rafmacalaba/datause-encoder-data huggingface dataset:
be rejected.<sup>12</sup>

# 6 Calculating TRIs and Deadweight Losses

The estimated import demand elasticities allow us to examine the trade restrictiveness and welfare losses associated with the existing tariff structure in 88 countries in our sample for which tariff schedules are available.<sup>13</sup> More importantly, this can be done within a theoretically-sound framework. The literature has traditionally measure trade restrictiveness using a-theoretical measures such as simple and import-weigthed tariffs.<sup>14</sup> As argued by Anderson and Neary (1992, 1994, 2003) these have little theoretical foundations. Import-weighted averages tend to be downward bias, as for example, they put zero weight on prohibitive tariffs and simple average tariffs put identical weights on tariffs that may have very different economic signi fi cance. Anderson and Neary (1992, 1994) propose a trade restrictiveness index (TRI), which has a theoretically sound averaging procedure. TRI is de fi ned as the uniform tariff that yields the same real income, and therefore national welfare, as the existing tariff structure. Deadweight loss measures can also be constructed using TRIs and theoretically consistent estimates of import demand elasticities, which in turn allows for comparisons of welfare distortions associated with each country’s tariff structure.

To calculate the TRI, one would ideally need to solve a full- fl edged general equilibrium model for the uniform tariff that could keep welfare constant given the observed tariff structure. Feenstra (1995) provides a simpli fi cation of TRI, which only requires information on import demand elasticities, share of imports and the current tariff schedule. The major drawback of such simpli fi cation is that it ignores the general equilibrium of cross price effects. Nonetheless, as an illustration of the fi rst order effect of trade distortions, the simpli fi ed TRI is informative:<sup>15</sup>

> 12 We found similar patterns when regressing on the median elasticity by country. The coefficient on GDP is 0.03 and on GDP per capita - 0.02 and both are statistically signi fi cant at the 1 percent leve.

> 13 Data sources for tariff data are United Nations’ Comtrade and the Integrated Database of the WTO. In this paper, we abstract from measuring the trade restrictiveness of non-tariff barriers, as well as the role of tariff preferences in eroding trade restrictiveness. For an attempt to do so, see Kee, Nicita and Olarreaga (2004b).

> 14 If NTB measures are to be considered, trade economist often use simple or import-weigthed coverage ratios of NTBs.

> 15 See Equation (3.5) in Feenstra (1995). Note that given our setup, the derivation in Feenstra (1995) is equivalent to deriving the TRI that would keep GDP at its maximum level given the existing tariff structure.

18
Kathlon and Sughd regions. The share of these cotton products in total exports steadily increased from 2 to 24 percent between 1995 and 1997 before slowly declining to fall below 5 percent after 2005. The manufacture of chemicals and chemical products mainly include inorganic chemicals and plastics products.

**Figure 3. Tajikistan: Sectoral Composition of Exports, 1992-2010**

(percent of total exports) <!-- Start of picture text --> 120% 1,000<br>900<br>100%<br>800<br>700<br>80%<br>600<br>60% 500<br>400<br>40%<br>300<br>200<br>20%<br>100<br>0% 0<br>Manufacture of basic precious and non-ferrous metals<br>Growing of crops; market gardening; horticulture<br>Manufacture of textiles<br>Manufacture of chemicals and chemical products<br>Manufacture of food products and beverages<br>Scrap metal<br>Total Export (in million $, RHS)<br><!-- End of picture text --> Source: COMTRADE database and authors’ calculations.

# _Shifting Trading Partners_

There is also a shift in Tajikistan’s major trade partners (Figure 4). In 1992, the top-5 export markets were per order of importance the EU8, the CIS, the EU15+, the Central Asia and the Middle East and North Africa<sup>4</sup> . In 1997 when the number of Tajikistan trading partners covering 90 percent of its total exports jumped from 15 in 1992 to 46, the top-5 export markets were the EU15+, the CIS, East Asia and Southeast Europe. In 2010, the top-5 export markets were the CIS, the EU15+, the EU8, Southeast Europe and East Asia. The slight product diversification around 1997 illustrated in Figure 3 seems to have been accompanied with a slight market diversification away from its traditional CIS partners.

> 4 The EU8 covers the following countries: Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic and Slovenia. The CIS covers Belarus, Moldova, Russia and Ukraine. The EU15+ covers the EU15 countries plus Norway, Switzerland and Turkey.

5
Table 1.9: Heavy truck sales in Russia

||**_1995_**|**_2000_**|**_2005_**|**_2006_**|**_2007_**|**_2008_**|**_2009_**|
|---|---|---|---|---|---|---|---|
|Units ('000 trucks)||||||||
|Total Sales|**129.2**|**190.3**|**205.9**|**256.6**|**366.9**|**360.5**|**102.0**|
|**Russian producers**|119.1|171.1|155.8|193.2|230.3|211.3|76.1|
|**CIS producers**|5.8|11.5|9.6|10.3|11.6|9.2|2.2|
|**Non-CIS producers**|4.3|7.7|40.5|53.1|125|140|23.7|
|Market shares (% of total)||||||||
|**Russian producers**|92.2%|89.9%|75.7%|75.3%|62.8%|58.6%|74.6%|
|**CIS producers**|4.5%|6.0%|4.7%|4.0%|3.2%|2.6%|2.2%|
|**Non-CISproducers**|3.3%|4.0%|19.7%|20.7%|34.1%|38.8%|23.2%|

Source: Rosstat, Authors estimates.

**In contrast, there has been a rapid increase in the importance of the domestic market.** Between 2003 and 2007, domestic sales nearly doubled while exports of tractors to Russia and other countries stayed broadly constant; in addition, during the global financial crisis of 2008-2009, the domestic market became the main sales channel for Belarus’s trucks (Figure 1.8). Public procurement, as a part of agriculture modernization program, contributed to the increase in domestic demand. Even after substantial recovery in track sales to Russia in 2010, domestic market accounted for two thirds of total sales as compared to only one third in 2003.

**But the increase in importance of the domestic market is not sustainable.** It is based on protectionism which limits competition with other possible sources and has been financed by spending policies followed by the Government of Belarus that are not sustainable given the current macroeconomic situation.

Figure 1.9: Sales of Trucks 2003-2010 <!-- Start of picture text --> 22.5<br>12.0<br>9.2 9.1 10.0 5.1<br>6.2 14.8<br>3.2 3.9 4.3 4.3 4.2 15.9<br>13.7 1.7<br>8.7 8.4 8.9 8.9 9.3 1.7 5.3<br>2.5<br><!-- End of picture text --> Source: Belstat, Authors estimates.

**In sum, the period of high growth in recent years has come to an end unless SOEs adapt to the radical changes in the external and domestic environment.** Excess capacity, which easily contributed to rapid growth in productivity, is already exhausted; low investment in improving quality of products along with rapidly increasing real wages has reduced competitiveness. The biggest challenge looking forward is to respond to the changes that will result from Russia’s accession into the WTO and from the gradual increase in consumer’s sophistication in that market.

13
27 Appendix Table 1. Description of variables and data sources

|**Variable**|**Description**|**Source**|
|---|---|---|
|Dependent variab|les||
|Stock Return<br>Synchronicity|Measure of how much individual stock returns move together, or<br>are correlated to the market return. A higher measure of<br>synchronicity indicates that there is not much firm-specific<br>information, and hence indicates lower efficiency.|Based on Morck,<br>Yeung, and Yu<br>(2000).<br>Constructed from<br>Datastream data<br>on returns,<br>exchange rates,<br>and CRSP.|
|Transaction<br>Costs|Measure of the implicit and explicit costs of trading in a particular<br>market. It is based on a methodology that exploits the percentage<br>of zero-return days in a year. Since a trade only happens when the<br>benefit of doing so exceeds the costs, the relative frequency of<br>zero-returns (no trades) is a proxy for transaction costs.|<br>Based on<br>Lesmond,<br>Trzcinka, and<br>Ogden (1999).<br>Constructed from<br>Datastream data<br>on returns.|
|Zero Return<br>Weeks|The percentage of zero-return weeks that are observed for a firm.<br>The measure is then aggregated over firms to create the country<br>average.|Datastream and<br>S&P Emerging<br>Markets Database.|
|Independent varia|bles||
|GDP per capita|Gross domestic product divided by mid-year population.|World Bank,<br>World<br>Development<br>|
|||Indicators|
|Extent of<br>disclosure index|Measures the strength of minority shareholder protections against<br>directors’ misuse of corporate assets for personal gain. This<br>particular indicator captures the transparency of related-party<br>transactions.|<br>World Bank,<br>Doing Business.|
|Extent of<br>director liability|Measures the strength of minority shareholder protections against<br>directors’ misuse of corporate assets for personal gain. This|World Bank,<br>Doing Business|
|index<br>Ease of|particular indicator captures the extent of liability for self-dealing.<br>Measures the strength of minority shareholder protections against|World Bank,|
|shareholder<br>suits index|directors’ misuse of corporate assets for personal gain. This<br>particular indicator captures the ability to sue officers and<br>directors for misconduct|Doing Business|
|Short selling|.<br>A dichotomous variable that indicates whether short sales are|Endo and Rhee<br>|
|practiced<br>Stock Lending<br>|practiced in a stock market.<br>A dichotomous variable that indicates whether stock lending is<br>|(2005)<br>Endo and Rhee<br>|
|allowed|allowed in a stock market.|(2005)|
|<br>Settlement|<br>Time to confirmation in the settlement cycle for securities traded|<br>Handbook of|
|period<br>Equity Inflows|<br>in the stock exchange, provided as_x_in T +_x_.<br>Portfolio equity flows into the country in a given year. It includes<br>transaction in shares and stocks and similar documents.|Stock Markets.<br>IMF, International<br>Financial<br>Statistics|
|Options and|A dichotomous variable that indicates whether any option or|Handbook of|
|<br>futures trading|<br>future derivative is traded in the country.|Stock Markets.|
visualize a future that most parents had not considered before for their children, we decided to use videos instead of other media formats. The entertainment media used in this trial were produced by Impact(Ed), formerly Discovery Learning Alliance, a US-based non-profit media firm that works with ministries of education in developing countries to produce television and mass media initiatives. The study videos are an excerpt from the 10-hour video series “My Better World”, an entertaining animation program that also includes real people sharing their experiences in inspiring mini documentaries, often set against a pan-African village life. These everyday role models reflect the animated story’s highlighted themes with their personal stories of struggle, courage, and perseverance.

The community screenings of the aspirational videos were implemented in the following way. Community mobilizers invited eligible households, living within a radius of two kilometers of a randomly selected local school, to attend community events that would discuss “relevant issues for parents”. When households were visited to deliver such invitations, no mention was made about the main themes of the videos and screening discussions to avoid any potential self-selection of participants against or in favor of education for children. Using data from the listing exercise, the target child (6-9 years old) and both parents were invited to two sessions that would take place in the local school over two subsequent weekends. The implementing NGO was responsible for organizing and staffing the community screenings. NGO female facilitators were trained by the authors about which messages to highlight during the post-screening discussions based on the aspirational video content. To host a maximum of 75 households in the local schools for the screenings and discussions, two parallel screenings (in two different classrooms) took place in most treated communities. The day of the screenings, a registration desk was set at the entrance to ensure that invited were admitted to the up only participants venue. Snacks and a gift bag with a few early-grade literacy booklets were handed to each household head as they completed registration. The community leader welcomed attendees, briefly highlighting the importance of primary education, and explaining the ground rules for participation. The public endorsement of the community leader of the activity led by the NGO facilitators had the purpose of minimizing possible disruptions during the screenings and discussions. The two sessions per community lasted a total of four hours per screening. The aspirational video-clips were on average 5 minutes long and a total of 11 videos were screened in 2 weekends. The videos involved short documentaries and animations of people living in similar social and economic circumstances to those observed in the communities where this study took place. After the video screenings, NGO facilitators led a discussion by asking questions to participants to reinforce the key messages shown in the videos. In total, the screenings and discussions lasted less 8
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# **10. REFERENCES**

Abila, R.O. (1995). _Structure, Conduct and Performance of Kisumu Fish Marketing System._ MSc Thesis, University of Nairobi.

Abila, R.O. (1996). _Economic Analysis of the Domestic and Export Marketing of Kenya’s Nile Perch and its Products._ Proceedings of 6<sup>th</sup> FAO Expert Consultation on Fish Technology in Africa, Kisumu.

Abila, R.O. (1998). _Four Decades of the Nile Perch Fishery in Lake Victoria: Technological Developments, Impacts and Policy Options for Sustainable Utilisation_ . Paper presented at the workshop Prospects for the Sustainable management of Lake Victoria, Mwanza.

Abila, R.O. (2000). _The Development of the Lake Victoria Fishery: A Boon or Bane for Food Security?_ IUCN, Nairobi.

Abila, R.O. (2002). _A Socio-Economic Analysis of the Fishery Cooperatives of Lake Victoria (Kenya)._ PhD Thesis, University of Hull.

Abila, R.O. and Jansen, E.G. (1997). _From Local to Global Markets: The Fish processing and Exporting Industry on the Kenyan Part of Lake Victoria: Its Structure, Strategies and SocioEconomic Impacts._ Centre for Development and the Environment, University of Oslo, Oslo.

Abila, R.O. and Jansen, E. (1997). _From Local to Global markets: The Fish Processing and Exporting Industry on the Kenyan Part of Lake Victoria: Its Structure, Strategies and SocioEconomic Impacts_ .

AFIPEK (2000). _Code of Good Manufacturing Practice (GMP) for the Handling and Processing of Fish and Fishery Products in Kenya_ . Kenya Fish Processors and Exporters Association, Nairobi.

Bankole, A., Ogunkoba, O. and Ademola Oyejide, T. (2000). _Quantifying the Trade Impact of Sanitary and Phytosanitary Standards: What is Known and Issues of Importance for SubSaharan Africa_ **.** World Bank, Washington DC.

Bokea, C. and Ikaiara, M. (2002). _The Macroeconomy of the Export Fishing Industry in Lake Victoria (Kenya)._ IUCN, Nairobi.

Cato, .C. (1998). _Seafood Safety - Economics of Hazard Analysis and Critical Control Point (HACCP) Programmes._ FAO, Rome.

Cato, J.C. and Lima dos Santos, C.A. (1998a). _Costs to Upgrade the Bangladesh Frozen Shrimp Processing Sector to Adequate Technical and Sanitary Standards and to Maintain a HACCP Program._ NE-165, University of Massachusetts, Amhurst.

78
34 IV. Recent Theoretical Developments on the Relation Between Trade and Growth As pointed out in Section III the traditional policy and empirical literature on trade and growth have not been based on firm theoretical grounds. In fact, as Helpman (1988, p. 3) has recently pointed out "current theory is not suitable to deal in a satisfactory way with these alternative views (on the relation between trade policy and growth)".<sup>23</sup> The most serious problem of traditional growth theory -- that is, of Solow's neoclassical growth model and its many variants -- is that in equilibrium (i.e., in the steady state) the rate of growth of output is independent of policy related parameters. This means that two identical countries that only differ with respect to their vectors of policies will still grow, in equilibrium, at the same jate. An additional and well-known property of these models is that the equilibrium rate of growth of an economy is independent of its savings rate. Thus, if two otherwise identical countries have different savings ratios, they will still grow at the same rate in the steady state. The model predicts, however, that they will have different equilibrium levels of income per capita.

These limitations of traditional growth models can be illustrated with a simple aggregate production function. Let Y be total output, F( ) be a constant returns to scale production function, K the capital stock, L the labor force and A a parameter that represents "technology". Further, let's assume that technological progress is of the "labor augmenting" type. Aggregate output is then determined in the following way: 23Findlay (1984) and Smith (1984) provide excellent surveys on the relation between growth and trade. They don't deal, however, with the role of domestic commercial policy; their main interest is, in fact, to explain how growth will affect trade patterns.

24See Dixit (1976).
included in their benchmark specification, in order to focus more directly on the strength of the internal instruments for inequality. My version of HOZ’s benchmark specification is reported in the first column of Table 3. Consistent with their findings, lagged inequality is positively but insignificantly correlated with growth, while twice‐lagged inequality is negatively and strongly significantly correlated with growth. The main difference with HOZ’s benchmark specification is that the estimated effect of twice‐ lagged inequality on growth is slightly more negative (‐0.67 in my version, as opposed to ‐0.55 in theirs). The estimated long‐run effect (the sum of the two coefficients) is negative and equal to ‐0.51. The magnitude of this estimated adverse growth effect of higher inequality is again substantial: a 0.1 point increase in the Gini would lower cumulative growth over the following five years by five percentage points, or about one percentage point per year.

The first column of Table 4 contains my replication of the benchmark specification in CC, which I take to be Column 1 in Table 1 of that paper. CC regresses growth on lagged income, lagged human capital inequality, and a set of control variables. Once again, I drop the control variables in order to focus directly on the strength of internal instruments for inequality. This change has only small effects on the estimated magnitude of the negative growth effect of human capital, which remains substantial: a one‐standard deviation increase in human capital inequality of 0.24 Gini points in their sample lowers cumulative growth over the following five years by 5.7 percent, or about 1.1 percentage points per year. However, the significance of the estimated effect is lower than in CC’s reported results, falling just short of significance at the 10 percent level.

At the bottom of Tables 2, 3 and 4, I detail the internal instruments used in the SYS‐GMM estimator. In the differenced equation, OBT use the second through fifth lags of log per capita GDP, together with second and third lags of inequality and redistribution, as instruments. In the levels equation, OBT use the first and second lagged differences of inequality and redistribution as instruments. In the case of HOZ, the instruments in the differenced equation consist of twice‐lagged levels of income, inequality, and the three control variables, while in the levels equation they use the lagged difference of income, inequality, and the control variables. For CC, the instrument set consists of

> (ii) ∆����� in the differenced equation, which serves as its own instrument under HOZ’s maintained assumption that lagged inequality is predetermined, i.e. ��������������0, ��0 .  This correction substantially strengthens identification relative to HOZ’s benchmark specification, by introducing missing instruments that by construction are perfectly correlated with explanatory variables.  However, it has only minimal effects on the point estimates in the SYS‐GMM estimator.

13
# **4.10 Differentials by Contract**

There are good economic reasons why, in many circumstances, it is in the interests of both the employer and the employee to form a long-nm employment relationship, thereby helping to build and retain firm-specific skills. The estimated wage differential associated with permanent employment in a firm is estimated to be statistically insignificant. Hence, there does not seem to be a trade-off between enhanced job security and wages. The contrary view that job security endows insider workers with more bargaining power that makes it possible to achieve higher wages does not seem to hold in Ghana. The result that greater job security seems not to lead to lower wages suggests that insider workers benefit from trade unions.

The productivity differential associated with a permanent contract is positive but not statistically significantly different from zero. Moreover, temporary workers are not less productive than permanent workers, indicating that the former may be working hard in an attempt to obtain a permanent contract.

# **4.11 Differentials by Marriage or Ethnicity**

The marriage premium is not statistically significantly different from zero. This finding is surprising because results from wage analyses in industrial countries usually show that married workers earn higher wages than unmarried workers. This finding reflects that 36
5

**rable 2: MACROECONC:0o INDICATORS DURING THE 1980S**

||**1978-81**<br>**19**|**82-86**|
|---|---|---|
||**Low**<br>**Middle**<br>**Low**<br>**Income**<br>**income**<br>**Income**|**Middle**<br> <br>**Income**|
|**GDP (Gr**|**owth)**<br>**2.77**<br>**4.83**<br>**2.48**|**1.99**|
|**CA/GDP**|**-7.3.7**<br>**-5.52**<br>**-7.81**|**-5.12**|
|**INVjGDP**|<br>**20.6**<br>27.1<br>**18.5**|**22.7**|
|**RER**|**1.029**<br>1.014<br>**1.247**|**1.040**|
|**EXSHCK/G**|**DP a/**<br>**-4.50(LY)**|**-3.82**(MY)|
|**DOD/GDP**<br>**Definiti**|<br>**33.8**<br>29.3<br>54.2<br>**ons:**|46.6|
|GDP|=<br>Real gross domestic product||
|CA|=<br>Current account||
|INV|Real public + private investment||
|RER|=<br>Real exchange rate index (3980=100).<br>value of RER means a real depreciation.|An increase irn the|
|DOD|=<br>Putlic or publicly guaranteed debt<br>tables.|m World Bank Debt|
|Notes:|Cwn calculations.<br>All values a:-e average<br>relevant period. Sample of 93 developing<br>income; MY=middle income.<br>(Income per<br>1986).|values duringthe<br>countries. LY='ow<br>capita above S450 irn|
|a/|Source: Fain.i et al. (1989). Estimate of the<br>lower terms of trade and higher interest<br>compared with 1978-81, as expressed as a share<br>Formula for the computation of the welfare lo<br>equation4.|welfare loss due to<br>rates during 1982-86<br> **of**t'e average32P.<br>ss is given below|