rafmacalaba/datause-encoder-data huggingface dataset:
_Step 3:_ Poverty and inequality outcomes are measured as follows. The poverty headcount ratio is measured as the proportion of individuals with a household per capita income below the poverty line. We use the upper-middle-income-country (UMIC) poverty line, which is calculated globally by the World Bank and takes the value of $5.50 per-person per-day in 2011 Purchasing Power Parity (PPP).<sup>11</sup> The headcount ratio is complemented by the poverty gap index, which measures how far below individuals are from the poverty line, as a proportion of the line. Finally, inequality is measured using the Gini coefficient, with 0 representing perfect equality, and 1 representing perfect inequality. Since the Gini is more sensitive to changes in the middle of the distribution, we use the 90/10 ratio as complementary measure. This ratio compares the income of the 90<sup>th</sup> percentile to the income of the 10<sup>th</sup> percentile.

Finally, taking advantage of the global scale of the CEQ initiative, the findings are benchmarked to international comparators. To choose comparators relevant to Turkey, we used the following criteria: a) the country is (at least) upper-middle-income, and b) a CEQ study has been produced in the last 5 years. As a result, we benchmark Turkey’s performance in comparison to the following eight countries: Argentina, Brazil, Chile, Mexico, Croatia, Poland, Russia, and the United States.

# **5. Overall Impact of Taxes and Spending on Poverty and Inequality**

# **_5.1 Impact on Income Inequality_**

The overall system of taxes and spending in Turkey leads to a significant reduction in income inequality. Figure 2 shows the overall impacts on the Gini coefficient and the 90/10 ratio following the income concepts described in Figure 1. The country achieves a reduction in Gini of about 0.07, down from 0.44, in the base scenario when old-age contributory pensions are treated as deferred income. The reduction is around 0.11 when those pensions are considered as transfers. Before any fiscal intervention, Market Income Gini is 0.48 (0.44 if pensions are added). The Disposable Income Gini drops to 0.42 once direct taxes and direct transfers are considered. Indirect taxes have a slight unequalizing effect, making the Gini coefficient of Consumable Income increase to 0.44. Of all the fiscal tools, in-kind benefits in education and health have the largest impact on inequality; the Gini coefficient of Final Income drops to 0.38.

> 11 Turkey’s National Institute of Statistics (TUIK) does not calculate a national poverty line based on local cost of food and non-food basic needs. It stopped doing so in 2009.

17
- Fourth, the effects land market regulations had on the efficacy of specific

- projects.

The first research finding – that housing demand across the world was highly regular – provided some perspective on how the Bank could most effectively structure many of the components of the housing projects. In retrospect, this finding may now seem almost naïve, but it is important to remember that it was only 10 years prior to that review that the first cross-country empirical study of housing investment was undertaken, by Burns and Grebler (1977). In addition, and more importantly, in the year that study was undertaken public housing production and ownership was the main vehicle of housing assistance throughout much of the developing world. For instance, at that time, China, the 15 countries established since the 1991 dissolution of the former Soviet Union, as well as India, Korea, Mexico, Argentina, and many African countries all had very active public housing programs.<sup>13</sup> Many of these countries also proscribed or discouraged private production of housing.

The work on housing demand also provided a basis for understanding what kind of standards and units were affordable to low-income families. It sought to demonstrate how and why the unaffordable standards pursued in most places left the public sector not only unable to meet housing demand, but also drove so much housing supply into the informal sector and illegality. It also showed why it was so important to focus on the details about the kind of housing units that the poor could afford without the aid of subsidies. That is, the work emphasized why the units produced could be sustainably financed only if modest housing standards appropriate to beneficiaries’ lower income levels were used.

> 13 At about the time of the last review, Tolley in work subsequently published in 1991, demonstrated the importance of rents based on a cost of capital perspective in China, contributing to China’s initial housing reform program. Shortly thereafter, in 1992, Renaud wrote  “Why the Former Soviet Union Needs Housing Markets.” (1992)

8
OD

# TABLE 19

# AVERAGE RATE OF GROWTH OF OUTPUT.<sup>INPUTS. AND TFP</sup> 01-0 FOR 1976-78. D011<sup>FOR 1979-80. AND 01-2 FOR 1981-65</sup> 00-PR FOR PRIVATE FIRMS AND 00-PU<sup>FOR PUBLIC FIRMS</sup>

|VARIABLE|LABEL|MEAN|**NINILUM**<br>VALUE|MAXIMUM<br>VALUE|
|---|---|---|---|---|
|-------|___--------------------------<br>DO-PR D1a0 --|-------------|----------------|---------<br>|
|VOL|AVERAGE LABOR GROWTH RATE<br>|0.054<br>|-0.242<br>|0.192<br>21|
|mGK|AVERAGE GROWTH RATE OF CAPITAL<sup>STOCK</sup>|0.082<br>|-0.006<br>|0-6<br>|
|VGCX|AVERAGE RATE OF GROWTH OF<sup>OUTPUT</sup><br>|0.067<br>|-0.220<br>|0.257<br>|
|VGCI|AVERAGE GROWTH RATE OF INTER<sup>INPUTS</sup><br>|0.044<br>|-0.234<br>1|0.184<br>0182|
|MTFP|AVERGE TFP GROWTH RATE|0.017|-0.59|.|
||OO-PR01O<br>--|-------------|----------------|---------|
|----------|------------------------------<br> <br>||||
|NGL|AVERAGE LABOR GROWTH RATE<br>|0.006<br>|-0.074<br>|0.140<br>|
|MGN|AVERAGE GROWTH RATE OF CAPITAL<sup>STOCK</sup><br>|0.050<br>|-0.033<br>|0.166<br>|
|MGCX|AVERAGE RATE OF GROWTH<sup>OF OUTPUT</sup><br>|-0.153<br>|-0.502<br>|0.107<br>|
|NGCI|AVERAGE GROWTH RATE OF INTER<sup>INPUTS</sup><br>|-0.057<br>|-0.359<br>|0.245<br>0014|
|KTFP|AVERGE TFP GROWTH RATE|-0.128|-0.402|-.|
|----------|------------------------------<br>DO=PRD1=2 -|--------------|----------------|---------|
|mc.-|AVERAGE LABOR GROWTH RATE|0.051<br>|0.002<br>|0-215<br>|
|NGK|AVERAGE GROWTH RATE OF CAPITAL<sup>STOCK</sup>|0.044<br>|-0.137<br>|0.149<br>|
|MGCX|AVERAGE RATE OF GROWTH OF OUTPUT<br>|0.140<br>|0.010<br>|0.280<br>2|
|NGCI|AVERAGE GROWTH RATE OF INTER<sup>INPUTS</sup>|0.129<br>|-0.008<br>|0.94<br>|
|NTFP|AVERGE TFP GROWTH RATE|0.034|-0.078|0.098|
|----------|------------------------------<br>DO-PU 01t0 -|--------------|----------------|---------|
|NGL|AVERAGE LABOR GROWTH RATE|0.049<br>|-0.015<br>|0.237<br>|
|MGK|AVERAGE GROWTH RATE OF CAPITAL<sup>STOCK</sup>|0.077<br>|-0.040<br>|1.007<br>|
|NGCX|AVERAGE RATE OF GROWTH OF OUTPUT<br>|0.007<br>|-0.762<br>|0.552<br>|
|NGCI|AVERAGE GROWTH RATE OF<sup>INTER INPUTS</sup><br>|-0.039<br>|-0.861<br>|0.382<br>2|
|NTFP|AVERGE TFP GROWTH RATE|-0.005|-0.198|0.30|
|------------------|----------------------<br>°O=PU Di--l----<br>|------------------------<br>|-----------<br>||
|NGL|AVERAGE LABOR GROWTH RATE|0.004<br>|-0.131<br>|0.180<br>|
|#GK|AVERAGE GROWTH RATE OF CAPITAL<sup>STOCK</sup><br>|0.002<br>|-0.046<br>|0.086<br>0558|
|HGCX|AVERAGE RATE OF GROWTH OF OUTPUT<br>|-0.115<br>|-0.633<br>|.<br>|
|MGCI|AVERAGE GROWTH RATE OF INTER<sup>INPUTS</sup>|0.073<br>|-0.569<br>|0.607<br>|
|KTFP|AVERGE TFP GROWTH RATE|-0.181|-0.911|0.378|
|--------|____-------------------------<br>OOPU<br>D142 -|--------------|----------------|---------|
|NGL|AVERAGE LAaOR GROWTH RATE<br>|-0.007<br>|-0.322<br>|0.144<br>|
|MGK<br>|AVERAGE GROWTH RATE OF CAPITAL<sup>STOCK</sup><br>|0.021<br>|-0.043<br>028|0.396<br>0367|
|NGCX<br>|AVERAGE RATE OF GROWTH OF<sup>OUTPUT</sup><br>|0.083<br>|-.5<br>|.<br>0190|
|NGCI|AVERAGE GROWTH RATE OFINTER<sup>INPUTS</sup>|0.025|-0.296|.<br>|
|MTFP|AVERGE TFP GROWTH RATE|0.057|-0.077|0.209|
**Table 2A: Coefficients and elasticities of how the Economic Freedom Index and its subcomponents affect returns to** **<u>schooling</u>**

|**Heritage Foundation Subindex**|**Marginal Returns to Education**|**Marginal Returns to Experience**|
|---|---|---|
|**Economic Freedom Index**|0.089**|0.086**|
||(2.44)|(2.48)|
||[0.61]|[1.06]|
|**Business Freedom**|0.050**|0.007|
||(2.91)|(0.90)|
||[0.24]|[0.14]|
|**Freedom from Corruption**|0.027*|0.010|
||(1.87)|(0.49)|
||[0.09]|[0.06]|
|**Financial Freedom**|0.001|0.014|
||(0.01)|(0.72)|
||[0.002]|[0.15]|
|**Fiscal Freedom**|0.047*|0.027|
||(1.90)|(1.10)|
||[0.47]|[0.47]|
|**Freedom from Government**|0.027*|0.045**|
||(1.70)|(3.51)|
||[0.25]|[0.74]|
|**Investment Freedom**|0.033*|0.051**|
||(1.53)|(2.78)|
||[0.21]|[0.57]|
|**Monetary Freedom**|0.001|0.032**|
||(0.04)|(2.08)|
||[0.005]|[0.47]|
|**Property Rights**|0.060**|0.017|
||(3.45)|(0.91)|
||[0.31]|[0.16]|
|**Trade Freedom**|0.014|0.006|
||(0.54)|(0.29)|
||[0.10]|[0.08]|
|**F(8, 108)**<sup>**a**</sup>|1.11|2.06**|

Each estimate is from a separate regression of returns to schooling or returns to experience using the model specification used in Table 1A. The complete results are available from the authors on request t-statistics corrected for clustering are in parentheses. Associated elasticities are in brackets.

aTest that the sum of the 9 subindex effects equals the effect of the overall Economic Foundation Index 21
In terms of LPS, most programs (82 percent) have somebody in charge of industry relations. About half of the programs (52 percent) communicate with local firms to gauge their needs or collect data on graduates’ employment or employers’ satisfaction with the graduates. Less common (35-40 percent) are agreements with industry to hire program graduates or train faculty. Most programs support students’ job search; for instance, they train students for job interviews (69 percent), run an employment center (60 percent), and, most often, provide job market information (81 percent). As for _faculty_ , the average program has 20 instructors. On average, these are wellqualified (82 and 49 percent have bachelor’s and graduate degrees, respectively) and experienced (56 percent have at least five years of industry experience) although less than half currently work in industry. They are mostly male and teach part-time. In most programs (85 percent), faculty are evaluated more than once a year, and about half of the programs provided professional training to most faculty the previous year. When hiring faculty, most programs (88 percent) seek practical experience. In terms of _other practice_ s, two-thirds of programs frequently review and update their administrative records, and 50-70 percent rely on admission requirements such as exams, interviews, or test scores in mandatory national exams. In the average program, faculty members comprise the largest share of the HEI’s governing board (40 percent of board members).

As panel B shows, in the average program most students are part-time, male, and younger than 25 years old. Most incoming students lack basic skills; the main deficiency is in numerical skills (reported by 82 percent of programs), followed by reading and writing. The average program has a theoretical duration of 5.2 semesters, is 11.5 years old, and enrolled 222 students the year prior to the survey. HEIs in our sample are mostly private (70 percent), are not universities, and lack high-quality accreditation. The average institution is 38 years old and offers 22 programs.

15
# **IX.   REFERENCES**

- Adenikinju, Adeola and Olumuyiwa Alaba, 2005: “EU-ACP Economic Partnership Agreements: Implication for Trade and Development in West Africa,” TPRTP, University of Ibadan, Nigeria.

- Busse, Mathias, Axel Borrmann, and Harold Grossman, 2004. “The Impact of ACP/EU Economic Partnership Agreements on ECOWAS Countries:  An Empirical Analysis of Trade and Budget Effects.”  HWWA–Hamburg Institute of International Economics, Hamburg, July.

- Doe, Lubin, 2006: “Reforming External Tariffs in Central and Western African countries,” Working Paper WP/06/12, IMF.

- __________, 2006: “Harmonization of Domestic Consumption Taxes in Central and Western African Countries,” Working Paper WP/06/8, IMF

- Ebrill L., Stotsky J, and Gropp Reint, 1999: “Revenue Implications of Trade Liberalization,” IMF.

- Hinkle, Lawrence and Richard Newfarmer, 2005: “Risks and Rewards of Regional Trading Arrangements in Africa: Economic Partnership Agreements (EPAs) Between the EU and SSA,” Mimeo, World Bank, 2005.

IMF, 1998: “Trade Reform and Regional Integration in Africa.” ____, 1998: “Impact of EMU on Selected Non-European Union Countries.”

- Koranchelian, Taline, 2006: “The Association Agreement between Algeria and the European Union: Economic Implications and Challenges,” IMF.

Massa, Coulibaly 2004: “WAEMU Experiences,” ECOWAS.

Newfarmer, Richard, Lawrence Hinkle and Mombert Hoppe, 2005: “Beyong Cotonou:

- Economic Partnership Agreements in Africa,” in Trade, Doha, and Development: A Window into the Issues World Bank, Washington DC.

- Olarreaga M., Kee H. L, and Nicita, Alessandro, 2004: “Import Demand Elasticities and Trade Distortions,” World Bank Policy Research Working Paper.

- Pritchett, Lant and Geeta Sethi, 1993: “Tariff Rates, Tariff Revenue, and Tariff Reform: Some New Facts,” World Bank Policy Research Paper, 1143.

- Robinson, Sherman, Jeffrey Lewis, and Karen Thierfelder: “Free Trade Agreements and the SADC Economies”, Journal of African Economies, Volume 12, Number 2, PP 156–206

- Stephens, Christopher and Kennan, Jane, 2005: “EU-ACP Economic Partnership Agreements: The Effects of Reciprocity,” Institute of Development Studies, Sussex.

17
of timber and fish processing industries pale in comparison to the MVA content from gold processing. Lesotho is an exception. Its garments manufacturing industry accounts for 55 percent of total MVA and has been the key driver of the 4.3 percent growth rate in per capita MVA. However, an upcoming diamond exports sector is likely to overcome the dominance of MVA from this sector in the future (Table 3.3).

**Table 3.3: Africa’s manufacturing sector is dominated by natural resource processing industries which are the divers of growth today**

||**Manuf**<br>|**acturing Perf**<br>|**ormance of C**<br>|**ountries inSub-S**<br>|**aharan Africa**<br>||
|---|---|---|---|---|---|---|
|**Country**|**MVA**<br>**per**<br>**capita**<br>**US $**<br>**(1990)**|**MVA**<br>**per capita**<br>**US $**<br>**(2010)**|**MVA**<br>**per capita**<br>**[compound**<br>**annual**<br>**growth**<br>**rate (%)**<br>**1990-**<br>**2010)]**|**Resource-**<br>**based**<br>**manufacturing**<br>**share of MVA**<br>**in 2009**|**Low tech**<br>**manufacturing**<br>**share of MVA**<br>**in 2009**|**Medium &**<br>**High tech**<br>**manufacturing**<br>**share of MVA**<br>**in 2009**|
|Angola|26|66|4.8%|46%|41%|12%|
|Benin|21|23|0.4%||||
|Burkina Faso|26|37|1.9%||||
|Burundi|16|9|-2.9%||||
|Cameroon|126|148|0.8%|75%|24%|2%|
|Cape Verde|108|139|1.2%||||
|Central<br>African<br>Republic|21|16|-1.3%|76%|16%|8%|
|Chad|22|15|-1.8%||||
|Comoros|14|12|-0.9%||||
|Congo|62|83|15%|81%|6%|13%|
|Cote d‘Ivoire|112|99|-0.6%|70%|13%|17%|
|Dem. Rep. of<br>the Congo|16|5|-5.7%||||
|Eritrea|9|9|0.2%||||
|Ethiopia|8|9|0.3%|67%|20%|13%|
|Gabon|163|200|1%|76%|16%|8%|
|Gambia|19|16|-0.7%||||
|Ghana|20|23|1.6%|86%|7%|6%|
|Guinea|12|17|1.7%||||
|Guinea- Bissau|26|16|-2.2%||||
|Kenya|49|47|-0.3%|68%|19%|13%|
|Lesotho|44|103|4.3%|36%|55%|9%|
|Liberia|34|17|-3.6%||||
|<br>Note: MVA = Ma|<br>nufacturing|<br>Value Added|<br>||||
|_Source: Economic_<br>Source: UNCTAD/U|_Developme_<br>NIDO Report|_nt in Africa, R_<br>: 2011|_eport 2011 –_|_Special Issue(UNC_|_TAD/UNIDO), Ch_|_apter 2, page 27._|

The direct impact of the emergence of China as the global manufacturing dynamo has been the decline in manufacturing sector jobs in the non-resource processing industries such as light manufacturing. Resource processing industries are capital intensive and do not create many jobs 37
## _3.3 A Note on Aggregation_

For comparability with the previous literature, and to serve as a foundation for the village-panel estimation, we also show results for the village analog of (1) (setting aside the jackknife dimension): In Appendix 1 we show in detail how this equation can be aggregated from a household version (like (1)), and thus how the coefficients can be compared across household and village-level specifications. A few key points are worth highlighting. First, given our chosen functional form, it is easy to aggregate from the household-level regression by simple averaging, which is equivalent to using either village means, or village dummies, as instruments for the appropriate household specification. The estimated effect of inequality will be numerically identical, whether estimated at the household or village level. Second, our specification allows us to better highlight the distinction between the village and household-level analysis: Important differences in key parameter estimates emerge in the details of empirical implementation, especially the loss of household-level controls, and the use of an internally consistent sample that properly aggregates (i.e., only the non-attritted, balanced panel). Third, following the insights of Devereux (2007), we show that our jackknifed specification (1) can be derived as the reduced form growth equation for Deaton’s (1985) measurement-error-correcting estimator of the village-level specification (2). This provides “bonus” justification for using Equation (1) as our base specification. We are thus able to also implement the Deaton (1985) estimator for the village-level Equation (2).

# 4.0 Empirical Results

## _4.1 Data_

The data come from annual household surveys conducted by the Survey Department of the Research Center on the Rural Economy (RCRE). The survey collected detailed household-level information on income and other household characteristics. The starting point is a sample from 1987 of 4,847 households drawn from 82 continuously observed villages in nine provinces.<sup>16</sup> Originally planned as an annual longitudinal survey, by the end of our sample (2002) there was significant attrition of households, on the order of 30 percent.<sup>17</sup> We are able to follow a “pure” balanced panel of 3,424 households every year between 1987 and 2002, excluding 1992 and 1994 when there was no survey.

> 16 The complete RCRE survey covers over 22,000 households in 300 villages in 31 provinces and administrative regions.  RCRE’s complete national survey is 31 percent of the annual size of the NBS Rural Household Survey.  By agreement, we have obtained access to data from 9 provinces (Anhui, Gansu, Guangdong, Henan, Hunan, Jiangsu, Jilin, Shanxi and Sichuan), or roughly one third of the RCRE survey.

> 17 The RCRE survey continued past 2002; however, there were significant changes in survey design from 2003 that introduced serious comparability problems for income.  The post-2002 data cannot be pooled with previous surveys.

_Inequality and Growth in Rural China_ , Page 11
Figure 11. Percentage of Population Aged 12-24, 1950-2050, United Nations Medium Variant Projections <!-- Start of picture text --> Figure 10a. Selected Countries with Peak in Youth Population before 2000 Figure 10b. Selected Countries with Peak in Youth Population in 2000-2010<br>Percentage of Population Aged 12-24, U.N. Medium Variant Projections Percentage of Population Aged 12-24, U.N. Medium Variant Projections<br>35 35<br>Costa Rica<br>30 30 Mexico<br>Brazil<br>Viet Nam<br>25 25 Indonesia<br>20 20<br>15 15<br>10 Thailand 10<br>China<br>5 5<br>0 0<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year Year<br>Figure 10c. Selected Countries with Peak in Youth Population in 2010-2030 Figure 10d. Selected Countries with Peak in Youth Population after 2030<br>Percentage of Population Aged 12-24, U.N. Medium Variant Projections Percentage of Population Aged 12-24, U.N. Medium Variant Projections<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>Philippines Kenya<br>10 Malaysia 10 Democratic Republic of the Congo<br>Egypt Iraq<br>Peru Senegal<br>5 India 5 Pakistan<br>0 0<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year Year<br>Percentage Percentage<br>Percentage Percentage<br><!-- End of picture text --> 37
estimates for the STE sample.

An important finding from the estimates in Tables 3 and 4 is that an increased agricultural productivity and the resulting higher rural income leads to employment transformation within the services sector in both the rural and STE samples: households appear to substitute away from traditional services provided by smaller enterprises, and into services provided by larger and more skilled enterprises, especially in education. Similar transformative effects are found for large-scale manufacturing located in the STEs. The effects on manufacturing in the rural areas are very different; a positive agricultural productivity shock results in a robust growth of small-scale manufacturing employment with no impacts on employment of relatively large-scale enterprises.

# **6. Conclusions**

This paper provides a theoretical and empirical analysis of the role of agricultural productivity in nonfarm employment growth, and structural transformation in the labor market, using a broader framework that goes beyond rural-urban dualism, and focuses on the rural towns and smaller cities which we term as small-town economy (STE). We develop a simple model that formalizes the demand and labor market linkages between the STEs and the surrounding villages. A positive productivity shock to agriculture does not necessarily increase village income; given a labor endowment, the village income increases only when the response of wage with respect to productivity shocks (i.e, wage elasticity) is lower than a threshold.<sup>25</sup> When the elasticity of demand for the town good in the village is low so that most of the town good is sold outside the local economy, the labor market linkage predominates and a higher rural wage due to agricultural productivity increase reduces employment in small towns. In contrast, when demand for goods and services produced in small towns comes mainly from the adjacent rural areas, the demand effect can more than offset the negative wage effect, and lead to higher labor allocation to the production of town good. Another key insight from the model is that when a significant part of village income is spent on goods and services in small towns, ignoring them will lead to serious underestimation of the employment

> 25In a more general model with a positive labor supply curve at the household level, a positive shock to agriculture is expected to increase village income in general.

28
**Table 11 Impact of changes in the share of Children on poverty**

||Fist-Differ|ence||Panel Fixe|d-effects||Generalize|d Method of|Moments|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of WAP|0.802**|0.546*|0.619**|1.095**|0.627|0.389|0.814|0.900**|0.750*|
||(0.349)|(0.321)|(0.301)|(0.525)|(0.400)|(0.374)|(0.644)|(0.456)|(0.402)|
|Initial GDP-pc||1.726*|2.520*||1.297|1.154||0.790|-0.714|
|||(1.021)|(1.324)||(2.214)|(2.146)||(2.702)|(2.532)|
|Schooling (years)|||0.343|||1.889|||0.997|
||||(2.144)|||(4.363)|||(4.199)|
|British colony|||-0.543||||||-0.0850|
||||(2.178)||||||(5.575)|
|French colony|||0.281||||||-6.921|
||||(1.438)||||||(4.266)|
|Non-colony|||0.449||||||-1.578|
||||(1.199)||||||(1.957)|
|Landlocked|||-1.294||||||-6.117|
||||(1.635)||||||(7.199)|
|Latitude|||-0.0281||||||0.0248|
||||(0.0370)||||||(0.0910)|
|Observations|350|341|298|350|341|302|350|341|298|
|Countries|105|104|82|105|104|86|105|104|82|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Author’s estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

23