rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8 Main Regression Results**

|VARIABLES|I|II|III|IV|V|VI|VII|
|---|---|---|---|---|---|---|---|
|NetJobCr|-0.160|-0.162|-0.168|-0.211|-0.199|-14.020|-23.481|
||(0.083)*|(0.073)**|(0.081)**|(0.088)**|(0.092)**|(5.436)**|(14.195)*|
|Log(labor)|0.070|0.055|0.065|||3.307|2.293|
||(0.005)***|(0.005)***|(0.005)***|||(0.395)***|(0.503)***|
|Age|0.001|0.000|0.001|0.003|0.004|-0.003|-0.120|
||(0.000)***|(0.000)|(0.000)***|(0.000)***|(0.000)***|(0.018)|(0.029)***|
|Foreign|0.241|0.205|0.225|0.334|0.320|13.830|10.461|
||(0.017)***|(0.018)***|(0.015)***|(0.019)***|(0.019)***|(1.442)***|(1.620)***|
|Log(Pro)<br>||||0.051<br>(0.011)***||||
|Growth<br>|||||0.132<br>(0.033)***<br>|||
|Observations|13845|13845|13845|10279|13669|13845|3671|
|R2/R2(p)|0.224|0.206|0.218|0.199|0.179|0.211|0.136|
|Robust standard<br>sector, survey ye<br>dummy for exp<br>employment gro<br>dependent variab<br>the probit regress|errors clustere<br>ar, and country<br>orters to one<br>wth as a contro<br>le, (VII) uses<br>ions show the|d by country,<br>fixed effects.<br>for only direc<br>l instead of em<br>revenue genera<br>marginal effect|sectors, year<br>(I) is main resu<br>t exporters, (I<br>ployment leve<br>ted from expo<br>s at their mean|are in parenth<br>lt, (II) uses co<br>V) uses log o<br>l, (VI) uses %<br>rts (only firms<br>values. *** p<|eses. In the re<br>nservative defi<br>f labor produ<br>of revenues g<br>that export ar<br>0.01, ** p<0.0|gressions, I al<br>nition of expor<br>ctivity as con<br>enerated from<br>e included). C<br>5, * p<0.1.|so control for<br>ting, (III) sets<br>trol (V) uses<br>exports as the<br>oefficients for|

26
Fertility Decisions in Italy _.” Journal of Population Economics_ 15: 549–73.

Del Boca, D., and D. Vuri. 2006. “The Mismatch between Employment and Childcare in Italy: The Impact of Rationing.” Working paper No. 983, Institute for the Study of Labor (www.iza.org).

Duflo, E. 2001. “Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment.” _American Economic Review_ 91 (4): 795–813.

Elacqua, G., M. Schneider, and J. Buckley. 2006. “School Choice in Chile: Is It Class or the Classroom?” _Journal of Policy Analysis and Management_ 25 (3): 577–601.

Encina, J., and C. Martínez. 2009. “Efecto de una Mayor Cobertura de Salas Cuna en la Participación Laboral Femenina: Evidencia de Chile.” Working Paper No. 303, University of Chile, Department of Economics. Santiago, Chile.

Ganuza, E., and R. Paes de Barros. 2001. “Liberalización, Desigualdad y Pobreza: América Latina y el Caribe en los 90.” UNDP, ECLAC.

Gallego, F., and A. Hernando. 2008. “School Choice in Chile: Looking at the Demand Side.” Unpublished Manuscript, Pontificia Universidad Católica de Chile.

Gelbach, J. 2002. “Public Education for Young Children and Maternal Labor Supply.” _American Economic Review_ 92: 307–22.

<mark>Gimenez-Nadal, J. I., and A. Sevilla. 2012. “Trends in Time Allocation: A Cross-Country</mark> <mark>Analysis.”</mark> _<mark>European Economic Review</mark>_ <mark>56 (6): 1338–59.</mark> 26
|RailwayRehabilitation Project|HenryG. R. Kerali|
|---|---|
|RailwayRehabilitation Project|HenryG. R. Kerali|
|RailwayRehabilitation Project|HenryG. R. Kerali|
|RailwayRehabilitation Project|HenryG. R. Kerali|
|RailwayRehabilitation Project|HenryG. R. Kerali|
|RailwayRehabilitation Project|HenryG. R. Kerali|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|Improvement to Financial Reportingand AuditingProject|Pazhayannur K. Subramanian|
|BiodiversityRestoration Project|Michel Simeon|
|BiodiversityRestoration Project|Michel Simeon|
|BiodiversityRestoration Project|Michel Simeon|
|Oil Spill Response Project|Abdelmoula M. Ghzala|
|Oil Spill Response Project|Abdelmoula M. Ghzala|
|Oil Spill Response Project|Abdelmoula M. Ghzala|
|Oil Spill Response Project|Abdelmoula M. Ghzala|
|Oil Spill Response Project|Abdelmoula M. Ghzala|
|Population and FamilyPlanningProject|Ramesh Govindaraj|
|Population and FamilyPlanningProject|Ramesh Govindaraj|
|Population and FamilyPlanningProject|Ramesh Govindaraj|
|Population and FamilyPlanningProject|Ramesh Govindaraj|
|Population and FamilyPlanningProject|Ramesh Govindaraj|
|Population and FamilyPlanningProject|Ramesh Govindaraj|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|FISCAL TECHNICAL ASSISTANCE PROJECT|Hana Brixi|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
|Payment System and Bank Modernization Project|Miguel Navarro-Martin|
**31**

# 13: Feed demand Soybeans

Trade liberolization ,s Price Guarontee

1.8 -.

<!-- Start of picture text --> i.7I<br>1 6- ,<br>~u4/ 1<br>v 5 i : i>1<br>.,,<br>09<br>7A<br>06<br>1974 75 76 77 78 79 80 81 82 83 84 85<br>Years<br>q - price guarontee t trade lb<br><!-- End of picture text -->

# 14: Gov. supply adjustment

# Soybeanis

<!-- Start of picture text --> Trade liberaiizot;vr ws Pr-ce Guarantee<br>4<br>3~~~~~~~~~~~~~~~~<br>09<br>0.8 /<br>c<br>- 0.7<br>0.6-<br>05-<br>0)4<br>0.3<br>0.2<br>1974 75 7d 77 78 73) 80 81 82 8 6<br>Teors<br>a price )ua3rantee 4- trade lib<br><!-- End of picture text -->
The Realities of Primary Care: Variation in Quality of Care Across Nine Countries in Sub-Saharan Africa Kathryn Andrews, Ruben Conner, Roberta Gatti and Jigyasa Sharma<sup>1</sup>

# **JEL Classification:** I10, I18, C20, C83

**Keywords:** Health facilities, primary health care, Service Delivery Indicators, surveys

> 1 Kathryn Andrews (kandrews@worldbank.org); Ruben Conner (rconner1@worldbank.org); Roberta Gatti <u>(rgatti@worldbank.org); Jigyasa Sharma (jsharma3@worldbank.org), all in the Human Development Chief</u> Economist’s Office at the World Bank. The World Bank Service Delivery Indicators (SDI) team can be reached at <u>sdi@worldbank.org. This report benefited from consultations with the AFR Human Development leadership team</u> and at World Bank Nairobi Country Office (October 2019).  This paper has benefited from review and feedback from Gerard Kambou (on behalf of Albert G. Zeufack) and Mamta Murthi _._ We are grateful to Victor Alegana (KEMRI-Wellcome), Jishnu Das (Georgetown University), Ritva Reinikka (Aalto University), and researchers from KEMRI-Wellcome Trust Research Programme and Strathmore University, Nairobi for contributions and useful comments. We are indebted to Christophe Rockmore and Waly Wane for their leadership and ongoing support of the SDIs. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the World Bank or its Executive Directors.
the regions of the world. Table A1 reports the country and regional coverage of the sample (22% of the sample is in Africa, 20.7% of the sample in East Asia, 17.4% in Eastern Europe, 21% in Latin America, 9% in MENA and 9% in South Asia). This wide coverage allow us to document several cross country correlations between the investment in job training and country level indicators as the country’s level of development, institutional quality or general educational attainment. Finally, the surveys have the advantage of collecting information on the training flows. This information is likely to be a more accurate measure of recent job training than in surveys attempting to measure the stock of training at the firm level (see e.g., Bassanini et al, 2005).<sup>10</sup> In sum, the _Enterprise Surveys_ are an excellent source of micro data on employer provided training but also of cross country statistics. They are by far one of the most comprehensive sources of comparable firm level data in the developing world (see e.g., Aterido, HallwardDriemeier and Pages, 2011, Almeida and Fernandes, 2008, Ibarraran et al, 2009). Moreover, even when comparing with data for developed countries (e.g., as in Black and Lynch, 2001, for the US) they have the advantage of having a high response rates on information for job training and have the advantage of not exploring recall information. Moreover, because the data is collected at the firm level, it contains information simultaneously for firm and some worker level characteristics.<sup>11</sup> We measure training intensity at the firm level by computing a dummy variable that equals one when the firm reports having supplied formal training to their workers. The exact question in the survey is: “ _Do you offer formal (beyond “on-the-job”) training to your permanent employees?_ ” On average, 39% of the firms in the sample report offering job training to their employees. However, figure 1 illustrates well the wide

> 10 The _Enterprise Surveys_ also collect information on the extensive training margin for some countries, including the percentage of skilled and unskilled workers trained at the firm level as well as some information on the training hours. We have also found robust size training premiums for the share of both skilled and unskilled workers (not reported but available on request).

> 11 The _Enterprise Surveys_ have been used in the study closely related topics by Pierre and Scarpetta (2004), Almeida and Aterido (2008), Almeida and Fernandes (2008), Rosholm et al (2007) or Almeida (2009). A previous version of this survey was also used by Frazer (2006) and Teal (1996) for Ghana and by Kahyarara and Teal (2008) for Tanzania (World Bank Regional Program on Enterprise Development). They have also been extensively used in studying the related topic on how the business environment affects firm growth and performance (e.g., Dollar, Hallward-Driemeier and Megistae, 2005, Aterido, HallwardDriemeier and Pages, 2011)

10
# **Table A6: Panel B – Nested Logit Regressions, 2006**

Log Odds ratio between Owning with and without a Mortgage

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|0.68 ***|0.06|0.06|-0.49 ***|0.19|-0.14|3.16 ***||-3.83|0.12|
|Estonia|2.40 ***|0.10|0.24 ***|-0.83 ***|1.13 ***|-0.23|-0.45 ***||0.51|-0.15|
|Hungary|1.18 ***|0.02|0.19 ***|-0.21|-0.17|-1.02 ***|0.06|0.43|-3.97 **|-0.27|
|Latvia|0.35|0.25 *|-0.03|0.07|0.40 **|0.05|1.09 ***|3.12 **|-1.83|0.27|
|Lithuania|2.80 ***|0.18|0.35|-0.70 **|0.60|-0.44|-0.12||4.73|-1.25|
|Poland|1.21 *|0.13|0.26 ***|-0.02|0.22|-0.57 **|0.81 ***|3.60|0.58|-0.80 **|
|Slovak Republic|0.46|-0.04|0.19 ***|-0.60|-0.12|-0.46 ***|3.58 ***|-3.83 ***|-4.49 ***|0.04|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.43|0.11|0.13 **|-0.17|-0.29 *|-1.30 ***|0.66 ***|-3.91 ***|-0.98|-0.99 **|
|Belgium|3.30 ***|-0.01|0.23 ***|-1.27 ***|0.49 ***|-1.01 ***|-0.11|-7.29 ***||-0.79 ***|
|Cyprus|2.80 ***|0.03|0.15 **|-0.85 ***|0.14|-0.64 *|-0.13|-6.60 ***|-2.28|-0.89|
|Denmark|-1.12 ***|0.11|0.12|-2.28 ***|0.86 ***|0.79 **|-1.09 ***||-16.80 ***|1.03 ***|
|Finland|2.50 ***|0.08|0.08 **|-0.90 ***|0.53 ***|-0.24|-0.17 **|-4.10 *|-11.82 ***|-0.49 **|
|France|2.70 ***|0.03|-0.13 **|-0.91 ***|0.65 ***|0.08|-0.08|-9.10 ***|-12.25 ***|-0.94 ***|
|Greece|0.73 **|0.11|0.10|-0.13|-0.40|-0.57|0.45 **|3.36|2.93|0.78 ***|
|Ireland|2.45 ***|-0.09|0.18 ***|-0.73 ***|0.14|-1.17 ***|0.38|-2.72|-7.55 ***|-1.16 ***|
|Italy|2.26 ***|0.13 **|0.25 ***|-0.61 ***|0.00|-0.89 ***|0.50 ***|-0.09|-3.84 ***|-0.97 ***|
|Luxembourg|2.94 ***|0.22|-0.05|-1.07 ***|0.26|-0.46|0.92 ***|-18.42 ***|-17.71 ***|-1.08 ***|
|Netherlands|1.43 ***|-0.14|0.10|-0.90 ***|0.52 ***|-0.72 ***|2.68 ***||-11.69 ***|-0.01|
|Portugal|1.53 ***|-0.04|0.30 ***|-0.59 ***|0.07|-1.33 ***|1.58 ***|1.12|-4.82 *|-1.07 ***|
|Spain|2.22 ***|0.03|0.41 ***|-0.79 ***|0.04|-1.13 ***|0.37 ***|-4.16 ***|-3.81 ***|-0.60 ***|
|Sweden|0.99|-0.15|-0.08|-0.59|0.22|-0.78|0.91|-3.16|-6.11 **|-0.54|
|United Kingdom|1.86 ***|-0.09|0.10 *|-0.71 ***|0.14|-0.91 ***|-0.12||-11.57 ***|-0.63 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

41
World Bank (2002). The World Bank group ́s energy program poverty reduction sustainability and selectivity. World Bank, Washington.

World Economic Council (1999). The challenge of rural energy poverty in developing countries. WEC and FAO, London.

Yepez-Garcia, A., Luna Tovar, P., and San Vicente Portes, L. (2010). Subsidies in he energy sector in Mexico. World Bank, mimeo.

**Annex 1: Probit for energy consumption**

|||**LPG**|**Ele**|**ctricity**|**Natu**|**ralGas**|**G**|**asoline**|
|---|---|---|---|---|---|---|---|---|
||**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|**Coef.**|**Std. Err.**|
|||||**Urb**|**an**||||
|Decile 2|-0.060|(0.023) ***|0.003|(0.013)|-0.010|(0.015)|-0.114|(0.031) ***|
|Decile 3|-0.069|(0.023) ***|-0.002|(0.009)|-0.030|(0.016) *|-0.185|(0.029) ***|
|Decile 4|-0.106|(0.030) ***|-0.004|(0.012)|-0.023|(0.014)|-0.231|(0.031) ***|
|Decile 5|-0.126|(0.031) ***|-0.007|(0.009)|-0.075|(0.029) ***|-0.290|(0.032) ***|
|Decile 6|-0.117|(0.031) ***|-0.010|(0.014)|-0.089|(0.026) ***|-0.355|(0.029) ***|
|Decile 7|-0.121|(0.031) ***|-0.016|(0.017)|-0.111|(0.025) ***|-0.402|(0.027) ***|
|Decile 8|-0.066|(0.044)|-0.021|(0.021)|-0.139|(0.034) ***|-0.468|(0.022) ***|
|Decile 9|-0.087|(0.043) **|-0.023|(0.020)|-0.146|(0.030) ***|-0.519|(0.016) ***|
|Decile 10|-0.080|(0.060)|-0.028|(0.019)|-0.264|(0.056) ***|-0.603|(0.018) ***|
|**N**|2|0453|2|0453|20|453||20453|
|**Wald**|721.040|(0.000) ***|63.500|(0.000) ***|1781.990|(0.000) ***|16499.360|(0.000) ***|
|||||**Ru**|**ral**||||
|Decile 2|-0.188|(0.020) ***|0.079|(0.025) ***|-<br>|-|-0.093|(0.028) ***|
|Decile 3|-0.201|(0.034) ***|0.101|(0.048) **|-<br>|-|-0.172|(0.043) ***|
|Decile 4|-0.231|(0.040) ***|0.147|(0.058) **|-<br>|-|-0.203|(0.051) ***|
|Decile 5|-0.273|(0.042) ***|0.102|(0.036) ***|-<br>|-|-0.254|(0.059) ***|
|Decile 6|-0.319|(0.046) ***|0.100|(0.044) **|-<br>|-|-0.303|(0.051) ***|
|Decile 7|-0.351|(0.042) ***|0.066|(0.047)|-<br>|-|-0.388|(0.058) ***|
|Decile 8|-0.329|(0.039) ***|0.085|(0.052)|-<br>|-|-0.396|(0.063) ***|
|Decile 9|-0.306|(0.066) ***|0.101|(0.063)|-<br>|-|-0.409|(0.067) ***|
|Decile 10|-0.363|(0.061) ***|0.022|(0.070)|-<br>|-|-0.560|(0.047) ***|
|**N**|5|359||5359||||5359|
|**Wald**<br>Notes: ***, **<br>Clustered stan<br>The control va<br>Dependent va|217.06<br>, * denote si<br>dard errors a<br>riables were<br>riable = 1 if th|(0.000) ***<br>gnificance at 1%, 5<br>t state level<br>North, Center, Empl<br>e household does n|194.25<br>% and 10 %<br>oyment typ<br>ot report sp|(0.000) ***<br>e, Male, Formal, In<br>ending on that en|digenous lang<br>ergy source|uage, Log (hous|779.37<br>ehold size), S|(0.000) ***<br>killed|

32
# **Risk-Based Supervision of Pension Funds:**

## **A Review of International Experience and Preliminary Assessment of the First Outcomes**

Gregory Brunner, Richard Hinz, and Roberto Rocha<sup>1</sup> The World Bank, Washington DC

> 1 This paper is part of a broader project on risk-based supervision coordinated jointly by the World Bank and the IOPS.  The authors are grateful to Constantinos Stephanou, David Madero, Dirk Broeders, Dimitri Vittas, Heinz Rudolph, Laura Ard, Luiz Mario Hernandez Acevedo, and Peter Skjodt, for useful inputs and comments on an earlier draft.
**Table 2: COVID-19 mortality costs in Côte d'Ivoire across time while taking into account age**

|||||**Panel A: Centr**|**al values**<br>**Mortal**|**ity costs**|||
|---|---|---|---|---|---|---|---|---|
||**Value of a stati**|**stical lifeyear**|**3**|**% discount rate**|||**5% discount rat**|**e**|
|**Elasticity **|3% discount rate|5% discount rate|2020|2021|2022|2020|2021|2022|
|1|3 9622.38|497 33.86|5 309 399.00|2 2703 624.09|4 873 552.81|6 664 336.68|28 497 499.41|6 117 264.27|
|1.4|1 3996.16|17 567.93|1 875 485.77|8 019 801.11|1 721 527.99|2 354 102.35|10 066 422.72|2 160 855.14|
|||||**Panel B: Low**|**values**<br>**Mortal**|**ity costs**|||
||**Value of a stati**|**stical lifeyear**|**3**|**% discount rate**|||**5% discount rat**|**e**|
|**Elasticity **|3% discount rate|5% discount rate|2020|2021|2022|2020|2021|2022|
|1|184 90.44|23 209.13|2 477 719.53|10 595 024.57|2 274 324.65|3 110 023.79|13 298 833.06|2 854 723.33|
|1.4|6 531.54|8 198.37|875 226.69|3 742 573.85|803 379.73|1 098 581.09|4 697 663.93|1 008 399.06|

Notes: A constant value per statistical life year (VSLY) is estimated by using two discounting rates: 3% and 5%. Based on information from the WHO's Global Health Observatory Data Repository, we estimate that the remaining life expectancy is half of the Ivoirian life expectancy at birth. The term elasticity refers to the value of a statistical life (VSL) 's income elasticity, or the proportional change in the VSL brought on by a change in income. For 2013, the USA's GNI per capita (PPP, current international US$) was US$ 54 380. This amount is used as the basis for all calculations. The low levels of VSL are equal to US$ 4,200,000 for the USA. All VSL estimates are adjusted for inflation using the GDP deflator, as well as for potential changes in real income. All estimates are in US$.

7
issuers. Only 13 African countries have seen corporate bond issues; in all these cases, the issues have been highly concentrated in the telecommunications and banking sectors.<sup>12</sup> While there have been some signs of improvement, such as large initial public offerings (IPOs) on the Ghanaian, Kenyan, and Nigerian stock exchanges, it is unlikely that these advances will make capital markets sustainable. Attempts at establishing regional markets have been less successful than expected, as the example of the regional exchange in Abidjan shows, as do the continuous political struggles related to creating a similar regional exchange for the Central African Monetary Union.

However, many countries in Sub-Saharan Africa have not only seen economic growth pick up in recent years, but financial deepening and broadening have occurred in them as well. While this might be partly driven by demand and partly by international capital inflows, improvements in the institutional framework of finance—such as the establishment of commercial courts and alternative dispute resolution systems, the establishment or improvement of collateral registries and credit reference bureaus, and macroeconomic stability—have certainly contributed to this improvement. Stronger confidence in Africa’s financial systems is also reflected in the quintupling of private capital inflows over the period 2000 to 2007;<sup>13</sup> these surpassed donor inflows for the first time in 2006, thus reversing a long-term trend. This surge of private capital into Africa was part of increased capital flows to emerging markets resulting from the worldwide liquidity glut. However, as cross-country comparisons show,<sup>14</sup> these flows partly also reflect improved macroeconomic fundamentals in many African countries, such as macroeconomic stability and fiscal discipline. When looking behind this aggregate, however, one

> 12 IMF 2008. 13 IMF 2008. 14 IMF 2008.

7
<!-- Start of picture text --> Land<br>° ___tatge firms relative to small small Firms ________ ________<br>Z<br>: tt 4<br>° 4 ' |<br>@<br>FS HFSS FESS FSS SS SS KS<br><!-- End of picture text -->

<!-- Start of picture text --> Land<br>High growth states relative to low growth States states relative to low growth States to low growth States low growth States States<br>- +14<br>2<br>SESEFEESFEES SSSI SSS SSS<br><!-- End of picture text --> <!-- Start of picture text --> 7 Land<br>:<br>Lod<br>t<br>pot<br>FE SESE ESS EFS SS SS SS<br><!-- End of picture text --> <!-- Start of picture text --> 7 Land Land Land<br>: High growth states relative to low growth States states relative to low growth States to low growth States low growth States States ° ___tatge firms relative to small small Firms ________ ________<br>Lod Z<br>t : tt 4<br>pot - +14 ° 4 ' |<br>FE SESE ESS EFS SS SS SS 2 @<br>SESEFEESFEES SSSI SSS SSS — FS HFSS FESS FSS SS SS KS<br>oo Fixed Capital Capital (excluding land) 2 HighFixedgrowth Capitalstates relative(excluding to low growthland)States ) FixedLarge Capital firmsLarge Capital firms Capital firms firms relative (excludingto small Firmsland) (excludingto small Firmsland)to small Firmsland) small Firmsland) Firmsland)land)<br>| ya / |<br>|<br>FF EFPPEPIS EFPPEPIS : : .<br>SEES — OC PPPEEPEEPISER — SSIS IIIS POSSE<br><!-- End of picture text -->

<!-- Start of picture text --> FixedLarge Capital firmsLarge Capital firms Capital firms firms relative (excludingto small Firmsland) (excludingto small Firmsland)to small Firmsland) small Firmsland) Firmsland)land)<br>|<br>.<br>SSIS IIIS POSSE<br><!-- End of picture text -->

<!-- Start of picture text --> oo Fixed Capital Capital (excluding land)<br>|<br>|<br>FF EFPPEPIS EFPPEPIS<br>SEES<br><!-- End of picture text -->
In the more general case of non-linear income dynamics, OLS estimation of the cohort average equation (10) will still lead to consistent estimates of β1, β2 and β3, allowing one to determine if there are non-convexities in the income dynamics. However, the OLS estimates of the cohort-specific effects αc will be biased. To correct for this bias we need to impose further restrictions on the form of the measurement error. In particular, we assume that:

- i) The measurement errors are symmetric, so that E ¡ε<sup>3</sup> i,t¢ = 0

- ii) The measurement errors are stationary, so that E ¡ε<sup>2</sup> i,t¢ = σ<sup>2</sup> ε<sup>isconstant</sup> over time. We can allow the variance of the measurement errors to differ across cohorts.

- iii) The measurement errors are independent of the true values within a cohort, so εi,t is independent of Yi,t<sup>∗foralliinagivencohortc.</sup>

Under these assumptions, based on equations (7) and (8), one can easily show that as nc →∞, the OLS estimate of αc, αbc converges as follows: where Ec ¡Yi,t<sup>∗</sup> −1¢ is the cross-sectional mean in income for individuals in cohort c at time t − 1. Under assumptions (ii) and (iii) we have that the cross-sectional variance of income is: Rearranging then gives: b Equation (15) can then be used to obtain an estimate, σ<sup>2</sup> ε<sup>ofσ2</sup> ε<sup>basedon</sup> the sample cross-sectional variance of observed incomes, and on an estimate of θ = V ar ¡Yi,t<sup>∗</sup> ¢ / V ar (Yi,t). Proxies for this reliability ratio can be obtained from validation studies (see Bound, Brown and Mathiowetz, 2001). We calculate σb<sup>2</sup> ε 12