rafmacalaba/datause-encoder-data huggingface dataset:
-38-

# **RefreZnJU**

Azariadis, Costas and Allan Drazen. "Threshold Externalities in Economic Development," N.B.E.R. Working Paper, May, 1988.

Barro, R. J., "A Cross-Country Model of Growth, Saving and Investment," N.B.E.R. Working PaDer, no. 2855, February, 1989.

Bernanke, B. S., "Crazy Explanations for the Productivity SLowdown: Comment," 1988.

Cohen, D., and J. Sachs, "Growth and External Debt Under Risk of Debt Repudiation," European Economic Review, vol. 30, pp. 529-560, 1986.

Dechert, W. D. and K. Nishimura, "A Complete Characterization of Optimal Growth Patterns in an Aggregated Model with a Non-Concave Production Function," Journal of Economic Theory, vol. 31, pp. 332-354, 1983.

Eaton, J. and M. Gersovitz., "Debt with Potential Repudiation: Theoretical and Empirical Analysis," Review of Economic Studies, vol. 48, 1981.

Froot, K. A., "Buybacks, Exit Bonds, and the Optimality of Debt and Liquidity Relief," International Economic Review, vol. 30, no. 1, 1989.

Khan, M. S. and N. Ul Haque, "Foreign Borrowing and Capital Flight," I.M.F. Staff PaRers, pp. 606-628, 1985.

King, R. G. and S. Rebelo, "Public Policy and Economic Growth: Developing Neoclassical Implications," mimeo, September, 1988.

Lindert, P., "Relending to Sovereign Lenders," U. C. Davis, mimeo, 1987.

Lucas, R., "On the Mechanics of Economic Development," Journal of Monetary Economics, vol. 21, 1988, pp. 3-42.

Majumdar, M. and T. Mitra, "Intertemporal Allocation with a Non-Convex Technology: The Aggregative Framework," Journal of Economic Theory, vol. 27, pp. 101-136, 1982.

"Dynamic Optimization with a Non-Convex Technology: The Case of A linear Objective Function," Review of Economic Studies, pp. 143-151, 1983.

Majumdar M., T. Mitra and Y. Nyarko, "Dynamic Optimization Under Uncertainty: Non-Convex Feasible Set," in Joan Robinson and Modern Economic Theory: Essays in Honor of Joan Robinson, G. Feiwell Ed., Ch. 19, Macmillan, 1989.

Norton, 136.

Psacharopoulos, G., and A. M. Arriagada, "The Educational Composition of the Labor Force: An International Comparison," World Bank Reprint Series, Number 402, 1986.
prediction of _σj,t_<sup>_m_as:</sup> Close inspection of equation (6) reveals the manner by which effective choice set variation ‘switches on’ the instrument. If there was only one market (i.e. the whole of Kenya) in which individuals choose to enrol, then the denominator of (6) would be constant across all markets _m_ . Hence, identification would rely exclusively on the non-linear transformation of _Xk,t_<sup>_mβ_0andis</sup> sensitive to misspecification. In contrast, if enrollment choices take place in many distinct markets, the denominator of (6) varies across _m_ . Since we have data available on a large number of Kenyan – districts in two time periods, identification in our case is driven by choice set variation and not by specific functional assumptions.

As stated above, this instrumentation strategy will allow us to estimate the causal effect of others’ enrollment decisions on my own enrollment under the assumption that there are no quality spillovers between schools. Technically, the assumption implies that the utility a student derives from going to public school, _δj,t_<sup>_m_,doesnotdependonwhoisenrolledinprivateschoolandeither</sup> – the observed or unobserved quality of going to private school and vice versa. In our view the assumption of ‘no-spillovers’ is a valid first-order approximation to the production of school value. The main mechanism that could violate it is if education acquisition was driven by a pure signaling motive. In this case, what matters for utility is not just the absolute quality of the public sector, but rather the difference between private and public sector.

We cannot rule out that signaling may play some part in enrollment decisions, thus should be kept in mind as an alternative explanation of our results here. Signaling could serve as a mechanism linking FPE to enrollment shifts toward private schools if the entrance of marginal pupils to public schools creates a strong incentive for more affluent households to differentiate themselves – not in terms of skills acquired, but credentials – from these new public school pupils.

34
||(8)<br>0.470|[0.048]***<br>1.510|[0.472]***<br>0.226<br>[0.072]***<br>-0.065<br>[0.142]<br>0.133<br>[0.036]***<br>Yes|Yes<br>624<br>0.33<br>2273<br>0.000||
|---|---|---|---|---|---|
|Efects|(7)<br>ds<br>0.509|[0.047]***<br>1.419|[0.476]***<br>0.276<br>[0.072]***<br>-0.052<br>[0.143]<br>Yes|Yes<br>624<br>0.32<br>2260<br>0.000||
|untry Fixed|(6)<br>g 5-year Perio<br>0.475|[0.048]***<br>0.701|[0.416]*<br>0.31<br>[0.213]<br>1.539<br>[0.497]***<br>0.163<br>[0.037]***<br>Yes|Yes<br>624<br>0.33<br>2270<br>0.000|at 1%.|
|ns with Co|(5)<br>Counts durin<br>0.534|[0.047]***<br>0.543|[0.422]<br>0.383<br>[0.215]*<br>1.493<br>[0.503]***<br>Yes|Yes<br>624<br>0.32<br>2250<br>0.000|* signifcant|
|l Estimatio|(4)<br>able: Patent <br>0.476|[0.049]***<br>2.354|[0.398]***<br>-0.168<br>[0.167]<br>-0.278<br>[0.190]<br>Yes|No<br>624<br>0.31<br>2146<br>0.000|nt at 5%; **|
|ve Binomia|(3)<br>pendent Vari<br>0.548|[0.047]***<br>-1.534<br>[2.261]<br>0.957|[0.405]**<br>Yes|Yes<br>624<br>0.32<br>2242<br>0.000|; ** signifca|
|tion: Negati|(2)<br>De<br>0.396|[0.045]***<br>1.478|[0.319]***<br>Yes|No<br>624<br>0.31<br>2132<br>0.000|ifcant at 10%|
|Knowledge Production Func|(1)<br>0.577|[0.045]***|Groups) (t-1)<br>(t-1)<br>phic Groups) (t-1)<br>Yes|Yes<br>624<br>0.32<br>2236<br>0.000|dard errors in brackets. * sign<br>of patents.|
|Cumulative|)|orld (t-1)<br>Rate|Group (t-1)<br>World (Income <br>graphic Group <br>World (Geogra<br>U.S.||Notes: Stan<br>SP= Stock|
|e 8:|(t-1|the w<br>ent|come<br> the <br> Geo<br> the <br> the <br>ects|||
|abl|try|of <br>llm|In<br> of <br> of <br> of <br> to <br> ef|es<br>on||
|T|coun|rest <br> Enro|Rest<br> Rest<br> Rest<br> Rest<br>ports<br> fxed|mmi<br>tions<br>R2<br>persi<br>val||
||P|P <br>y|P <br>P <br>P <br>P <br>x<br>ry|du<br>a<br> <br>is<br>p-||
||(log) S|(log) S<br>Tertiar|(log) S<br>(log) S<br>(log) S<br>(log) S<br>(log) E<br>Count|Time <br>Observ<br>Pseudo<br>Over-d<br>Over.||

29
**_maize_** (United States), no. 2, yellow, f.o.b. (free on board) U.S. Gulf ports; **_rice_** (Thailand), 5 percent broken, white rice, milled, indicative price based on weekly surveys of export transactions, government standard, f.o.b. Bangkok; **_wheat_** (United States), no. 1, hard red winter, ordinary protein, export price delivered at the U.S. Gulf port for prompt or 30 days shipment; s **_oybeans_** (United States), c.i.f. (cost, insurance, freight) Rotterdam; **_palm oil_** (Malaysia), 5 percent bulk, c.i.f. N. W. Europe; and **_crude oil_** , average price of Brent, Dubai and West Texas Intermediate, equally weighed. Finally, the manufacture unit value (MUV) was used as a proxy of global deflator index. The MUV is a US dollar trade weighted index of manufactures exported from 15 economies (Brazil, Canada, China, Germany, France, India, Italy, Japan, Mexico, the Republic of Korea, South Africa, Spain, Thailand, the United Kingdom, and the United States). More details on the prices along with the MUV can be found at the World Bank’s Commodity Price Database.<sup>**1**</sup>

The _S/U_ ratio was calculated as the ratio of end-of-season stocks to total consumption taken from the U.S. Department of Agriculture’s Production, Supply, and Distribution Online.<sup>**2**</sup> The exchange rate measure was the International Monetary Fund’s Special Drawing Rights rate representing an index of the U.S. dollar against four currencies, namely the Euro, Pound sterling, Japanese yen, and U.S. dollar.<sup>**3**</sup> The 10-year US Treasury bill was used as interest rate proxy, taken from the U.S. Federal Reserve’s Selected Interest Rates database.<sup>**4**</sup> Last, GDP was taken from the World Bank’s World Development Indicators database.<sup>**5**</sup>

# Estimation

Prior to estimating the model, we examined the stationarity properties of all variables, applying unit root tests to levels with and without trend as well as first differences. Two tests were used, the ADF (Augmented Dickey-Fuller) and the PP (Phillips-Perron). Results are reported in table 1. The statistics indicate overwhelming rejection of stationarity and trend stationarity for all five food prices and the crude oil price in favor of stationarity in first differences (see upper panel of table 1). Stationarity and trend stationarity were rejected for all three macroeconomic variables and GDP, again in favor of stationarity in first differences (see middle panel of table 1). However, the unit root test results for the _S/U_ ratios were mixed. The statistics of maize and rice point to difference stationarity, and to a lesser extent, this is the case with wheat and palm oil. However, both unit root statistics indicate stationarity for soybeans (see lower panel of table 1).

> **1** <u>http://worldbank.org/prospects/commodities</u>

> **2** <u>www.fas.usda.gov/psdonline</u>

> **3** <u>http://www.imf.org/external/np/fin/data/rms_five.aspx</u>

> **4** <u>www.federalreserve.gov/releases/h15/data.htm</u>

> **5** <u>http://data.worldbank.org/indicator</u>

— 5 —
Figure 4. Aveage Total **PAlent** Cosd In **Kenya,** by Hosptal **and** Type of Procedure <!-- Start of picture text --> 0 Cost ot Prooduro 0 Cost of Hosptalziation<br>17.5<br>$15.25<br>15<br>12.5<br>10 10.7<br>5 - $99 $30 $5.24 $4.37<br>2.5 4,11 $QI91<br>0 O__ z2v| - 121 I w Lain~~J, .' ''2. : 6 7=f-V ~~ _F:1! 7 1117A I . . .. .._<br>KENI KEN1 KEN2 KEN2 KEN3 KEN4<br>(n.17) (n.10) (n-5) (n=11) (n-11) (n-45)<br>D&C MVA D&C MVA MVA MVA<br>'Cost In U.S. dogars. US$1 = 27.7 Kenya ShilIlings.<br><!-- End of picture text -->

Table 2. Average Cost of Treating a D&C or MVA Patient in Kenya, by Hospital and **Cost** Category

|**H_ospita**<br>and Type of<br>lrocedure|Number<br>of<br>Casew|Medical<br>instruments|Re-<br>**deriatlon**|Diposable<br>Suppiles|Drnp|Staff|SubTotal|loptalbaUn|**Tol**|
|---|---|---|---|---|---|---|---|---|---|
|KENI<br>(D&C)|17|0.00<br>(NC)|0.00<br>(NC)|0.29<br>(7%)|0.57<br>(14%)|1.40<br>(35%)|2.26<br>(57%)|1.73<br>(43%)|3.99|
|KENI<br>(MVA)|10|0.30<br>(10%)|0.14<br>(5%)|0.18<br>(6%)|0.60<br>(19%)|096<br>(31%)|2.18<br>(71%)|0.91<br>(29%)|3.09|
|KEN2<br>(D&C)|_5_|0.00<br>(NC)|0.00<br>(NC)|1.31<br>(9%)|0.65<br>(4%)|2.59<br>(17%)|4.55<br>(30%)|10.70<br>(70%)|15.25|
|KEN2<br>(MVA)|11|0.31<br>(6%)|0.16<br>(3%)|0.17<br>(3%)|0.58<br>(11%)|1.47<br>(28%)|2.69<br>_(51%)_|2.55<br>(49%)|**524**|
|KEN3<br>(MVA)|I1|0.32<br>(11%)|0.14<br>(5%)|0.29<br>(10%)|0.39<br>(13%)|0.84<br>(29%)|1.98<br>(67%)|0.96<br>(33%)|2.94|
|KEN4|45|0.31|0.08|0.23|0.58|1.56|2.76|1.61|4.37|
|(MVA)||(7%)|(2%)|(5%)|(13%)|(36%)|(63%)|(37%)||

Costs in U.S. dollars; as of May 1991. US$I - Ksh 27.7 (NC) = not calculated (%) = Percentage of average total cost by hospital 10
is another significant predictor for women to stay out of the labor force if they were already so. On the other hand, holding any educational qualification also significantly reduces the probability that a publicly employed woman drops out of the labor force. Younger, unmarried women, and women living in smaller households are more likely to move from the public to the private formal sector.

**Table 1: Determinants of employment status transitions between 2012 and 2018**

|Initial status 2012||Public|Wome<br>Formal|n|Out|of labor force (O|LF)|
|---|---|---|---|---|---|---|---|
|Destination status<br>2018|Public<br>Formal|Private<br>Formal|Private<br>Informal|OLF|Public<br>Formal|Any<br>Employment|OLF|
|Age|-0.018|0.011*|0.008|-0.012|-0.021*|0.023***|-0.023***|
||(0.016)|(0.010)|(0.011)|(0.010)|(0.011)|(0.004)|(0.003)|
|Age^2|0.001|-0.003*|-0.003|0.004|0.003*|-0.002***|0.002***|
||(0.002)|(0.002)|(0.002)|(0.003)|(0.001)|(0.001)|(0.000)|
|Primary school|0.209|-0.088|-0.036|-0.408**|0.034|-0.063***|0.048***|
||(0.142)|(0.062)|(0.083)|(0.163)|(0.031)|(0.012)|(0.016)|
|Secondary school|0.239|-0.067|-0.087|-0.502***|0.158***|-0.050***|-0.003|
||(0.132)|(0.059)|(0.064)|(0.152)|(0.031)|(0.014)|(0.013)|
|Post-Secondary|0.238|-0.075|-0.085|-0.499***|0.373***|-0.072***|-0.057**|
||(0.127)|(0.048)|(0.062)|(0.148)|(0.042)|(0.017)|(0.024)|
|Married|0.051|-0.049***|0.005|0.032|-0.137***|-0.039**|0.108***|
||(0.029)|(0.009)|(0.017)|(0.031)|(0.044)|(0.011)|(0.024)|
|Household size|0.010|-0.013*|-0.000|0.072|-0.004|0.000|-0.002|
||(0.013)|(0.004)|(0.001)|(0.011)|(0.008)|(0.002)|(0.001)|
|Region dummies|✓|✓|✓|✓|✓|✓|✓|
|Occupation dummies|✓|✓|✓|✓|☓|☓|☓|
|Observations|811|812|812|908|790|6,618|6,618|

_Notes_ . *** p<0.01, **<0.05, *<0.1 _._ A linear probability model’s coefficient estimates and standard errors are reported. This table uses panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. The sample is restricted to those aged at least 20 years old in 2012 and at most 59 years old in 2018. All control variables refer to 2012. Panel weights are used.

Table 2 shows the regression output on formality-sector transitions for men. We consider men who were employed either in the public sector or informally—which we define as the two absorbing states—at baseline and examine the transition probability to the public formal sector, private formal sector and private informal sector in 2018. Predictably, those better educated are found to be much more likely to secure formal employment, be it in the public or in the private sector. Having a secondary or a post-secondary level of education is strongly associated with moving out of the private informal sector. The better educated are also much more likely to keep their public formal jobs once they secure them. They are much less likely to move to the private, 20

**Official Use**
# **4.2 Projecting the Pollution Impact of Alternative Policies**

China affords a unique opportunity for projection of alternative futures, because for many parts of China the future already exists. As Figures 8, 13 and 14 indicate, China's provinces and major urban areas exhibit great disparity in the present strictness of environmental regulation, the share of production in large enterprises, and the degree of state ownership of industry. Shanghai, for example, has a high proportion of production in large facilities, a low proportion in state enterprises, a high (although unfortunately, falling) degree of regulatory strictness and, as a result, low levels of pollution intensity per unit of industrial output. Sichuan's statistics are the converse in most cases.

For much of China, it will be many years before current conditions in Shanghai are replicated. Thus, although we cannot predict the future path of technological progress with any accuracy, we can develop conservative projections for much of China based on actual conditions in the more advanced provinces. And even among the latter, there is sufficient diversity of characteristics to permit use of econometrically-estimated relationships to estimate the consequences of moving toward leading-edge status in the dimensions which matter most for pollution intensity.

China's second advantage is its wealth of data. Large databases made available to us by NEPA have enabled us to base our entire forecasting exercise on the econometric estimation exercises summarized in this paper. To our knowledge this has not previously been possible, in China or any other country. We have used the NEPA databases in three related studies: (1) determinants of industrial air and water pollution intensity; (2) the impact of air emissions on atmospheric pollutant concentrations; and (3) the cost of pollution abatement. For our air pollution scenarios, we have completed the forecasting exercise by joining our estimates to the empirical findings of Xu. et. al. (1994) on the health impact of air pollution.

# **4.3 Projecting Pollution Damage**

# **Emissions Intensities and Loads**

Our analysis begins with projections of industrial pollution loads, which are based on our econometric studies of variations in pollution intensity for COD, S02 and TSP. Figures 11 and 12 summarize the significant intensity factors: The effective pollution levy; large plant share of output; SOE share of output; and the shares of exceptionally 'clean' and 'dirty' sectors. Assuming that the economic reforms will continue, we have extrapolated from recent trends in these variables to project their future paths.

We treat the effective pollution levy as a policy variable in three scenarios: (1) Holding the air and water pollution levies constant at their 1993 levels, which would represent regress for water but progress for air (in light of the decline since 1987); (2) an annual increase of 5%/year, which would be approximately reflect the recent national trend for water 19
WPS3977

# **Quantifying the Value of U.S. Tariff Preferences for Developing Countries**<sup>*****</sup>

## Judith M. Dean<sup>†</sup> John Wainio<sup>‡</sup>

### **Abstract**

In recent debates, trade preference erosion has been viewed by some as damaging to developing countries, and by others as insignificant, except in a few cases. However, little data have been available to back either view. The objective of this paper is to improve our measures of the size, utilization and value of all US non-reciprocal trade preference programs, in order to shed some light on this debate. Highly disaggregated data are used to quantify the margins, coverage, utilization and value of nonagricultural and agricultural tariff preferences, for all beneficiary countries in the US regional programs and in the GSP. Results show that US regional tariff preference programs are generally characterized by high coverage of beneficiary countries’ exports, high utilization by beneficiary countries, and low tariff preference margins (except on apparel). For 29 countries, the value of US tariff preferences was 5% or more of 2003 dutiable exports to the US, even after incorporating actual utilization. Most of this value is attributable to non-agricultural tariff preferences, and to apparel preferences in particular. These results suggest that preference erosion may be significant for more countries than many had thought.

#### JEL: F13

Keywords: trade preference erosion, development, utilization

### World Bank Policy Research Working Paper 3977, August 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Revised version of paper presented at the "Preference Erosion:  Impacts and Policy Responses," International Symposium, WTO, Geneva, June 13-14, 2005.  We are grateful to Maya Shivakumar and Nick Grossman for their superb work assembling and analyzing data.  We also thank Pat Thomas for her valuable help constructing tables and Peg MacKnight for her help with the USITC Dataweb.  The views expressed here do not necessarily reflect the views of the USITC, or any of its individual Commissioners, or the USDA.

> † U.S. International Trade Commission.

> ‡ U.S. Department of Agriculture.
35 Most likely, less expensive, shorter-term programs with fewer work disincentives could be designed to attack unemployment, while reducing Income Insacurity among the poor.

In terms of capital market effects, the literature suggests that underfunded public pension systems do not appear to massively depress private savings, though they certainly do not seem to Increase It. Fully funded private pensions In the United States apparently Increase capital formation modestly, though it Is not clear whether this positive effect carries over to developing economies with less extensive capital markets. It has also been alleged that private pension plan growth can generate healthier capital markets, though pension plans in the developing countries examined here have thus far fallen short of spurrlng massive new private savings.

Retirement Systems' Labor Market Effects Retirement systems provide old-age Income, and some might think that their main labor market effects would therefore be limited to the end of the worklfe. Indeed, retirement plans do exert a powerful effect on workers' choice of retirement age. In addition, however, retirement systems affect a myriad of labor supply decisions made by younger and middle-aged workers as well, depending on the way the benefit formulas are structured, and also depending on the way benefits are financed. Effects of retirement systems on labor demand are also important and varied. In the case of a voluntarily-supplied company pension plan, the retirement promise is an integral part of the firm's compensation package; hence pension financing and benefit formulas are endogenously determined with labor demand, and not external to it. Governmental social security systems also have important effects on labor demand, especially when they cover only portions of the workplace, are supported by payroll taxes making covered workers relatively expensive, and when they offer benefits which favor early retirement. These effects are sketched in this review of how retirement systems affect labor markets. Retirement Systems and Labor SuRDlV: A great deal of microeconomic research during the 1980s examined the effects of retirement systems on retirement behavior.2 8 Specifically, studies have shown that workers with generous pensions retire earlier than those with lower pension benefits. Also,