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# Essays in Applied Panel Data Econometrics and Machine Learning

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Publikationstyp: | Dissertation |

Publikationsstatus: | Published |

URI (zitierfähiger Link): | http://nbn-resolving.de/urn:nbn:de:bsz:352-2-f2jehdzt50hd0 |

Autor/innen: | Minhas, Ghalib |

Erscheinungsjahr: | 2018 |

Zusammenfassung: |
This dissertation consists of three chapters and has been written during my studies in the doctoral program Quantitative Economics and Finance at the University of Konstanz. The first chapter measures government growth in OECD economies and shows how this growth is driven by the different expenditure components from 1991 to 2012. Chapter 2 explains how financial constraints affect the employment adjustment at the firm level. Chapter 3 examines the fundamental and financial determinants of downsizing employment in manufacturing firms. In what follows, I briefly describe the individual chapters, and discuss their main mechanisms and results.
In Chapter 1, we use Wagner's law (1883) to study the long-run relationship between different components of government expenditures growth in OECD countries w.r.t. their GDP/capita growth. As compared to recent panel data studies on Wagner's law, our study contributes by decomposing government expenditures into different categories. In a first step, we consider general government expenditure in total (TGGE). In the second step, TGGE are dissected into modes: community consumption, investment and payments, and transfers. In the third step, we consider types of government expenditures, i.e. current and capital expenditures. In the fourth step, TGGE are decomposed into functions (excluding defence): social protection, health, education, economic affairs, law and order, recreation, culture and religion (LORCR), environmental protection, housing and community amenities, and general public services. We use a panel cointegration model to regress each government expenditure category on GDP per capita. In particular, we use the error correction model (ECM) proposed by Westerlund (2007). To estimate the long-run elasticities, pooled mean group (PMG) (Pesaran, Shin, and Smith 1999) and mean group (Pesaran and Smith 1995) estimation techniques are applied. In addition, we also control for common cross correlated effects as proposed by Pesaran (2006) to account for cross-sectional dependence in the relationship between government expenditures and GDP per capita. We find a negative long-run elasticity for total government expenditures relative to GDP, which suggests that Wagner's law is not valid in its strict version. The reason is that only transfers have a positive long-run elasticity, whereas we find negative elasticities for community consumption, and investment and payments. We also find negative long-run elasticities for both types of government expenditures (current and capital expenditures). Among the seven functional categories, we find the highest positive elasticities for health, and the lowest negative elasticity for economic affairs. Chapter 2 is a joint study with Jesse Würsten (University of Leuven). It investigates the causes of employment adjustment in the presence of firm-level financial constraints. We adopt an approach that directly describes financial constraints of firms based on their creditworthiness. We analyze Belgian firm level panel data of firms in the manufacturing and non-manufacturing sectors for the period 2005 – 2015. To evaluate the creditworthiness of firms, we use a financial distress index based on the Altman Z-score plus model (Altman, 2012). The Altman Z-score model is computed based on revenue, liquidity, debt, equity, and cash flow of firms. This allows classifying firms as distressed, grey, and safe, depending upon their median degree of financial distress. Distressed firms are found out to be highly leveraged with debt and more constrained to internal funds (cash flow) than other firms. We then applied a recursive modeling technique in a structural PVAR model to isolate the effects of financial factors (cash flows and interest expenses) from fundamental factors (employee productivity and employee cost). This estimation strategy allows us to estimate the pure effect of financial conditions on employment adjustments. We show that the firm’s financial constraints (cash flow and interest expenses) affect employment adjustment over-time depending upon the degree of the firm’s financial distress. The availability of internal funds (cash flow) is important in explaining employment adjustment in manufacturing firms that are highly distressed due to their low net-worth. Conversely, the availability of external funds is important in explaining employment adjustment in non-manufacturing distressed firms. All of these effects are independent of fundamental factors like employee productivity and employment cost. Chapter 3 is also a joint study with Jesse Würsten (University of Leuven). We examine the fundamental and financial drivers of falling employment rates for manufacturing firms by using “Two Trees” average treatment effect approach in a random forest model. Following Chapter 2, we adopt a structural approach to identify the causal effect of fundamental and financial factors on downsizing employment from a firm’s perspective. In particular, we employ a machine learning classification technique of random forest models, as described in Breiman (2001). We demonstrate that the random forest model provides unbiased and more accurate estimates, compared to standard decision tree classification model. To isolate the effect of the independent variables on the target variable, the random forest model provides partial dependence plots for each independent variable. We use the partial dependence plots to identify treatment groups and use the "Two Trees" algorithm of Athey and Imbens (2015) to quantify the impact of fundamental and financial factors on the falling employment rate. As in chapter 2 we classify the firms in our sample according to their creditworthiness and define three clusters: distressed, grey, and safe firms. Firms are clustered according to the time series median of the financial distress index over 2005-2015. We estimate the average treatment effects by dividing the sample into three periods: pre-crisis (2005-2007), during crisis (2008-2009), and post crisis (2010-2015) We show that the Random forest model has a 10-15% higher out of sample Area Under the Curve (AUC) than the than the standard tree model. Our estimates show that distressed firms have a 16.6%-points higher probability to reduce the workforce compared to grey and safe firms. In the financial crisis subsample, the difference in probabilities increases to 18.12%-points. In the pre-crisis period, firms with cash flows per employee below 523,454 Euro have a 16.4%-point higher probability to reduce their workforce and the difference in probabilities increases to 18.9%-points in the financial crisis period. Depending on the subsample, firms with high interest expenses per employee have a 19-24%-point higher probability of reducing the workforce. Among the fundamental factors, the cost of employees has the strongest effect on the probability to reduce the workforce. Firms with a payroll above 57,501 Euros per employee have a probability to reduce the workforce that is 23.7%- points higher than firms with employment costs below this threshold. The results indicate that financial factors play an important role in the decision to reduce the workforce. Moreover, the financial crisis 2008- 2009 amplifies the negative effects of being not creditworthy, having low cash flows and high employment cost. |

Prüfungsdatum (bei Dissertationen): | 16. Jul 2018 |

Hochschulschriftenvermerk: | Konstanz, Univ., Diss., 2018 |

Fachgebiet (DDC): | 330 Wirtschaft |

Link zur Lizenz: | Nutzungsbedingungen |

Prüfsumme:
MD5:2b34bb2abf0eb38cae2fea5d4e7f0e0a

MINHAS, Ghalib, 2018. Essays in Applied Panel Data Econometrics and Machine Learning [Dissertation]. Konstanz: University of Konstanz

@phdthesis{Minhas2018Essay-44314, title={Essays in Applied Panel Data Econometrics and Machine Learning}, year={2018}, author={Minhas, Ghalib}, address={Konstanz}, school={Universität Konstanz} }

Minhas_2-f2jehdzt50hd0.pdf | 42 |