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We examine the potential secondary impacts of raising the minimum wage in a jurisdiction on the efficiency of its foster care placement system. We employ the Adoption and Foster Care Analysis and Reporting System (AFCARS) restricted-access dataset representing all children in the US foster care system for the years 2000–21. We calculate placement efficiency as the number of yearly moves experienced by a child in the foster care system, with a decrease in the number of placements interpreted as benefitting children both psychologically and behaviourally. This increase in stability benefits the foster parents and corresponds to a smaller financial strain on the public. After including various controls and state-year fixed effects, our Tobit and Ordinary Least Squares (OLS) analyses find that increased minimum wages are significantly associated with lower levels of placement disruption. We take this as promising initial descriptive evidence for the secondary effects of the minimum wage on reducing the financial burden, and increasing the placement efficiency, of the foster care system.

There is extensive evidence of the negative effects of the fiscal size of government – the spending, taxing, and ownership roles of the state – on economic performance (Bergh & Henrekson, 2011). But how do governments get big? As the size of government is measured as part of the Fraser Institute's Economic Freedom of the World index, we observe a wide variety of countries as having very large governments. We see the Nordic countries, which are among to be the most democratic in the world. We see a variety of autocracies. We see small countries like Lesotho, Eswatini, and Timor-Leste. When governments spend and own a large proportion of the economy, is there any ready explanation as to why?

The approach here is narrative-based, though informed strongly by data. It is likely that that a large number of variables influence the size of government, and many of them may influence different aspects of government size. Factors that cause a government to begin nationalising industries may be different from the factors that cause it to expand its pension system, for example. In the academic literature, there are many explanations that very plausibly tell us why some states are a bit bigger or a bit smaller, on the margin.¹ What we wish to do instead is to identify commonalities among groups of countries with very large governments, where the commonality plausibly has a direct pathway to the countries' size of governments. For simplicity's sake, we are considering the top quartile of countries with the largest governments, or 42 countries.

Again in contrast to the academic literature, we are not even making a claim to the ‘average treatment effect’ of the three variables we eventually identify. None of them is a mechanical law and each narrative has a counter-example. Rather, we wish to establish certain basic descriptive facts concerning the size of government. A great deal of confusion has arisen as a result of the lack of clarity regarding such basic descriptive facts; for instance the suggestion that the size of government should be removed from Economic Freedom of the World because of its lack of positive correlation with the rest of the index (Ott, 2018; cf. Murphy, 2022a). And although the issue is beginning to get rectified, there has long been confusion about the sustainability of Nordic ‘socialism’ (Lawson & Powell, 2019, pp. 5–14; Christensen et al., 2023). In this article, we hope to similarly facilitate improved discourse concerning the nature of the size of government.

Today, Economic Freedom of the World scores 165 jurisdictions. Of those, the top quartile of countries in terms of the size of government includes countries that are wealthy and countries that remain undeveloped. Jurisdictions rarely have uniformly low or high measures when it comes to the size of government as some components and subcomponents are negati

We examine the relationship between industry-specific regulations and startup birth rates (entry) and startup ‘deaths’ (closings) in the United States and Canada during 2012–19. Our sample contains data on thousands of active and closed technology startups in the United States and Canada that were founded between January 2012 and June 2019. We use the Mercatus Center's RegData database to capture the intensity of national-level regulations. Our findings suggest that more regulated industries may exhibit lower rates of entry and that more regulated industries are associated with a greater likelihood of a startup closing. These findings seem more robust for the US than for Canada. We discuss startup funding as a potential mechanism by which regulation may impact startup closings, using our fieldwork interviews with over 100 technology startup executives and venture capital investors.

Peter Dwyer, Lisa Scullion, Katy Jones, Jenny McNeill & Alastair B. R. Stewart, The Impacts of Welfare Conditionality: Sanctions, Support and Behavioural Change. Policy Press. 2022. pp. 218. £80 (hbk). ISBN: 978-1447320111. £26.99 (pbk). ISBN: 978-1447343738. £26.99 (ebk). ISBN: 978-1447343745

Behavioural conditions have always been attached to the receipt of social security benefits for the UK's unemployed. These conditions have increased in number and scope in recent decades, alongside heavier sanctions for non-compliance, as part of a trend across OECD countries (Watts & Fitzpatrick, 2018). The UK Welfare Reform Act 2012 was a landmark in this process. Its “work search requirement” obliges claimants of unemployment benefit – now Universal Credit (UC), previously Jobseeker's Allowance (JSA) – to take “all reasonable action” and “any particular action specified by the Secretary of State, for the purpose of obtaining work” (s. 17). In practice this means that a Department for Work and Pensions (DWP) ‘Work Coach’ can now compel an unemployed UC claimant, under threat of a financial penalty, to apply for a job of their choosing. Those who leave a job voluntarily and then apply for UC can now also face a “sanction” (s. 49).

Heralding the 2012 Act, its architect, the then Secretary of State for Work and Pensions Iain Duncan Smith (2010), argued in a speech that “reinforced conditionality” to ensure claimants “take reasonable offers of work” was necessitated by the benefit system's regrettable drift towards one that “Beveridge warned against”, in which “idleness” had become “institutionalised” (see Beveridge, 1942, p. 58). In the same speech, he claimed that in the 2000s British companies had been “unable to get British people to fill” some job vacancies, so “workers from overseas stepped in”. Around this time, Duncan Smith referred to a television documentary in which some unemployed benefit claimants would not get “on a bus” to a nearby city to widen their job search (BBC, 2010). The issues Duncan Smith referred to may be persisting; there were 5.6 million UK people of working age on out-of-work benefits in December 2023, just below the record high of 5.9 million in 1993, despite 0.9 million unfilled job vacancies (see Andrews, 2024).

This review article focuses on the £2 million Economic and Social Research Council (ESRC)-funded ‘Welfare Conditionality: Sanctions, Support and Behaviour Change’ research programme (often abbreviated to ‘WelCond’). Led by Peter Dwyer, a Professor of Social Policy at the University of York, and involving academics from six universities, it ran from 2013 to 2018, just as the 2012 Act's new policies were being slowly rolled out across the country. The project's main purpose was to test