This study compares Non-English-Speaking migrant women’s unemployment situation with that of Australian born women, using the currently available 1% sample from the Australian 2016 Census data. A new general probit model is used to estimate the probability of unemployment for both groups combined, as well as for each of these groups separately to show the difference in unemployment rates between the two groups. Our contribution is to show how to estimate and test the imbalance in unemployment rates between the two groups. We show that the unemployment rate for Non-English-Speaking migrant women (7.2%) is significantly higher than for Australian born women (5.1%), but the gap is narrowing over time. We show that the unemployment rate for Non-English-Speaking migrant women increases when they are married and have children and when they have poor English proficiency and lower education levels. This implies that there is a significant higher probability of unemployment for Non-English-Speaking migrant women across human capital and demographic controls. We have discussed other dimensions of unemployment such as discrimination and cultural activities and shown that these are the main factors behind the relatively higher unemployment rate for Non-English-Speaking migrant women.

We examine the Economic Policy Uncertainty (EPU) transmission over the Exchange Rate Volatility (ERV) for 8 Emerging Market Economies (EME) using the recent panel VAR methodology of Abrigo and Love (Stata Journal 16:778–804, 2016). The econometric investigation reveals that: (a) both domestic and US-EPU shocks exert positive effects on the ERV, (b) the contribution of the US-EPU to the ERV fluctuations overcomes the own EPU’s share, (c) the ERV acts as a possible transmission channel of the US-EPU to the domestic economic activity, (d) the domestic EPU increases in response to a higher US-EPU and vice versa and (e) the latter is surprisingly and markedly sensitive to EME macroeconomic conditions. Our findings are robust to different sensitivity analyses, provide novel insights into EPU international spillovers, and have interesting policy implications for EME decisions makers and investors.

In the recent years, the burgeoning non-performing assets (NPAs) have become a matter of concern and scrutiny in India as the surge in NPAs impinge on the credit services of the banks, make the banks vulnerable to external shocks, leave them with less cushion in case of idiosyncratic shocks and thus, leading to the abrasion of their productive capital. In this backdrop, some very normative questions become inevitable. How has the technical efficiency of the banks in India changed over time especially after the asset quality review, 2016? How does undesirable output like non-performing assets (NPA) impact the technical efficiency of banks in India? Does technical efficiency have anything to do with the ownership of banks? These are some of the questions we endeavour to answer through our study by employing three cornerstone methodologies namely DEA, Malmquist productivity index and SFA in the banking sector for the period 2014–2020.The results obtained from employing DEA and SFA both points toward the heterogeneity in the technical efficiency of public sector banks and private sectors banks operating in India. The results obtained from DEA are majorly three-fold. Firstly, private sector banks have fared better than the public sector banks, while the SFA scores show that the public sector ownership promotes efficiency. Secondly, the technical efficiency of public sector banks has consistently been falling from 2014 to 2017 only to rise in the later years, evidence corroborated by the SFA scores also. This trend is in line with the slew of measures adopted by the government and RBI like AQR and mergers of banks subsequently. Although according to the Malmquist productivity decomposition results, we find that productivity of banks have been falling for the period 2014–2020. Thirdly, the non-performing assets are detrimental for the efficiency of the banks. Like DEA, the SFA results also shows the presence of technical inefficiency in the Indian banking sector and a similar trend in the technical efficiency wherein the scores decline from 2014 through 2017 and then they rise subsequently.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40953-021-00247-x.