rafmacalaba/datause-encoder-data huggingface dataset:
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25

|Country,<br>L<br>economy, or<br>territory|ast rtishe<br>P<br>Date|d<br>censu<br>opulation<br>(1000) *|s<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, untless indicated)|Source for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|Yean,e People's<br>Dem.<br>Rep. of|May 73|1590 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on UNPVSR<br>4/87|
|Yugoslavia<br>Zaire<br>Zambia|Nar81<br>Jul84<br>Sep 80|22425 J<br> 29671 F<br>5680 F|UNPVSR<br>7/86 (official est.)<br>U.N. 1985 revision (prelim.)<br>Bank projection from census|BasedanUNPVSR<br>4/87<br>U.N. 1968 revision (prelim.)<br>1980-85, Bank sector report;<br>1985-90, Bank projection|Based on UNPVSR<br>4/87<br>U.N. 1988 revision (pretim.)<br>1975-80, Bank sector report;<br>1985-90, Bank projection|
|Ziffbabwe|Aug<br>02|<br>7SS0 F|Bank<br>projection<br>from census|Bank est. bared on official<br>contraceptive<br>prevalence<br>rate<br>of 26X|U.N. 1988 revision (pretim.)|

Notes: When a source for an estinmte is directly cited (e.g., UNPVSR 4/87), the estimate is taken as is from that publication. When an estimate Is "based on" a source, the estimate is not reported in but derived using data in that publication. When a source is given for a fertility or mortality estimate prior to 1985-90, the source for the corresponding 1985-90 estimate is a projection using the earlier estimate. Citations for the main documents referred to here (usually by abbreviations) are in the list of references.

- Nature of population estimate: F a de facto; J = de jure. No census, or no fertility ormortatity estinmtes and no projections made (the latter for smatt countries).

- a Census was for entire peninsula. b 55 people.

CBR Crude birth rate. CMR Crude death rate.

Celade Centro Latino Americano de Demograffa (1987). DHS Demographic **and** Health Survey. INED Institut National d'Etudes DOmographiques (Monnier 1987). TFR Total fertility rate. UNPVSR United Nations, Populatioa **n** d Vitat Statistics Report. USBOC United States, Bureau of the Census (1985).
frastructure, constraints to doing business, competition, among others. The survey uses the two-stage stratified random sampling strategy. I use the global standardized version of the dataset which uses a standardized sampling strategy and questionnaire. All monetary data are converted into 2009 USD prices using the GDP deflator and exchange rates for the respective countries. The final dataset, therefore, is repeated cross-section data of firms in 10 SSA countries<sup>13</sup> surveyed between 2006 and 2018. To account for these time and country variations in the dataset, year and country fixed effects are applied respectively in the estimation.

The dataset reports annual revenue and cost of inputs rather than physical measures of outputs and inputs. Productivity in this paper is measured using two indicators: value added per worker and sales per worker. Value added is computed as total sales revenue less the cost of raw materials and intermediate inputs (Hjort and Poulsen, 2019). Additional firm outcomes used paper include number of employees (full-time, temporary), total labor cost, and labor cost per worker. The age of the firm and foreign/domestic ownership status of firms are also included in the data.

Two measures of self-reported power outage intensity are explored in the firm analysis: (i) outage frequency measured as the average number of times a firm experienced power outages in a typical month; and (ii) the number of hours without electricity in a typical month, measured by the product of the frequency and average duration of outages in a typical month. Arguably, these self-reported measures of outage intensity, are not without biases. However, administrative data on outage intensity are virtually non-existent in many African countries, thus making the self-reported measures the best possible means of measuring outage intensity. Additionally, given the prevalence and regular nature of power cuts in the study area, the extent of bias associated with recall, if any, will be minimal, other things being equal.

Finally, the GPS location of firms in the WBES dataset is not publicly available due 13Benin, Cˆote d’Ivoire, Congo DR, Ghana, Malawi, Mauritius, Mozambique, Sierra Leone, Togo, Zambia 12
- 31 -

# For What Purposes?

To generate information useful in improving educational practice at the school level.

The relatively small impact on the schools of national-level summative research evaluation studies in the U.S. has been well recognized. Current practice (and theory) emphasizes the formative use of monitoring and evaluation information by decision-makers in a position to act incrementally to improve practice.

Large-scale summative evaluation has, so far, failed on two dimensions. One is utilization. Recognition that implementation conditions vary from site to site, and of the long gestation period for large studies, has tended to make findings irrelevant at the level where decisions are taken. Decision-makers at local levels have been reluctant to accept findings that are based on national samples, whether relevant or not, unless they support directions for policy and practice determined primarily by local factors. A second is theory failure: education has yet to evolve a generally accepted theory which enables decision-makers in diverse contexts to interpret and apply general findings with confidence.

This is not to argue that educational research should cease. Indeed, it is of considerable importance that it be strengthened. The move towards research at the school level in the U.S. indicates the continuing vitality of such inquiry.

However, it does suggest that priority should be given to the purpose with the most likely immediate payoff -- improvement at the school level.

Moreover, a clear and strong assistance agency policy position in support of formative evaluation might help reduce the resistance of governments to "evaluation" that is seen as judgmental of their efforts. Such a policy position would be reinforced by designs which explicitly incorporate analysis of the decision-making needs at different levels.

Donors and governments will continue to make summative judgments regarding the effectiveness of investments. These could be significantly strengthened over current practice by the information generated for formative purposes. Suimmativ **e** valuations of educational outcomes have, in any case, played almost no role in project evaluation. Any special studies that are contemplated should be cast in a formative mold, and they ought to be centered on school effectiveness.
# Figure **9** Ghana Relative Price of Cocoa Deflated by maize prices

<!-- Start of picture text --> ratio at domestic and world prices<br>20<br>15<br>10<br>5<br>0<br>1953 1958 1963 18 1973 1978 19b83<br><!-- End of picture text --> year + cocoa/maize cocoa/maize $ 3YMA$ prices
<!-- Start of picture text --> Sovereign funding Bank funding and<br>pressures solvency pressures<br>cmc——_><br>* ”<br>Se Kv7<br>4 Bank lending<br>Bank profitability<br>Economic growth ¥<br>7val ‘ * .<br>wt Na<br>Tighter regulations Realigning and<br>(Basel 2.5, Basel 3, ———————————» refocusing business<br>EBA and national models<br>regulatory measures)<br><!-- End of picture text -->
compliance costs between developed and developing countries. To examine whether foreign investments in Vietnam are more attracted to the pollution intensive sectors, we examined the pollution intensity of FDI. Table 5 presents the results on the composition of foreign investments. Column (1) shows the OLS estimates and column (2) shows fixed effect estimates using dirty and clean dummies. The dependent variable is log industry-level foreign investment.

**Table 5: Composition of Foreign Direct Investments**

|**Dependent Variable: Ln**<br>**(FDI)**|**(1)**|**(2)**|
|---|---|---|
||**OLS**|**FE**<br>**(dirty/clean)**|
|Lagged FDI|0.390***|0.387***|
||[2.92]|[2.88]|
|Log air pollution|-0.182|-0.132|
||[0.97]|[0.62]|
|Log water pollution|0.08|0.073|
||[0.93]|[0.83]|
|Log Toxic Pollution|0.656***|0.682***|
||[3.63]|[3.62]|
|Log Industrial<br>productivity per worker|-0.193|-0.177|
||[1.12]|[1.00]|
|Tax|-0.073|-0.109|
||[0.41]|[0.57]|
|Constant|-9.690***|-10.695***|
||[5.27]|[4.00]|
|Observations|65|65|
|R-squared|0.43|0.46|
|Number of Dirty dummy<br>variables||2|

Absolute value of t statistics in brackets significant at 10%; ** significant at 5%; *** significant at 1% We find that FDI is significantly higher in toxic pollution intensity sectors. The coefficient on toxic pollution intensive FDI is 0.65*** in column (1) and 0.68*** in column (2) indicating that foreign investment was more attracted to toxic pollution intensive sectors in Vietnam. The coefficients on air and water pollution intensity are not 23
_How FuM_ **_HMm Chlmue Ir4Wi Gru?_** _31_

# TAILE 9: OmciAL ENERGY DATA FOR CHNE INDUSTRY BY BRANCH, 1980-198S

A. Energy consumption in tons of **standard** coal per 100 million yuas of GVIO at 1980 pricu

|branch|<br>1980|1981|1982|1983|1984|1985|
|---|---|---|---|---|---|---|
|1|164549|156603|150471|147084|142632|132.|
|2|99124|99492|96161|93911|93926|92062|
|3|162468|157580|155472|152577|151079|156652|
|4|83429|76069|73443|69541|65960|60040|
|_5_|148546|136517|127091|118445|110716|98176|
|6|32538|31160|29269|25823|22886|18894|
|7|217821|21552S|215280|20959S|193233|180283|
|8|36959|37293|33711|38041|33546|34263|
|9|131594|127529|128454|135213|128038|100577|
|10|32537|31258|29761|30040|28645|28135|
|11|701|666|712|662|633|717|
|12|6839|6371|6721|5813|5054|5224|
|13|173163|144298|161236|168497|165408|154314|
|14|11482|12205|12169|12370|11549|11476|
|1S|77476|73383|70686|66480|61444|58634|
|total|78411|72380|70374|67045|62592|56872|

B. Annual percentage rise in GVIO per ton of standard coal consumed

|brmnch|1981|1982|1983|1984|1985|
|---|---|---|---|---|---|
|1<br>|5.07|4.08<br>|2.30<br>|3.12<br>|7.56<br>|
|2|-0.37|3.46|2.40|-0.02|2.02|
|3<br>|3.10|1.36<br>|1.90<br>|0.99<br>|-3.56<br>|
|4|9.68<br>|3.58<br>|5.61<br>|5.43|9.86<br>|
|_5_<br>|8.81<br>|7.42<br>|7.30|6.98<br>|12.77<br>|
|6|4.42|6.46|13.34|12.83|21.13|
|7|1.07<br>|0.11|2.71|8.47|7.18|
|8|-0.90|10.63|-11.38|13.40|-2.09|
|9|3.19|-0.72|-5.00|5.60|27.30|
|10|4.09|5.03|-0.93|4.87|1.81|
|11|5.26|-6.46|7.55|4.58|-11.72<br>|
|12|7.35|-5.21|15.62|15.02|-3.2S|
|13|20.00|_-10.51_|-4.31|1.87|7.19|
|14|-5.92|0.30|-1.62|7.11|0.64|
|**_15_**<br>|**_5.58_**|3.82|6.33<br>|8.20|4.79<br>|
|total|8.33|2.85|4.97|7.11|10.06|

Source: Data **taken** or calculated from Energy 1986, p. 16.
World Bank. 2005. “Zambia Poverty and Vulnerability Assessment.” Discussion Draft.

- World Bank. 2006. “Malawi Poverty and Vulnerability Assessment: Investing in Our Future.” World Bank Report No. 36546-MW.

- World Bank. 2008. World Development Indicators. World Bank Publications, Washington, DC, USA.

- World Bank. 2009. _Conditional Cash Transfers: Reducing Present and Future Poverty,_ eds: Fiszbein, Schady, and Ferreira. World Bank Publications, Washington, DC, USA.

- Yamano, Takashi and Thom Jayne. 2004. “Measuring the Impacts of Working-age Adult Mortality on Small-scale Farm Households in Kenya.” _World Development_ 32(1): 91–119.

24
# **Table 5. Impacts on sectoral outputs due to the carbon tax (% change from the baseline)**

||Scen|ario 1|||Scen<br>|ario 2<br>|||||Scen<br>|ario 3<br>||||Scen<br>|ario 4<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|r|||Cas|e a|Cas|e c|Ca|se e|Ca|se a|Ca|se c|Cas|e e|Ca|se c|Ca|se e|
|ecto||l||l||l||l||l||l||l||l||l|
|S|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|Formal|Informa|
|S1|-1.23|1.44|0.30|0.54|1.00|0.06|4.14|0.51|0.08|1.25|0.65|1.18|3.67|1.32|-0.76|0.68|-1.04|0.79|
|S2|3.29|2.91|0.62|-0.26|-0.83|-1.94|3.03|0.93|2.06|1.55|1.58|1.03|3.18|1.63|3.45|3.18|3.29|2.89|
|S3|-2.78|-0.13|0.64|-0.08|2.31|-0.08|10.1|1.15|0.37|0.06|1.77|0.14|6.88|1.11|-2.55|0.36|-3.32|0.28|
|S4|-12.9|-11.2|-5.5|-5.03|-1.33|-1.65|-12.8|-12.0|-10.1|-8.8|-9.06|-7.94|-12.7|-11.6|-13.1|-11.8|-13.5|-12.2|
|S5|1.23|1.40|0.62|0.62|0.24|0.18|1.69|1.25|1.23|1.30|1.23|1.25|2.08|1.78|1.82|2.11|1.96|2.34|
|S6|1.71|2.65|2.08|2.30|2.03|1.90|6.37|5.96|2.43|2.95|2.66|3.02|5.53|5.46|5.44|6.48|5.89|7.17|
|S7|-1.06|0.85|0.43|0.52|1.12|0.32|4.25|0.73|0.16|0.80|0.69|0.76|3.33|0.81|0.09|1.04|-0.08|1.19|
|S8|-9.55|0.00|-6.67|0.00|-5.25|0.00|-9.49|0.00|-7.19|0.00|-6.21|0.00|-9.52|0.00|-7.86|0.00|-9.78|0.00|
|S9|2.64|2.68|1.70|1.47|1.08|0.73|5.25|3.95|2.38|2.25|2.27|2.06|4.73|3.70|5.07|4.54|5.47|4.92|
|S10|0.19|1.65|0.50|0.76|0.62|0.29|0.67|0.97|0.39|1.28|0.43|1.14|0.42|1.25|1.02|1.34|1.14|1.47|
|S11|0.96|1.39|0.58|0.64|0.33|0.22|1.78|1.32|0.83|1.05|0.77|0.91|1.64|1.36|1.89|2.10|2.04|2.31|
|S12|-0.01|0.00|0.32|0.00|0.46|0.00|0.95|0.00|-0.13|0.00|-0.22|0.00|0.39|0.00|1.75|0.00|2.00|0.00|
|S13|-8.25|0.00|1.78|0.00|7.38|-0.67|-7.96|6.05|-1.48|0.00|1.02|0.38|-8.08|6.13|-8.35|0.00|-8.51|5.97|
|S15|0.40|0.16|0.25|0.12|0.15|0.09|0.60|-0.06|0.32|0.14|0.30|0.13|0.60|-0.03|-0.30|0.09|-0.36|0.13|
|S16|-0.21|-2.15|0.57|-2.36|0.91|-2.49|4.16|-3.11|0.45|-2.03|0.76|-2.01|3.69|-2.59|0.08|-0.52|-0.07|-1.02|
|S17|0.09|0.45|0.20|0.13|0.23|-0.04|0.82|0.28|0.17|0.55|0.17|0.59|0.75|0.75|0.33|0.62|0.35|0.68|
|S18|-0.04|1.45|0.04|0.41|0.06|-0.11|0.00|0.35|-0.30|1.94|-0.46|2.26|-0.42|2.18|-0.14|0.80|-0.15|0.91|
65 Table D.4: Resources, Industrialization & Sectoral Employment / Informality, Panel

|Dep.Var. =|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
|Empl. Sh. of ... (_t_)|MFG|FIRE|SUM|NTR|NTR2|NTR3|GOVT|GOVT2|WAGE|SELF|
|MFGSERV_t_|0.34**|0.06|0.40|-0.29|-0.54**|-0.51***|-0.10|-0.15**|0.14|-0.27|
||[0.154]|[0.111]|[0.243]|[0.239]|[0.233]|[0.099]|[0.083]|[0.063]|[0.337]|[0.271]|
|NRXGDP_t−_10|0.30*|-0.04|0.26|0.21|0.49***|0.38***|0.18**|0.04|-0.31|0.30|
||[0.166]|[0.071]|[0.217]|[0.153]|[0.152]|[0.104]|[0.076]|[0.167]|[0.257]|[0.228]|
|DEINDU1980_−t_|-0.42|-0.03|-0.45|-0.23|-0.47***|-0.12|0.10|-0.17|0.30|-0.51***|
||[0.368]|[0.204]|[0.563]|[0.161]|[0.153]|[0.191]|[0.126]|[0.127]|[0.199]|[0.17]|
|**NRXGDP**|-0.04|-0.10|-0.14|0.51|1.03***|0.90***|0.28*|0.19|-0.45|0.57|
|**- MFGSERV**|[0.19]|[0.13]|[0.29]|[0.35]|[0.30]|[0.15]|[0.14]|[0.18]|[0.49]|[0.42]|
|**DEINDU**<br>-|0.76***|-0.10|-0.85**|0.06|0.07|0.40*|0.20|-0.03|0.16|-0.24|
|**- MFGSERV**|[0.27]|[0.14]|[0.41]|[0.20]|[0.15]|[0.21]|[0.15]|[0.13]|[0.28]|[0.23]|
|Observations|124|124|124|124|124|124|120|93|99|99|
|Country, Year FE|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Controls|Y|Y|Y|Y|Y|Y|Y|Y|Y|Y|

_Notes:_ This table shows the correlation between the employment share of each sector/type of employment in urban areas in _t_ and measures of natural resource exports ( _t_ -10), industrialization/FIRE-ization ( _t_ ), and deindustrialization (1980- _t_ ). Robust SE clustered at the country level. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
missing elements to obtain a complete price matrix. The imputation process filled in missing values using the first-feasible methodology according to the following order: 1) median of the 20 nearest neighboring districts; 2) province median of that month; 3) national median of that month; 4) median price of 20 neighboring districts of the quarter; 5) province median of that quarter; and 6) national median of that quarter.

In order to account for differences in price and quality between domestic and imported wheat and rice, we calculate price averages for domestic and imported varieties separately. (The survey includes questions on the percentages of imported wheat and rice that a household consumes; these percentages are used to calculate total expenditure for these items.) Finally, the value of expenditure on food away from home is included in the calculation of total food consumption, but not included in the calculation of calorie intake since quantity data on such food was not collected.<sup>26</sup> We adjust consumption estimates using both NRVA price data and the official consumer prices index data to compare consumption across regions and over time. For food prices, we use an NRVA-based, Laspeyres price index estimated by quarter for each region.<sup>27</sup> For non-food items, we use a non-food price index developed by the Central Statistics Organization that accounts for temporal, but not spatial, differences in prices. Real consumption is relative to the chosen base: urban areas in the Central region in quarter one; the capital, Kabul, is located in the Central region.

To sharpen the focus on food security, we also examine: (i) per capita daily calorie intake; (ii) the food consumption score – a measure of household dietary diversity; and (iii) per capita daily protein intake.<sup>28</sup> The first measure is related to United Nations Millennium Development Goal 1.9 – to reduce by half the proportion of population below a minimum level of dietary energy consumption. The second two measures reflect different dimensions of household food security that relate to nutrient intake. Several studies from developing

> 26 Expenditure on food away from home accounts for approximately 2% of household food expenditure. 27 The food price index is based on a reference bundle of goods consumed by relatively poor households; the reference bundle was constructed to reflect regional diversity in consumption patterns. See Diewert (1993) for a discussion of the Laspeyres price index, as well as other indices.

> 28 Since consumption data were collected at the household level, it is not possible to address the issue of individual nutritional status, which depends on the intra-household allocation of resources. There is some evidence that in times of difficulty, households allocate more food to children. (Block _et al._ , 2004)  In the robustness tests, we include the number of children in the household as an additional control; the results do not change.

11
<!-- Start of picture text --> Peru United Kingdom<br>25% Other Social 25% Other Social<br>15% Spending 15% Spending<br>Cash Transfers Cash Transfers<br>5% 5%<br>-5% Health -5% Health<br>-15% -15%<br>Education Education<br>-25% -25%<br>-35% Other Taxes -35% Other Taxes<br>-45% -45%<br>Vat/Sales Tax Vat/Sales Tax<br>-55% -55%<br>-65% Income Tax -65% Income Tax<br>I II III IV V I II III IV V<br>United States<br>25% Other Social<br>15% Spending<br>Cash Transfers<br>5%<br>-5% Health<br>-15%<br>Education<br>-25%<br>-35% Other Taxes<br>-45%<br>Vat/Sales Tax<br>-55%<br>-65% Income Tax<br>I II III IV V<br>capita capita<br>Amount per capita as % of GDP per Amount per capita as % of GDP per<br>capita<br>Amount per capita as % of GDP per<br><!-- End of picture text -->

# **Health**

In Latin America, per capita health expenditures to the poorest quintile vary from 1.9 percent of GDP per capita in Mexico and Peru, to 4.7 percent in Argentina, and average 3 percent. However, the country that spends most on health for the poor – Argentina – still remains well below the level of the United Kingdom (7.6 percent), while it spends similar amounts than the United States (4.6 percent).

Notice that – aside from Nicaragua – public health spending is quite pro-rich: on average, the government spends on the poorest income quintile 70 percent of what it spends on the richest quintile. In addition, many Latin American countries have a two-tier health system (usually linked to people’s work status) which contrasts the universal health system of the United Kingdom. Accordingly, in Latin America universal health spending declines with people’s income, while contributory health spending – which still has a redistributive component, but only among participants – strongly increases (see Figure 5). This dual health system is more reminiscent of the US health system than of the universal European ones.

# **Education**

Most countries in our Latin American sample invest in the education of the poor comparable amounts than the United Kingdom or the United States: the Latin American 11
# **Pioneering Firms in Fragile and Conflict-Affected States: Why and How Development Finance Institutions Should Support Them**

Paul Collier,<sup>1</sup> Neil Gregory<sup>2</sup> and Alexandros Ragoussis<sup>3</sup>

**Acknowledgments.** The authors would like to thank Djeneba Doumbia and Leo Bureau-Blouin for excellent research assistance; Kruskaia Sierra-Escalante and Paddy Carter for their thorough review and comments, as well as participants of the 2019 DFI Forum on Private Investment in Fragile Environments for helpful discussions.

**JEL Codes:** O31, L2, O19, D81

**Keywords:** Firms; Innovation; Pioneering; Fragility; Risk; Development Finance.

> 1 Blavatnik School of Government, University of Oxford and International Growth Centre, London. 2 International Finance Corporation.

> 3 International Finance Corporation.
for the size of the source and destination manufacturing sectors in the following augmented exponential model: where _Yis_ is initial value added of sector _s_ in source country _i_ and _Yjs_ is initial value added of sector _s_ in destination country _j_ .<sup>26</sup> The presence of the two _Y_ terms controls for the general effects of financial development by holding constant the scale of the source and destination sectors of FDI. This means that in comparison to coefficients _β_ 1 and _β_ 2 in equation 1, coefficients _γ_ 1 and _γ_ 2 will only capture the FDI-specific impacts of financial development on relative bilateral FDI in financially vulnerable sectors. This also implies that we can gain some insights into the presence and relative strength of the general effects of financial development on FDI by examining the signs, sizes, and statistical significance of the differences ( _β_ 1 _− γ_ 1) and ( _β_ 2 _− γ_ 2).

Table 1 summarizes our interpretation of the key coefficients in equations 1 and 2.

Table 1: Interpretation of coefficients in equations (1) and (2)

|Evidence for|SFD|Expected|DFD|Expected|
|---|---|---|---|---|
|||sign||sign|
|Overall effect|_β_1|+|_β_2|?|
|FDI-specifc effects|_γ_1|+|_γ_2|?|
|General effects|_β_1 _−γ_1|+|_β_2 _−γ_2|?|

# _Effects of financial development at the different margins of FDI_

We are also interested in the effects of financial development at the different margins of FDI. In the spirit of Bernard et al. (2011), we define the extensive margin as the number of bilateral FDI projects ( _Nijs_ ) and the intensive margin as the average size of bilateral FDI projects conditional on positive FDI ( _FDINijsijs_ ). The volume of bilateral FDI ( _FDIijs_ ) between two countries _i_ and _j_ in a given sector _s_ is the product of these two margins: _FDIijs_ = _Nijs_ <u>(</u> _FDINijsijs_ <u>).</u>

> 26As explained below, data availability forces us to use, in all years, the 2002 value added data.

11
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