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This section investigates and identifies whether there is a causal link between CAL and productivity. It analyses the channels through which CAL may impact on productivity in the spirit of Rajan and Zingales (1998) and Raddatz (2006). The first hypothesis is that industries that are more dependent on external financing may have higher growth rates in Poland after the liberalization of capital flows. The second hypothesis is related to the empirical literature that there is a non-conclusive effect of CAL effect on the performance of manufacturing sectors. As discussed in Section 5.2, those effects occur as the liberation impacts through different capital transmission mechanisms such as deposits and securities channels, credits and loans transactions and lastly profits channels.

In order to answer the hypotheses the following methodology with two parts was employed: standard sector financial dependence productivity estimation strategy (Model 1a), and an alternative CAL sector productivity estimation strategy (Model 1b).

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In light of the empirical literature, each of these models were estimated by the Difference-in-Differences estimation. In this model, the estimation outcomes can be observed for two different manufacturing sectors for two time periods, one period is before CAL and the second period after it. In this case, financial and liquidity dependent sectors are exposed to a treatment of CAL in the second period, but not in the first period. Non-financial and non-liquidity dependent sectors are not exposed to the CAL treatment during either period. If the same units within a sector are observed over the years, the average gain in the non-financial or liquidity dependent sectors is subtracted from the average gain in the financial or liquidity dependence sectors.

An employment of Differences in Difference estimation will allow to remove biases in the CAL period comparisons between financial or liquidity dependent, and non-financial or non-liquidity dependent sectors, that could be the result from permanent differences between two different type of sectors, as well as biases from comparisons over time in the financial dependent sector, that could be the result of trends. Moreover, in order to overcome omitted variable concerns, it is necessary to employ Difference-in-Differences estimation strategies following Rajan and Zingales’ (1998), Raddatz’ (2006) and Levchenko, Ranciere, and Thoening (2007).

In order to find the answer to this CAL impact through financial dependence, the financial dependent productivity estimation strategy (Model 1a) was adopted to define country CAL differences influence on productivity. In order to make a prediction about country liberalization differences, between industries based on an interaction and between a country and industry characteristic, which follows the way of previous studies such as Rajan and Zingales’ (1998) and Raddatz’ (2006). We estimate the following specification in the panel of sectors and time:

𝑇𝐹𝑃_𝑖𝑡 = 𝛽₀+ 𝛽₁𝑖𝑚𝑓_𝑡+ 𝛽₂𝐶𝐻𝐴𝑇_𝑖^𝑘+ 𝛽₃𝐶𝐻𝐴𝑇_𝑖^𝑘∗ 𝑖𝑚𝑓_𝑡+ 𝛾𝑋_𝑖𝑡+ 𝜀_𝑖𝑡 (1a) where –t-time unit, i-sector unit, k is defined as Finance, Liquidity I and II, ε-error terms On the left-hand side, the dependent variable of total factor productivity (𝑇𝐹𝑃_𝑖𝑡) in industry j over the period between 1995 and 2007, which is measured by the non-parametric approach, Hicks-Moorsteen Index (TFP index) and the non-parametric approach of OLS estimation of TFP (TFP OLS). The left-hand side variables are measures for over a

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period of time from 1996 to 2007 for 22 manufacturing sectors. The vector of control variables 𝑋_𝑖𝑡 contains the last year share of the sector in total fixed asset investments 𝐼𝑁𝑉𝐸𝑆𝑇𝑀𝐸𝑁𝑇_{𝑖,𝑡−1}, as well as the beginning-of-a period of openness variable 𝑂𝑃𝐸𝑁𝑁𝐸𝑆𝑆_𝑖,𝑡 is the sum of exports and imports as a share of the gross output in the sector.⁴⁰

The selection of control variables was done on the basis of existing literature such as human capital index, concentration index, and percentage changes in exports or imports. However, these other independent variables apart from investment and openness variables were not statistically significant in the empirical analysis. Both investment and openness variables have endogeneity character, it is not possible to totally control this, but it can be captured partially by lagged variables.

De facto CAL measure (𝑖𝑚𝑓_𝑡) is a binary measure (0/1) that the liberalization event is dated for the country and then compared. De facto measure of CAL is the most standard in the empirical literature. Compared to de jure measure it is suspect to less endogeneity problems as the measure construction is based on the legal regulations changes, then in actual changes in capital flows or interest rates. The parameter of interest is 𝛽3, or the effect of the interaction of de Facto CAL measure (𝑖𝑚𝑓_𝑡) and sector financial and liquidity dependence (𝐶𝐻𝐴𝑇_𝑖^𝑘). This empirical estimation strategy relies on the two variations of financial and liquidity dependence variables (𝐶𝐻𝐴𝑇_𝑖^𝑘).

A first variation of these variables is on/off sector-level where “1” indicates financial/liquidity dependence sectors and otherwise “0” is non-financial/non-liquidity dependence sectors that are based on the financial/liquidity ratio with respect to the median of this ratio.

A second variation is the value of each financial/liquidity ratio at the level of the year 1995. The financial sector dependence variable (𝐶𝐻𝐴𝑇_𝑖^{𝐹𝑖𝑛𝑎𝑛𝑐𝑒}) was built based on the

40 In this estimation model, we have also used different lags of investments and openness variables, but these variables were not statistically significant. Moreover, other variables were also included in the estimation, such as the sectoral concentration index, the ratio of privatized companies to the total number of companies in this sector, used as measure for privatizations. Also, these variables were not statistically significant for this estimation period. In order to analyse European integration, a dummy variable (0/1) was added into the estimation, where ‘1’ is after the year 2004 Poland jointed the EU, and then ‘0’ is before the year 2004. Again, this variable did not provide any statistically significant results.

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financial ratio as share equity to Gross Value Added. Then, liquidity sector dependence variables were defined based on two liquidity ratios, the first variable (𝐶𝐻𝐴𝑇_𝑖𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝐼

) is Cash Conversion Cycle (CC) and the second variable (𝐶𝐻𝐴𝑇_𝑖𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝐼𝐼) is the inventory ratio to sales. All specifications include a set of fixed effects (sector effect, time effect and sector x time effects).The fixed effects significantly improve in alleviating simultaneity issues by controlling omitted variable.

An alternative approach is to analyse the effect of financial liberalization on productivity through various capital transmission mechanisms (Model 1b). We employ empirical strategies parallel to the standard sector financial depended productivity estimation strategy mentioned above. As Levchenko et al. points out, it does not allow for researchers to identify the magnitude and direction of the overall effect of financial liberalization. This alternative CAL sector productivity estimation strategy (Model 1b), which is included in the equation, is the treatment effect (sector) level and interaction with the treatment effect on country level and industrial financial dependence. In particular, this is the following set of estimation specifications:

𝑇𝐹𝑃_𝑖𝑡 = 𝛽₀+ 𝛽₁∗ 𝑖𝑚𝑓_𝑡+ 𝛽₂𝐶𝐻𝐴𝑇_𝑖^𝑘+ 𝛽₃∗ 𝐶𝐴𝐿 𝑆𝑒𝑐𝑡𝑜𝑟_𝑖𝑡+ 𝛽₄𝐶𝐻𝐴𝑇_𝑖^𝑘∗ 𝐶𝐴𝐿 𝑆𝑒𝑐𝑡𝑜𝑟_𝑖𝑡+ 𝛾𝑋_𝑖𝑡+ 𝜀_𝑖𝑡 (1b)

where –t-time unit, i-sector unit, k is defined as Finance, Liquidity I and II, ε-error terms

In this specification, defined is the period of liberalization for Poland by the usage of on-off measures (𝑖𝑚𝑓_𝑡). The variable 𝑖𝑚𝑓𝑡 takes the value of ‘0’ before the liberalization episode, and ‘1’ after it. This variable indicates whether the observation is from before or after treatment as this liberalization policy. Then, following Raja-Zingales and the Raddatz-type model, non-financially and non-liquidity intensive sectors are used as a control group compared to the financially and liquidity intensive sectors. Similar to Model (1b) we use the same vector as control variable 𝑋_𝑖𝑡 as in Model (1a), this contains the last year share of the sector as total fixed asset investments 𝐼𝑁𝑉𝐸𝑆𝑇𝑀𝐸𝑁𝑇_{𝑖,𝑡−1}, as well as the beginning-of-a period of openness variable 𝑂𝑃𝐸𝑁𝑁𝐸𝑆𝑆_𝑖,𝑡 is the sum of exports and imports as a share of the gross output in the sector. The coefficient of interest 𝛽₄ is the variable, which is defined as interaction of the proxy of CAL sector

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measure (𝐶𝐴𝐿 𝑆𝑒𝑐𝑡𝑜𝑟_𝑖𝑡 ) and sector financial and liquidity dependence (𝐶𝐻𝐴𝑇_𝑖^𝑘). This coefficient describes if the changes in the productivity of sectors with high liquidity needs or financial dependence were caused as the results of changes in each of the capital transmission mechanisms after the CAL on the country level, then 𝛽₄ can be

‘positive’ or ‘negative’ and economically significant. This empirical strategy is similar in a certain way to Lenchenko, Ranciere and Thoening’s (2006) approaches, with respect to their analysis of the impact of De jure CAL measure on output.

All specifications include a set of fixed effects (sector, time effects and sector x time effects) to alleviate simultaneity issues by controlling omitted variables. This also investigates the accuracy of the CAL sector measures approach (𝐶𝐴𝐿 𝑆𝑒𝑐𝑡𝑜𝑟_𝑖𝑡) compared to the CAL country index (𝑖𝑚𝑓_𝑡) in order to see if this methodology provided an advantage to correct the omitted variable problem, due to its inability to include sector and time effects in capital control estimations.

As mentioned above, this methodology aims to identify the effect of CAL from differential effect across industries in Poland and to identify through which channels this effect happens. However, this methodology does not allow for the magnitude of the overall effect of this liberalization on productivity.