rafmacalaba/datause-encoder-data huggingface dataset:
||(0.014)|(0.028)|(0.013)|(0.029)|(0.013)|(0.029)|
|---|---|---|---|---|---|---|
|Courts: Obstacle Severity|0.011|0.052|-0.041|-0.214|-0.037|-0.196|
||(0.213)|(0.401)|(0.199)|(0.401)|(0.199)|(0.400)|
|Crime Losses (% of sales)|-0.013|0.080|-0.022|0.064|-0.023|0.070|
||(0.046)|(0.106)|(0.045)|(0.106)|(0.046)|(0.105)|
|Taxes: Obstacle Severity|-0.160|-0.168|-0.174|-0.190|-0.176|-0.192|
||(0.166)|(0.166)|(0.163)|(0.166)|(0.163)|(0.167)|
|Labor Laws: Obstacle Severity|0.004|0.003|0.003|0.005|0.000|0.002|
||(0.174)|(0.174)|(0.170)|(0.169)|(0.171)|(0.169)|
|Square of Financially|3.844|2.575|2.169|-0.327|1.858|-0.497|
|Constrained (CI avg.)|(4.863)|(4.763)|(4.659)|(4.657)|(4.652)|(4.644)|
|Financially Constrained (CI||-0.407||-0.128||0.031|
|avg.) * Initial Employment||(1.421)||(1.376)||(1.379)|
|Financially Constrained (CI||-0.703||-0.525||-0.574|
|avg.) * Age of Firm||(1.311)||(1.301)||(1.303)|
|Financially Constrained (CI||-7.446||-7.409||-7.773|
|avg.) * Exports||(6.168)||(5.455)||(5.485)|
|Financially Constrained (CI||0.072**||0.078**||0.076**|
|avg.) * Foreign Ownership||(0.035)||(0.033)||(0.033)|
|Financially Constrained (CI||-1.424||-1.185||-1.465|
|avg.) * Firm Bought Assets||(1.765)||(1.752)||(1.745)|
|Financially Constrained (CI||0.242||0.423||0.386|
|avg.) * Power Outage||(0.508)||(0.472)||(0.478)|
|Financially Constrained (CI||0.014||0.024||0.023|
|avg.) * Time Tax||(0.057)||(0.057)||(0.057)|
|Financially Constrained (CI||-0.264||-0.714||-0.749|
|avg.) * Courts: Obstacle Severity||(0.752)||(0.767)||(0.770)|
|Financially Constrained (CI||0.147||-0.709||-0.564|
|avg.) * Informal Competition||(1.844)||(1.834)||(1.822)|
|Financially Constrained (CI||-0.208||-0.253||-0.240|
|avg.) * Crime Losses||(0.220)||(0.193)||(0.187)|
|Financially Constrained (CI||2.860||0.776||0.995|
|avg.) * GDP per capita (logs)||(1.781)||(1.623)||(1.596)|
|Financially Constrained (CI||1.795***||1.503***||1.461***|
|avg.)* Growth rate of GDPper ca|pita|(0.545)||(0.477)||(0.475)|
|Corruption in the rows below is Ov<br>Petty Corruption Depth (columns 5|erall Corrup<br>-6)|tion (columns|1-2), Petty C|orruption Incid|ence (column|s 3-4) and|
|Square of Corruption (CI avg.)|-0.021|-0.016|0.002***|0.003***|0.002***|0.003***|
||(0.017)|(0.014)|(0.001)|(0.001)|(0.001)|(0.001)|
|Corruption (CI avg.) * Initial Emp|loyment|-0.189||0.022||0.020|
|||(0.157)||(0.019)||(0.023)|
|Corruption (CI avg.) * Age of||0.118||-0.006||-0.001|
|Firm||(0.143)||(0.015)||(0.017)|

39
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|---|---|---|---|---|---|---|
|Education (years of)|0.009***<br>(0.00)|0.008***<br>(0.00)|0.004<br>(0.00)|-0.002<br>(0.00)|-0.003<br>(0.00)|-0.007<br>(0.01)|
|Single (vs. Married)|-0.172***|-0.183***|-0.157***|-0.142***|-0.183***|-0.174***|
||(0.02)|(0.03)|(0.04)|(0.03)|(0.04)|(0.05)|
|Women*Single|0.238***|0.205***|0.128**|0.188***|0.173***|0.131*|
||(0.03)|(0.04)|(0.06)|(0.04)|(0.05)|(0.08)|
|Has children under 10y|0.071***<br>(0.02)|0.102***<br>(0.03)|0.071*<br>(0.04)|0.077***<br>(0.03)|0.086**<br>(0.04)|0.110**<br>(0.05)|
|Women*children under 10y|-0.014|-0.034|-0.200**|-0.04|-0.019|-0.183*|
||(0.03)|(0.04)|(0.08)|(0.04)|(0.05)|(0.11)|
|People over 60y in household|0.054**|0.064**|0.058*|0.046|0.055|0.072|
||(0.02)|(0.03)|(0.03)|(0.04)|(0.05)|(0.05)|
|Women*People over 60y|0.007|0.019|0.042|0.003|0.022|0.002|
||(0.03)|(0.04)|(0.05)|(0.05)|(0.06)|(0.08)|
|Home ownership|-0.027**|0.008|0.01|-0.040**|0.014|0.054|
||(0.01)|(0.02)|(0.03)|(0.02)|(0.02)|(0.04)|
|Household size|-0.019***|-0.028***|-0.024***|-0.019***|-0.019***|-0.022**|
||(0.00)|(0.01)|(0.01)|(0.00)|(0.01)|(0.01)|
|_Constant_|0.343|0.254|0.118|0.362|0.224|0.054|
||(0.29)|(0.32)|(0.41)|(0.33)|(0.36)|(0.51)|
|N|5912|3404|1810|3242|1886|929|
|R2|0.293|0.309|0.272|0.359|0.378|0.354|
|Adj. R2|0.28|0.287|0.228|0.338|0.343|0.284|
|Fixed-Effects|District|District|District|District|District|District|

Notes: *, **, *** denote significance at the 10 percent, 5 percent and 1 percent levels respectively. Robust standard errors in parentheses. N: number of observations. District-level fixed effects, their coefficients are not presented in the table. Baseline category for city=Cairo.

50
Table 7: COVID-19 and Electricity Consumption in Ghana: Dynamic Effects

|||||logof Co|nsumption||||
|---|---|---|---|---|---|---|---|---|
||A|ll|Resid|ential|Non-Re|sidential|HeavyI|ndustries|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|DID (Month> April=0)|0.430***<br>(0.001)|0.431***<br>(0.001)|0.457***<br>(0.001)|0.459***<br>(0.001)|0.195***<br>(0.003)|0.195***<br>(0.003)|-0.361***<br>(0.021)|-0.357***<br>(0.022)|
|DID (Month> April=1)|0.173***<br>(0.001)|0.180***<br>(0.001)|0.196***<br>(0.001)|0.203***<br>(0.001)|-0.019***<br>(0.003)|-0.016***<br>(0.003)|-0.159***<br>(0.022)|-0.159***<br>(0.022)|
|DID (Month> April=2)|0.162***<br>(0.001)|0.142***<br>(0.001)|0.180***<br>(0.001)|0.158***<br>(0.001)|0.009***<br>(0.003)|-0.002<br>(0.003)|-0.087***<br>(0.024)|-0.052*<br>(0.028)|
|DID (Month> April=3)|0.141***<br>(0.001)|0.152***<br>(0.001)|0.155***<br>(0.001)|0.166***<br>(0.001)|0.030***<br>(0.003)|0.038***<br>(0.004)|-0.097***<br>(0.022)|-0.123***<br>(0.025)|
|DID (Month> April=4)|0.138***<br>(0.001)|0.098***<br>(0.001)|0.147***<br>(0.001)|0.104***<br>(0.001)|0.062***<br>(0.004)|0.047***<br>(0.004)|-0.107***<br>(0.024)|-0.089***<br>(0.026)|
|DID (Month> April=5)|0.196***<br>(0.001)|0.166***<br>(0.001)|0.209***<br>(0.001)|0.177***<br>(0.001)|0.086***<br>(0.004)|0.074***<br>(0.004)|-0.071***<br>(0.024)|-0.054**<br>(0.025)|
|DID (Month> April=6)|0.130***<br>(0.001)|0.098***<br>(0.001)|0.142***<br>(0.001)|0.107***<br>(0.001)|0.034***<br>(0.004)|0.017***<br>(0.004)|-0.090***<br>(0.023)|-0.065***<br>(0.024)|
|DID (Month> April=7)|0.183***<br>(0.001)|0.176***<br>(0.001)|0.198***<br>(0.001)|0.190***<br>(0.001)|0.062***<br>(0.004)|0.062***<br>(0.004)|-0.064***<br>(0.022)|-0.071***<br>(0.024)|
|DID (Month> April=8)|0.278***|0.278***|0.439***|0.440***|0.111***|0.114***|-0.044*|-0.057**|
||(0.010)|(0.010)|(0.022)|(0.022)|(0.011)|(0.011)|(0.023)|(0.025)|
|Climate Ctrls|No|Yes|No|Yes|No|Yes|No|Yes|
|Customer FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District X Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.711|0.711|0.687|0.687|0.775|0.775|0.859|0.859|
|Mean dep. Var|383.115|383.115|123.781|123.781|419.210|419.210|152470.284|152470.284|
|Obs|41958788|41958777|37299880|37299869|4602780|4602780|52317|52317|

Notes: Dependent variable is the log of monthly electricity consumption. DID (Month> April= _ω_ ) represents the DID estimates for the _ω_ months since the start of COVID lockdown (April, 2020). The DID estimates are derived from the expression _DID_<sup>_ω_</sup> = _β_<sup>ˆ</sup> 2020<sup>_ω−_</sup><sup><u>1</u></sup> 2 �2019 _k_ =2018<sup>_β_ˆ</sup> _k_<sup>_ω_.Climate Ctrls include the monthly average</sup> temperature and total precipitation . Standard errors clustered at district level in parenthesis. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 33
# 3.2.1 Macro stabilization variables

We include the log of _government consumption_ as a share of GDP, _lkg_ , calculated as the log of csh_g from the PWT 10.0. This variable is supposed to capture growthreducing effects through distortionary taxation (Afonso and Furceri 2010) or public debt issuance. The negative association with growth is motivated by the fact that we include the positive effects that government consumption may have on growth separately, for example, through spending on infrastructure. As our model describes long-run growth, it is also important not to conflate the short-term positive stimulus effect that increased government consumption can have during economic downturns.<sup>7</sup> _Inflation_ is measured as the log change of _v_c/q_c_ (household consumption in current national prices/household consumption in constant national 2017 prices) from the national accounts module of the PWT 10.0, which has greater availability than inflation data from the World Bank’s World Development Indicators (WDI).<sup>8</sup> The _real exchange rate_ , _lrer_ , is calculated as the log of the GDP price level (in PPP) over the nominal exchange rate: _pl_gdpo/xr_ , both taken from the PWT 10.0. Since _xr_ is measured as national currency/US$, an increase in _lrer_ reflects a real appreciation, which is expected to have a negative effect on output and growth through various channels (e.g., Rapetti 2019; Levy-Yeyati, Sturzenegger, and Gluzmann 2013).<sup>9</sup>

# 3.2.2 Financial variables

To measure countries’ _financial development_ , we use the log of domestic credit to the private sector as a percentage of GDP, _lcredit_ , taken from the WDI. The literature on financial development and its relationship to growth has grown extensively over the past decades (e.g., Buzzi and Russo 2019) and various studies and data sets on more detailed aspects have appeared (e.g., Svirydzenka 2016). Credit over GDP, however, has the clear advantage of wide availability across countries and over time and has been used by several seminal studies in the literature on finance and growth. Brunnermeier et al. (2021) confirm that credit expansion is rather a sign of financial deepening than the prelude for financial busts.

To gauge the effects of _financial crises_ , we rely on the database from Laeven and Valencia (2020), which has coverage until 2017. Our data set contains dummy variables for individual years for banking, currency, or sovereign debt crises and a

> 7 For similar reasons, we also do not include fiscal deficit variables, which are highly cyclical and hence tend to smooth out over the five-year averages.

> 8 We add 1 to this variable to avoid negative numbers, which cannot be translated into logs.

> 9 We also calculated deviations of the real exchange rate from an HP-filtered trend, as well as volatility measures, but the results did not provide additional insights.

-8-
|risk2_religion||0.104||0.140|||
|---|---|---|---|---|---|---|
|||(0.46)||(0.47)|||
|risk2_trust||-0.147<br>(1.66)*||0.103<br>(0.78)|||
|Worker|||-227.556|-160.414|-230.357|-0.049|
||||(1.65)*|(0.91)|(2.07)**|(0.35)|
|Smoking|||178.560|254.001|80.636|-0.046|
||||(1.20)|(1.35)|(0.69)|(0.32)|
|Fincome|||0.008|0.012|0.005|0.304|
||||(1.06)|(1.25)|(0.85)|(2.91)***|
|Education|||312.897|761.388|149.948|0.652|
||||(1.36)|(2.94)***|(0.70)|(2.37)**|
|Rcancer|||679.293|781.307|498.976|0.565|
||||(1.95)*|(1.68)*|(1.87)*|(2.30)**|
|Uncertainty|||-198.846|-268.940|-156.097|-0.444|
||||(1.27)|(1.20)|(1.27)|(1.98)**|
|Health check|||-1.315|-70.683|-70.971|0.234|
||||(0.01)|(0.40)|(0.65)|(1.68)*|
|Religion|||-263.355|-334.927|62.958|0.071|
||||(1.14)|(0.92)|(0.28)|(0.28)|
|Trust|||430.417|454.759|401.105|0.414|
||||(2.77)***|(2.24)**|(3.37)***|(2.93)***|
|Constant|||-518.225|-1,144.424|-252.289|0.504|
||||(1.28)|(2.32)**|(0.85)|(0.51)|
|Observations|833|833|833|833|833|833|
|Log-likelihood|-7363.41|-7340.67|-7300.07|-7296.83|-7304.22|-1745.13|
|R-squared|-0.12|-0.07|0.03|0.02|0.03|0.05|
|**Typical person’s WT**|**P-risk coefficient**||||||
|Constant|0|0|747.02|678.45|706.77|4.853|
|Level term|22.67|51.31|1.677|10.192|5.990|0.023|
|Squared term|0|-0.506|0|-0.106|-0.065|-0.0002|
|Marginal WTP<br>(Yuan)|226,700|353,138|16,770|68,410|39,351|29,208|
|Average WTP<br>(Yuan)|226,700|433,119|489,371|514,386|496,763|110,801|
|Robust t statistics in p<br>1%.|arentheses.significant|at 15%, * significa|nt at 10%, ** signi|ficant at 5%; *** s|ignificant at||

**Table 9-2: WTP Function of Cancer Mortality Risk Reduction**

|**Model**<br>**(Dependent Variable)**|**Model 1**<br>**(WTP)**|**Model 2**<br>**(WTP)**|**Model 3**<br>**(WTP)**|**Model 4**<br>**(WTP)**|**Model 5**<br>**(WTP)**|**Model 6**<br>**Log(WTP)**|
|---|---|---|---|---|---|---|
|Risk Reduction|0.777|-0.886|7.579|32.562|2.481|0.020|
||(0.07)|(0.03)|(0.59)|(0.92)|(0.33)|(1.84)*|
|Risk Reduction<sup>2</sup>||-0.247||-0.321|-0.088|-0.0003|
|||(1.86)*||(0.59)|(1.08)|(2.02)**|
|risk_worker|4.511|-3.758|3.014|-0.776|||
||(0.62)|(0.19)|(0.45)|(0.03)|||
|risk_smoking|-4.592|-13.559|-9.048|-21.485|||
||(0.69)|(0.90)|(1.43)|(1.15)|||
|risk_fincome|-0.000|-0.002|-0.000|-0.001|||
||(1.59)|(2.45)**|(0.99)|(0.84)|||
|risk_education|16.603|46.923|-5.069|-23.982|||
||(2.06)**|(1.77)*|(0.62)|(0.76)|||
|risk_Rcancer|11.219|49.315|-10.449|-1.980|||
||(0.95)|(1.45)|(0.92)|(0.05)|||
|risk_uncertainty|-6.441|-21.249|9.326|42.298|||
||(0.56)|(0.63)|(0.95)|(0.93)|||

31
-118-

We are **now** able to match 4 and 6/8 cylinder cars separately. Table A.5 and A.6 compare the results of the single match using census data only with the differentiated matches for 4 and 6/8 cylinder cars for the Brazil/US and Mexico/US comparison respectively. It appears that the PPPs of the differentiated matches are higher than the PPPs of the single match, which implies that the original comparison using census data only showed an overvalued output **in** Brazil and Mexico.

**TABLE A.5**

Gross Value of Output and PPPs for Passenger Cars, Brazil (1975)/USA (1977)

||Bra|zil<br>(1975)|USA|**(197.71**||
|---|---|---|---|---|---|
||million<br>1975<br>cruzeiros|<br>million PPP<br>1977<br>Cr./USS<br>US**S**|million<br> 1975<br>cruzeiros|million<br>1977<br>US**S**|<br>PPP<br>Cr./USS|
|Match using<br>Census<br>only (without model<br>differentiation)<br>Passenger<br>cars|Data<br> <br>9,286.5|2,338.2<br>3.97|189,834.1|47,796.3|3.97|
|MaWch using<br>Augmente<br>Information|d|||||
|4 cylinder<br>cars<br>|5,683.8<br>|1,265.4<br>4.49<br> <br>|16,099.5<br>|3,584.2<br>|4.49<br>|
|6& 8 cylinder<br>cars|3,602.7<br>--------------------------|718.5<br>5.01<br>----------------------------|221,676.9|44,212.1|5.01|
|Passengercars|9,286.5|1,983.9<br>4.68|223,776.4|47,796.3|4.97|

Source: see text.

## **TABLE** A.6

# Gross Value of Output and PPPs for Passenger Cars, Mexico (1975)/USA (1977)

|Me|xico(197|5)|USA|(1977)||
|---|---|---|---|---|---|
|million<br>1975<br>pesos|million<br>1977<br>US**$**|PPP<br>Ps./US$|million<br> 1975<br>pesos|million<br>1977<br>US S|<br>PPP<br>Ps./US$|
|Match using Census Data<br>only (without model<br>differentiation)<br>Passenger<br>cars<br>12,223.3|1,338.3|9.13|436,562.1|47,796.3|9.13|
|Match using Augmented||||||
|Information||||||
|4cylinder<br>cars<br>6,038.0<br> <br>|611.2<br>|9.88|35,407.7<br>|3,584.2|9.88|
|6 & 8cylinder<br>cars6,185.3<br>**------------------------**|561.0<br>**-------------------------**|11.02<br>**----**|487,436.0|44,212.1|11.02|
|Passenger<br>cars<br>12,223.3|**..**<br>1,172.2|10.43|522,843.7|47,796.3|10.94|

Source: see text.
9 TABLE 1. Sample description.

|Country<sup>8</sup>|**Bond**|**Terms**<br>**Announced**|**Issue**<br>**Date**|**Maturity**<br>**Date**|**Ratings**<sup>**9**</sup>|**Liquidityl°**|
|---|---|---|---|---|---|---|
|**1) Argentina**(18%)"1|Pars<br>Discounts|Apr 1992|4/07/93<br>4/07/93|3/31/23<br>3/31/23|B1, BB-|LI<br>L2+|
|2)Brazil(35%)|ParS(A,B)<br>Discounts (A, B)|Jul 1992<br>|4/15/94<br>4/15/94|4/15/24<br>4/15/24|B1,B+|LI-<br>L2|
|3) Mexico (19%)|Pars(A,B)<br>Discounts(A, B, C|Jan 1990<br>, D)|3/28/90<br>3/28/90|12/31/19<br>12/31/19|Ba2, BB|LI<br>L2+|
|4) Venezuela (12%)|Pars(A,B)<br>Discounts|Jun 1990|12/18/90<br>12/18/90|3/31/20<br>3/31/20|Ba2, --|Ll-<br>L3|
|5) Ecuador (1.8%)|Pars<br>Discounts|May 1994|2/28/95<br>2/28/95|2/28/25<br>2/28/25|n.r., n.r.|L2<br>L2|
|6) Bulgaria (4%)|Discounts(A)|Mar 1994|7/28/94|7/28/24|n.r., n.r.|L2|
|7)**Poland**(6%)|Pars<br>Discounts|Mar 1994|10/27/94<br>10/27/94|10/27/24<br>10/27/24|Baa3, BBB-|L2<br>L2+|
|**8)**Nigeria (0.9%)|Pars|Oct 1991|1/21/92|11/15/20|n.r., n.r.|L2|
|9) Philippines (3%)|Pars (B)|Feb 1992|12/01/92|12/01/17|Ba2, BB|L2-|

9 Up to now, the other Brady agreements are: Costa Rica (Terms announced: May 90, Issue date: 5/21/90), Uruguay (Terms announced: Nov 90, Issue date: 2/18/91), Jordan (Terms announced: Jan 93, Issue date: 12/23/93) and Dominican Republic (Terms announced: Aug 93, Issue date: 8/30/94).

9 Moody's, Standard and Poor's respectively. The rating are as June 21, 1996. The following table summarizes the grade rank: <!-- Start of picture text --> Investmentgrade Speculative grade<br>Moody S&P Moody S&P<br>Aaa AAA Bal BB+<br>Aal AA+ Ba2 BB<br>Aa2 AA Ba3 BB-<br>Aa3 AA- BI B+<br>Al A+ B2 B<br>A2 A B3 B-<br>A3 A-<br>Baal BBB+<br>Baa2 BBB<br>Baa3 BBB-<br><!-- End of picture text --> n.r. = not rated. Note: Baal/BBB+ or below is the rating requirement designed by JPMorgan to define an "emerging market" in the context of external debt market. _Source:_ JPMorgan, _Emerging Market Analytics,_ June 21 1996.

**10** The liquidity rating for Brady bonds provided by JPMorgan is:

> L I Benchmark Average bidloffer<3/8 and bond _quoted_ by the _main brokers._

L2 Active Average bid/offer<3/4 and bond _quoted_ by at least half of the _main brokers._ L3 Traded Average bid/offer<2 and bond _quoted_ by at least one _main broker._ L4 Mostly Illiquid Average bid/offer<3 and bond _quoted_ by at least one _main broker._ L5 Illiquid Bond rarely or never _quoted_ by _main brokers._ Note: A bond is considered _quoted_ in categories LI, L2 and L3 if it is priced 75% of the time; it is considered quoted in category L4 if it is priced 25% of the time. The _main brokers_ designed by JPMorgan are: Eurobrokers, Tullets, Tradition, Cantor, Chapdelaine and RMJ. _Source:_ JPMorgan, _Introducing the Emerging Markets Bond Index Plus._ July 12, 1995.

% of total amount outstanding in the Brady Market in parenthesis. _Source:_ Merril Lynch, _7he 1995 Guide to Brady Bonds,_ 12/06/94.
policies in Argentina is confirmed by the results simulating the _inputs effect_ in Figure 5. Had inputs been the only element changing in the production function between 2006 and 2012, then average test scores would have increased only marginally during that period.

_Figure 5: Growth Incidence Curve Capturing the Microsimulation Results, 2006-2012_ <!-- Start of picture text --> Observed change<br>2006-2012<br>Efficiency Effect<br>Inputs effect<br>Residual effect<br>0 20 40 60 80 100<br>Percentiles of PISA Distribution<br>Source: authors' own simulations using estimation results<br>60<br>40<br>20<br>0<br>Change in Reading Test Scores<br>-20<br><!-- End of picture text --> As was the case for the period characterized by a decline in learning outcomes, the recovery period is explained, to a large extent, by the efficiency effect or the changes in parameters in the production function. Nevertheless, as shown in Figure 6, the set of parameters that change favorably during the recovery period are those related to students’ characteristics and socioeconomic background. For instance, had the only element changing in the production function been the wealth parameter, it would have been enough to explain an increase of 18 points in learning outcomes between 2006 and 2012. The changes in the parameters for mothers’ education are also contributing to the recovery during that same period.

In terms of the change in parameters of school characteristics and institutional setting during the period 2006 to 2012, learning outcome effect of public schools remains lower than in private schools despite the overall gains in reading test scores during those years. The system’s effectiveness in transforming educational inputs into results improved, though only marginally, in areas such as the proportion of teachers with completed tertiary education and, to a lesser extent, the decision making at the school level (formulating school budgets).

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_3_ bias exists against semi-fabricated and processed commodities. Next, using detailed information on tr&de barriers compiled by UNCTAD and the World Bank an attempt is made to determine if Asian countries' tariffs and nontariff barriers (NTBs) escalate in the same manner, and to the same degree, as developed countries' trade restnictions. The analysis also attempts to identify specific commodity processing chains where liberalization could make an important contribution to Asian intra-trade. The study concludes with an overall assessment of the priorities that should be assigned to trade barrier escalation issues in Asia and also provides several specific suggestions for a regional multilaterml liberalization initiative.

# II. The Data and Methodology

In this study a World Bank classification scheme was used to identify different levels of fabrication for 48 commodities exported in primary and processed form by Asian developed and developing countries (see Appendix I for full details on the components of each stage identified in terms of SITC products). At a minmnum, the scheme distinguishes between a primary and processed stage product (i.e., the primary stage of the coffee chain consists of green and roasted coffee beans (SITC 071.1), while the processed stage consists of coffee extracts (SITC 071.2)). In other instances, a semi-fabricated stage or stages are identified (i.e., the cocoa chain consists of cocoa beans (primary stage), cocoa powder and butter (intermediate stages), with chocolate being the final stage item). Table 4 contains a ist of commodities covered by this classification scheme, and identifies major Asian country producers and exporters of these products.<sup>3</sup> Statistics on Asian countries' 1990 imports of each processing chain's stages were drawn from United Nations Series D trade tapes, as were similar data for several earlier years. In a few cases, trade data for individu2i commodity processing chains were aggregated into broad product groups (e.g., foodstuffs; ores, minerals and nonferrous metals; or energy products) to focus on broad trends in primary and processed products' import 3"Leakages me y occur as one goes up the processing chains since some products may be employed as inputs for production processes which are not a formal part of the chain. For example, cotton fiber may be used for the manufacture of rubber tires and not appear in the textile stages of the cotton chain. Such leakages may cause the magnitude of the shift to processed commodity imports to be understated and may also affect the accurv'y of estimates of the es-alation in tariffs and nontariff barriers. If trade barriers are lower for products where leauges occur than for goods included in the formal definition of the processing chain, the extent and magnitude of escalation will be overstated.