rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Comparison of Turkey and Germany Findings**

||**Turkey (2005-2012)**|**Germany (2000-2007)**|
|---|---|---|
|Activity Effect|51%|15%|
|Sectoral Structural Effect|-2%|-6%|
|Firm Structural Effect|-2%|-4%|
|Intensity Effect|-13%|1%|
|_Aggregate Energy Consumption_<sup>**10**</sup>|_27%_|_5%_|

Second, although their magnitudes by country differ significantly, the activity effect has been the main driver of the change in aggregate energy consumption in both countries with 51% for Turkey and 15% for Germany. Third, both the sectoral structural effect and the firm structural effect had a more contractionary effect on aggregate energy consumption for Germany (-6% and -4%, respectively) than for Turkey (-2% for both effects). This implies that, although across-sector and across-firm within-sector production reallocation has been towards energy efficient sectors/firms in both Germany and Turkey, these effects were much stronger in Germany. Finally, while the energy intensity effect for Germany was almost stagnant over 2000-2007 with a mere 1% increase, the energy intensity of a specific firm decreased by on average an impressive 13% over the 2005-2012 period.

To recap, using a firm level decomposition methodology, we documented that, rather than across sectors structural change, and within sector across-firm structural change, the activity effect and the firm intensity effect were the drivers of the change in energy consumption in Turkish manufacturing industry over the 2005-2012 period. With the superiority of firm level analysis over sector level analysis, we could show in this study that the energy efficiency increases within sectors seen in Table 2a and Table 2b are from the increase in firm level efficiency rather than shift of production towards more energy efficient firms within sectors.

As for policy implications, we believe that there are two areas that need to be investigated further for designing policies aiming at higher energy efficiency in Turkey. First, the very small role of the production shift from higher energy intensive sectors to lower energy intensive sectors (sector structural effect) as well as from higher energy intensive firms to lower energy intensive firms within-sectors (firm structural effect) might be indicating serious market failures and barriers that prevent more energy efficient firms from increasing their share in total production in Turkish manufacturing industry. Therefore, addressing the source of this low-level dynamism could yield useful results for enhancing energy efficiency.

> 10 In Petrick (2015), the aggregate CO2 emissions is decomposed, instead of the aggregate energy consumption. We calculated aggregate energy consumption for that study by deducting the effects of fuel mix change and emission factor of fuels from the aggregate CO2 emissions presented in that study.

17
Pará’s low HDI performance is mainly caused by lack of income. Disaggregating the HDI reveals that Pará is performing relatively well in education and health, while income generation shows difficulties. Education improved 15 percent during 1991-2000 and reached 0.81 in 2000 (see Table 4.6a). This placed Pará sixteenth in the educational ranking. In health, measured by life expectancy at birth, Pará ranked higher (number 12) and increased by 5.8 percent during 1991-2000 and reached 0.72 in 2000, only 0.05 points below Rio Grande do Sul that ranks number one (Table 4.6b). In income, Pará ranked twentieth, down from a seventeenth place in 1991. Income in Pará only increased 4.6 percent in the 1990s (Table 4.6b). The seven states below Pará in the raking, all started from a very low initial and experienced income growth rates double or triple that of Pará.

|**Table 4.6a:**|**HDI and HDI-E**|**ducation**<br>**1991-2000**|||
|---|---|---|---|---|
|**Hum**|**an Development**<br>**Municipalities**<br>**(HDI-M)**|**Index-**|**H**<br>**Ed**|**DI-M**<br>**ucation**|
|||**2000**<br>**Growth (%)**<br>**1991-2000**||**2000**<br>**Growth (%)**<br>**1991-2000**|
|Distrito Federal||0.844<br>5.9|Distrito Federal|0.935<br>8.3|
|São Paulo||0.814<br>5.3|Santa Catarina|0.906<br>12.1|
|Rio Grande do Sul||0.809<br>6.9|Rio Grande do Sul|0.904<br>9.4|
|Santa Catarina||0.806<br>8.9|Rio de Janeiro|0.902<br>7.7|
|Rio de Janeiro||0.802<br>6.9|São Paulo|0.901<br>7.6|
|Paraná||0.786<br>9.3|Amapá|0.881<br>16.6|
|Goiás||0.770<br>8.8|Paraná|0.879<br>13.0|
|Mato Grosso do Sul||0.769<br>8.0|Goiás|0.866<br>13.2|
|Mato Grosso||0.767<br>10.2|Roraima|0.865<br>15.3|
|Espírito Santo||0.767<br>9.8|Mato Grosso do Sul|0.864<br>11.8|
|Minas Gerais||0.766<br>9.7|Mato Grosso|0.860<br>16.0|
|Amapá||0.751<br>8.8|Espírito Santo|0.855<br>11.9|
|Roraima||0.749<br>5.5|Minas Gerais|0.850<br>13.2|
|Rondonia||0.729<br>11.3|Rondonia|0.832<br>14.9|
|Tocantins||0.721<br>13.6|Tocantins|0.827<br>24.1|
|**Pará**||**0.720**<br>**8.6**|**Pará**|**0.815**<br>**14.8**|
|Amazonas||0.717<br>7.3|Amazonas|0.813<br>14.9|
|Rio Grande do Norte||0.702<br>13.6|Bahia|0.785<br>27.6|
|Ceará||0.699<br>17.0|Rio Grande do Norte|0.779<br>21.4|
|Bahia||0.693<br>15.3|Ceará|0.772<br>27.9|
|Acre||0.692<br>11.6|Sergipe|0.771<br>22.3|
|Pará||0.692<br>12.6|Pará|0.768<br>19.3|
|Sergipe||0.687<br>13.2|Acre|0.757<br>21.5|
|Paráíba||0.678<br>16.1|Maranhão|0.738<br>28.9|
|Piauí||0.673<br>14.7|Paraíba|0.737<br>28.0|
|Maranhão||0.647<br>17.4|Piauí|0.730<br>24.8|
|Halagaos||0.633<br>18.3|Alagoas|0.703<br>31.4|
|_Source_: UNDP.|||||

27
where _V_<sup>�</sup><sup>_h_</sup> is the proportional change in household real income. In the remainder of the paper, we estimate the different components of equation (7) and study them in detail. We also use equation (7) to build ex-post counterfactual distributions. Let _x_<sup>_h_</sup> 0<sup>betheobserved,</sup> ex-ante level of real household income (from the data compiled in the household surveys). The counterfactual real income _x_ �<sup>_h_</sup> 1<sup>is</sup> Much of what we do below hinges on the comparison of the ex-ante and ex-post distributions of income.

Since our propositions are derived from an agricultural household model, which is not often used in the trade literature, it is important to bear in mind some of the key features of our approach when interpreting the results. If we take the standard models at face value, it is clear that several data limitations would prevent us from fully accurately estimating the welfare effects of trade stemming from those models. These limitations are inherent to household surveys. For example, information on returns to capital, especially corporate profits and so on, is often patchy and typically missing altogether in the household surveys. Consequently, it is very difficult to map the results from those standard models to our empirical results which are based on household surveys. There might therefore be some discrepancies between some of our results and standard trade theory. For example, Stolper-Samuelson effects resulting in differential impacts on the returns to capital vs labor, or to skilled vs unskilled labor, may not be captured by our analysis. However, most of the discussion about poverty, inequality and household welfare is (to a large extent) based on household surveys, which remain the dominant instrument for measuring household consumption and income portfolios. Such surveys are thus a natural starting point for analyzing the distributional impacts of trade policy.

Our results also have interesting implications for trade theory. We argue that the most innovative feature of our approach is household heterogeneity, which is often missing in standard trade models. Heterogeneity takes two forms. One is consumption heterogeneity and non-homothetic preferences. In particular, since the poor and the rich allocate their 9
seminal article in that we elect to focus on a binary classification of construction intensity per district (above or below the aggregated number of schools per 1,000 children planned for the whole country). Our analysis also focuses on the contrast between two cohorts for which primary school construction would have provided a different treatment dose. In the ECED data, the vast majority of mothers are born after 1957-1962 (the comparison cohort used in Duflo (2001)) because the sampling was based on being mothers to young children in 2013. Thus, our analysis contrasts women born between 1966 and 1970 with those born between 1971 and 1975. Since the timing of construction was variable within districts and older children are unlikely to benefit from newly constructed primary schools, the latter cohort (1971-1975) is expected to have accrued most of the benefits of increased access to elementary education generated by INPRES.

We start by estimating models that confirm the effect of planned school construction on the education of mothers interviewed in 2013. For the effect on the mother’s children, we focus on a reduced-form or intent-to-treat analysis because the impact of school construction over the second generation may result from multiple mechanisms. In Figure A1, we reproduce these findings using data from the 2010 Census. The additional data allows us to examine the parallel-trends hypotheses (at least in the dimension of maternal schooling) which sustain our empirical design but, due to aforementioned child-mother age restrictions in our working sample, could not be directly performed.<sup>12</sup> Our full econometric specification based on this reasoning and focused on maternal educational outcomes is as follows: where _Intensed_ is an indicator function for districts where INPRES construction was more intensive than the national level, _Y oungc_ flags mothers in 1971 to 1975 birth-cohorts. Fixed-effects for district of birth ( _μd_ ) and year-of-birth ( _φc_ ) guarantee that the parameter _τ_ is interpreted as a difference-in-differences coefficient. We complement the model with interactions between other

> 12We also rely on the fact that studies of INPRES utilizing alternative data sources have shown that the parallel trend assumption seems to hold (Akresh et al., 2018; Duflo, 2001; Mazumder et al., 2019).

16
In the above expression, the distribution adjustment factor is equal to a weighted average of changes in the slope of the Lorenz curve. An assessment based uniquely on the distributional factor in (2.16) or (2.18) would be consistent with Kakwani and Pernia (2000) interpretation of pro-poor growth. Accordingly, growth would be considered propoor only when the distributional factor is positive.

# **_3. Poverty Implications of Economic Growth in Indonesia_**

# **_3.1.  The Era of Sustained Growth and Poverty Reduction_**

Indonesia has a long history of high achievements in growth and poverty reduction, even in the face of adverse conditions. In the 1980s, the country suffered a serious deterioration in its terms of trade with the rest of the world. In particular, the price of oil (one of Indonesia’s main exports and source of government revenue) fell by 63 percent between 1981 and 1986 (Ravallion and Huppi 1991). The government responded with two devaluations<sup>10</sup> in 1983 and 1986, and a sweeping reform of budgetary and structural policies. The rate of economic growth slowed down during the adjustment period, but remained positive and above the rate of growth of the population. Poverty continued to decline over the period.

These positive achievements were mainly due to pre-existing conditions and to government commitment to promoting broad-based economic growth and pro-poor implementation of economic reforms (World Bank 1990). The low level of inequality in the distribution of living standards made it possible for modest growth rates in per capita expenditure to lead to substantial poverty reduction. In the 1970s and early 1980s, the

> 10 On March 30, 1983 the rupiah was devalued from 703 to 970 to the US dollar. On September 12, 1986 the price of a dollar was increased from 1,134 to 1,644 rupiahs (World Bank 1990).

14
_Z_ * ), namely that an equal proportionate increase in all consumptions leaves measured poverty unchanged.

Second, the consumption level at which the kink occurs in the Chen-Ravallion schedule of relative poverty lines is appreciably higher than for our proposed new schedule. For the Chen-Ravallion relative poverty lines the kink was at a consumption level of $3.24 per day at 1993 PPP, while the new schedule of relative poverty lines proposed in the last section has a kink at $1.95 a day at 2005 PPP. If we had instead chosen max($.125, _Ci_ / 3) as the relative poverty line at 2005 PPP, the kink would be at a consumption level of $3.75 a day instead of $1.95. There are 18 countries with PCE in the interval ($1.95, $3.75), i.e., there are an extra 18 countries in the segment where the absolute line is no longer binding. Alternatively, if we use max($.120, _Ci_ / )3 as the relative poverty line (given that $1.20 is our estimate of the mean poverty line at 2005 PPP corresponding to $1.08 at 1993 PPP) then we find that there is an extra 16 countries for which the absolute line is no longer binding.

So our new data on national poverty lines suggest that relative poverty is a more prominent concern amongst a number of developing countries than found by RDV. This echoes our earlier finding that the overall elasticity of the poverty line to the mean in our sample is quite high—less than unity but similar to some past estimates for developed countries.

# **6. PPP’s for the poor**

The above calculations have used PPP’s constructed for international comparisons of mean consumption, not for comparing poverty lines. In principle, it would be better to have PPPs weighted to the average consumption bundle of people near the poverty line (as was argued by RDV). It is unclear how much this matters for measuring poverty lines in the poorest countries, since national poverty lines tend to be approximately equal to mean consumption in those countries. In the context of our new data set on national poverty lines, it should first be recalled that we also find that the national poverty lines tend to be roughly equal to mean consumption for our reference group of the poorest countries. However, that does not mean that the PPP for aggregate consumption is appropriate for those countries, since the underlying prices are expenditure weighted, not population weighted. Even so, it remains likely that it will be at higher income levels that the divergence between aggregate consumption PPP and a “PPP for the 20
# **Table 4 - Preferential Treatment and Political Connection Strength**

The table investigates the extent to which the political connection strength affects the favorable treatment of politically connected firms by banks. Panels A and B show the results using two proxies for connection strength respectively: being in the same industry that is under the chairman’s supervision and being headquarted in the same municipality as the chairman. A unit of observation is a loan at the firm-bank-quarter level. The dependent variables are loan terms at origination and ex post performance respectively. Please refer to Appendix A for variable definitions. The statelevel yearly GDP growth and unemployment rate are included as controls in all specifications. All regressions include _firm*bank_ and _bank*quarter_ fixed-effects. Standard errors are reported in parentheses and are clustered at the state level. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.

||LoanV|olume|Loans|pread|Mat|urity|D(Col|lateral)|D(De|fault)|
|---|---|---|---|---|---|---|---|---|---|---|
|**Panel A Same Industry**|||||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Chairman|0.386|0.345|-0.015|-0.016|0.118***|0.099**|-0.023***|-0.020***|0.008***|0.008***|
||(0.280)|(0.271)|(0.012)|(0.012)|(0.040)|(0.038)|(0.007)|(0.006)|(0.002)|(0.002)|
|Chairman*SameInd|2.341***|2.414***|-0.023|-0.022|-0.069|-0.051|0.012|0.013*|0.013***|0.010***|
||(0.232)|(0.220)|(0.018)|(0.019)|(0.045)|(0.049)|(0.009)|(0.007)|(0.001)|(0.002)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Quarter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry*Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-Square|0.021|0.021|0.414|0.415|0.201|0.202|0.368|0.372|0.021|0.023|
|N|594,534|594,534|594,534|594,534|594,534|594,534|594,534|594,534|480,354|480,354|
|**Panel B Same Municipalit**|**y**||||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Chairman|0.700***|0.658**|-0.019*|-0.019*|0.108***|0.092**|-0.023***|-0.020***|0.006***|0.007***|
||(0.239)|(0.245)|(0.011)|(0.012)|(0.039)|(0.035)|(0.006)|(0.005)|(0.002)|(0.002)|
|Chairman* SameMuni|1.544*|1.568*|-0.012|-0.010|-0.034|-0.014|-0.045**|-0.044**|0.021***|0.019***|
||(0.925)|(0.923)|(0.019)|(0.018)|(0.128)|(0.120)|(0.019)|(0.016)|(0.009)|(0.008)|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Quarter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry*Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|R-Square|0.021|0.021|0.414|0.415|0.201|0.202|0.368|0.372|0.021|0.022|
|N|594,534|594,534|594,534|594,534|594,534|594,534|594,534|594,534|480,354|480,354|

38
PPR Working Papar Serlba

|||ALhr|Dala|Contact<br>f<br>aD|
|---|---|---|---|---|
|WPS242|A Multi-Level Model of School<br>Effectiveness in a Developing<br>Country|Marlaine E. Lockheed<br>Nicholas T. Longford|July 1989|C. Cristobal<br>33640|
|WPS243|Averting rinancial Crisis -<br>Kuwait|Fawzi H. Al-Sultan|July 1989|R. Simaan<br>72167|
|WPS244|Do Caribbean Exporters Pay<br>Higher Freight Costs?|Alexander J. Yeats|July 1989|J. Epps<br>33710|
|WPS245|Developing a Partnership of<br>Indigenous Peoples, Conservation<br>and Land Use Planners in Latin Am|Peter Poole<br>ists,<br>erica|August 1989|S. Davis<br>38622|
|WPS246|Causes of Adult Deaths in<br>Developing Countries: A Review<br>of Data and Methods|Richard Hayes<br>Thierry Merte ,s<br>Geraldine Lockett<br>Laura Rodrigues|July 1989|S. Ainsworth<br>31091|
|WPS247|Macroeconomic Policies for<br>Structural Adjustment|Carlos A. Rodriguez|August 1989|R. Luz<br>61588|
|WPS248|Private Investment, Government<br>Policy, and Foreign Capital in<br>Zimbabwe|Mansoor Dailami<br>Michael Walton|August 1989|M. Raggambi<br>61696|
|WPS249|The Determinants of Hospital<br>An Analysis of Ethiopia|Ricardo B.-Dicowsky<br>David W. Dunlop|August 1989|V. Israel<br>48121|
|WPS250|The Baker Plan: Progress,<br>Shortcomings, and Future|William R. Cline|August 1989|S. King-Watson<br>33730|
|WPS251|Patents, Appropriate<br>Technology and North-South<br>Trade|Ishac Diwan<br>Dani Rodrik|August 1989|S. King-Watson<br>33730|
|WPS252|Do the Secondary Markets<br>Believe in Life After Debt|V. A. Hajivassiliou|August 1989|S. King-Watson<br>33730|
|WPS253|Public Debt, North and South|Helmut Reisen|August 1989|S. King-Watsin<br>33730|
|WPS254|Future Financing Needs of the<br>Highly Indebted Countries|Ishrat Husain<br>Saumya Mitra|August 1989|S. King-Watson<br>33730|
|WPS255|The External Debt Difficulties of<br>Low Income Africa|Charles Humphreys<br>John Underwood|August 1989|S. King-Watson<br>33730|
|WPS256|Cash Debt Buybacks and the<br>Insurance Value of Reserves|Sweder van Wijnbergen|||
Table 1. Explaining decile shares in transition countries (with the overall EBRD index)

|Decile|**First**|**Second**|**Third**|**Fourth**|**Fifth**|**Sixth**|**Seventh**|**Eighth**|**Ninth**|**Tenth**|
|---|---|---|---|---|---|---|---|---|---|---|
|Growth rate|-0.265<br>(2.80)**|-0.066<br>(0.81)|-0.010<br>(0.13)|0.001<br>(0.01)|0.002<br>(0.03)|-0.011<br>(0.20)|-0.004<br>(0.08)|-0.003<br>(0.05)|0.135<br>(1.34)|0.221<br>(0.58)|
|Inflation|-0.019<br>(3.43)**|-0.019<br>(3.89)**|-0.016<br>(3.47)**|-0.011<br>(2.83)**|-0.008<br>(2.33)*|-0.005<br>(1.59)|-0.001<br>(0.48)|0.005<br>(1.75)|0.021<br>(3.60)**|0.052<br>(2.33)*|
|EBRD_total|-0.067|-0.072|-0.059|-0.038|-0.025|-0.013|-0.003|0.018|0.084|0.176|
||(3.16)**|(3.90)**|(3.40)**|(2.51)**|(1.83 )|(1.09)|(0.29)|(1.51)|(3.67)**|(2.04)*|
|Exp_gdp|-0.001|0.0003|0.0003|0.001|0.0004|0.0001|-0.0002|0.00001|0.002|-0.002|
||(0.85)|(0.36)|(0.41)|(0.94)|(0.78)|(0.16)|(0.50)|(0.02)|(2.17)*|(0.62)|
|Polity2|0.008|0.008|0.007|0.006|0.005|0.004|0.002|-0.001|-0.005|-0.034|
||(3.01)**|(3.45)**|(3.37)**|(3.19)**|(3.00)**|(2.63)**|(1.73)|(0.77)|(1.78)|(3.20)**|
|Dincome|-0.013|-0.001|0.007|0.013|0.015|0.016|0.016|0.025|0.040|0.012|
||(0.96)|(0.06)|(0.60)|(1.27)|(1.46)|(1.67)|(1.65)|(2.13)*|(1.69)|(0.21)|
|Quarterly|-0.005|-0.015|-0.012|-0.016|-0.018|-0.018|-0.013|-0.005|0.013|0.155|
||(0.09)|(0.29)|(0.25)|(0.36)|(0.42)|(0.43)|(0.31)|(0.09)|(0.13)|(0.64)|
|Semiannual|0.030|-0.004|-0.010|-0.021|-0.020|-0.038|-0.052|-0.065|-0.040|0.153|
||(0.47)|(0.08)|(0.19)|(0.42)|(0.43)|(0.84)|(1.14)|(1.17)|(0.36)|(0.59)|
|Annual|-0.005|-0.010|-0.011|-0.013|-0.009|-0.006|-0.001|0.007|0.038|0.091|
||(0.39)|(0.95)|(1.02)|(1.33)|(0.93)|(0.67)|(0.16)|(0.67)|(1.70)|(1.78)|
|Constant|0.577<br>(6.71)**|0.721<br>(9.61)**|0.764<br>(10.91)**|0.793<br>(12.67)**|0.860<br>(15.01)**|0.937<br>(18.28)**|1.058<br>(21.98)**|1.187<br>(21.36)**|1.202<br>(11.19)**|1.901<br>(5.44)**|
|No of obs|177|177|177|177|177|177|177|177|177|177|
|R-square|0.546|0.606|0.604|0.589|0.510|0.379|0.200|0.280|0.312|0.539|

Note: Statistically significant coefficients (at 1 and 5 percent levels) denoted by respectively two and one asterisks; _z_ values between brackets Dincome is a binary variable taking value of 1 is survey is income-based and 0 is expenditure- or consumption-based. Quarterly, semiannual, annual are binary variables for survey reference period (the omitted variable is monthly). Inflation rate is expressed in natural logs. All regressions include country dummies.

16
|||Tabl|e 2: Basic r|egressions||||
|---|---|---|---|---|---|---|---|
|Equation number|2-1|<br>2-2|<br>2-3|<br>2-4|2-5|2-6|2-7|
|Model variant|Ability &<br>morality|Morality-<br>ability cor-<br>relation|“Institutions<br>rule”<br>variables|Morality &<br>institutions|Weighted<br>Least<br>Squares|Tanzania<br>dropped|Additional<br>geography<br>variables|
|No secondary|-3.798***|-2.906***||-2.012***|-1.981***|-2.221***|-1.846***|
|schooling (%)|(-4.49)|(-3.57)||(-3.31)|(-2.69)|(-3.24)|(-3.25)|
|Morality|0.048***|0.036***||0.017*|0.015*|0.020**|0.017**|
||(6.31)|(4.63)||(1.94)|(1.73)|(2.20)|(2.06)|
|Morality-ability||5.334***||3.616***|3.679***|2.845***|3.472***|
|correlation||(3.77)||(3.71)|(3.97)|(2.68)|(3.35)|
|Distance from|||0.021***|0.010**|0.011**|0.009*|0.012**|
|equator|||(3.60)|(2.07)|(2.12)|(1.80)|(2.06)|
|trade share of|||0.171|0.184|0.064|0.171|0.131|
|GDP (log)|||(0.94)|(1.24)|(0.39)|(1.15)|(0.72)|
|Rule of law index|||0.669***|0.449***|0.413***|0.453***|0.428***|
||||(8.01)|(4.75)|(4.39)|(4.77)|(4.36)|
|Log of population|||||||-0.019<br>(-0.23)|
|Log of land area|||||||-0.028<br>(-0.039)|
|Landlocked|||||||-0.309*<br>(-1.81)|
|Constant|8.463|7.889|7.610|7.246|7.826|7.424|8.173|
||(36.87)|(30.79)|(9.44)|(9.24)|(9.62)|(9.19)|(6.01)|
|R<sup>2</sup>|.52|.62|.63|.77|.75|.77|.77|
|Observations|74|74|74|74|74|73|74|
|Mean dep. var.|9.14|9.14|9.14|9.14|9.14|9.17|9.14|

Dependent variable is log of 2008 GDP per capita (PPP-adjusted). Test statistics (from robust standard errors) are in parentheses. A *,** and *** respectively indicate significance at .10, .05 and .01 levels for two-tailed tests.

37