rafmacalaba/datause-encoder-data huggingface dataset:
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Roads

## **Achievements**

Malawi has been spending heavily on its road network in recent years, and as a result has achieved better levels of road quality. During the early 2000s, Malawi was devoting nearly 4 percent of its GDP to road sector spending, one of the highest ratios in southern Africa. The results are evident when Malawi’s road sector is benchmarked against that of its peers. Paved road density is comparatively high. Paved road quality is somewhat better, while unpaved road quality is substantially better with nearly 90 percent of the network in good or fair condition, compared with less than 60 percent elsewhere (table 2). Malawi has also made significant progress on the institutional reform agenda for roads. Malawi has introduced a road fund that meets most of the best-practice institutional design features.

**Table 2. Malawi’s road indicators benchmarked against Africa’s low- and middle-income countries**

||Unit|Low-income<br>countries|**Malawi**|Middle-income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1,000 km<sup>2</sup><br>of arable land|86.6|**141.2**|507.4|
|Unpaved road density|km/1,000 km<sup>2</sup><br>of arable land|504.7|**164.7**|1,038.3|
|GIS rural accessibility|% of rural population within 2 km of all-<br>season road|21.7|**26.2**|59.9|
|Paved road traffic|Average annual<br>daily traffic|1,049.6|**600.6**|2,786.0|
|Unpaved road traffic|Average annual<br>daily traffic|62.6|**44.5**|12.0|
|Paved network condition|% in good or fair condition|80.0|**85.5**|79.0|
|Unpaved network condition|% in good or fair condition|57.6|**89.4**|58.3|
|Perceived transport quality|% firms identifying roads as major business<br>constraint|23.0|**16.4**|10.7|

_Source:_ Gwillliam and others 2009. Derived from AICD national database, http://www.infrastructureafrica.org/aicd/tools/data. _Note:_ GIS = geographic information system.

## **Challenges**

Road preservation expenditure still falls significantly short of what is needed to preserve the network in good condition. Based on the Road Network Evaluation Tool (RONET) analysis, Malawi’s recent spending on road maintenance falls about 24 percent short of what is needed to sustain the infrastructure (figure 4). One of the issues is that the fuel levy of around $0.07 per liter, while fairly typical of southern Africa, falls substantially short of the $0.25 per liter needed to fully finance optimal maintenance requirements. This benchmark level is probably higher than what could in practice be applied, and certainly well above the highest level observed in the region (in Tanzania, at around $0.16 per liter). Government transfers to the road fund currently make up for part (although by no means all) of this difference, reaching an implicit spending level of almost $0.19 per liter.

7
Ravallion, M., & Chen, S. (2011). Weakly Relative Poverty. The Review of Economics and Statistics, 93(4), 1251–1261. https://doi.org/10.1162/REST_a_00127 World Bank. 2018. Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. World Bank, Washington, DC. License: Creative Commons Attribution CC BY 3.0 IGO World Bank. 2016. Poverty and Shared Prosperity 2016: Taking on Inequality. Washington, DC: World Bank. doi:10.1596/978-1-4648-0958-3. License: Creative Commons Attribution CC BY 3.0 IGO Zhai, F (2018), “China’s belt and road initiative: A preliminary quantitative assessment,” Journal of Asian Economics, in press. https://doi.org/10.1016/j.asieco.2017.12.006

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**Figure 1:  Regional Distribution of Business Services Exports**

<!-- Start of picture text --> 600<br>1990 1995 2000 2001 531<br>515<br>500<br>400 375<br>300 266<br>200<br>130<br>114<br>100 58 91<br>11 14 21 19 3 4 14 17 6 11 16 15<br>0<br>Africa and the South Asia LAC East Asia and OECD<br>Middle East the Pacific<br>In Billion US Dollars<br><!-- End of picture text -->

# **Figure 2**

<!-- Start of picture text --> Average Growth Rate of Exports of Business Services for<br>selected countries, during 1995-2000<br>Ind ia 43%<br>Israel 28%<br>D o minica 20%<br>B raz il 20%<br>M aurit ius 19%<br>N icarag ua 15%<br>Est o nia 14%<br>N ig eria 14%<br>R o mania 13%<br>A ust ralia 11%<br>C hina 11%<br>U nit ed St at es 11%<br>C anad a 11%<br>B arb ad o s 8%<br>Jamaica 7%<br>Peru 6%<br>Euro A rea 6%<br>A rg ent ina 5%<br>Ghana 4%<br>Jap an 1%<br><!-- End of picture text --> Source: IMF Balance of Payments Statistics; Note: -The “Business Services” category includes Total Services minus Transportation, Travel and Government Services. Alternatively, Business Services consist of: Communication, Construction, Insurance, Financial, Computer & info, Other business, Personal, cultural and recreational services, as well as Royalties and License fees.

The resulting occupational shifts in importing countries and the implied adjustment costs have, however, raised the specter of protectionism. This specter may never materialize, because of the technical infeasibility of protection or the triumph of good sense. But it would still be wise to preempt protectionism, to ensure that any adjustment pressures are dealt with through desirable domestic assistance rather than inefficient barriers to trade. And if the GATS cannot secure existing openness, then what hope is there of eliminating entrenched restrictions? Hence:

- Goal 1: _WTO Members would lock-in the current openness of cross-border trade for a wide range of services.  Certain services, such as those that involve the mobility of capital, would be exempt._

6
(2013) have recently documented. However, as a robustness check we replicate our results by constructing weights according to district population, and there is no important change.<sup>8</sup> The ASER test is only given to children living in rural areas. Therefore, we are unable to estimate the effect of the TSC on urban children. However, as the TSC is an exclusively rural program, we would not expect an effect in urban areas. More information about this data, including survey forms and summary reports, is available online at Pratham’s website: `http://www.pratham.org/M-20-3-ASER.aspx` .

# **4.2**

We estimate OLS linear probability models of achieving the three binary educational outcomes: recognizing letters, recognizing single-digit numbers, and recognizing the larger numbers. In order to account for fixed geographic heterogeneity and for an overall time trend, we use district and year fixed effects. Therefore, separately for each outcome, our regression is: where _i_ indexes six-year-old children, _d_ indexes districts, and _t_ indexes years of birth (2001, 2002, and 2003). _Achievement_ is an indicator that takes on 1 if the child demonstrated the relevant ability on the test and 0 otherwise. _TSC_ is reported TSC latrines per capita built in a district by the end of the first year of the child’s life, as in figure 2.<sup>9</sup> Notice that there is no _i_ subscript on TSC because it is the same for all children born in the same district in the same year. _δ_ and _α_ are district and year fixed effects. As recommended by Cameron

> 8Indeed, for example, our main result estimate of 0.75 in column 1 of panel A of table 3 for the effect on reading slightly increases to 0.90 when weighted, with a standard error clustered at the district level of 0.39.

> 9In fact, the ASER data report the district in which the child currently lives, not the district where she lived during her first year of life. However, permanent rural-to-rural migration is rare in India, other than of women without children moving for marriage (Munshi and Rosenzweig, 2009). Demand for latrines is unfortunately sufficiently low that it is unlikely that the TSC would _cause_ anyone to migrate, so migration is unlikely to be a threat to our identification.

13
* 23 -

Table 4: Results of HLM variance component analysis: Philippines secondary, 1983 (percent of total variance accounted for)

|Score|School|Individual|
|---|---|---|
|Science**teat**score|43|**57**|
|Math*maticstest<br>score|52|48|
|Positive attitude toward science|21|79|
|Negative attitude toward science|17|83|
|Positive attitude toward school|10|90|
|Negative<br>attitudw toward school|11|89|

47. In the second stage, for each of the six outcome measures, we fit an unconditional model that includes one random student level variable (SES) and two fLxed student level characteristics (sex and age)<sup>5</sup> . Table 5 summarizes the results from the six models. All three student-level characteristics were significant predictors of science and mathematics achievement; male students scored significantly higher on both tests than did female students, although the male advantage in science was only about 15% of a standard deviation and in mathematics only about 10% of a standard deviation, neither of which is considered meaningful (Cohen 1969). Male students also held less positive attitudes toward both science and school than did female students. Students from higher SES backgrounds had higher achievement and more positive attitudes than did students from lower SES backgrounds, ceteris paribus. Although the residual variance of SES was allowed to vary among schools, the SES differentiation estimates were not reliable (reliabilities range from .01 to .12). Therefore, in the remaining analyses, SES is treated as a fixed variable (residual variance set at zero). Significant school-level random effects exist even after controlling for individual level student characteristics, and the achievement and attitude estimates are quite reliable (.62 to .94). These differences between

> 5The other student background characteristics were too highly correlated with SES to be included **as** independent variables.
A common feature in returns to migration based on wages is that independent of using data from 1995 or 1999 the return to migration are increasing with education for SE-NE migrants and decreasing for NE-SE migrants. Retums to migration for SE-NE migrants with at least secondary education have increased between 1995 and 1999. Returns to migration for NE-SE migrants slightly decreased for migrants with primary I and above education during 1995-99.

In sum, the findings in this section provide some evidence that returns to migration have been decreasing for NE-SE migrants and increasing for SE-NE migrants during 1995-99. These findings are consistent with the increased migration to the Northeast and the decreased migration to the Southeast documented earlier. The predicted positive returns to migration for NE-SE migrants indicate that people migrating from the NE to the SE in search of higher remuneration. The estimated lower and generally negative returns to migration for SE-NE migrants indicates that it is likely that nonmonetary factors play a role in SE-NE migration such as lower levels of violence and warmer climate. The negative returns to migration for SE-NE migrants may also indicate that costs of living in the Southeast are substantially higher than in the Northeast and that the spatial deflators suggested by Ferreira, Lanjouw, and Neri (1999) might not be sufficient to fully account for regional differences in the cost of living.' As already mentioned, we only observe income in the place where the individual decides to locate. The crux of the problem of measuring returns to migration is that we only observe income in the place where the individual is now locating, and we do not observe the counterfactual (what the person would have eamed had he/she not migrated). If a SE-NE migrant were unemployed prior to migration, but found employment in the NE, negative returns to NE migration might be consistent with an economic explanation of migration. Unemployment in the SE in 1999 was for the whole 3.2-percentage-points higher than in the NE (Table 4.6). Differences between states are even more pronounced. Rio Grande do Norte and Piaui have an unemployment rate of 9.2 percent and 3.4 percent respectively, compared to 15.8 percent in metropolitan Sao Paulo. Given that 75.1 percent of all migrants from the SE originated in the State of Sao Paulo, high unemployment might therefore well be responsible for a lazy share of the migration.12

> " This is further highlighted by the fact that if we repeat our analysis without spatial deflation, the findings do not change significantly.

> 12 A research question that emerges is why labor markets within the SE do not exhibit the flexibility to absorb the unemployed and leave migration as a viable solution. An attempt to address the impact of unemployment on the returns to migration would be to weigh returns of migration with respective probabilities for unemployment within a state. Further research is needed here.

29
# Reference List

Andors, Stephen 1977. _China's Industrial Revolution_ . New York: Pantheon Books.

Appleton, Simon and Lina Song. 2005. "The Myth of the 'New Urban Poverty'? Trends in Urban Poverty in China, 1988-2002." Presented at Urban China in Transition, New Orleans, Louisiana, January 15, 2005.

Balcerowicz, Leszek. 11-18-2003. "Post-Communist Transition in a Comparative Perspective." Presented at Practitioners of Development Seminar Series, Washington, DC, November 18, 2003.

Baumol, William J. 2002. _The Free Market Innovation Machine_ . Princeton: Princeton University Press.

Cheung, Kui-yin and Ping Lin. 2004. "Spillover Effects of FDI on Innovation in China: Evidence From the Provincial Data." _China Economic Review_ 15(1): 25-44.

Chow, Gregory 1985. _The Chinese Economy_ . New York: Harper and Row Publishers. Djankov, Simeon and Peter Murrell. 2002. "Enterprise Restructuring in Transition: A Quantitative Survey." _Journal of Economic Literature_ 40(3): 739-792.

Dollar, David, Shi, Anqing, Wang, Shuilin, Xu, Lixin Colin. 2003. _Improving City_ _Competitiveness Through the Investment Climate: Ranking 23 Chinese Cities_ Washington, DC: World Bank.

Duckett, Jane 1998. _The Entrepreneurial State in China: Real Estate and Commerce_ _Departments in Reform Era Tianjin_ . London: Routledge.

Fan, C. Cindy. 2004. "Migration in China: A Review of Recent Findings and Policy Recommendations." Prepared for the 11th Five Year Plan of China. Washington, DC: World Bank.

Feldman, Maryann and Roger Martin. 2004. "Jurisdictional Advantage." NBER Working Paper 10802. Cambridge, Mass.: National Bureau of Economic Research.

Ferguson, Roger W. Jr. and William L. Wascher. 2004. "Lessons From Past Productivity Booms." _Journal of Economic Perspectives_ 18(2): 3-28.

Gore, Lance L. P. 1998. _Market Communism: The Institutional Foundation of China's Post-Mao Hyper-Growth_ . Hong Kong: Oxford University Press.

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30 Table A.6: Regressions for fixed investment with exogenous instruments (economic and structural controls), unbalanced 5-year average panel, 1980–2009<sup>_†_</sup>

||**A.E5**|**A.E6**|**A.E7**|**A.E8**|
|---|---|---|---|---|
|Lagged investment|0.608<br>(0.22)<sup>_∗∗∗_</sup>|0.523<br>(0.23)<sup>_∗∗_</sup>|0.657<br>(0.08)<sup>_∗∗∗_</sup>|0.321<br>(0.26)|
|Output|0.415|0.454|0.368|0.755|
||(0.23)<sup>_∗_</sup>|(0.24)<sup>_∗_</sup>|(0.10)<sup>_∗∗∗_</sup>|(0.26)<sup>_∗∗∗_</sup>|
|Output growth|1.415|1.513|1.395|1.053|
||(0.42)<sup>_∗∗∗_</sup>|(0.35)<sup>_∗∗∗_</sup>|(0.26)<sup>_∗∗∗_</sup>|(0.68)|
|Cost of capital|0.423|1.573|0.824|0.848|
||(1.30)|(1.48)|(0.94)|(2.14)|
|Trade openness|-0.157|-0.083|-0.024|0.319|
||(0.20)|(0.21)|(0.16)|(0.52)|
|Financial openness|-0.140|-0.195|-0.187|-0.148|
||(0.08)<sup>_∗_</sup>|(0.08)<sup>_∗∗_</sup>|(0.06)<sup>_∗∗∗_</sup>|(0.10)|
|Financial|-0.131|0.292|-0.185|-0.133|
|development|(0.39)|(0.15)<sup>_∗∗_</sup>|(0.20)|(0.42)|
|Institutional|0.453|0.256|0.201|1.068|
|quality|(0.23)<sup>_∗∗_</sup>|(0.33)|(0.08)<sup>_∗∗_</sup>|(0.55)<sup>_∗_</sup>|
|Business|-0.250|-0.268|0.031|-0.720|
|environment|(0.34)|(0.30)|(0.17)|(0.53)|
|Institutional|0.028|0.118|0.081|-0.192|
|structure|(0.14)|(0.12)|(0.09)|(0.27)|
|Wald _χ_<sup>2</sup>|5,766<sup>_∗∗∗_</sup>|9,832<sup>_∗∗∗_</sup>|16,822<sup>_∗∗∗_</sup>|1,817<sup>_∗∗∗_</sup>|
|Hansen J|33.215|28.811|39.364|20.809|
|AR(2) z|-1.339|-1.457|-1.481|-0.459|
|Instruments|41|41|43|37|
|N (countries)|337 (105)|333 (105)|337 (105)|408 (106)|

> _†_ All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. Period fixed effects and a constant term were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.
Panel E. Core CPI (Percent)

||1970-2016|1970-85|1986-2000|2001-16|
|---|---|---|---|---|
|Factor||All cou|ntries||
|Global|4.7<br>(12.5, 1.7 - 15.4)|8.4<br>(14.2, 6.9 - 14.5)|5.4<br>(17.5,1.3 - 29.1)|29.8<br>(27.8, 9.7 - 42.1)|
|Group|14.1<br>(18.8, 2.9 - 26.6)|16.5<br>(18.9, 5.2 - 31.8)|10.6<br>(15.3, 3.1 - 25.6)|3.5<br>(5.8, 2.1 - 7.7)|
|Global + Group|26.5<br>(27.9,10.9 - 40.4)|28.6<br>(29.6,11.6 - 44.3)|31.3<br>(29.2,11.9 - 44.9)|33.0<br>(30.0,13.8 - 46.9)|
|Factor||Advanced|economies||
|Global|7.9<br>(14.5, 3.1 - 21.4)|8.5<br>(16.0, 7.7 - 22.6)|5.3<br>(16.1, 2.3 - 26.3)|31.9<br>(29.5, 25.0 - 44.1)|
|Group|17.7<br>(21.9, 8.7 - 34.8)|16.9<br>(21.0, 5.2 - 35.7)|17.9<br>(17.1, 2.3 - 29.2)|3.1<br>(5.8, 2.0 - 5.4)|
|Global + Group|34.6<br>(34.9,18.9 - 46.4)|40.1<br>(35.5,13.2 - 53.7)|31.8<br>(30.9,20.6 - 46.6)|35.1<br>(33.0,20.6 - 47.7)|
|Factor||EM|DEs||
|Global|1.7<br>(8.0, 1.3 - 9.1)|10.1<br>(8.9, 2.5 - 12.9)|26.3<br>(26.2, 0.7 - 29.1)|22.2<br>(23.3, 0.6 - 29.0)|
|Group|<br>2.9<br>(11.5, 1.5 - 13.5)|<br>9.4<br>(11.5, 5.3 - 19.2)|<br>7.0<br>(6.9, 4.6 - 10.9)|<br>6.4<br>(7.6, 4.2 - 13.2)|
||<br>11.8|<br>17.2|<br>22.4|<br>11.8|
|Global + Group|(15.0,3.0 - 16.5)|(18.7,5.6 - 27.5)|(24.9,5.9 - 33.6)|(22.9,1.1 - 31.1)|

Notes: The contribution of global and group inflation factors is estimated over the three subsample periods with the dynamic factor model (two-factor model) for each of the five different inflation measures: import prices, PPI, headline, GDP deflator, and core CPI. The data set includes 38 countries (25 advanced economies and 13 EMDEs) except import prices for 21 countries (17 advanced economies and 4 EMDEs). The first argument in the first row indicates the unweighted median across countries. The first argument in the second row (in parentheses) is the mean variance share across countries. The second and third arguments in the second row (in parentheses) indicate the interquartile range (25th and 75th percentiles) of variance shares. CPI = consumer price index; EMDEs = emerging market and developing economies; GDP = gross domestic product; PPI = producer price index.

32
- 4 -

suffering from extreme floods, which are led by substantial increases in (mean) peak discharges in the regional three major rivers, Ganges, Brahmaputra and Meghna (Mirza, 2002). One of his scenarios predicts that the volume of water in the Ganges would increase by 5 to 15 percent, depending on changes in temperature.

Finally, closely related to the above, changing hydrology and possible extreme events must of necessity impact sediment risks and measures. More sediment, along with other factors such as changed composition of water, could raise the probability that a hydropower project suffers greater exposure to turbine erosion. When a major destruction actually occurs, the cost of recovery would be enormous. An unexpected amount of sediment will also lower turbine and generator efficiency, resulting in a decline in energy generated.

This paper mainly addresses the first issue by analyzing hydrological and weather time series under the assumption of long-run statistical stability. For the purpose of discussing extreme rainfall events and the resulting floods, some other approaches are needed, such as frequency analysis (e.g., Stedinger _et al_ ., 1993; Khaliq _et al_ ., 2006). A number of probability distribution assumptions are tested in this regard (e.g., Malamud and Turcotte, 2006; Bhunya _et al_ ., 2007). Even a nonparametric Bayesian method is applicable (O’Connell, 2005). With a regional climate model, Kay _et al_ . (2006) demonstrate the detailed estimates of flood frequency for 15 catchment areas in the United Kingdom. In the multivariate context, Samaniego and Bárdossy (2007) investigate extreme events in connection with various physiographical and geographic factors. Notably, however, all these models necessitate long hydrological time series, preferably on a daily or hourly basis. To account for the sediment handling issues, even more detailed data elements may be required.

Methodologically, the current paper adopts a simple multivariate stochastic approach, the vector autoregression (VAR) model. This seems a natural extension from the conventional univariate time series analysis (e.g., Lettenmaier and Wood, 1993; Salas, 1993; Mohammadi _et al_ ., 2006; Wong _et al_ ., 2007). Mohammadi _et al_ . (2006) apply an autoregressive moving average (ARMA) model for forecasting a river flow in Iran. Using data on monthly river
**Table 5. Share of Sector Value-Added by Regional Market of Russia**

(in percent)

|Good|Central F|ar East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|Sector<br>Total|
|---|---|---|---|---|---|---|---|---|---|---|---|
|MAR|10.0|2.8|2.9|15.2|8.8|31.3|8.5|3.7|14.7|2.0|100.0|
|AGR|20.0|3.2|1.3|15.4|22.7|28.2|2.8|2.1|1.5|2.7|100.0|
|AIR|7.9|2.8|2.1|11.6|10.0|22.1|32.9|3.6|5.5|1.4|100.0|
|CHM|16.3|0.4|1.8|12.4|10.9|41.6|9.1|2.4|0.1|4.9|100.0|
|CNM|21.3|2.3|1.5|9.6|13.9|25.2|18.2|4.9|1.6|1.4|100.0|
|COL|0.3|11.8|7.3|74.2|3.8|2.7|||||100.0|
|CON|12.6|1.2|1.0|9.0|17.4|25.1|22.9|4.9|4.0|1.9|100.0|
|CRU||1.0|3.7|2.2|8.4|21.7|||62.8|0.2|100.0|
|ELE|11.1|3.2|2.9|14.4|9.5|26.2|17.9|4.4|7.9|2.4|100.0|
|FIN|9.1|3.4|3.0|12.7|9.2|22.3|24.2|4.4|9.8|1.9|100.0|
|FME|23.0|0.6|1.9|10.9|4.8|40.9|2.8|1.4||13.7|100.0|
|FOO|15.2|5.6|2.2|10.6|14.6|17.1|20.6|10.8|0.3|2.8|100.0|
|GAS||0.3|0.7|0.8|2.5|4.8|||90.8|0.0|100.0|
|HEA|8.9|3.7|3.1|12.3|9.4|21.1|26.5|4.6|8.5|1.9|100.0|
|HOU|9.2|3.7|2.8|12.1|10.3|21.2|28.4|4.7|5.7|1.9|100.0|
|MWO|14.7|4.3|1.0|7.4|6.8|38.8|17.3|7.2|1.1|1.4|100.0|
|NFM|1.3|4.1|4.7|57.9|2.5|23.6|3.6|2.2||0.1|100.0|
|OIL|12.1|4.7|1.5|17.3|10.0|38.9|4.4|7.2|3.9||100.0|
|OTH|12.3|3.2|3.6|14.0|7.8|29.3|10.0|5.2|11.7|3.0|100.0|
|OTI|30.8|1.3|1.6|5.7|9.1|20.9|22.5|5.8|0.2|2.1|100.0|
|PIP|7.8|2.7|2.5|10.5|8.9|28.9|6.4|3.6|27.7|1.0|100.0|
|PST|9.3|3.5|2.8|12.2|10.1|22.0|23.9|4.5|9.9|1.9|100.0|
|RLW|11.3|3.3|3.0|14.6|9.9|25.9|16.6|4.5|7.8|3.0|100.0|
|SCI|10.1|3.6|2.8|11.9|8.9|26.0|18.9|4.7|11.3|1.7|100.0|
|TMS|9.1|3.3|2.7|12.1|9.9|22.4|25.3|4.3|8.9|1.8|100.0|
|TPP|9.3|5.9|20.5|17.8|3.5|17.6|9.2|9.8|0.8|5.7|100.0|
|TRD|8.5|3.3|2.8|12.5|9.3|22.3|19.0|4.2|16.1|2.0|100.0|
|TRK|9.3|3.2|2.8|12.5|10.4|22.6|21.1|4.3|11.8|2.0|100.0|
|TRO|9.2|3.1|2.7|11.7|10.5|23.0|21.9|4.2|11.8|1.9|100.0|
|CLI|9.0|3.7|2.8|11.7|10.5|20.4|30.6|4.7|4.6|1.9|100.0|
|Total|329.2|99.4|96.2|453.3|284.4|734.1|465.5|128.4|340.9|68.7|3000.0|

Source: Regions of Russia and authors calculations.

41
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