rafmacalaba/datause-encoder-data huggingface dataset:
According to the National Statistical Office, there are at least five factors that explain the level of economic growth achieved between 1996 and 2001. These include: (1) a good performance of the export sector, particularly coffee, cocoa and cotton; (2) investments associated with the privatization program; (3) the expansion of the timber industry; (4) increased salaries in the public sector; and (5) job creation and multiplier effects associated with the construction of the Chad-Cameroon pipeline. The National Statistical Office also explains that the poor performance of the economy between 2001 and 2007 is due mainly to the fact that growth occurred in low productivity sectors such as the urban informal sector and traditional agriculture.

The data presented in table 2.1 also reveal a significant amount of inequality in the distribution of per adult equivalent expenditure. The share of the richest decile is equal to almost 12 times that of the poorest decile in 1996, about 11 times in 2001 and 9.3 times in 2007. Furthermore we note that, for all three years, the share of expenditure of every decile up to the sixth is strictly less than its population share (10 percent). For the seventh decile, the share of expenditure is about 8 percent in 1996, and a little over 10 percent in 2001 and 2007. The Gini measure of inequality has hovered around 40 percent in 1996 and 2001 and declined slightly to about 39 percent in 2007.

# **Changes in Poverty over Time**

Figure 2.1 presents a picture summarizing the evolution of aggregate poverty from 1996 to 2007 based on TIP curves associated with poverty measures which are members of the Foster-Greer-Thorbecke (FGT) family. The acronym TIP stands for the _three I’s of poverty_ because the curve provides a graphical summary of _incidence_ , _intensity_ and _inequality_ dimensions of aggregate poverty based on the distribution of poverty gaps (Jenkins and Lambert (1997)<sup>9</sup> . These dimensions are shown as follows: (1) the length of the non-horizontal section of the curve reveals poverty _incidence_ ; (2) the _intensity_ aspect of poverty is represented by the height of the curve; and (3) the degree of concavity of the

> 9 This curve is constructed in four steps: (1) rank individuals from poorest to richest on the basis of the welfare indicator **y** ; (2) compute the relative poverty gap of individual **i** as **gi=max{(1-yi/z), 0}** where **z** is the poverty line; (3) form the cumulative sum of the relative poverty gaps divided by population size; and (4) plot the resulting cumulative sum of poverty gaps as a function of the cumulative population share.

9
|**Expenditure line (COICOP)**|**Corresponding**<br>**HS code**|<br>**Weighted**<br>**average import**<br>**tariffs**|**Import share**<br>**in retail****|**Import price**<br>**elacticity*****|
|---|---|---|---|---|
|Expenditures for fat|209|15.0|0.5|-1.8|
|Expenditures for other meats|2|34.2|0.7|-1.4|
|Expenditures for fish and seafood|3|8.0|42.1|-1.3|
|Expenditures for vegetable oil, margarine and other grease<br>|15|14.3|74.2|-1.0|
|Expenditures for eggs|407|0.2|0.2|-1.0|
|Expenditures for potatoes|701|13.8|6.8|-1.2|
|Expenditures for vegetables and melons|7|13.8|32.1|-0.8|
|Expenditures for fruits and berries|8|3.4|89.8|-1.0|
|Expenditures for sugar and confectionery|17|6.1|22.0|-1.0|
|Expenditures for tea, coffee, cocoa|9|12.9|76.6|-1.0|
|Expenditures for non-alcoholic drinks|2201, 2202*|10.9|17.2|-0.9|
|Expenditures for other food|food average|15.9|26.3|-1.2|
|Expenditures for alcohol and tobacco|22, 24*|7.7|11.7|-1.5|
|Expenditures for clothing|61, 62*|10.1|41.5|-1.9|
|Expenditures for footwear|64|2.3|52.4|-1.0|
|Expenditures for fabrics|59, 60*|5.8|30.9|-1.0|
|Expenditures for household appliances|85|5.3|74.7|-1.2|
|Expenditures for furniture|94|12.4|10.4|-0.9|
|Expenditures for health care|30|7.7|64.6|-0.9|
|Expenditures for maintenance of private vehicles|2710, 8708*<br>25% for 19.2%|1.2|18.5|-1.4|
|Expenditures for purchase of cars and other vehicles|of import|4.8|98.9|-1.2|
|Expenditures for personal care|34|10.6|77.7|-0.8|
|Expenditures for for food purchased for animals and for cultivation|||||
|of landplot|23|4.2|47.9|-1.0|

_Notes_ .

* Average import tariffs were weighted by import volume (from world in 2015). Data of tariff rates is taken from TRAINS database (as of 2014, partner – world).

** Data on share of import goods in retail sales is obtained from Belstat yearbook on retail trade<sup>18</sup> and monthly bulletins on retail trade.

*** Elasticities are from Shymanovich G. (2013). Absent elasticities were assumed as following: for “bread”, “other dairy products”, and “poultry” equaled to “pastry”, “sour cream and cream”, and “pork” respectively; for “eggs” equaled to -1; for “other food” as average of elasticities for food products; for “alcohol and tobacco” as average elasticity of alcohol and tobacco weighted by import; for “maintenance of private vehicles” as average elasticity for HS2710 and HS8708 weighted by import volume.

_Sources:_ TRAINS database, Shymanovich G. (2013), Belstat.

# **Figure 9. Incidence of import duties by deciles**

<!-- Start of picture text --> 0.8 USD PPP % 1.4<br>0.7 1.2<br>0.6<br>1.0<br>0.5<br>0.8<br>0.4<br>0.6<br>0.3<br>0.4<br>0.2<br>0.1 0.2<br>0.0 0.0<br>1 2 3 4 5 6 7 8 9 10<br>% of disposable income (right axis)<br>USD PPP per day<br><!-- End of picture text --> _Note_ . Deciles are ranked by disposable income. _Source_ : own estimates based on HBS data.

> 18 <u>http://www.belstat.gov.by/ofitsialnaya-statistika/realny-sector-ekonomiki/vnytrennia-torgovlya/roznichnaya-torgovlya/publikatsii_6/index_702/.</u>

18
# The Intensive Margin in Trade

Ana M. Fernandes Peter J. Klenow Sergii Meleshchuk World Bank Stanford and NBER IMF Martha Denisse Pierola Andr ́es Rodr ́ıguez-Clare<sup>_∗_</sup> Inter-American Development Bank UC Berkeley and NBER Keywords: _intensive margin of trade, extensive margin of trade, productivity distribution, trade costs, welfare, Pareto_ JEL Classification: _F1, F12_

> _∗_ We are grateful to Arnaud Costinot, Caroline Freund, Cecile Gaubert, Keith Head, Sam Kortum, Thierry Mayer, Eduardo Morales and Jesse Perla for useful discussions, to seminar participants at various institutions, and to Matthias Hoelzlein and Nick Sander for outstanding research assistance. The World Bank provided access to the _Exporter Dynamics Database_ . Research for this paper has in part been supported by the World Bank’s Multidonor Trust Fund for Trade and Development and the Strategic Research Program on Economic Development, as well as the Stanford Institute for Economic Policy Research. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or its member countries. The views expressed herein are those of the authors and should not be attributed to the IMF, its Executive Board, or its management.
||**10.** Separation of release from fnal determination and payment of Customs duties.<br>**11.** Perishable goods enjoy preferential treatment concerning the separation of release.<br>**12.** No pre-shipment inspection is required on Customs matters.<br>**13.** Authorized Operator (AO) programmes (LPI data)<br>**14.** Simplifcation of procedures (LPI data).<br>**15.** Simplifcation of procedures (DB data).<br>**16.** Working hours of Customs personnel are adapted to commercial needs.<br>**17.** Mandatory use of a third-party customs broker is not required.|
|---|---|
|Border agency<br>cooperation|**1.** Cooperation between domestic agencies on the ground (IPD data).<br>**2.** Government agencies delegate controls to Customs authorities.|
|(internal)|**3.** Regular meetings are held to improve cooperation, and the private sector is included.|
|Border agency<br>cooperation|**1.** Working days and hours are aligned with other neighboring countries.<br>**2.** Procedures and formalities are aligned with other neighboring countries.|
|(external)|**3.** Common facilities are developed and shared with other neighboring countries.<br>**4.** There are joint controls with neighboring countries.|
|Consularization|**1.** The country does not impose consular transaction requirements.|
|Governance<br>and impartiality|**1.** Structures and functions of the Customs agency are publicly available.<br>**2.** A code of conduct is published and made available to all employees.|

**3.** Information on disciplinary procedures and penalties for misconduct are publicly available.

**4.** Ethics policy is consistent with international norms and a help desk exists to guide staff on ethical issues. **5.** The mechanisms for financing customs are legally defined and information is publicly available.

**6.** An audit function for internal systems is established, adequately empowered and operational. **7.** Annual Customs reports are available and contain sufficient information on Customs activities. **8.** Frequency of irregular <u>payments</u> and bribes <u>(GCR</u> data).

Notes: Adapted from Mo ̈ıs ́e and Sorescu (2013) Annex 1. More detailed information is available there. The OECD used some external data, attributed here with relevant abbreviations. _GCR data_ are from the Global Competitiveness Report published by the World Economic Forum. _IPD data_ are from the Institutional Profiles Database compiled by CEPII. _LPI data_ are components of the World Bank’s Logistics Performance index. _DB data_ are components of the Doing Business - Trading Across Borders data.

40
The tax sharing system adopted by Indonesia is quite transparent and enables the Government of Indonesia to maintain a harmonized tax system with lower costs of tax administration. Nevertheless, the system suffers from a number of limitations. For most of the shared taxes, tax base sharing with provincial and local government opting to levy supplementary rates might be a superior alternative to current arrangements. Tax base sharing has the potential of introducing greater accountability in the system as local governments would have to justify tax rate imposition to local electorate. If tax base sharing is ruled out as an option then it would be better to consider using taxpayer place of residence as the criterion for allocation of personal income tax revenues. Such a criterion will lead to greater jurisdictional equity as it will enable residential

**Table 5.  Sharing of Central Taxes in Indonesia** (percent)

|Revenue source|Central<br>government|Originating<br>provincial<br>government|Originating<br>local<br>government|All local<br>governments in<br>originating<br>province|All local<br>governments<br>(equal share)|
|---|---|---|---|---|---|
|Personal income tax|80.0|8.0|12.0|n.a.|n.a.|
|Property tax|9.0|16.2|64.8|n.a.|10.0|
|Property transfer tax|n.a.|16.0|64.0|n.a.|20.0|
|Tobacco excise tax|98.0|0.6|0.8|0.6||
|Mining land rent|20.0|16.0|64.0|n.a.|n.a.|
|(Kab/Kota producers)||||||
|Mining land rent (prov.|20.0|80.0||||
|Mines)||||||
|Mining royalty –|20.0|16.0|32.0|32..0|n.a.|
|(Kab/Kota producer)||||||
|Mining Royalty prov.|20.0|26.0|0.0|54.0|0.0|
|Forestry license|20.0|16.0|64.0|n.a.|n.a.|
|Forestry royalty|20.0|16.0|32.0|32.0|n.a.|
|Reforestation|60.0|0.0|40.0|||
|Fishery royalty|20.0|n.a.|n.a.|n.a.|80.0|
|<br>Geothermal mining|20.0|16.0|32.0|32.0|n.a.|
|Oil||||||
|Base rate – kota/kab.|84.5|3.0|6.0|6.0|n.a.|
|Conditional rate -|0.0|0.1|0.2|0.2|n.a.|
|kota/kab.(education)||||||
|Base rate - province|84.5|5.0|0.0|10.0||
|Cond. Rate - province|0.0|0.17|0.0|0.33||
|<br>Natural gas||||||
|Base rate|69.5|6.0|12.0|12.0|n.a.|
|Conditional rate|n.a.|0.1|0.2|0.2|n.a.|
|(education)||||||

n.a. = not applicable. Note: 70% of oil and gas revenues are returned to special autonomy regions i.e. Aceh, Papua and Papua Barat _Source:_ Eckardt and Shah (2007), Marwanto (2011)

# **Table** **<u>6. Sharing of Provincial Taxes in Indonesia</u>**

|**Revenue Source**|**Provincial Share**|**Local Share**|
|---|---|---|
|Motor Vehicle tax|70%|30%|
|Vehicle Transfer Tax|70%|30%|
|Fuel Excise Tax|30%|70%|
|Groundwater extraction and use tax|30%|70%|

Source: Eckardt and Shah (2007) 12
|Gasoline|5.6|5.4|11.4|11.2|17.3|17.1|23.4|23.2|29.6|29.4|
|---|---|---|---|---|---|---|---|---|---|---|
|Biodiesel|10.0|643.7|26.5|673.9|55.9|704.8|116.3|736.3|277.8|767.5|
|Bioethanol|10.0|9.8|26.4|26.0|55.8|54.9|116.0|114.0|276.7|270.4|

**Table B5: Mandatory substitution of 5% in diesel-biodiesel and bioethanol-gasoline**

||**y**<br>|**1**<br>|**y**<br>|**2**<br>|**y**<br>|**3**<br>|**y**<br>|**4**<br>|**y **<br>|**5**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|
|**_Macroeconomic Indicators_**|||||||||||
|GDP|4.0|3.7|8.1|7.9|12.3|12.1|16.7|16.5|20.9|20.8|
|Trade Balance|5.1|4.8|10.4|10.1|15.9|15.6|21.4|21.2|26.9|26.7|
|Fiscal Result|2.1|2.0|4.3|4.1|6.5|6.3|8.6|8.5|10.7|10.6|
|Rate of Unemployment|9.2|9.4|6.9|7.1|4.6|4.7|2.2|2.3|0.0|0.0|
|**_Welfare Indicators_**|||||||||||
|Poorest Household|3.9|3.6|8.0|7.7|12.1|11.9|16.4|16.2|20.5|20.4|
|Richest Household|3.6|3.4|7.2|7.0|10.9|10.8|14.7|14.6|18.4|18.4|
|**_Sectoral Activity Level_**|||||||||||
|Agriculture|4.2|3.9|8.4|8.2|12.8|12.5|17.3|17.0|21.8|21.6|
|Manufactures|4.4|4.2|8.8|8.7|13.4|13.3|18.1|18.0|22.7|22.7|
|Services|3.9|3.7|7.9|7.7|12.1|11.8|16.3|16.1|20.4|20.3|
|Soy|4.1|3.7|8.3|8.0|12.6|12.3|17.0|16.7|21.5|21.2|
|Maize|4.1|3.8|8.4|8.0|12.7|12.4|17.2|16.9|21.7|21.5|
|Sugar Cane|4.4|5.7|8.9|10.3|13.5|15.1|18.3|19.9|22.9|24.7|
|Rest of agriculture|4.2|4.0|8.5|8.3|12.9|12.7|17.5|17.2|22.1|21.8|
|Soybean oil|4.9|4.6|9.9|9.6|15.0|14.7|20.2|20.0|25.4|25.2|
|Diesel|5.5|5.2|11.1|10.8|16.8|16.5|22.7|22.4|28.7|28.4|
|Gasoline|5.6|5.3|11.4|11.1|17.3|17.0|23.4|23.1|29.6|29.3|
|Biodiesel|10.0|642.8|26.5|673.1|55.9|704.0|116.3|735.5|277.8|767.0|
|Bioethanol|10.0|13524.6|26.4|14127.4|55.8|14727.5|116.0|15327.8|276.7|15914.3|

**Table B6: Increase of export taxes on soy and soybean oil (10%)**

||**y1 **<br>|<br>|**y2 **<br>||**y3 **<br>||**y4 **<br>||**y5 **<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|
|**_Macroeconomic Indicators_**|||||||||||
|GDP|4.0|3.9|8.1|8.0|12.3|12.2|16.7|16.5|20.9|20.7|
|Trade Balance|5.1|5.0|10.4|10.3|15.9|15.7|21.4|21.3|26.9|26.6|
|Fiscal Result|2.1|2.1|4.3|4.2|6.5|6.4|8.6|8.5|10.7|10.5|
|Rate of Unemployment|9.2|9.1|6.9|6.9|4.6|4.5|2.2|2.1|0.0|0.0|
|**_Welfare Indicators_**|||||||||||
|Poorest Household|3.9|3.9|8.0|7.9|12.1|12.1|16.4|16.3|20.5|20.4|
|Richest Household|3.6|3.5|7.2|7.1|10.9|10.8|14.7|14.6|18.4|18.3|
|**_Sectoral Activity Level_**|||||||||||
|Agriculture|4.2|2.9|8.4|7.1|12.8|11.4|17.3|15.8|21.8|20.3|
|Manufactures|4.4|4.6|8.8|9.1|13.4|13.7|18.1|18.4|22.7|22.9|
|Services|3.9|4.0|7.9|8.0|12.1|12.2|16.3|16.4|20.4|20.4|
|Soy|4.1|-4.9|8.3|-1.1|12.6|2.9|17.0|7.0|21.5|11.1|
|Maize|4.1|7.5|8.4|11.9|12.7|16.3|17.2|20.9|21.7|25.5|
|Sugar Cane|4.4|5.7|8.9|10.3|13.5|15.0|18.3|19.7|22.9|24.4|
|Rest of agriculture|4.2|8.0|8.5|12.4|12.9|17.0|17.5|21.6|22.1|26.4|
|Soybean oil|4.9|-2.5|9.9|2.2|15.0|7.1|20.2|12.0|25.4|16.8|
|Diesel|5.5|5.4|11.1|11.0|16.8|16.7|22.7|22.6|28.7|28.6|

35
_ 10 -

## B. Central Government Expenditures

41. Central Government expenditures grew at about 102 a year during 1982-87. This growth masks the changing composition of expenditures, since capital expenditures (i.e. mostly transfers to ENEE) fell while current expenditures rose.

## B.1. Introduction

42. The current structure of expenditures presents rigidities. As a result, the scope for decreasing expenditures within a possible adjustment program will likely be minor. For example, interest payments almost tripled in real terms during 1981-1987 and accounted for about 16Z of current expenditures in 1987, while expenditures on defense equalled 7.8Z of the total in 1987 (Table 5). It is generally believed that expenditures related to defense are distributed among various categories such as wages and salaries, debt service, investment, and goods and services. Total military expenditures are believed to have amounted to 232 of current revenues in 1987. Given the sensitive border problems, defense expenditures may prove to be difficult to reduce in the short-term. Furthermore, expenditures on the social sectors remained at one-third of the total during the period. This is mostly due to high education expenditures (i.e. mostly for teacher salaries). As a result, the share allocated to infrastructure, mostly for transport and natural resources which the country badly needs, declined over the years.

**Table 5:** Central Sovornment Expenditures by Function. 1981-87 **_(X_** of **total)**

||**1981**|**1982**|**1988**|**1984**|**1985**|**1986**|**198**|
|---|---|---|---|---|---|---|---|
|Administrative Expenditures|20.0|17.0|**16.8**|17.4|19.9|14.7|**14.6**|
|Social Sectors|**85.6**|**84.8**|**82.7**|30.7|**80.4**|**82.6**|<br>82.2|
|o/w Education|(20.2)|(21.2)|(19.7)|(19.7)|(20.1)|(20.8)|(20.8)|
|o/w H- Ith|(11.2)|(10.8)|(10.4)|(11.0)|**(8.8)**|**(9.8)**|(9.6)|
|Infrastructure|16.2|18.7|12.8|10.a|**9.1**|**11.8**|**10.6**|
|**o/w**Transport**and**Communication|s (9.0)|(7.3)|**(6.8)**|(6 7)|(4.8)|**(5.6)**|**(6.8)**|
|**@/w**Natural**Resources**|(7.2)|(6.4)|**(6.8)**|**(4.6)**|**(4.8)**|<br>(6.7)|**(4.8)**|
|**Defonse**|1386|**18.1**|12.4|14.0|14.0|**18.0**|**7.8**|
|**Debt**Service**2**|16.8|**21.4**|**26.7**|**27.8**|**26.6**|28.4|**84.8**|
||_100.0_|_100.0_|**160.6**|**160.6**|**106.6**|**_100.0_**|**_160.0_**|

Source: Ministry **of Finance.**

# _1_ Budgeted.

_J_ Includes amortization psyments. Tho] Ministry of Finance classiflos amortization paymonts **as** exponditures and **it** was not possible at this timo to difforentiate Interest from amortization payments In this table.
represented by full sets of time and product fixed effects plus an error term orthogonal to the tariff variables.

Lastly, not all imports which are in principle eligible for preferential treatment _de facto_ enter the EC market under that respective import program.<sup>8</sup> To incorporate less than full utilization rates into the model, let _λEC ,t_ ( _z_ ) denote the fraction of imports of product _z_ which are actually accorded the preferential rate _τEC ,k_ . Adding back in the time subscript, we obtain the reduced form estimation equation, in which _τ ≡_ (1 + tariff rate), as The coefficient _β_ 1 is expected to be negative (the conventional impact of lower tariffs triggering higher imports), while _β_ 2 _, β_ 3 _>_ 0 as changes in ACP and MFN market access conditions _ceteris paribus_ erode a GSP country’s position. In particular, a positive coefficient _β_ 2 would testify to intra-developing country trade diversion as lower ACP tariffs translate into lower GSP exports. Coefficient _β_ 4 is expected positive as US tariff cuts stimulate US imports which, however, decreases the import ratio; and finally _β_ 5 _, β_ 6 _<_ 0 for analogous reasons as above (changes in US MFN and NAFTA tariff schedules impairs GSP countries’ relative position, and a fall in the denominator causes the ratio to increase).

> 8 Generically, one important reason for intra-beneficiary trade diversion is probably the systematic difference in utilization rates across beneficiary groups. The latter is markedly higher for LDC and ACP countries as compared to ordinary GSP recipients. For instance, in 2001 the overall utilization rate for GSP recipients, i.e. the ratio of preferential to eligible imports, was around 45 percent (CEC, 2003, Table T3), whereas in contrast the corresponding figure for ACP imports reached 76 percent (Inama, 2003, p.966).

9
openness and domestic financial development through firm borrowing channel (Aghion et al. 2004, 2010). Incorporating borrowing by firm in the model may provide an additional channel for the interaction between financial development and financial liberalisation to affect output and investment. However, in spite of the fact that India started liberalising capital account in 1991, the pace and the extent of easing restrictions on capital flows remained low compared to other emerging economies. The access to foreign capital by Indian households and firms are still limited due to a wide array of capital control measures existing in the country. The de jure measure of capital account openness based on the Chinn-Ito index shows that India is relatively closed compared to other large emerging economies (Patnaik and Shah 2012) (see figure 6). Households in India are not allowed to borrow abroad. There are a number of restrictions on foreign borrowing by firms, and both macro and firm level data indicate low exposure of Indian firms to foreign capital.<sup>7</sup> Given the low level of access to foreign capital by Indian households and firms, an open economy setup through the financial channel may not be appropriate to replicate the post-reform business cycle stylised facts in India.

India liberalised current account at a faster pace than capital account.

> 7 Along with domestic financial deepening, opening up of the capital account, or financial liberalization, has been a major component of reforms in India since 1991. However, the access to foreign capital by Indian households and firms have remained limited. Households and banks in India are not allowed to borrow abroad. As far as borrowing by firms are concerned, Indian firms access foreign capital through two channels to leverage their operations. These are Foreign Direct Investment (FDI) and foreign borrowings. FDI in India (net inflows) has grown from USD 0.59 billion in 1993–94 to USD 30.76 billion in 2013–14 (Economic Outlook, Centre for Monitoring Indian Economy). However, the net FDI inflows in India accounts for only 1.78% of GDP in 2013–14. The share of net FDI inflows in India in total investment amounts to 5.24% in 2013–14. To compare with other emerging economies, for instance, net FDI inflows in Brazil in 2013 has been USD 80.84 billion, which is more than double the FDI inflows in India, while the net FDI inflows in China in 2013 has been USD 347.85, which is more than eleven times larger the FDI flows in India (World Development Indicators). Looking deep into the firm-level database, only 623 firms are found to have foreign promoter (ownership) in a base of 26,725 companies at the end of 31st March, 2014 (Prowess, Centre for Monitoring Indian Economy). India holds stock under foreign borrowings of USD 53.92 billion in 2012– 13 and 2013–14. The net inflow of foreign borrowings has accounted for only 0.63% of GDP in 2013–14. Again in a sample of 26,725 firms in the Prowess database, only a total of 642 companies are found to have had foreign borrowings over the years, while only 464 companies have executed for the financial year 2013–14.

28