rafmacalaba/datause-encoder-data huggingface dataset:
credit intervention. Section 4 discusses the data, which are drawn from a random household survey carried out in program and non-program villages. Both cross-sectional and panel data are used to evaluate the role of PRIME in mitigating seasonality and hard-core poverty. As PRIME is a special microfinance program, it is essential to evaluate it against regular microfinance. Section 5 presents the data in terms of the ultra-poor’s participation in PRIME vis-à-vis regular microfinance and analyzes the factors responsible for the participation of the ultra-poor in one of the programs. Section 6 reviews the methods and results and program effects on household welfare using cross-sectional data. Section 7 then discusses the methods and results using panel data. Section 8 provides a summary and policy conclusion.

# **2. Tackling Seasonal and Extreme Poverty in the Northwest with PRIME**

The first goal outlined in the United Nations Millennium Development Goals (MDGs) focuses on reducing poverty and hunger. The World Bank has vigorously promoted all member countries to attain this goal. The Government of Bangladesh has undertaken various steps toward achieving the MDGs and had made significant progress. Recent figures suggest Bangladesh’s achievements. Compared to the baseline poverty of 58.8 percent in 1990, the poverty rate in 2000 was 49.6 percent, roughly equivalent to a 1-percent poverty reduction per year (United Nations 2005). Results of the 2005 Household Income and Expenditure Survey (HIES) show even more gains in poverty reduction: The overall poverty rate in 2005 was 40 percent countrywide, with 28.4 percent in urban and 43.8 percent in rural areas (Table 1). Over the 2000–05 periods, the rate of poverty reduction was about 2 percent per year.

6
|4|Retail trade, except of motor vehicles and<br>motorcycles|6308|163|2.5|1.5|
|---|---|---|---|---|---|
|5|Human health activities|515|161|23.8|1.5|
|6|Manufacture of food products|818|30|3.5|0.3|
|7|Construction of buildings|3673|24|0.6|0.2|
|8|Food and beverage service activities|850|22|2.5|0.2|
|9|Financial service activities, except insurance<br>and pension funding|271|13|4.6|0.1|
|10|Wholesale trade, except of motor vehicles<br>and motorcycles|947|9|0.9|0.1|
|11|Land transport and transport via pipelines|2616|6|0.2|0.1|
|12|Manufacture of furniture|407|4|1.0|0.0|
|13|Manufacture of basic pharmaceutical<br>products and pharmaceutical preparations|127|4|3.1|0.0|
|14|Telecommunications|144|3|2.0|0.0|
|15|Manufacture of beverages|40|2|4.8|0.0|
|16|Accommodation|19|1|5.0|0.0|
|17|Fishing and aquaculture|106|0|0.0|0.0|
|18|Water collection, treatment and supply|137|0|0.0|0.0|
|19|Insurance, reinsurance and pension funding,<br>except compulsory social security|37|0|0.0|0.0|
|20|Employment activities|4|0|0.0|0.0|
||Total employees (in thousand)|33288|11019|24.9|100|

Source: ILOSTAT database, International Labor Organization A noticeable wage gap exists between average earnings of males and females across major industry groups without taking into account differences in workers’ characteristics. Pakistan’s Labor Force Surveys for 2014-2015 show a gender pay gap of 26 percent on average at the national level.<sup>19</sup>

**Table 6: Gender Wage Gap by Economic Activity in Pakistan in 2016**

|**S.No.**|**Economic activity (2016)**|**Men**|**Women**|**Wage Gap (%)**|
|---|---|---|---|---|
|||**Nomi**<br>**earnin**|**nal annual**<br>**gs (in US$)**||
|1|Manufacturing|1644|612|37.2|
|2|Activities of households as employers|1212|576|47.5|
|3|Electricity; gas, steam and air conditioning supply|3324|1596|48.0|
|4|Mining and quarrying|1716|912|53.1|
|5|Real estate activities|2196|1260|57.4|
|6|Professional, scientific and technical activities|2700|1596|59.1|
|7|Activities of extraterritorial organizations and bodies|4452|2724|61.2|
|8|Education|3240|2016|62.2|

> 19 Khan, Frida (2017).

18
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2655 Measuring Services Trade<br>Liberalization and its Impact on<br>Economic Growth: An Illustration|Aaditya Mattoo<br>Randeep Rathindran<br>Arvind Subramanian|August 2001|L.Tabada<br>36896|
|WPS2656 The Ability of Banks to Lend to<br>Informationally Opaque Small<br>Businesses|Allen N. Berger<br>Leora F. Klapper<br>Gregory F. Udell|August 2001|A. Yaptenco<br>31823|
|WPS2657 Middle-income Countries:<br>Development Challenges and<br>Growing Global Role|Peter Fallon<br>Vivian Hon<br>Zia Qureshi<br>Dilip Ratha|August 2001|D. Fischer<br>38656|
|WPS2658 How Comparable are Labor Demand<br>Elasticities across Countries?|Pablo Fajnzylber<br>William F. Maloney|August 2001|A. Pillay<br>88046|
|WPS2659 Firm Entry and Exit, Labor Demand,<br>and Trade Reform: Evidence from<br>Chile and Colombia|Pablo Fajnzylber<br>William F. Maloney<br>Eduardo Ribeiro|August 2001|A. Pillay<br>88046|
|WPS2660 Short and Long-Run Integration:<br>Do Capital Controls Matter?|Graciela Kaminsky<br>Sergio Schmukler|August 2001|E. Khine<br>37471|
|WPS2661 The Regulation of Entry|Simeon Djankov<br>Rafael La Porta<br>Florencio Lopez de Silanes<br>Andrei Shleifer|August 2001<br>|R. Vo<br>33722|
|WPS2662 Markups, Entry Regulation, and<br>Trade: Does Country Size Matter?|Bernard Hoekman<br>Hiau Looi Kee<br>Marcelo Olarreaga|August 2001|L. Tabada<br>36896|
|WPS2663 Agglomeration Economies and<br>Productivity in Indian Industry|Somik Lall<br>Zmarak Shalizi<br>Uwe Deichmann|August 2001|R. Yazigi<br>37176|
|WPS2664 Does Piped Water Reduce Diarrhea<br>for Children in Rural India?|Jyotsna Jalan<br>Martin Ravallion|August 2001|C. Cunanan<br>32301|
|WPS2665 Measuring Aggregate Welfare in<br>Developing Countries: How Well Do<br>National Accounts and Surveys Agre|Martin Ravallion<br>e?|August 2001|C. Cunanan<br>32301|
|WPS2666 Measuring Pro-Poor Growth|Martin Ravallion|August 2001|C. Cunanan<br>32301|
<!-- Start of picture text --> Figure 1<br>Number of Tobacco Clubs and Smallholder Tobacco Production<br>-<br>(1992 2002)<br>1992 1994 1996 1998 2000 2002<br>tobacco clubs smallholder burley output<br>total burley output<br>800<br>600<br>400<br>200<br>0<br><!-- End of picture text --> Sources: Tobacco Control Commission; National Statistical Office, Statistical Yearbook (various issues); and Government of Malawi, Economic Report (various issues).

5
-18-

The importance of the involuntary money holdings or "monetary overhang" problem is that it poses a threat to monetary and price stability. If asset holders reduce their cash holdings to their equilibrium levels, e.g., as a result of price liberalization, the monetary overhang would immediately leacd to an excess supply of money and thus to inflationary pressures. Unlike other countries which experience inflation as a result of money creation (flow), in Bulgaria inflationary pressures arise both from the excess money holdings (stoCk of money) and from money creation (flow). In fact, inflationary pressures could result even in the presence of a tight monetary policy. The magnitude of Bulgaria's monetary overhang problem can be'seen by comparing Bulgaria's money to GDP ratio with that of other developed and developing countries (see Table ' Bulgaria's ratio is above those of countries with more developed financial markets, such a& ` ,nany and the U.K.; above developing countries, such as Yugoslavia and Turkey; and above countries that also have a substantial monetary overhang problem, such as Algeria and Egypt. In fact, Bulgaria's money to GDP ratio is one the highest in the world.

Teble 10: International Conparison of Money to GDP Ratio (Percentage of GOP)

||19|84<br>|19|88<br>|
|---|---|---|---|---|
||Ml|M2|Ml|M2|
||-----|--<br>------<br>---|..<br>.....<br>..|......|
|Developed:<br>|||||
|Germany<br>|16.1<br>|54.4<br>|19.6<br>|61.6<br>|
|U.K.<br>|14.5<br>|39.5<br>|36.4<br>|85.8<br>|
|Spain<br>|21.1<br>|62.1<br>|25.6<br>|67.1<br>|
|Portugal|24.6|104.5|29.9|105.2|
|Eastern European:<br>|||||
|Butgarie<br>|n.a.<br>|n.a.<br>|48.2<br>|101.6<br>|
|Yugoslavia|15.5|54.1|9.1|38.2|
|Other Developing:<br>|||||
|Turkey|11.9|26.4|8.8|20.6|
|Egypt<br>|31.5<br>|65.6<br>|22.6<br>|59.8<br>|
|Algeria<br>.....<br>.........<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.|59.4<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>.<br>..<br>.|64.1<br>.<br>.<br>.<br>.<br>.<br>.|56.8<br>.<br>.<br>.<br>.<br>.<br>.<br>.|66.9<br>.<br>.<br>.<br>.|

Source: IMF, International Financial statistics; and World Bank's Economic Database. Note: To correct for price differences arising from corputing a ratio of an end-period stock and a GDP-flow estimate at average prices, stocks were converted to average-1980 prices using end-period Lrnsuner price index and computing its ratio to real GDP with base average-1980. In the case of Bulgaria average-1989 was used as a base year.

The monetary overhang problem poses a real challenge for the monetary authorities. This can be illustrated by using the quantity theory of money. Consider such an equation as,

# **(1)**

V is the actual income velocity of money, P is the price level, Y the real long-term income and M is the actual derrand for money. Now consider thiat equilibrium demand for money is M which
high weight, and rightly so. It is seen around the world that successfully operating e-systems have been extremely useful to tax administrations in reducing tax compliance time and cost for tax payers and direct contact between taxpayers and tax officials. So, the Doing Business’s paying taxes sub-indicator is also useful in judging a tax system’s simplicity.

Based on this reasoning, the two sub-indicators chosen as proxies for a measure of tax simplicity are TAXTIME and TAXPAY. As the data analysis shows in the following sections, while each of these indicators by themselves have a positive relationship with tax corruption, jointly they further strengthen the relationship.

It should be noted that the Doing Business (DB) reports come out with a lag of two years. For example, a DB 2010 report reflects the measures of various indicators as were recorded for the year 2008. Accordingly, the year 2008 data points of all other variables used in the paper correspond to “DB year” 2010; care has been taken in ensuring that the data for the same years have been matched for each country.

The Doing Business indicators have been criticized as they are not considered the most robust of measures, especially in the case of the Paying Taxes indicators. The methodology and the presentation of the data collected have also been questioned. However, the point is, they are the _only_ available set of data points that provide an objective, world-wide comparison of indicators of the complexity or simplicity of tax regimes.

The Doing Business report has recently been reviewed by an independent panel<sup>4</sup> constituted by the President of the World Bank. This panel has also relied, among others, on a study carried out by the International Tax Dialog (ITD) in 2008, which made various suggestions on improving the DB Paying Taxes indicator.<sup>5</sup> In general, the recommendations conclude that “the Panel accepts the need for tax indicators as a measure of the ease of doing business for small and medium-sized enterprises. It

> 4 Independent Panel Review of the Doing Business Report, June 2013,

> http://www.dbrpanel.org/sites/dbrpanel/files/doing-business-review-panel-report.pdf

> 5 The International Tax Dialog brings together the Inter-American Center of Tax Administrations, European Commission, Inter-American Development Bank, IMF, OECD, United Nations, and the World Bank.

7
Table 2: Regression results for employment elastiticy of growth: ECA

|Variables||1995 t|o 2010|||1995 t|o 2001|||2002 t|o 2007||2008 t|o 2010|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)<br>OLS|(2)<br>FOLS|(3)<br>ROLS|(4)<br> SGMM1|(5)<br>OLS|(6)<br>FOLS|(7)<br>ROLS|(8)<br> SGMM1|(9)<br>OLS|(10)<br>FOLS|(11)<br>ROLS|(12)<br> SGMM1|(13)<br>OLS|(14)<br>FOLS|
|EU10|||||||||||||||
|Output coeff.|0.324|0.240|0.251|0.369|-0.022|0.022|0.049|0.023|0.467|0.431|0.414|0.835|0.421|0.297|
|Output t-stats.|7.7|3.9|4.5|5.6|-0.3|0.2|0.5|0.1|3.7|2.1|2.9|5.4|6.0|1.8|
|Number of obs.|150|150|150|150|60|60|60|60|60|60|60|50|30|30|
|R squared|0.288|0.483|0.525||0.001|0.361|0.381||0.194|0.523|0.636||0.566|0.779|
|AB AR2 test (p value)||||0.375||||0.57||||0.048|||
|Hansen J statistics (p value)||||1||||0.782||||0.749|||
|Balkan and Turkey|||||||||||||||
|Output coeff.|0.169|0.092|0.145|0.251|-0.006|-0.087|-0.030|0.151|0.264|0.382|0.425|0.895|0.597|0.687|
|Output t-stats.|1.6|0.7|1.5|1.5|-0.1|-0.8|-0.6|1.0|0.7|0.8|1.6|1.6|3.2|2.4|
|Number of obs.|94|94|94|94|31|31|31|31|42|42|42|35|21|21|
|R squared|0.028|0.271|0.402||0.000|0.514|0.897||0.013|0.337|0.494||0.352|0.770|
|AB AR2 test (p value)||||0.377||||0.141||||0.542|||
|Hansen J statistics (p value)||||1||||0.999||||0.917|||
|CIS|||||||||||||||
|Output coeff.|0.124|0.082|0.063|0.091|0.110|0.083|0.039|0.055|0.145|0.081|0.049|0.114|0.176|0.126|
|Output t-stats.|4.5|2.6|2.9|2.2|2.4|1.8|1.6|0.7|2.6|1.2|1.7|2.0|3.3|2.2|
|Number of obs.|180|180|180|180|72|72|72|72|72|72|72|60|36|36|
|R squared|0.102|0.498|0.754||0.074|0.550|0.822||0.087|0.542|0.896||0.240|0.859|
|AB AR2 test (p value)||||0.912||||0.734||||0.187|||
|Hansen J statistics(pvalue)<br>Note: The dependent variabl<br>OLS stands for cross-sectional<br>ROLS stands for robust regres<br>The null hypothesis of the Ar<br>The null hypothesis of the Ha|e is emp<br>bivaria<br>sions. S<br>ellano-B<br>nsen J-s|loymen<br>te regre<br>GMM sta<br>ond AR(<br>tatistics|t growth<br>ssions. F<br>nds for<br>2) test i<br>is that t|1<br>. The outp<br>OLS includ<br>system ge<br>s that the f<br>he instrum|ut coeffic<br>e in addit<br>neralized<br>irst-diffe<br>ents are|ient is t<br>ion cou<br>method<br>renced e<br>not corr|he para<br>ntry and<br>of mom<br>rrors ex<br>elated w|0.734<br>meter esti<br>year fixed<br>ents regre<br>hibit no se<br>ith the res|mate of o<br>-effects.<br>ssions.<br>cond-ord<br>iduals.|utput gr<br>er serial|owth.<br>correla|0.597<br>tion.|||

13
Table 4: Coefficients on Main Variables when additional independent variables are included in regression (see Table 3, Column 2 for base regression)..

||DEPENDEN|TVARIABLE|ISPERCENT|OFREVEN|UESPAIDTO|UTILITIES|INBRIBES|
|---|---|---|---|---|---|---|---|
|**AdditionalIndependentVariable**|**None**<br>____|**RegionDummiee**<br>_________<br>~<br>~~~|**RegulationIndex**<br>~~~~~-<br>|**CorporateTax**<br>**RateIndex**<br>|**DemocracyIndex**|**YearsSince**<br>**Democracy**<br>|**Parliamentary**<br>**Dummy**<br>|
|**NumberofObservations**|1822|1822|1636|1636|1822|1822|1822|
|_CoefficientonAdditional IndependentVariable_|<br>**-**|**-**|_-0.0063_<br>|_0.0302_<br>|**_0.1610_**<br>|_0.0061_<br>|_O.5008**_<br>|
|||_-_ _|_(-0.05)_|**_(0.28)_**|_(0.94)_|_(0.16)_|_2.07_|
|_CoefficientsonothervariablesofInterest_||||||||
|FixedLineTelecomOperatorPrivatized|-0.6698***|-0.5192***|-0.6262***|-0.6233***|-0.6217***|-0.6605***|-0.5534***|
|(Dummy)|(4.07)<br>|(-2.72)<br>|(-3.57)|(-3.56)<br>|(-3.62)<br>|(-3.78)|(-3.49)|
|FixedLines|-0.0364***|-0.0488***|-0.0426**|-0.0426***|-0.0374***|0.0366***|-0.0148|
|(Per1000people)|(-3.30)|(-3.95)|(-3.33)|(-3.76)|(-3.38)|(-3.30)|(-0.98)|
|CellularCompanies|-0.2278***|-0.1922**|-0.1886**|-0.1907**|-0.2657***|-0.2272***|-0.1746**|
|(Number)|(-3.32)|(-2.58)|(-2.19)|(-2.21)|(-3.33)|(-3.30)|(-2.39)|
|Margin|0.0126**$|0.0125***|0.0113***|0.0113***|0.0125***|0.0126***|0.0128***|
|(SalesPricelessOperatingCostsas%ofoperatingcosts)|(3.70)|(3.68)|(3.17)|(3.18)|(3.66)|(3.70)|(3.75)|
|LargestShareholder- OtherPrivate--De Novo|0.6529***|0.6739***|0.5405**|0.5428**|0.6480***|0.6524***|0.6644***|
|(Dummy)|(2.84)|(2.91)|(2.21)|(2.23)|(2.81)|(2.83)|(2.88)|
|Overdue PaymentstoUtilities|-0.1641**|-0.1420*|-0.1696**|-0.1696**|-0.1555*|-0.1639**|-0.1565**|
|(Index- highervaluesmean lessoverdue payments)<br>|(-2.08)<br>|(-1.78)<br>|(-2.03)|(-2.03)|(-1.96)|(-2.08)|(-1.98)|
|DataSource:TheWorldBusinessEnviromnentSurvey(WBE|S)02000TheWo|rldBankGroup.||||||

T-statistics are in parentheses. * Significant at 10% level. ** Significant at 5% level. Significant at 10 percent level. Note: Regressions include all variables included in Column 2 of Table 3 including sector and size dummies. ' Regional dummies are dummnies for Central Europe, Southeastem Europe and the Commonwealth of Independent States.

32
As a proxy for trade cost in equation (3.11) this paper utilizes a variable ( _d_ ) constructed by the distance from the main port of entry of the product. Since about 80 percent of Mexico’s imports originate from the United States, the trade cost variable is constructed as the shortest driving distance (in thousand of kilometers) from each of the state’s capital to the United States border (Tijuana, Ciudad Juarez and Nuevo Laredo).<sup>15</sup> With the use of distance, the assumption is that the trade costs are constant across the time period of the analysis.

Because a primary concern is to measure the effect of tariff liberalization on domestic prices at the regional level, the empirical specification includes an interaction term between the distance and the tariff rate, so as to isolate empirically the regional effect of tariff movements in the pass-through. Therefore, the estimating equation is given by: The coefficients of interest are: γ that represents the tariff pass-through elasticity (i.e. the percentage increase in the import prices for a one percent increase in the tariff), and<sup>γ</sup> 1 that is its adjustment for each region. The pass-through is “full” or “complete” if γ = 1 and the pass-through is “incomplete” if γ < 1. Similarly, the effect of the pass-through will be identical in all regions if γ1 = 0 . On the other hand, if regional prices vary as a consequence of movement in the tariff, then γ1 ≠ 0 .<sup>16</sup> The econometric estimation of equation (3.12) follows Deaton (1985) and combines a time series of cross-sectional data set into a pseudo panel.<sup>17</sup> The data consist in domestic

> 15 About 80 percent of Mexican imports from US is through the northern border.

> 16 Furthermore, since the United States is the only country that borders Mexico from the north, a positive

> and significant coefficient<sup>γ</sup> 1<sup>may be interpreted as the fact that pass-through elasticities are higher for</sup> southern regions. Indeed this is likely in the case when a product is predominantly imported from other Latin American countries.

> 17 From a time series of cross section surveys, synthetic cohorts are defined as groups with fixed membership, whose individuals (or households) can be identified as they show up in the surveys. For this reason, groups are defined according to some time invariant variables. Means within each cohort are

14
# **Table B4:  Regulatory Quality**

## **Code Table**

## **Concept Measured**

**Representative Sources DRI** A14 _Regulations_ -- _Exports:_ A 2% reduction in export volume as a result of a worsening in export regulations or restrictions (such as export limits) during any 12-month period, with respect to the level at the time of the assessment. _Regulations_ -- _Imports:_ A 2% reduction in import volume as a result of a worsening in import regulations or restrictions (such as import quotas) during any 12-month period, with respect to the level at the time of the assessment. _Regulations_ -- _Other Business_ : An increase in other regulatory burdens, with respect to the level at the time of the assessment, that reduces total aggregate investment in real LCU terms by 10% _Ownership of Business by Non-Residents:_ A 1-point increase on a scale from "0" to "10" in legal restrictions on ownership of business by non-residents during any 12-month period. _Ownership of Equities by Non-Residents_ : A 1-point increase on a scale from "0" to "10" in legal restrictions on ownership of equities by non-residents during any 12-month period. **EIU** A9 Unfair competitive practices Price controls Discriminatory tariffs Excessive protections **GCS** A28 Administrative regulations are burdensome Tax system is distortionary Import barriers as obstacle to growth Competition in local market is limited Anti monopoly policy is lax and ineffective Environmental regulations hurt competitiveness Complexity of tax System **HER** A16 Regulation Government Intervention Wage/Prices Trade Foreign investment Banking **MIG** A21 _Unfair Competition_ . _Unfair Trade_ . **PRS** A23 _Investment Profile._ **WMO** A15 _Tax Effectiveness:_ How efficient the country’s tax collection system is. _Legislation:_ An assessment of whether the necessary business laws are in place.

85
**Table 4.13. Import quantity and value**

||**Qu**|**antity**||**Valu**|**e**||
|---|---|---|---|---|---|---|
||**Base**<br>**Level***|**% Ch**<br>**Sc. 2.a**|**ange**<br>**Sc. 2.b**|**Base**<br>**Level**|**% C**<br>**Sc. 2.a**|**hange**<br>**Sc. 2.b**|
|**Wheat**|1,707|5.39|9.43|852,991|5.25|9.00|
|**Barley**|75|1.38|2.11|26,710|1.24|1.71|
|**Maize**|1,920|2.32|4.01|958,308|2.18|3.60|
|**Other grains**|62|2.58|4.20|294,479|2.44|3.79|
|**Pulses**|14|3.33|5.32|24,903|3.19|4.91|
|**Tobacco**|223|-0.44|-0.99|330,050|-0.57|-1.38|
|**Sugar beet**|28|5.67|9.32|7,916|5.53|8.89|
|**Cotton (raw)**|855|2.72|5.18|1,014,863|2.58|4.77|
|**Oil seeds**|437|0.68|1.19|322,682|0.54|0.79|
|**Potatoes**|16|1.60|2.45|8,778|1.47|2.04|
|**Vegetables**|92|2.46|4.16|63,121|2.32|3.75|
|**Fruits**|84|4.05|7.22|74,676|3.92|6.80|
|**Nuts**|5|1.68|2.47|23,580|1.54|2.07|
|**Other crops**|4|5.40|9.04|8,446|5.26|8.61|
|**Milk**|34|0.01|-0.47|34,145|-0.12|-0.87|
|**Poultry and Eggs**|20|2.11|3.02|306|1.87|2.15|
|**Other An. Prod.**|241|2.47|3.34|20,183|1.98|2.61|
|**Other Agr. Prod.**|360|-1.00|-1.89|240,806|2.34|2.93|
|**Food Production**|1,092|8.31|14.10|360,165|-1.13|-2.28|
|**Mining**|16,369|-0.79|-1.09|1,091,780|8.16|13.65|
|**Consumer Manufac.**|9,920|1.62|1.99|16,369,273|-0.92|-1.48|
|**Int. and Cap. Goods**|74,091|-1.12|-1.65|9,919,764|1.49|1.59|

_Source: Results from TACOGEM-W. * Value added Thousand TRY_ Scenario 2.b entails a 50 percent increase in the urban water supply by decreasing irrigation water by the same amount, creating responses in the economy in the same direction as in Scenario 2.a, but evidently at higher magnitudes.

# **4.4 Scenario 3: Climate Change Scenario**

Climate change is expected to have a considerable impact on agricultural production. Climate change mainly affects agricultural productivity through reduced water availability (or runoff, i.e. the difference between rainfall and evapotranspiration)—both by reduced precipitation and increased temperatures. Most particularly, it has been reported that most of the Near East Region (including Turkey) will have a decrease in water availability of up to 40 mm per year, and that 50