rafmacalaba/datause-encoder-data huggingface dataset:
estimates of the effect of education on smoking behavior are very similar to ordinary least square estimates, suggesting that the effect of college education in reducing smoking prevalence is causal.

Rates of return to education, as traditionally calculated, only account for labor market earnings. If the effect of education on health and longevity is, as in the case of smoking reduction, causal, it makes sense to attribute additional returns to education. I have proposed (De Walque 2003; 2004) a method to estimate these extra returns in the context of the HIV/AIDS epidemic in Africa. Compared to HIV/AIDS case, the potential addition to the returns to education is significant but modest in the case of smoking, essentially because smoking kills at relatively late ages.

# **Bibliography**

The word “processed” describes informally reproduced works that may not be commonly available through library systems.

- Angrist, Joshua D. 1990. “Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records.” _American Economic Review_ 80(3): 313-36.

- Angrist, Joshua D., and Krueger, Alan B. 1992. “Estimating the Payoff to Schooling Using the Vietnam-Era Draft Lottery.” NBER Working Paper 4067. Cambridge, Mass.

- Becker, Gary S. 1993. _Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education_ . 3rd Edition. Chicago, IL: The University of Chicago Press.

- Becker, Gary S., Grossman Michael, and Murphy Kevin M. 1994. “An Empirical Analysis of Cigarette Addiction.” _American Economic Review_ 84(3): 396-418.

- Bedard, Kelly, and Olivier Deschenes. 2003. “The Long-Term Impact of Military Service on Health: Evidence from World War II Veterans.” Processed.

- Berger, Mark C., and J. Paul Leigh. 1989. “Schooling, Self-Selection and Health.” _The Journal of Human Resources_ 24(3): 433-55.

- Card, David. 1995. “Earnings, Schooling, and Ability Revisited.” _Research in Labor Economics_ 14: 23-48

- Card, David. 2001. “Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems.” _Econometrica_ 69(5): 1127-60.

- Card, David, and Lemieux, Thomas. 2000. “Going to College to Avoid the Draft: The Unintended Legacy of the Vietnam War.” Processed.

- Card, David, and Thomas Lemieux. 2001. “Draft Avoidance and College Attendance: The Unintended Legacy of the Vietnam War.” _American Economic Review Papers and Proceedings_ 91: 97-102.

- Chaloupka, F. J., and K. E. Warner. 2000. “The Economics of Smoking.” In J. Newhouse and A. Culyer, eds., _Handbook of Health Economics 1A_ . North-Holland, Amsterdam..

18
Benefits in Russia 25 payments to the cooperative, with membership of the cooperative vested in each owner. There would have to be quite strong penalties, reinforced by the legal system, to default on payments. There are numerous examples from OECD economies of contractual arrangments that might be adapted. In the case of groups of owners receiving compensation maintenance payments, receipt would be contingent on formulating a standard articles of association and forming a managing board for the cooperative or condominium. The latter would be required to provide certified financial accounts at specified intervals. For those groups of owners not receiving compensation grants, receipt of title would be contingent on adoption of similar articles of association by all owners in **a** building; the same accounting and other rules would obviously apply.

# **6: Types of Firms and their Settings**

Any discussion of benefits has to distinguish not only over the type of benefit but also with respect to the type of firm as given by its place in the labour market. This is important given the immense diversity of firms in addition to the problem of concentration in employment.

At first approximation, we can characterise the Russian economy in terms of numbers of semi-closed economies within which labour is largely immobile. In addition, we can try and characterize in terms of degree of concentration over regions and within regions. Russian industrial structure has generally been characterized by high concentration. At one pole, we have the large company town; but also structures still dominated by relatively few firms. Pzown et al (1993), using a large firm dataset for 1989, qualify these assumptions in several significant ways !. First, they argue that at national level, there -s little robust evidence of concentration; further, Russian industry is not generally characterized by very large firms and those very large firms that do exist are not generally in heavily concentrated industries. In addition, most very large firms are not in company towns, but are located in areas with reasonably diversified industrial bases.

Nevertheless, 90% of Russian cities have 10 firms or lecs and empocyment The dataset excludes firms in the military-industrial complex and hence may iikely understate concentration.
<!-- Start of picture text --> P lan,Pning, P and R 8om<br><!-- End of picture text --> World Dovwlpmont Report The property tax is the most widely used source of municipal tax revenue in the developing world, but its current yield is often insubstantial.

Poor policy often sets tax rates too low, offers excessive exemptions, and fails to adequately respond to inflation. Poor administration results in incomplete tax rolls, haphazard valuations, and low collection efficiency.

To increase the yield and improve the fairness of the tax, both the policy and administrative problems must be addressed. Tax reform too often consists of a one-time general revaluation or rate increase. Taken alone, neither has a sustained impact on the property' tax's performance.

Tax policy must ensure that rates are set high enough to make the tax worth collecting. Where significant inflation exists, a policy of annual adjustments in rates or valuations should be instituted.

Administrative reforms should support simple procedures for property discovery and valuation, suited to the characteristics of the local tax base and the skills to the taxing authority. Procedures for updating property records to reflect changes in the tax base deserve particular attention. Collection systems should be designed to make compliance convenient; and noncompliance subject to costly, swift, and sure penalties.

Central governments can achieve reform on a nationwide scale - even where the property tax is locally administered -by delivering standardi?ed packages of training and technical assistance to local governments.

This paper is a background paper for the 1988 World Development Report. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rhoda Blade-Charest, room S13-060, extension 33754.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Plauning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, inteTpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright C 1988 by the International Bank for Reconstruction and DevelopmentThe World Bank
Table 3: Empolyment Status for Workers in Rural Households

||||Work|Market Work||||
|---|---|---|---|---|---|---|---|
|||Wage<br>work|Within the<br>Household|Within the<br>Household|Household<br>Production|Market<br>Work|Any<br>Work|
|||(1)|(2)|(3)|(4)|(5)|(6)|
|Ages 6-15||||||||
||Rice Price|-0.04|0.008|-0.08|0.084|-0.115|0.003|
|||[0.019]**|[0.069]|[0.079]|[0.088]|[0.082]|[0.069]|
||Rice Price*Male|0.028|0.116|0.064|0.046|0.097|0.125|
|||[0.022]|[0.067]*|[0.062]|[0.082]|[0.061]|[0.066]*|
||Observations|9586|9586|9586|9540|9586|9586|
||R-squared|0.09|0.38|0.36|0.31|0.37|0.39|
|Ages 16-24||||||||
||Rice Price|-0.146|-0.036|-0.034|-0.121|-0.117|-0.031|
|||[0.084]*|[0.056]|[0.090]|[0.091]|[0.071]*|[0.046]|
||Rice Price*Male|-0.051|0.073|0.144|0.003|0.043|-0.032|
|||[0.097]|[0.079]|[0.109]|[0.126]|[0.089]|[0.068]|
||Observations|6059|6059|6059|6058|6059|6059|
||R-squared|0.14|0.11|0.15|0.2|0.13|0.09|
|Ages 25-64||||||||
||Rice Price|0.052|-0.063|-0.051|-0.086|0.001|-0.036|
|||[0.040]|[0.026]**|[0.047]|[0.045]*|[0.044]|[0.018]**|
||Rice Price*Male|0.022|0.081|0.005|0.041|-0.008|0.031|
|||[0.061]|[0.043]*|[0.060]|[0.084]|[0.050]|[0.028]|
||Observations|13324|13324|13324|13321|13324|13324|
||R-squared|0.12|0.05|0.1|0.15|0.1|0.03|

Note: * and ** denote significance at 10% and 5% level, respectively. Standard errors that correct for clustering on community/survey time are reported in parenthesis. All regressions include commune fixed effects, third order polynomial in age, a gender effect, number of completed grades, year effect, interaction of year effect with regions, interaction if commune accessibility with year, controls for the season of interview and whether the interview is at a rice harvest or planting time. In addition, all terms of the age polynomial, number of completed grades, year effect, interaction of year effect with regions, interaction of commune accessibility with year, controls for the season of interview and whether the interview is at a rice harvest or planting timeall are interacted with male indicator. Number of observations is smaller in household production because some people that report working outside household production, have missing data on household production.
23 Chart 11 <!-- Start of picture text --> COLOMBIA<br>27<br>26-<br>25-<br>24-<br>235<br>22-<br>21-<br>20 -<br>19<br>18<br>17<br>16-<br>15<br>14-<br>13<br>12-<br>1 1<br>10<br>9 19'65 19'67 19369 j 171 1973 19"75 1977 19'79 19'81 19'83 19`85<br>1966 1968 1970 1972 1974 1976 1978 1980 19Z2 1984<br>Yeor<br>0 Priv.lnvt./GDP + Dom.Cred./GDP<br><!-- End of picture text -->
# **3.1 Impact of the 2008 Global Food Price Increase on Undernourishment**

The World Bank index of food prices increased by 35 percent in 2008 compared to its annual average in 2007 and by roughly 50 percent in July 2008 compared to its level in July 2007. We consider a scenario in which prices increase by a ‘trend level’ of 5 percent from 2007 levels and scenarios with increases of 25 percent, 35 percent and 50 percent. Recognizing that the full extent of price movements in international commodity markets does not pass through into domestic consumer prices we also consider the case in which 20 percent of the change in international prices is not passed through. The results are presented in Table 3.

|**Table 3: Impact of f**<br>_% Increase from_|**ood price inc**|**reases on**<br>**20**|**undernouri**<br>**08- Full Pa**|**shment**<br>**ssthrough**||**2008 -**<br>**Passth**|**Partial**<br>**rough**|
|---|---|---|---|---|---|---|---|
|_2007_||**5%**|**25%**|**35%**|**50%**|**35%**|**50%**|
|Sub-Saharan Africa||1%|4%|5%|7%|4%|6%|
|Asia and Pacific<br>Latin America and||-4%|6%|11%|19%|8%|14%|
|Caribbean||0%|10%|16%|24%|12%|18%|
|Middle East and||||||||
|North Africa||-1%|13%|20%|31%|15%|24%|
|Total||-2%|6%|10%|16%|7%|12%|
|||||||**2008 -**|**Partial**|
||**Baseline**|**20**|**08- Full Pa**|**ssthrough**||**Passth**|**rough**|
|_(incidence in_<br>|**FAO**|||||||
|_millions)_|**2007**|**5%**|**25%**|**35%**|**50%**|**35%**|**50%**|
|Sub-Saharan Africa|236|239.3|245.5|248.6|253.4|246.4|250.2|
|Asia and Pacific|583|561.5|619.7|649.6|695.5|628.6|664.8|
|Latin America and||||||||
|Caribbean<br>Middle East and|51|50.8|56.2|59.0|63.2|57.0|60.4|
|North Africa|37|36.6|41.8|44.4|48.5|42.5|45.8|
|Total<br>_Notes:_Partial pass through o<br>international markets. The %<br>lower panel correspond to t|923<br>f price assumes t<br>increase numbe<br>he estimate perce|902.2<br>hat the price s<br>rs correspond t<br>ntage increase|974.9<br>hock faced by d<br>o increase over<br>under the vari|1012.2<br>omestic consu<br>2007 model b<br>ous scenarios|1069.4<br>mers is 80% of t<br>aseline. The inc<br>applied to the F|986.5<br>he price shock i<br>idence number<br>AO baseline for|1031.1<br>n the<br>s in the<br>2007.|

Under the 5 percent food price increase scenario, the number of total undernourished would have decreased to 902 million in 2008. This reduction would largely be due to economic growth with the rise in average incomes more than making up for the increase in food prices. A more extreme scenario considers the change in the food price index between June 2007 and June 2008 – 50% - and shows that this would imply a 16 percent increase in undernourishment in 2008 if global prices were fully passed through. The more moderate, and realistic scenario, involves taking the average price increase for 2008 (35%) and the partial (80%) pass through of global prices. This scenario implies that the undernourishment count for 2008 rose by 63 million
|**Table V. Regression A**|**Basic Specifi**<br>**nalysis: Actua**<br>**P**|**cation (BS)**<br>**l Bilateral C**<br>**anel A. U.S.**|**BS + Tim**<br>**urrent Accou**<br>**Investors**|**e Trend**<br>**nt vs. Optim**|**BS + La**<br>**al Portfolio**|**gged BCA**<br>**Shares**|
|---|---|---|---|---|---|---|
||**Alpha**|**L.Alpha**|**Alpha**|**L.Alpha**|**Alpha**|**L.Alpha**|
|**Relative Risk Aversion: 5**|||||||
|Basic VAR|0.002 ***|-0.000|0.001 **|-0.001|0.000 *|-0.000 *|
|+ RER in Levels|0.002 ***|-0.000|0.001 **|-0.001|0.000|-0.000 *|
|+ RER in Differences|0.002 ***|-0.000|0.001 **|-0.001|0.000 *|-0.000 *|
|+ Infl., NER in Levels|0.001 ***|-0.000|0.001 **|-0.001|0.000|-0.000|
|+ Infl., NER in Differences|0.002 ***|-0.000|0.001 ***|-0.001|0.000|-0.000 *|
|**Relative Risk Aversion: 10**|||||||
|Basic VAR|0.004 ***|-0.000|0.002 **|-0.002|0.001 *|-0.001 *|
|+ RER in Levels|0.003 ***|-0.000|0.002 **|-0.001|0.001 *|-0.001 *|
|+ RER in Differences|0.004 ***|-0.000|0.002 **|-0.001|0.001 *|-0.001 *|
|+ Infl., NER in Levels|0.003 ***|-0.000|0.002 **|-0.001|0.001 *|-0.001|
|+ Infl., NER in Differences|0.003 ***|-0.000|0.002 ***|-0.001|0.001|-0.001 *|
|**Relative Risk Aversion: 30**|||||||
|Basic VAR|0.011 ***|-0.000|0.006 **|-0.005|0.002 *|-0.002 *|
|+ RER in Levels|0.009 ***|-0.001|0.006 **|-0.004 *|0.002 *|-0.002 *|
|+ RER in Differences|0.011 ***|-0.000|0.006 **|-0.004|0.002 *|-0.002 *|
|+ Infl., NER in Levels|0.008 ***|-0.001|0.006 ***|-0.004 *|0.002 *|-0.002|
|+ Infl., NER in Differences|0.010 ***|0.001|0.006 ***|-0.003|0.002|-0.002 *|
|**Relative Risk Aversion: 100**|||||||
|Basic VAR|0.037 ***|-0.000|0.021 **|-0.016|0.008 *|-0.008 *|
|+ RER in Levels|0.030 ***|-0.005|0.021 ***|-0.014 *|0.007 *|-0.006 *|
|+ RER in Differences|0.036 ***|-0.000|0.021 **|-0.014|0.007 *|-0.007 *|
|+ Infl., NER in Levels|0.027 ***|-0.004|0.019 ***|-0.012 *|0.006 *|-0.005|
|+ Infl., NER in Differences<br>This table shows the regression <br>results for a U.S. investor. Three <br>account on contemporaneous and<br>time trend, and the basic specific<br>relative risk aversion parameter: 5<br>our five VAR specifications use<br>percentage of GNP. * means signi|0.034 ***<br>results of the bila<br> different regressi<br>lagged optimal po<br>ation including a <br>, 10, 30, and 100.<br>d to calculate ex<br>ficant at 10%, **|0.002<br>teral current ac<br>ons are reporte<br>rtfolio shares (a<br> lagged depende<br>Different serie<br>pected asset re<br>at 5%, and ***|0.020 ***<br>count on the m<br>d: our basic spe<br>lpha and L.alph<br>nt variable. The <br>s of model-predi<br>turns. The bilat<br>at 1% levels.|-0.011<br>odel-predicted <br>cification that <br>a, respectively),<br> results are sho<br>cted portfolio s<br>eral current ac|0.006<br>portfolio share<br>regresses the b<br>the basic spec<br>wn for differe<br>hares are consi<br>count data is|-0.006 *<br>s. It shows the<br>ilateral current<br>ification with a<br>nt values of the<br>dered based on<br>expressed as a|

50
<!-- Start of picture text --> Policy Research|<br>Fnanco& andPvoScr<br>Development<br>WPS 1115<br><!-- End of picture text -->

- This paper a product of the Finance and Private Sector Development Division, Policy Research Departnent-ispartofalargereffortinthedeparunentto understandthe determinantsoflong-rungrowth. The study was funded by the Bank's Research Support Budget under research project "How Do National Policies Affect Long-Run Growth?" (RPO 676-66). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Daniele Evans, room N9-089, extension 38526 (March 1993,45 pages).

What has the profession leamred from crosscountry regressions about the links between long-run growth and indicators of fiscal, monetary, trade, financial, and exchange-rate policies?

Levine and Zervos find that: . Indicators of financial development are strongly associated with long-run growth.

* Other individual policy indicators are only weakly linkled to growth.

= It is particularly difficult to find a consistent relawionship between inflation and long-run grr **Avth.** For example, the inclusion or exclusion of one or two countries (Nicaragua and Uganda) out of more than 100 countries in the sample can lead to reach three quite different conclusions: (1) that only very high inflation is bad for growth, (2) **that** very high inflation in itself is not bad for growth, but small increases in inflation in moderate-inflation countries slow growth, or (3) that inflation is unrelated to growth The connections between policy indicators and growth are quite sensitive to slight alterations in the right-hand-side variables and to Ismall changes in the sample of countries.

And the daunting array of methodological problems limiting our ability to Interpret crosscountry regressions implies that, _at best,_ they suggest interesting empirical regularities. Crosscountry regressions should not be used to predict by how much long-run growth will change when policies change. But beliefs about policy and growth that are not supported by cross-country evidence will tend to be viewed skeptically. So, future worik on the policy-growth nexus siiould integrate broad cross-country analyses with country case studies and investigations of specific finns.

The Policy ResearchWorklng Paper Series disseminates the fidings of work under way in theBanlc An objective of the series is to get these findings out qciickly. even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissenination Center
respectively) and 57 percent for tobacco growers in Malawi. Fertilizer usage data are not available for Rwanda and are missing for a large share of the Kenya coffee sample.

In addition to information on harvest and input use, this analysis also requires an estimate of the date of harvest. Since our long recall indicator will refer to only one harvest, we restrict the analysis to crops that are harvested only once during the agricultural calendar. Coffee and tobacco are both single harvest crops. Maize and sorghum, however, can have multiple harvests during the same year. In Rwanda, where the month of harvest information is recorded in the questionnaire for staple crops, we restrict the analysis to households that indicated only one month of harvest, or two consecutive harvest months for maize or sorghum. In Malawi, seasonal rain patterns allow for one main and one secondary ( _dimba_ ) harvest per year. We include only the main harvest from rain-fed plots. In Kenya, the calculation of the harvest date is complicated by high degrees of variability in agricultural zones within the country. The analysis in Kenya is therefore limited to areas and crops with only one harvest per year and reliable information on the harvest month for the crop.<sup>5</sup> This includes only the Rift Valley for maize production, and the Eastern, Central, Western and Rift Valley regions for coffee production. For all three countries, the household interview can pertain to one of two agricultural seasons, depending on the last completed season relative to the month of the interview.<sup>6</sup> Thus, the results are not specific to only one agricultural season.

For the harvest date assigned to households, we would ideally use the actual harvest month for each household (a second best option would be the village-specific harvest month). In the Rwanda EICV, we have and use the direct reporting of the household’s harvest month for maize and sorghum. In Malawi and Kenya, and for coffee in Rwanda, we have no information on the month of the harvest (or reported harvest window) for either households or villages in the surveys. In these cases, we assume it is based on other sources. The harvest date is constructed based on input from local agronomists on the normal month of harvest by region to account for spatial variation in production seasons. The harvest month for maize in Malawi is assumed to be April for the southern region, May for the central region, and July for the more arid northern region. The harvest months for tobacco are assumed to be one month earlier. In

> 5 In Kenya, the design of the survey instrument makes the exclusion of irrigated plots more difficult. The incidence of irrigation in the Rift Valley, however, is quite low, with less than 5 percent of farms using irrigation on maize crops. These observations remain in the dataset, although their exclusion does not markedly change the results.

> 6 In Malawi, the data pertain to the 2002/03 and 2003/04 rainy seasons. In Kenya, it is the 2003/2004 and 2004/2005 rainy seasons. In Rwanda, it is the 2000/2001 season.

10
<!-- Start of picture text -->
|<br>mm:<br>|<br>|<br>TM<br>| Tex<br>| Domestic<br>L | 8<br>Op ©, ©; FDI<br>-fp | Export oe<br>7 o—__-°<br>|<br>-fy |<br>|<br>Adapted from<br>-f, Helpman (2006)<br>|<br>|<br><!-- End of picture text -->
# **Income Growth**

The ability to link the ENAMIN to the ENEU means that we can create a panel where profits are observed at two points in time, and extensive firm and personal entrepreneur characteristics that would determine selection into treatment are available for the initial period. While the data is not structured to permit the use of a difference in differences matching approach that would clear out any unobserved characteristics that could violate the CIA, the possibility of controlling for initial profits provides some additional defense for the CIA.

Table 6 reports unconditional average income growth by treatment status. It clearly appears that without controlling by any enterprise or personal characteristics nontreated firms exhibit higher – although still negative, on average – rates of income growth. Our OLS, matching and control function estimates (tables 7 through 9) reduce this gap but do not reverse its sign. Moreover, with both OLS and matching, receiving credit – particularly if from informal sources – is significantly related to lower rates of income growth. The negative results are strongest with the Kernel matching but they are weakly echoed by the nearest neighbor matching estimates. These estimated negative treatment effects on growth could suggest that the treatment effects on the first period income move firms toward their steady state sizes with attendant reduction in growth rates as they converge. Alternatively, as matching is done on after-treatment initial levels of profits, one could argue that the worse performing treated are matched to the best nontreated, thus downwardly biasing the estimated treatment effects.

# **Survival**

Table 10 shows the raw survival likelihood by treatment status: on average treated individuals show a 10% higher probability of survival. It thus appears that despite the lack of positive effect of the treatments on growth, they do increase the likelihood of micro-firm survival. Since control function techniques are not applicable in this context, 19