rafmacalaba/datause-encoder-data huggingface dataset:
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Within machinery and transport equipment, SITC 7, the effect of shifting weights was much larger. For exports to the world (Appendix Table 2), the index on a 1975 base rose more slowly than that on a 1963 base by more than a quarter of a per cent per year, as measured from a logarithmic regression, and the index on a 1986 base increased by about another third of a per cent less than that on the 1975 base. Thus, the shift of 20 plus years in the base reduced the rate of growth in prices of machinery and transport equipment by roughly .6 per cent each year. Countries apparently shifted their imports toward the products that were declining relatively in price much more extensively in machinery and transport equipment than in manufacturing as a whole.

# New and Rauidly Growing Countries

Two of the major changes that have taken place in the world market for exports of manufactures have been, first and most important, the rapid growth of Japanese production and exports, and second, the rapid growth of exports by the Asian NICs. That growth could affect the markets and world prices for manufactures in several ways, depending on the way in which entry into or expansion in the share of world markets was accomplished. The most readily observable method of entry would be entry into world markets at high prices and low market shares, followed by relative declines in the newcomers' prices as their productivity grew relative to others. Indexes with later country weights would decline in comparison with indexes based on earlier weights, because they would give more importance to the new countries with declining prices. A second method of entry would be that the new countries enter world markets by selling at low prices, gradually raising their market shares as buyers learn about and shift to the new sources of supply, but with no change in relative prices offered
those income sources, the WHT rates vary for each type of income but remain constant regardless of the amount of the earnings.

**Table 1. Central Government Budget Revenue, 2016**

|**Source**|Million<br>Turkish Lira<br>(TL)|Percent<br>of GDP|Share of<br>Government<br>Revenue|
|---|---|---|---|
|**Total Revenues (I+II+III)**|**738,585**|**28.3**|**100.0**|
|**I. Taxes**|**459,002**|**17.6**|**62.1**|
|**I.1) Taxes on Income, Profits and Capital Gains**|**139,574**|**5.4**|**18.9**|
|a) Corporate Tax|42,970|1.6|5.8|
|b) Personal Income Tax|96,605|3.7|13.1|
|Withholding Tax|89,752|3.4|12.2|
|Based on Declaration|4,522|0.2|0.6|
|Other|2,331|0.1|0.3|
|**I.2) Taxes on Property**|**10,606**|**0.4**|**1.4**|
|**I.3) Taxes on Goods and Services**|**268,165**|**10.3**|**36.3**|
|a) Domestic VAT + VAT on Imports|130,822|5.0|17.7|
|b) Special Consumption Tax|120,402|4.6|16.3|
|c) Gambling Tax|900|0.0|0.1|
|d) Special Communication Tax|4,976|0.2|0.7|
|e) Others|11,065|0.4|1.5|
|**I.4) Other Taxes**|**40,657**|**1.6**|**5.5**|
|**II. Social Security Contributions**|**184,446**|**7.1**|**25.0**|
|II.1) Retirement|122,963|4.7|16.6|
|II.2) Health|61,481|2.4|8.4|
|**III. Other Revenue* **|**95,138**|**3.6**|**12.9**|

Source: Ministry of Treasury and Finance.

*Other revenues include taxes on international trade and transactions, stamp duties, fees, government property income, grants and aids and special revenues, interest, shares and fines, capital revenues, collection from loans, revenue from special budget institutions and revenues from regularity and supervisory institutions.

Individuals are obliged by law to declare their income from different sources every year, and pay taxes accordingly. Any income that is taxed with WHT will be deducted from the total amount of taxes that should be paid. PITBD has a relatively complicated schedular structure whereby every individual is expected to calculate their taxes from different incomes and 6
# **1. Introduction**

Two narratives drive discussions of the development impact of high-skilled migration. The first is the idea of a brain drain, whereby the departure of doctors, teachers, engineers, scientists, and other highly skilled workers decimates the human capital and fiscal revenues of sending countries (Bhagwati and Hamada, 1974). Such fears lead to calls for policies to restrict the flow of highly skilled workers, such as demands that developed countries stop recruiting doctors from developing nations, and efforts by developing nations to restrict the ease of their highly skilled individuals migrating.<sup>1</sup> Contrasting with this is the view of a highly educated diaspora as a potent force for developing the local economy through remittances, trade, foreign direct investment (FDI), and knowledge transfers, with the experience of India and China in setting up technology firms as a result of diaspora working in Silicon Valley a prominent example (Saxeenian, 2002). Economists have also emphasized that the possibility of migrating may spur human capital accumulation, potentially leading to a net increase in the education levels of those in the home country.<sup>2</sup> However, what is sorely lacking in such discussions is empirical evidence as to what the experience has been in practice for countries facing high rates of high-skilled emigration. Recent large-scale data efforts have provided a much-improved evidence base with which to talk about the scale of high-skilled emigration (e.g. Docquier and Marfouk, 2004; Beine et al. 2007), and in Gibson and McKenzie (2010) we have used data from three island countries to investigate the determinants of migration and return migration decisions by the highly skilled. Yet quantitative evidence as to the extent to which the many theoretical channels operate in practice in determining the consequences of high-skilled emigration is almost non-existent. In particular, it is unclear whether it is common for highly-skilled emigrants from high migration countries to actually be engaging in knowledge transfers, trade, and FDI, or whether the experience of Chinese and Indian IT companies is so famous because it is the exception, not the rule. We also

> 1 For example, in 2009 the Algerian Government said it would restrict study abroad scholarships granted to high achievers in baccalaureate examinations in an effort to stem a worsening brain drain, and Uganda began requiring doctors who wish to pursue further studies abroad to make a written commitment to return to Uganda. On the receiving side, the World Federation of Public Health Associations adopted a resolution in 2005 supporting ethical restrictions on international recruitment of health professionals from developing countries.

> 2 See Mountford (1997), Vidal (1998), Stark et al. (1997) and Schiff (2006) for this theoretical debate, Beine et al. (2008) for cross-country empirical evidence, and Chand and Clemens (2008) for a case study in Fiji. Kapur and McHale (2005) provide a nice recent review of the literature.

2
# _2.1.2 Investment: Higher profits are not always reinvested, with Consumer Staples disinvesting and Travel and Leisure fixed assets growing strong_

Firms reinvest their profits and raise new money because of asset depreciation and to anticipate growth opportunities. Indeed, despite superior profitability, with a median of 17.3 percent, retained earnings to equity of Egyptian firms are similar to the peer group (Exhibit 2.5). However, Egyptian net fixed asset growth is not clearly higher than the peer group. Together with superior price to book ratios, this suggests a mixed picture on the sector level.

# **Exhibit 2.5 – Investment indicators**

_<u>Medians and means for 2003-07</u>_

||**Net fixed asse**<br>**growth(%)**|**t**<br>**Price to Book **|**Retained**<br>**earnings to**<br>**equity (%)**|
|---|---|---|---|
|**Peer group (WS)**||||
|Median|-3.9|1.3|17.7|
|Mean|1.2|1.7|18.4|
|**LMI countries, excl. Egypt (WS)**||||
|Median|-8.6|1.1|19.3|
|Mean|-5.8|1.5|20.7|
|**Top Tier, Egypt (WS)**||||
|Median|-5.3|1.8|17.3|
|Mean|3.2|2.3|18.5|

_Source: Worldscope; World Bank staff analysis Notes: Whole peer group: Colombia, Chile, Hungary, Indonesia, Israel, Jordan, Mexico, Morocco, Pakistan, Philippines, and Turkey. LMI countries: Colombia, Indonesia, Jordan, Morocco, and Philippines._ There are indeed differences on the industry level. Consistent with its lower profitability, Consumer Staples shows significantly lower retained earnings in 2007 (-20.9 pp; Exhibit 2.6). In addition, its fixed asset growth in 2007 is much lower than the peer group (-2.9 pp). Not coincidentally, the price book ratio is only lower for Consumer Staples. In contrast, with higher fixed asset growth and lower retained earnings, the Travel and Leisure sector seems to be reinvesting its profits significantly, consistent with its superior price to book ratio (+5.9 pp). Although fixed asset growth rates of the Industrial Cyclical and Construction and Materials sectors are slightly higher, with significantly higher retained earnings, they seem to be hoarding their profits.

Exhibit 2.7 shows the statistical differences of indicators between Egypt and the benchmark groups where we have corrected for differences in industry composition. Relative to the LMI group, Egypt has superior net fixed asset growth of 2.9 pp, indicating that the typical Egyptian firm invests more than is expected based on its income bracket. However, comparing to the peer group, the difference is negative and not statistically significant likely due to industry variation. The slightly lower retained earnings in combination with robust high profitability suggest that the median Egyptian firm is reinvesting its earnings. However, the significantly higher Egyptian price to book ratio implies that investment is relatively on the low side.

8
# **1 Introduction**

In recent decades China has experienced impressive growth. Over the last three decades GDP growth averaged 10% per year, and over 500 million people have moved out of poverty. China is now the world’s largest exporter, manufacturer and a substantial importer of raw materials<sup>1</sup> . The value of Chinese trade has practically doubled every four years in the last three decades<sup>2</sup> . Along with this impressive growth, there has been an increasing academic and policy debate about the effects of China on other countries’ performance, particularly in Latin America<sup>3</sup> . A cacophony of voices have been heard in the media discussing the economic and social impacts of the rise of China, with some arguing about the benefits while others are more skeptical<sup>4</sup> .

For Latin American countries, several questions regarding the growth of China remain unanswered. For instance, how much of the change in net exports and prices can be attributed to the growth of China? Did China’s growth affect relative wages, employment and informality in Latin America? How difficult was it for workers to move from a sector that received a negative shock to a sector that received a positive shock? This paper contributes to the debate providing some answers. More specifically, we conceptualize China’s growth as a negative trade shock for manufacturing exporters and a positive shock for mining and agricultural exporters. Then, we identify the size of these shocks for a large set of Latin American and Caribbean economies. Finally, using the identified shocks and a structural model of labor markets, we analyze the impact on wages and employment in three large countries: Argentina, Brazil and Mexico.

From the perspective of Latin American producers, the export and import growth of China was realized as a shift in aggregate demand faced by exporters. The size and direction of these

> 1See World Bank and Development Research Center of the State Council (2013).

> 2See Feenstra and Wei (2009).

> 3See for example Lora (2007) and Lederman _et al_ (2009).

> 4See for example the articles: “China and Latin America: Connected and Competing, _The European Financial Review_ , February 20<sup>_th_</sup> , 2013; “China and Latin America Magic,or realism?, _The Economist_ , January 1<sup>_st_</sup> , 2005 and “Deals Help China Expand Sway in Latin America”, _The New York Times_ , April 15<sup>_th_</sup> , 2009.

2
**<u>Table 12. Regression results</u>**

|||Cons|umption|(in log)|||||Income|(in log)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS|||GLL|AMM||O|LS||GLL|AMM|
||Coeff.|P-value|Coeff.|P-value|Coeff.|P-value|Coeff.|P-value|Coeff.|P-value|Coeff.|P-value|
|Intercept|7.79|0.00|8.94|0.00|9.09|0.00|7.44|0.00|7.80|0.00|7.56|0.00|
|_Household variables_|||||||||||||
|Agricultural index|-0.37|0.66|-0.12|0.89|-0.16|0.91|1.30|0.18|1.50|0.13|1.18|0.22|
|<br>Number of adults over 50 years|-0.17|0.00|-0.10|0.06|-0.09|0.52|-0.13|0.03|-0.09|0.18|-0.08|0.19|
|Number of adults 25-50 years|-0.10|0.04|-0.04|0.47|-0.03|0.54|-0.10|0.09|-0.05|0.40|-0.05|0.39|
|<br>Number of adults 15-24 years|-0.03|0.46|0.03|0.50|0.04|0.54|-0.07|0.16|-0.03|0.58|-0.03|0.57|
|Number of children 12-14 years|0.01|0.87|0.07|0.19|0.08|0.65|-0.12|0.03|-0.08|0.19|-0.07|0.25|
|<br>Number of children 6-11 years|0.03|0.33|0.01|0.91|0.01|0.97|-0.04|0.19|-0.02|0.87|-0.02|0.84|
|Number of infants 3-5 years|0.03|0.36|0.00|0.99|0.00|1.00|-0.03|0.46|0.00|0.97|-0.01|0.96|
|<br>Number of infants 0-2 years|-0.01|0.80|-0.01|0.89|-0.02|0.92|-0.05|0.24|-0.02|0.88|-0.02|0.84|
|Age of head|0.01|0.06|0.01|0.54|0.01|0.64|0.02|0.05|0.02|0.18|0.01|0.26|
|<br>Age of head<sup>2</sup>/100|-0.02|0.00|-0.02|0.00|-0.02|0.00|-0.02|0.00|-0.03|0.00|-0.02|0.00|
|Male head|0.00|0.93|-0.02|0.79|-0.04|0.55|0.12|0.06|0.15|0.04|0.14|0.05|
|Years of schooling of head|0.04|0.00|-0.02|0.49|-0.02|0.65|0.04|0.00|0.06|0.13|0.05|0.19|
|No job|0.16|0.02|0.16|0.03|0.14|0.59|0.16|0.05|0.17|0.04|0.16|0.07|
|<br>Income diversity|0.25|0.00|0.33|0.06|0.34|0.30|0.57|0.00|0.64|0.00|0.60|0.00|
|Land owner|-0.09|0.62|-0.76|0.14|-0.68|0.51|0.05|0.80|-0.13|0.83|0.07|0.90|
|_Region_|||||||||||||
|North West|-0.16|0.35|-0.27|0.14|-0.30|0.72|-0.37|0.06|-0.28|0.18|-0.33|0.13|
|North|-0.35|0.09|-0.39|0.08|-0.37|0.75|-0.02|0.93|0.27|0.31|0.25|0.35|
|North East|-0.98|0.00|-0.91|0.00|-0.90|0.22|-0.27|0.22|-0.23|0.35|-0.19|0.44|
|Artibonite|-0.31|0.03|-0.33|0.05|-0.36|0.30|0.31|0.06|0.31|0.12|0.29|0.16|
|Centre|-0.84|0.00|-0.63|0.00|-0.64|0.02|-0.13|0.54|-0.15|0.53|-0.17|0.50|
|West|0.13|0.42|0.03|0.87|-0.02|0.95|0.49|0.01|0.57|0.01|0.57|0.01|
|Grande'Anse|-0.85|0.00|-0.74|0.00|-0.79|0.45|-0.81|0.00|-0.68|0.00|-0.72|0.00|
|Nippes|-0.17|0.30|-0.11|0.58|-0.11|0.64|-0.07|0.72|-0.05|0.84|0.04|0.86|
|South|0.34|0.02|0.24|0.15|0.19|0.58|0.10|0.55|0.21|0.29|0.09|0.68|
|Southeast|ref||ref||ref||ref||ref||ref||
|_Community variables_|||||||||||||
|<br>% Land owners|0.33|0.38|0.47|0.60|0.27|0.90|1.24|0.00|1.32|0.20|1.68|0.14|
|Community index|-0.17|0.17|-0.60|0.22|-0.55|0.77|-0.09|0.50|-0.72|0.21|-0.49|0.49|
|<br>Average years of schooling|0.06|0.07|0.06|0.06|0.06|0.51|0.03|0.38|0.06|0.15|0.04|0.27|
|_Household* Community variables_|||||||||||||

30
differentials in productivity in an African context where households have complex structures. First household size is usually high in rural Africa and plots are not necessary managed at the household level but at the individual level. Second it is not uncommon (in Mali for example) to have three generations living together and the person declared as the head of the household might just be the patriarch whose influence on productivity is in fact limited. Since the head of the household does not have the observable and nonobservable characteristics as the other household members, the scope of the conclusions drawn from these types of studies is limited in terms of public policy. The second type of method consists of estimating a production function with the gender dummy as an independent variable; the estimation being done at the plot level. The advantage of this method over the previous is that it is better able to isolate the difference in productivity caused by gender among all the factors that influence productivity. While this method does help identify a gender gap, the principal limitation is that the model is not built to identify the root causes of the gender difference which is critical for identifying and prioritizing areas for policy interventions (Kilic et al, 2013).

Following Kilic et al (2013) and Oseni et al (2013), we use the Oaxaca-Blinder (OB) decomposition to assess gender differentials in agricultural productivity. We start by estimating a production function that models plot level agricultural productivity as a function of the gender of the plot manager and other factors that may contribute to productivity. The model to be estimated is: where y is the natural log of output per hectare (a measure of productivity), g is the gender dummy of the plot manager, x is a K+1-dimension vector including the intercept (x0), land size, non-labor and labor inputs, individual characteristics of the managers, household and community variables, u is the random error term which is assumed to be independently and identically distributed as _N(0,σ_<sup>_2_</sup> _)_ . This model provides an indication of any significant gender productivity gap.

We also estimate a model independently for male and female-managed plots defined as: 10
**Figure 4: US Goods and Services Imports YTY Growth, Aug 2008 – Dec 2009**

<!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>0%<br>‐10%<br>‐20%<br>‐30%<br>‐40%<br>‐50%<br>‐60%<br>Total services Total goods<br>Industrial supplies & materials Consumer goods, except automotive<br>Aug‐08 Sep‐08 Oct‐08 Nov‐08 Dec‐08 Jan‐09 Feb‐09 Mar‐09 Apr‐09 May‐09 Jun‐09 Jul‐09 Aug‐09 Sep‐09 Oct‐09 Nov‐09 Dec‐09<br><!-- End of picture text --> Source: Own illustration. Data: Bureau of Economic Analysis, International Accounts Products, quarterly imports of goods, balance of payments-based data, seasonally adjusted.

# **3.2 Globalization in the Long-Run versus “Crisis Elasticities” of Trade**

A second explanation of the collapse in world trade in the most recent downturn is that economic globalization has, over decades, resulted in a steady increase in the income elasticity of world trade, so that the relatively large GDP decline in recent years has been matched by a historic decline in the volume of world trade. Freund (2009) reports estimates showing a monotonic increase in the income elasticity of world trade over successive decades since the 1960s, rising from 1.94 in the 1960s to 3.69 in the 2000s. As a result, for an identical percentage drop in GDP now leads to a greater percentage decline in trade than previously. Thus, a 1 percent reduction in real income lowered real trade by around 2 percent in the 1960s, which has increased gradually to 3.7 in recent years.

The income elasticity of import demand _εM_ , for example, is given by: Using quarterly GDP and import data, we approximated the US income elasticities of import demand as follows: where subscript _t_ denotes the quarter,  _M t_  _M t_  _M t_  4 , and  _Yt_  _Yt_  _Yt_  4 . _Figure 5_ (left-hand panel) confirms the increase in the income elasticity of imports for the US over the last four decades. We did not show the data for 2008:Q4-2009:Q4, as the drop and subsequent increase were extremely high.

Note that greater economic openness _per se_ – the result of trade liberalization or technological change or changing business strategies – does not imply a higher income elasticity of import demand. To the contrary, it other things equal, a higher import propensity, _M/Y_ **_,_** lowers the 8
**Figure 15.  Historical inequality trends (UK, France, Spain)**

<!-- Start of picture text --> 0.2<br>0.15<br>0.1<br>0.05<br>0<br>1910 1920 1930 1940 1950 1960 1970 1980 1990<br>UK FR SP<br>Income top 1%<br><!-- End of picture text --> Source: Perry et al. (2006)

# **IV. Why is reducing inequality important?**

As noted in the introduction there are several reasons why policy makers should be concerned with such levels of high inequality, including that high inequality appears to lower long-run growth. In this section we review three of them.

# _IV.1 Inequality and crime_

First, there is now significant evidence that high crime and violence levels lower growth prospects (see Alessina and Perotti, 1996 for macro evidence of how crime levels lower growth) More recently, Alaimo et al. 2007 have presented micro evidence based on 10,000+ Latin American firms suggesting a negative impact of crime on firm productivity. In turn, as shown by Fajnzylber, Lederman and Loayza (1998) income inequality appears to be a major determinant of crime and violence levels (see Panel A, Figure 16 for the correlation between the Gini coefficient and the % of firms that find crime to be a major constraint to growth and Panel B for the correlation between income Ginis and homicide rates. Nor surprisingly then, the percentage of firms that find crime and violence to be a major constraint to growth in Latin America is much larger than in other regions (Panel C, Figure 16).

21
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Upper MRAD per person per year =51.0 ***** change in l-hr max ozone (ppm) Central MRAD per person per year 34.0 ***** change in 1-hr max ozone (ppm) Lower MRAD per person per year 17.0 ***** change in I-hr max ozone (ppm) Respiratory Svmptoms. Results of Krupnick et al_3<sup>8</sup> can be used to estimate the effects of ozone on respiratory symptoms. As noted above, this study examined the daily occurrence of upper and lower respiratory symptoms among a panel of adults in Southern California. A Markov process model, which incorporated the probability of illness on the prior day and controlled for aumocorrelation, was developed to determine the effects of air pollution on healthMany regression models included both ozone and particulate matter. Therefore, to prevent double counting, the effects of these pollutants were apportioned according to the regression results when the pollutants were examined together. The marginal effect of ozone was calculated by incorporating the stationary probabilities as described in the paper. The high estimate is obtained from the specification that includes all other pollutants, the central estimate includes only some of the pollutants and the low estimate is one standard error below the central estimate. Therefore, the following ranges were determined for the number of symptom days related to a change in the annual average of 1-hour daily maxinum ozone: Upper change in symptom days per year per person 96.6 * change in 1-hour max ozone (ppm) Central change in symptom days per year per person = 54.75 ***** change in 1-hour max ozone (ppm) Lower change in symptom days per year per person = 28 11 ***** change in 1-hour max ozone (ppm) Eve Irritation. Schwarez et al.57 provide empirical estimates relating oxidants to eye irritation using the data from a population beginning nursing school in Los Angeles, as described above. Using