rafmacalaba/datause-encoder-data huggingface dataset:
Figure 5.2: TURKEY, evaluation of TFP measures under different imputation methods

# I. Kernel<sup>1</sup> estimates of TFP densities

<!-- Start of picture text -->
-2 0 2 4 6 8<br>log-TFP<br>Complete case ICA method<br>Random ICA ICA on inputs<br>EM alg. [1] EM alg. [2]<br>EM alg [3]<br>1<br>.8<br>.6<br>.4<br>Density of TFP<br>.2<br>0<br><!-- End of picture text -->

# II. Table of descriptive statistics of TFP measures

||**# Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Complete case|818|1.84|1.01|-5.25|6.41|
|ICA meth.|1805|3.45|1.20|-3.36|7.85|
|Random ICA|1805|4.16|1.28|-2.67|8.91|
|ICAon inputs|1481|1.37|1.23|-5.64|5.87|
|Em alg [1]|2646|2.87|0.97|-4.05|7.44|
|Emalg [2]|1802|1.33|0.88|-5.84|6.13|
|Em alg [3]|1802|1.51|0.88|-5.65|6.31|

# III. Correlation matrix between TFP measures

||Complete<br>case|ICA meth.|Random ICA|ICA on<br>inputs|Em alg [1]<br>E|m alg [2]<br>Em alg [3]|
|---|---|---|---|---|---|---|
|Complete case|1.000||||||
|ICA meth.|0.969|1.000|||||
|Random ICA|0.954|0.998|1.000||||
|ICA on inputs|0.992|0.974|0.956|1.000|||
|Em alg [1]|0.990|0.993|0.986|0.985|1.000||
|Em alg [2]|0.990|0.927|0.908|0.969|0.964|1.000|
|Em alg [3]|0.991|0.932|0.914|0.969|0.968|1.000<br>1.000|

Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. Complete case: TFP measure from the sample without replacement of missing values; likewise, input-output elasticities are obtained from estimating equation (1) in the complete case (see I-O elasticities in Table 9.2). ICA method: TFP measure with inputs and output replaced by ICA method and input-output elasticities from Table 8.2. Random ICA: TFP measure with inputs and output replaced by random ICA method and input output elasticities from Table 9.2.

ICA on inputs: TFP measure when only inputs are imputed by the ICA method (not sales).The I-O elasticities and semielasticities used are in Table 9.2.

Em alg. [1]: TFP measure obtained under imputation of inputs and output by the EM algorithm described in section 5.1.1. Likewise, the I-O elasticities are in Table 9.2.

Em alg. [2]: In this case the EM algorithm used is that described in section 5.1.2. The I-O elasticities are in Table 9.2. Em alg. [3]: The description of the EM algorithm is in section 5.1.3. The I-O elasticities are in Table 9.2. Source: Authors’ estimations with ICSs.

84
depend on government responses and mitigating strategies (Koren and Peto, 2020). For instance, government restrictions and shutdowns disrupt supply chains and the labor market. Layoffs, furloughs, and wage cuts, in turn, affect consumer demand for various services through income and wealth effects. Consumers could also cut back spending on certain goods and services and increase precautionary saving due to elevated economic uncertainty.

To gauge the role of supply and demand-driven shocks, we interact data on government responses, such as social distancing and lockdown restrictions, with information on the spread of the pandemic. Government responses are correlated with the spread of the pandemic, with highly affected countries expected to implement stricter measures. However, variations in the timing and spread of the pandemic and government measures across countries allow us to identify the impact of the pandemic before and after government measures are put in place. This will provide important evidence on how much of the effects are driven by supply-side restrictions arising from social distancing and lockdown measures and demand-driven changes in economic activities.

We employ fixed effects models and exploit the temporal and spatial variations in the spread of COVID-19, as measured by the number of confirmed cases and deaths, across 182 countries that are currently affected by the disease.<sup>2</sup> As of May 20, over 5 million people have been infected by COVID-19, and over 320,000 people have deceased from the disease. While the spread of the pandemic is relatively swift, there are spatial and temporal variations in the exposure and intensity of the pandemic, which allows us to identify the impacts of the pandemic.

We find that demand for some services jumped (dipped) immediately as countries experienced COVID-19 and associated business closures and lockdowns. As countries detect their first case of COVID-19 in March 2020, we observe significant spikes and dips in demand for some services. Demand for services that require face-to-face interaction, such as transport, hotel and restaurant, tourism, and retail, has substantially contracted, with hotel and restaurant services worst hit. On the contrary, demand for services that require less face-to-face interaction or provide solutions to reduced personal interaction, such as ICT and delivery, has increased. For instance, countries that have recorded 10 or more confirmed cases saw a 63-79 percent decline in demand for hotel and restaurant services, and a 46-77 percent increase in the demand for ICT services.

> 2 We have dropped 13 small island nations and territories from our analysis sample owing to high volatility in their internet search data, which makes identifying search trends problematic.

4
model is correct) except in rare cases in which ∆<sup>ˆIV</sup> has lower asymptotic variance than ˆ∆<sup>BPtheLATEhappenstobeclosetotheATE.</sup> ATE<sup>and</sup>

# 3 Monte-Carlo simulations

To examine the properties of the BP and IV estimators in finite samples and with misspecifications, we conducted Monte-Carlo simulations across a range of parameter values. These parameter values represent a wider selection compared to those used in previous work by Angrist (1991) and Bhattacharya, Goldman, and McCaffrey (2006), and prove useful in understanding the performance of these estimators in practical applications. The wider range of parameters considered here qualitatively affects the nature of the recommendation. For instance, we find that for some combinations of pT and pY , deviations from normality in the BP model result in significant bias, in contrast to the results of Bhattacharya, Goldman, and McCaffrey (2006) over more limited simulations. Also, Angrist’s (1991) finding of near-effi ciency of IV disappears when we add an exogenous covariate to the model. Table 1 compares the parameter ranges used in the different papers.

||Our simulations|Angrist (1991)|Bhattacharya et al. (2006)|
|---|---|---|---|
|pT|0.1−0.9|0.2−0.5|0.5|
|pY|0.1−0.9|0.5−0.9|0.0−0.7|
|ρ|0.0−0.7|0.5|0.1−0.5|
|∆ATE|0.05−0.16|0.10|0.00−0.42|
|n|400−30,000|400−800|5000|
|Number of covariates|0 or 1|0|1|

Table 1: Ranges of parameter values used in various studies.

## 3.1 Data-generating processes

Our data-generating processes (DGPs) are all based on the following latent-index model: 12
4

**Table 1. School Access, Repetition, Grades Attained, and Indigenous Population in Latin America and the Caribbean**

|Sub-region/<br>Country|School A<br>On time|ccass(%.<br>Ever|Repetition<br>(%)|Yea in<br>Primary|Grades<br>Attaine|Indigenous<br>d<br>(%)|
|---|---|---|---|---|---|---|
|South America|..|93|45|7.1|4.2||
|Argentina<br>|..<br>|..<br>|26<br>|..<br>|..<br>|0.1<br>|
|Bolivia<br>|61<br>|90<br>|38<br>|4.8<br>|2.2<br>|66<br>|
|Brazil|69|92|54|7.7|3.9|1|
|Chile|44|98|11|6.4|_5.5_|9|
|Colombia|43|83|44<br>|6.2<br>|4.5<br>|1<br>|
|Ecuador|82|100|28|6.2|_5.2_|21|
|Paraguay|100|100|28|6.3<br>|4.8|3<br>|
|Peru|66|97|29|6.5|_5.1_|40|
|Uruguay|43<br>|100<br>|18|6.3<br>|5.7<br>||
|Venezuela|76|95|28|6.4|4.5|1|
|CentralAmerica|..|81|47|6.7|4.1||
|Costa Rica<br>|43<br>|99<br>|23<br>|6.4<br>|5.2<br>|1<br>|
|El Salvador|65|73|51|7.0|4.0|21|
|Guatemala|52|72|47|6.4|4.0|50|
|Honduras|83|93|49|6.9|4.5|7|
|Nicaragua|73|78|60|6.8|2.7|3|
|Panama|82|99|26|6.5|5.4|6|
|**Gulf**of**Meico**|..|96|33|_5.8_|4.1||
|Haiti|1|44|54|4.8|1.3||
|Mexico|96|100|32|5.7|4.3|11|
|Dominican Republic|<br>76|74|_55_|6.7|3.4||
|Anglophone Caribbe|an<br>..|100|7|5.6|4.9||
|Bahamas|2|100|0|6.0|_5.7_||
|Barbados|87|99|18|5.7|_5.0_||
|Dominica|95|98|26|6.3|5.2||
|Grenada|88|100|_15_|6.0|4.4||
|Guyana|73|100|8|6.0|5.4|3|
|<br>Jamaica|82|100|4|5.3|4.7||
|St. Kitts and Nevis|21|100|2|6.0|5.9||
|St. Vincent|87|99|23|6.3|5.2||
|St. Lucia|96|100|4|5.8|5.4||
|Trinidad and Tobago|<br>100|100|8|5.7|4.8||
|Overall|41|93|42|6.8|4.2||

Sour=cs: **Sie (1992); nigos pouliom euimuw: Ber** (1990). Note: .. **available.**
Figure 6: Politician approval with time trend, by bandwidth <!-- Start of picture text --> VC approval VC approval Councillor approval<br>60 90 120 150 180 60 90 120 150 180 60 90 120 150 180<br>Number of days Number of days Number of days<br>WEO approval MP approval<br>60 90 120 150 180 60 90 120 150 180<br>Number of days Number of days<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>-.2 -.2 -.2<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br><!-- End of picture text --> _Notes:_ Regressions include linear time trend and linear time trend interacted with the variable indicating post campaign survey date.

35
# **1. Introduction**

Do stock prices improve efficiency by directing capital towards more productive uses? A widely held view, dating back at least to Hayek (1945), is that stock prices are useful signals since they aggregate information about fundamentals that is not otherwise known to firms’ managers.<sup>1</sup> In this sense, the stock price of a company might be informative to the manager when making a real investment decision.<sup>2</sup> In a very practical way, informative prices enable superior decision-making (Fama and Miller (1972)).

In this paper I identify a limitation to this view. More precisely, in a model where informed traders in secondary markets internalize that stock prices are signals to firms’ managers, I show that trading volume and price informativeness depend on the quality of managers’ prior information. In other words, the amount of private information that is aggregated into the stock price through the trading process is a function of managers’ initial information. The learning channel is limited because prices are less informative for firms with low quality of managerial information, precisely the case in which managers would like to learn more from the stock market. This happens despite the fact that some market participants are endowed with perfect information. As a result, stock prices are not as useful in guiding capital towards its most productive use.

Aside from identifying a fundamental limitation to the allocational role of stock prices theoretically, I make two novel empirical contributions. Using a sample of U.S. publicly traded companies, I document a positive correlation between the quality of managers’ information and stock price informativeness. I find a stronger correlation for firms with higher institutional ownership, which further suggests the presence of strategic behavior. Also, contrary to the conventional view that less informed managers should rely more on stock prices when making investment decisions, I find no differences in the sensitivity of investment to stock prices for different levels of managerial information. The evidence suggests that while firms do learn from prices, the learning channel and its effects on real investment are limited.

I model learning from prices as follows. There is a continuum of publicly traded firms facing a real investment opportunity with uncertain net present value. Firms use stock prices to update their prior about their own fundamentals. Informed and noise traders submit demands for the firm’s shares in a secondary market. Informed traders are strategic in that they internalize the effect of their trades on both prices and on the firms’ inference problem.

> 1Subrahmanyam and Titman (1999) argue that prices are useful to managers because they aggregate investors’ signals about future product demand.

> 2This mechanism has received empirical support in the recent work of Durnev et al. (2004), Chen et al. (2007) and Bakke and Whited (2010).

2
**Selected Bibliography**

- Aberbach, Joel D., Robert D. Putnam, and Bert A. Rockman. 1981. _Bureaucrats and Politicians in Western Democracies,_ Boston, MA.: Harvard University Press.

- Acs Z. J., and D. A. Gerlowski. 1996. _Managerial Economics._ New Jersey, Prentice Hall.

- Australian Public Service and Merit Protection Commission, 1998. _State ofthe Service Report_ 1997-9, Canberra: Public Service Commissioner.

- Axelrod, Robert, and Keohane, Robert. 1985. "Achieving Cooperation Under Anarchy: Strategies and Institutions". _World Politics._ 38-1: 226-254.

- Bendor, Jonathan, and Mookherjee, Dilip. 1987. "Institutional Structure and the Logic of Ongoing Collective Action". _The American Political Science Review._ 81-1: 129-154.

- Carter, N, R. Klein, and P. Day (1995) _How Organizations Measure Success: The Use of Performance Indicators in Government._ London: Routledge.

- Dilulio, Jr. John D. 1994. "Principled Agents: The Cultural Bases of Behavior in a Federal Government Bureaucracy." _Journal of Public Administration Research and Theory, 3:_ 277-318.

- Evans Gord and Manning Nick. 2000. "A practical approach to assessing central government policy-making institutions in cabinet government: learning lessons from recent World Bank institutional analyses". Unpublished World Bank paper, (http://wwwl.worldbank.org/publicsector/toolkits.htm)

- Fink, Arlene. 1995. _How to Sample Surveys. The Survey Kit,_ California: Sage Publications.

- Girishankar, Navin. 1999. _Reforming Institutions for Service Delivery: A frameworkfor Development Assistance with an Application to the Health, Nutrition and Population Portfolio,_ Policy Research Working Paper No. 2039, Washington D.C., The World Bank.

- Gore, Al. 1993. _Creating a Government that Works better and Costs Less: The Report of the National Performance Review._ New York: Plume.

- Greene, William H. 2000. _Econometric Analysis,_ Fourth Edition, New Jersey: Prentice Hall.

36
and to make sex within marriage safer by increasing HIV testing, promoting condom use, and reducing spousal age differences may help address the problem of HIV infection among this group of young women.

Although it seems reasonable to posit that females who marry young are at relatively high risk of contracting HIV, the actual prevalence of HIV among young wives remains unknown. In determining policy approaches and prevention efforts, it is important to determine whether the rate of HIV infection among young married adolescents is indeed as high as or higher than that of other women their age who are sexually active but unmarried.

Clark (2004) documents the increased risk of HIV infection for young married females by comparing prevalence data among the partners of young married females and the boyfriends of unmarried females the same age. She reports that in Kenya 30.0 percent of male partners of young wives are HIV–positive, while only 11.5 percent of partners of unmarried females the same age are seropositive. In Zambia 31.6 percent of partners of young wives and 16.8 of partners of unmarried females the same age are HIV–positive (Bruce and Clark 2004).

Clark (2004) shows that HIV infection rates peak among married women 15–24 before gradually declining. In contrast, the HIV infection rates for married men peak at 30–34. These are generally the ages when women and men marry. She also shows that the HIV prevalence rate is significantly higher among married women and men than among unmarried, sexually active women and men the same age. She finds that being married raises the risk of being HIV–positive by 75 percent among sexually active women 15–19. These findings suggest that early marriage is a risk for contracting HIV, although, as noted earlier, the study relies on a small sample from two urban areas of Kenya and Zambia.

Bongaarts (2007) draws the opposite conclusion. His analysis, based on DHS in Ghana and Kenya and on cross-country comparisons, suggests that late marriage and a long interval between first sex and first marriage are risk factors for HIV infection.

Data from the first five DHS that include HIV testing for a nationally representative sample of the adult population and much larger sample sizes allow the risks early marriage poses for HIV infection to be assessed (Table 3). The datasets are 12
pendix 1. The first assertion follows from: Asymptotic independence of the first and second term on the right hand side of eq. (3) ˆ follows from the independence of _θ_<sup>ˇ</sup> and _qS − XSθ_<sup>ˇ</sup> and the fact that the information matrix of the model for _q_ ˆ _S_ is block diagonal in _θ_ and ( _ρ, σ_<sup>2</sup> _, τ_ ) respectively. As for independence between the third and the first two terms, note that the estimator _ω_ ˇ is a function of _uS_ + _eS_ and hence is uncorrelated with _rO_ = _uO − E_ ( _uO|uS_ + _eS_ ), which together with the normality assumptions implies independence.

To emphasize that the expectations of the first two terms are taken with respect to the sampling distribution of _ω_ we have used the symbol _Eω_ . The expressions for the asymptotic covariance of the first and third terms of eq. (3) are obvious. Details on the covariance matrices of the three terms and estimators for them are discussed in Appendix 1. □

# **3 Data**

Figure 1 (left panel) shows the map of Malawi, our study area. Malawi is a landlocked country in the South-East of Sub-Saharan Africa, situated between Zambia, Mozambique, and Tanzania, with a large lake alongside its Eastern border. Its territory has an elongated shape that is divided into a Northern, Central, and Southern region, with over 80 percent of the population residing in the Central and Southern regions (see Table 1). The country is still highly rural with just over 15 percent of the population (in 2010) residing in urban areas. The three largest cities span all three regions: Lilongwe (the capital) in the Central region with a population of 674,448 (2008), Blantyre in the Southern region with a population of 661,256 (2008) , and Mzuzu in the Northern region with a population of 133,968 (2008) (Malawi NSO 2008).

Both approaches considered in this paper rely on the 2010-11 Malawi Third Integrated Household Survey (IHS3), which is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project (Malawi National Statistical Office (NSO) and World Bank, 2012). The survey contains high-quality data on household consumption expenditure that is used to measure poverty, defined as the percentage of individuals whose total annual household consumption per capita fall below the national poverty line. Household expenditure and the poverty line are expressed in Malawi kwacha using February/March 2010 prices.<sup>4</sup> The poverty line, the sum of the

> 4For details on the price deflator used in the Malawi IHS3, we refer the reader to Malawi National

12
|Kenya Microfinance for Water Services Project<br>Patrick Nduati Mwangi|
|---|
|Kenya Microfinance for Water Services Project<br>Patrick Nduati Mwangi|
|Kenya Microfinance for Water Services Project<br>Patrick Nduati Mwangi|
|Bolivia Urban Wastewater Methane Gas Capture<br>Miguel Vargas-Ramirez|
|VN - Program 135 Phase 2 Support Credit<br>Steven N. Schonberger|
|VN - Program 135 Phase 2 Support Credit<br>Steven N. Schonberger|
|VN - Program 135 Phase 2 Support Credit<br>Steven N. Schonberger|
|BuildingCapacitytoprepare Impl. of Integrated Social Protection SyGustavo C. Demarco|
|BuildingCapacitytoprepare Impl. of Integrated Social Protection SyGustavo C. Demarco|
|BuildingCapacitytoprepare Impl. of Integrated Social Protection SyGustavo C. Demarco|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|National Rural Livelihoods Project<br>Gayatri Acharya|
|CapacityBuildingof Arab Water Council<br>Claire Kfouri|
|CapacityBuildingof Arab Water Council<br>Claire Kfouri|
|CapacityBuildingof Arab Water Council<br>Claire Kfouri|
|CapacityBuildingof Arab Water Council<br>Claire Kfouri|
|CapacityBuildingof Arab Water Council<br>Claire Kfouri|
|CAR - LICUS for support to Community-Driven Development and SocMargo A. Hoftijzer|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|GZ-Village and Neighborhood Development Project<br>Lina Abdallah Saeed Abdallah|
|CapacityBuildingfor HighlyPathogenic Avian Influenza Prevention aShiyongWang|
|**Table 4. Value-Added by Sector as a pe**|**rcent of the V**|**alue-Added**|**of the Fede**|**ral Okrug**<br>(p|ercent)||||
|---|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|Sector Total|
|Railway transportation|3.6|3.2|2.9|3.1|2.7|3.5|3.2|3.1|
|Truck transportation|1.7|1.8|1.7|1.5|1.7|1.6|1.6|1.7|
|Pipelines transportation|0.3|0.2|0.3|0.5|0.6|0.3|0.2|0.3|
|Maritime transportation|0.6|0.4|0.5|0.6|0.8|0.8|0.5|0.6|
|Air transportation|0.5|0.8|0.6|0.4|0.5|0.6|0.5|0.6|
|Other transportation|0.8|0.9|0.8|0.7|0.8|0.7|0.7|0.8|
|Telecommunications|1.2|1.5|1.2|1.1|1.0|1.2|1.2|1.2|
|Science & science servicing|1.4|1.5|1.2|1.4|1.4|1.2|1.6|1.4|
|Financial services|7.5|9.2|6.5|6.4|6.7|7.2|7.9|7.5|
|Ferrous metallurgy|2.8|1.4|0.7|0.7|2.8|1.4|0.2|1.5|
|Non-ferrous metallurgy|2.2|0.5|0.7|0.9|2.8|10.7|12.3|2.9|
|Chemical & oil-chemical industry|1.6|1.6|1.4|3.2|0.3|1.5|0.2|1.6|
|Mechanical engineering & metal-working|5.5|6.2|2.9|8.6|2.4|2.7|5.0|5.1|
|Timber & woodworking & pulp & paper industry|4.7|1.0|0.4|1.0|0.2|1.6|1.4|1.3|
|Construction materials industry|0.8|1.3|1.1|0.9|0.5|0.7|0.5|0.9|
|Food industry|6.6|5.0|4.9|2.8|1.0|2.6|6.2|3.8|
|Other industries|0.8|1.5|0.6|0.8|0.1|0.4|0.3|0.8|
|Public services, culture and arts|5.8|7.2|5.0|4.9|4.4|5.2|6.2|5.6|
|Agriculture & forestry|4.7|5.9|15.2|9.5|2.5|9.0|5.7|7.1|
|Coalmining|0.4|0.0|0.2|0.0|0.1|3.2|1.3|0.5|
|Housing and communal services|2.9|3.8|2.8|2.4|1.8|2.6|3.0|2.8|
|Construction|6.2|9.5|12.3|8.4|3.6|5.2|2.9|7.4|
|Electric industry|3.0|3.0|2.5|3.0|2.4|3.1|3.0|2.9|
|Gas|0.0||0.1|0.1|2.7|0.0|0.1|0.5|
|Crude oil extraction|2.1||4.1|5.5|17.3|0.8|1.2|4.8|
|Oil refining and processing|1.0|0.6|1.0|2.1|0.2|1.5|1.1|1.0|
|Other goods-producing sectors|1.0|0.6|0.5|0.9|0.9|0.8|0.9|0.8|
|Post|0.3|0.4|0.3|0.3|0.3|0.3|0.4|0.3|
|Wholesale and retail trade|29.8|30.3|27.0|27.6|37.4|29.0|30.2|30.5|
|Textiles and apparel|0.5|0.7|0.5|0.4|0.3|0.4|0.5|0.5|
|Okrug Total|9.2|27.7|9.4|19.6|17.6|12.0|4.4||

38