metadata: authors: 0: author: Iimi,Atsushi
count: Kazakhstan
docna: 0: docna: Estimating the Impacts of Transport Corridor
            Development in Kazakhstan : Application of Dynamic Panel
            Data Models to Firm Registry Data
docty: Policy Research Working Paper
abstracts: cdata!: Large-scale transport infrastructure
            investment can facilitate structural transformation by
            changing firm behavior. Although its impact is evident over
            the long term, an important empirical challenge is potential
            endogeneity of infrastructure placement. By using the
            dynamic panel data regression, the paper examines the
            impacts of massive road corridor investment under the Nurly
            Zhol program in Kazakhstan. The paper takes advantage of
            detailed micro shipping data to capture historical changes
            in transport connectivity over the past 10 years. While the
            average travel speed has slightly increased, transport costs
            have been nearly halved. The estimated translog cost
            functions indicate that local market accessibility is the
            most important factor to boost firm productivity. The
            elasticity was 0.24 in absolute terms. Inventory is found to
            be a major cost factor for firms. It is found that a
            10-percent improvement in accessibility to large cities,
            such as Astana and Almaty, could allow firms to reduce their
            inventory by 8.7 percent. The market accessibility is found
            to foster firm agglomerations, but agglomeration economies
            do not seem to translate into higher firm productivity. This
            is possibly because the Kazakh economy still lacks effective
            forward or backward linkages across industries.
display_title: Estimating the Impacts of Transport
            Corridor Development in Kazakhstan : Application of Dynamic
            Panel Data Models to Firm Registry Data
listing_relative_url: /research/2022/09/33908609/estimating-impacts-transport-corridor-development-kazakhstan-application-dynamic-panel-data-models-firm-registry-data
topicv3: Transport Economics m1326662 162
publishtoextweb_dt: 2022-09-29T00:00:00Z
dois: 10.1596/1813-9450-10196
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 914707
model_extractions: Policy Research Working Paper 10196 Estimating the Impacts of Transport Corridor Development in Kazakhstan Application of Dynamic Panel Data Models to Firm Registry Data _Atsushi Iimi_ Transport Global Practice September 2022
Policy Research Working Paper 10196

# **Abstract**

Large-scale transport infrastructure investment can facilitate structural transformation by changing firm behavior. Although its impact is evident over the long term, an important empirical challenge is potential endogeneity of infrastructure placement. By using the dynamic panel data regression, the paper examines the impacts of massive road corridor investment under the Nurly Zhol program in Kazakhstan. The paper takes advantage of detailed micro shipping data to capture historical changes in transport connectivity over the past 10 years. While the average travel speed has slightly increased, transport costs have been nearly halved. The estimated translog cost functions indicate that local market accessibility is the most important factor to boost firm productivity. The elasticity was 0.24 in absolute terms. Inventory is found to be a major cost factor for firms. It is found that a 10-percent improvement in accessibility to large cities, such as Astana and Almaty, could allow firms to reduce their inventory by 8.7 percent. The market accessibility is found to foster firm agglomerations, but agglomeration economies do not seem to translate into higher firm productivity. This is possibly because the Kazakh economy still lacks effective forward or backward linkages across industries.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Estimating the Impacts of Transport Corridor Development in Kazakhstan: Application of Dynamic Panel Data Models to Firm Registry Data**

Atsushi Iimi<sup>¶</sup> Eastern and Southern Africa, Transport The World Bank Group Key words: Transport connectivity, firm inventory, cost function, agriculture production, Dynamic panel regression.

JEL classification: D22; D24; Q12; C26.

> ¶ Corresponding author.
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# **I. Introduction**

Large-scale infrastructure investment can facilitate structural change of an economy. The literature is generally supportive of this: There is a wide range of direct and indirect effects of large infrastructure investment (e.g., Datta, 2012; Donaldson, 2018; Duranton and Venables, 2018; Ferraz and Coutinho, 2019; Banerjee et al., 2020; IMF 2020; Li et al., 2020). However, the long-term impact remains open to debate when causality is considered (e.g., Jiang et al., 2017; Valila, 2020). One of the most important empirical difficulties comes from potential endogeneity between economic outcomes and infrastructure placement (e.g., Datta, 2012; Banerjee et al., 2020). While infrastructure investment is generally believed to raise the productivity of an economy, public infrastructure is often placed where economic productivity is inherently high. Therefore, regardless of its real impact, positive correlation is likely to be observed between them.

Using micro data from Kazakhstan, the current paper attempts to address the endogeneity issue and examine the effects of large-scale transport infrastructure investment. Kazakhstan is a large landlocked country with a low population density. Transport connectivity has long been an important challenge to the country. In the early 2010s, the government embarked upon a massive infrastructure investment program, Nurly Zhol, to redevelop public infrastructure. In the transport sector, the government doubled road investment in regional corridors connecting major cities to Western Europe and Western China. The paper shows that the improved regional connectivity allowed firms to reduce their inventory, increasing their productivity.

In the literature, there are at least two approaches to deal with the endogeneity problem of infrastructure investment: The first approach is the computational general equilibrium (CGE) model, which captures various demand and supply effects as a system of equations. The CGE approach can analyze causality of different economic activities, including transport infrastructure investment. A number of regional and national CGE models exist (e.g., Kim et al., 2004; Nguyen and Wigle, 2011). Brocker et al. (2010), for instance, examine the welfare impact of the TransEuropean Transport Network on the regional economies. Despite their computational sophistication and capability of multisectoral analysis, one of the disadvantages of the CGE
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models is that it is often difficult to interpret the predicted effects in practical terms – i.e., what causes what, and why.

Structural equation modeling (e.g., Li et al., 2018) and input-output models (e.g., Yu, 2018) have similar characteristics to CGE. For example, Jiang et al. (2017) propose a structural equation model to relate transportation demand and supply to the overall economic production, showing that transport investments had significant heterogeneous impacts on regional economic growth in China. From the practical point of view, unfortunately, it is not so straightforward to interpret the results and hard to understand how transport investment, employment, transport demand and income (or consumption) are actually interlinked with each other.

The second approach to deal with the endogeneity is the instrumental variable (IV) method (e.g., Datta, 2012; Banerjee et al., 2020). It is a partial equilibrium model but allows more direct interpretation of results. The current paper follows this approach but combines it with the dynamic panel data regression. The empirical validity will entirely depend on the selection of IVs and ex post statistical tests, such as overidentifying restrictions. Recent studies have shown that quasi-random (or quasi-exogenous) factors, such as geographic conditions, physical nature of infrastructure and historical events, are valid instruments, because they are less relevant to current economic outcomes but may have influenced subsequent infrastructure developments (e.g., Jedwab and Maradi, 2012; Donaldson, 2018). Datta (2012) used the nature of highway infrastructure development as a network. If a highway connects two cities, other places between them can also benefit “unintentionally.” Banerjee et al. (2020) argue that the transport infrastructure placement in China was historically determined by proximity to old cities and treaty ports, which could be used to construct valid IVs.

Unlike the existing studies, the current paper applies dynamic panel data regression, taking advantage of rich micro data on both the transport connectivity and firm sides in Kazakhstan. Panel data analysis requires detailed time-series data on transport connectivity, which is often unavailable. In general, the transport sector has few available data. Moreover, transport infrastructure consists of long-lived capital assets that normally impact an economy gradually. Thus, it is even more challenging to collect time-series data that capture their relatively modest
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changes. This paper exploits stop-by-stop freight shipping data collected by the Asian Development Bank (ADB) over the period 2010-2019.<sup>1</sup> The data allows to estimate granular changes in transport costs and times at the individual road level, suggesting that transport connectivity was changed substantially over the last decade.

On the firm data side, the paper takes advantage of Kazakh comprehensive firm registry data with location identifiers, balance sheets and income statements, covering all formal sectors for the period of 2010-18. By matching this with the above connectivity data, panel data regression is performed, which is a powerful way to construct valid IVs systematically and avoid the endogeneity problem.

One of the disadvantages of the partial equilibrium approach may be that the bigger picture, i.e., what happens to the overall economy, might be misunderstood. To mitigate such a risk, the paper considers both direct and indirect effects of transport connectivity in the model and examines non-firm activities by applying the same methodology to the agriculture sector, which can help to understand the overall effects of improved regional corridors.

The remaining sections are organized as follows: Section II provides an overview of the country and sectoral context related to transport connectivity in Kazakhstan. Section III develops our empirical strategy. Section IV describes our data, and Section V presents the main estimation results and discusses policy implications. Section VI discusses the impacts of improved connectivity on agriculture. Then, Section VII concludes.

> 1 See ADB (2020) for more details. We would like to express our special thanks to the Central Asia Regional Economic Cooperation (CAREC) Corridor Performance Measurement and Monitoring (CPMM) team for sharing relevant raw data.
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<!-- Start of picture text --> 600 m Railway Railway m Road Road<br>- = Inland water Inland water water ® Maritime Maritime<br><<< 500 Air @ Pipeline<br>2c<br>5400400<br>5<br>= 300<br>g<br>ie)££ 200<br>°==<br>Oo 100<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2009 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017 2017<br><!-- End of picture text --> <!-- Start of picture text --> 4500 Railway @ Road Road<br>_ 4000 = Inland water_TM water_TM Maritime<br>522 3500 m Airii @ Pipelineipeliipeli<br>c<br>2 3000<br>£ 2500<br>a<br>& 2000<br>o99 1500<br>& 1000<br>& 500<br>0<br>2007 2008 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> 600 m Railway Railway m Road Road 4500 Railway @ Road Road<br>- = Inland water Inland water water ® Maritime Maritime _ 4000 = Inland water_TM water_TM Maritime<br><<< 500 Air @ Pipeline 522 3500 m Airii @ Pipelineipeliipeli<br>2c<br>5400400 2 3000<br>5 £ 2500<br>= 300 a<br>g & 2000<br>ie)££ 200 o99 1500<br>==<br>°== & 1000<br>Oo 100 & 500<br>0 0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2009 2010 2011 2012 2013 2014 2015 2016 2017 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017 2017 2007 2008 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2011 2012 2013 2014 2015 2016 2017 2014 2015 2016 2017 2015 2016 2017 2016 2017<br>_ 0.17 0.17 0.164<br>g ois 0-147 3 0.15<br>5° 0.134 «0.431 5° 0.137 0.135 0.132<br>3®%®% 0.13 0.116 0.119 3% 0.13 0.114::<br>8rr0.11 8 0.11<br>30.090.09 3 0.09<br>& 0.07 & 0.07<br>££<br>>0.050.05 > 0.05<br>°°<br>2F0F00 oe”o”o” o”ioio o”9?9? 2== i.os”os” oe”2”2” o”o”o” ov©© 9?<br>Market Access Index (0 to to 1) Market Access Index (0 to to 1)<br><!-- End of picture text --> <!-- Start of picture text --> 0.17 0.164<br>3 0.15<br>5° 0.137 0.135 0.132<br>% 0.13 0.114::<br>3%<br>8 0.11<br>3 0.09<br>& 0.07<br>£<br>> 0.05<br>2== i.os”os” oe”2”2” o”o”o” ov©© 9?<br>Market Access Index (0 to to 1)<br><!-- End of picture text --> <!-- Start of picture text --> _ 0.17<br>g ois 0-147<br>5° 0.134 «0.431<br>3®%®% 0.13 0.116 0.119<br>0.11<br>30.090.09<br>& 0.07<br>>0.050.05<br>2F0F00 oe”o”o” o”ioio o”9?9?<br>Market Access Index (0 to to 1)<br><!-- End of picture text -->
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**Figure 20. Market accessibility and firm agglomeration by sector, 2018**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Market Access Index (0-1)<br>Agriculture Fitted values<br>Manufacturing Fitted values<br>Retail & Wholesale Fitted values<br>1500<br>1000<br>500<br>Number of firms per district<br>0<br><!-- End of picture text -->

# **III. Empirical Models**

As discussed above, one of the most important empirical issues is the endogeneity of infrastructure placement. While public infrastructure is considered to have various positive impacts on an economy, it is often placed where productivity is already inherently high because of unobserved reasons for researchers. To deal with this problem, the paper uses the ArellanoBond (1991) dynamic panel data regression. The fixed-effects regression model can mitigate the endogeneity bias under certain assumptions (e.g., parallel trend assumption) but cannot eliminate it in general (e.g., Nickell, 1981).<sup>8</sup> Using the same data as the current paper, IMF (2020) estimates the impact of transport infrastructure under Nurly Zhol on firm revenues and profits by the fixed-effects method. However, the fixed-effects model may lead to biased estimates if there are any time-variant individual effects that are correlated with the residuals.

In addition, the previous study does not necessarily explain why the close presence of improved regional transport corridors increased firm revenue and profit, but not employment. It may be more useful to build an empirical model based on some economic theories so that the underlying results chain could be understood better.

> 8 As will be shown below, the dynamic panel model estimator is more suitable to our data where there is a strong first-order serial correlation in the first-difference residuals.
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To address these issues, the current paper starts with the traditional theory of production. Suppose that firm _i_ at time _t_ minimizes its production cost ( _c_ ) to produce output _y_ (total production or sales) by choosing inputs _X_ ’s. Five inputs are considered: labor ( _L_ ), energy ( _E_ ), fuel ( _F_ ), interest paid ( _I_ ) and other inputs ( _M_ ). Then, the firm _i_ ’s production cost in equilibrium can be written as a function of input prices, _W_ , conditional on output _y_ : The firm’s productivity is also assumed to depend on a variety of time-invariant firm-specific characteristics, _Ui_ , such as ownership structure and the number of years in operation as proxy of firm’s experience in the industry, as well as time-variant characteristics around firms (denoted by _Zit_ ). All time-invariant firm-specific effects are removed by the first difference transformation of the dynamic panel model.

For the time-variant firm characteristics, the paper considers both direct and indirect impacts of transport connectivity. For the direct effect, three types of connectivity are examined: (i) transport connectivity to the nearest large city ( _CITYit_ ), (ii) transport connectivity to the nearest boarder point ( _BPit_ ), and (iii) market access index ( _MAIit_ ). In theory, the MAI is the most general measurement capturing various dimensions of transport accessibility in a country. It is defined by the sum of purchasing power or market capacity, _m_ , inversely weighted by the degree of impediment ( _d_ ) between two locations.<sup>9</sup> For _m_ , the city population is used as a proxy of the market capacity. _d_ is measured by transportation time or costs from an origin to a large city: These connectivity variables are expected to capture the direct impacts of connectivity on production costs. For instance, better transport connectivity means lower costs of transporting inputs and outputs. Better connectivity may also allow firms to save other transaction costs.

> 9 This is based on a conventional gravity framework. The literature also uses the negative exponential weights. See for instance Elbadawi, Mengistae and Zeufack (2006) and Lall and Mengistae (2005).
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Unlike the existing literature, we also incorporate the indirect effects separately. At least two types of indirect effects are particularly relevant to the transport-growth nexus: First, the profitability of firms is affected by inventory ( _Qit_ ). In practice, annual inventory costs are estimated at between 7 to 16 percent of the value of held inventory. Since a firm normally keeps inventory of 10 percent of products, it implies that an average firm spends about 0.7-1.6 percent of total revenue to carry inventories (Brown, 2011).

The firm’s inventory is affected by transport connectivity. The traditional economic order quantity (EOQ) or ( _S_ , _s_ ) model indicates that the optimal inventory level is determined by the size of market demand, the costs of ordering and transportation, and inventory holding costs (e.g., Arrow _et al_ . 1951). Thus, if purchasing costs are high, for instance, because of unreliable transport infrastructure and slow customs clearance, firms would likely hold greater inventory to prepare for unexpected disruption in the supply of materials and equipment. The empirical evidence is supportive of this: Shirley and Winston (2004) find that firm inventory declined by 7 cents for every additional dollar of investments in the highway capital in the United States. In India, firms in cities located along the improved highway network (i.e., Golden Quadrilateral project) reduced their inventory by 7 operating days’ supply (Datta 2012).

Second, agglomeration economies are another indirect effect. In theory, firms are located where other firms exists because of the benefits from shared input markets and lower transport and transaction costs (e.g., Krugman, 1991; Holl, 2004; Procher, 2011; Lee et al. 2012; Mare and Graham, 2013; IMF 2020). The current paper measures agglomeration economies at two different levels: The number of total firms registered at time _t_ where firm _i_ is located, denoted by _Nit_ ,<sup>10</sup> and the number of firms in the same industry as firm _i_ in the same location, _Mit_ .<sup>11</sup> Hence, we have six time-variant transport-related variables: , B , M . ZZ= {CCMMC MM, QQ, NN, MM} As in the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976), the following translog cost function is considered:

> 10 In our data, firms’ locations are identified by district.

> 11 For industrial classification, the International Standard Industrial Classification (ISIC) sections are used.
- 17 -

1 1 i i 2 y + 2 h h h + pp pp −pp yy 1 kk wwkk kk kk wwkkww kk ln cc = ∑αα ln cc + ββ ln yy++ 2ββ ln yyln yy+ ∑ββh h hln ww+ ∑∑ββ ln ww+ ln ww ∑ββkk yywwh kk ln yyln wwh kk + ∑ββhkk+zzkk ln zzkk ∑∑ββkk zzkkzz ln zzkk ln zz (2) + ∑ββkk yyzzkk ln yyln zzkk kk wwkkzz kk ii ii ∑∑ββ ln ww ln zz uu + uu + εε To have a well-behaved cost function, the following symmetry and homogeneity restrictions are imposed: h h h h h h h = 0 wwkkww (3) ww wwkk zzkkzz zz zzkk kk wwkk kk wwiiww kk yywwkk kk wwkkzz ββ = ββ , ββ = ββ , ∑ββ = 1 , ∑∑ββ = 0 , ∑ββ = 0 , ∑ββ To estimate Equation (2), the Arellano-Bond (1991) dynamic panel data model is applied. By applying the dynamic panel model, the unobserved firm-specific fixed-effects _ui_ are eliminated, and the potential endogeneity among the variables in the equation is also addressed simultaneously. Given the parameters estimated, the price elasticity of factor demand can be calculated using the Allen’s partial elasticities of substitution between inputs _k_ and _h_ (e.g., Uzawa, 1962; Berndt and Wood, 1975): The paper also investigates the inventory and agglomeration effects measured by _Q_ , _N_ or _M_ . A simple EOQ model suggests that the optimal inventory increases with the demand size (represented by firm’s total production or sales, _y_ ) and decreases with transport connectivity _vk_ and interest rate ( _INT_ ):
To control for the size difference across firms, the number of employees ( _L_ ) is included with other firm-specific characteristics, _X_ . For transport connectivity _V_ , the same variables are used: _CITY_ , _BP_ and _MAI_ . Again, the equation is estimated by the dynamic panel data model.

Agglomeration economies can also be influenced by transport connectivity. To examine this, the following equation is estimated by the dynamic panel model: where _Njt_ is the number of firms registered in location or district _j_ at year _t_ . The dynamic panel data model not only eliminates any time-invariant location-specific characteristics _uj_ and but also addresses the potential endogeneity of transport connectivity _vk_ .

# **IV. Data**

On the firm data side, our sample data come from the official firm registry data in Kazakhstan, which comprises about 25,000 to 55,000 enterprises registered in the whole country after duplicated observations are excluded ( **Figure 21** ). It covers most formal enterprises, including both small and medium-sized and large firms, across all sectors, from agriculture to manufacturing and services. The data is available for the period of 2010 to 2018, constituting a very rich panel data, though unbalanced. In this paper, the period of 2012 to 2018 is only used because the available shipping data, which is used to measure transport connectivity, is less reliable in the first two year of the data monitoring program. Excluding observations with some missing data, about 41,000 observations are used in the cost analysis, i.e., to estimate Equation (2). Fewer observations are used for the inventory analysis (Equation (5)) and the agglomeration economies (Equation (6)), because of different specifications including more lagged dependent
<!-- Start of picture text --> 60,000<br>if<br>2<br>5 50,000<br>© 40,000<br>mo}<br>v<br>g 30,000<br>oO<br>2<br>45 20,000<br>3<br>2 10,000<br>a<br>a<br>)<br>2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text -->
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emergency repair costs. The traditional approach often measures straight-line distance or makes assumptions on average speed or unit transportation costs per km, depending on road characteristics, which may not be true. The following analysis primarily uses the cost-based connectivity variables. The results remain largely the same even if the time-based variables are used.<sup>13</sup>

**<u>Table 1. Border points used for the analysis</u>**

|Borderpoint|Neighboringcountry|
|---|---|
|Aul|Russian Federation|
|Kairak|Russian Federation|
|Zhaisan|Russian Federation|
|Ganyushking|Russian Federation|
|Kurmangazy|Russian Federation|
|Taskala|Russian Federation|
|Pogodaevo|Russian Federation|
|Tazhen|Uzbekistan|
|Saryagash|Uzbekistan|
|Zhibek Zholy|Uzbekistan|
|Konysbayeva|Uzbekistan|
|Aisha Bibi|Kyrgyz Republic|
|Merke|Kyrgyz Republic|
|Kordai|Kyrgyz Republic|
|Karasu|Kyrgyz Republic|
|Dostyk|China|
|Khorgos|China|
|Aktau|Caspian Sea|

The summary statistics are shown in **Table 2** . The data comprises a wide range of firms from small companies to large enterprises. Depending on industry, cost structures look different: Some consumed a lot of energy, and others are more labor-intensive. The number of firms existing at the same location also varies substantially: Some enterprises coexist with other companies in the same locality, and others are more isolated.

For transport connectivity variables, the transport costs to the nearest city varies from nearly zero to US$14 with an average of US$1.29. It means that some firms are located about 200 km away from the nearest city. The costs of transporting goods to the nearest border point are much higher, depending on where firms are located: The cost range from US$5 to US$80, meaning

> 13 The cost- and time-based variables are different but closely related to one another. If a road is in poor condition, it is likely to take more time to move from one place to another. Transportation costs also tend to be higher because vehicle operating costs, which comprise fuel costs, maintenance costs and driver’s time costs, are high.
- 21 -

that some firms are about 2,000 km away from the nearest border point. On average it costs about US$35 per ton.

# **<u>Table 2. Summary statistics: Firm registry data</u>**

|Variable|Abb.|Obs|Mean<br>|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Cost analysis:|||||||
|Total production or operating costs|_C_|41,410|<br>342,164|3,939,146|1.00|<br>257,000,000|
|(1,000 tenge)|||||||
|Average wage (total payroll expenses|_Wl_|41,410|<br>1,451|6,412|0.02|<br>833,377|
|divided by the average number of<br>employees during the year) (1,000 tenge)|||||||
|Energy costs divided by total asset value|_We_|41,410|<br>0.9|99.1|0.00|<br>19,744|
|Fuel costs divided by total asset value|_Wf_|41,410|<br>0.3|6.2|0.00|<br>566|
|Financing costs divided by total asset|_Wi_|41,410|<br>6.7|551.9|0.00|<br>62,446|
|value|||||||
|Other costs divided by total asset value|_Wm_|41,410|<br>16.1|839.3|0.00|<br>146,091|
|Total revenue (1,000 tenge)|_Y_|41,410|<br>510,205|6,484,125|1.00|<br>410,000,000|
|Transport costs to the nearest city ($/ton)|_CITY_|41,410|<br>1.29|1.91|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|41,410|<br>35.83|17.73|5.36|<br>80.98|
|point ($/ton)|||||||
|Market Access Index based on transport<br>costs|_MAI_|41,410|<br>0.25|0.34|0.01|<br>1.00|
|Amount of inventories at the end of the|_Q_|41,410|<br>83,400|1,056,314|0.50|<br>104,000,000|
|period (1,000 tenge)|||||||
|<br>Number of firms located in the same|_N_|41,410|<br>2,260|2,719|1|<br>9,317|
|district|||||||
|Number of firms in the same industry<br>that are located in the same district|_M_|41,410|<br>186|281|1|<br>1,891|
|Inventory analysis:|||||||
|Amount of inventories at the end of the|_Q_|17,475|<br>113,447|1,290,961|0.00|<br>104,000,000|
|period (1,000 tenge)|||||||
|Transport costs to the nearest city ($/ton)|_CITY_|17,475|<br>1.37|2.02|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|17,475|<br>35.61|17.85|5.36|<br>80.98|
|point ($/ton)|||||||
|<br>Market Access Index based on transport|_MAI_|17,475|<br>0.22|0.32|0.01|<br>1.00|
|costs|||||||
|Interest rate (financing costs divided by<br>total|_I_|17,475|<br>6.43|507.44|0.00|<br>62,446|
|Total revenue (1,000 tenge)|_Y_|17,475|<br>740,949|8,362,113|1.00|<br>410,000,000|
|Number of employees|_L_|17,475|<br>33.49|29.72|1|<br>999|
|Share of the major business revenue in<br>total revenue|_diverse_|17,475|<br>0.07|0.44|-38.58|<br>1.00|
|Agglomeration analysis:|||||||
|<br>Number of firms located in each district|_N_|1,104|<br>239.28|744.48|1|<br>9,317|
|Transport costs to the nearest city ($/ton)|_CITY_|1,104|<br>2.00|2.69|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|1,104|<br>36.64|14.81|5.36|<br>80.98|
|point ($/ton)|||||||
|<br>Market Access Index based on transport<br>costs|_MAI_|1,104|<br>0.08|0.11|0.01|<br>1.00|
- 22 -

# **V. Main Estimation Results**

First, the cost function is estimated by the dynamic panel data model. The estimated full equations are presented in **Appendix 2** . With one lagged dependent variable included on the righthand side, the Arellano-Bond tests indicate that autocorrelation exists in first differences but the second-order serial correlation also exists, violating the validity of the second lagged endogenous variables as instruments for their current values. Thus, three lagged variables are used as covariates. The differenced residuals are found to have no second-order autocorrelation. The Sargan-Hansen tests of overidentifying restrictions are broadly accepted.

Based on the estimated coefficients, the output elasticity is calculated in relation to our direct and indirect connectivity impacts ( **Table 3** ). The first column result is associated with the specification with all six variables included. Among the direct effects, only the connectivity to the nearest city or market ( _CITY_ ) has a statistically significant impact on firm production. The elasticity is estimated at -0.237, meaning that a 10 percent improvement of city connectivity could increase firm production by 2.4 percent.

Other types of transport connectivity, i.e., _BP_ and _MAI_ , do not have a clear impact on firm production. One may wonder if there is multicollinearity among them. Even if only one of them is included in the model, the results remain the same: The coefficient of the transport cost to the nearest border point is negative as expected, but it is statistically insignificant. The MAI has a positive elasticity as expected, but it is also insignificant. A policy implication is straightforward: In the Kazakhstan context, local connectivity is the most important factor to increase firm production, thereby boosting the domestic economy. Connectivity to border points, i.e., neighboring countries, and inter-city connectivity within the country, which is captured by MAI, may also be important. But their economic impacts remain inconclusive.

For the indirect effects, firm inventory has a strong impact on production. This is consistent with common business practices: The more inventories, the more costly. The elasticity is estimated at -0.32 to -0.64, depending on model specification. The elasticities are all statistically significant.
- 23 -

Therefore, it can be concluded that firm productivity decreases with inventory. If inventories are reduced by 10 percent, firm production increases by 3 to 6 percent. As will be seen below, the level of inventory is related to transport connectivity.

Contrary to our prior expectation, agglomeration economies are found to be insignificant. In the last column model, the output elasticity associated with the number of firms at the same locality, _N_ , is estimated to be negative at the 10 percent significance level, meaning that firm production would be reduced when more firms agglomerate together at the same place, i.e., diseconomies of agglomeration. This may be able to be interpreted as a fact that industrial backward and forward linkages are still weak in Kazakhstan. Firms’ locational choice may remain suboptimal to maximize the firm production as a whole.

**<u>Table 3. Output elasticity with respect to direct and indirect transport impacts</u>**

||(1)<br>||(2)<br>|<br>|(3)<br>|<br>|(4)<br>|<br>|
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|_CITY_|-0.237|(0.145) *|-0.162|(0.094) *|||||
|_BP_|-0.106|(0.142)|||-0.131|(0.142)|||
|_MAI_|-0.029|(0.178)|||||0.077|(0.133)|
|_Q_|-0.637|(0.382) *|-0.322|(0.054) ***|-0.340|(0.059) ***|-0.336|(0.060) ***|
|_N_|-0.046|(0.545)|-0.160|(0.108)|-0.152|(0.113)|-0.201|(0.120) *|
|_M_|0.122|(0.463)|-0.322|(0.529)|-0.253|(0.547)|-0.322|(0.570)|

Regarding the production inputs, the implied factor demand elasticities are computed. As expected, the elasticities are all negative and significant, meaning that the demand for production factors would decline when their prices increase ( **Table 4** ). Labor demand is the most priceelastic, followed by energy and financial services. Fuel demand is the least elastic, implying that firms cannot reduce their use of fuel even if fuel prices increase. By contrast, if wages increase, firms are likely to adjust their demand for labor, resulting in a significant increase of unemployment. The results are robust against specifications.

The output elasticities associated with factor prices are also calculated ( **Table 5** ). The highest elasticity is found for labor. When wages decline, firm production could be stimulated significantly. The elasticity is estimated at about 0.45 in absolute terms. Interest rates also have a relatively large elasticity, indicating that firms may have considerable financial constraints and a
- 24 -

heavy interest payments burden in Kazakhstan. The fuel and energy prices have a fewer impacts on firm production.

**<u>Table 4. Implied factor demand elasticity</u>**

||(1)<br>|<br>|(2)<br>|<br>|(3)<br>|<br>|(4)<br>|<br>|
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|Labor|-0.986|(0.088) ***|-0.959|(0.087) ***|-0.950|(0.087) ***|-0.946|(0.087) ***|
|Energy|-0.704|(0.026) ***|-0.710|(0.019) ***|-0.698|(0.025) ***|-0.698|(0.025) ***|
|Fuel|-0.626|(0.121) ***|-0.576|(0.160) ***|-0.518|(0.218) **|-0.476|(0.268) *|
|Interest|-0.734|(0.016)***|-0.731|(0.016)***|-0.730|(0.017)***|-0.729|(0.017)***|

**<u>Table 5. Implied output elasticity associated with factor prices</u>**

||(1)||(2)||(3)||(4)||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|Labor|-0.451|(0.088) ***|-0.427|(0.085) ***|-0.448|(0.088) ***|-0.456|(0.090) ***|
|Energy|-0.129|(0.031) ***|-0.140|(0.030) ***|-0.130|(0.031) ***|-0.130|(0.031) ***|
|Fuel|-0.057|(0.027) **|-0.049|(0.026) *|-0.044|(0.027) *|-0.041|(0.027)|
|Interest|-0.266|(0.033)***|-0.258|(0.032)***|-0.275|(0.033)***|-0.278|(0.034)***|

Since the impact of inventory on firm production is found to be significant, it is further investigated whether and how firm inventory is affected by transport connectivity. To estimate the simple EOQ model (Equation (5)), the dynamic panel data regression is applied. It is found that the market access index has a significant impact on firm inventory ( **Table 6** ). This is consistent with the existing literature (e.g., Shirley and Winston, 2004; Datta 2012). The elasticity is estimated at about -0.87. This implies that besides the observed direct impact of local transport connectivity on firm production, firm inventory is an important cost factor, which is also affected by transport infrastructure, especially market accessibility. Thus, to reduce firm inventories, it is important to connect them to large cities and markets within the country.

Notably, the firm inventory behavior looks different across industries. When the sample data is divided into six subsectors, the impact of market accessibility is particularly significant in the agriculture, mining, power, water and construction industries, and possibly in the manufacturing industry. The inventory elasticity is negative at -0.73 for manufacturers with a p-value of 12 percent. Market accessibility within the country is of particular importance for these industries.
- 25 -

For other covariates, the estimation results follow economic theory. As expected, the interest rate mostly has negative coefficients: When inventory costs are higher, the level of inventory is reduced. The coefficient of total sales or production is found to be positive and significant. This is also consistent with the economic order quantity model: The greater demand, the higher inventory. All these results seem to be supportive of our empirical model specification.
**<u>Table 6. Inventory equation: Arellano Bond dynamic panel regression</u>**

|Sector|All||Agricul<br>Mining|ture &<br>|Manufa|cturing|Power/<br>Constru|Water/<br>ction|Retail &<br>Wholesa|<br>le|Transp|ort|Other s|ervices|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|βQt-1|0.239|(0.062) ***|0.214|(0.113) *|0.100|(0.137)|0.204|(0.105) *|0.056|(0.173)|0.319|(0.169) *|0.349|(0.079) ***|
|βQt-2|0.056|(0.027) **|0.029|(0.038)|-0.066|(0.041) *|-0.008|(0.048)|0.0026|(0.0453)|0.005|(0.052)|0.117|(0.041) ***|
|βQt-3|0.081|(0.037) **|||||||||||||
|βCITY|-0.056|(0.051)|0.059|(0.085)|-0.191|(0.146)|-0.249|(0.115) **|0.412|(0.355)|-0.003|(0.099)|-0.076|(0.104)|
|βBP|-0.012|(0.056)|0.040|(0.107)|0.144|(0.099)|0.101|(0.159)|-0.201|(0.166)|-0.116|(0.158)|-0.043|(0.066)|
|βMAI|-0.865|(0.436) **|-1.310|(0.776) *|-0.727|(0.468)|-1.096|(0.556) **|0.177|(0.322)|-1.008|(1.040)|-0.664|(0.694)|
|βI|-0.0004|(0.0004)|-0.051|(0.016) ***|-0.022|(0.108)|-0.033|(0.013) **|-0.0014|(0.0002) ***|-0.018|(0.002) ***|0.000|(0.000)|
|βY|0.065|(0.027) **|0.027|(0.049)|0.068|(0.042) *|-0.003|(0.046)|0.176|(0.084) **|0.168|(0.073) **|-0.021|(0.058)|
|βL|0.331|(0.044) ***|0.296|(0.096) ***|0.355|(0.087) ***|<br>0.432|(0.131) ***|<br>0.527|(0.122) ***|<br>0.104|(0.107)|0.419|(0.079) ***|
|βdiverse|-0.022|(0.044)|0.047|(0.051)|0.091|(0.203)|0.195|(0.318)|0.222|(0.232)|-0.304|(0.301)|-0.080|(0.079)|
|t=2014|||0.031|(0.034)|0.026|(0.032)|-0.039|(0.022) *|-0.007|(0.041)|0.055|(0.037)|0.004|(0.022)|
|t=2015|-0.008|(0.020)|0.018|(0.040)|0.071|(0.053)|-0.014|(0.039)|-0.051|(0.069)|0.016|(0.047)|-0.016|(0.031)|
|t=2016|0.119|(0.046) ***|0.171|(0.086) **|0.162|(0.097) *|0.060|(0.108)|0.060|(0.095)|0.097|(0.094)|0.193|(0.061) ***|
|t=2017|0.112|(0.047) **|0.178|(0.083) **|0.208|(0.099) **|0.060|(0.113)|0.127|(0.098)|0.025|(0.097)|0.140|(0.063) **|
|t=2018|0.115|(0.046) **|0.239|(0.083) ***|0.247|(0.095) ***|<br>0.074|(0.121)|0.061|(0.126)|-0.056|(0.104)|0.104|(0.069)|
|constant|3.739|(0.833)***|6.251|(1.376)***|7.104|(1.706)***|5.157|(1.216)***|7.166|(1.526)***|3.874|(1.744)**|3.194|(0.840)***|
|Obs.|17475||4014||2592||3019||1914||2296||7886||
|Group|7614||1553||1344||1335||1104||986||3451||
|Wald chi|133.02||49.7||45.1||31.93||1756.26||307.4||91.91||
|Arellano-|Bond test:||||||||||||||
|AR(1)|-6.19|***|-3.41|***|-2.4|***|-4.35|***|-1.16||-2.77|***|-5.82|***|
|AR(2)|-1.63||0.98||-1.86|*|-0.53||1.09||-1.63||-2.48|**|
|AR(3)|-1.06||-0.55||1.34||0.32||0.82||0.98||1.01||
|Hansen|||||||||||||||
|test|38.02||33.35||37.99||25.68||35.57||30.72||34.53||

Note: The dependent variable is the firm's total value of inventory in logarithm. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% statistical significance, respectively.
Another type of indirect effect, which is the impact of transport connectivity on firm agglomeration, is examined by the dynamic panel regression ( **Table 7** ). When one lagged dependent variable is included as a covariate, the Sargan-Hansen test can be rejected, indicating that additional instruments are not exogenous. With three lagged variables, the overidentifying restriction condition is met. Not surprisingly, there are significant agglomeration economies: More firms are located where other firms exist. The lagged values of _N_ have significantly positive coefficients.

It is also shown that the market accessibility has a significant positive impact on agglomeration economies. The elasticity is estimated at 0.168 with a standard error of 0.076. Thus, even though agglomeration economies may not result in stimulating firm production and economic growth, the improved market accessibility can certainly foster agglomeration in the country.

Interestingly, local transport connectivity ( _CITY_ ) or regional connectivity ( _BP_ ) does not seem to be conducive to agglomeration economies. The domestic market integration at the more general level is required in this regard.

The transport impact on firm agglomeration also varies across sectors ( **Table 8** ). The estimated impact is particularly strong in the manufacturing, retail and wholesale, and other service industries. The elasticity is highest at 0.19 in the retail and wholesale sector, followed by 0.16 in the manufacturing industry and 0.03 in other services. The policy implication is complex: In general, the Kazakh economy exhibits agglomeration economies: Large firm agglomerations, presumably around large cities, are likely to grow further. Improved transport connectivity, i.e., market accessibility facilitates this process even more, especially in the manufacturing and service industries. Nonetheless, agglomerations economies do not have a clear impact on firm production, possibly indicating that the Kazakh economy still lacks effective forward or backward linkages across industries. This may also represent a potential risk of urbanization without growth. To stimulate growth from the transport point of view, local connectivity is key.
- 28 -

**<u>Table 7. Agglomeration economies: Arellano Bond dynamic panel regression</u>**

||Coef.|Std.Err.||Coef.|Std.Err.||
|---|---|---|---|---|---|---|
|βNt-1|0.128|(0.083)||0.479|(0.078)|***|
|βNt-2||||0.131|(0.049)|***|
|βNt-3||||0.074|(0.042)|*|
|βCITY|0.031|(0.088)||0.014|(0.031)||
|βBP|-0.032|(0.170)||-0.012|(0.063)||
|βMAI|0.481|(0.181)|***|0.168|(0.076)|**|
|t=2013|0.307|(0.048)|***|0.174|(0.037)|***|
|t=2014|0.347|(0.034)|***|0.221|(0.031)|***|
|t=2015|0.444|(0.062)|***|0.167|(0.047)|***|
|t=2016|0.512|(0.065)|***|0.197|(0.045)|***|
|t=2017|0.707|(0.064)|***|0.362|(0.055)|***|
|t=2018|1.231|(0.085)|***|0.800|(0.080)|***|
|constant|4.441|(0.950)|***|1.632|(0.612)|***|
|Obs.|1104|||1098|||
|Group|162|||157|||
|Wald chi|1107.8|||1738.0|||
|Arellano-B|ond test:||||||
|AR(1)|-4.30|***||-4.81|***||
|AR(2)|-0.61|||-0.72|||
|Hansen|||||||
|test|56.37|***||10.53|||
**<u>Table 8. Agglomeration economies by sector: Arellano Bond dynamic panel regression</u>**

||Agriculture &<br>Mining|Manufacturing|Power/<br>Constru|Water/<br>ction|Retail &<br>Wholesale|Transpor|t|Other services|
|---|---|---|---|---|---|---|---|---|
||Coef. Std.Err.|Coef. Std.Err.|Coef.|Std.Err.|Coef. Std.Err.|<br>Coef.|Std.Err.|Coef.<br>Std.Err.|
|βNkt-1|0.553 (0.068) ***|-0.072 (0.113)<br>|0.433|(0.092) ***|-0.120 (0.123)|<br>0.404|(0.084) ***|0.727 (0.041) ***|
|βNkt-2|0.200 (0.032) ***|-0.027 (0.086)<br>|0.196|(0.052) ***|-0.005 (0.076)|0.253|(0.052) ***|0.035 (0.027)|
|βMt|-0.137 (0.230)|0.771 (0.137) ***|0.712|(0.146) ***|1.914 (0.314)|***<br>0.743|(0.161) ***|0.450 (0.100) ***|
|βMt-1|-0.181 (0.082) **|0.386 (0.117) ***|-0.189|(0.111) *|0.174 (0.176)|-0.420|(0.173) **|-0.704 (0.118) ***|
|βMt-2|0.426 (0.179) **|-0.122 (0.199)|-0.225|(0.183)|-0.810 (0.326)|***<br>0.010|(0.163)|0.455 (0.120) ***|
|βCITY|-0.015 (0.016)|0.072 (0.048)|0.009|(0.018)|0.065 (0.065)|-0.005|(0.015)|0.006 (0.011)|
|βBP|-0.026 (0.031)|-0.029 (0.070)|0.002|(0.037)|-0.039 (0.100)|0.069|(0.030) **|0.044 (0.023) *|
|βMAI|-0.023 (0.026)|0.160 (0.062) ***|0.007|(0.029)|0.191 (0.106)|*<br>0.010|(0.021)|0.031 (0.019) *|
|t=2013|0.134 (0.114)|-0.134 (0.072) *|-0.044|(0.073)|-0.046 (0.149)|-0.011|(0.074)|0.164 (0.053) ***|
|t=2014|0.334 (0.110) **|-0.020 (0.072)|-0.123|(0.079)|-0.316 (0.154)|**<br>-0.059|(0.080)|0.156 (0.061) ***|
|t=2015|0.202 (0.061) ***|-0.128 (0.058) **|-0.017|(0.061)|-0.139 (0.147)|0.030|(0.055)|0.477 (0.069) ***|
|t=2016|0.195 (0.047) ***|-0.014 (0.063)|-0.096|(0.053) *|-0.177 (0.124)|0.091|(0.061)|0.126 (0.056) **|
|t=2017|0.335 (0.084) ***|-0.003 (0.069)|-0.087|(0.076)|-0.373 (0.172)|**<br>0.057|(0.081)|0.254 (0.068) ***|
|t=2018|0.706 (0.190) ***|-0.029 (0.101)|-0.027|(0.115)|0.019 (0.236)|-0.112|(0.125)|0.208 (0.095) **|
|constant|-0.003 (0.140)|-1.609 (0.411)***|-0.700|(0.325)**|-2.762 (0.793)|***<br>-1.051|(0.303)***|-0.686 (0.116)***|
|Obs.|1656|694|1827||556|732||2407|
|Group|269|119|318||104|122||465|
|Wald chi|3510.3|1531.9|3345.5||1202.8|12878||12433|
|Arellano-Bon|d test:||||||||
|AR(1)|-8.22 ***|-3.71 **|-5.85|***|-2.88 ***|-5.55|***|-8.95 ***|
|AR(2)|-1.93 *|0.08|-2.00|**|-1.34|-0.92||2.32 **|
|AR(3)|1.18|-0.30|0.21||-1.97 **|1.05||-1.61|
|Hansentest|91.61 ***|46.08|66.57|***|49.01|54.75|*|91.22 ***|
# **VI. Discussion – Agriculture Sector**

The above analysis based on the official firm registry data covers almost all sectors in the entire economy, but one, agriculture. The database includes some 20,000 formal enterprises, which employ about 100,000 people in total. Although large agrobusinesses in the formal sector are included, a large number of small agricultural producers, such as self-employed farmers, are not included.<sup>14</sup> One may wonder if the improved transport corridors would affect their agricultural productivity.

To examine this question, the dynamic panel data method is applied to crop production data at the district level: Where _q_ denotes the total production of a particular crop at location _j_ at time _t_ . Four major crops are examined: cereal, potato oilseed and vegetable. The first two crops are traditional food crops in the country, but potato exports have been increased rapidly. Oilseeds and vegetables are also becoming increasingly important as export crops. These cash crops are expected to more sensitive to transport connectivity, _vk_ . No other time-variant variable is included in the equation. Again, the same approach is used to avoid the potential endogeneity between crop production and transport connectivity. All time-invariant characteristics, such as agroclimatic productivity of land, are removed by first differencing.

Four major crops grown in Kazakhstan are considered: cereal, potato, oilseed and vegetables. District-level panel data are available for these four crops in 199 districts during the period of 2011-19 ( **Table 9** ). The analysis only uses the data for 2012 to 2018.

> 14 In Kazakhstan, about 1 million people are estimated to engage in agriculture. According to WDI, for instance, the total labor force in Kazakhstan is about 9 million, of which about 16 percent are estimated to be working in the agriculture sector.
- 31 -

|**Table 9. Summary statistics: Crop production data**|
|---|

|Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Crop production at each district (ton)|_q_||||||
|Cereal||1,292|1,125,720|1,487,816|<br>0.2|9,017,332|
|Potato||1,481|<br>189,000|236,465|<br>5.4|1,516,768|
|Oilseeds||1,133|<br>133,064|209,732|<br>0.2|1,741,450|
|Vegetables||1,550|184,637.5|413,477.0|<br>120.0|3,696,046|
|Transport time to the nearest city (hour)|_CITY2_|<br>5,456|<br>1.69|1.63|<br>0.05|<br>13.70|
|Transport time to the nearest border point (hour)|_BP2_|5,456|<br>13.25|7.69|<br>1.12|<br>48.55|
|Market Access Index based on transport time|_MAI2_|5,456|<br>0.06|0.06|<br>0.00|<br>0.55|

The dynamic panel regression is performed for each of the crops. First, the standard lagged value instruments are found to be largely valid, except for the model for cereal production ( **Table 10** and **Table 11** ). Second, cereal production is least sensitive to transport connectivity. The statistical significance of the estimated coefficients is generally low. It seems that the way of producing cereal in Kazakhstan would not change dramatically because of transport connectivity.

It is found that local market connectivity has almost always have a positive impact on crop production, meaning that these crops are produced more when farms are away from local populated areas. This can be interpreted as the fact that agricultural production normally takes place in nonurban areas.

The regional connectivity measured by transport time to the nearest border point is important to produce more potatoes, oilseeds and vegetables. These are among the important emerging export crops from the country. Particularly, the estimated coefficients are statistically significant for oilseeds and vegetables. The elasticity is about -0.3, meaning that a 10 percent reduction in transport time to the border point (including border crossing time) could increase oilseed and vegetable production by 3 percent. This impact is significant.

Last, the impact of domestic market accessibility measured by _MAI_ on crop production is heterogeneous across crops, possibly depending on the domestic consumption behavior. For potatoes and vegetables, the domestic market access is important. The coefficients are estimated to be positive and significant. It implies that these crops are important not only as export commodities but also for domestic consumption. Particularly, vegetables are often perishable. The
- 32 -

proximity to the domestic markets is crucial. By contrast, the coefficients of MAI are negative for cereal and oilseed production, meaning that the domestic market accessibility does not really matter to these commodities.
**<u>Table 10. Cereal and potato production: Arellano Bond dynamic panel regression</u>**

||Cereal||||Potato||||
|---|---|---|---|---|---|---|---|---|
||Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|
|_qt-1_|0.237 (0.164)|0.213 (0.163)|0.245 (0.131) *|0.257 (0.156) *|0.533 (0.159) ***|0.649 (0.164) ***|0.666 (0.155) ***|0.539 (0.167) ***|
|_qt-2_|0.059 (0.106)|0.041 (0.105)|0.065 (0.086)|0.067 (0.101)|||||
|_qt-3_|0.111 (0.105)|0.107 (0.106)|0.119 (0.090)|0.128 (0.102)|||||
|_CITY2_|0.277 (0.170) *|0.437 (0.234) *|||0.135 (0.068) **|-0.077 (0.063)|||
|_BP2_|-0.126 (0.155)||-0.028 (0.153)||-0.065 (0.057)||-0.062 (0.057)||
|_MAI2_|-0.431 (0.353)|||-0.647 (0.399) *|0.605 (0.214) ***|||0.481 (0.196) **|
|t=2012|||||0.050 (0.048)|0.015 (0.043)|0.000 (0.044)|0.054 (0.046)|
|t=2013|||||0.027 (0.038)|0.036 (0.030)|0.015 (0.035)|0.047 (0.032)|
|t=2014|0.047 (0.060)|0.066 (0.052)|0.061 (0.056)|0.081 (0.051)|0.030 (0.044)|0.056 (0.034) *|0.025 (0.038)|0.061 (0.036) *|
|t=2015|0.362 (0.062) ***|0.395 (0.050) ***|<br>0.382 (0.059) ***|0.377 (0.050) ***|<br>0.067 (0.041) *|0.066 (0.032) **|0.035 (0.034)|0.092 (0.039) **|
|t=2016|0.204 (0.084) **|0.258 (0.083) ***|<br>0.234 (0.084) ***|0.208 (0.073) ***|<br>0.033 (0.048)|0.017 (0.039)|-0.014 (0.036)|0.055 (0.048)|
|t=2017|0.217 (0.134) *|0.266 (0.127) **|0.215 (0.125) *|0.245 (0.119) **|0.046 (0.055)|0.085 (0.040) **|0.048 (0.049)|0.083 (0.041) **|
|t=2018|0.141 (0.156)|0.191 (0.165)|0.147 (0.140)|0.145 (0.146)|0.007 (0.070)|0.021 (0.063)|-0.012 (0.062)|0.035 (0.066)|
|constant|6.305 (3.341)*|7.884 (4.049)*|7.138 (3.013)**|4.804 (2.930)*|7.296 (2.502)***|3.943 (1.842)**|3.912 (1.866)**|6.675 (2.452)***|
|Obs.|952|952|952|952|1481|1481|1481|1481|
|Groups<br>Wald|162|162|162|162|188|188|188|188|
|chi2|130.1|92.0|90.37|130.92|299.0|113.17|117.76|272.15|
|Arellano|-Bond test:||||||||
|AR(1)|-3.04 ***|-2.98 ***|-3.52 ***|-3.24 ***|-2.7 ***|-2.99 ***|-3.11 ***|-2.65 ***|
|AR(2)|-1.21|-1.01|-1.36|-1.16|1.51|1.43|1.45|1.46|
|AR(3)<br>Hansen|0.68|0.75|0.77|0.64|-0.54|-0.30|-0.35|-0.42|
|test|50.00***|50.60***|50.90***|49.72***|33.93|34.77|36.07|33.13|
- 34 -

**<u>Table 11. Oilseed and vegetable production: Arellano Bond dynamic panel regression</u>**

||Oilseed||||Vegetable||||
|---|---|---|---|---|---|---|---|---|
||Coef. Std.Err.|Coef. Std.Err.<br>|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|Coef. Std.Err.|
|_qt-1_|0.266 (0.156) *|0.266 (0.162) *|0.248 (0.167)|0.235 (0.160)|0.242 (0.120) **|0.216 (0.173)|0.470 (0.141) ***|0.069 (0.125)|
|_CITY2_|0.154 (0.160)|0.308 (0.186) *|||0.245 (0.094) ***|-0.232 (0.086) ***|||
|_BP2_|-0.373 (0.182) **||-0.313 (0.180) *||-0.336 (0.093) ***||-0.323 (0.102) ***||
|_MAI2_|-0.554 (0.317) *|||-0.692 (0.355) **|1.040 (0.221) ***|||1.074 (0.217) ***|
|t=2012|0.508 (0.144) ***|0.679 (0.106) ***|0.552 (0.140) ***|0.599 (0.117) ***|0.022 (0.060)|0.017 (0.049)|-0.058 (0.063)|0.105 (0.048) **|
|t=2013|0.381 (0.107) ***|0.537 (0.080) ***|0.417 (0.103) ***|0.525 (0.081) ***|0.008 (0.054)|0.091 (0.039) **|-0.010 (0.047)|0.114 (0.044) ***|
|t=2014|0.249 (0.140) *|0.468 (0.101) ***|0.287 (0.137) **|0.461 (0.105) ***|-0.056 (0.062)|0.099 (0.043) **|-0.066 (0.053)|0.110 (0.047) **|
|t=2015|0.677 (0.163) ***|0.913 (0.103) ***|0.720 (0.160) ***|0.878 (0.106) ***|0.022 (0.061)|0.134 (0.044) ***|-0.030 (0.055)|0.187 (0.049) ***|
|t=2016|0.776 (0.159) ***|1.033 (0.111) ***|0.845 (0.153) ***|0.992 (0.112) ***|0.019 (0.066)|0.116 (0.048) **|-0.063 (0.059)|0.194 (0.054) ***|
|t=2017|0.749 (0.175) ***|1.021 (0.130) ***|0.781 (0.176) ***|1.030 (0.132) ***|-0.095 (0.081)|0.125 (0.050) **|-0.095 (0.070)|0.120 (0.055) **|
|t=2018|0.531 (0.171) ***|0.772 (0.132) ***|0.575 (0.170) ***|0.768 (0.130) ***|-0.021 (0.076)|0.149 (0.058) ***|-0.049 (0.064)|0.170 (0.063) ***|
|constant|6.168 (1.682)***|6.787 (1.633)***|7.879 (1.876)***|5.006 (1.580)***|12.340 (2.019)***|8.411 (1.855)***|6.573 (1.743)***|13.382 (1.912)***|
|Obs.|1133|1133|1133|1133|1550|1550|1550|1550|
|Groups|154|154|154|154|197|197|197|197|
|Wald|||||||||
|chi2|148.55|157.4|132.4|140.13|156.6|44.37|145.09|88.11|
|Arellano|-Bond test:||||||||
|AR(1)|-2.87 ***|-2.84 ***|-2.67 ***|-2.68 ***|-3.14 ***|-1.98 **|-4.33 ***|-1.59|
|AR(2)|-0.94|-0.96|-1.03|-1.09|0.86|0.5|1.16|-0.11|
|AR(3)<br>Hansen|1.31|1.36|1.47|1.32|-0.29|0.71|0.1|0.54|
|test|34.30|35.63|35.22|32.00|27.93|25.77|35.27|21.27|
# **VII. Conclusion**

Large-scale infrastructure investment can facilitate structural change of an economy, stimulating economic growth and diversification. The literature is generally supportive of this. However, the real impact remains open to argument. One of the most important empirical difficulties comes from potential endogeneity between economic outcomes and infrastructure investment.

The paper examined the impacts of massive road investment under the Nurly Zhol program in Kazakhstan. Methodologically, the paper exploited detailed micro shipping data to estimate how the transport connectivity changed over the last 10 years. On average the transport costs along the major regional corridors are estimated to have been halved, while average travel speed has not been changed much, if not, only slightly increased.

However, realized benefits from improved corridors vary depending on location. The domestic market accessibility was particularly improved along the major regional corridors connecting Astana to Almaty, Astana to Pavlodar and Oskemen, and Almaty to Taraz and Shymkent. The regional connectivity has been improved in several regions, such as East Kazakhstan, West Kazakhstan, Zhamby and Almaty City, but other inland areas are still left far from the external market.

Improved transport infrastructure stimulated the demand for regional and national freight transport, creating more enterprises and jobs in the trucking industry. With intensified competition, the price cost margins are considered to have been narrowed. Average transport costs declined from 10 U.S. cents per ton-km in the early 2010s to 6 U.S. cents in the late 2010s.

More formally, the translog cost function was estimated by the dynamic panel data regression. It was shown that local connectivity is the most important factor to boost firm production. The elasticity was estimated at 0.24 in absolute terms. Inventory is also found to be a major cost factor to firms.
- 36 -

1. To reduce firms’ inventories, it is crucial to improve the accessibility to large cities, such as Astana and Almaty. The elasticity is estimated at about -0.87. This inventory effect is particularly significant in the agriculture, mining, manufacturing power, water and construction industries.

It is also shown that market accessibility contributed to fostering firm agglomerations. This is particularly strong in the manufacturing, retail and wholesale, and other service industries. With the standard agglomeration economies, it is likely that more and more firms are located where other firms exist. Thus, large cities would likely continue growing. Notably, however, agglomeration economies do not have a direct impact on firm production. This is considered to mean that the Kazakh economy still lacks effective forward or backward linkages across industries.

In the agriculture sector, it was found that regional connectivity is important to produce crop production, particularly, for potatoes, oilseeds and vegetables, which have been emerging as important export commodities for the country. Domestic market connectivity is also important for potatoes and vegetables. By contrast, cereal production does not seem to change with connectivity.

In conclusion, the large-scale transport infrastructure investment in Kazakhstan clearly improved transport connectivity in various ways, which led to some structural change in the economy, boosting the transport industry. The induced impacts of the improved connectivity vary across sectors. Overall, the economy benefited positively, but with better local connectivity to district centers and more enhanced accessibility to large cities, the country could have benefited more from its transport infrastructure.