rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A4 – Testing for Pre-Trends, Given Exposure to the Energy Sector in August 2014**

This table tests for the existence of pre-trends in loans to firms in the energy sector, given the exposure of banks to the energy sector in August 2014. The sample period is from January 2013 to August 2014. The dependent variables are in logs. _Total Lendingf,b,m_ is the total lending value to firm _f_ by bank _b_ in month _m. Working Capitalf,b,m_ and _Investmentf,b,m_ are total lending value destined to working capital and investment, respectively. The sample is at the firm-bank-month level of observation. <u>Standard errors are double clustered at the bank and month levels. *** p<0.01, ** p<0.05, * p<0.1.</u>

||Total Le<br>|ndingf,b,m<br>|Working<br>|Capitalf,b,m<br>|Investm<br>|entf,b,m<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ExposureEnergyb,Aug14|-0.018||-0.058*||0.035||
||(0.025)||(0.030)||(0.056)||
|ExposureEnergyb,Aug14*TimeTrendb,m|0.001|0.001|0.001|0.002|0.001|0.001|
||(0.002)|(0.002)|(0.001)|(0.002)|(0.001)|(0.002)|
|Observations|7,160|7,160|7,160|7,160|7,160|7,160|
|R-squared|0.001|0.058|0.004|0.045|0.004|0.090|
|Bank-firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Month FE|Yes|-|Yes|-|Yes|-|
|Firm-month FE|No|Yes|No|Yes|No|Yes|
|SD(ExposureEnergyb,Aug14)|6.2|6.2|6.2|6.2|6.2|6.2|

40
numbers out of 10 are correctly discriminated – when children need to identify the highest number from a pair of numbers. For the recognition of missing numbers, children also perform very poorly (on average 0.20 correct answers out of 10). And for more advanced numeracy skills, addition and subtraction, children score, on average, 0.20-0.30 correct answers out of 20 numbers.

Table 1: Sample Descriptive Statistics at Baseline

||Mean|St.Dev|Min|Max|N|
|---|---|---|---|---|---|
||_H_|_ousehold_|_charac_|_teristics_||
|Age of target child’s father|43.88|10.72|15|97|9,029|
|Age of target child’s mother|31.45|8.11|15|98|8,907|
|Hh education: none|0.53|0.50|0|1|9,393|
|Hh education: Islamiyya / other|0.06|0.23|0|1|9,393|
|Hh education: any primary|0.20|0.40|0|1|9,393|
|Hh education: any secondary|0.14|0.35|0|1|9,393|
|Hh education: higher than secondary|0.07|0.26|0|1|9,393|
|Household size|8.61|3.95|2|37|9,393|
|No. of rooms|2.88|1.60|1|20|9,393|
|Food: always eat enough|0.18|0.38|0|1|9,393|
|Food: not always eat enough|0.72|0.45|0|1|9,393|
|Food: sometimes/frequently not eat enough|0.10|0.30|0|1|9,393|
|||_Targ_|_et Chil_|_d_||
|Female (=1)|0.51|0.50|0|1|9,393|
|Age|7.31|1.04|6|9|9,393|
|Out-of-school|0.17|0.38|0|1|9,393|
|EGRA letter recognition|0.35|3.63|0|92|9,285|
|<br>EGRA nonword reading|3.98|6.56|0|29|116|
|EGRA oral passage reading|11.09|1.75|0|56|9,285|
|EGRA reading comprehension|0.02|0.27|0|5|9,285|
|EGRA listening comprehension|2.88|1.79|0|5|9,285|
|EGRA letter dictation|0.80|1.23|0|5|9,285|
|EGRA word dictation (correct word)|0.05|0.40|0|5|9,285|
|EGRA word dictation (all letters correct)|0.06|0.42|0|5|9,285|
|EGMA number recognition|0.43|1.87|0|20|9,273|
|EGMA number discrimination|1.60|2.49|0|10|9,285|
|EGMA missing number|0.20|0.85|0|10|9,285|
|<br>EGMA addition|0.30|1.60|0|20|9,285|
|EGMA subtraction|0.20|1.35|0|20|9,285|

19
43 Table 1 Total External Debt, 1982 and 1987 ($ billions and percentages)

||1982|1987|Percent Chanae|
|---|---|---|---|
|All Developing Countries|781.2|1,167.1|49.0|
|Highly Indebted|391.2|527.3|34.8|
|Other|392.1|639.9|63.2|
|of which:||||
|Algeria|16.7|22.9|37.1|
|China|8.4|30.2|259.5|
|Egypt|26.2|40.3|53.8|
|Greece|11.2|23.1|106.3|
|Hungary|9.0|19.0|111.1|
|India|25.6|46.4|81.2|
|Indonesia|26.5|52.6|98.5|
|Malaysia|11.3|21.7|92.0|
|Pakistan|11.6|16.3|40.5|
|Poland (a)|31.0|42.1|35.8|
|Portugal|13.6|18.2|33.8|
|Thailand|12.2|20.7|69.7|
|Turkey|19.7|40.8|107.1|
|Subtotal|223.0|394.3|76.8|
|Latin America||||
|World Bank|332.8|442.0|32.8|
|ECLA|331.0|410.5|24.0|
|Latin America-6 major||||
|World Bank|281.2|363.3|29.2|
|ECLA|285.1|331.3|16.2|

a. Initial year is 1985.

Source: Calculated from World Bank, World Debt Tables 1988, Vol. II; ECLA, Balance Preliminar de la Economia Latinoamericana, 1987 and 1988.
Finally, our indicator for non-parent adult mortality _(primedeath)_ represents the primeage (15-49 years) adult death of a household member, excluding parents, which occurred before the child turned 15. We also include district fixed-effects ( ∂ _v_ ) for the district in which the child resided at baseline; as noted above, by 2004, some children had relocated to new communities which could be as far away as Dar es Salaam. This basic specification is later augmented to include interactions of _orphan_ and _primedeath_ with a set of individual and household characteristics from baseline.

The data for this study are drawn from a 13-year longitudinal study of people originally residing in the Kagera region of Tanzania in 1991. The Kagera Region of Tanzania is located on the western shore of Lake Victoria, bordering Uganda to the north and Rwanda and Burundi to the west. The population (1.3 million in 1988, about 2 million in 2004) is overwhelmingly rural and primarily engaged in producing bananas and coffee in the northern districts and rain-fed annual crops (maize, sorghum, cotton) in the southern districts. The Kagera Health and Development Survey (KHDS) is a longitudinal socioeconomic survey conducted from September 1991 to January 1994 covering the entire Kagera region (see World Bank, 2004, and <u>http://www.worldbank.org/lsms/) and a follow-up round in 2004 to re-interview all</u> surviving respondents. Because adult mortality of the working age population (15-50) is a relatively rare event and HIV/AIDS was unevenly distributed in Kagera, the KHDS household sample was originally stratified based on the agro-climatic features of the region, levels of adult mortality from the 1988 Census (including both high and low mortality areas), and household-level indicators thought to be predictive of elevated adult illness or mortality. This was done in order to capture a higher percentage of households with a death during the panel 1991-1994 while retaining a control group of households without a death. As a result of the sampling scheme, orphan rates in the KHDS 19911994 are more than twice as high as would be observed in a non-stratified random sample although by 2004 rates look close to the population estimates.<sup>8</sup>

> prime-age death in the household, there could be an additional effect. However, less than 5 percent of children experience both events so we exclude this interactive effect in our estimation.

> 8 It is interesting to note that the KHDS 2004 sample is remarkably similar to a random sample of households from Kagera, despite the fact that the baseline sample was not a simple random sample and given the (moderate) attrition since the baseline. The households in the KHDS 2004 had similar characteristics to the CWIQ survey in 2004 (including characteristics such as: household size, female

5
A divergence in the cost-of-living across households has consequences for the measurement of income inequality. Muellbauer (1974) examines the different impacts on UK households of price changes between 1964 and 1970 and finds that the COL of low-income households grew faster than the CPI captured. Ignoring this kind of error overstates the reduction in inequality from 1964 to 1970 by 13-15 percent.

# **3 Methodology**

Consumption patterns vary across households at different income levels for various reasons. Engel’s law postulates that the share of the household budget spent on food declines with income <u>(Chai and Moneta 2010). Shares of the household budget spent on housing are also typically higher</u> for poor households than for rich ones. Wealthier households tend to spend proportionally more on transportation, education, insurance, and pensions than poorer households and allocate their assets differently (Erosa and Ventura 2010). These differences in consumption patterns and heterogeneous changes in consumer prices lead to different levels of inflation faced by different households. Increases in the COL varying by household income could have significant implications for poverty and inequality, potentially affecting a range of government policies (Gürer <u>and Weichenrieder 2020).</u> The standard CPI measures the costs of the basket of goods and services purchased by the “typical” household. But because the consumption basket purchased by a household is likely to differ from the CPI basket, the inflation rate it faces will likely differ from the CPI inflation rate. Such differences can be especially large in periods of high inflation.

A large body of economic research analyzes other factors affecting differences in the rates of inflation measured by the CPI and the COL approaches. Among the most important drivers are substitution bias, incomplete quality adjustment, and the treatment of housing expenses. The overall conclusion of these studies is that CPI inflation is likely to overstate COL inflation (Jacobs, <u>et al. 2014).</u> Formally, the CPI inflation is calculated based on the Lowe index<sup>2</sup> :

> 2 The Lowe index is a fixed-basket type index in which the quantity vector (or expenditure shares) corresponds to a reference period that may be different from the price periods compared. Although most countries claim that they use a Laspeyres (or “Laspeyres-type”) index for their national CPI (UN 2009), the claim is not strictly accurate. In the

6
Islands and shorelines were delineated using recent Google Images. The bathymetry of the hydrodynamic model was updated using secondary data from different sources, including the surveyed river bathymetry data from the IWM, Ocean bathymetry data (C-Map from the IWM). Setback distances of the polders in the study regions were measured; and the shorelines of different segments of the study area were developed based on the foreshore areas.

To generate the storm surge model, cyclonic wind and pressure field were added to the hydrodynamic model as inputs of meteorological parameters. Historic cyclone data on (i) radius of maximum wind, (ii) maximum wind speed, (iii) cyclone track forward speed and direction, (iv) central pressure and (v) natural pressure from the Bangladesh Meteorological Department were used to estimate the cyclonic wind field and pressure. Holland Single Vortex theory was applied to generate the cyclonic wind field (Holland 1980). The generated cyclonic wind and pressure fields for the cyclone track were then used in the hydrodynamic model to simulate the cyclonic storm surges.

Figure 4 describes the setup of the mathematical model and describes how storm surge heights were estimated for this analysis.

**Figure 4: Mathematical Model Setup**

<!-- Start of picture text --> MATHMATICAL MODEL SETUP<br>STORM SURGE<br>WAVE MODEL<br>MODEL<br>Model grid<br>Hydrodynamic<br>Boundary condition Cyclone model<br>model<br>Bed resistance<br>Hydrodynamic<br>model<br>+<br>Cyclonic wind field<br><!-- End of picture text --> 10
Table 8: Transitions between Formal and Informal states, conditional on moving, by age in percent.

|**Argentina**|**From Formal to**<br>**Informal**|**From Informal to**<br>**Formal**|**Difference**|
|---|---|---|---|
|15-18|42.3|10.3|32.0%|
|19-24|46.4|27.8|18.6%|
|25-44|44.1|31.7|12.4%|
|**Brazil**||||
|15-18|48.3|30.5|17.8%|
|19-24|46.7|46.0|0.7%|
|25-44|47.5|44.6|2.8%|
|**Mexico**||||
|15-18|47.7|34.5|13.1%|
|19-24|51.4|50.2|1.1%|
|25-44|47.7|46.6|1.1%|

Figure 1: Number of observations at each initial state before a transition is made in Argentina, by age <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>15-18<br>40% 19-24<br>25-44<br>30%<br>20%<br>10%<br>0%<br>OLF Studying Unemployed Self employed Formal Informal Unpaid<br><!-- End of picture text --> 24
<!-- Start of picture text --> 25<br>207 atU‘A<br>at<br>¢ 1<br>15 ef \<br>: '<br>7 1<br>A 4 1‘<br>10 aN Pd '<br>”N 4a \ad7 1H is’‘<br>4ow Ae 1 a} ,<br>oo” 4 ’ XN Cad ¢<br>5 . ee 7 i) ni 7a V42<br>y ¢ had<br>manneii | t l [<br>PMSN DdMMMok MMM6 SSAA HDALMGDOGK eCGG OASEGG aSONDgAY Ad -gragram 5 gray6 4<br>-5<br>Direct Investment Portfolio Investment Other Investment<br>12<br>10 n<br>UU N<br>’ ‘\<br>8 ‘ 4<br>KN a \ °<br>! \ c \ oor?<br>é aA , \ As ad<br>4 1 Vv it<br>oe |)<br>‘ s/ \ U<br>4 a ! \<br>Aasd‘ ‘ \Li re i<br>_ J \<br>2-7 ¢<br>oe | I; ' !<br>PMMAPD SY SS? MMMMI? PO HP MMMSE PH IFMM FMMG GFBS RSS WAPSPP OOSAWd AY A MorisA 0 OO 1<br>-2<br>“4<br>Direct Investment Portfolio Investment Other Investment<br><!-- End of picture text -->
Reform and Inequality during the Transition: An Analysis Using Panel Household Survey Data, 1990-2005 Branko Milanovic and Lire Ersado<sup>1</sup> Key words: Inequality, transition, economic policy JEL Classification: D31; P3 Number of words: about 8,080 1 World Bank Research Department and Carnegie Endowment for International Peace; and World Bank Europe and Central Asia Department respectively. Corresponding author <u>bmilanovic@worldbank.org. We</u> are grateful to Ruslan Yemtsov, Cem Mete and participants at seminars at the World Bank, School for Advanced International Studies in Washington, and International Economic Association in Istanbul, where the paper was presenrted, for useful comments and suggestions. The opinions expressed in the paper are authors’ own and should not be attributed to the World Bank or its affiliated organizations.
increasingly globalized in terms of costs structures – in other words the extent to which linkages are crossing borders.<sup>6</sup> The rise in direct imports as share of total costs, from Table 7, stands out across manufacturing, though it is far less important for services. Services remain largely non-traded on a cost basis, with imports representing one-third to one-quarter of the share we see for goods production.<sup>7</sup> Table 7: **Direct imports as a share of gross costs**

||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|
|Agr, Forestry, Fisheries|0.032|0.046|0.044|0.046|0.054|0.055|
|Processed Foods|0.079|0.096|0.096|0.091|0.091|0.092|
|Energy Extraction|0.099|0.151|0.249|0.243|0.267|0.274|
|Minerals nec|0.057|0.094|0.063|0.063|0.079|0.084|
|Beverages and Tobacco Products|0.064|0.076|0.077|0.078|0.099|0.101|
|Textiles|0.121|0.132|0.155|0.155|0.161|0.147|
|Wearing Apparel|0.128|0.132|0.151|0.149|0.171|0.154|
|Leather Products|0.129|0.168|0.152|0.163|0.148|0.139|
|Wood Products|0.080|0.097|0.110|0.110|0.117|0.117|
|Paper Products, Publishing|0.070|0.105|0.096|0.096|0.091|0.095|
|Chemical, Rubber, Plastic Products|0.125|0.144|0.155|0.151|0.165|0.176|
|Mineral Products nec|0.058|0.069|0.083|0.082|0.098|0.109|
|Ferrous Metals|0.111|0.136|0.121|0.112|0.125|0.151|
|Metals nec|0.149|0.202|0.182|0.169|0.193|0.238|
|Metal Products|0.064|0.089|0.107|0.107|0.122|0.142|
|Transport Equipment|0.107|0.145|0.159|0.165|0.190|0.198|
|Machinery and Equipment nec|0.103|0.133|0.174|0.187|0.191|0.195|
|Manufactures nec|0.105|0.120|0.141|0.154|0.145|0.148|
|Electricity, Gas, Water|0.033|0.058|0.068|0.065|0.077|0.097|
|Construction|0.049|0.053|0.066|0.069|0.082|0.090|
|Trade and Transport Services|0.037|0.043|0.055|0.051|0.065|0.077|
|Other Commercial Services|0.022|0.030|0.031|0.035|0.041|0.048|
|Public Services|0.023|0.038|0.035|0.032|0.034|0.037|
|Dwellings|0.005|0.010|0.005|0.004|0.005|0.009|

# **4 Services at country and region level**

We now turn to an analysis of individual countries and regions. This means that rather than working with globally aggregated social accounting data, we have now subdivided all flows to allow indexing by region. We will not explore bilateral

> 6As has been noted elsewhere, the split of bilateral trade into intermediate and final use in GTAP is somewhat crude. In our view this is less of a problem here, where we focus on aggregate trade with world.

> 7While not explored here, there is also the question of the origin of value added for specific types of inputs across sectors. Francois and Woerz (2008) find that rising shares for imported service inputs, on a gross value basis, have predictable effects across sectors, depending on which are more of less service intensive in an input basis.

14
A novel finding is that the key ingredients for a good BE also differ according to the level of development of the country in which a firm is located. Access to bank financing and capacity agglomeration are more important for facilitating firm productivity growth in lower-income countries. Interestingly, trade credit access plays a positive role only in middle and lower-middleincome countries. However, most BE elements prove to be income-invariant in terms of facilitating both employment and labor productivity growth.

Our paper is closely related to two literatures. The first related literature examines how the institutional and business environments affect firm performance.<sup>2</sup> Early studies use smaller samples and focus on a few specific factors such as infrastructure, including the internet (Clarke et al., 2015), property rights, and corruption (see, for example, Dollar et al., 2005, 2006; HallwardDriemeier et al., 2006; Cai et al. 2011, Clarke and Xu, 2004; Cull and Xu, 2005).<sup>3</sup> Some recent studies, including an urban economics literature, investigate how various aspects of the agglomeration environment – i.e., capacity agglomeration, as in this paper, or the size and/or density of the city in which a firm is located – matter for job creation (Clarke et al. 2016) or levels of sub-national productivity within specific countries (Li et al., 2015; Combes and Gobillon, 2015). The second related literature uses sub-national regional data to shed light on key determinants of economic development. Acemoglu and Dell (2010) show that institutions are likely key reasons for sub-national disparities in earnings in the Americas that cannot be explained by observed variations in human capital. Gennaioli et al. (2013) provide evidence that human capital plays a prominent role in accounting for sub-national differences in development and firm performance.

We differ from the above literature in many aspects. Most importantly, we employ a comprehensive list of key factors that encompass basic institutions, quality of human capital, infrastructure, access to finance, access to land, labor regulation, tax burdens, and agglomeration. As a consequence, we are able to reduce possible omitted variable bias and, thereby, more convincingly identify the relative impacts of different BE elements on firm performance. The literature on key determinants of development has focused heavily on the importance of institutions (Knack and Keefer, 1995; Acemoglu et al., 2001; Rodrik et all, 2004), finance (Levine, 1997; Rajan and Zingales, 1998; Demirguc-Kunt and Maksimovic, 1998; King and Levine, 1998),

> 2 See Xu (2011) for a recent survey of this literature.

> 3 See also Demirguc-Kunt et al. (2006) for a study of the impacts of BE variables on incorporation decisions. Fernandes (2008), meanwhile, studies the impacts of the BE in Bangladesh using WBES data.

6
||Policy Research Workn|g<br>er ie|Contact|
|---|---|---|---|
|~~~~~~|~~~~~AZau||for paper|
|WPS785 How Did the Asian Countries Avoid<br>the Debt Crisis?|Ishrat Husain|October 1991|S. King-Watson<br>31047|
|WPS786 Fiscal Policy for Managing<br>Indonesia's Environment|Sadiq Ahmed|October 1991|B. Prasertwaree<br>82477|
|WPS787 Private Investment Under Macroeco<br>nomic Adjustment in Morocco|- Klaus Schmidt-Hebbel<br>Tobias Muller|October 1991|S. Jonnakuty<br>39074|
|WPS788 How Expectations Affect Reform<br>Dynamics in Developing Countries|Fra,-cesco Daveri|October 1991|S. Jonnakuty<br>39074|
|WPS789 Intrahousehold Inequality and the<br>Theory of Targeting|Lawrence Haddad<br>Ravi Kanbur|October 1991|J. Sweeney<br>31021|
|WPS790 Reforming and Privatizing Hungary's<br>Road Haulage|Esra Bennathan<br>Jeffrey Gutman<br>Louis Thompson|October 1991|B. Gregory<br>33744|
|WPS791 Measuring Real Exchange Rate<br>Instability in Developing Countries:<br>Empirical Evidence and Implications|Lant Pritchett|October 1991|K. Cabana<br>37947|
|WPS792 Reducing Labor Redundancy in<br>State-Owned Enterprises|Jan Svejnar<br>Katherine Terrell|October 1991|B. Gregory<br>33744|
|WPS793 Decollectivization and the<br>Agricultural Transition in Eastern and<br>Central Europe|Karen M. Brooks|October 1991|C. Spooner<br>30464|
|WPS794 How Do NatWnal Policies Affect<br>Long-Run Growth? A Research<br>Agenda|William Easterly<br>Robert King<br>Ross Levine<br>Sergio Rebelo|October 1991|R. Martin<br>39065|
|WPS795 Economic Stagnation, Fixed Factors,<br>and Policy Thresholds|William Easterly|October 1991|R. Martin<br>39065|
|WPS796 Excess Liquidity and Monetary<br>Overhangs|Gerard Caprio, Jr.<br>Patrick Honohan|October 1991|W. Pitayatonakarn<br>37666|
|WPS797 Using Field Visits to Improve the<br>Quality of Family Planning, Health,<br>and Nutrition Programs: A Supervisor'<br>Manual|Richard Heaver<br>s|October 1991|0. Nadora<br>31091|
|WPS798 Agriculture's Decline in Indonesia:<br> <br>Supply or Demand Determined?|Will Martin<br>Peter G. Warr|October 1991|M. Sanchez<br>33731|
|WPS799 Growth in Open Economies<br>|Sergio Rebelo|November 1991|R. Martin<br>39065|
**Table 3.7: Marginal Propensities to Lose a Job**

||||**on the 2ndquarter**||
|---|---|---|---|---|
|**Variables**|**2007-2008**||**2008-2009**|**2009-2010**|
|**By sex**|||||
|Ml|-0.0155|***|-0.0133 ***|-0.0298 ***|
|ae<br> <sup>**(1)**</sup>|(0.0039)||(0.0041)|(0.0035)|
|**By schooling level**|||||
|Secondar|-0.0107|**|-0.0014|-0.0125 ***|
|y|(0.0045)||(0.0049)|(0.0040)|
|High School|-0.0154<br>|***|-0.0118 **<br>|-0.0113 ***<br>|
||(0.0047)||(0.0050)|(0.0042)|
|Pfil|-0.0389|***|-0.0298 ***|-0.0296 ***|
|roessona<br> <sup>**(2)**</sup>|(0.0050)||(0.0056)|(0.0045)|
|**By region**|||||
|<br>Ct i|0.0039||0.0014|-0.0015|
|ener regon|(0.0078)||(0.0078)|(0.0067)|
||0.0049||-0.0242 ***|0.0010|
|Central gulf region|(0.0099)||(0.0091)|(0.0086)|
|Nth tl i|-0.0003||-0.0230 ***|-0.0164 **|
|or cenra regon|(0.0083)||(0.0078)|(0.0067)|
||0.0067||-0.0027|-0.0167 **|
|North pacific region|(0.0094)||(0.0091)|(0.0071)|
|Ctl ifi i|0.0050||-0.0120|-0.0075|
|enra pacc regon|(0.0081)||(0.0078)|(0.0067)|
|South pacific region|-0.0232<br>|**|-0.0223 **<br>|-0.0190 **<br>|
||(0.0108)||(0.0110)|(0.0092)|
|Pil i|-0.0163|*|-0.0216 ***|-0.0180 **|
|ennsuar regon|00085||00084|00071|
||(.)||(.)|(.)|
||0.0015||-0.0036|-0.0094|
|Border|(0.0079)||(0.0078)|(0.0065)|
|**B**<sup>**(3)**</sup>|||||
|**y age**<br>From 26 to 35 years|-0.0392<br>|***|-0.0197 ***<br>|-0.0222 ***<br>|
||(0.0046)||(0.0052)|(0.0044)|
|F 36 t 45|-0.0421|***|-0.0329 ***|-0.0255 ***|
|rom o year|00048||00053|00045|
||(.)||(.)|(.)|
||-0.0360|***|-0.0271 ***|-0.0078|
|From 46 to 55 years|(0.0054)||(0.0059)|(0.0053)|
|F 56 t 65|-0.0116|*|0.0148 *|0.0209 ***|
|rom o years|(0.0068)||(0.0079)|(0.0071)|
|**By position in the household**|||||
|<br>Household head|-0.0318|***|-0.0410 ***|-0.0256 ***|
||(0.0044)||(0.0045)|(0.0040)|
|Pseudo R2|0.1395||0.1302|0.134|
|N|42915||42170|40336|

_Source:_ Own calculations using rotating panel from ENOE between 2nd quarters of 2008, 2009 and 2010. _Note_ : a. Level of instruction is compared to "No level of instruction and primary." 24