY = BXX + MM variables and an intercept term, and where p is the lag length, B is a matrix of coefficients, and tt tt is a N × 1 vector of residuals. YY XX tt MM The model is specified as: As part of the identification strategy, the following sign restrictions are imposed on a four-variable VAR model as:

> 4 For an extensive review, see Kaminsky, Lizondo, and Reinhart (1998); Frankel and Saravelos (2012); and Chamon and Crowe (2012).

5
||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Handwshing facilities**|||||
|HW facilities stocked with water and soap (=1)|0.67|0.057<br>(0.045)|0.071<br>(0.044)|0.062<br>(0.043)|
|Water and soap anywhere in the house (=1)|0.768|0.056<br>(0.034)|0.065**<br>(0.033)|0.067**<br>(0.033)|
||||[8.4%]|[8.7%]|
|**Hand cleanliness**|||||
|Hands Cleanliness Index|7.403|0.205<br>(0.172)|0.226<br>(0.158)|0.21<br>(0.151)|
|**Self-Reported handwashing behaviour**|||||
|HW with soap and water previous to eat|0.402|0.056|0.069|0.095**|
|||(0.050)|(0.049)|(0.044)|
|HW with soap and water before food prep.|0.694|-0.052<br>(0.041)|-0.047<br>(0.040)|-0.042<br>(0.039)|
|HW with soap and water after fecal contact|0.656|-0.0121<br>(0.050)|-0.0000743<br>(0.049)|0.0199<br>(0.043)|
|HW with soap and water before feeding child|0.194|0.0387<br>(0.034)|0.0526*<br>(0.031)|0.0546*<br>(0.030)|
||||[27%]|[28%]|
|**Structured observations**|||||
|Observed HW with soap previous to feeding baby|0.076|-0.001<br>(0.048)|0.001<br>(0.054)|0.04<br>(0.060)|
|Observed HW with soap after fecal contact|0.342|-0.028<br>(0.059)|-0.021<br>(0.054)|-0.022<br>(0.056)|
|Observed HW with soap previous to preparing food|0.099|0.067|0.077**|0.068*|
|||(0.041)|(0.038)|(0.038)|
|||[67.6%]|[77.7%]|[68.6%]|
|Observed HW with soap previous to eat|0.189|0.096*|0.101**|0.115**|
|||(0.050)|(0.047)|(0.049)|
|||[50.7%]|[53.4%]|[60.8%]|
|Controls||NO|YES|YES|
|District dummies||NO|NO|YES|
|Clustered standard errors in parentheses; *** p<0.01 ** p<0.0|5 * p<0.1||||

65
manufacturing firm sample. As in the manufacturing sample, agglomeration economies seem to be larger in the core.

**Table 5: Summary Statistics, Services Firms**

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|_exi_|5,268|0.0786|0.2691|0|1|
|ln_empli_|5,073|3.4509|1.4482|0|10.1075|
|ln_agei_|5,248|2.3347|0.8359|0|4.6052|
|_fdii_|5,173|0.1170|0.3214|0|1|
|_techi_|5,322|1.1469|1.0568|0|4|
|ln_customsr_|3,246|2.3345|0.8063|0|4.1961|
|ln_outager_|3,441|2.2134|1.4052|-2.6159|6.3783|
|ln_licenser_|3,407|3.4647|0.7524|0.6931|5.8278|
|_creditr_|3,468|39.8169|22.9619|0|100|
|_corrupr_|3,379|22.6259|19.9234|0|100|
|_agg_sizer_|5,322|29.1557|26.8645|0.1511|92.8144|
|_agg_exr_|5,322|29.5193|29.1430|0|100|
|_agg_indr_|5,322|31.8127|28.5548|0.3247|100|

Regarding regional determinants, the coefficient signs are somewhat ambiguous. Almost none of these effects are significant in non-core regions. In core regions, only credit and corruption show the expected coefficient signs, while customs, electricity and license show inverse effects. We believe that these inverse signs reflect an endogeneity problem – that is, services firms that are located in regions with higher congestion costs (urban agglomerations) are more likely to export. As in the manufacturing sample, regional determinants matter more for firms in core regions.

Appendices 8 to 10 report the regression results by firm size. Many variables now show ambiguous results. Due to the low number of observations, the results should be interpreted with caution.

25
- Manning, Nick, Ranjana Mukherjee, and Omer Gokcekus. March 2000. _The experience ofpublic officials in Bolivia: What works in a weak institutional environment?_ Unpublished World Bank document.

- Milgrom, Paul, and J. Roberts. 1992. _Economics, Organization, Management,_ New Jersey: Prentice Hall.

- Miller, Gary J. 1992. _Managerial Dilemmas: The Political Economy of Hierarchy._ New York: Cambridge University Press.

- Mnookin and Komhauser. 1988. "Bargaining in the Shadow of the Law: The Case of Divorce," _Yale Law Journal_ 88: 950-972

- Moe, Terry M. 1984. "New Economics of Organization." _American Journal of Political Science,_ Vol. 28, No. 4. pp. 739-777.

- . 1990. "The Politics of Structural Choice: Toward a Theory of Public

- Bureaucracy." In Oliver E. Williamson (ed.), _Organization Theory: From Chester Barnard to the Present and Beyond._ New York: Oxford University Press.

- Nunberg, Barbara, and John Nellis. 1995. Civil Service Reform and the World Bank. _World Bank Discussion Papers;_ No. 161.

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. 1994b. Performance Management in Government: Performance Measurement and Results- Oriented Government, _Public Management Occasional Papers_ 1994 No. 3.

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- http://www.oecd.org/puma/sigmaweb/acts/civilservice/cslaws.htm; Civil Service Legislation: Checklist on Secondary Legislation (and other Regulatory Instruments) at http: //www. oecd. org/puma/sigmaweb/pubs/ENGPDF/sp 14. pdf, both from OECD's SIGMA program.

- Osbome, David and Peter Plastrik. 1997. _Banishing Bureaucracy: The Five strategies for Reinventing Government._ New York: Plume.

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38
# **4. Choice of Leasing vs. Buying (Privatizing) Land from the State by Russian Enterprises**

**_Hypothesis 2:_** _Russian enterprises often prefer to apply to lease land (rather than to purchase it) because obtaining ownership of land is accompanied by higher administrative outlays, longer delays, and frequent refusals by bureaucrats._

In this hypothesis we constructed a model to explain the variation in the dependent variable: _rate of land lease transactions_ ( Υ3 ). This variable was constructed using the information reported by respondents to the ARCS survey as the ratio of the number of land lease transactions to the total number of land transactions (lease plus privatization) in each of the surveyed regions.

The first step in testing the above mentioned hypothesis was the construction of a correlation matrix to examine simple paired relations between the variable of interest and other potential explanatory variables (and to examine the extent to which the explanatory variables are interrelated). The following administrative barriers were considered: procedure duration, number of stages, the cost of a procedure, frequency of unofficial payments etc. (for the list of variables see Annex).

Although Procedure No.2 (purchase of land for construction – see Page 10 for the list of procedures) deals with both leasing (Procedure No.2.B) and privatization of land (Procedure No.2.A), all the following analysis is limited to Procedure No.3 (lease or privatization of land under privatized buildings). This is because the number of observations for Procedure No.2 is not sufficient to produce regional averages<sup>26</sup> . From the correlation matrix we have concluded that the duration of the procedure, its official cost, total cost, and rate of refusal are all significantly correlated with the rate of land lease transactions.

In its generic form, the tested models look as follows: where: Θ is a vector of control variables from a list shown in the Annex; and τ _A_ is the logarithm of duration of land plot privatization procedure No. 3.a. by ARCS companies.

Table 4.1 shows that the most pronounced administrative barrier influencing the decision of a business either to buy or to lease the land in the surveyed regions is the procedure duration. This variable is significant in all settings with a positive coefficient.

> 26 Procedure No.1 is not included because it is dealing only with temporary leasing of land plots, i.e. it does not have a “twin” procedure covering privatization of the state and municipal lands.

17
the co-movement of commodity price returns and stock market uncertainty, especially in the time period starting from 2007. On the other hand, from a statistical point of view, real interest rates do not seem to have much effect on the co-movement of price returns.

Future research on this topic should explore even further the dynamic behavior of commodity prices, the degree and evolution of market interrelations and their determinants. If increasing interdependencies across commodities occur, they may limit the effectiveness of diversification strategies aimed at reducing price risk. The steady expansion of the biofuel industry in the United States and the European Union may further strengthen the extent and size of correlations between agricultural and energy commodity prices in the near future. The growing financial integration of commodity markets may also continue stimulating cross-market dependencies, while the underlying factors driving these associations and their dynamics may keep changing over time.

# **References**

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- **Alquist, Ron, and Olivier Coibion.** 2013. “The Comovement in Commodity Prices: Sources and Implications.” Manuscript.

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- **Beck, Stacie.** 1993. “A Rational Expectations Model of Time Varying Risk Premia in Commodities Futures Markets: Theory and Evidence.” _International Economic Review_ , 34(1): 149–168.

- **Beck, Stacie.** 2001. “Autoregressive Conditional Heteroscedasticity in Commodity Spot Prices.” _Journal of Applied Econometrics_ , 16(2): 115–132.

- **Bollerslev, Tim.** 1990. “Modelling the Coherence in Short-Run nominal Exchange Rates: a Multivariate Generalized ARCH Model.” _The Review of Economics and Statistics_ , 498–505.

- **Breusch, Trevor Stanley, and Adrian Rodney Pagan.** 1980. “The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics.” _The Review of Economic Studies_ , 47(1): 239–253.

- **Byrne, Joseph P, Giorgio Fazio, and Norbert Fiess.** 2013. “Primary Commodity Prices: Co-Movements, Common Factors and Fundamentals.” _Journal of Development Economics_ , 101(C): 16–26.

- **Cashin, Paul, C. John McDermott, and Alasdair Scott.** 1999. “The Myth of Co-Moving Commodity Prices.” IMF Working Paper 99/169.

12
<!-- Start of picture text --> Figure 7: Average per capita Remittances, by consumption deciles<br>A. Sri Lankan Rupees, 2012/13 prices B. Share of total income<br>1200 0.09<br>0.08<br>1000<br>0.07<br>800 0.06<br>0.05<br>600<br>0.04<br>400 0.03<br>200 0.02<br>0.01<br>0<br>0<br>poorest 3 5 7 9<br>decile poorest 3 5 7 9<br>decile<br>Per capita consumption deciles<br>Per capita consumption deciles<br>2002 2006/07 2009/10 2012/13 2002 2006/07 2009/10 2012/13<br><!-- End of picture text --> Source: Own estimates based on HIES <!-- Start of picture text --> Figure 8: Average per capita Samurdhi, by consumption deciles<br>A. Sri Lankan Rupees, 2012/13 prices B. Share of total income<br>180<br>0.09<br>160 0.08<br>140 0.07<br>120 0.06<br>100 0.05<br>80 0.04<br>0.03<br>60<br>0.02<br>40<br>0.01<br>20<br>0<br>0<br>poorest 3 5 7 9<br>poorest 3 5 7 9<br>decile<br>decile<br>Per capita consumption deciles<br>Per capita consumption deciles<br>2002 2006/07 2009/10 2012/13 2002 2006/07 2009/10 2012/13<br>Source: Own estimates based on HIES<br>Note: Total net income after any tax and transfers<br><!-- End of picture text --> 19
8 The results of this analysis suggest that government spending, especially when financed by credit, contributes to the transitory CPI divergences which have been noted. Neither the money stock, not private credit has any 8 independent explanatory power, nor does the value of exportsl. A sample of the regression results is contained in Table 1.

# 2.2 Dynamic impact of core prices on periphery inflation.

Turning to quarterly data in order to explore the short-term dynamics of inflation transmission, regression equations were estimated for inflation in the largest countries in the CFA zone, i.e. Cote d'Ivoire and Cameroon, and in Lesotho and Swaziland. An appropriate type of model to capture this kind of transmission is the error-correction model, according to which there is both a short-term and a long-term transmission between core and periphery inflation. A surge in core inflation leads to an early response in periphery inflation; to the extent that the change in the price level at the core does not immediately pass through to the periphery, there is a gradual catching-up process to the equilibrium core-periphery price relationship. We hypothesize that this long term relationship is one of proportionality, subject to a possible linear drift. Thus, long-term price trends in, for example, Cote d'Ivoire, may be represented by: log P*,, -=a + log P1 ,.+fT.

Here T represents a linear time trend. The short-term dynamics of the price level are composed of a partial adjustment to changes in core inflation plus convergence to the long-term price trend: A log P,, - yA log Pi,. - t(log P., log PSCb)-I.

For this representation to be valid with no time trend (i.e. the null hypothesis P-0), the core and periphery price levels should be cointegrated. Figure 3 plots the gaps between core and periphery price levels: the figure is in logs and shows that deviations of plus or minus about 10 per cent have been observed. There is no clear trend apparent in the gaps, suggesting again that the series may have a common trend. Nevertheless, using standard tests, the quarterly data series cannot reject 18 Export performance does influence private, and possibly government, consumption spending. Fooled cross-country regressions (also based on deviations from mean share) of government consumption found domestic credit to government significant (t-2.2), with exports less so (t-1.8). The RSQ in that regression, which included a lagged dependent variable was 0.63. For private consumption, also with a lagged dependent variable, government consumtpion had a t-statistic of 3.0 and exports 2.7, with an RSQ of 0.85.