rafmacalaba/datause-encoder-data huggingface dataset:
for the younger children even though the overall impacts when the older ages are included are mixed. The more detailed presentation of the estimates for Group II in Table D.2 indicates that among two year olds, the proportions with diarrhea are significantly reduced for all three durations and for two and three year olds the proportions with worms are significantly reduced for those with 17+ months of duration.

**Table 8. Summary of Estimated Intent-to Treat Positive and Negative Program Impacts** **<u>for Nearest Neighbor Matched Estimates with Control for Attrition, Philippines ECD Study</u>**

||Number o<br>wi<br>|f Significant<br>th Program<br>|Coefficients<br>Effect<br>|Mean M<br>Significant E<br>ofsam<br>|agnitude of<br>ffects (in terms<br>ple SDs)<br>|
|---|---|---|---|---|---|
||Positive|Negative|%Positive|Positive|Negative|
|**Group I: Predominately positive**|**program i**|**mpacts**||||
|Cognitive skills|9|0|100.0|0.553|na|
|Expressive language|7|0|100.0|1.085|na|
|Gross motor skills|7|0|100.0|0.791|na|
|Self-help|7|0|100.0|0.334|na|
|Fine motor skills|5|0|100.0|0.647|na|
|Social-emotional skills|10|1|90.9|0.545|–0.468|
|Weight-for-height z Score|7|1|87.5|0.234|–0.370|
|Receptive language|6|2|75.0|1.43|–0.475|
|Proportion wasted|6|3|66.7|–0.014|0.010|
|**Group II: Mixed or virtually no**|**program im**|**pacts**||||
|Proportion with diarrhea|3|2|60.0|–0.035|0.016|
|Proportion with worms|2|2|50.0|–0.085|0.067|
|Proportion stunted|3|5|37.5|–0.04|0.048|
|Height-for-age z Score|0|1|0|na|–0.292|
|**Group III: Predominately negati**|**ve program**|**impacts**||||
|Proportion anemic|0|8|0|na|0.089|
|Hemoglobin count|0|9|0|na|–0.539|
|**Totals or averages across groups**||||||
|All groups|49|34|59.0|0.454|–0.192|
|Group I|41|7|85.4|0.623|–0.326|

Notes: “Significant” refers to the 0.05 level. “na” means “not applicable.” Group III, predominantly negative program impact, includes the proportion anemic and hemoglobin count. For these two indicators there are a fairly large number of significant coefficient estimates, and all indicate negative program impact. This is a bothersome result although these two are only 13 percent of the total indicators that we consider, and we do not have a good explanation as to why the program had this effect. Unless anemia were of much greater concern than other aspects of child development reflected by the indicators in Group I (which does not seem to be the case), these negative program effects should not be interpreted to dominate in the overall evaluation of the program. It would be desirable to learn why they occur, however, and to consider whether it is worth the costs of rectifying whatever causes these negative outcomes.

20
**Figure 1: FDI and non-FDI Capital Inflows**

<!-- Start of picture text --> Median inflows for all EMs in % of trend GDP<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>‐0.5<br>‐1<br>‐1.5<br>FDI non‐FDI<br>Figure 2: Components of non-FDI Capital Inflows<br>Median inflows for all EMs in % of trend GDP<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>‐0.5<br>‐1<br>‐1.5<br>Other flows Portfolio equity Portfolio debt<br>1990q1 1991q1 1992q1 1993q1 1994q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1<br>1990q1 1991q1 1992q1 1993q1 1994q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1<br><!-- End of picture text --> 6
Isolation and Subjective Welfare: Evidence from South Asia<sup>*</sup> Marcel Fafchamps<sup>†</sup> Oxford University Forhad Shilpi<sup>‡</sup> The World Bank JEL codes: D60, I31, R20 Keywords: geographical isolation; consumption adequacy; subjective well-being

* We benefitted from comments from Ravi Kanbur, Mukesh Eswaran, John Hoddinott, John Pender, Martin Ravallion, and participants to a seminar at Cornell and IFPRI. We also received excellent comments on an earlier version of this paper from participants to the Cornell/LSE/MIT Conference on Behavioral Economics, Public Economics and Development Economics; participants to the Global Poverty Research Group workshop held in June 2004; and from participants to seminars at LSE, CERDI, the University of British Columbia, Bath University, and the World Bank. We would like to thank Migiwa Tanaka for excellent research assistance. The support of the World Bank and of the Economic and Social Research Council (UK) is gratefully acknowledged. The work was part of the programme of the ESRC Global Poverty Research Group.

† Department of Economics, University of Oxford, Manor Road, Oxford OX1 3UQ. Email: marcel.fafchamps@economics.ox.ac.uk.

‡ DECRG, The World Bank, 1818 H Street N.W., Washington DC 20488 USA..
Table 4: Subsample Analysis

||Wo<br>C/R|rld Bank a<br>PS1|id 1995–2<br>PS2|014<br>PS3|
|---|---|---|---|---|
|Mission|0.11<sup>_∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|0.07<sup>_∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|
||(0.05)|(0.03)|(0.03)|(0.03)|
|Ethnic dummies|Yes|Yes|Yes|Yes|
|Mean dependent variable|0.43|0.36|0.29|0.35|
|_R_-squared|0.52|0.66|0.66|0.68|
|Observations|844|799|604|794|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A.

_Note_ : “C/R” stands for coast or river subsample. “PS” refers to different subsamples obtained by propensity score matching. Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km from water source; Caloric Suitability Index; Malaria Ecology Index; and a tropics dummy (which is also interacted with mean altitude). Cells that are more than 90 percent covered by barren land or more than 90 percent covered by forest are excluded from the sample. The rivers include the Nile, Niger, Senegal, Zambezi, and Congo together with its tributaries the Ubangi and Kasai.<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 19
**Table 2. Odds ratios from logit “Model 1” of extended formality by data source**

||**Pooled**|**ELMPS**<br>**2012 in est.**|**ELMPS**<br>**2018 in est.**|**Economic**<br>**Census**|**Establishm**<br>**ent Census**|
|---|---|---|---|---|---|
|**Data (ELMPS 2018 in est. omit)**||||||
|ELMPS 2012 in est.|1.349**|||||
||(0.147)|||||
|Economic Census|0.400***|||||
||(0.035)|||||
|Establishment Census|0.532***|||||
|**Ent. size (one worker omit.)**|(0.045)|||||
|2|1.655***|1.462|1.887***|1.814***|1.628***|
||(0.094)|(0.292)|(0.358)|(0.130)|(0.011)|
|3-4|2.414***|2.096**|1.394|2.651***|2.750***|
||(0.141)|(0.506)|(0.368)|(0.275)|(0.019)|
|5-24|5.661***|7.228***|9.753***|7.441***|5.108***|
||(0.402)|(3.428)|(5.122)|(0.622)|(0.051)|
|**Ent. age (one year old omit.)**||||||
|0|0.728**|0.685|0.339*|0.825|0.831***|
||(0.078)|(0.442)|(0.167)|(0.094)|(0.013)|
|2|1.165|0.995|0.668|1.622***|1.181***|
||(0.108)|(0.420)|(0.308)|(0.162)|(0.015)|
|3|1.461***|1.903|0.959|1.601***|1.353***|
||(0.168)|(0.730)|(0.456)|(0.172)|(0.019)|
|4|1.619***|2.400*|0.937|1.830***|1.410***|
||(0.150)|(0.958)|(0.439)|(0.199)|(0.018)|
|5|1.946***|3.073*|2.116|2.255***|1.547***|
||(0.190)|(1.451)|(1.127)|(0.280)|(0.020)|
|6|2.063***|1.678|2.681|2.507***|1.706***|

22
application of all our fictitious candidates, we observe whether the fictitious candidate’s CV has been viewed. This indicator is used as a dependent variable for the second recruitment stage.

After clicking on a longlisted candidate and obtaining more information from their full CV, the employer can invite the applicant for an interview. Like previous correspondence audit studies, we collect information on whether our fictitious candidate has received a callback for an interview (either via phone or via email). Upon receiving a callback, we immediately reject the interview offer. We use an indicator for receiving a callback as a dependent variable for the third recruitment stage.

Note that, among our two treatments, gender is visible to the employer at all stages - the employer can filter by gender, they can decide to click only on candidates of a certain gender (using photo and name to identify gender), and they can see the same variables in the full CV. On the other hand, employers can only view socioemotional skills treatment after they click on the longlisted candidate and see their full CV – in other words, employers cannot filter by socioemotional skills (unless they put those skills in a keyword search) in order to create a longlist, and they cannot see those skills in a filtered longlist, as only name, photo, current position, and city of residence are visible at that stage.

_Figure 1: Employers’ screening process after application_ <!-- Start of picture text --> Stage 1: Employer uses filters to make the long list<br>No specific information on candidates yet, but employer can filter using various criteria<br>including keywords<br>Stage 2: Employer screens the long list and decides whether to click on the candidate’s<br>CV<br>Employer observes each applicant's name,<br>Among treatments, only gender is visible<br>photo, current position, city<br>Stage 3: Employer screens the full CV and decides whether to invite the candidate for an<br>interview<br>Both gender and socioemotional skill<br>Employer is able to view the entire CV<br>signals are visible<br><!-- End of picture text -->

# **3. Results**

The results are organized around two main blocks. First, we consider the aggregate effect of the gender treatment and differentiate the treatment effect for the three recruitment stages. We then move to the socioemotional skills treatment and show the aggregate results, as well as differentiating between genders. Table 4 provides the descriptive statistics for our sample. About 71 percent of our applicants made it to the longlist, 32 percent had their CVs clicked on (i.e. their full CVs viewed) and 6 percent received a callback for an interview.<sup>16</sup> Figure 2 provides the distribution of CVs on the three outcome variables.

> 16 Callback rates in the studies using a similar methodology range from around 3 percent (e.g., in Kroft et al., 2013) to 30 percent (e.g., in Booth et al., 2012).

12
**_55_**

# **Table 8. Determinants of Stock and Flow Funding in State and Local Pension Plans** (standard errors in parentheses)

||Dependent Variable|
|---|---|
|Explanatory Variable|**AST_PBO**<br>**Flow Fund**|
|_A. Pension Board Composition_||
|<br>BDELAC|-0.20"<br>0.08|
||(0.08)<br>(0.17)|
|BDELRT|-0.61'<br>0.27|
||_(0.35)_<br>(0.72)|
|_B. Pension Management Practices_|<br>|
|<br>BDBENOK|-0.01<br>-0.01|
||(0.06)<br>**(0.1 1)**|
|BDACTOK|<br> <br>0.13'<br>0.30"|
||(0.07)<br>**_(0.15)_**|
|LIABINS|0.13"<br>-0.19"|
||(0.05)<br>**(0.09)**|
|ACCINHS|<br><br>0. ! 1<br>-0.02|
||(0.05)<br>**(0.09)**|
|_C. Pension Reporting Practices_||
|<br>REPSOLO|_0.05_<br>-0.01|
||(0.05)<br>**(0.09)**|
|GIVERPT|<br><br>0.09<br>0.003|
||(0.08)<br>**_(0.15)_**|
|AMORTPER|<br><br>-0.001''<br>-0.002|
||(0.01)<br>(0.004)|
|AMORTMS|<br><br>-0.31<br>-0.34"|
||(0.09)<br>(0.16)|
|ACTUARUC|<br><br>0.03<br>-0.13|
||(0.08)<br>(0.17)|
|_D. Pension Assumptions_||
|<br>COLA|-0.03<br>0.10|
||(0.06)<br>(0. 1)|
|WDOT|<br> <br>-0.04<br>0.02|
||(0.04)<br>(0.07)|
|WDOTMS|<br><br>-0.35<br>0.08|
||(0.24)<br>(0.44)|
|PORTABLE|<br><br>-0.13'<br>0.02|
||(0.05)<br>**(0. 0)**|
|NEXPROR|-0.01<br>0.02|

Olivia S. Mitchell - Public Pension Governance and Performance
# **Table A8.3: Multiyear analysis for countries with a high QFD (greater than 2 percent of GDP in the reference year)**

QFD as percent of GDP QFD as percent of revenues <!-- Start of picture text --> 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 0.0% 50.0% 100.0% 150.0% 200.0% 250.0% 300.0% 350.0% 400.0% 450.0%<br>HIGH QFD AND IMPROVING HIGH QFD AND IMPROVING<br>Ethiopia_2011 2.0% H Ethiopia_2011 398.7%<br>Ethiopia_2012 1.7% Ethiopia_2012 352.6%<br>More than 50 percent of units<br>South Africa_2012 4.0% South Africa_2012 104.1% H generated are from hydro<br>South Africa_2013 3.9% South Africa_2013 101.7%<br>South Africa_2014 3.4% South Africa_2014 90.2%<br>Senegal_2012 3.3% Senegal_2012 82.4%<br>Senegal_2013 2.2% Senegal_2013 55.1%<br>HIGH QFD AND MIXED HIGH QFD AND MIXED<br>Malawi_2011 1.8% Malawi_2011 114.5%<br>Malawi_2012 1.3% H Malawi_2012 55.2%<br>Malawi_2013 2.3% Malawi_2013 84.5%<br>Malawi_2014 2.5% Malawi_2014 82.0%<br>São Tomé and Príncipe_2012 5.6% São Tomé and Príncipe_2012 144.2%<br>São Tomé and Príncipe_2013 6.8% São Tomé and Príncipe_2013 192.1%<br>São Tomé and Príncipe_2014 6.1% São Tomé and Príncipe_2014 174.1%<br>Madagascar_2011 1.9% Madagascar_2011 121.0%<br>Madagascar_2013 2.3% H Madagascar_2013 140.3%<br>Madagascar_2014 2.2% Madagascar_2014 145.8%<br>Gambia, The_2012 5.1% Gambia, The_2012 93.1%<br>Gambia, The_2013 4.6% Gambia, The_2013 84.2%<br>Gambia, The_2014 5.8% Gambia, The_2014 115.2%<br>HIGH QFD AND WORSENING HIGH QFD AND WORSENING<br>Botswana_2010 1.8% Botswana_2010 93.9%<br>Botswana_2011 1.7% Botswana_2011 87.7%<br>Botswana_2012 3.7% Botswana_2012 200.9%<br>Botswana_2013 3.4% Botswana_2013 174.0%<br>Guinea_2011 1.4% H Guinea_2011 181.6%<br>Guinea_2013 2.1% Guinea_2013 247.5%<br><!-- End of picture text --> _Source_ : World Bank staff calculations using utility data.

98
# **2 Estimates of Empty Driving**

In this section, I survey estimates of empty driving from industry reports, local and national governments, and academic studies. The settings span many countries and range from the late 1970’s to the present day. In terms of coverage, the mix of studies significantly over-weights North America and the European Union and under-weights Europe & Central Asia and the Middle East & North Africa. On the time dimension, the median study was conducted in 2009, with an overall large span of 40 years between the earliest and latest studies. As a result, controlling for the role of time will be relevant. For more details on geographic and temporal coverage, see Appendix A.

These estimates span different sampling methodologies, measure different objects, and cover different segments of the trucking market. Empirical estimates of empty trips have been generated by a variety of data sources. The dominant source is survey data, such as Colombia’s Ministry of Transportation Freight Origin-Destination Survey program as used in Gonzalez-Calderon et al. (2012a). Firm surveys begin with a census of firms in the sector, and ask a sample of firms what their overall fleet-level empty trip share is. Vehicle surveys instead use a sample of registered vehicles and ask the vehicle’s operator how often that vehicle was empty. Finally, site surveys stop a random sample of trucks as they pass a fixed site (such as a rest stop or administrative checkpoint), and interview the drivers for their empty or loaded status. These variations may result in different weights or accuracy for different segments of the market. Trip audits or diaries, such as the National Survey of Transport of Goods by Road conducted in Ireland and studied in Council (2017), offer deeper information on truck driving patterns. A sample of trucks is tracked over the course of several days, and the loaded or empty status tracked over that period. Finally, digitization of the trucking economy has generated new datasets of mobile app transactions, as studied in Heilmann (2020). App data has had limited penetration into the economics and transportation literature, but holds potential for future work which takes advantage of detailed histories of trips. However, app data also depends on the market share of the particular application or marketplace, and may generalize poorly to the rest of the market.

Most surveys aggregate over all segments of the trucking freight market within a country, but there are exceptions. Studies may distinguish between short haul and long haul trips, as in Osborne et al. (2014). Beilock and Kilmer (1986) present figures for specific trailer types: refrigerated (reefer) and specialized trailers. Lam et al. (2019) separately measure shares of empty miles by firm organization, finding that logistics services providers have lower rates of empty trips than (owner) operators. Finally, a few studies explicitly cover international trips, including Nathan Associates Inc. (2013)’s study of corridors in West and 5
Income growth of poor net sellers in excess of 3 percent is still somewhat puzzling as the direct impact of lower rice prices is negative on this household category. The low intensity of net sellers who are poor could explain this. As indicated in Figure 6, over 40 percent of the poorest rural households in the bottom decile of total per capita expenditures are in the least intense net seller category, with the value of rice sales between zero and 10 percent of total expenditures. This share is significantly lower for households in other deciles.

**Figure 6. Intensity of net sellers of rice by income level (rural households).** Percent of total expenditures

<!-- Start of picture text --> 100<br>Greater than<br>80 30%<br>60<br>Greater than<br>40 10% and less<br>than 30%<br>20<br>Greater than<br>0 0% and less<br>than 10%<br>1 2 3 4 5 6 7 8 9 10<br>Deciles of total per capita expenditures<br><!-- End of picture text --> Note: Intensity is expressed as the value of rice sales net of purchases as percent of total expenditures. Totals do not always add to 100 percent bec _aus_ e some households did not report any sales or purchases of rice and could not be categorized according to intensity of rice sales or purchases. Source: 2000 HIES (rural households).

Furthermore, a very small share of the poor rural net sellers of rice is in the category of highest intensity of net sales, with the value of rice sales exceeding 30 percent of total expenditures. The low intensity of the poorest rural net sellers is a plausible explanation of why their income has increased in spite of declining rice prices. Total income of poor rural net sellers could have also grown due to direct second-round (labor market) and multiplier effects.

Although growth incidence curves indicate the trends in income growth at different welfare levels, they provide no basis for establishing causality or even simply correlation with changes in productivity and rice prices. To establish this causality, we examine the impact on the change in total consumption of changes in rice productivity, 21
2

# **Acknowledgments**

We are indebted to the task team leaders of the 2000, 2007 and 2012 databases, Davidson Gwatkin and Caryn Bredenkamp, without whose efforts the 2018 database would not have been possible. We are grateful to Leander Buisman who assisted in the processing of the microdata, to Caryn Bredenkamp, Tania Dmytraczenko, Olivier Dupriez, Rose Mungai, Minh Cong Nguyen, Marco Ranzani, Aparnaa Somanathan, Ajay Tandon and Joao Pedro Wagner De Azevedo who provided access to microdata, and to Qinghua Zhao for help with PovcalNet. We are grateful to Rantimi Adetunji, Nastassha Arreza, Amanda Kerr, Lingrui Liu, Jie Ren and Margarida Rodrigues for their tireless research assistance. We acknowledge the contributions of our collaborators on several projects whose ideas helped shape the 2018 database, including Daniel Cotlear, Tania Dmytraczenko and Owen Smith at the World Bank, Gabriela Flores at WHO Geneva, and Gisele Almeida at PAHO/WHO. Finally, we are grateful to Michele Gragnolati, Christoph Kurowski and Magnus Lindelow for their support, and to Tony Fujs, Karthik Ramanathan and Tariq Khokhar for engineering the online products. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.

# **Accessing the database**

A portal version of the 2018 database with visualization functionality can be accessed at <u>http://datatopics.worldbank.org/health-equity-and-financial-protection/. The data set itself can be</u> accessed and downloaded, indicator by indicator, or in its entirety, from <u>https://datacatalog.worldbank.org/node/142861 from which model Stata ‘do’ files can be downloaded</u> to replicate the datapoints in the HEFPI data set.

# **Citing the database**

The reference citation for the data is: Wagstaff, Adam, Eozenou, Patrick, Neelsen, Sven and Smitz, Marc. 2018. The Health Equity and Financial Protection Indicators Database 2018. World Bank: Washington, DC.

* Corresponding author: Adam Wagstaff. Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA. Tel: +1 202 473 0566, awagstaff@worldbank.org
**Figure 9:** Relationship between HCI and education expenditure (as a percent of GDP)

<!-- Start of picture text --> SGP<br>JPN KOR<br>SWE<br>KAZ USA<br>RUS<br>CHN VNM<br>GEO<br>KIR<br>TLSNAM ZWE<br>ZAF<br>ETH LSO<br>LBR<br>SSD TCD<br>2 4 6 8 10 12<br>Domestic government education expenditure (% of GDP)<br>High income Upper middle income Lower middle income Low income<br>1<br>.8<br>.6<br>HCI<br>.4<br>.2<br><!-- End of picture text -->

**Figure 10:** Relationship between HCI and education expenditure (as a percent of GDP), stratified by income level

<!-- Start of picture text --> High income Upper middle Lower middle Low income<br>0 5 10 15 0 5 10 15 0 5 10 15 0 5 10 15<br>Domestic government education expenditure (% of GDP)<br>1<br>.8<br>.6<br>HCI<br>.4<br>.2<br><!-- End of picture text --> 19
in South Africa, Lucas (1987) finds that remittances positively affect the accumulation of cattle and crop productivity. Likewise, evidence on Burkina Faso suggests that intercontinental migration positively affects household income diversification into livestock production, while it negatively affects non-farm activities (Konseiga, 2004). Mendola (2004) finds that in rural areas of Bangladesh, international migration enhances adaptation of new farming technologies. In the specific case of Albania, McCarthy et al. (2006), using data from the 2002 and 2003 Albania LSMS, provide evidence of net increases in agricultural (and total) income despite significant reductions in the allocation of labor to crop production. They attribute the improvement to a shift towards livestock as a result of the capital inflows and labor loss from migration.

Conversely, a number of other studies, dating back to the early 1980s, have posited that remittances, by facilitating the substitution of labor for leisure, may indeed result in non-increasing – or even lower – levels of income due to reduced labor efforts, and in lower productivity, due to the loss of the most productive and better educated segments of the population (Lipton, 1980; Palmer, 1985). This same hypothesis is also supported in studies by Azam and Gubert (2002), Itzigsohn (1995) and Germenij and Swinnen (2004), with remittances potentially contributing to farm inefficiencies. Similarly, Funkhouser (1992) and Rodriguez and Tiongson (2001) find lower labor participation as a result of the receipt of remittances. Amuedo-Dorantes and Pozo (2006) find no impact on male labor hours, though document changes in its composition, with less hours being worked in formal ( _vis a vis_ informal) sector. Along these same lines, when investigating the direct role of migration on agricultural production in rural China, Rozelle et al. (1999) found that migration exerts a direct negative effect on agricultural yields. As family members leave the farm, yields fall sharply, suggesting that on-farm labor markets are conspicuously absent in those parts of China. Finally, deBrauw and Rozelle (2002) show that the benefits of migration are mainly related to consumption and accumulation of durable goods, as well as house improvements, rather than to productive investments.

# **IV. Description of the Data**

6