rafmacalaba/data-use-mentions-extended huggingface dataset:
development financing (Wolf, 2005). The composition of capital flows matters (Tong and Wei, 2011), and for a large open economy such as the Turkish, which presented an average current account deficit of 7.5 percent of GDP in 2010-2013, counting with long-term foreign investment matters. In addition, beyond the macroeconomic implications, firm-level evidence for some developing economies points to the existence of positive productivity spillovers over local suppliers (Javorcik, 2004; Kugler, 2006), as well as other positive pecuniary and knowledge-related externalities.

The purpose of this paper is twofold: first, assessing whether foreign direct investment in Turkey has contributed to growth by generating the micro-level externalities; second, if that is the case, empirically identifying significant driving factors for foreign direct investment, in order to help understand the reasons for Turkey ́s limited success in attracting inflows, and propose ways to increase the appeal to foreign investors.

The rest of the paper is structured as follows: section two presents firm-level evidence of the impact of FDI on productivity in Turkey; section three presents a cross country approach to the determinants of FDI attraction in Eastern Europe and Turkey; section four draws policy implications for Turkish policy makers; and section five concludes.

# **2. FDI and Growth in Turkey: Evidence from Firm Level Data**

In order to respond to the question on the impact of foreign direct investment on growth, we examine whether multinational firms increased the productivity of Turkish firms who received FDI between 2005 and 2012. We also analyze how this affects the productivity of domestic firms operating in the same or different sectors than the foreign affiliates of the multinationals.

## **2.1. Data and empirical approach**

Our data come from the ORBIS database (also known as AMADEUS for European countries) which is compiled by Bureau van Dijk Electronic Publishing (BvD). It covers 60 countries worldwide, including both developed and emerging countries. ORBIS has financial accounting information from detailed harmonized balance sheets and profit and loss
_nd_ = 0), as there is no insample part in this case, we generate the whole census vector **y** _d_<sup>(</sup><sup>_l_)</sup> = **y** _dr_<sup>(</sup><sup>_l_)</sup> from the model (6.13) by setting _γd_ = 0 (limiting value as _nd →_ 0).

Obtaining an analytical estimator for the MSE of the EB predictor _δ_<sup>ˆ</sup> _d_<sup>_EB_</sup> of _δd_ in general is challenging. Molina and Rao (2010) proposed a parametric
boosted international trade by reducing the costs of matching buyers and sellers. For example, Lendle and Olarreaga (2017) find that the impact of distance on crossborder trade flows—across 61 countries and 40 product categories—is about 65 percent smaller for eBay transactions relative to total international trade.

This paper also adds to an emerging literature which seeks to identify the impact of industrial robots on a range of economic outcomes. Taking into account both the displacement and productivity effects of automation, Acemoglu and Restrepo 2017 find that the use of one more robot per 1,000 workers reduced the aggregate employment to population ratio by about 0.34 percentage points from 1990 to 2007 in the United States. For a sample of 17 advanced economies, Graetz and Michaels (2018) calculate that robot densification from 1993-2007 raised the annual growth of labor productivity by about 0.36 percentage point (compared to a mean growth of 2.4 percent). They also find significant negative implications of robots for the employment of lowskilled workers. Maloney and Molina (2016) look for evidence of polarization in labor markets in developing countries as a result of spreading automation and trade, but find limited evidence to date – although they flag automation in China as a key development still in relatively early stages that should be watched. Using administrative data on Mexican exports by municipality, sector and destination from 2004 to 2014, Artuc, Christiaensen, and Winkler (2019) show that higher exposure to U.S. automation did not affect overall wage or manufacturing employment. Yet, there were two counteracting forces; exposure to U.S. automation reduced manufacturing wage employment in areas where occupations were initially more susceptible to being automated, but exposure increased manufacturing wage employment in other areas.

4
where ̈<sup>i</sup> t<sup>isa(timeandcountrydependent)binaryvariableequaltooneifthereis</sup> no data on the immigrants’ skill in country i, and equal to zero otherwise.

Shortcomings. Our methodology is imperfect and could be improved in many ways. However, we believe that our estimates can be reasonably used to examine the changes in the international distribution of migration rates, to test for the (push and pull) determinants of skilled migration. Let us discuss some shortcomings.

A ...rst limit of our analysis is that the set of receiving countries is restricted to OECD nations. Generally speaking, the skill level of immigrants in non-OECD countries is expected to be very low, except in a few countries such as South Africa (1.3 million immigrants in 2000), the Gulf countries (9.6 million immigrants in Saudi Arabia, United Arab Emirates, Kuwait, Barhain, Oman and Qatar), some Eastern Asian countries (4 million immigrants in Hong-Kong and Singapore only). According to their census data, about 17.5 percent of adult immigrants are tertiary educated in these countries (17 percent in Bahrain, 17.2 percent in Saudi Arabia, 14 percent in Kuwait, 18.7 in South Africa). Considering that children constitute 25 percent of the immigration stock, we estimate the number of educated workers at 1.9 million in these countries. The number of educated people in the rest of the world lies between 1 and 4 million (if the proportion of educated immigrants among adults lies between 2.5 and 10 percent). This implies that focusing on OECD countries, we should capture a large fraction of the world-wide educated immigration (about 85 percent). Nevertheless, we are aware that by disregarding non-OECD immigration countries, we probably underestimate the brain drain for a dozen of developing countries (such as Egypt, Sudan, Jordan in the neighborhood of the Gulf States, Namibia, Zimbabwe and other countries which send emigrants to South Africa
- 18 -

funds: KHB loans, capital gains from the sale an earlier house, the return of a chonsei deposit, financial savings, assistance for relatives, loans frobi other financial institutions (not necessarily second mortgage loans), the chonsei deposit coming from a tenant, and other sources. The income of the borrower, the price of the unit and the ratio of housing price to household income show the link between a strategy and who uses it. There is a clear difference between firsttime buyers and borrowers who sold an earlier home.

36. Over 28 strategies were actually ir. use in 1985; the 12 most frequent covering 83.5 percent of sample borrowers are presented in Table 3. The data clearly show the inefficient and fragmented methods of financing housing. KHB loan-to-value (LTV) ratios are very low and consistently below 30 percent. This defeats the first-time ownership objective of KHB: less than half the borrowers were first time owners. This lending strategy reflects the monopolistl.- fosition of KHB, its public status, and its lack of funds. KHE's n.&formance is judged according to the number of loans it makes. Besides, low LTV ratios minimize default risk and eliminate the need for income tests, thereby greatly reducing administrative costs.

37. Among the 47 percent of the borrowers who were first time home-owners, the majority (49 percent) relied on their previous chonsei deposit and other financial assets. The chonsei deposit provided 28 percent of the funds. Those with lower incomes had to sublet part of their unit to finance their unit, their house/income ratio was the highest in the group at 5.5 times. This confirms that chonsei renters are used in the case of high housing price/income ratios. The general survey of the Korean population and the surveys of KHB borrowers clearly show the large degree of fragmentation and inefficiency which could be eliminated by the financial liberalization of housing finance and the effective development of a mortgage market.

# V. COMPOUNDING FINANCIAL REPRESSION
the macro CGE part and the micro module and therefore a major difficulty consists of satisfactorily mapping the sources of incomes from the CGE model to the micro model. For example, in the CGE model it is possible to clearly distinguish labor remunerations from capital earnings; however, in the micro data, for the large group of self-employed people, incomes are a mix of labor and capital returns. For this group, an imputed wage is estimated and the remaining amount is classified as capital income (see Annex 2 for details). Furthermore, the micro-simulation module defines an exogenous household income ( _Yho_ ) as all non-labor income components like transfers, imputed rents, capital remuneration, etc. This exogenous income is not modified during the simulations. Thence, although consistency between macro and micro is always pursued, the changes in capital remunerations predicted by the CGE are not reflected in the micro data. The

> 11 For simplicity we only distinguish between food and non-food products.

9
The nature of the measurement is revealed by estimating equation (2) seven times, with village-averaged log total consumption from modules 1 to 7 as the measured dependent variable,

* _y_ and the village average of log consumption from the benchmark individual diary taken as the approximation to the true _y_ . We find that θ ˆ > ,0 λ ˆ < 1 in all seven regressions, with λ<sup>ˆ</sup> ranging between 0.42 and 0.66 and always statistically significantly less than one. The three survey designs with lower variance than in the benchmark design have the strongest degree of mean ˆ reverting error, with .042 > λ> .054. For these designs, the shrinkage due to the λ<sup>2</sup> term in the denominator of equation (5) outweighs the effect of adding the variance of the random noise ˆ term. Furthermore, finding λ < 1in all regressions implies that there is a negative correlation between the errors and the true values (as proxied by the village average of log consumption from the ‘gold standard’ design).

# **IV. Evidence from Food Engel Curve Regressions**

Consider a simplified version of the food Engel curve of Deaton and Paxson (1998), where the demographic composition and control variables are ignored to reduce clutter: The food share for household _i_ , _w f_ , _i_ depends on household total consumption,<sup>_x_</sup> _i_<sup>and</sup> household size,<sup>_n_</sup> _i_<sup>along with a random disturbance,</sup><sup>_u_</sup> _i_ . The data on log per capita consumption, ln( _x n_ ) _i_ are affected by measurement error in<sup>_x_</sup> _i_<sup>and since these errors occur</sup> through both food and non-food, the food share is also affected unless there are equi-proportional errors in both
the surface. Data are available from 1988 to present except mid-1990 through 1991 and this analysis uses data until 2009.

# **Statistical procedures and calibration of the runoff models**

In order to relate the BWI to the flow gauge data, we use the XL-stat software to run a quadratic univariate regression equation. The independent variable is BWI and the dependent variable is the flow data. Initial results from a simple coincident model did not account for a natural lag that occurs between the period of time when the moisture falls on the surface and the arrival of the water at the downstream gauge in the basin. Therefore, we performed several lagged relationships, realizing that the duration of the lag would depend on the basin‘s size, topography, soil type, and land-use. In performing this analysis we used monthly data, allowing lag to occur 14
specific determinants of crime. Our daily and monthly fixed effects account for variation in crimes over the course of a week (for instance, there may be more crimes on Fridays) and the year (for instance, there is less outdoor activity in the winter and generally lower crime). Our results are robust to several alternative sets of fixed effects, including a month _×_ year fixed effect. To alleviate concerns about smooth variation in temperature within months, we also show that our results are robust to including a day-of-year fixed effect (see Table A1).

_βk_ is the coefficient of interest and measures the approximate percentage change in daily crimes if the maximum temperature is in temperature bin _k_ relative to the 60-65<sup>_◦_</sup> F bin. We cluster standard errors at the county level (Abadie et al., 2017) and weight our regressions by the total population of the county in each year, as captured in the ACS.

We estimate this fixed effects Poisson model using maximum likelihood (Hausman et al., 1984; Wooldridge, 1999; Correia et al., 2019). We choose a Poisson model both because of the count and skewed nature of the outcome crime data and because of the properties of the fixed effects Poisson estimator. Since there are many county-days with no crimes, our data has many zeros. The Poisson model accounts for these zeros more easily than a linear fixed effects model with log( _Cidmy_ ). It also avoids the bias caused, when the share of zeros is non-trivial, by some common methods of transforming data to account for zeros (Nichols et al., 2010).

In our primary analysis _Cidmy_ represents the count of total crimes in a county on a given day. We conduct several alternative analyses to examine how heat impacts different types of crimes or impacts crimes in neighborhoods with different characteristics. In those analyses the specification is the same, but we change the outcome to be the count of crimes in a particular category.
tall (i.e. children in the top right quadrant) will tend to have W/H scores in the normal range. For the most part, wasted children will be those in the bottom right quadrant-children who are fairly tall but also fairly light in weight.

In Figure 2 the oval circle diagonal to the x- and y-axes capturing most wasted children lies outside the framed area. Thus, it makes it clear most children who are both stunted and underweight are not wasted, i.e., children can be stunted and underweight, and be underweight and wasted, but are unlikely to be stunted _and_ wasted. In Figure 3 the principal cluster lies more to the direction of south-west than the one in Figure 2, indicating higher incidence of stunting and underweight in Bangladesh than in Brazil. Although there are more cases of simultaneous stunting, underweight and wasting in Bangladesh, only 9% of children fall in this category. We would therefore not expect to see a country with high inequalities in all three dimensions of malnutrition.

# COMPARISONS **OF MALNUTRITION LEVELS WITH OTHER SURVEYS**

Malnutrition indicators computed along the lines indicated in section III were checked against WHO Global Database on Child Growth and Malnutrition to ensure their comparability [26]. Table 2 shows that the discrepancies between WHO reference values and our estimates are within +/-10% for most cases. Exceptions are: Egypt (stunting) and Romania (stunting) probably due to the different survey years from reference survey years; Guatemala (stunting) and the Philippines (all) most likely attributable to the regionally representative surveys; and Nicaragua (stunting) and Pakistan (wasting) possibly because of dropped observations due to insufficient consumption information.
Figure 7. Decomposition of inter- and intra-skills components into the composition and structure effects.

<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>-0.1<br>-0.2 intra comp intra struc<br>inter comp inter struc<br><!-- End of picture text -->

# **VI. Conclusions and policy implications**

This paper analyzes the movements in the gender wage gap in Georgia using the lens of industrial and occupational segregation .and reveals that the gender wage gap in Georgia is present mainly due to the gender wage inequality within industries and skill categories rather than due to the industrial and skill-based segregation.

Industrial and occupational segregation have been shown to be important determinants of gender wage gaps in a range of countries. Using the Duncan index, we establish that industrial and occupational segregation in Georgia during 2004 – 2015 was high. We then quantify the role of industrial and occupational segregation in relation to the within-industry or withinoccupation gender wage inequality by employing the decomposition approach proposed in Brown et al. (1980). This analysis demonstrates that, despite the high degree of industrial and occupational segregation, it is the gender wage gaps within sectors (public and private), industries and skill categories that are the primary factor underlying the presence of the gender wage gap in Georgia. This is in part because female-dominated industries and occupations do not pay much less than the economy–wide average, because women are concentrated in the industries and occupations that have benefitted during expansionary periods and were not as affected by the 2008 crisis as male-dominated industries. Our findings also highlight the growing role of the private sector in contributing to the gender wage gap contraction in Georgia. Importantly, we establish that at least a half
The questionnaire was tested on a pilot sample of 19 countries, on all continents, across all World Bank income groups and all with Panalpina offices.<sup>8</sup> A revised questionnaire was sent to all respondents in February 2005, and they were asked to benchmark their answers to January 2005.<sup>9</sup> An expanded questionnaire was sent to the 42 landlocked countries in the sample. It contained an additional section on transit transport and customs clearance at each border, as well as the associated documents, time and costs.

The questionnaire asks respondents to identify the likely port of export. For many countries, especially in Africa and the Middle East, this may not be the nearest port. Due to high port fees, inadequate inland infrastructure, or problems at border crossings, freight-forwarders avoid some ports. For example, Cotonou, Benin’s main port, is seldom used due to perception of corruption and high terminal handling fees. Respondents also identify the likely destination of their cargo. This serves as another quality check of the data, to confirm that this is a viable trade destination compared with the available trade statistics.

The survey then goes through the exporting procedures, dividing them into four stages: pre-shipment activities such as inspections and technical clearance; inland carriage and handling; terminal (port) handling, including storage if a certain storage period is required; and finally customs and technical control. At each stage, the respondents describe what documents are required, where do they submit these

> 8 These are Bangladesh, Chile, Dominican Republic, Germany, Honduras, Italy, Japan, Jordan, the Republic of Korea, Latvia, Malaysia, Mexico, Netherlands, Nigeria, Singapore, South Africa, Sri Lanka, Thailand and the United States. 9 The survey is available at

> - <u>http://rru.worldbank.org/Documents/DoingBusiness/Methodology/TradingAcrossBorders/Logistics survey.pdf.</u>

8
a self-administered questionnaire, which includes standard modules on household, parental and student socioeconomic characteristics, one question on education expectations (¿What is the highest education level you expect to achieve?) and, in the ECE 2015 only, two Likert scales to measure students’ perceptions about their ability to understand and complete different tasks related to math and reading comprehension (respectively). We test the impact of the intervention on education expectations and students’ self-perception of their own abilities in these two subjects to improve our understanding of the mechanisms.

> 17 Two other ECE studies take place annually in Peru, a census assessment of students in Year 2 and a sample assessment for Year 4s. For our study, only data from the Year 8 ECE is relevant.

12
Figure 25: Education profile of Temporary employees. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Georgia Kyrgyzstan Turkey Armenia<br>Without Instruction (SP) Without Instruction (EP) Primary (SP)<br>Primary (EP) Secondary (SP) Secondary (EP)<br>Tertiary (SP) Tertiary (EP)<br><!-- End of picture text --> Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point.

It should be noted, however, that, like in the case of Latin American countries, the improvement in the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in the Annex of the paper.

When we analyze what has happened at the salary level in the period of consideration (Figure 26), two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by the economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.10

> 10As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time). However, since there are non-standard forms of employment not identified in the database, our standard employment category could, in fact, incorporate non-standard
_Figure 13: Legal and business context for women and promotion of gender equality in schools_ <!-- Start of picture text --> 4.8<br>Philippines<br>Lao PDR<br>4.7<br>4.6 Peru<br>Nigeria Congo, Dem. Rep.<br>4.5<br>Vietnam<br>Indonesia<br>4.4<br>Ghana<br>4.3<br>Bangladesh<br>4.2 Mongolia<br>4.1 Pakistan<br>Uganda<br>4<br>40 50 60 70 80 90 100<br>Women, Business and Law Index 2023<br>Disagrees")<br>(5="Strongly agrees", 1= "Strongly<br>School should promote gender equality<br><!-- End of picture text --> Source: CGD–World Bank 2022 survey and Women, Business and Law, 2023 (World Bank).

# 5 Other Priorities in Education Outcompete Investments in Inclusion

Although at least 80 percent of all respondents in the 2022 survey claim they would support accommodations to facilitate the inclusion of children with disabilities in regular classrooms, investing in a program for students with disabilities is the least often selected option in the discrete choice experiment (Figure 14). Less than 50 percent of all 2022 respondents<sup>15</sup> selected investing in a program for students with disabilities, preferring in-service teacher training (60 percent) or early-grade reading (62 percent). The second-lowest frequency option was investing in laptops (38 percent). Officials from the EAP region were as likely as other respondents to select the project supporting inclusion (47 percent of respondents) than outside of the region (48 percent of respondents). In the EAP region, the project supporting inclusion was selected more often (47 percent) than the provision of free meals in primary schools (38 percent