rafmacalaba/datause-encoder-data huggingface dataset:
# **8. Conclusion**

Governments with sufficient resources are better able to solve market failures and provide the right environment for growth to thrive. Hence, the tax level (and its structure) are crucial for inclusive growth **.** In this paper we revisit the relationship between taxes and growth but extend it to study the implications for inclusive growth measured by the prosperity gap. We then look at potential channels through which taxes may impact growth outcomes.

growth. Future growth and inclusive growth are subject to a threshold around 13 percent of tax to GDP after which future (inclusive) growth rises. Countries also transition from low-income to middleincome status around this threshold. The implications of these thresholds are that countries that have low levels of tax revenue could significantly increase inclusive growth if they effectively mobilize tax revenues. Moving from a tax threshold of 7 percent of GDP (the level of tax is some of the worst performing economies) to 15 percent of GDP is associated with an additional 10 percentage points of cumulative growth over the next ten years. In the case of inclusive growth, the same increase in tax to GDP ratio results in the reduction of the prosperity gap by about half in ten years; that is, the factor by which the average individual in a country has to multiply their income to get to $25 per day, which is “the income of a typical person living in a country that moves from middle income to high income status” (Prinsloo et al. 2023). Given that tax-to-GDP ratios are volatile and evidence from an event study, it is reasonable to interpret our findings as in line with the standard recommendation for countries with low tax-to-GDP levels to aim for levels around 15 percent. Three channels may explain why this threshold exist: increased spending on health and education, lower government spending and growth volatility, and more progressive taxes.

18
,.. .,-....,...,,.. .,y,p,..ide acievmn data '' Infrmtio on.the pefrac ofstudets sin"- aeted grde 6 4 :8 - ¢mani.* abnd, n tb ongrn tn sec¢tor p^ierfrac ove ti The -e econoai stats, ender, yp 6uof comaniy typet ddofschool. a oth .:liatQ of suit reOt. Thrytasidboiebderbn nniaasesmet bciv eewlbedtr butdt -wqo prncpas teh an itLnl and reginl3ed~~ hna1rotsai4 te maae b 4. tder .h ucet*for flarch TalLer ot *aS Resac and Ev.Zuatio:xs rsio 'fDivi the w:ationa Idcaioa 'reStinan cetrmWf =ge5sc il aO~a.re~~iii.t 4rdy. da erto l: ntuet o e aonl eesetwldb 4ee=pe wit th nitneo h i1e Xr3ut*l)es~m Tenrau of Blmnt iduaii an th eat& f3ncaia Outw n Sprt =df a1E prvd it*ratin4 * is~ W omutes ham, been orao to advise ib kiWB a *itrna Advieso **_* OMV4jo_** _'t.OUQy tea4z_ Wtncl MvsO> Oouuxt *s *oial 4e.L and extin .... Th powe as {e~~ie the .**....:.... u***f tsW ,si l*e..tin1mee i!1 Il _*x.e *t_ Ew col ~tOml 1 ntaio. an ult oto ii.vUb raie ~~n over the past two decades, there has been little progress in monitoring and evaluation of lank-supported projects. Most evaluations have emphasIzed the 21.
level as well. The value of the 8-digit HS exports from each Latin American country to the U.S. is used as weights to aggregate the data on tariffs from the 8-digit of the HS to the 3-digit of the ISIC.

Political Organization of Importers Previous papers on the political economy of trade have used corporate Political Action Committees (PAC) data for manufacturing fi rms to determined whether a sector is organized or not. These measures identify which domestic producers’ lobbies are capable of playing a signi fi cant role in the determination of trade policy. The model and the discussion shown above, however, made clear that the in fl uence played by exporters and importers might be very important in explaining tariff preferences. This paper is fi rst to elaborate a measure of political organization for importers. Using the lobbying in service database kindly provided by Kishore Gawande, we identify which manufacturing and agriculture products present organized lobbying by service fi rms. The measure of importers’ political organization is driven by the requirements of the theoretical model presented above, and which is summarized by the variable Ik<sup>i.</sup> Gawande (2002) organizes service fi rms’ corporate PAC contributions for the fi ve election cycles between the years of 1991 and 2000. Contributions reach a total of $483 million over the fi ve election cycles period and are organized at the SIC 4-digit level. Of course, not all of the contributions paid can be directly imputed to in fl uence trade policy. For instance, it is hard to believe that fi nancial institutions and insurance companies lobby for trade purposes. Following this line of argument, only contributions from retail and wholesale fi rms are used to measure the political activity of service fi rms in this paper. Trade related contributions from service fi rms amount to $68 million during the period from 1991-2000. Before proceeding with the determination of the political organization variable, an average across the fi ve election cycles was taken by SIC 4-digit code.

The theoretical model requires importers’ to be either politically organized or not by agricultural and by manufacturing code. In order to fi lter contributions from SIC 4-digit service codes to the ISIC 3-digit and HS 8-digit agricultural and manufacturing codes we 23
information on deposits, interest rates, and bank fundamentals. We study the impact of systemic risks by adding to such regressions standard measures of country and exchange rate risks, along with bank-level measures of exposures to those risks.

During crisis periods risks are likely to change daily and news events are expected to affect depositor behavior in ways not likely to be captured by the panel estimations. Thus, we also run time-series regressions for each country using daily information on deposits, interest rates, and systemic factors, as well as a chronology of news that are expected to impact on the market’s perception of systemic risk. We discuss each of these approaches and present the corresponding empirical results below. A description of all the variables used, along with the data sources is presented in Appendix Tables 1 and 2. Summary statistics are reported in Appendix Table 3

# **3.1 Traditional Panel Estimates**

Our baseline panel specification is as follows, where _i_ is the bank and _t_ is the period identifier.

_D_ stands for the depositor reaction, alternatively the log of deposits, the change in deposits, and the interest rate on deposits. For the case of Argentina, we examine the behavior of deposits and interest rates in pesos and U.S. dollars separately. In the case of Uruguay, because prior to the crisis deposit dollarization exceeded 80 percent, we concentrate only on U.S. dollar deposits and interest rates.

_F_ stands for bank fundamentals and is a matrix of bank level ratios that are intended to capture banks’ asset quality, profitability, and capitalization levels. In particular, we include the 13
**Table** 21 Pension fund **assets (as** a **p.raentage** of **ODP)**

||1970|1971|1980|1985|1990|
|---|---|---|---|---|---|
|Ut.|17|15|23|47|55|
|Us|17|20|24|29|35|
|Germany|2|2|2|3|3|
|Japan|**0**|**1**|2|4|5|
|**Canada**|1|13|17|23|28|
|Netherlands|29|36|46|68|77|
|Sweden|22|2.|30|29|28|
|Iwitierland|38|01|51|59|69|
|Denmark|5|5|7|12|15|

**lourc:t** Nat!e,nal llow-of-Fun4s data.

**Table** 3i **Life insurance** and **pension** fund assets **(as a percentage** of GDP)

||1970|1975|1980|1985|1990|
|---|---|---|---|---|---|
|UK|43|37|46|83|97|
|uS|37|37|42|49|59|
|**_Ge'_**ny|10|11|14|19|22|
|Japan|8|10|13|20|41|
|Canada|31|28|31|39|46(3)|
|Netherlands|45|51|63|86|107|
|Sweden|42|48|51|55|63|
|Switzerland|51|1is|70|82|n/a|
|Denmark|14|14|19|31|n/a|
|Memo Items5||||||
|France|6|7|7|9|13(1)|
|Italy|n/a|4|3|6|12(2)|

(1) 1988 (2) 1987 (3) 1989 6a
accessing the service.

The household’s problem can be solved by the Lagrangian method.

The FOCs are: In the CES human capital production function, the elasticity of substitution _σ_ determines the degree of complementarity and substitutability between _Zgt_ and _Zit_ , where _σ_ = 1 _−_ <u>1</u> _ρ_<sup>.According</sup> to the L’Hˆopital rule, as _ρ →−∞_ , _σ →_ 0. The CES human capital production function approaches the Leontief function, _u_ (min( _Zgt, Zit_ )) and _Zit_<sup>_∗_=</sup><sup>_Z_</sup> _gt_<sup>_∗_.As</sup><sup>_ρ→_1,</sup><sup>_σ→∞_,</sup><sup>_Zgt_and</sup> _Zit_ are CES perfect substitutes, _u_ (( _θ_ ) _Zgt_ + (1 _− θ_ ) _Zit_ ). In this case, the conditionalities of a CCT on _Zgt_ could lead households to decrease their demand for _Zit_ . If _−∞ < ρ <_ 1, _Zgt_ and _Zit_ are complements and we can derive the optimal ratio of _Zit_ to _Zgt_ . In this case the first-order conditions are necessary and sufficient for an interior solution given concavity of the technology in terms of _Zit_ to _Zgt_ .<sup>35</sup> From the FOCs in (8), we solve _∂L/∂Zit_ and _∂L/∂Zgt_ for _λ_ , then we take the ratio of<sup>_<u>Zit</u>_solvefor</sup><sup>_Zit_:</sup> _Zgt_<sup>and</sup> By using the FOCs in (8) to solve _∂L/∂Zit_ and _∂L/∂ct_ for _λ_ , we obtain equation (10). A similar

> 35This approach is similar to Heckman and Cunha [2007] for dynamic complementarity between _It and It_ +1.

14
from the World Integrated Trade System. An important advantage of our data is that they are very disaggregated, at the product (6-digit) level. Furthermore, we have a long sample, ranging from 1993 to 2007 (plus data in previous years for trade flows and MFN tariffs). On the negative side, the panel is unbalanced, with a significant amount of missing data, especially in the first half of the sample.

The aggregate trade data used in Section III.3 are from the IMF direction of trade statistics. These data go back further than the WITS data and a great effort is made to ensure they capture aggregate bilateral flows.

# **III.2 – Tariffs**

Table 1 shows the (simple) average preferential and MFN tariffs, plus the difference between the two ( _margin_ ), for each of the ten ASEAN members since the outset of AFTA in 1993 (or since the entry year for the later entrants). Blank spaces reflect missing data (the year 2001 is not shown because preferential tariffs are missing for all members that year). The overall trend is of falling MFN and preferential rates, but this is not always the case, even though we are looking at average levels. The most notable exception is Thailand, where during the Asian crisis MFN tariffs temporarily peaked at high levels.

These points become clearer when we look at figure 1, where we display the time series of the three variables from table 1 for the countries where data is available for most years, Indonesia, Malaysia, and the Philippines. The downward trend in intra-ASEAN tariff rates is very transparent for all of them, although the speed and the magnitude of the changes vary, both being higher for the initially more protected countries, Indonesia and Philippines. In those countries external tariffs have fallen as well, but the changes have been minimal since 2003. The pattern of the average MFN tariff in Malaysia has been more erratic, having gone up right after the Asian currency crisis and come down only slightly since 2005, but remaining above the pre-crisis level. With respect to the average margin of preference, the pattern again differs among the three countries: it is generally (but not always) increasing for Indonesia and Malaysia, and it is mostly (but not always) decreasing in the Philippines, reflecting the large drops in the MFN tariff there. Figure 1 therefore makes clear the 12
# **Annex Table 1: Big Five Inventory**

Rating 1 “mostly like me” to 5 “mostly not like me” in describing characteristics about self.

||Rating||Rating|
|---|---|---|---|
|1.Istalkative|----|23.Tendsto belazy|----|
|2. Tends to find fault with others|----|24. Is emotionallystable,not easilyupset|----|
|3.Doesa thoroughjob|----|25.Isinventive|----|
|4. Is depressed,blue|----|26. Has an assertivepersonality|----|
|5.Is original, comes up with newideas|----|27. Canbe coldandaloof|----|
|6.Isreserved|----|28.Perseveres until thetask isfinished|----|
|7. Is helpful and unselfish with others|----|29. Can be moody|----|
|8. Canbe somewhatcareless|----|30. Valuesartistic,aesthetic experiences|----|
|9. Is relaxed,handles stress well|----|31. Is sometimes shy,inhibited|----|
|10. Is curious about many different things|----|32. Is considerate and kind to almost<br>everyone|----|
|11.Isfullofenergy|----|33.Doesthings efficiently|----|
|12. Startsquarrels with others|----|34. Remains calm in tense situations|----|
|13.Isa reliable worker|----|35.Prefers work that isroutine|----|
|14. Canbetense|----|36.Is outgoing, sociable|----|
|15. Is ingenious,a deepthinker|----|37. Is sometimes rude to others|----|
|16. Generates a lot of enthusiasm|----|38. Makes plans and follows through with<br>them|----|
|17.Hasa forgivingnature|----|39. Getsnervous easily|----|
|18. Tends to be disorganized|----|40. Likes to reflect, playwith ideas|----|
|19. Worriesa lot|----|41.Hasfewartisticinterests|----|
|20. Has an active imagination|----|42. Likes to cooperate with others|----|
|21.Tendsto be quiet|----|43.Is easily distracted|----|
|22.Is generallytrusting|----|44.Is sophisticatedin art,music, or literature|----|

BFI scale scoring (“R” denotes reverse-scored items) Extraversion: 1, 6R, 11, 16, 21R, 26, 31R, 36 Agreeableness: 2R, 7, 12R, 17, 22, 27R, 32, 37R, 42 Conscientiousness: 3, 8R, 13, 18R, 23R, 28, 33, 38, 43R Neuroticism: 4, 9R, 14, 19, 24R, 29, 34R, 39 Openness: 5, 10, 15, 20, 25, 30, 35R, 40, 41R, 44 Source: John, Naumann and Soto, 2008 26
suggests an overall increase of about 86 ug/m<sup>3</sup> during this season – a figure which is close to the MiniVol result for kitchens.

Table 5 Regression Results for Exposure Distribution Statistics

||`Mean`|`Maximum`|`Minimum`|`St. Dev.`|`Median`|
|---|---|---|---|---|---|
|`Brick`|`249.683`|`5,765.622`|`24.741`|`642.883`|`27.575`|
||`(1.78)`|`(1.94)`|`(1.10)`|`(1.83)`|`(0.48)`|
|`Mud`|`36.592`|`-635.003`|`-5.529`|`-85.740`|`41.120`|
||`(0.27)`|`(0.22)`|`(0.25)`|`(0.25)`|`(0.74)`|
|`Thatch`|`201.391`|`2,405.807`|`31.963`|`339.155`|`46.685`|
||`(1.50)`|`(0.84)`|`(1.48)`|`(1.01)`|`(0.85)`|
|`Wood`|`322.702`|`8,782.403`|`-14.011`|`1045.098`|`-9.783`|
||`(2.45)*`|`(3.15)**`|`(0.66)`|`(3.17)**`|`(0.18)`|
|`Dung`|`203.429`|`4,407.647`|`-14.287`|`590.197`|`-28.595`|
||`(1.70)`|`(1.74)`|`(0.74)`|`(1.97)*`|`(0.58)`|
|`Kitchen layou`|`t`|||||
|`Attached`|`257.893`|`6,584.797`|`27.972`|`669.463`|`67.878`|
||`(2.17)*`|`(2.62)**`|`(1.46)`|`(2.25)**`|`(1.40)`|
|`Detached`|`274.299`|`3,480.145`|`63.093`|`400.850`|`181.843`|
||`(2.01)*`|`(1.20)`|`(2.87)**`|`(1.17)`|`(3.25)**`|
|`High Dust`|`88.887`|`498.348`|`22.689`|`42.903`|`85.526`|
||`(0.86)`|`(0.23)`|`(1.36)`|`(0.17)`|`(2.02)*`|
|`Constant`|`49.277`|`-1847.06`|`46.929`|`-121.12`|`73.523`|
||`(0.32)`|`(0.57)`|`(1.90)`|`(0.32)`|`(1.17)`|
|`Observations`|`101`|`101`|`101`|`101`|`101`|
|`R-squared`|`0.20`|`0.27`|`0.29`|`0.24`|`0.31`|

``` Absolute value of t statistics in parentheses ``` ```
* significant at 5%; ** significant at 1%
```

# **5.  Conclusions and Policy Implications**

This study has identified several sources of IAP exposure risk that can be mitigated by Bangladeshi villagers at feasible cost. We believe that self-interest will motivate villagers to act, once they become convinced that the problem is serious, and that their actions will be cost-effective. Serious mitigation will require the approval of male household heads, who control many of the relevant resources, and one key to persuasion is provided by our findings on male exposure risk. Our experiments, as well as the survey and monitoring work reported in a previous paper, have undermined the conventional view that women face most of the IAP exposure risk. Men also face serious risk, for two principal reasons. First, dangerous particulates from kitchen fires spread immediately to living areas, so males’ avoidance of cooking areas does not protect them.

14
- 70 -

# APPENDIX TABLE 5 (Continued)

BELIZE TRANSPORT COST PROFILE FOR EXPORTS TO THE UNITED STATES IN 1986

|||U.S.|BELIZE N|OMINAL FREI|GCHT<br>RATE|BELIZE<br>TARIFF|"OTHERS"|NOMINAL FRE|IGHT RATE|"0TTHERS"<br>TARIFF|SHARE IN<br> <br>BELIZE'S|
|---|---|---|---|---|---|---|---|---|---|---|---|
|OES|TSuSA|<br>IMPORTS(S)|TOTAL|VESSEL|AIR|RATE|TOTAL|VESSEL|AIR|RATE|EXPORTS|
|79|6923295|<br>182,832|<br>8.10|8.20|6.60|3.22|6.68|4.73|19.70|3.09|0.364|
|80|7003522|<br>48,408|8.54|8.54|0.00|8.50|4.62|4.02|23.92|8.51|0.096|
|81|7003552|<br>85,500|0.00|0.00|0.00|8.50|6.20|5.05|8.95|8.60|0.170|
|82|7004514|<br>43,920|**8.50**|8.50|0.00|10.00|6.17|4.36|14.55|10.00|<br>0.088|
|83|7004544|<br>77,902|13.58|0.00|3.51|10.00|5.70|4.24|7.85|10.01|<br>0.155|
|84|7108080|<br>50,725|17.09|17.09|0.00|0.00|3.67|3.34|3.93|4.91|0.101|
|85|7271100|<br>98,062|6.46|0.00|6.46|0.00|24.11|23.96|97.63|2.38|0.195|
|86|7728500|36,416|6.87|6.87|0.00|0.00|8.85|6.08|14.65|4.96|0.073|
|87|7732500|74,135|10.08|8.66|21.47|0.00|5.76|4,11|10.15|3.10|0.148|
|88|7912700|<br>118,225|15.16|0.00|15.16|0.00|4.88|2.70|5.81|0.19|0.236|
|89|7912800|10,344|30.43|0.00|30.43|0.00|7.85|3.95|14.43|-.13|0.021|
|90|7930000|<br>30,958|2.63|2.63|0.00|0.00|15.15|20.36|4.43|0.00|0.062|
|91|8000035|1,272,450|1.58|1.20|2.18|0.00|2.53|4.65|1.97|0.00|2.537|
|92|8010000|11,400|0.96|0.00|0.96|0.00|1.60|1.80|4.35|0.00|0.023|
|93|9999500|49,911|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.099|

- OS OESCRIPTION

- 79 Parts nspf _for_ motor vehicles

- 80 Leather athletic footwear nspf, for **cen** youths, and boys el Men's leather cement footwear nspf

- 82 Womn's casual leather footwear nspf, valued over 52.50 per pair

- 83 Women's leather ce ent footwear nspf, valued over 52.50 per pair

- **84** Non-optical measuring or checking instruments, and parts

- 85 Furniture of unspun fibrous rattan oaterials and parts thereof .nspf **86** Closures, includes caps, lIds, and other Closures, of rubber or plastics

- 87 Gaskets, of rubber or plastics

- 88 Leoather, other than patent leather, cut or snaped for conversion Into uppWrs

- 89 Leather, other than patent leather, cut or shaped for conversion into footwar, other than uppers 90 waste and scrap, nspf

- 91 U.S. products returned after export without being advanced in value or Improved In condItIon abroad, nes 92 Articles roleWorted, undor lease to foreign manufacturer 93 Under **5251** formal and infortal **entries** estimated
# Improving Access to Banking: Evidence from Kenya

FRANKLIN ALLEN, ELENA CARLETTI, ROBERT CULL, JUN “QJ” QIAN, LEMMA SENBET, AND PATRICIO VALENZUELA<sup>∗</sup> JEL CODES: G2; O1; R2.

KEY WORDS: Equity Bank, bank penetration, bank account, minority language, microfinance.

> ∗ Allen is from the University of Pennsylvania, Carletti is from the European University Institute, Cull is from the World Bank, Qian is from Boston College, Senbet is from University of Maryland, and Valenzuela is from the University of Chile. We thank Financial Sector Deepening Trust Kenya (FSD Kenya), and in particular David Ferrand, for providing the household surveys. We have benefited from helpful comments from Thorsten Beck, Sankar De, Samuel Makome, Randall Morck, Jonathan Robinson, Moses Sichei, John Staley, and seminar participants at Wesleyan University, the 2011 NBER African Successes conference in Zanzibar and the 2012 Summer Research Conference in Finance at the Indian School of Business in Hyderabad. We are grateful to the Gates Foundation, NBER, and the authors’ respective institutions for financial support. We are responsible for all the remaining errors.
Policy ReseaRch WoRking PaPeR 4751

# **Abstract**

This paper examines the effect of regionalism on unilateral trade liberalization using industry-level data on applied most-favored nation tariffs and bilateral preferences for ten Latin American countries from 1990 to 2001. The findings show that preferential tariff reduction in a given sector leads to a reduction in the external (most-favored nation) tariff in that sector.

External liberalization is greater if preferences are granted to important suppliers. However, these “complementarity effects” of preferential liberalization on external liberalization do not arise in customs unions. Overall, the results suggest that concerns about a negative effect of preferential liberalization on external trade liberalization are unfounded.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand the consequences of regionalism. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at cfreund@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
standard deviations of idiosyncratic component (i.e. _σ_ Δ _uit_ ) and of the differenced commodity price data (i.e. _σ_ Δ _CPit_ ), would equal one if all variation were idiosyncratic (i.e. _R_<sup>_2_</sup> = _σ_ Δ _uit_ / _σ_ Δ _CPit_ ). The second statistic, equal to the ratio between the standard deviations of the common factor (i.e. _σFt_ ) and that of the idiosyncratic component (i.e. _σuit_ ), would be zero if all variation were idiosyncratic (i.e. _σFt_ / _σuit_ = 0). Finally, we present the factor loadings ( _λi_ ) from equation (1); they should be larger for series that are more related to the common factor.

**Table 2. Cross Sectional Heterogeneity in Commodity Prices**

||_σ_Δ_uit_/_σ_Δ_CPit=R_<sup>_2_[1]</sup>|_σFt_/_σuit_<br>[2]|_λi_<br>[3]|
|---|---|---|---|
|Aluminum|0.915|0.307|-0.301|
|Bananas|0.919|0.198|-0.248|
|Beef|0.910|0.191|-0.235|
|Cocoa|0.975|0.166|-0.215|
|Coffee|1.000|_0.023_|-0.057|
|Copper|0.728|**0.581**|-0.863|
|Cotton|0.879|0.421|-0.342|
|Hides|0.996|_0.036_|0.043|
|Jute|0.975|0.223|-0.281|
|Lamb|0.970|_0.083_|-0.075|
|Lead|0.944|0.354|-0.597|
|Maize|0.798|**0.774**|-0.527|
|Palm|0.923|**0.517**|-0.445|
|Rice|0.688|1.610|-1.128|
|Rubber|0.819|**0.608**|-0.653|
|Silver|0.403|**2.545**|-2.031|
|Sugar|0.149|**1.770**|-3.759|
|Tea|0.976|0.153|-0.163|
|Timber|0.597|0.405|-0.396|
|Tin|0.685|**0.640**|-1.041|
|Tobacco|0.999|_0.022_|-0.028|
|Wheat|0.773|**0.747**|-0.508|
|Wool|0.973|0.283|-0.154|
|Zinc|0.929|0.402|-0.674|

_Notes_ : [1] ratio of standard deviation of idiosyncratic component to standard deviation of differenced data, if all variation idiosyncratic then tends to one (i.e. _R_<sup>_2_</sup> = _σ_ Δ _uit_ / _σ_ Δ _CPit_ ); [2] standard deviation of common to idiosyncratic component, if all variation idiosyncratic then this tends to zero (i.e. _σFt_ / _σuit_ = 0); and [3] The factor loadings ( _λi_ ) from equation 1. We highlight in bold cross sectional units where the ratio of the common to the idiosyncratic variability is greater than 0.5, indicating that the majority of variation is explained by the factor.

17