rafmacalaba/datause-encoder-data huggingface dataset:
Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey”

# **3. A literature review on the determinants of FDI in Turkey**

Existing literature dealing with foreign direct investment in Turkey is heterogeneous, presents different focuses and draws from dissimilar theoretical approaches and methodological tools (see Table 1 in the Annex). In order to synthesize existing empirical evidence, we group the studies depending on the determinants of FDI they discuss and the methodology applied.

A first set of articles departs from OLI’s eclectic paradigm (see Dunning, 2001) which tries to determine the location, ownership and internalization factors that influence the establishment of multinational enterprises in Turkey. Collection of survey data at the firm level is usually the method adopted by these studies.

Tatoglu and Glaister (1998) conducted a survey on motivations for foreign direct investment in Turkey on a purposive sample of 316 foreign equity ventures. There is evidence that among the most relevant factors influencing location in Turkey are its market size (and the related growth rate) and certain government policies such as the possibility to repatriate profits.

In a similar fashion, Tatoglu and Glaister (2000) examine the location, transaction and firm related factors in the establishment of foreign companies in Turkey. For this purpose, they adopt a managerial approach, drawing mainly from industrial organization models, internalization theory and the eclectic paradigm. Erdilek (2001) has criticized this research for being << _narrowly focused_ >> and providing << _no in-depth analysis of the economic, political and social factors that determine Turkey’s attractiveness as a host country to foreign investors_ >>. Tatoglu et al (2003) analyze the determinants for foreign ownership in Turkish manufacturing (fully owned enterprise, joint venture, etc.). Their findings suggest that there are country-related factors that influence the formula of foreign investment access to the host economy. The authors recommend Turkish policy makers to continue pursuing structural transformation and privatization, but these suggestions do not seem to be clearly derived from the empirical results.

Deichmann et al (2003) use a conditional logit model in order to identify the location patterns of multinational enterprises in Turkey. The location decision will depend more on the characteristics of the industry (productivity, agglomeration of similar firms) and on local conditions (coastal access, infrastructures, etc.). Public investment’s share on regional GDP does not seem to be an influencing factor. Another group of scholars has looked at the geographical distribution of FDI within Turkey, 6
**Figure 1: Cognitive development of children age 36-72 months in Ecuador**

<!-- Start of picture text --> 1 10 b y w e a lth q u a rtiles: 1 1 0 b y pla ce o f re sid en ce:<br>rich e st 2 5 %<br>1 0 0<br>1 00<br>9 0 rural<br>90<br>80 8 0<br>70 7 0<br>u rb a n<br>60 p o ore st 2 5 % 6 0<br>3 6 4 2 4 8 54 6 0 66 7 2 3 6 4 2 4 8 54 6 0 6 6 7 2<br>1 10 b y m a tern a l ed u ca tio n: 1 2 o r m ore yea rs 1 1 0 b y pa terna l ed uc atio n:<br>12 or m ore ye ars<br>1 00 1 0 0<br>90 7 -11 yea rs 9 0<br>7 -1 1 yea rs<br>80 8 0 6 ye ars<br>6 yea rs<br>70 7 0<br>60 0-5 yea rs 6 0 0-5 yea rs<br>3 6 4 2 4 8 54 60 66 7 2 3 6 4 2 4 8 5 4 6 0 6 6 7 2<br>a ge in m o nth s a g e in m o n th s<br>medianTVIP score<br>medianTVIP score<br><!-- End of picture text --> _Source_ : Paxson and Schady (2005).

hsladovich 29
11 _Tax Reform in Transition_ countries over the past two decades (e.g., the top rate has been reduced from 60 to 40 percent, a relative reduction of one third). In contrast, Bulgaria has increased its rates with the top rate rising from 40 percent in 1992 to 52 percent in 1993. Bulgaria's top rate is the highest from among the 10 counties presented in McLure's study, exceeding those of the two notable outliers -- Sweden and Japan<sup>3</sup> . Although the 1997 amendment to Bulgaria's personal income tax has reduced the top-bracket rate to its 1992 level of 40 percent, several countries have further reduced, in the late 1980s and early 1990s, their income tax rates (notably Colombia and Indonesia), which makes the contrast even more striking.

However, a comparison of income tax rates in EMEs, reveals that part of the high income tax rate 'syndrome' may be a regional peculiarity of these economies. The following nine countries: Armenia, Azerbaijan, Czech Rep., Hungary, Poland, Romania, Tajikistan, Turkmenistan, and Uzbekistan, representing a spectrum of EMEs all have extremely high top marginal tax rates, often higher than Bulgaria's top rate. Interestingly, the minimum marginal tax rate is also rather low, between 6 percent in Romania and 15 percent in the Czech Republic<sup>4</sup> . This suggests prima faciae that, much like Bulgaria, other transitional economies may also face design problem in their income tax systems. This particularly so if the underlying income distribution in those countries is not adequately taken into account in designing appropriate tax rates and tax brackets. Recent analyses of income inequality and poverty trends in EMEs by Milanovic (1995) and Hassan and Peters (1996), show some striking similarities in the patterns of poverty and inequality in these economies. In particular, the pattern of inequality of income (e.g., Gini coefficient) in Bulgaria is found similar to that in Poland and Romania. Therefore, to the extent that the income tax system in Poland and Romania may suffer from similar deficiencies as Bulgaria's system, particularly low exemption 3/Countries included in the study are: Australia, Canada, Columbia, Indonesia, Israel, Japan, Mexico, Sweden, UK and USA (Table 15.1, p. 282).

4/There is an acute lack of country studies on the effects of taxes in the course of economic transition. Some exceptions include studies of Czechoslovak Republic (Coulter _et al._ 1993), Romania (Shome, Haindl and Schenone 1993) and former Soviet Republics (Shome and Escolano 1993).
Scholz, and Seshadri (2011) suggest that financial aid leads to an increase in educational attainment for borrowing-constrained families. The findings in Levine, Levkov, and Rubinstein (2008) imply that an increase in banking competition leads to an increase in credit supply and a decline in labor market discrimination. In standard consumer theory, borrowing constraints prevent consumption smoothing and produce a greater dependence on current income than predicted by intertemporal welfare optimization theories (such as the permanent income hypothesis).

However, a recent and rather controversial literature suggests that financial deepening in the form of a greater supply of credit or of a greater diversity of financial products might have ambiguous distributional and welfare impacts, depending on how social welfare is measured. Three reasons are cited to postulate this ambiguity. First, general equilibrium effects distort price distributions. Indeed economy-wide increases in credit supply could affect the distribution of prices of products and assets in a way that may offset the partial equilibrium positive welfare impacts for a substantial fraction of households. Second, there is evidence that social preferences are present in locational choices (Krysan and Farley, 2002), consumption decisions (Bertrand and Morse, 2016), and educational production functions (Sacerdote, 2011). With such externalities, an unconstrained market equilibrium could be less efficient than a constrained market equilibrium. Third, the expansion of the supply of credit towards less creditworthy or towards more risky products can lead to large and potentially suboptimal welfare losses during downturns.

Ouazad and Rancière (2016a) use transaction-level micro data to estimate the empirical relevance of the first mechanism, that is, general equilibrium effects. The paper puts forward a novel general equilibrium model of a metropolitan area to estimate the impact of the relaxation of lending standards on the distribution of housing prices. The model introduces endogenous borrowing constraints in a locational choice model, where heterogeneous households choose location across block groups based on the variation of local amenities. The paper introduces borrowing constraints by noting that the households’ choice set is constrained by mortgage originators’ lending standards. Conditional on their choice set, households choose across locations based on a comparison of the bundle of local amenities and housing prices. The probability of a given choice set is determined by the product of the probabilities of approval of 18
variable, the spending coefficient collapses, becoming undistinguishable from zero. Statistically, the interaction is driven by a positive correlation between spending and governance quality, aligning with what has been documented in the literature.

Quantitatively, these effects are considerable. Doubling spending is estimated to significantly increase transport infrastructure performance as measured by the LPI:Timeliness by roughly 0.27 index points. For example, this corresponds to improving the transport service reliability of Mozambique to that of Cambodia. However, keeping governance fixed statistically, the effect on transport reliability of one additional dollar of public spending is reduced by a factor of 6 Evaluating effect sizes through standardized coefficients reveals that governance quality actually explains the bulk of statistical variation in transport reliability.

In other words, increasing spending and improving governance in parallel enhances transport system performance on average 6 times faster than increasing spending alone. Figure 3 illustrates the strength of this interaction, i.e. how the marginal effect of public road spending per capita melts once governance quality is taken into account. Statistically, results suggest that only about 8% of variation in transport reliability may be explained by investment spending, while about 44% is explained by a country’s governance quality.

Apart from that, population density and high rates of urbanization are significantly associated with better transport infrastructure resilience while dynamic economies, as measured by GDP growth, show more transport service irregularities.

To conclude, various empirical specifications, across infrastructure types, suggests that governance quality is a major determinant of infrastructure quality. Often enough, it outweighs the relevance of natural risk or socio-economic factors.

_Figure 3: Relation between transport resilience and public road spending per capita_ <!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0 200 400 600 800 1000 1200 1400<br>Annual public road spending per capita in constant 2009 USD (OECD + BOOST)<br>WB Logistic Performance Index: Timeliness (1-5)<br><!-- End of picture text --> Spending and governance improve together Increase in spending alone 7
urban areas, employ children as maids for domestic services including babysitting, cooking and other chores. These children (not related to their employer) are less likely to be in school.

**Table 3.  Deprivation status for school aged children (aged 5-17) by relationship, 2011**

||Non-relative, employed by|All other|
|---|---|---|
|Child Status|the household|children|
|In school|0.20|0.65|
|Belowpovertyline*|0.04|0.34|

Source: Computed from WMS and HCES 2011. Note: * Household level indicator.

Table 3 shows enrollment status in 2011 was 20 percent for these children compared to 65 percent for all children. However, a monetary poverty indicator puts these children in the better off category. These children are rarely employees in poor households and are most often girls employed by urban families.

# **5. Discussion**

# **_5.1 Exit from overlapping deprivations_**

The analysis in the previous sections points to considerable progress in reducing multidimensional poverty in Ethiopia. The previous sections presented results from single and overlap deprivation analysis. The single deprivation analysis shows that there has been substantial progress in various dimensions of wellbeing. Likewise, the evolution in overlapping deprivations shows that there have been improvements in overlapping deprivations; it indicates that more households are having fewer deprivations. This is illustrated in Figure 7 using three different combinations in urban and rural areas.

21
<!-- Start of picture text --> Box-Cox transformation (log) — ||| - e e<br>Nat log shift transformation — ||| - e e<br>Nat log transformation | @— || - e@ e0e e<br>-20 0 20 40 60<br>Design bias at municipal level<br><!-- End of picture text -->

<!-- Start of picture text --> Box-Cox transformation (log) |— e e<br>Nat log shift transformation |— e e<br>Nat log transformation {++ ee 8@ e<br>0 10 20 30 40<br>MSE at municipal level (x100)<br><!-- End of picture text -->
For the IV approach to be valid and allow valid inferences of our variables of interest, we need two conditions to hold.

- a) _Exclusion Restriction_ : The instrument is uncorrelated with the error term, that is, Cov(Zi, εi1) = 0 . Or, to put it differently, the instrument has no direct effect on  Yi. This means that β1= 0 in Equation (6).

- b) Zi _is correlated with_ Ei, our opportunity measure. Cov(Zi, Ei) ≠0.

The literature on economic development suggests a few different potential instruments.

- a) _Log of Wheat Sugar Suitability Ratio_ :  Abundance of land suitable for growing wheat relative to that suitable for growing sugarcane (Easterly, 2007).

- b) _Share of Tropical Land_ . Percentage of land area in geographical tropics (Sachs, 1997).<sup>9</sup>

- c) _Ethnic Fractionalization_ . Ranges from 0 and 1 and describes how ethnically segregated a society is (Alesina et al., 2003).

- d) _Legal Origin_ .<sup>10</sup>

- e) _Latitude_ of Country Centroid.<sup>11</sup>

- f) _Longitude_ of Country Centroid.<sup>12</sup>

Table A.2-3 in Appendix 2 presents a correlation table across all these measures. We choose agricultural endowments as our preferred instrument, which was first introduced by Easterly (2007). The variable “wheat–sugar suitability ratio” is defined as _LWHEATSUGAR=log[(1+share of arable land suitable for wheat) / (1+share of arable land suitable for sugarcane)]_ .

The idea behind this instrument is that the higher the ratio (share of arable land suitable for wheat to sugar in total arable land) in a society, the lower would be economies of scale in production, and the society would be less likely to need structural inequality (in its most extreme form, slavery) for efficiency. As a result, societies with a higher wheat-sugar ratio of arable land are likely to be more equal in terms of opportunities.

As Easterly points out, the wheat–sugar ratio could simply be proxying for whether the country is in the tropics. There is certainly a strong correlation (correlation= −0.66, t-statistic= −10.75), but also enough variation in this measure within tropical and non-tropical areas alike to suggest that the two measures are not the same. The measure comes from FAO and takes into account factors like soil, rainfall, temperature and elevation. Easterly (2007), using different studies, argues that this measure has

> 9 This instrument is similar to settler mortality, so we will not use it to avoid concerns about the validity of the historical data in the latter. See Acemoglu et al. (2012) and Albouy (2012) for a debate on the validity of settler mortality data.

> 10 Indicates which type of legal system the country ascribes to: British, French, Germany, Scandinavian or Communist.

> 11 Ranges from 0 at the Equator to 90 (North or South) at the poles and refers to a geographic coordinate that specifies the north-south position of a point on the Earth's surface.

> 12 Ranges from −180 to 180 and refers to the angle between a plane containing the Prime Meridian and a plane containing the North Pole, South Pole and the location in question.

9
Brück, T., M. d’Errico, and R. Pietrelli. 2019. “The Effects of Violent Conflict on Household Resilience and Food Security: Evidence from the 2014 Gaza Conflict.” _World Development_ 119 (July): 203–23. https://doi.org/10.1016/j.worlddev.2018.05.008.

- Bundervoet, T., P. Verwimp, and R. Akresh. 2009. “Health and Civil War in Rural Burundi.” _Journal of Human Resources_ 44 (2): 536–63.

- Chamarbagwala, R., and H.E. Moran. 2011. “The Human Capital Consequences of Civil War: Evidence from Guatemala.” _Journal of Development Economics_ 94 (1): 41–61. <u>https://doi.org/10.1016/j.jdeveco.2010.01.005.</u>

- Clementi, F., A.L. Dabalen, V. Molini, and F. Schettino. 2017. "When the Centre Cannot Hold: Patterns of Polarization in Nigeria," _Review of Income and Wealth_ 63(4): 608–32. <u>https://doi.org/10.1111/roiw.12212.</u>

- Corral Rodas, P.A., V. Molini, and G. Oseni. 2019. "No Condition Is Permanent: Middle Class in Nigeria in the Last Decade." _Journal of Development Studies_ 55 (2): 294–310. <u>https://doi.org/10.1080/00220388.2017.1366453.</u>

- Croke, K., A. Dabalen, G. Demombynes, M. Giugale, and J. Hoogeveen. 2012. “Collecting High Frequency Panel Data in Africa Using Mobile Phone Interviews.” Policy Research Working Paper 6097, World Bank, Washington, DC.

- Dabalen, A.L., and S. Paul. 2014. “Effect of Conflict on Dietary Diversity: Evidence from Côte

- d’Ivoire.” _World Development_ 58 (June): 143–58.

<u>https://doi.org/10.1016/j.worlddev.2014.01.010.</u>

- D’Souza, A., and D. Jolliffe. 2013. “Conflict, Food Price Shocks, and Food Insecurity: the Experience of Afghan Households.” _Food Policy_ 42 (October): 32–47.

<u>https://doi.org/10.1016/j.foodpol.2013.06.007.</u>

- Enamorado, T., L.F. López-Calva, C. Rodríguez-Castelán. 2014. “Crime and Growth Convergence:

- Evidence from Mexico.” _Economics Letters_ 125 (1): 9–13.

<u>https://doi.org/10.1016/j.econlet.2014.07.033.</u>

- Ichino, A., and R. Winter-Ebmer. 2004. “The Long-Run Educational Cost of World War II.” _Journal of Labor Economics_ 22 (1): 57–86. https://doi.org/10.1086/380403.

- International Crisis Group. 2017. _Herders against Farmers: Nigeria’s Expanding Deadly Conflict._ Africa Report 252. Brussels: International Crisis Group.

<u>https://d2071andvip0wj.cloudfront.net/252-nigerias-spreading-herder-farmer-conflict.pdf.</u>

- IOM (International Organization for Migration). 2018. _Nigeria: Displacement Tracking Matrix Round XXI Report—February 2018_ . Grand-Saconnex, Switzerland: IOM.

<u>https://reliefweb.int/report/nigeria/nigeria-displacement-tracking-matrix-round-xxi-reportfebruary-2018.</u>

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29
Table 4a: Shares of plants with financial loans by <u>young/old status for ASI plants</u>

|||||Ol|d share|||||You|ngsha|re||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||1989 <br>|1994 <br>|2000 <br>|2005 <br>|2010 <br>|Average<br>|1989 <br>|1994 <br>|2000 <br>|2005 <br>|2010 <br>|Average<br>|
|15|Food products and beverages|0.87|0.87|0.89|0.90|0.92|0.89|0.91|0.87|0.92|0.90|0.88|0.90|
|16|Tobacco products|0.21|0.55|0.53|0.51|0.48|0.46|0.52|0.03|0.22|0.48|0.45|0.34|
|17|Textiles|0.95|0.94|0.94|0.94|0.94|0.94|0.97|0.93|0.92|0.91|0.92|0.93|
|18|Wearing apparel; dressing and dyeing of fur|0.98|0.94|0.92|0.95|0.94|0.95|0.98|0.93|0.93|0.87|0.92|0.93|
|19|Leather tanning; luggage, handbags, footwear|0.91|0.90|0.91|0.88|0.94|0.91|0.95|0.91|0.87|0.81|0.87|0.88|
|20|Wood and wood products; straw and plating articles|0.78|0.78|0.74|0.85|0.86|0.80|0.76|0.72|0.79|0.80|0.79|0.77|
|21|Paper and paper products|0.95|0.95|0.95|0.95|0.94|0.95|0.98|0.93|0.96|0.97|0.95|0.96|
|22|Publishing, printing and media reproduction|0.84|0.85|0.90|0.90|0.92|0.88|0.96|0.93|0.94|0.89|0.96|0.94|
|23|Coke, refined petroleum and nuclear fuel|0.79|0.85|0.92|0.83|0.96|0.87|0.71|0.88|0.76|0.82|1.00|0.84|
|24|Chemicals and chemical products|0.92|0.92|0.91|0.90|0.88|0.91|0.93|0.91|0.89|0.83|0.88|0.89|
|25|Rubber and plastic products|0.98|0.95|0.96|0.95|0.93|0.96|0.97|0.94|0.92|0.89|0.87|0.92|
|26|Other non-metallic mineral products|0.82|0.81|0.79|0.79|0.74|0.79|0.82|0.83|0.76|0.69|0.72|0.76|
|27|Basic metals|0.97|0.97|0.96|0.97|0.96|0.97|0.97|0.96|0.94|0.97|0.95|0.96|
|28|Fabricated metal products, except machinery|0.95|0.95|0.94|0.94|0.94|0.94|0.95|0.95|0.92|0.90|0.91|0.92|
|29|Machinery and equipment, n.e.c.|0.95|0.94|0.93|0.95|0.94|0.94|0.94|0.95|0.87|0.89|0.91|0.91|
|30|Office, accounting and computing machinery|0.96|0.96|0.84|0.85|0.95|0.91|0.95|1.00|0.77|0.75|0.66|0.83|
|31|Electrical machinery and apparatus, n.e.c.|0.97|0.96|0.96|0.95|0.93|0.95|0.90|0.91|0.93|0.86|0.88|0.90|
|32|Radio, television, and comm. equipment|0.96|0.92|0.96|0.89|0.92|0.93|0.95|0.95|0.98|0.79|0.80|0.89|
|33|Medical, precision and optical instruments, watches|0.95|0.93|0.93|0.90|0.92|0.93|0.98|0.95|0.68|0.86|0.78|0.85|
|34|Motor vehicles, trailers and semi-trailers|0.96|0.95|0.96|0.94|0.96|0.96|0.89|0.91|0.90|0.88|0.84|0.88|
|35|Other transport equipment|0.95|0.96|0.95|0.95|0.95|0.95|0.97|0.86|0.89|0.97|0.89|0.92|
|36|Furniture, manufacturing n.e.c.|0.88|0.87|0.89|0.92|0.88|0.89|0.87|0.86|0.88|0.90|0.80|0.86|
||Nationwide|0.83|0.88|0.90|0.90|0.90|0.88|0.91|0.80|0.88|0.86|0.86|0.86|

Notes: See Table 3a. Plants at least 3 years of age are coded as old.