Trade Liberalization and Industry Dynamics: A Difference in Difference Approach

Recent models of trade with firm heterogeneity predict that opening to trade reduces the number of firms, increases the average size of firms, and decreases firms’ markups. This paper uses a large dataset for 28 manufacturing industries and 46 countries to test these predictions. The econometric analysis based on the treatment effects literature shows that on average, trade liberalizations do not decrease the number of firms nor increase the average size of firms. Markups appear to decrease during the three years after the liberalization. We also find that the number of firms and the average size of firms increase in comparative advantage industries.Trade Liberalization, Industry Dynamics, Treatment Effects

Copayments for Ambulatory Care in Germany: A Natural Experiment Using a Difference-in-Difference Approach

In response to increasing health expenditures and a high number of physician visits, the German government introduced a copayment for ambulatory care in 2004 for individuals with statutory health insurance (SHI). Because persons with private insurance were exempt from the copayments, this health care reform can be regarded as a natural experiment. We used a difference-in-difference approach to examine whether the new copayment effectively reduced the overall demand for physician visits and to explore whether it acted as a deterrent to vulnerable groups, such as those with low income or chronic conditions. We found that there was no significant reduction in the number of physician visits among SHI members compared to our control group. At the same time, we did not observe a deterrent effect among vulnerable individuals. Thus, the copayment has failed to reduce the demand for physician visits. It is likely that this result is due to the design of the copayment scheme, as the copayment is low and is paid only for the first physician visit per quarter.Copayments, ambulatory care, difference-in-difference, count data, zeroinflated-model, SOEP

Copayments for Ambulatory Care in Germany: A Natural Experiment Using a Difference-in-Difference Approach

In response to increasing health expenditures and a high number of physician visits, the German government introduced a copayment for ambulatory care in 2004 for individuals with statutory health insurance (SHI). Because persons with private insurance were exempt from the copayments, this health care reform can be regarded as a natural experiment. We used a difference-in-difference approach to examine whether the new copayment effectively reduced the overall demand for physician visits and to explore whether it acted as a deterrent to vulnerable groups, such as those with low income or chronic conditions. We found that there was no significant reduction in the number of physician visits among SHI members compared to our control group. At the same time, we did not observe a deterrent effect among vulnerable individuals. Thus, the copayment has failed to reduce the demand for physician visits. It is likely that this result is due to the design of the copayment scheme, as the copayment is low and is paid only for the first physician visit per quarter.copayments, ambulatory care, difference-in-difference, count data, zeroinflated- model

Copayments for Ambulatory Care in Germany: A Natural Experiment Using a Difference-in-Difference Approach

In response to increasing health expenditures and a high number of physician visits, the German government introduced a copayment for ambulatory care in 2004 for individuals with statutory health insurance (SHI). Because persons with private insurance were exempt from the copayments, this health care reform can be regarded as a natural experiment. We used a difference-in-difference approach to examine whether the new copayment effectively reduced the overall demand for physician visits and to explore whether it acted as a deterrent to vulnerable groups, such as those with low income or chronic conditions. We found that there was no significant reduction in the number of physician visits among SHI members compared to our control group. At the same time, we did not observe a deterrent effect among vulnerable individuals. Thus, the copayment has failed to reduce the demand for physician visits. It is likely that this result is due to the design of the copayment scheme, as the copayment is low and is paid only for the first physician visit per quarter.copayments, ambulatory care, difference-in-difference, count data, zero-inflated-model

Trade Liberalization and Industry Dynamics: A Difference in Difference Approach

Recent models of trade with firm heterogeneity predict that opening to trade reduces the number of firms, increases the average size of firms, and decreases firms’ markups. This paper uses a large dataset for 28 manufacturing industries and 46 countries to test these predictions. The econometric analysis based on the treatment effects literature shows that on average, trade liberalizations do not decrease the number of firms nor increase the average size of firms. Markups appear to decrease during the three years after the liberalization. We also find that the number of firms and the average size of firms increase in comparative advantage industries.

Assessing the Frontiers of Ultra-Poverty Reduction: Evidence from CFPR/TUP, an Innovative Program in Bangladesh

This paper uses household panel data to provide robust evidence on the effects of BRAC’s Targeting the Ultra-poor Program in Bangladesh. Our identification strategy exploits type-1 errors in assignment, comparing households correctly included with those incorrectly excluded, according to program criteria. Evidence from difference-in-difference matching and sensitivity analysis shows that participation had significant positive effects on income, food consumption and security, household durables, and livestock, but no robust impact on health, ownership of homestead land, housing quality and other productive assets. Using quantile difference-in-difference, we find that the income gains from program participation is smaller for the lowest two deciles.Ultra-poor, CFPR/TUP, BRAC, Bangladesh, Microfinance, Bangladesh, Assignment Error, Difference-in-Difference, Matching, Heteroskedasticity-Based Identification

Farm level impact of rural development policy: a conditional difference in difference matching approach

We use a conditional difference-in-difference matching estimator and a 2003-2007 balanced panel drawn from the FADN Italian sample to evaluate the impact at the farm level of the implementation of the first Italian Rural Development Programme (RDP). We find that, in average, farms receiving at least a RDP payment increased family labor, while they did not increase total labour employed on farm. In addition, they experienced an increase in labor profitability and added value, even though the estimate significance varies accordingly to the matching method used. Our findings, suggest that the implementation of the first RDP produced a positive direct impact on rural GDP, while it did not prove to be effective in terms of rural employment growth.Common Agricultural Policy, Rural Development Policy, conditional diff-in-diff matching, Agricultural and Food Policy, Q12, Q18, C14,

Income Taxes and Entrepreneurial Choice: Empirical Evidence from Germany

Entrepreneurial activity is often regarded as an engine for economic growth and job creation. Through tax policy, governments possess a potential lever to influence the decisions of economic agents to start and close small businesses. In Germany, the top marginal income tax rates were reduced exclusively for entrepreneurs in 1994 and 1999/2000. These tax reforms provided two naturally defined control groups that enable us to exploit the legislation changes as "natural experiments". First, the tax rate reductions did not apply to freelance professionals (Freiberufler), and second, entrepreneurs with earnings below a certain threshold were not affected. Using data from two different sources, the SOEP and the Mikrozensus (LFS), we analyse the effect of the tax cuts on transitions into and out of self-employment and on the rate of self-employment. We apply a "difference-in-difference-in-difference" estimation technique within a discrete time hazard rate model. The results indicate that the decrease in tax rates did not have a significant effect on the self-employment decision.Taxation, entrepreneurship, natural experiment, difference-in-difference-in-difference estimation

Using State Administrative Data to Measure Program Performance

This paper uses administrative data from Missouri to examine the sensitivity of job training program impact estimates based on alternative nonexperimental methods. In addition to simple regression adjustment, we consider Mahalanobis distance matching and a variety of methods using propensity score matching. In each case, we consider estimates based on levels of post-program earnings as well as difference-in-difference estimates based on comparison of pre and post-program earnings. Specification tests suggest that the difference-in-difference estimator may provide a better measure of program impact. We find that propensity score matching is generally most effective, but the detailed implementation of the method is not of critical importance. Our analyses demonstrate that existing data available at the state level can be used to obtain useful estimates of program impact.Noexperimental Methods, Matching, Difference-in-Difference