rafmacalaba/datause-encoder-data huggingface dataset:
O.A.M.D.G.

respectively<sup>ω</sup> and κ are random village level factors; and _u_ , ε and η are individual 2 2 level error terms, with _E_ [ε _in_ , _uin X_ ] = 0 , _Var_ [ε _in X_ ] = σε , _Var_ [ _uin_<sup>_X_</sup> ]<sup>=</sup> σ _u_ ; and 2 2 _E_ [ _vn_ ,<sup>ω</sup> _n X_ ] = 0 , _Var_ [ _vn_<sup>_X_</sup> ] = σ _v_ , _Var_ [ω _n X_ ] = σω and likewise for the relation between the assets and _D_ .

2 2 Also, _Cov_ [θ _in_ , θ _jn_ ] = σω where θ _in_ = ω _n_ + ε _in_ ; _Cov_ [υ _in_ , υ _jn_ ] = σ _v_ where υ _in_ = _vn_ + _uin_ 2 and _Cov_ [ξ _in_ , ξ _jn_ ] = σκ where ξ _in_ = κ _n_ +η _in_ . We allow for this latter correlation of household error terms at the village level with clustering. - Note that the capture rate is measured at the departmental level. We can assume that individual errors, although correlated at the municipal level, are not correlated at the departmental level beyond municipalities (the average ‘Departamento’ stretches across c500km). Under this assumption standard errors clustered at the municipal level only are still valid. With SUR, also _Cov_ ε[ , _u_ ] ≠ 0 , _Cov_ [ε , η ] ≠ 0 and _Cov_ [ _u_ , η] ≠ 0 as explained above.

We estimate the equations with OLS, controlling each time for sample selection by including the Inverted Mills Ratio calculated from the SISBEN sample. Results-Table 2 shows the results.

30
# **Appendix:**

# **Historical Variations in Protection over Time and the Value of Bindings**

Assessing the value of a binding at or above the initial applied rate requires information on the variation that would otherwise occur in the tariff over time (Francois and Martin 2004). The higher the variability of protection for any given average tariff rate, the higher will be the cost of protection because the cost of protection rises with the square of the tariff. A doubling of protection increases the cost of protection by a factor of four. Tariffs vary substantially over time in response to a range of factors, including changes in intellectual fashions regarding the desirability of liberal trade policy and responses to changes in the trade policy of trading partners. The great volatility of unrestrained protection in the United States is evident in Figure A.1 which shows average tariffs on imports into the United States between 1790 and 2007.

# **_Figure A.1 Average import tariff collections: United States, 1791-2007, %_**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1790179818061814182118291837184518531861186918771885189319011909191719251933194119491957196519731981198919972005<br>Tariff rate %<br><!-- End of picture text --> Source: US Dept of Commerce (2008).

Gardner and Kimbrough (1989) suggest that the US customs tariff series should be divided into the period before full implementation of the income tax in 1918, and the subsequent period in which tariffs were much less important as a revenue source. A key feature of the graph is the extraordinary variation in the tariff rates within both of those periods, and particularly prior to the implementation of Kennedy and Tokyo Round commitments from the late 1960s. The variability of the tariff in the 19<sup>th</sup> century was substantial, with tariffs trending upwards between 1790 and the late 1820s, oscillating around a lower average level for the next forty years, and peaking again in the late 1860s. What the graph makes clear is that—in the absence of tariff bindings—what comes down can quickly go back up again. After the diminution of the revenue imperative in 1918, for instance, average tariffs more than tripled by 1934. Worse, this increase took place in the context of rising tariffs in other major trading countries, with disastrous consequences for 30
wards, or because land planning and zoning at the city level have different implications for the tolerance of municipal authorities to informal settlements across the city.

In particular, we run the following regression where _c_ is a constant term, _F_ is a dummy variable for formal sector, _Z_ are variables describing provision of services by local authorities, and _wi_ are ward dummies. Results are reported in Table 5. In the first column, we do not include ward dummies. We find that inclusion of dummies significantly improves the explanatory power in terms of _R−squares._ Another interesting feature of this result is that rates of return are 19 percent and 22 percent higher in the informal sector with exclusion and inclusion of dummies for wards respectively. This is similar to what we have found in regression (10). As expected, we find that provision of these services have no explanatory power when we run a separate regression for the formal sector. In the informal sector, point estimates suggests that provision of community tap for water supply raises the rate of return, implying a higher probability of destruction. Therefore, informal settlements which rely on community tap for water are more vulnerable in the sense that they face a higher risk of destruction. An interpretation for this result could be that if the government were committed in recognizing a particular informal settlement, households living in that settlement would rely more on individual connections, or some form of piped water supply.

# **4.5 Interpreting Results from Previous Literature**

The remaining of the empirical part of the paper tries to make the link between our approach and empirical findings of Jimenez (1984), Friedman, Jimenez and Mayo (1988). Taking a demand-side approach in which renters and owners are not distinguished, they run hedonic (log) price and (log) rent regressions for urban areas of the Philippines to assess the premium given to dwelling values by property rights. Jimenez (1984) looking at data from Davao, 1979, found a 18 percent differential for the rents of rental units and 20
7 commercial banks. Today, the government's ability to finance expenditures and enterprise losses -,-:--through -. a large inflation tax is sharply reduced.

Table 1

|(p|ercent o|f**GDP**|**,**unless|otherw|ise noted|)|||
|---|---|---|---|---|---|---|---|---|
||_198P_|_1i8r_|_19_|_19r_|_19_|_im_|_1m9m_|M|
|**Donad Credit**|65|67|**72**|73|91|46|**22**|**i5**|
|**Cluuoa anGowmaot**|9|IS|26|32|**55**|7|4|**3**|
|**Clabn_**am<br>**1trpr**<br>**uand Housduoh**|**_56_**|52|46|41|37|39|**13**|12|
|**Money**<br>-51<br>||**57**|61|64|sO|**62**|31|16|
|Curnw**OuldnmBub**|9<br>**p11**|10<br>|10||12|Y|4|4|
|**Rubl**BDepodh|42<br>|**47**<br>|**S1**<br>|S3<br>|**67**<br>|43<br>|14<br>|6<br>|
|**FXDepouiIs**|**0**|**0**|**0**<br>|**0**<br>|**1**|11|13|6|
|**Nomnal GDP (IL Diao)'**<br> <br>|06<br>|830<br>|_373_<br>|<br>_951_<br>|639<br>|1923<br>|37811<br>|323693<br>|
|**_elmn_**<br>**Rate (ab1eU**<br> <br>|0.63<br>|**0.53**<br>|0.61<br>|9|23|169|**415**<br>|1247<br>|
|lo-d<br>Per Capit GDP<br>**(ES**at**unrts exchange**ra)|**40R8**|4887|4977|-|-|-|614|1747|

Data for the USSR.

**b** December GDP annualized.

For the years 1989-1991 no data for GDP per capita are reported as there was not a well functioning foreign exchange market.

# The new tussian banks: small, but entrepreneurial

The two-thousand new commercial banks are either entrely new banks - so called "zero' banks - or regional spin-offs from the thr sectoral state banks. By now both 'zero" and "spin-of banks are represented among the largest commercial banks (Table 2). For example Inikombank is a zero-bank, Mosbusinessbank a former Zhilsotsbank branch, and ICB ifi St. Petersburg a former Promstroi branch.

"Zero' banks were usually created by groups of enterprises, sometimes with only a small number of large enterprises as shareholders (so called "pocket" banks), sometimes with hundreds of shareholders. The heavy concentration in bank shareholding has been reduced over time.
Table 8: Falsification test

|Dependent variable|CPIA A|CPIA B|CPIA C|
|---|---|---|---|
|number of cumulative PSG conditions|-.00009|.002|.004|
||(.007)|(.006)|(.004)|
|PSG conditions, squared|.000005|-.000009|-.00002|
||(.00004)|(.00003)|(.00002)|
|economic management conditions|.022<br>(.013)<sup>_∗_</sup>|.|.|
|econ. mgmt. cond., squared|-.0003<br>(.0001)<sup>_∗∗_</sup>|.|.|
|structural policies conditions|.|.009<br>(.004)<sup>_∗∗_</sup>|.|
|struct. pol. cond., squared|.|-.00005<br>(.00002)<sup>_∗∗_</sup>|.|
|social policies conditions|.|.|.016|
||||(.008)<sup>_∗∗_</sup>|
|soc. pol. cond., squared|.|.|-.0002|
||||(.00009)<sup>_∗∗_</sup>|
|log of per capita GDP|.216|.225|.192|
||(.076)<sup>_∗∗∗_</sup>|(.059)<sup>_∗∗∗_</sup>|(.069)<sup>_∗∗∗_</sup>|
|aid over GDP|-1.426|-.779|-1.182|
||(.604)<sup>_∗∗_</sup>|(.421)<sup>_∗_</sup>|(.622)<sup>_∗_</sup>|
|Political Rights|-.076|-.134|-.109|
||(.032)<sup>_∗∗_</sup>|(.023)<sup>_∗∗∗_</sup>|(.029)<sup>_∗∗∗_</sup>|
|_country fxed efects_|_yes_|_yes_|_yes_|
|_year fxed efects_|_yes_|_yes_|_yes_|
|Observations|1761|1761|1761|
|Number of countries|139|139|139|
|Number of instruments|125|125|125|
|Wald statistic|135.05|234.43|174.08|
|_p-value_|0.0001|0.0001|0.0001|
|Hansen J-test|92.23|101.21|99.12|
|_p-value_|0.789|0.559|0.617|
|Dif-in-Hansen test|23.63|29.31|30.46|
|_p-value_|0.701|0.397|0.342|

Regression results from estimating GMM model with CPIA A, CPIA B and CPIA C as dependent variables. CPIA cluster A average measures the quality of economic management, CPIA cluster B average the quality of structural policies and CPIA cluster C average measures the quality of policies for social inclusion and equity. * significance at 10%; ** significance at 5%; *** significance at 1%.

40
27

# Table 3 Unit Costs of Peeding Programs (MCH ind School Feeding) Costs in USS of 198

|Categorv|Range|Median|<br>Mean|Number of Programs|
|---|---|---|---|---|
|All Programs|19.25- 300|74.48|88.51|52|
|Asia<br>|32.1- 300<br>|70.01<br>|91.29*<br>|<br>21|
|North Africa<br>|65.53- 104.7<br>|87.34<br>|88.63<br>|5|
|Sub-Saharan Africa<br>|55.80- 96.25<br>|81.46<br>|78.95<br>|5|
|Latin America|19.25- 272.54|67.18|87.96|21|
|School Feeding<br>|19.25- 208.59<br>|81.46|88.74|11|
|MCH|26.75- 272.54|73.84|85.64|37|
|Small(<br><100,000)<br> <br>|26.75 - 272.54<br> <br>|96.48<br>|121.92<br>|7|
|Medium (1<br>500,000)<br> <br>|24.38-<br>96.25<br>|68.11<br>|62.99<br>|10|
|Large_(_<br>2 500,000)|19.25- 139.0|96.90|89.85|7|
|Gov't. Operated<br>|19.25- 272.54<br>|75.11<br>|91.73<br>|37|
|NGO Operated|24.38- 300|77.98|89.70|14|

Source: Calculated from Appendix Table 1.

Notes:

* Falls to 80.32 for South Asia only. Costs are in $ of 1988, cost per '000 calories/day/year
**Figure 3. Comparative Annual Average Unemployment Rate**

<!-- Start of picture text --> 20.0<br>18.0<br>16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>% Unemployed when migrated<br>2.0<br>National average unemployment rate<br>0.0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> _Source: Eurostat; IASCI; Soros Foundation_

**Figure 4. National and Migrant Unemployment Rates**

<!-- Start of picture text --> 25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>BU CZ ES LV LT<br>HU PL SL SK RO<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> _Source: Eurostat. BU=Bulgaria; CZ=Czech Republic; ES=Estonia; LV=Latvia; LT=Lithuania; HU=Hungary; PL=Poland; SL=Slovenia; SK=Slovakia; RO=Romania Source: Eurostat; IASCI; Soros Foundation_ There is also evidence that a brain drain effect has occurred with earlier migrants being less skilled than later migrants. Figure 5 illustrates the increasing proportion of migrants with tertiary education compared to the general population. The human capital stock has been falling and evidence from the World Bank Enterprise Surveys, in Figure 6, indicates that Romanian firms are finding it hard to recruit appropriate skilled employees. Even after the negative impacts of the financial crisis, the 2013 Enterprise Survey found that over a third of firms found lack of skills to be a major or very severe obstacle. The League of Romanian Students Abroad undertakes an annual survey and found that, in 2014, just a quarter of current students plan to return to Romania on graduation and only 14 percent of those who had completed under4
**Table 2: Which groups deserve support from the government?**

||||families with<br>|<br>||||
|---|---|---|---|---|---|---|---|
||disabled|elderly|children|working poor|unemployed|war veterans|nobody|
|**EU**||||||||
|France|0.78|0.66|0.44|0.71|0.40|0.20|0.02|
|Sweden|0.83|0.73|0.52|0.56|0.70|0.29|0.01|
|Germany|0.67|0.48|0.77|0.55|0.42|0.27|0.02|
|Italy|0.59|0.68|0.52|0.58|0.61|0.12|0.01|
|Great Britain|0.70|0.79|0.47|0.60|0.31|0.66|0.01|
|**NEW EU**||||||||
|Latvia|0.86|0.80|0.87|0.55|0.52|0.43|0.00|
|Bulgaria|0.82|0.81|0.74|0.51|0.61|0.30|0.00|
|Czech Rep.|0.81|0.80|0.77|0.40|0.31|0.23|0.02|
|Slovenia|0.80|0.74|0.76|0.81|0.55|0.31|0.00|
|Slovakia|0.77|0.80|0.85|0.62|0.42|0.27|0.01|
|Romania|0.75|0.87|0.65|0.53|0.48|0.45|0.00|
|Lithuania|0.74|0.74|0.76|0.55|0.51|0.24|0.01|
|Estonia|0.74|0.64|0.83|0.50|0.59|0.28|0.00|
|Croatia|0.71|0.70|0.53|0.49|0.64|0.35|0.00|
|Poland|0.64|0.56|0.48|0.37|0.30|0.22|0.00|
|Hungary|0.63|0.67|0.78|0.52|0.49|0.30|0.02|
|**NON EU**||||||||
|Moldova|0.95|0.97|0.94|0.75|0.68|0.92|0.00|
|Kyrgyzstan|0.93|0.83|0.62|0.32|0.47|0.58|0.00|
|Montenegro|0.91|0.88|0.57|0.60|0.64|0.46|0.01|
|Kosovo|0.90|0.82|0.67|0.69|0.91|0.81|0.00|
|Ukraine|0.89|0.79|0.80|0.46|0.47|0.66|0.01|
|Tajikistan|0.88|0.78|0.57|0.55|0.36|0.60|0.00|
|<br>Macedonia|0.87|0.64|0.66|0.73|0.88|0.51|0.01|
|Mongolia|0.85|0.77|0.49|0.46|0.34|0.39|0.01|
|Georgia|0.84|0.83|0.59|0.31|0.59|0.52|0.00|
|<br>Belarus|0.84|0.68|0.78|0.48|0.26|0.60|0.00|
|Uzbekistan|0.82|0.66|0.65|0.40|0.45|0.45|0.00|
|Serbia|0.82|0.76|0.70|0.62|0.71|0.54|0.00|
|Azerbaijan|0.81|0.74|0.66|0.59|0.79|0.63|0.00|
|Kazakhstan|0.80|0.74|0.60|0.37|0.41|0.57|0.00|
|Turkey|0.80|0.77|0.65|0.74|0.70|0.67|0.01|
|Bosnia and||||||||
|Herzegovina|0.79|0.85|0.59|0.55|0.80|0.58|0.00|
|<br>Russia|0.76|0.75|0.73|0.40|0.29|0.49|0.00|
|Armenia|0.76|0.82|0.73|0.55|0.61|0.67|0.00|
|Albania|0.75|0.75|0.45|0.60|0.65|0.27|0.00|
|Total|0.75|0.71|0.64|0.53|0.44|0.43|0.01|
|_Legend: Between_<br>_Source:_LITS II|_0 and 0.25_|_Bet_|_ween 0.25 and 0_|_.5_<br>_Betwe_|_en 0.5 and 0.7_|_5_<br>_More tha_|_n 0.75_|

12
# **Decentralization, Economic Development, and Growth in Turkish Provinces**

Mehmet Serkan Tosun<sup>*****</sup> University of Nevada, Reno and Serdar Yilmaz<sup>******</sup> World Bank

# **JEL classification:** H77, H87, N45, O53

**Key words:** Decentralization; intergovernmental relations; Turkish provincial cities; economic development and growth

The authors thank Ayse Guner, Bilin Neyapti, Mediha Agar and Mark Thomas for helpful comments and suggestions, and Dilek Uz for excellent research assistance. The findings, interpretations, and conclusions are entirely those of authors, and do not represent the views of the World Bank, its executive directors, or the countries they represent. Corresponding author: Serdar Yilmaz.

* Department of Economics, College of Business Administration, University of Nevada-Reno, Reno, Mail Stop 0030. Nevada 89557; <u>tosun@unr.edu; phone: +1-775-784-6678.</u>

** Social Development Department, World Bank, 1818 H Street, NW, Washington D.C. 20433; <u>Syilmaz@worldbank.org; phone: +1-202-473-9350.</u>
# EXECUTIVE SUMMARY

Rural residents in many countries could benefit from the expansion of formal and semi-formal microfinance if financial institutions can serve clients with more useful and less-expensive financial products than those available from moneylenders. However, significant expansion of the microfinance sector is only feasible if it can be accomplished without subsidy, or at an initial cost that the government can support and in a way that will enable it to become self-sustainable. This report (i) examines five Indonesian microfinance programs that currently serve low-income clients in low-density areas; (ii) reviews the legal and regulatory issues that affect the Indonesian microfinance market; (iii) discusses lessons learned; and (iv) recommends ways governments and donors could promote the sector's health and sustained growth internationally.

## **Program Descriptions**

The Indonesian central government or a local government owns the five programs examined in this report. Four were established between 1979 to 1989. Four are involved exclusively in financial services or support to these services, while one also provides other services to clients. The programs vary considerably in size and geographic scope (Table 1).

## _Badan Kredit Kecamatan (BKK)_

financial institutions are owned by the South Kalimantan provincial government and located in sub-district capitals. The province has 110 units for its 109 sub-districts. Field staff travel to surrounding villages to transact business. The 34 BKK units created before 1992 make loans and accept deposits. The 76 units created after 1992 only make loans.

## _Lumbung Kredit Pedesaan_ (LKP) is a

system of semi-formal financial institutions owned by the provincial government of Nusa Tenggara Barat

**Table 1: Scope of Programs in 1996**

||Year<br>Founded|Number of<br>Provinces|Number of<br>Units|
|---|---|---|---|
|**BKK (South**<br>**Kalimantan)**|1985|**1**|110|
|**LKP (Dompu**<br>**Ditit**|1989<br>|1<br>|4<br>|
|**src)**<br>|1989<br>|Oa<br>|323<br>|
|P4K<br>|1979<br>|6b<br>|NA<br>|
|**BKD**<br>(excludes<br>inactive units)|1898|3|4,806|

a/ PHBK is expanding to three more provinces. **b/** P4K is expanding to 12 more provinces.

(NTB). LKP is very similar in structure and function to BKK. This review examines only the four LKP units in Dompu District.

_Program Hubungan Bank dan KSM (PHBK)_ is a microfinance program sponsored by the Central Bank of Indonesia (BI) and the German government's Agency for Technical Cooperation (GTZ). The program operates in 10 provinces and is expanding to 3 more. In March 1996, 323 banks or bank branches were participating in this program, and the number was growing rapidly. The program provides technical assistance to private and state-owned banks, non-government organizations (NGOs), and borrower groups to help them develop group lending skills. Client groups can obtain loans and open savings accounts.

v
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE meeting the MDGs for water supply and sanitation with lower-cost technologies than previously used, such as stand posts, boreholes, and improved latrines, could reduce the associated price tag by $85 million annually. Similarly, meeting transport-connectivity standards using lower-cost road-surfacing technologies—such as the single-surface treatment—could reduce the associated price tag by $108 million. Under this ―moderate‖ scenario, the total infrastructure spending needs are reduced by 14 percent from $2.009 billion to $1.729 billion per year.

**Figure 19. Zimbabwe’s infrastructure spending needs are among highest in the region relative to GDP**

<!-- Start of picture text --> Estimated infrastructure spending needed to meet targets, as percentage of GDP<br>SSA<br>LIC-Fragile<br>LIC-NoFragile<br>SADC<br>Resource-Rich<br>MIC<br>Congo, Dem. Rep.<br>Zimbabwe<br>Ethiopia<br>Kenya<br>Sudan<br>Uganda<br>South Africa Capex O&M<br>Cape Verde<br>Botswana<br>% of GDP 0 10 20 30 40 50 60 70<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

_Note:_ LIC = low-income countries; MIC = middle-income countries; SADC = Southern African Development Community; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

Zimbabwe already spends a sizable amount ($0.8 billion per year) to meet its infrastructure needs (table 17). Characteristically, for Zimbabwe, as much as 83 percent of the total is allocated to operations and maintenance (O&M) and only 17 percent to capital expenditures. The emphasis onlow-income countrieO&M spending underscores the fact that maintaining already-existing infrastructure stock is both a challenge and a priority for Zimbabwe. Operating expenditure is entirely covered from budgetary and state-owned enterprise (SOE) resources and payments by infrastructure users. Around 25 percent of capital-expenditure funding comes from public sources. Overall, the central government directly accounts for slightly more than 2 percent of the public spending, with the rest being channeled via parastastal entities. Non–Organisation for Economic Co-operation and Development (Non-OECD) financiers account for an impressive 63 percent of total capital spending. The private sector accounts for another 10 percent, and official development assistance (ODA) flows at around 1 percent also play a smaller, but nonetheless significant, role.

Zimbabwe‘s existing infrastructure spending amounts to a staggering 23 percent of GDP (figure 20). This represents a very high level of effort, even when compared to the average for low-income states. Relative to its peer group, Zimbabwe, due to international sanctions, is much less reliant on ODA. Instead, its power sector capital investments are heavily reliant on non-OECD funding. Public investments on the other hand, are scarce in the power sector, but are more pronounced in the road and water sectors. The ICT sector, similar to the comparator group, is predominately financed by private 44
assess regional preferences in services. Brown and Stern (2001) and Dee _et al_ . (2003) employ multi-country numerical models with many of the same features of Markusen, Rutherford and Tarr. Their models contain three sectors, agriculture, manufacturing and services, and are thus also rather stylized.

The model described in this paper is closest to the small open economy models developed by Balistreri, Jensen and Tarr (2011) and especially Jensen and Tarr (2012). Balistreri, Jensen and Tarr (2011) have shown that there is an imperfect competition analogy to trade diversion in goods whereby preferential commitments in to foreign investors in services could be immizerising. Jensen and Tarr (2012) extended the analysis to include the impact of improved trade facilitation and the reduction of non-tariff barriers in Armenia. But since Balistreri, Jensen and Tarr (2011) and Jensen and Tarr (2012) employed small open economy models, they were not capable of endogenously assessing the terms of trade gains from improved market access in preferential trade arrangements.<sup>8</sup>

# **3. Overview of the Model**

This paper builds on the algebraic structure of the model of Jensen and Tarr (2010; 2012) and of Balistreri, Jensen and Tarr (2011). Here we provide a general description of the structure described there and provide more details where we depart from that structure. We employ the GTAP 8.1 data set as our basic data set.

The key extension of the earlier models we developed of Armenia, Kenya and Tanzania is that we adopt a multi-region model, rather than a small open economy model, since we need to consider the possible effects on the member countries of the EACU. That is, we need to account for the “market access” effects on Kenya, Tanzanian, Ugandan and Rwandan exports of a reduction of import tariffs by the partner countries and by agreements to facilitate trade, lower non-tariff barriers within the free trade area or allow access to service providers within the FTA.

Although the general theory of the welfare effects of preferential trading arrangements does allow for the impact of changes in partner country tariffs on the home country’s market

> 8 Wonnacott and Wonnacott (1981) have demonstrated the theoretical importance of assessing improved market access in regional agreements, and Harrison, Rutherford and Tarr (2002) have shown numerically that assessing market access is very important in determining the value of a preferential trade agreement.

6