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CHAPTER 12

CHAPTER 12. VALUING IMPACTS FROM OBSERVED BEHAVIOR: DIRECT ESTIMATION OF DEMAND CURVES. DIRECT ESTIMATION OF THE DEMAND CURVE. Knowing one point on the demand curve and its slope or elasticity

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CHAPTER 12

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  1. CHAPTER 12 VALUING IMPACTS FROM OBSERVED BEHAVIOR: DIRECT ESTIMATION OF DEMAND CURVES

  2. DIRECT ESTIMATION OF THE DEMAND CURVE • Knowing one point on the demand curve and its slope or elasticity • We know only one point on the demand curve, but previous research provides an estimate of either the elasticity or slope of the demand curve. We first suppose the demand curve is linear and then suppose the demand curve has constant elasticity.

  3. Linear Demand Curve • A linear demand curve assumes that the relationship between the quantity demanded and the price is linear; that is, the demand curve can be written as: q = a0 + b1p

  4. Linear Demand Curve Where, q is the quantity demanded at price p, a0 is the quantity that would be demanded if price were zero (the intercept), and b1 indicates the change in the quantity demanded as a result of a one unit increase in price (the slope). If you know one point on the demand curve and its slope , then you can compute other points on the curve straightforwardly.

  5. Linear Demand Curve For a linear demand curve, price elasticity of demand equals: ed= b1p/q

  6. Constant Elasticity Demand Curve Some goods have a constant elasticity demand curve, that is, lnq = lna0 + b1lnp

  7. Constant Elasticity Demand Curve Slope and elasticity estimates of demand curves can often be obtained from prior research. When this happens, you need to consider possible internal and external validity problems (i.e., how valid is the estimate [internal – how was it measured and computed] and can it be used in this instance [external – how similar is the case in question to the research case]).

  8. Extrapolating from a Few Points If we know a few points on the demand curve, we can use them to predict another point of relevance to policy evaluation. There are two important considerations when extrapolating:

  9. Extrapolating from a Few Points Different functional forms lead to different answers. Furthermore, the further we extrapolate from past experience, the more sensitive the predictions are to assumptions about the functional form.

  10. Extrapolating from a Few Points • The validity of attributing an outcome change to the policy change (i.e. other variables are assumed to remain constant) may be questionable. • More observations provide greater validity.

  11. Econometric Estimation with Many Observations • Model specification. The econometric model should include all explanatory (so-called independent) theoretically-relevant variables, even if one is not specifically interested in their effect. • Excluding a theoretically important variable is one form of specification error. • Using the incorrect functional form is another form of specification error.

  12. Types of data Sometimes you can generate your own data but, more often, limited resources require one to use data available at lower costs (previously published data, data originally collected for other purposes, and/or sampling administrative records or clients).

  13. Considerations in the choice of data • Level of aggregation: individual or group. • Individual level data are preferred because most theory is based on individual utility maximization. • Aggregate data can lead to less precise estimates. • Cross-sectional and time series data. Cross-sectional data generally provides estimates of long-run elasticities, while time series data usually provides estimates of short-run elasticities.

  14. Identification • In a perfectly competitive market, price and quantity result from the simultaneous interaction of supply and demand. • Changes in price and quantity can result from shifts of the supply curve, shifts of the demand curve, or both. • In the absence of variables that affect only one side of the market (demand or supply, but not both), it may not be possible to estimate separate supply and demand curves.

  15. Instrumental Variables • To identify the demand curve, you need a variable that affects supply but not demand. This variable systematically shifts supply but not demand, thereby tracing out the demand curve. • To identify the supply curve you require a variable that affects demand but not supply.

  16. ESTIMATING THE MARGINAL EXCESS TAX BURDEN • Government projects are often financed using money raised through taxes. • A tax on a good, such as an excise tax, typically results in deadweight loss. • Social surplus is lost in transferring the tax revenue from consumers and producers to the government. • This loss (or leakage) occurs whenever there is a behavioral response to a tax -- for example, an excise tax on a consumption good causes purchases of the good to fall or a tax on earnings causes workers to reduce their work hours.

  17. ESTIMATING THE MARGINAL EXCESS TAX BURDEN • The marginal value of the forgone consumption or forgone hours of work is the deadweight loss of the tax. • As a consequence of this loss, the social cost of raising a dollar of revenue through a tax is usually larger than one dollar, sometimes substantially larger. • The social surplus lost from raising an additional dollar of tax revenue is known as the marginal excess tax burden (METB).

  18. ESTIMATING THE MARGINAL EXCESS TAX BURDEN

  19. ESTIMATING THE MARGINAL EXCESS TAX BURDEN Estimates of the average value of the METB for all taxes combined range from about 8 cents per tax dollar raised to 46 cents per tax dollar raised. Table 12.1: The Marginal Cost of Public Funds (Browning) Proportional Tax Degressive Tax Progressive Tax Marginal deadweight cost 8 13 16 Table 12.2: Comparison of Marginal Deadweight Cost Estimates Ballard, Shoven and WhalleyJorgenson and Yun Capital 46 92 Labor 23 48 All 33 46

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