rafmacalaba/datause-encoder-data huggingface dataset:
|Total household member above 17 years old|||||||0.027||
|---|---|---|---|---|---|---|---|---|
|<br>squared|||||||(4.83)***||
|Proportion of hh member under 15||0.443||||0.560||0.309|
|||(5.627)***||||(6.578)***||(9.42)***|
|Household head with higher education||0.072|0.157|0.074||||0.095|
|||(2.463)**|(4.12)***|(2.94)*||||(5.91)***|
|Household size|||||-0.045<br>(4.89)***||||
|Total number of hh member b/w 7 and 12|||||0.039<br>(1.63)*||||
|Household head with secondary education|||||||||
|**Household characteristics and appliances**|||||||||
|Bath|-0.095<br>(2.57)**||||||||
|Electric oven||||0.039|||0.063||
|||||(1.90)*|||(2.01)**||
|Number of room|||-0.085<br>(2.69)***||||||
|Water||||0.069||||0.087|
|||||(2.36)**||||(3.34)***|
|Gas cylinder||0.113<br>(1.95)*|||||||
|**Village mean**|||||||||
|Proportion of hh with heating system in village||||||||2..51<br>(5.25)***|
|Proportion of population 15 to 59 by village|||||||-11.04<br>(3.95)***||
|Proportion of household with telephone by|||||||4.65||
|village|||||||(2.75)**||
|Proportion of population under 15 in each||||3.583|||||
|village||||(6.65)***|||||
|Proportion of hh connected with water per||||||||-5.50***|
|<br>village||||||||(5.58))|
|Proportion of hh with hot water per village||||-0.010||||1.79|
|||||(6.51)***||||(4.01)***|
|Proportion of hh with electricity by village||||||||-14.62|
|||||||||(8.38)***|

53
<mark>Jordà, Ò., Schularick, M. and Taylor, A.M., 2011. Financial crises, credit booms, and external imbalances: 140 years of lessons.</mark> _<mark>IMF Economic Review</mark>_ <mark>,</mark> _<mark>59</mark>_ <mark>(2), pp.340-378.</mark> <mark>Lown, C., & Morgan, D. P. (2006). The credit cycle and the business cycle: new findings using the loan officer opinion survey.</mark> _<mark>Journal of Money, Credit and Banking</mark>_ <mark>, 1575-1597.</mark> <mark>Lown, C., & Morgan, D. P. (2001). The credit cycle and the business cycle: new findings using the survey of senior loan officers.</mark> _<mark>Federal Reserve Bank of New York</mark>_ <mark>Lown, C. S., & Morgan, D. P. (2002). Credit effects in the monetary mechanism.</mark> _<mark>Federal Reserve Bank of New York Economic Policy Review</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>(1), 217-235.</mark> <mark>Minsky Human, P., 1986. Stabilizing an unstable economy, New Haven, CN: Yale University Press.</mark> <mark>Rünstler, G., 2002. The information content of real-time output gap estimates: an application to the euro area.</mark> <mark>Schüler, Y.S., Hiebert, P. and Peltonen, T.A., 2015. Characterising the financial cycle: a multivariate and time-varying approach.</mark> <mark>Schularick, M. and Taylor, A.M., 2012. Credit Booms Gone Bust: Monetary Policy.</mark> _<mark>Leverage</mark>_ _<mark>Cycles</mark>_ <mark>.</mark> <mark>Stock, J.H. and Watson, M.W., 2007. Why has US inflation become harder to forecast?.</mark> _<mark>Journal of Money, Credit and banking</mark>_ <mark>,</mark> _<mark>39</mark>_ <mark>, pp.3-33.</mark> <mark>Strohsal, T., Proaño Acosta, C. and Wolters, J., 2015.</mark> _<mark>How do financial cycles interact? evidence from the us and the uk</mark>_ <mark>(No. 2015-024). SFB 649 Discussion Paper.</mark> 25
**ANNEX Table 1 ‐ Gulf countries and Resource Rich SSA**

|**_Dependent variable_**|First Stage<br>Corruption<br>index|Second Stage<br>log(GDP per<br>capita)|First Stage<br>Political<br>stability|Second Stage<br>log(GDP per<br>capita)|First Stage<br>Political risk<br>rating|Second Stage<br> <br>log(GDP per<br>capita)|
|---|---|---|---|---|---|---|
||**_Column 1_**|**_Column 2_**|**_Column 3_**|**_Column 4_**|**_Column 5_**|**_Column 6_**|
|Constant|1.913|4.033|6.641|0.526|45.308|‐0.090|
||(0.189)***|(0.74)***|(0.244)***|(‐1.495)***|(1.815)***|(0.346)|
|IGGDP|4.319<br>(1.371)***|1.936<br>(1.829)|4.693<br>(2.072)**||57.417<br>(13.833)***||
|IPGDP|0.586|3.093|4.922|0.547|31.829|1.851|
||(0.74)|(0.58)***|(1.252)***|(2.23)***|(8.419)***|(0.546)***|
|FDIGDP|1.823<br>(0.668)***||2.859<br>(1.177)**||‐16.582<br>(7.277)**||
|M2GDP|0.009<br>(0.005)*|0.009<br>(0.005)*|‐0.008<br>(0.006)|0.005<br>(2.875)***|0.035<br>(0.045)||
|DOMCREGDP|0.010<br>(0.002)***|0.017<br>(0.005)***|0.008<br>(0.003)***|0.002<br>(5.922)***|0.113<br>(0.018)***|0.010<br>(0.001)***|
|NETGDP|‐2.394<br>(0.673)***|4.778<br>(1.055)***|0.571<br>(0.922)|0.653<br>(3.071)***|‐23.939<br>(8.828)***|3.762<br>(0.497)***|
|CREER|0.285|‐0.424|0.115|0.257|2.423|‐0.324|
||(0.343)|(0.314)|(0.454)|(‐1.039)***|(3.023)|(0.251)|
|CONT|0.141<br>(0.161)||1.279<br>(0.203)***||11.875<br>(1.352)***||
|EXPGDP|‐0.012|0.041|‐0.005|0.003|‐0.084|0.034|
||(0.004)***|(0.004)***|(0.005)|(10.552)***|(0.032)***|(0.002)***|
|CORR-IV||0.183<br>(0.377)|||||
|POL‐IV||||0.075<br>(10.292)***|||
|POLRISK_IV||||||0.097<br>(0.007)***|
|**Adjusted R2**|0.154|0.768|0.744|0.817|0.505|0.843|

Resource rich SSA countries and Gulf countries are included. The estimation technique is OLS panel regression. The standard errors given in paranthesis are heteroskedasticity corrected. *** stands for significance at 1%, ** significance at 5% and * significance at 10%. Time dummies are included but the results are not reported in the table due to space limitation. GDP per capita is in constant 2000 US$. IGGDP is public investment in % of GDP. IPGDP is private investment in % of GDP. FDIGDP is foreign direct investment in % of GDP. M2GDP is M2 money in % of GDP. DOMCREGDPis domestic credit to private sector in % of GDP. NETGDP is net interest income in % of GDP. CREER is change in real effective exchange rate. EXPGDP is exports in % of GDP. CONT is the continuity dummy which is 1 for Gulf countries and Botswana and 0 otherwise. CORR‐IV is the estimated value of the corruption index from the first stage. POL‐IV is the estimated value of the political stability index from the first stage. POLRISK‐IV is the estimated value of political risk rating from the first stage.

32
Table 7A: Effects of Role Model on Financial Investment on Childrens Education

||**Rural(R)**|**Urban(U)**|**R–U**|**Rural(R)**|**Urban(U)**|**R–U**|
|---|---|---|---|---|---|---|
|Father Edu|0.052***|0.124***|-0.072***|0.053***|0.126***|-0.073***|
||(0.006)|(0.012)|(0.013)|(0.006)|(0.012)|(0.013)|
|Role Model Edu|-0.009|-0.044***|0.036*|-0.009|-0.045***|0.036*|
||(0.014)|(0.015)|(0.021)|(0.013)|(0.015)|(0.020)|
|Number of School Age Children||||-0.021<br>(0.019)|-0.055*<br>(0.030)||
|Constant|0.365***|0.868***||0.255**|0.761***||
||(0.104)|(0.188)||(0.112)|(0.205)||
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|8485|7281|15766|8485|7281|15766|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). The dependent variable is educational expenditure adjusting for inflation. Inflation rates are calculated using price of rice separately for rural and urban areas. A peer is someone who was born in the respondents district within _±_ 3 years of respondents birth. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls include cognitive ability index and its square.

Table 7B: Role Models and Intergenerational Educational Mobility

||**Rural(R)**|**Urban(U)**|**R–U**|**Rural(R)**|**Urban(U)**|**R–U**|
|---|---|---|---|---|---|---|
|Father Edu|0.238***|0.384***|-0.146**|0.200***|0.351***|-0.151**|
||(0.039)|(0.056)|(0.063)|(0.037)|(0.053)|(0.060)|
|Father Edu Sq|0.015***|0.011***|0.005|0.016***|0.011***|0.005*|
||(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|
|Father Edu_×_Role Model|-0.011**|-0.014***|0.003|-0.010**|-0.014***|0.004|
||(0.004)|(0.004)|(0.006)|(0.004)|(0.004)|(0.006)|
|Role Model Edu|0.229***|0.103|0.126|0.189***|0.104|0.085|
||(0.061)|(0.118)|(0.124)|(0.057)|(0.112)|(0.118)|
|Role Model Edu Sq|-0.004|0.002|-0.006|-0.003|0.002|-0.004|
||(0.004)|(0.005)|(0.006)|(0.003)|(0.005)|(0.006)|
|Constant|5.988***|6.389***|-0.401|4.722***|5.406***|-0.6836|
||(0.285)|(0.682)|(0.676)|(0.272)|(0.683)|(0 .671)|
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|8292|4111|12403|8292|4111|12403|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). A role model is someone who was born in the respondent’s district but is within _±_ 3 of respondent’s father age and belongs to top 50 percent education level within that age group of that district. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls include cognitive ability index and its square.

39
increase in their gains from 0.6 percent of baseline income to 0.8 percent, developing countries see a more significant gain, from 0.8 percent to 2.0 percent. At the global level, the gains would increase by 61 percent—amounting to US$461 billion in 2015. These gains are substantially lower than previous estimates (World Bank 2002, 2004). For example, in World Bank (2002), the gains for developing countries increased by a multiple of 3.2 rather than the more modest multiple of 2.3 reported in this chapter. There are essentially two main reasons for this result. The first lies in the fact that the trade barriers are substantially lower compared with the earlier work—incorporating trade reforms between 1997 and 2001, preferences ,and, significantly in the case of China, baseline policy changes. The second reason is the lower assumed elasticity between openness and productivity in the agricultural sectors, which has been reduced from 0.75 to 0.5.<sup>25</sup> Table 4 reports the average change in the growth of productivity in manufacturing relative to the baseline labor productivity growth rate for selected countries and regions in 2015.<sup>26</sup> The largest impacts are in the regions that have a relative comparative advantage in manufacturing—for example, South Asia. The predominant agricultural exporters—Argentina, Brazil, and the rest of Latin America, for example—see relatively negligible productivity impacts from reform. Because productivity is assumed to be labor augmenting, the macro impacts will be reflected by changes in the share of labor employed in manufacturing relative to aggregate value added.

> 25. See van der Mensbrugghe (2005) for further details.

> 26. It should be noted that baseline productivity is calibrated to line up with exogenous assumptions regarding per capita GDP growth. The openness shifter is calibrated so that in the baseline it represents 40 percent of total sectoral productivity, and the uniform shifter is calibrated to achieve the overall GDP growth target. Productivity in this version of the model is labor augmenting only.

16
sector to match the 2018/19 NLSS to real GDP growth rates in each sector. Those household heads whose sector could not be distinguished because the household contained multiple enterprises or those who were not working at all were assigned a weighted average of the per capita real GDP growth from agriculture, industry, and services.

The backcasted series starts by assuming that the 2018/19 NLSS effectively corresponds to 2019, for the purposes of mapping it to the macroeconomic data. This seems like a reasonable assumption since the data were collected between September 2018 and October 2019, covering two-thirds of 2019. We apply the growth rate between 2018 and 2019 to the survey estimate to backcast an estimate for 2018, then for 2017, and so on for all the other years until 2009.

The main formula for the backcasts can be written: where gt−1cons,s = pass× gt−1s −pt−1 C<sup>s</sup> is household consumption for households whose head is employed in sector s . pass is the assumed pass-through rate value between growth in national accounts and in household consumption. g<sup>s</sup> is real sectoral GDP growth in sector s . p is population growth.

The pass-through rate is initially assumed to be the same across sectors and across richer and poorer Nigerians.<sup>13</sup> The main results assume a pass-through rate of one, that is, they assume that growth in national accounts (in real, per capita terms) is fully passed onto household consumption. Nevertheless, sensitivity analysis, which applies different pass-through rates to check the robustness of the results, is presented in Section 6. The backcasted consumption vector is converted to US$ 2011 PPP terms to estimate poverty rates at the international poverty line.<sup>14</sup>

# _<u>4.2. Survey-to-survey imputation</u>_

Survey-to-survey imputations use non-monetary indicators and household consumption data from a “baseline” or “training” survey to impute consumption into some “target” survey that contains the same non-monetary indicators. The survey-to-survey exercise is conducted in three main steps.

> 13 This assumption is relaxed in Section 6, using “growth incidence curves” estimated using the survey-to-survey imputations.

> 14 The CPI value is 2.34 (for 2018/19) and the 2011 PPP conversion factor is 83.58. For more details on the PPP and CPI data used for global poverty measurement, see Atamanov et al. (2018) and Lakner et al. (2018).

9
International Monetary Fund (2001 a). Direction<sup>of TradeStatistics Yearbookand</sup> various Ouarterly Updates. Washington,<sup>DC:IMEF,2001.</sup> Intemational Monetary Fund (200 lb). International<sup>Capital Markets: Developments.</sup> Prospects, and Key Policy Issues. By a Staff<sup>Team led by Donald J. Mathieson and Garry</sup> J. Schinasi. Washington, DC: IMF, August 2001.

Intemational Monetary Fund (2001 c). Intemational<sup>FinancialStatistics. Washington,DC:</sup> IMF, July 2001.

International Monetary Fund (2001 d). World<sup>EconomicOutlook. Washington,DC:lIMF,</sup> Oct. 2001.

Intemational Monetary Fund (2002a). Intemational<sup>FinancialStatistics.CD-Rom.</sup> Washington, DC: IMF, May 2002.

Intemational Monetary Fund (2002b). World<sup>EconomicOutlook. Washington,DC: IMF,</sup> April 2002.

Japan. Ministry of Finance. New Mivazawa<sup>Initiative(financialsupportalreadv</sup> indicated). As of February 2, 2000. May 2002 <http://www.mof.go.jp/English/if/el eO42a.htm>.

Japan. Ministry of Finance. O&A about The<sup>New Initiative to Overcome the Asian</sup> Currency Crisis (New Mivazawa Initiative).<sup>13May 2002</sup> <http://www.mof.go.jp/English/qa/myO0l.htm>.

Japan Bank for Intemational Cooperation.<sup>"JBICFY2001Survey: TheOutlook of</sup> Japanese Foreign Direct Investment (1 3'h Annual<sup>survey)."English languageedition.</sup> Tokyo: JBIC, March 2002.

Jones, Ronald W. and Henryk Kierzkowski.<sup>"A Frameworkfor Fragmentation."</sup> Fragmentation: New Production Pattems in<sup>the World Economy.Eds. S.W.Amdtand H.</sup> Kierzkowski. Oxford: Oxford University Press,<sup>2001.17-34.</sup> Kaplan, Ethan and Dani Rodrik. "Did the Malaysian<sup>Capital ControlsWork?"NBER</sup> Working Paper 8142. Cambridge, MA: National<sup>Bureauof EconomicResearch,2001.</sup> Kartadjoemena, H. S. "ASEAN and the Intemational<sup>Tradingsystem:RegionalTrade</sup> Arrangement vs. the WTO." ASEAN Beyond<sup>the RegionalCrisis:Challenges and</sup> Initiatives. Ed. M. Than. Singapore: ISAS, 2001.<sup>203-242.</sup> Kawai, Masahiro. "Building institutions and<sup>resolution of the East Asian crisis." Reform</sup> and Recovery in East Asia. Ed. Peter Drysdale.<sup>New York:Routledge,2000.283-327.</sup> 138
Table 5: Structural Estimates for Upstream Goods

|VARIABLES|(1)|(2)|_M _<sup>_k_</sup><br>_j,t_<br>(3)|(4)|(5)|
|---|---|---|---|---|---|
|_σ_<sup>_k_</sup>|0.308***|0.274**|0.276**|0.333***|-0.358|
||(0.116)|(0.113)|(0.114)|(0.108)|(0.344)|
|_σ_<sup>_k _</sup>_×_ln(_distj_)|-0.0492|-0.0401|-0.0411|-0.0618**|0.121|
||(0.0320)|(0.0310)|(0.0311)|(0.0296)|(0.0883)|
|_σ_<sup>_k _</sup>_× HOMEj_|0.0102**|0.00547|-0.0307***|-0.00123|-0.00819|
||(0.00447)|(0.00426)|(0.00907)|(0.0157)|(0.0147)|
|_V sh_<sup>_k_</sup><br>_t_||-1.583***|-1.685***|-1.854***|-1.476***|
|||(0.258)|(0.290)|(0.368)|(0.348)|
|ln(_WV _<sup>_k_</sup>)||-0.270***|-0.240***|0.240**|0.127|
|||(0.0544)|(0.0601)|(0.117)|(0.105)|
|(ln(_WV _<sup>_k_</sup>))<sup>2</sup>||-0.0520***|-0.0524***|0.00176|-0.00332|
|||(0.00798)|(0.00878)|(0.0155)|(0.0149)|
|_Ctnr_<sup>_k_</sup><br>_t_||-0.298***<br>(0.106)|-0.460***<br>(0.111)|0.0481<br>(0.133)|0.111<br>(0.150)|
|_σ_<sup>_k _</sup>_× V sh_<sup>_k_</sup><br>_t _<sup>_× HOMEj_</sup>|||-0.00824<br>(0.00945)|-0.0197**<br>(0.00934)|-0.0272***<br>(0.00877)|
|_σ_<sup>_k _</sup>_×_ln(_WV _<sup>_k_</sup>)_× HOMEj_|||-0.00663***<br>(0.00225)|0.00990<br>(0.00887)|0.00285<br>(0.00764)|
|_σ_<sup>_k _</sup>_×_(ln(_WV _<sup>_k_</sup>))<sup>2</sup> _× HOMEj_|||-0.000220|0.00144|0.000719|
||||(0.000279)|(0.00101)|(0.000815)|
|_σ_<sup>_k _</sup>_× Ctnr_<sup>_k_</sup><br>_t _<sup>_× HOMEj_</sup>|||0.0585***<br>(0.00759)|0.0929***<br>(0.0165)|0.114***<br>(0.0187)|
|_σ_<sup>_k _</sup>_×_(ln(_distj_))<sup>2</sup>|0.00167|0.00111|0.00118|0.00217|-0.00950*|
||(0.00213)|(0.00207)|(0.00207)|(0.00195)|(0.00563)|
|_IHST_( <sup>�</sup><br>_X_<sup>_k_</sup><br>_jt_<sup>)</sup>|0.789***|0.802***|0.801***|0.800***|0.825***|
|_,_|(0.0258)|(0.0321)|(0.0322)|(0.0306)|(0.0340)|
|Observations|5,892,852|5,892,852|5,892,852|5,892,852|5,892,852|
|Year FE|YES|YES|YES|YES|YES|
|Flexible distance controls|NO|NO|NO|YES|YES|
|U.S. trade policy controls|NO|NO|NO|NO|YES|
|Pseudo R2|0.420|0.443|0.446|0.453|0.500|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The LHS variable on all models is _M_<sup>_k_of</sup> _j,t_<sup>, the total value imported in Puerto Rico from place of origin</sup><sup>_j_</sup> product _k_ in year _t_ . All models are estimated using the PPML estimator on Puerto Rico’s import data pooled across observations at the year, HS6 digit product, and place-of-origin level, with year fixed effects included in the estimation model. Pharmaceutical products are excluded from the estimation. IHST denotes the Inverse Hyperbolic Sine Transformation.

35
10 responsible for raising their own funds. However, their debt does enjoy benefits such as equal standing with Treasury debt for bank investments, enabling these credit agencies to borrow in credit markets at rates only slightly above Treasury rates. Table 3 contains statistics for the government-sponsored credit agencies. No comparable data on subsidies are available since the subsidy occurs in the below market funding of the credit agency and not on a per loan basis. The primary programs here are housing oriented, with smaller programs for agriculture and student loans. The outstanding loans from the Student Loan Marketing Association have been converted into direct loans and are thus included within that category in Table 1.

TABLE 3

|**LENDIN**|**G**BY**MAJOR GOVERNMENT-SP**<br>ENTERPRISES,<br>1989|**ONSOREBD**<br>NlLionts of$|
|---|---|---|
|ENTERPRISE|NEWLOANS|OUTSTANDING LOANS|
|**Federtl**<br>Home Loan<br>Banks|260,000<br>(57%)|143,856<br>(19%)|
|Federal Home Loan<br>Mortgage Corporation|77,499<br>(17%)|318,377<br>(42%)|
|Federal National<br>Mortgage Assocation|54,857<br>(12%)|252,067<br>(33%)|
|Farm Credit Banks<sup>7</sup><br>____________ ____________(12%)|55,262|48,225<br>(6%)|
|Student Loaf Marketing<br>Association|6,365<br>(1%)|0<br>(0%)|
|lTOTAL<br>._,_____(_______<br>_(10|453,983<br>____<br>0%)|761,706<br>(100%)|

SOURCE: "special Analysis F," in SIeft Ana Dles* udaet of th U rted Steteo Government. Fis ao Year WY, Execut0v ceo to resent Officeof nwagement and sucget (wasngton: U.S. Goverment Printing Office, 198).

6 The Federal Home Loan Bank System makes a large number of short terms loans to its member institutions, resulting in the new loan figure being larger than the outstanding loan figure.

7 See note 6 for the Federal Home Loan Bank System.

The outstanding loans of the Student Loan Marketing Association have been converted into direct loans and are included uuder that category.
# **National Drought Insurance for Malawi**

Joanna Syroka (CRMG, ARD), Antonio Nucifora (AFTP1, PREM)<sup>*</sup>

> * The authors would like to thank Tim Gilbo and David Rohrbach for very useful comments, and extensive support to the development of this risk management instrument. The Authors would like to thank participants to the World Bank 2008 PREM Economists' Forum, in April 2008. All errors remain our responsibility.

1
surveys for 4 years, the resulting panel data set consists of 80 observations. The panel is balanced with 20 observations per year.

# **6. EMPIRICAL RESULTS**

The results of the fixed-effects estimation of the model for four types of durable goods are presented in Table 2. Before discussing the results, it is worthwhile to look at the goodness of fit of the model specification. The F statistic test indicates that the “growth” and “convergence” variables are jointly significant for all the four durable goods at conventional levels. In the first three equations the two variables are jointly significant at the one percent level and in the last equation at the 5 percent level. In all equations these variables are also significant on their own at the conventional (10 percent or better) significance levels (Table 2). Note also that the F-test does reject the null hypothesis that group-specific effects are not significant except for the first equation.<sup>30</sup> This implies that if the model is estimated without taking the group effects into account (i.e., if a single overall intercept is included instead of country dummies); the resulting estimated coefficients would be biased.

What do the estimated regression coefficients tell us? The coefficient on the real GDP, or “growth” variable is equal to around 1 for three types of goods, suggesting that a 1 percent increase in real GDP (at the mean of real GDP index) is generally associated with a 1 percent increase in the quantity of a given durable good ownership rate (at the mean). The coefficient of this variable is less significant, both in terms of its statistical significance and its magnitude, in the equation for cars. In this equation the coefficient suggests that a 1 percent increase in real GDP would be associated with a 0.4 increase in the number of cars (Column 4, Table 2).

> 30 Note that the estimated model is a one-way model, i.e., it does not include time dummies. Estimating the two-way (with time dummies) is not possible since our “growth” variable does not change across groups, it only changes across time (years). The inability to include time effects also precludes us from purging the “growth” variable of the potential price effects (i.e., people buying more durable goods because those goods become cheaper and not because real incomes increase). However, those price effects would apply equally to all classes.

- 15 -