rafmacalaba/datause-encoder-data huggingface dataset:
reference week). Not surprisingly, under the revised standards, the measured share of agricultural employment in overall rural employment is significantly smaller than under the previous standards. This also highlights the need for household surveys to collect data on multiple, potentially overlapping, activities.

The remainder of this paper is organized as follows. Section 2 provides some background on the 19<sup>th</sup> ICLS standards. Section 3 describes the data sources. Section 4 uses multi-topic household survey data from Ghana, Malawi and Nigeria to illustrate how male and female farmers respond to questions about the intended uses of their agricultural produce and the share of farmers considered to be employed by the revised standards under different measurement approaches, paying specific attention to gender differences. In addition, the section explores the over-time variability in reports about the intended use of output. Section 5 compares male and female employment-to-population ratios under the previous (1982) and revised (2013) standards. Section 6 discusses implications for labor statistics in developing countries and concludes the paper.

# **2 The 19**<sup>**th**</sup> **ICLS resolution and its operationalization in household surveys 2.1 Background**

# **_Own-use production in national accounts and labor statistics_**

The 19<sup>th</sup> ICLS standards are rooted in a century-old debate on the appropriate delineations and statistical treatment of market and non-market production in the context of national accounts and labor force statistics. Historically, national income accounting emphasized the monetary economy (Benería 1999), but the 1953 United Nations systems of national accounts (SNA) made an exception for primary producers (such as farmers). Such production was recommended to be included irrespective of whether it was exchanged or not. This position was formalized in the 1993 SNA, which specified that household production of agricultural goods for own-use falls within the SNA production boundary, and hence, ought to be included in GDP – at least in countries where it is thought to be quantitatively important. Conversely, services for own use (such as cleaning, childcare, and meal preparation) have always been excluded from the SNA production boundary (and from GDP). This distinction between goods and services for own use has been extensively criticized, particularly (but not exclusively) by feminist economics scholars, who argue that national accounts statistics hide unpaid work performed mostly by women and do not recognize the productive value of these activities (Waring 1990; Benería 1999; Goldschmidt-Clermont and PagnossinAligisakis 1999; Young 2000; Goldschmidt-Clermont 2000; Anker 2011). Even though the topic was debated at two United Nations World Conferences (Swiebel 1999), the broad distinction between own-use goods (included in GDP) and own-use services (excluded from GDP) has been maintained in the 2008 5
_Gender Equality and Economic Development: The Role for ICTs_ 27 Thomas, D. (1990). “Intrahousehold Resource Allocation: An Inferential Approach.” _Journal of Human Resource_ s. Vol. 25, pp. 634-664.

United Nations (2002). _Arab Human Development Report 2002: Creating Opportunities for Future Generations._ United Nations Development Programme, Arab Fund for Economic and Social Development.

Whelan, Karl (2000). “Computers, Obsolescence, and Productivity.” _Finance and Economics Discussion Series_ . 2000-6, Federal Reserve Board, Washington, DC.

World Bank (1993). _World Development Report_ . Washington, D.C.

World Bank (2001). _Engendering Development_ . Washington, D.C.

World Bank (2003a). _Gender Equality and the Millennium Development Goals_ . Washington, D.C., April.

World Bank (2003b). _Engendering ICT: Ensuring Gender Equality in ICT for Development_ . Washington, D.C., September.

World Bank (2003c). _ICT and MDGs: A World Bank Group Perspective_ . Washington, D.C., December.
# Learning from Financial Crises

Jamus Jerome Lim and Geoffrey Minne<sup>_∗_</sup> Keywords: Financial crises, foreign banks, institutional learning JEL Classification: G21, G01, F34

> _∗_ This paper was substantially completed when the latter author was a consultant at the World Bank. Minne acknowledges financial support from Fonds de la Recherche Scientifique (FRS-FNRS), and the KCP II Window 2 Grant TF095266 “Analyzing the Impact of the Financial Crisis on International Bank Lending to Developing Countries” funded the data collection for the bank ownership dataset used in this paper, which can be downloaded from: http://siteresources.worldbank.org/INTKNOWLEDGEFORCHANGE/Resources/4915191386966650966/PRWP6590.zip. Comments from Jonathon Adams-Kane, Lucy Goodhart, Pierre-Guillaume M ́eon, and Kim Oosterlinck, as well as seminar participants at the Centre Emile Bernheim and Midwest Political Science Association, are gratefully acknowledged. However, the findings, interpretations, and conclusions expressed in this article are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
_COTE D’IVOIRE: Volatility, Shocks and Growth_ around the trend. During the peak years of Cote d’Ivoire’s productivity, 1976 to 1980, cocoa prices soared over trend. They crashed thereafter, and actual prices and the trend fell precipitously. In nominal (unlogged) dollars, cocoa prices rose to over $3,000 per metric ton in 1980, then fell to about $1,000 by 2000. In constant 1995 (unlogged) dollars, adjusted by the US CPI, real prices declined to one-seventh of their peak price over twenty years to 2000.

**Figure 4 Natural Logarithms International Real Cocoa Prices, 1960-2003**

<!-- Start of picture text --> International C ocoa Prices<br>(Inflation adjusted, base year=2000)<br>5<br>Cocoa<br>P rices<br>HP Trend<br>4.5<br>4<br>3.5<br>3<br>2.5<br>2<br>Ye a r<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000<br><!-- End of picture text -->

**Figure 5 : Cocoa Production Versus Trend in Cote d’Ivoire, 1961-2002**

<!-- Start of picture text --> Cocoa Production in Cote d'Ivoire<br>M illlions of M etric T onnes (FAO)<br>1.6<br>1.4 Cocoa Production (mt)<br>HP Trend<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Millions<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text --> Figure 5 depicts total national cocoa production in Cote d’Ivoire, in millions of metric tons. It grows dramatically over time. Annual coca output cycles across trend, with the oscillations at least somewhat co-incident with the oscillations of cocoa price around
1 and 4 potential fertility years for women of ages 16 to 45. In total, I observe 3,148 years of potential fertility with births in 920 of them (29.2 percent). Table 4 shows the annual birth probabilities of married women by 5-year age groups. The annual birth probabilities decrease from 0.405 for ages 16 to 20 to 0.06 for ages 41 to 45.<sup>17</sup>

# 3.2 Malawi Second Integrated Household Survey

The MDICP data do not include detailed measures of household consumption. For this reason, I use the Malawi Second Integrated Household Survey (IHS-2) to impute a better measure of consumption of households in the MDICP sample. The dataset, gathered by the Malawi National Statistics Oce in 2004-2005, is part of the World Bank's Living Standards Measurement Study program. It includes comprehensive data on consumption and expenditures of households, as well as on local prices. Specically, the dataset includes a measure of annual consumption expenditure aggregates in real value. The survey was elded in 26 out of Malawi's 27 districts, including all three MDICP districts. After restricting the sample to households in the districts covered by MDICP and excluding households without a woman as a head of the household or as a wife of the head, the estimation sample includes 612 households. 220 of these households are from Balaka, 188 from Mchinji, and 204 from Rumphi.<sup>18</sup>

# 3.3 Mortality Statistics

I supplement the use of the MDICP data with statistics about survival after infection and child mortality from other studies that use data from samples with repeated HIV testing and frequent follow-ups. These data provide more precise information on times of infection and death than MDICP does. Hallett et al. (2008) estimate probabilities of survival after infection by tting a Weibull distribution to survival data presented by Todd et al. (2007) 17The annual birth probability is dened as the proportion of alive and married women of specic age that give birth.

> 18Detailed information on the Malawi Second Integrated Household Survey can be obtained from the World Bank's website at http://econ.worldbank.org.

20
growth of Indian carriers in the period 1990-2005, of nearly 15 percent yearly in passenger traffic and 11 percent yearly in cargo traffic (Directorate General of Civil Aviation, 2006).<sup>12</sup> Until 2002, private sector competition in the insurance market was proscribed, severely limiting the range of insurance services on offer. Market penetration of insurance quickly increased following the entry of private and foreign insurers. After decades of public monopoly, premiums were equal to only 1.9 percent of GDP in 1999-2000, but they jumped to 2.86 percent of GDP by 2002-2003 (Insurance Regulatory and Development Authority, 2004). Government projections at the time of liberalization suggested that market participation by foreign firms in 2005 would reach only 5 percent of the market, but by November 2005, private firms with foreign shareholding had acquired a 34 percent market share. Over the same period, there was limited contraction by Indian public sector incumbents (Department of Public Enterprises, 2003).<sup>13</sup> In sum, liberalization led to a metamorphosis of services in India from a narrow range of products of substandard quality and poor distribution to the current environment in which service providers are highly competitive and offer their consumers, including manufacturing firms, a wide range of new and high quality services products.

# **IV. Empirical Strategy**

In this paper, we investigate whether there is a systematic link between liberalization in services sectors and the performance of firms in downstream manufacturing industries. This exercise requires three pieces of information: a measure of policy reform in services, a performance measure for manufacturing firms and information on the linkages between different sectors of the economy.

> 12 Recently, certain private providers, such as Kingfisher Airlines, have experienced financial difficulties 13 National Insurance Company Limited, Calcutta,  New India Assurance Company Limited, Mumbai, and United India Insurance Company Limited Chennai each cut their staffs by 10 percent, while Oriental Insurance Company Limited, New Delhi cut its staff by 14 percent (India Knowledge @ Wharton,  2006).

14
Both public and private investment spending is highest in construction and other manufacturing. Public investment also emphasizes spending on education/health. These different expenditure patterns suggest that income given to different agents will have very different impacts on the structure of the economy. For example, income to the government that is spent on current consumption will increase demand for non-traded goods.

**Table 12: Share of value added to factors by sector (percent) in Niger, 2009**

||Agricultural<br>Labor|Unskilled<br>labor (non-<br>agriculture)|Skilled<br>labor|Capital|Land|Value<br>added|
|---|---|---|---|---|---|---|
|Food agriculture|25.6|0.0|0.0|37.2|37.2|100.0|
|Other agriculture|26.2|0.0|0.0|36.9|36.9|100.0|
|Mines|0.0|14.1|2.1|83.8|0.0|100.0|
|Processed food|0.0|13.2|0.6|86.2|0.0|100.0|
|Other manufacturing|0.0|15.9|0.9|83.2|0.0|100.0|
|Construction|0.0|37.6|0.9|61.5|0.0|100.0|
|Utilities|0.0|11.2|31.0|57.8|0.0|100.0|
|Services|0.0|88.3|4.6|7.1|0.0|100.0|
|Public services|0.0|21.7|64.3|14.0|0.0|100.0|
|Education/Health|0.0|21.3|78.3|0.5|0.0|100.0|
|Total|11.5|29.4|10.7|31.8|16.6|100.0|

_Source:_ Authors’ calculations using the microeconomic SAM for Niger, 2009.

**Table 13: Expenditure shares by agent (percent) in Niger, 2009**

||Rural HH|Urban HH|Capital<br>owners|Government<br>purchases<br>for current<br>consumption|Public<br>Investment|Private<br>Investment|
|---|---|---|---|---|---|---|
|Food agriculture|46.44|29.68|36.43|0.0|6.95|8.50|
|Other agriculture|4.38|7.45|2.47|0.0|0.09|0.39|
|Mines|0.00|0.00|0.00|0.0|2.80|3.26|
|Processed food|13.17|12.83|16.18|0.0|0.08|0.08|
|Petroleum|3.05|4.45|3.65|0.0|0.00|0.00|
|Other manufacturing|12.53|12.57|12.76|0.0|22.54|34.37|
|Construction|0.61|0.52|0.30|0.0|30.47|47.47|
|Utilities|2.74|2.48|4.78|0.0|4.98|0.00|
|Services|16.90|29.18|21.22|0.0|3.75|4.07|
|Public services|0.00|0.00|0.00|53.1|0.00|0.00|
|Education/Health|0.19|0.85|2.21|46.9|28.33|1.87|
|Total|100.00|100.00|100.00|100.00|100.00|100.00|

_Source:_ Authors’ calculations using the microeconomic SAM for Niger, 2009.

15
Niger’s economic growth has accelerated from an annual average of 4.4 percent during the period 2002-2007 to 5.5 percent during the period 2007-2012. This was achieved despite profound political disturbances, two severe droughts in 2009 and 2011, and an increasingly adverse external environment. Inflation has been below 4 percent in most years since 2000. Until 2008, inflation tended to increase on account of food shortages whenever a food crisis occurred. More recently in 2010 and 2012, inflation remained low despite severe food crises. This reflects on the one hand the impact of the food safety net -- during years of crisis food is <!-- Start of picture text --> Figure 1: Economic growth and inflation,<br>2000-2012<br>12.0%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>-2.0%<br>-4.0%<br>Real GDP (change, %) CPI (annual average change, %)<br><!-- End of picture text --> _Source_ : Authors’ calculations from government data, IMF database and WDI.

distributed free or at subsidized prices to vulnerable households with funding from government and donors. On the other hand, this also reflects the lack of purchasing power of vulnerable households who contribute little to effective demand even during years following harvest failures.

**Table 1: GDP growth 1992-2012 and contributions to growth [expenditure side]**

||**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|**Average**<br>**2002-**<br>**2007 **|**Average**<br>**2007-**<br>**2012**|
|---|---|---|---|---|---|---|---|---|
|**_Growth rates_**|||||||||
|**Gross domestic product**<br>**(100%)**|**0.6%**|**9.6%**|**-1.0%**|**10.7%**|**2.2%**|**11.2%**|**4.4%**|**5.5%**|
|Public consumption (15%)|6.0%|6.6%|7.1%|-2.6%|-4.4%|25.7%|7.7%|6.4%|
|Private consumption (73%)|-0.5%|2.8%|11.2%|-4.7%|5.4%|5.7%|2.6%|3.3%|
|Gross fixed capital<br>formation (33%)|2.2%|48.5%|4.6%|52.8%|3.0%|-4.5%|15.6%|17.8%|
|Exports of goods and<br>services (22%)|7.9%|21.0%|4.8%|19.5%|2.2%|27.2%|4.0%|13.8%|
|Imports of goods and<br>services (44%)|3.1%|31.8%|29.2%|14.6%|5.4%|0.5%|6.9%|14.1%|
|**_Contribution to growth_**|||||||||
|Public consumption|0.9%|1.0%|1.1%|-0.4%|-0.7%|3.5%|1.2%|1.0%|
|Private consumption|-0.4%|2.1%|7.8%|-3.6%|3.6%|4.0%|2.0%|2.3%|
|Gross fixed capital<br>formation|0.5%|11.1%|1.4%|17.3%|1.3%|-2.0%|3.0%|6.5%|
|Net exports of goods and<br>services|0.4%|-5.9%|-9.7%|-2.9%|-2.2%|5.8%|-1.7%|-3.7%|
|Exports of goods and<br>services|1.3%|3.7%|0.9%|4.0%|0.5%|6.0%|0.7%|3.0%|
|Imports of goods and<br>services|-0.9%|-9.6%|-10.6%|-6.9%|-2.7%|-0.3%|-2.0%|-6.2%|

_Note_ : numbers in brackets are shares of total.

_Source:_ Government of Niger, IMF Niger data base, and World Bank WDI.

5
**Table 3: Average applied tariffs levied on WTO agricultural products by scenario, %**

|**Regions**|**Scenarios**||||
|---|---|---|---|---|
||**Baseline**|**B**|**C**|**D**|
|Brazil|4.8|4.7|4.7|4.8|
|China|7.8|5.3|6.3|7.5|
|EU-27|15.9|6.6|6.6|10.2|
|India|59.2|54.6|54.6|59.2|
|Japan|29.8|14.0|14.0|20.4|
|United States of America|4.8|2.1|2.1|3.0|
|**World Bank Classification**|||||
|All countries|14.6|9.0|9.2|11.9|
|Developing countries (no LDCs)|13.3|11.3|11.7|13.2|
|High income countries|15.5|7.5|7.6|11.1|
|LDCs|12.5|12.2|12.5|12.5|
|**WTO Classification**|||||
|Developed WTO|15.4|7.0|7.0|10.4|
|Developing WTO non LDCs|13.7|11.2|11.6|13.6|
|_Normal Developing WTO_|15.1|12.3|12.3|15.0|
|_RAM WTO_|13.4|12.8|13.0|13.4|
|_SVE WTO_|10.7|7.8|9.5|10.5|

Source: Laborde and Martin (2011a). Detailed results available at http://gatt.ifpri.info/dda0/

# **3 Non-Agricultural Market Access Proposals**

The central feature of the modalities for non-agricultural market access is the use of a nonlinear tariff-cutting formula. The formula is applied on base rates equal to existing bound tariffs or average applied MFN rates (period 1999-2001) plus 25 percent for currently unbound tariff lines. The tariff formula in this case is the highly nonlinear Swiss formula, which reduces the highest tariffs by the most. The Swiss formula requires tariffs in _ad valorem_ terms, and all tariffs are to be converted into _ad valorem_ terms and bound in those terms.

The Swiss formula is defined as: where _t1_ is the tariff after application of the formula; _t0_ is the tariff rate before application of the formula, and _ai_ is a coefficient for group _i,_ which differs between developed and developing 9
# **Supplementary information**

Table SI.1. Determinants of civil war risk: 1946-2003 Natural resource endowments _aggravating_ the problem of weak states – indicator variables

|Context variable|Natural resources<br>Context variable<br>Interaction|Fearon and Laitin<br>(2003) civil war|Prio \ Uppsala civil<br>war|
|---|---|---|---|
|Anocracy|Indicator variables ofproduction|||
||Lujalaprimarydiamondproduction(observations 6920)|-0.749 (0.460)|-0.493(0.293)|
||Anocracy|0.346(0.245)|0.405**(0.170)|
||**Interaction**|**1.596* (0.621)**|**0.753* (0.453)**|
||Lujala secondarydiamondproduction(observations 6920)|0.098(0.299)|0.374*(0.215)|
||Anocracy|0.419 (0.262)|0.485**(0.192)|
||**Interaction**|**0.392 (0.457)**|**0.041 (0.322)**|
||Lujala oilproduction dummy- offshore(observations 6920)|-0.090(0.392)|-0.118(0.244)|
||Anocracy|0.456*(0.254)|0.346*(0.185)|
||**Interaction **|**0.299 (0.539)**|**0.635* (0.356)**|
||Lujala oilproduction dummy- onshore(observations 6920)|0.006(0.271)|0.162(0.196)|
||Anocracy|0.474(0.303)|0.362(0.233)|
||**Interaction**|**0.068 (0.401)**|**0.227 (0.290)**|
|Political instability|Indicator variables ofproduction|||
||Lujalaprimarydiamondproduction(observations 6920)|-0.308(0.382)|-0.398(0.267)|
||Instability|0.410*(0.236)|0.146(0.184)|
||**Interaction**|**0.888 (0.700)**|**0.763 (0.534)**|
||Lujala secondarydiamondproduction(observations 6920)|0.141(0.270)|0.389**(0.196)|
||Instability|0.424(0.263)|0.283(0.203)|
||**Interaction **|**0.373 (0.495)**|**-0.084 (0.373)**|
||Lujala oilproduction dummy- offshore(observations 6920)|-0.222(0.374)|0.069 (0.220)|
||Instability|0.339(0.252)|0.163(0.195)|
||**Interaction**|**0.878 (0.550)**|**0.348 (0.406)**|
||Lujala oilproduction dummy- onshore(observations 6920)|-0.026(0.253)|0.318*(0.180)|
||Instability|0.377(0.329)|0.352(0.247)|
||**Interaction**|**0.247 (0.461)**|**-0.222 (0.339)**|
|Number of years with<br>riots in thepast 5years|Indicator variables of production|||
||Lujalaprimarydiamondproduction(observations 6450)|-0.343(0.497)|-0.614(0.370)|
||Riots|0.209**(0.086)|0.145**(0.065)|
||**Interaction**|**0.070 (0.185)**|**0.113 (0.120)**|
||Lujala secondarydiamondproduction(observations 6450)|0.096(0.325)|0.182(0.241)|
||Riots|0.219**(0.091)|0.127*(0.072)|
||**Interaction**|**0.005 (0.148)**|**0.096 (0.100)**|
||Lujala oilproduction dummy- offshore(observations 6450)|0.098(0.421)|0.080(0.278)|
||Riots|0.231***(0.088)|0.158**(0.067)|
||**Interaction**|**-0.061 (0.160)**|**0.080 (0.278)**|
||Lujala oilproduction dummy- onshore(observations 6450)|0.036(0.150)|0.205(0.211)|
||Riots|0.197(0.126)|0.162*(0.090)|
||**Interaction **|**-0.029 (0.299)**|**-0.008 (0.107)**|

36
# Table II-1

|Number of PHN related econom<br>reports completed sinc|ic and sector<br>e 1969,|
|---|---|
|by year||
|FY<br>Number|Cumulative|
|by year|by year|
|____________________________<br> <br>||
|1969<br>3|3|
|1970<br>5|8|
|1971<br>1|9|
|1972<br>0|9|
|1973<br>3|12|
|1974<br>2|14|
|1975<br>3|17|
|1976<br>1|18|
|1977<br>2|20|
|1978<br>2|22|
|1979<br>6|28|
|1980<br>2|30|
|1981<br>5|35|
|1982<br>4|39|
|1983<br>5|44|
|1984<br>10|54|
|1985<br>10|64|
|1986<br>7-|71|
|Total<br>71|71|

Source: Internal Documents Unit

# Table II-2

The number of PHN related sector and economic reports completed since 1969, by region and time period

|Region<br>------------------------------------------|<72<br>---------------------|72-75|76-79|80-83|>83|Total|
|---|---|---|---|---|---|---|
|S.Asia|3|0|3|3|3|12|
|E.Africa|1|2|0|6|7|16|
|W.Africa|2|0|2|2|2|8|
|E.Asia/P|2|4|2|3|4|15|
|LAC|1|0|2|2|5|10|
|EMENA|0|2|1|1|6|10|
|--------------------------------------------------<br>Total|--------------<br>9|8|10|17|27|71|

Source: Internal Documents Unit 15
|Share in total expenditure<br>8.5<br>12.9|5.8|
|---|---|
|_Fuel plus transport_||
|Per capita expenditure||
|(YER/year)<br>15,086<br>48,790|8,040|
|Share in total expenditure<br>9.7<br>14.0|7.0|
|Source: Based on HBS (2005/2006) and SAM (2009).||

# **Options for Petroleum Subsidy Reform**

This paper analyzes two major scenarios: an accelerated reform path (Reform Option 1) where all subsidies are cut within one year, and a more gradual reform option (Reform Option 2) that phases out subsidies over a period of three years (Table 5). In the accelerated scenario, subsidies would be eliminated from an estimated 391 billion YER in 2009 to zero in 2011.<sup>9</sup> This would, _ceteris paribus_ , imply a reduction of the fiscal deficit by one-half, from 6.9 percent to 3.5 percent of GDP, with remaining surplus from reform of 215 billion YER. In the gradual reform scenario, subsidies are phased out by equal amounts (130 billion YER) from 2011 to 2013. _Ceteris paribus_ , the total savings from reform are smaller due to continued fiscal costs for subsidies in 2011 and 2012, thus the fiscal deficit is reduced more slowly.

Table 5. Fuel Subsidy Reform Options

||Re<br>|form 1, ac<br>|celerate<br>|d<br>|Refor<br>|m 2, gra<br>|dual<br>|
|---|---|---|---|---|---|---|---|
||2009|2011|2012|2013|2011|2012|2013|
|**Fuel subsidy (change)**||||||||
|Subsidy (percent)||–100|0|0|–33|–50|–100|
|Subsidy (billion YER)||–391|0|0|–130|–130|–130|
|Subsidy (million $US)||–1,777|0|0|–593|–592|–593|
|Remaining subsidy (billion YER)|391|0|0|0|261|130|0|
|Remainingsubsidy (in million US$)|1,777|0|0|0|1,185|593|0|
|**Fiscal deficit (change)**||||||||
|Fiscal deficit (percent of GDP)|6.9|3.5|3.5|3.5|5.8|4.6|3.5|
|Fiscal deficit (in billion YER)|352|176|0|0|293|235|176|
|Fiscal deficit (in million US$)|1,600|800|0|0|1,333|1,067|800|
|Surplus from reform/spending||215|0|0|72|72|72|

Sources: IFPRI, based on IMF (2010), MoF (2010), and World Bank (2010).

In both the accelerated and the gradual reform scenarios, we consider three alternative uses of savings from the subsidy: first, total savings are used for deficit reduction, second, we also consider the impact of using part of the savings from reform for direct transfers to the poorest households. Finally, given Yemen’s need for infrastructure investment and the expected high returns from such investment, we consider the impact of scaling up infrastructure investment with productivity-spillovers in a third subscenario.

> 9 It is important to note that 2009 was a year with relatively low international oil prices resulting in lower fiscal costs for the subsidy. Therefore, the following analysis may underestimate the effects of subsidy reduction.

8