rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4.   Sales for Constant Returns to Scale Sectors: Determined by Constant Elasticity of Transformation Production Structure**

<!-- Start of picture text --> Russia sales<br>Oblast r sales (not oblast r) Rest of world sales<br>σT = 5<br>CET<br><!-- End of picture text --> Output (CRTS good g in Oblast r)

*The elasticity of transformation is equal to 4 in crude oil, natural gas and coal; otherwise 5.

78
**Table 5. Effect of Democracy and Culture**

||Log(cases/1|000 residents)|Pandemic m|ortalityrate|
|---|---|---|---|---|
||(1)|(2)|(1’)|(2’)|
|Democracy|0.208|1.029***|-0.009**|0.006|
||(0.184)|(0.246)|(0.004)|(0.006)|
|Individualistic culture|-0.364|2.953|0.012|-0.005|
|(imputation-augmented)|(1.540)|(2.246)|(0.025)|(0.023)|
|Trust|0.174|-6.208***|-0.003|-0.000|
|(imputation-augmented)|(1.110)|(1.806)|(0.015)|(0.014)|
|Democracy×Delay D||0.469*|||
|||(0.280)|||
|Individualistic×Delay D||-3.709||0.108*|
|||(3.219)||(0.056)|
|Trust×Delay D||2.569*|||
|||(1.542)|||
|Democracy×Elderly||-13.826***<br>(3.255)|||
|Democracy×Obesity||||-0.081**|
|||||(0.039)|
|Democracy ×SO2||0.592***<br>(0.195)|||
|Individualistic×Elderly||36.648*<br>18998|||
|||(.)|||
|Individualistic×SO2||-5.398***<br>(1.328)|||
|Trust×Obesity||35.529***|||
|||(9.199)|||
|DelayD|-0.236|0.181|-0.004|-0.043**|
||(0.169)|(1.249)|(0.003)|(0.021)|
|DelayI|0.183|-0.023|0.009***|0.011***|
||(0.146)|(0.155)|(0.003)|(0.003)|
|Elderly|-4.939**|-13.830*|0.011|0.003|
||(2.306)|(7.496)|(0.030)|(0.033)|
|Obesity|-1.400|-16.559***|0.038|0.070*|
||(1.534)|(3.917)|(0.030)|(0.041)|
|Urban share|1.221**|0.766*|-0.016*|-0.017**|
||(0.536)|(0.435)|(0.009)|(0.009)|
|SO2|0.391***|2.293***|0.001|-0.000|
||(0.124)|(0.517)|(0.002)|(0.002)|
|λ||0.519**<br>|||
|||(0.264)|||
|ρ||2.829*|||
|||(1.659)|||
|Country Char.|Yes|Yes|Yes|Yes|
|Continental Dummy|Yes|Yes|Yes|Yes|
|Missing indicators for Individualistic<br>Culture and for Trust|Yes|Yes|Yes|Yes|
|Obs.|140|140|140|140|
|Adjusted R<sup>2</sup>|0.723||0.293|0.325|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors in columns. Country Char. means basic country characteristics including log(GDP PC), log(population), average temperature, and two dummies indicated the distance to China, and missing indicators for the cultural indicators. For column (1) and (2), we have also tried using the subsample with the culture variables not missing, which has 58 observations. The results are quite similar for the culture and democracy variables, except that the coefficient of Individualistic Culture is positive and significant (with a coefficient of 6.21). Since doing this would imply the loss of more than half of the sample for which all other variables are available, we opt to use the sample that imputes missing cultural variables. In all the regressions, the missing indicators for the cultural variables are statistically insignificant.

For each regression, we have tried the OLS version and the spatial model, and we report only the preferred version: that is, the OLS if the two terms representing spatial interactions are both insignificant; and the spatial model if either one is statistically significant.
# **4. Results**

# **a. Regression results**

# **i. Association in growth rates**

Tables 5 and 6 report panel regression results based on specification (3).<sup>3</sup> The coefficient on lagged financial sector value added growth represents the percentage change in sector's growth rate in response to 1 percent higher financial growth.<sup>4</sup> Table 5 [about here] The regression results indicate that various sectors exhibit different degrees of sensitivity to financial growth and contractions. All the statistically significant coefficients are positive, indicating a positive association between growth in the financial sector and the real sectors. Specifically, construction, public utilities, and transportation sectors exhibit statistically significant sensitivity to growth and contractions in the financial sector. Construction is the most sensitive with a coefficient of 0.17 (statistically significant at 1 percent confidence level) indicating that a 1 percent increase in financial sector value added growth translates into a 0.2 percent increase in construction sector value added growth the following year. The coefficient of 0.08 for public utilities sector is also significant at the 1 percent level, followed by a 0.05 coefficient for the transportation sector regressions (significant at the 10 percent level).

> 3 As a robustness check, we also conducted panel regressions excluding the lagged value added per worker term (fourth term on the RHS of equation (3)) due to concern about possible serial correlation in the errors. The coefficients on lagged financial sector growth, except for government sector, are robust to the exclusion of the 3-lag ―convergence factor.‖

> 4 The panel estimation was performed with fixed effects specification, as the coefficients are statistically significantly different under the random effects as reported in the Hausman test results.

10
was also obtained from the World Federation of Exchanges. However, coverage is even more limited (only 32 countries and 4 years of data).

# **b. Controls**

The raw correlations between potential FDIs (shown separately for institutions and markets in Tables 2 and 3) are quite high This reflects the fact that the development of different markets and dimensions is affected by similar underlying determinants (both structural and policy-based) and proceeds on broadly parallel tracks as economies mature. This underlines the need for introducing structural controls. After some experimentation, the following set of controls was retained:

- _GDP per capita_

- _Poverty gap_<sup>_23_</sup>

- _Population size_

- _Population density_

- _Fuel exports to GDP_<sup>_24_</sup>

- _An offshore dummy_ (i.e., whether the country was defined by the IMF as an offshore center in 2007)

In view of the high correlation between income and poverty, the poverty gap was first regressed against per capita income. The residual of this regression was then substituted for the poverty variable. We also included an interacted term between country income level (GDP per capita) and country size (population). Except for the interacted term, all variables are expressed in logs, reflecting broadly constant elasticities.<sup>25</sup> We test a pooled OLS model, which for reasons of data availability, we restrict to the period 2000-05. The model is thus specified as: _LogYit_ = α + β 1 × _Log_ ( _GDPCAPit_ ) + β 2 _xLog_ 1( + _POVERTYit_ ) + β 3 × _Log_ ( _POP_ SIZEit_ ) + β 4 × _Log_ ( _POP_ DENSITYit_ ) + β 5 × _OFFSHOREi_ + β 6 × log(1 + _FUELit_ ) + β 7 _xPOP_ SIZEit xGDPCAPit_ + _YEAR_ DUMMIES_ + ε _it_ Where _Yit_ is the FDI for country _i_ at time _t._ The benchmark indicator is the residual. The regionally adjusted benchmark is obtained by adding regional dummies.26

> 23 This variable measures the proportion of the population under the poverty line, times the average distance from the poverty line. A value of zero was adopted for all high income countries (Source: Povcal Net, WorldBank)

> 24 The alternative of using a broader index that included all mining exports was also explored but the fit was found to be inferior, suggesting that the impact of oil exports on financial development is sector specific.

> 25 As a robustness check, we also estimated, for those indicators for which we have sufficient data, an alternative model based on the transformation of all variables into 25 quintiles and taking the inverse normal distribution to make the new variable normal. Results (see Appendix Table 1) are very close to those obtained with our standard specification.

> 26 We dealt with the outliers using an iterative algorithm that gradually eliminates outliers within the onepercent tails.

14
the sample distribution is not dependent on any particular set of parameter values, but represents results representative of the full distribution of parameter values.

We present the distribution of the results below for three key variables: welfare change as a percent of consumption, output change and skilled employment changes. A full compendium or results with the sample distributions and confidence intervals is in Jensen Rutherford and Tarr (2004). For each report variable, we calculate the percentage of solutions associated with a given result for the variable. Figure 3.2 shows that the welfare gains as a percent of consumption are, in most cases, between 6% and 8%. The minimum value is 4.5% and the maximum value is 11.4%. The statistics show that only 6.4% of the solutions are below a welfare gain of 6% and that 13.0% are above a gain of 8%. More than 80% of the solutions yield a gain between 6% and 8%. This shows that the welfare results are very robust within the six to eight percent of consumption range.

In figure 3.3, we focus on the employment impacts in the six sectors where the impacts are the greatest: the three sectors with the largest increase in employment and the three sectors with the largest decline in employment. We only show the results for skilled labor, as the results for unskilled labor are very close to the results for skilled labor. We assume total employment is unchanged, so employment must expand in some sectors and contract in others. The sectors where employment expands the most are: ferrous metallurgy, non-ferrous metallurgy and chemical industry. The manufacturing sectors where employment declines the most are: mechanical engineering, light industry and food industry. The results for all six sectors show that our central results are robust to most parameter configurations, and in particular that the expanding (declining) sectors are expanding (declining) for virtually all configurations. The figure also shows that the magnitude of the results for the expanding sectors is more uncertain 59
In Table IX, the determinants of a control change occurring within five years of crosslisting are analyzed using a Cox proportional hazard rate model. This methodology is useful in predicting discrete events (recorded as failures) in a panel setting and has been used by Pagano et. al (2002) in examining the decision by firms to cross-list. It estimates the effects of a set of covariates X influencing the probability of undergoing a control change at time t (the probability is referred to as the hazard rate h(t)). The model is written as: where β is a vector of coefficients and ho(t) is the baseline hazard rate (that is the hazard rate when all X variables are set to 0). The Cox proportional hazard rate estimator is a semi parametric estimator and is referred to as proportional hazard because it assumes that the hazard ratio h(t)/ho(t) is constant for any two firms at any point in time.

Table IX reports the estimates as exponentiated coefficients (exp (β1), exp(β2),...) rather as coefficients (β1, β2, ....), because exponentiated coefficients can be readily interpreted as the effect of unit change in independent variable X on the hazard ratio h(t)/ho(t).

The set of determinants include both firm-level characteristics and characteristics of the home country of the cross-listing firm. The set of firm characteristics include age of the firm, size (measured as logarithm of total assets), leverage ratio, Tobin’s Q, total asset growth and total foreign income growth of the company. I also use lagged values of the following country-level variables: GDPCAP is the log of GDP per capita of the home economy, TVT_GDP is stock market total value-traded to GDP which is defined as total shares traded on domestic stock market exchange to GDP, COMMON is a legal origin dummy which takes the value 1 for English common law countries and 0 otherwise, PROPERTY is the property rights regulation index from LLSV (1999) and the Index of Economic Freedom, and is a measure of the degree to which the government protects private property, and CAPCONTROL is an index of capital control regulations, as in Harrison et. al. (2003), constructed from the International Monetary 28
CIF/FOB ratios, have shown that a deterioration in the infrastructure from the median to the 25<sup>th</sup> percentile raised transport costs by 12 percentage points and reduced trade volumes by 28 percent. The issue of inadequate infrastructure is of particular relevance for Central Asia.

# **_Density_**

Estache and Goicoechea (2005) have shown that Central Asia has low levels of transport and communications’ infrastructure. For instance, railway density is on average 5.4 rail-km per 1,000 km2, i.e. one-third of the average railway density of low- and middle-income countries. This is a particular cause for concern given that approximately 90% of total freight transport in Central Asian countries during 2000 was by rail (see Raballand et al. 2005). It is therefore fair to say that low infrastructure levels are likely to affect Central Asian trade, and that the lack of investment in existing infrastructure increases transport costs.

Moreover, it can be shown (see Appendix 2) that even after controlling for low income levels, the density of infrastructure in Central Asia remains severely lacking.

# **_Maintenance and services_**

Low network density is only part of the problem, which is compounded further by the poor quality of the transport infrastructure. The Asian Bank of Development Bank (2006) documented a trend of deteriorating material and infrastructure, primarily as the result of inadequate maintenance (notably for railway infrastructure). According to Pomfret (2005), the insufficient funding earmarked for infrastructure maintenance is a phenomenon common to all former Soviet countries, where national networks often take precedence over regional networks. In some instances, governments even opted to set up alternative roads or railway lines to avoid crossing neighboring countries (largely for political reasons) despite the existence of regional lines. Consequently, existing infrastructure were left to deteriorate progressively.

Inadequate road maintenance has far-reaching implications for trade and transaction costs. As amply demonstrated in Sub-Saharan African contexts, bad roads result in damaged trucks and increased time spent on maintenance, thereby reducing the productive capital rate of return. In turn, this hampers the ability of intermediaries to link isolated producers (especially farmers) to markets. In extreme cases, markets may suffer total collapse, leading 7
# **Appendix: Environmental Database of the Model: Documentation of the Sources and Construction**

## **_A.1. Non-CO2 emission accounting_**

- 1.1. The environmental component of the data for the WTO model consists of emissions of seven air pollutants: PM, SO2, CO, NOx, CnHm, VOC, and CO2, which constitute the most commonly observed source of air pollution in the Russian Federation (Table A1). Data on non-CO2 emissions comes from the SUST-RUS database, which in turn relies on several publications of the government of Russia as explained below. The CO2 emission accounting is based on fuel-specific emission intensity and is discussed in part 0.

Table A1. Pollutants ( _p_ ) in the model

|**Pollutant**|**Abbreviation**|**Environmental impacts**|**Health effects**|
|---|---|---|---|
|Particulate<br>matter|PM|Soiling and damage to<br>materials, smog|Lung cancer, asthma,<br>birth defects|
|Sulphur dioxide|SO2|Acid rain, atmospheric<br>particulates|Asthmatic, alterations<br>in the lungs|
|Carbon<br>monoxide|CO|Leads to increased<br>concentrations of methane<br>and tropospheric ozone|Headache, nausea,<br>dizziness, seizures|
|Nitrogen oxide|NOx|Acid rain, eutrophication<br>in coastal waters|Difficulty breathing,<br>fluid build-up in the<br>lungs|
|Hydrocarbons|CnHm|Smog, leads to increased<br>concentrations of<br>tropospheric ozone|Affects the central<br>nervous system|
|Volatile organic<br>components|VOC|Damage to soil and<br>groundwater|Damage to liver,<br>cancer, headaches|
|Carbon dioxide|CO2|Climate change, ocean<br>acidification|High concentration:<br>rapid heart rate,<br>clumsiness|

Source: SUST-RUS 2012(a); EPA: Health Effects of Air Pollution 85
48 Table III.2 Cont'd

|Country (Year)|Government<br>(Percent)|Commerciala<br>(Percent)|<br>NGO<br>(Percent)|Otherb<br>(Percent)|
|---|---|---|---|---|
|Near East<br>Lcbnon (1984)|||||
|Clinical|1.2|46.4|52.4|0.0|
|Nonclinical<br>Egypt (1984)c<br>Clinical|0.6<br>45.4|78.5<br>50.5|20.6<br>3.0|0.0<br>1.1|
|Nonclinical<br>Horocco<br>(1984)C|19.1|19.4|0.5|1.0|
|Clinical|71.1|27.3|--|1.7|
|Nonclinical|55.6|42.6|--|1.9|

# Source: CPS; DHS; ESCAP (1987).

- a. Includes private physicians, hospitals, pharmacies, and any other private, non-NGO.

- b. Unspecified source, may encompas* NGOs when private, nonprofits are not a category, and **may** include commercial vhere it is not a separate category.

- co IUD and male and female sterilization.

**PP. 3686-tSR**
**Table 5. The effect of alternative specifications on scale parameters estimates, RLMS 19942017**

|||**Poole**|**d OL**|**BUC**||
|---|---|---|---|---|---|
|**Sen**|**sitivity scenarios**|Baseline<br>elasticity|Additional<br>child|Baseline elasticity|Additional<br>child|
|**_1_**|Exectations|0.649***|0.080***|0.410***|0.050*|
||p|_(0.03)_|_(0.01)_|_(0.09)_|_(0.03)_|
|**_2_**|Rf|0.497***|0.057*|||
||eerence group|_(0.06)_|_(0.02)_|||
|**_3_**|Life satisfaction|0.762***|0.117***|0.659***|0.043*|
|||_(0.03)_|_(0.01)_|_(0.11)_|_(0.03)_|
|**_4_**|Measurement error|0.571***|0.089***|0.342***|0.056*|
|||_(0.04)_|_(0.01)_|_(0.10)_|_(0.03)_|
|**_5_**|Utitd l|0.577***|0.020*|0.306***|-0.013|
||nresrce sampe|_(0.03)_|_(0.01)_|_(0.09)_|_(0.02)_|
|**_6_**|Pensioners|0.560***|0.064***|0.352*|0.046*|
|||_(0.04)_|_(0.01)_|_(0.15)_|_(0.03)_|

**Note:** Standard errors in parentheses are calculated using delta-method. All regressions include the same controls as in Table 1.

31
Table 4: Regressions for fixed investment with interaction terms (variables of interest), unbalanced 5-year average panel, 1980–2009<sup>_†_</sup>

||**I1**|**I2**|**I3**|**I4**|**I5**|
|---|---|---|---|---|---|
|Financial|0.017|0.028|0.034|0.102|0.097|
|development|(0.11)|(0.11)|(0.12)|(0.16)|(0.15)|
|Financial|||0.052|0.166|0.084|
|structure|||(0.14)|(0.24)|(0.23)|
|Fin. dev. _×_||||-0.153|-0.057|
|fn. struc.||||(0.22)|(0.21)|
|Institutional|-1.158|-1.152|-1.396|0.162|0.136|
|quality|(0.60)<sup>_∗_</sup>|(0.60)<sup>_∗_</sup>|(0.91)<sup>_∗_</sup>|(0.08)<sup>_∗∗_</sup>|(0.10)|
|Institutional|-0.934|-0.935|-1.221||0.098|
|structure|(0.48)<sup>_∗_</sup>|(0.47)<sup>_∗∗_</sup>|(0.75)||(0.09)|
|Inst. qual. _×_|0.692|0.697|0.869|||
|inst. struc.|(0.34)<sup>_∗∗_</sup>|(0.33)<sup>_∗∗_</sup>|(0.51)<sup>_∗_</sup>|||
|Economic controls|Yes|Yes|Yes|Yes|Yes|
|Structural controls|No|Partial|Full|No|Yes|
|Wald _χ_<sup>2</sup>|18,421<sup>_∗∗∗_</sup>|18,225<sup>_∗∗∗_</sup>|9,250<sup>_∗∗∗_</sup>|10,117<sup>_∗∗∗_</sup>|13,206<sup>_∗∗∗_</sup>|
|Hansen J|29.435|28.634|30.755|28.874|29.156|
|AR(2) z|-1.927<sup>_∗_</sup>|-1.853<sup>_∗_</sup>|-1.003|0.165|0.105|
|Instruments|45|46|47|41|44|
|N (countries)|321 (105)|321 (105)|229 (82)|236 (82)|236 (82)|

> _†_ All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. Period fixed effects, a constant term, and economic (all specifications) and additional structural controls (I2–I4) were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

institutional structure alone (without an interaction term) may yield a coefficient statistically indistinguishable from zero, as the two cancel out.

For financial development, including an interaction term with financial structure leads to the coefficient on all three being statistically insignificant. This echoes the result in column (B8) of Table 1, and could be due to a more restrictive sample being employed when financial structure is included. However, another reason can be surmised by examining the coefficient on the interaction term: since it is negative (and relatively large), allowing for interaction effects likely means that the negative conditioning effect of financial structure on development may potentially give rise to a statistically insignificant coefficient on the independent term.

Finally, we should also note that, across all specifications, institutional quality tends to be statistically significant,<sup>12</sup> but not so for financial development. While we hesitate to rule out financial development altogether due to the more restrictive sample in most of the specifications in Table 4, it is nonetheless the case that—as it was in Table 3—the significant impact of financial development on investment is a somewhat more fragile result.

> 12 Even for column (I5), where the coefficient on institutional quality is insignificant, _p_ = 0 _._ 158.

16
illustrated in Figure 1. The evaluation data includes sociodemographic variables, participation in formal schooling and training, labor-market outcomes, and detailed information on participation in the MVTP. Attrition reached 5.4 percent at baseline, 9.6 percent at the first follow-up survey, and 15 percent at the second follow-up survey. As shown in Table 1, and relative to similar interventions with similar timespan for the measurement of treatment effects, the attrition rate in this study is thus moderate.<sup>8</sup> Appendix Tables S1.1 and S1.2 provide results of tests for differences in baseline variables across treatment and control units for the six- and eighteen-month follow-up samples conditional on response status. For most baseline covariates, no systematic differences are observed between individuals in the treatment and control groups after conditioning on response status. Only for a small number of baseline covariates is the null of equality of means by treatment status rejected, after conditioning on response status. Finally, Appendix Table S1.3 reports the overall rate of attrition by treatment status. The null hypothesis that the overall attrition rate is of equal magnitude between treatment and control groups cannot be rejected.

Mean covariate balancing tests for the two experimental designs are shown in Table 2, ne for the allocation of training slots (left panel) and one for the allocation of weekly newsletters to individuals who received treatment (right panel). Baseline data show that the typical applicant is twenty-three years old, a woman (65 percent), and poor (82 percent lived in gers.<sup>_9_</sup> Almost half of the participants are married (45 percent), have children (42 percent), and live with their parents (47 percent). On average, they show high levels of formal

> 8 Table 1, Column 9, shows attrition rates that range from 18.5 (Card et al., 2011) to 46 percent (Cho et al. 2013). Alzua et al. (2016) reported no attrition because their reliance on administrative data.

> 9 Gers are traditional Mongolian dwellings. They are portable, round tents covered with skins or felt that are used by several distinct nomadic groups in Central Asia. Mongolia is divided into Ulaanbaatar, the capital, and twenty-one provinces (aimags). Ulaanbaatar is divided into six main and three small suburban districts (khoroos), which are further divided into smaller administrative units called khesegs. Poor khesegs are called gers, or impoverished neighborhoods, which, in most cases, lack water, drainage, and electric infrastructure.

15