Readings MT Flashcards
(73 cards)
(Tackling youth unemployment in Uganda Alfonsi et al)
Special setting?
Misallocation of talent if the poor face barriers to education and training; youth unemployment key policy issue. Set in Uganda, where 60% of unemployed are under 25.
Types of interventions: vocational training (supply side), apprenticeships (demand side).
Firms in Uganda typically very small and lots of unemployed workers.
(Tackling youth unemployment in Uganda Alfonsi et al)
Theory?
Firms get ++ payoff from hiring a worker (output) but must pay a cost (wage). The probability that they will receive a payoff depends on the skill of the worker: lower skill workers raise the risk that the firm has to pay the cost but does not receive the payoff.
Apprenticeships insure the firm against this risk by paying the wage if the firm agrees to hire and train the worker. Apprenticeships benefit the firm more because they can teach non-transferrable skills and retain the worker for a lower wage
Vocational training makes it more worthwhile for the firm to hire the worker by providing him with better skills that increase the payoff. Vocational training benefits the worker more because they get transferrable skills and can bargain for higher wages each time they change the employer.
(Tackling youth unemployment in Uganda Alfonsi et al)
Empirical design?
Two-sided experiment involving both workers and firms which allows to compare supply and demand side interventions – vocational training (VT) and firm-provided training (FT) through apprenticeships respectively. As both interventions are fielded in the same setting we can directly compare their impacts on worker outcomes.
Supply-side subjects: disadvantaged youth entering the labour market.
Demand side subjects: small and medium size enterprises in both manufacturing and service sectors. Workers assigned to VT were randomly assigned into two treatments: 1) the first group completed their six months of training and then transitioned into the labour market; 2) the second group were matched to firms operating in the same sector as the worker had been trained in, and in the same region.
Workers not offered VT were randomly assigned as follows: (i) matched to firms; (ii) matched to firms and those firms offered a wage subsidy in order to hire the worker and train them on-the-job for six months; (iii) held as a control group.
Randomisation of matching: firms are allocated randomly to receiving workers from any of these treatments. As workers are observationally equivalent only at the point of application to vocational training, we mostly present ITT estimates for worker outcomes based on random assignment to treatment at the point of application. The job ladder model to pinpoint the exact mechanisms driving the results.
Assumption: if VT workers have more certifiable skills than FT workers, and their balance of skills is more tilted towards sector – rather than firm-specific skills, they will more quickly move up the job ladder and their wage profiles will diverge away from FT workers.
(Tackling youth unemployment in Uganda Alfonsi et al)
Data?
156 employed workers, 40 firms in each labour market. 13% attrition rate. Match an average of 8 workers per market.
(Tackling youth unemployment in Uganda Alfonsi et al)
Key findings?
- FT more likely to report being firm trained whereas VT no change: firms are less willing to train workers that have already been vocationally trained in sector specific skills.
- The ITT estimates show that VT workers and FT workers all report being significantly more likely to have relevant skills than those in the control group, but the figure is higher for VT workers.
- VT workers have measurably higher sector-specific skills and indeed report their skills to be transferable across firms; FT workers report having more firm specific skills; workers transitioning into the labour market through VT and FT training routes perform very different tasks in firms as measured even years post intervention.
Overall effect:
- FT and VT workers are more likely to be employed over the control group
- VT and FT workers have ++ hours worked
- both groups earnings rise but more for VT: this is driven by them having more stable employment and working more months over the year.
- Among those actually hired by the firm, the share of FT workers who were employed at the matched firm for at least 6 months is 57%. Hence for the majority of hired workers, retention with the firm lasts longer than the period of the wage subsidy itself, suggesting these firms were constrained to begin with.
- Workers are far more mobile: when unemployed they get back onto the job ladder more quickly than control
- FT workers tend to end up in firms with lower productivity than VT
(Tackling youth unemployment in Uganda Alfonsi et al)
Conclusions?
Benefit-cost ratio is above one for both interventions however if we were to start from scratch, the wage subsidy intervention would just break even & costs for VT would still need to more than double
FT skills give job right away but VT skills more portable. Over time, FT employment rate converges to control group but VT workers employment increases over time. FT workers do well initially.
Reason why firms/workers don’t invest in training is still unclear - maybe due to credit constraints. For policy implications, providing better training can be a starting point.
(Tackling youth unemployment in Uganda Alfonsi et al)
Things to consider in experiment?
- when we instrument for take-up by the assignment, the labour market outcome for VT and FT are actually more similar
- basically a lot more people took up VT
- the gap is driven by firm rather than worker - firm interest is key limiting factor - many firms don’t want to grow as they are usually ran by self-employed people who are just waiting to find a job so they don’t want to expand
(Beyond GDP, Jones et al)
Special setting/Motivation?
GDP is a flawed measure of economic welfare as many factors affect living standards within a country that are incorporated imperfectly if at all, in GDP.
Therefore we want to compare living standards of people in different countries taking into account welfare measures (e.g. consumption, leisure, inequality and mortality using the standard economics of expected utility.
(Beyond GDP, Jones et al)
Theory?
Comparing welfare across countries using a common spec for preferences
Utilitarian expected utility calculation gives equal weights to each person
The welfare metric could be an equivalent variation (what is the proportion to adjust to equal welfare)
or it could be compensating variation (by what factor to increase to raise welfare)
these two methods produce different results especially for poor countries and the paper in particular reports the equivalent variation
(Beyond GDP, Jones et al)
Empirical design?
Calculate consumption-equivalent measure of welfare to see what proportion of consumption in one country, given its values of these factors, would deliver the same EU as values in another country and make an individual indifferent between the two countries.
Utility from leisure: constant Frisch elasticity of labour supply (hold marginal utility of consumption fixed, the elasticity of labour supply with respect to wage is constant)
(Beyond GDP, Jones et al)
Data?
Micro data (Household surveys ) - 13 countries welfare provide publicly available multi-country datasets to construct some of these welfare measures omitted: morbidity (disease prevalence), the quality of the natural environment, crime, political freedom and intergenerational altruism
(Beyond GDP, Jones et al)
Key Findings?
GDP +ves:
- excellent indicator welfare across broad range of countries: correlation with welfare of 0.98
- difference still can be important - deviation is big - welfare seems dispersed than income
How do we reconcile the large deviations w the high correlations:
- scales are so different
- income varies by more than factor of 63,000 % in our sample whereas deviations are 25-50%
Average Western European living standards look v close to US when taken into account life expectancy, leisure, lower levels of inequality. Same w non-European being poorer bc of these same reasons.
Life expectancy v important to welfare growth
(Beyond GDP, Jones et al)
Conclusions?
Developing countries should focus on inequality issue
Developing countries not just to focus on boosting incomes but also health, life expectancy directly.
(Global Inequality of Opportunity. Milanovic)
Setting?
Less than 3% of world’s population live in countries where they weren’t born. More than 2/3rds global inequality between individuals is due to national income differences.
(Global Inequality of Opportunity. Milanovic)
Theory?
Income can be written as a fn of country-specific circumstances, own-specific circumstances whose effort also depend on country, person’s own effort and a random shock that can also be called luck
(Global Inequality of Opportunity. Milanovic)
Empirical design?
Every country divided into 100 groups of equal sizes to compare positions (e.g. 2nd percentile in China w/ 70th in Nigeria) and this allows us to define income classes in the same way across countries. Income within all percentiles except highest is homogenous (i.e. within all percentiles it doesn’t vary too much its all a similar amount)
Regression:
- annual ave. household per cap income in PPP is regressed on country’s GDP per cap PPP and inequality in income distribution
- both variables on RHS strictly exogenous to an individual effort
2 approaches about taking into account pop size:
- individual viewpoint (size is irrelevant) and world as it is (accounting for size)
To test robustness: replace GDP per capita with ave. number of years of education of population over the age of 15
To examine location premium:
run similar regression but with person’s own income ventile (each ventile contains 5% population ranked from poorest to richest) - for each ventile separately, regress ventile income on country’s GDP/capita and Gini coefficient
(Global Inequality of Opportunity. Milanovic)
Data?
2008 data from 118 countries’ household surveys representing 94% of world’s population and 96% of world dollar income
(Global Inequality of Opportunity. Milanovic)
Key findings?
Base case regression 1:
- elasticity of own income wrt to country’s GDP per cap is 0.866
- gini coefficient enters with a negative sign (i.e. more unequal country on average reduces one’s income)
- overall explains a lot of the variability of individual percentile incomes across the world
- the increase of country’s average educational level by 1 year + of schooling associated with huge increase in individual incomes
Regression 2 with ventiles:
- take all people in given ventile of country’s income distribution - some 90% of variability of incomes will be explained by GDP/cap and Gini coefficients of the countries where they live
- locational premium holds for everyone but premium is less for those in lower ventiles of income distribution
- more than 1/2 variability in income globally is explained by circumstances given at birth
(Global Inequality of Opportunity. Milanovic)
Conclusions?
Benefit of higher mean income is proportionately greater for the rich classes so policy implications should target within country inequality
(Returns to Capital Microenterprises. De Mel)
Special setting?
Sri Lanka small and informal firms
3 areas in Sri lanka
If returns to low level capital stock are low, individuals without access to capital will face big disadvantage (poverty trap) but if they’re big they could grow so experiment tests this
(Returns to Capital Microenterprises. De Mel)
Empirical design?
RCT to identify effect of cash /in kind investments on profitability of enterprises irrespective of if they apply for credit at market interest rates.
Examine heterogeneity of returns to test which theories can explain why firms have marginal returns well above i rate
Randomly allocate 4 types of grans (2 smaller of each and 2 bigger of each), randomization stratified within district and zone and there’s control group
(Returns to Capital Microenterprises. De Mel)
Things to be worried about in the experiment?
Random allocation of grants to ensure changes in capital stock not associated with entrepreneurial ability, demand shocks, and other factors affecting profitability
Treatment influences hours work not j capital stock so validity assumption violated - therefore adjust profits to reflect
LATE is weighted ave. of marginal returns to capital w/ marginal return to each firm weighted by how much that firm’s capital stock responds to treatment
(Returns to Capital Microenterprises. De Mel)
Key findings?
- all 4 treatments = higher capital stock, using logs (dampens effect of outliers), still +ve effect on capital stock, more able owners have larger impact
- shock associated with ROR 5.85% and these treatment impacts appear to be flat/decreasing - no evidence of increasing returns
- more treatment = more hours
- cash better than in-kind
- heterogeneity of returns supports the view that high marginal returns from treatment reflect credit constraints rather than missing insurance markets
(Returns to Capital Microenterprises. De Mel)
Conclusions?
Outcome of interest is profits - likely to be under/overreported in response to treatment have to think abt this.
Policy: need to understand how these entrepreneurs make investment decisions - why don’t they take advantage of high ROR (i rate maybe)?
