rafmacalaba/datause-encoder-data huggingface dataset:
_-4_ terms of debt stock, at the end of 1996, Turkey's total extemal debt reached 43% of GNP, as compared to 24% for Brazil, 49% for Mexico, 50% for Thailand, and 60% for Indonesia.

**Table 1**

**Turkey: Net Capital Inflows (US$ million)**

||1990|1991|1992|1993|1994|1995|1996|1997|
|---|---|---|---|---|---|---|---|---|
|Financial Account|4,037|(2,397)|3,648|8,963|(4,194)|4,643|8,763|8,616|
|Direct Investment|700|783|779|622|559|772|612|554|
|Portfolio Investment|547|623|2,411|3,917|1,158|237|570|1,634|
|Equity|(45)|56|300|431|994|120|198|(42)|
|Debt|592|567|2,111|3,486|164|117|372|1,676|
|Other Investment|2,790|(3,803)|458|4,424|(5,911)|3,634|7,581|6,428|
|of which: short-term|3,000|(3,020)|1,396|3,054|(5,127)|2,305|5,945|1,761|
|Monetary Authority|(130)|(1,060)|336|1,024|1,397|1,632|1,339|1,097|
|General Govrnment|503|330|(1,310)|(1,953)|(2,516)|(1,991)|(2,232)|(1,406)|
|Banks|1,510|(2,199)|(374)|1,265|(4,612)|1,692|4,494|1,256|
|Other Sectors|835|(880)|1,806|4,088|(180)|2,301|3,980|5,481|
|Net Erros & Omissions|(469)|948|(1,190)|(2,222)|1,766|2,355|(1,782)|(2,523)|

Sources: International Financial Statistics and Central Bank of Turkey.

||**_Turkey:**|**Net Ca**|**T**able<br>**pital Inflo**|2<br>**ws (% o**|**f GNP)**||||
|---|---|---|---|---|---|---|---|---|
||1990|1991|1992|1993|1994|1995|1996|1997|
|FinancialAccount|2.7%|-1.6%|2.3%|4.9%|-3.2%|2.7%|4.7%|4.4%|
|Direct Investment|0.5%|0.5%|0.5%|0.3%|0.4%|0.5%|0.3%|0.3%|
|Portfolio Investment|0.4%|0.4%|1.5%|2.2%|0.9%|0.1%|0.3%|0.8%|
|Euity|0.0%|0.0%|0.2%|0.2%|0.8%|0.1%|0.1%|0.0%|
|Debt|0.4%|0.4%|1.3%|1.9%|0.1%|0.1%|0.2%|0.9%|
|Other Investment|1.8%|-2.5%|0.3%|2.4%|-4.5%|2.1%|4.1%|3.3%|
|of which: short-term|2.0%|-2.0%|0.9%|1.7%|-3.9%|1.3%|3.2%|0.9%|
|Monetary Authority|-0.1%|-0.7%|0.2%|0.6%|1.1%|1.0%|0.7%|0.6%|
|General Govrnment|0.3%|0.2%|-0.8%|-1.1%|-1.9%|-1.2%|-1.2%|-0.7%|
|Banks|1.0%|-1.4%|-0.2%|0.7%|-3.5%|1.0%|2.4%|0.6%|
|Other Sectors|0.5%|-0.6%|1.1%|2.2%|-0.1%|1.3%|2.2%|2.8%|
|Net Erros & Omissions|-0.3%|0.6%|-0.7%|-1.2%|1.3%|1.4%|-1.0%|-1.3%|

Sources: International Financial Statistics and Central Bank of Turkey.

**Table** 3

||**Net Ca**|**pital Inflows**|**of Selecte**|**d Countrie**|**s (% of GN**|**P)**|||
|---|---|---|---|---|---|---|---|---|
||1990|1991|1992|1993|1994|1995|1996|1997|
|**Mexico**|3.3%|8.2%|7.6%|8.6%|3.9%|-4.4%|1.3%|-|
|**Brazil**|-1.3%|-1.3%|1.6%|1.8%|1.5%|4.3%|-|-|
|**Thailand**|10.8%|12.2%|8.7%|8.6%|8.7%|13.3%|10.8%|-10.0%|
|**Indonesia**|4.1%|4.6%|4.6%|3.7%|2.3%|5.3%|5.0%|0.7%|

Source: International Financial Statistics.
# **Table IVa: Agricultural Outcomes**

||(1)<br>Chan|(2)<br>ge inGarde|(3)<br>nSize|(4)<br>C|(5)<br>hange in T|(6)<br>itle|(7)|(8)<br>Chang|(9)<br>e in Prod|(10)<br>uctive Asse|(11)<br>ts(ln)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Treated|0.96***|0.91***|1.04***|0.67***|0.69***|0.64***|-0.85|-0.32|-1.04|-0.60|-1.50|-0.62|
||(0.18)|(0.21)|(0.23)|(0.05)|(0.05)|(0.06)|(0.64)|(0.59)|(0.78)|(0.74)|(1.20)|(1.09)|
|Treated*Very far||0.20|||-0.21**||||-0.79|-0.95|||
|||(0.30)|||(0.10)||||(1.09)|(1.13)|||
|Treated*Female Head||0.06|||0.07||||1.54|2.19*|||
|||(0.37)|||(0.08)||||(1.35)|(1.29)|||
|Treated*Maize kg (BL)|||-0.00|||0.00|||||0.00|0.00|
||||(0.00)|||(0.00)|||||(0.00)|(0.00)|
|Maize kg (BL)|-|-|||||||||||
||0.00***|0.00***|-0.00*|0.00|0.00|0.00|-0.00*|-0.00**|-0.00*|-0.00**|-0.00|-0.00|
|Fertilizer Subsidy|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)<br>3.52***|(0.00)|(0.00)<br>3.63***|(0.00)|(0.00)<br>3.49***|
|||||||||(0.69)||(0.71)||(0.69)|
|Female HH Head|-0.06|-0.08|-0.06|0.04|0.01|0.04|0.30|0.14|-0.40|-0.86|0.33|0.16|
||(0.21)|(0.26)|(0.20)|(0.04)|(0.01)|(0.04)|(0.63)|(0.60)|(0.69)|(0.62)|(0.62)|(0.58)|
|Age of HH Head|0.01|0.01|0.01|0.00|0.00|0.00|0.07|0.12|0.06|0.11|0.07|0.12|
||(0.03)|(0.03)|(0.03)|(0.01)|(0.01)|(0.01)|(0.13)|(0.13)|(0.13)|(0.12)|(0.13)|(0.13)|
|Age of HH Head squared|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|-0.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Primary School|-0.02|-0.03|-0.03|-0.01|-0.00|-0.01|0.76|0.48|0.76|0.47|0.83|0.51|
||(0.20)|(0.20)|(0.19)|(0.04)|(0.04)|(0.04)|(0.89)|(0.82)|(0.88)|(0.80)|(0.93)|(0.87)|
|HH Size|-0.08|-0.08|-0.08|0.00|0.00|0.00|0.07|0.08|0.09|0.11|0.09|0.09|
||(0.05)|(0.05)|(0.05)|(0.01)|(0.01)|(0.01)|(0.20)|(0.20)|(0.20)|(0.20)|(0.20)|(0.20)|
|Constant|0.66|0.70|0.63|-0.06|-0.05|-0.05|0.98|-2.92|1.24|-2.64|1.19|-2.79|
||(0.61)|(0.61)|(0.62)|(0.13)|(0.11)|(0.13)|(2.83)|(2.77)|(2.81)|(2.73)|(2.72)|(2.65)|
|Observations|469|469|469|432|432|432|477|477|477|477|477|477|
|R-squared|0.147|0.148|0.147|0.519|0.535|0.519|0.032|0.127|0.039|0.139|0.035|0.128|

_Standard Errors in Parenthesis (* p<0.10, ** p<0.05, *** p<0.01)_ 33
at least one country per region with two countries each from Sub-Saharan Africa and South Asia— the two regions where the prevalence of extreme poverty is the highest in the world. Our choice of countries was also constrained by our interest in having data on important covariates of poverty, including completed years of schooling, asset ownership, literacy, access to electricity, agriculture as source of income, piped drinking water and improved sanitation. Table 2 shows the dispersion in these selected covariates of poverty with Colombia, Indonesia, and Nigeria have relatively high levels of education (at least an average of 7 years of schooling for the head of households) while India and Pakistan have relatively low levels of education (an average of about 5.5 years of schooling).<sup>11</sup> We have no data on years of schooling for Mali and Tajikistan. The downside of limited data for some covariates in some countries is offset by the fact that we analyze pooled data from eight different countries that are carefully selected.

In addition to select nonmonetary covariates of poverty, we also use principal components analysis (PCA) to compute an asset index in each country to proxy wealth. The asset information that is available for each country varies, but our interest in examining the poverty covariates is to assess whether within each country the covariates are more strongly correlated with poverty as measured by the per-capita or square-root allocations. PCA extracts a latent, underlying variable which in our case, we interpret as a proxy for wealth, from a set of related indicators. (For examples and details of this, see Pritchett and Filmer, 2001; Vyas and Kumaranayake, 2006; Filmer and Scott, 2012; Harttgen and Vollmer, 2013.) The idea is to exploit variation in a range of related variables and estimate the principal components or factors that are orthogonal to each other. In this case, we have data on the ownership of different household assets for each country, such as having

> and 2017 for Colombia. By convention, the decimal indicates the share of the survey conducted in the second year. For example, 75% of the Nigeria survey was conducted in 2019.

> 11 These figures are to be interpreted with caution due the highly varied survey years. The data for India is much older than the data for the other countries.

10
low return activities, of tradeable and nontradeable activities, and of di ff erent types of occupational categories in the existing literature (Ranis and Stuart, 1973; Lanjouw and Feder, 2001, Haggblade, Hazell and Reardon, 2006). This is con fi rmed in the context of Bangladesh, where nearly 39 percent of non-farm worker are engaged in a myriad of service related occupations. Among di ff erent subsectors, a quarter of nonfarm workers are employed in manufacturing. In this paper, we focus mainly on the distinction between high and low wage employment and self-employment in the non-farm sector. Motivation for this classi fi cation of the non-farm activities is derived from the evidence that in every developing country, a substantial segment of the non-farm activities o ff er returns that are lower than the agriculture wages. For instance, in Bangladesh, about 28% of the wage workers in the nonfarm sector earn less than the median agricultural wage rate, These types of nonfarm activities may ensure bare subsistence, but they by no means o ff er an escape from poverty. What drives the growth of the relatively high return non-farm activities is thus a more pertinent issue from the policy perspective.

Standard occupational and sectoral classi fi cation of the non-farm activities can not be used to distinguish between high and low return activities. Table 1 reports the broad occupational and sectoral classi fi cations of non-farm activities by types of non-farm employment (high return, low return and self-employment) using the HIES 2000 data from Bangladesh. The high return wage works are de fi ned as those activities which pay more than the median agricultural wage in the locality and vice versa. Of all services workers, 18 percent are engaged in low return wage employment, 43 percent in high return wage employment and 35 percent in self-employment. Services workers predominate in all three types of employment (high return, low return and selfemployment). For instance, 45 percent of all low return wage employment, 37 percent of high return wage employment and 39 percent of self-employed are engaged in services occupations. Similar patterns are evident in the case of sectoral classi fi cation of non-farm activities with 11
<!-- Start of picture text --> Ethiopia 2015 - 2016 (|===—_ :<br>Malawi 2015 - 2016 -| EE ety Niger 2011) 2011) (iis<br>Mali 2017)<br>Malawi 2009 - 2010) ==— Ethiopia 2018 2018 - 2019) 2019) Ss[iE[iE iii<br>Nee 201) ———==——<br>Tanzania 2010] Ethiopia 2013 2013 -2014-- SSS.(( [i i ii i<br>Tanzania 2010 >) ===<br>Mali 2014) Niger 2014) 2014) EEE<br>Tanzania 2012} ==<br>Ethiopia 2018-209 5 —=== — Mal 2017 ssEEEE<br>Tanzania 2008 |=a<br>Ethiopia 2013 -20147- === Nigeria 2019) 2019) [Ei ==<br>Tanzania 2014 | ===<br>Ethiopia 2013 -2014- = Mali 2014; [is- [is-s-<br>Ethiopia 2011 -2012-§ =F"<br>Malawi 2018 - 2019) == Malawi 2018 2018 - 2019 2019 - (i [i i ii ==<br>Nigeria 2019” GH<br>asSs Ss |Ss Ss | Ss | |<br>“ Share of total population rectedby drought Damages11 attributed to drought10 drought1010 shocks (Millions100 (Millions100100 of 20221,000 USD),2,000log4,000 scaled 20221,000 USD),2,000log4,000 scaled1,000 USD),2,000log4,000 scaled USD),2,000log4,000 scaled2,000log4,000 scaledlog4,000 scaled4,000 scaled scaled<br>Ea i | Ey<br>Malawi 2012 - 2013 ll<br>Nigeria 2019 _<br>Niger 2014 .<br>Niger 2014<br>Niger 2011<br>Malawi 2015<br>- 2016 -<br>0% a 10% 20%Ss30% | 1ce10 100 1,000|<br>Share of total population affected by flood Damages attributed to flood shocks (Millions of 2022 USD), log scaled<br>| by | Ey<br><!-- End of picture text -->

<!-- Start of picture text --> :<br>Niger 2011) 2011) (iis<br>Ethiopia 2018 2018 - 2019) 2019) Ss[iE[iE iii<br>Ethiopia 2013 2013 -2014-- SSS.(( [i i ii i<br>Niger 2014) 2014) EEE<br>Mal 2017 ssEEEE<br>Nigeria 2019) 2019) [Ei ==<br>Mali 2014; [is- [is-s-<br>Malawi 2018 2018 - 2019 2019 - (i [i i ii ==<br>asSs Ss |Ss Ss | Ss | |<br>Damages11 attributed to drought10 drought1010 shocks (Millions100 (Millions100100 of 20221,000 USD),2,000log4,000 scaled 20221,000 USD),2,000log4,000 scaled1,000 USD),2,000log4,000 scaled USD),2,000log4,000 scaled2,000log4,000 scaledlog4,000 scaled4,000 scaled scaled<br>| Ey<br><!-- End of picture text -->
Papers estimating the impact of financial sector volatility on financial/corporate sector profits and associated tax revenues are limited. EC (2010) estimates that the GDP weighted average share of the contribution of the financial sector to total corporate tax collection in the EU 29 was 20% before the crisis and fell to 17% in 2008. Moreover, they expected accumulated losses to reduce future tax payments via loss carry forward. IMF (2009) finds that the decline in financial sector profits during 2008/2009 affected tax revenues by 0.2 percent of GDP in the UK and US during 2008/2009. In this section, I examine whether financial sector profitability affects tax outturns beyond the impact through the GDP cycle so that corporate profits and tax revenues as a share of GDP are affected in the ECA region.

# <u>Empirical Estimation I:  Tax Revenues and GDP Composition</u>

In this section, I take a closer look at the revenue implications of potential changes in GDP composition; specifically changing net exports to GDP, given growth. As global capital markets have weakened leading to reduced external financing, many countries have had to contain their current account deficits. Another factor tending to reduce imports and increase net exports has been much weaker consumption demand in many ECA countries. As a result, consumption based tax revenues have declined. I explore how value added taxes are affected by changes in net exports. Value added taxes are likely to decline more than the amount estimated by declining GDP if net exports rise relative to GDP. Figure 3 shows how net exports tend to be anti-cyclical, being more negative when GDP growth is high and higher when GDP growth is low in ECA countries. In other words, net export patterns may worsen revenue declines during recessions and improve them during booms.

**Figure 3:  Net Export as a share of GDP & GDP growth rate show negative association 2000-2012**

<!-- Start of picture text -->
-20 -15 -10 -5 0 5 10 15<br>GDP Growth rate (%)<br>40<br>20<br>0<br>Net Export (% of GDP)<br>-20<br>-40<br><!-- End of picture text -->

_Source:_ World Bank staff estimates based on government accounts. _Note:_ Extreme values (GDP growth rate >20%; Net Export/GDP >40% or <-40%) are excluded from this graph<sup>17</sup>

> 17 This amounts to leaving out Azerbaijan for one year due to a very high growth rate and to omitting BosniaHerzegovina in the pre-2004 period, Kosovo, and Moldova for some years.

13
Figure 3: Correlation between services trade barriers and the share of embodied foreign services for textiles sector in year 2000 <!-- Start of picture text --> IRL<br>RUS<br>HUN<br>LTUDNKGRC CZE<br>BEL<br>MEX<br>CAN<br>POLDEU SWE<br>NLD<br>IDN<br>BGRAUT FIN<br>ESP<br>PRT ROM AUS<br>KOR<br>CHN<br>GBRFRA ITA<br>BRA<br>TUR USA<br>IND<br>JPN<br>0 10 20 30 40 50<br>Financial services trade restriction index<br>1<br>.8<br>.6<br>.4<br>.2<br>Share of embodied foreign financial services<br>0<br><!-- End of picture text --> Notes: The vertical axis represents the share of embodied foreign financial services among the embodied foreign and domestic financial services for year 2000. The horizontal axis represents financial services trade restriction index, using the data from the World Bank Services Trade Restriction Indexes (STRI). See Appendix 2 for the country names corresponding to these country codes.

31
volatile capital flows, provide institutional investors with instruments that satisfy their demand for safe and stable long term yields, reduces financial instability associated with asset price bubbles, and provides a reliable source of capital to fund public and private ventures under market discipline and scrutiny. In addition, fixed income securities are crucial for the efficient conduct of monetary policy especially if the central bank has to bring down inflation or maintain inflation target without a peg to a major currency.

With regard to domestic borrowing the DeMPA focuses on primary market aspects to evaluate whether market-based mechanisms are being used to issue debt. The rationale for the indicator is that borrowing activities in local currency in the domestic market—particularly in the primary wholesale or institutional market—should be transparent and predictable to provide the government with a mechanism to finance its expenditures in a cost-effective manner while minimizing the risks (DeMPA, 2009). International practice has shown that the government can benefit from providing market participants and investors with advanced notice of borrowing plans and other market activities and by acting in accordance with their stated intention when issuing new Treasury-bonds or undertaking activities such as buyback of government Treasurybills and Treasury-bonds. This approach can lead to lower costs by providing certainty for investors, increasing liquidity, broadening the investor base, and creating a level playing field for investors. It entails the publication of a borrowing plan for Treasury bills and bonds, the preparation of an annual plan for total borrowing in local currency market and the availability and quality of procedures for such activity. On domestic debt management, the DeMPA find that small states perform moderately in using market-based mechanisms in domestic borrowing (Dimension 1) and strong on the availability and quality of documented domestic borrowing procedures (Dimension 2) (Table 3)

**Table 3: Domestic Borrowing – Assessment of Each Dimension**

|DPI 8 Domestic borrowing|Score<br>C and<br>above|Score<br>D|Not<br>rated|
|---|---|---|---|
|_Dim 1: Extent of market based mechanisms and_|1|12|4|
|_the publication of a borrowing plan_|(6%)|(71%)|(24%)|
|_Dim 2:Availability and quality of documented_|11|3|3|
|_procedures_|(65%)|(18%)|(18%)|

Source: DeMPA reports

# **Common Elements in Small States That Have Helped to Develop Debt Markets**

Picking up on the DeMPA findings to highlight policy options that have worked well in some small states, this section of the paper selects the development of domestic debt markets as an example for other small states to analyze. Although, developing a debt market in a regional 23
widely discussed improvements in educational mobility in India in last few decades should be interpreted with due caution because they are based on IGRC estimates from coresident samples, and thus are likely to be substantially biased downward, and the apparent improvements in IGRC may largely (or at least partly) be driven by declining coresidency rates in younger generations.

# **8. Conclusions**

We take advantage of two rich data sets from Bangladesh and India to explore the direction and magnitude of coresident sample selection bias in the two most widely used measures of intergenerational persistence: intergenerational regression coefficient (IGRC) and intergenerational correlation (IGC). The evidence reported in this paper shows that the worry about coresidency bias is well-justified when the focus is on estimating IGRC, by far the most popular measure among development economists.<sup>27</sup> The IGRC estimates, in general, suffer from substantial downward bias in coresident samples vindicating the skepticism among researchers and journal editors about the usefulness of data with coresidency restriction. The bias in IGC estimates is, however, much smaller in magnitude, less than one-third of that in the IGRC estimates on an average. We discuss theoretical explanations and intuitions behind the empirical results. The biases in both IGC and IGRC converge to zero as coresidency rate converges to 100 percent, but, even with high coresidency rates, IGC estimates are preferable.

Robust evidence on the direction of bias, i.e., that the estimates of intergenerational persistence are highly likely to be biased downward in coresident samples can be useful in understanding changes in economic mobility over time, given that coresidency rates are usually lower in the younger generations. This adds a caveat to the optimistic picture of intergenerational educational mobility found in some countries such as India based on declining IGRC estimates from coresident samples over time.

Our analysis also suggests that the IGC estimates are much less sensitive to the vari-

> 27The same negative conclusion is likely to hold for other related measures of mobility where the focus is on a the slope parameter of a regression without normalization to take into account changes in variances.

29
4. Getting out of debt. Practical steps for seeking help, for example, debt management, assessment tools and debt recovery.

5. Sound financial management, for example, using credit wisely, borrowing responsibly, feeling confident.

In addition, the NMDA was featured prominently in the storyline, and the main character and her husband seek advice from an NDMA employee who helps them find their way back to financial responsibility. At the end of each episode in which the NDMA was highlighted, the actual NDMA toll-free number was displayed on screen with a public announcement offering NDMA’s free financial advisory service.

A detailed synopsis of the show is available in Appendix 1 of this paper. Further, a short summary video clip of the storyline is available in the Web Appendix.

# **4. Research Design and Timeline**

# _4.1 Research Design_

Evaluating the impact of a nationally televised soap opera is quite challenging. First, it is difficult to separate the effect of the soap opera’s message from other messages on similar issues that individuals and families may be receiving from alternate sources. Second, certain types of individuals may self-select into watching a particular soap opera, and hence any subsequent behavior change is confounded by these selection issues. Third, it is difficult to identify the cause of any subsequent behavior change as the message itself because the effect of the message is confounded by the mode of delivery. Specifically, since on-screen actors typically have a mass following, many of those watching may simply follow what their favorite actors advocate irrespective of the content of the message.

To overcome these concerns, the project used a random encouragement design methodology with the following setup. First, a listing exercise of the study population was conducted during which basic socio-economic and TV viewership information was gathered from all households. The evaluation and hence the listing exercise was concentrated in the area of urban Gauteng (with 2,500 respondents from Johannesburg and 500 respondents living in Pretoria). The listing 13
10 roughness of paved roads in Ethiopia and Ghana decreased by 15 percent and 8 percent respectively between 1984 and 1988, and in Sudan and Nigeria increased by 3 percent and 2 percent respectively over the same time period (Harral and Faiz, 1988, Table A-2; Mason and Thriscutt, 1989, Table 3). At times the actual roughness of the road pavement will thus be higher than the long-term average, while at others it will be lower. This creates an awkward inconsistency. A sub-optimal road maintenance regime delivers perverse signals to users. When road maintenance is reduced, roughness increases and this increases VOCs: lower road quality thus costs road users more, while higher quality costs less. The assumption of constant average roughness is nevertheless unavoidable in the interests of simplifying the analysis.

Table 1. Changing Condition of Brazil's Road Network (percent and weighted average)

||**-1979-**||**-1984--**||**Cbange**<br>**1979-1984**|
|---|---|---|---|---|---|
|Road**Condition**|**km**|et|Ian|perce|percent|
|**Good**|10,000|24|14,000|30||
|Fair|23,000|**58**|19,000|42||
|Poor|7,000|**18**|13,000|28||
|**Weightod Averae**<br>**weghs**(a)||_1S6_||163|**4.5**|
|**weigbts**(b)||**135**||141|3.8|
|**(a)**Good - **1.0,**Fair =<br>|1.5, Poor - 2.5.<br>|||||
|(bh**(iood**- 1.U,**tatr**=|**1U,**roor - X.u.|||||

Note: **lbe increm in the** -ber of **roads in** good **condition was** caused by **new constuion.** About 6,000m **of newpaed roadswes** added to thedetwork, **while2,000km** formerly i good **codon decined** to fair. **Soums (Hd and Fa4z, 1988, Boz 1-3)** 25. Assumption IV assumes that vehicle operating costs are a linear function of the average roughness of the road pavement. This is a reasonable assumption. Although they are not strictly a linear function of the average roughness, they are nearly so and the discrepancy is of little practical importance. This is illustrated in Figure 1 for Tunisia. Since the figures are based on generic VOC figures, the relationship will hold in other countries as well.