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52

# **Table 28. USA: exhaust emission control history for new gasoline-powered automobiles**

U. S. Federal pollutant standard (g/km)

|Year|CO|HC|NOx|
|---|---|---|---|
|Uncontrolled|54|5.4|2.5|
|1968|32|3.7|3.1|
|1970|21|2.4|3.1|
|1972|17|1.9|3.1|
|1973|17|1.9|1.9|
|1974|17|1.9|1.9|
|1975|9.3|0.9|1.9|
|1977|9.3|0.9|1.2|
|1980|4.4|0.25|1.2|
|1983|2.1|0.25|0.6**(09)b**|

a) Applicable at 80,000 km.

b) Applicable at 100,000 **km.** Source: [OECD 1988a, pp. 89]

**Table 29. Japan: Exhaust emission control history for new gasoline-powered automobiles**

||P|ollutant stand|ards|Measurement|<br>Test|
|---|---|---|---|---|---|
|Year|CO|HC|NOx|Unit|Cycle|
|1966|3|-|-||4mode|
|1969|2.5|-|-|%|4 mode|
|1973|26.0|3.8|3.0|g/km|10 mode|
|1975|2.7b|0.39|1.6|g/km|11 mode|
||85.0|9.5|11.0|g/test|11 mode|
|1976|2.7|0.39|1.20|g/km|10 mode|
||85.0|9.5|9.0|g/test|11 mode|
|1978|2.7|0.39|0.48|g/km|10 mode|
||85.0|9.5|6.0|e/test|11 mode|

a) Applicable to **all** new model vehicles that are greater than 1000 kg equivalent inertia weight. b) The figures show the maximum pennissible limits of the standards applicable at 30,000 km durability and apply to new model vehicles only. Source: [OECD 1988a, pp. 89]
extreme. The proportion of SMEs not using financing products at all is significant, however, ranging from 13% in Chile to 51% in Venezuela and 65% in Mexico.

Table 5 shows that the use of other banking services and products is substantial; almost all SMEs use them across countries. SMEs seem to take advantage of several products including, in order of importance, payment of taxes, internet banking, insurance products, transfers, other payments (including payments to suppliers and employees), automatic debit payments, and debit cards. To a lower extent they use foreign exchange, credit card for executives, and collection of receivables. Venezuela again appears to be an outlier; SMEs there basically just use internet banking.

Finally, the FRS (Inmark Group) surveys provide data that permit gauging the degree of involvement of SMEs with the banking system across countries, as measured by the percentage of SMEs that use a different number of banking products (Figure 17). FRS (Inmark Group) classifies as “involved” clients those that use between 7 and 8 products, as “valuable” clients those that use between 9 and 10 products, and finally as “star” clients those that use more than 10 products. The evidence suggests that Peru and Chile are ahead of the Latin pack in terms of SME involvement with banks; about 51% and 42% of surveyed SMEs, respectively, used 7 or more banking services or products (from 1 or more banks) in 2006. At the other extreme are Colombia and Mexico, where only 30% and 18% of surveyed SMEs, respectively, used 7 or more banking products and services in 2006.

38
that allow the link between PPP and inequality. Section 4 provides robust evidence that confirms the effect of inequality on PPP and of a stable ‘dynamic Penn effect’. While the empirical results in Sections 3 and 4 are based on a time series of the ICP PPPs to date, Section 5 widens the study by proposing an alternative preference-based methodology for estimating PPPs and then using it to calculate counterfactual PPPs that provide a useful comparison with the ICP PPPs. Section 6 concludes the paper with a summary of the principal results and a discussion of issues that could feature in the future agenda of the ICP.

# **2. Analytical Framework and the Link Between PPP and Inequality**

We model a system of two economies that are linked by trade. The consumption basket of the representative individual of either of these economies is composed of three goods<sup>7</sup> and the economies are allowed to engage in bilateral trade in two of these goods. Thus, the market for commodities of any economy is constituted by the domestically produced non-tradable good as well as by the tradable goods produced either domestically or imported from abroad. Note that although each of these economies is endowed with the technology to produce all the commodities, whether a particular tradable good is produced in an economy is determined by the nature of product specialization as directed by the terms of trade. Trade between the economies is assumed to be free<sup>8</sup> and balanced. The productions of the goods in the economies are organized by constant returns to scale production technologies that utilizes a single factor of production. We shall refer to this factor of production as capital. The factor of production is perfectly mobile within an economy but is immobile across the economies. There is perfect competition in both the product and factor markets.

Given this overview of the nature of the economies, we first detail their production structures. Henceforth, we refer to the economies as ‘ _home_ ’ ( _h_ ) and ‘ _foreign_ ’ ( _f_ ) and use as subscripts the letters _h_ and _f_ to distinguish the various exogenous and endogenous variables of the respective economies. Given the assumptions of our model, the production of non-tradable and tradable goods denoted by _X_ , _Y1_ and _Y2_ in that order, are given by:

> 7   In a perfectly competitive setting (as assumption that we invoke subsequently), three is the minimal number of commodities that ensures bilateral trade as also the presence of at least one non-tradable.

> 8   Defined as trade that does not involve any transaction costs in the form of tariffs/subsidies or transportation costs.

8
|Theimpact of higher migrants’dependencyratios onthel|ikelihoodf<br>|orbrainga <br>|in<br>|
|---|---|---|---|
|Migration Possibilities|Low<br>(S1d)|Medium<br>(S2d)|High<br>(S3d)|
|_Annual growth rates_||||
|Population|0.9%|0.8%|0.7%|
|Real Gross Domestic Product|3.5%|3.4%|3.3%|
|Per capita real disposable income|3.1%|3.2%|3.2%|
|Per capita domestic human capital|2.9%|2.6%|2.2%|
|Real physical investment|5.2%|5.2%|5.2%|
|Real educational investment|6.1%|6.2%|6.3%|
|Labor supply, unskilled|1.1%|1.1%|1.0%|
|Labor supply, skilled|3.8%|3.5%|3.1%|
|Wage, unskilled|2.4%|2.4%|2.4%|
|Wage, skilled|-0.1%|0.4%|0.9%|
|Physical capital remuneration|-1.7%|-1.7%|-1.7%|
|Educational investment costs|0.1%|0.4%|0.6%|
|Physical investment costs|0.1%|0.1%|0.2%|
|Consumer price index<br>_Others_|0.1%|0.2%|0.2%|
|Proportion of domestic skilled workers after 25 years|11.0%|10.3%|9.4%|
|Skilled workers migration rate after 25 years|2.5%|3.1%|3.8%|
|Elasticity of per capita domestic human capital accumulation t|o skilled mig|ration|-0.48|
|Source: authors’ calculations.||||

Raising further the dependency ratio of migrant families – that is, beyond the source country average – could be theoretically envisaged. Yet, within reasonable limits, results suggest that higher dependency ratios in migrant families would only marginally improve the response of domestic human capital accumulation to migration, without changing its sign.

# _Lower Dependency Ratios in the Source Country_

Net brain gain is predicated on the possibility for residents to invest more in education, in response to better incentives (higher return on education) and higher savings with remittances. Such a propensity to invest depends in turn on the share of disposable income which is not allocated to incompressible consumption, and thus on the dependency ratio: the lower this ratio, the higher the propensity to reap emerging investment opportunities in human capital.

In the next simulations, we advance further the position of our average economy on the path of demographic transition (see Annex 2). As such, dependency ratios are 16
# **A Appendix: Additional material**

Table A1: Year and country fixed effects

|_Dependent Variable:_||||Migration|Flows||||
|---|---|---|---|---|---|---|---|---|
||WGI|CC|WGI|GE|C|PI|IC|RG|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|WGI CC in _o_|0.16***<br>(0.01)|0.14***<br>(0.01)|||||||
|WGI CC in _d_|–0.25*** <br>(0.09)|–0.27***<br>(0.06)|||||||
|WGI GE in _o_|||0.28*** <br>(0.02)|0.23***<br>(0.01)|||||
|WGI GE in _d_|||–0.06<br>(0.10)|–0.15**<br>(0.07)|||||
|CPI in _o_|||||0.11*** <br>(0.01)|0.14***<br>(0.01)|||
|CPI in _d_|||||–0.09<br>(0.07)|–0.09**<br>(0.05)|||
|ICRG in _o_|||||||0.09*** <br>(0.01)|0.10***<br>(0.01)|
|ICRG in _d_|||||||–0.11*<br>(0.06)|–0.07*<br>(0.04)|
|Constant|4.97***|5.29***|4.19***|4.87***|4.84***|4.90***|4.93***|4.61***|
||(0.23)|(0.32)|(0.27)|(0.33)|(0.22)|(0.32)|(0.20)|(0.35)|
|Gravity controls<br>Year FE<br>Country FE|No<br>Yes<br>Yes|Yes<br>Yes<br>Yes|No<br>Yes<br>Yes|Yes<br>Yes<br>Yes|No<br>Yes<br>Yes|Yes<br>Yes<br>Yes|No<br>Yes<br>Yes|Yes<br>Yes<br>Yes|
|Adj. R-Square|0.46|0.75|0.47|0.75|0.45|0.75|0.45|0.75|
|N|6890|6053|6890|6053|6594|6053|5248|4813|

Robust standard errors clustered by county in parenthesis. ***, **, and * indicate statistical significance at the 1 _%_ , 5 _%_ , and 10 _%_ levels, respectively.

20
consumption brackets. Evidence abound in the HIES data that transfers from government in the form of child allowance, pensions and other social assistance programs are important components of household consumption (see next section). Transfers from family members abroad and within the country are also important sources of consumption in Azerbaijan. International donor supports are significant, particularly for the displaced population. The outcome of such measures would undoubtedly lead to reduction in inequality, while at the same time alleviating poverty. It is unlikely this explains away all the observed extremely low inequality in Azerbaijan, but it could be a contributing factor.

Secondly, the data generating mechanism may be systematically biased and does not represent well the various segments of the Azeri population. This scenario gives rise to the following three hypotheses:

- **Hypothesis 2:** The inequality in the data is suppressed due to the fact that the HIES data have fewer representatives of households in the upper income echelon. This situation could arise, for instance, if some households in the richest category, decline participation in the survey. From communications with Azeri officials with the knowledge of the HIES data, there is high likelihood and clear evidence that the rich households tend to decline responding to survey questionnaires.

- **Hypothesis 3:** Households in the lower income echelon are inadequately represented in the data. Several factors could lead to survey data unrepresentative of the poorest households such as their location. The poorest households tend to live in inaccessible locations and in areas with poor infrastructures such as roads. In such conditions, lack of or poor supervision of the interviewers and data collection process, may lead to replacement of the poor households with better off  ones in more accessible location or to many missing households in the data, most of which are poor.  Also it is not implausible to think the poor may decline participation in the survey.

- **Hypothesis 4:** The data do not fully represent both the upper and lower income echelons. In the scenario where households in the upper and lower welfare echelon are “truncated”, the resulting data would have unnaturally high homogeneity and therefore low inequality.

Thirdly, various forms of transfers and deficiency in the data generating mechanism could lead to the observed low inequality:

- **Hypothesis 5:** Combination of factors stated as conditions for hypotheses 1 through 4 are responsible for extremely low inequality in Azerbaijan.

Finally respondents, although they participated in the survey, were less forthcoming on the questions about their wealth. This is a common problem in most household surveys through out the developing world; Azerbaijan is not an exception. This led us to our final hypothesis:

- **Hypothesis 6:** Respondents, especially those participating households in the upper income echelon, were concealing their incomes and expenditures, thus depressing income and consumption disparity in the data.

7
In estimating equations [10] and [11], we use urban household disposable income as our measure of urban wages and rural household per capita income as our measure of rural wages. This is analogous to NEG-wage equation studies which use GDP per capita or worker as a proxy for wages in aggregate data (e.g. Redding and Venables, 2004; Au and Henderson, 2006; Hering and Poncet, 2010a). In contrast to both Au and Henderson (2006) and Hering and Poncet (2010a), the definition of urban implied by our proxy for urban wages includes all urban households in a prefectural region, even if they live outside the "city proper" (i.e. outside the "districts" of Chinese prefectures). Further discussion of the dataset used in the estimation of equations [10] and [11] can be found in Appendix A.

# _4.1. Econometric Issues_

Broadly speaking, the empirical NEG literature provides two strategies for the estimation of equations similar to [10] and [11]. The first is a two-stage strategy in which a gravity trade equation is estimated (Redding and Venables, 2004). This allows for the construction of a measure of RMA which is then used in the estimation of the wage equation. The second is a direct estimation strategy which aims to measure RMA much more directly, thereby cutting out the first stage of the two-stage strategy (Hanson, 1997). Generally, use of the gravity approach is not possible with within-country data because of the lack of domestic trade data, although with some heroic assumptions one could impute such data. Two-stage estimation has been applied to China by Hering and Poncet (2010a, 2010b). Internal bilateral trade flows have been constructed using Chinese regional input-output tables, but this can only be done at the provincial level, meaning that crucially, somewhat arbitrary allocation rules then need to be adopted to calculate RMA at the prefectural level. In our context, these rules would involve assuming, for example, that _G U_ and _G R_ are constant across regions within provinces (Hering and Poncet, 2010a, p 665). Second, and perhaps more importantly, the provincial input-output tables do not allow for an easy distinction to be made between urban and rural goods. This makes it difficult to calculate separate measures of RMA for the urban and rural sectors.

17
productivity, and can be modeled as, with _yi_ = _yi_<sup>_∗_if</sup><sup>_zi_= 1and</sup><sup>_yi_notobservedif</sup><sup>_yi_= 0.</sup> _xi_ is a vector including the individual, household, and location characteristics. In the selection stage take the individual characteristics of the household head, and include the variables gender, age, and education. In the outcome stage we take the individual characteristics of the enterprise owner, and include the same variables as in the selection stage. The households characteristics, access to credit and shocks, remain equal in both stages, as well as the location characteristics distance to the next population center.<sup>8</sup>

# **3.2.4 Panel Data Analysis**

Finally, we can make use of the panel properties for Nigeria and Uganda to estimate panel regressions.

Formally we estimate, where _{Yeht, Y_<sup> ̄</sup> _eht, Y_<sup> ̄ ̄</sup> _eht}_ is labor productivity at the enterprise level. The set _{_ **x**<sup>_′_</sup> _eht_<sup>_,_ ̄</sup><sup>**x**</sup><sup>_′_</sup> _eht_<sup>_,_ ̄ ̄</sup><sup>**x**</sup><sup>_′_</sup> _eht_<sup>_}_containsvectorsofindividualcharacteristicsoftheenterpriseownerat</sup> the enterprise level. The set _{_ **w** _ht_<sup>_′,_</sup><sup>**w** ̄</sup> _ht_<sup>_′,_</sup><sup>**w** ̄ ̄</sup> _ht_<sup>_′}_contains vectors of household characteristics</sup> observed at the household level. Finally, _{_ **z**<sup>_′_</sup> _eht_<sup>_,_ ̄</sup><sup>**z**</sup><sup>_′_</sup> _eht_<sup>_,_ ̄ ̄</sup><sup>**z**</sup><sup>_′_</sup> _eht_<sup>_}_isasetofvectorsofenterprise</sup> characteristics at the enterprise level. Let _{_ **_β_**<sup>_j_</sup> _,_ **_γ_**<sup>_j_</sup> _,_ **_δ_**<sup>_j_</sup> _}_ denote the associated vectors of coefficients for equation _j_ , where _j_ = 1 _,_ 2 _,_ 3. The _λ_<sup>_j_</sup> _t_<sup>aretimeeffectsforequation</sup><sup>_j_,</sup><sup>_μh_</sup> are fixed effects at level _k ∈{h}_ , _νh_ are random effects at level _k ∈{h}_ , and _u_<sup>_j_</sup> _eht_<sup>are</sup> idiosyncratic errors for equation _j_ .

As individual characteristics we include the gender, age, education of the enterprise owner, and the information if the owner is a migrant. As household characteristics we include the number of adults, shocks, and access to credit. As location characteristics we include firm size and months of enterprise operation.<sup>9</sup>

> 8 See Appendix B.4 for summary statistics and a detailed definition of the variables used in the Heckman selection model.

> 9 See Appendix B.5 for summary statistics and a detailed definition of the variables used in the panel data analysis.

11
|**Table 7: Per**|**Capita Inco**<br>|**me Classifica**<br>|**tion of Palan**<br>|**pur Household**<br>|**s by Caste i**<br>|**n 1983/4**<br>|
|---|---|---|---|---|---|---|
||Very Poor|Poor|Secure|Prosperous|Rich|%<br>(No. of<br>hhs)|
|Thakur|0.067|0.233|0.267|0.233|0.200|1.00<br>(30)|
|Murao|0.037|0.222|0.111|0.333|0.296|1.00<br>(27)|
|Dhimar|0.231|0.231|0.154|0.231|0.154|1.00<br>(13)|
|Gadariya|0.083|0.250|0.333|0.083|0.250|1.00<br>(12)|
|Dhobi|0.250|0.0|0.500|0.250|0.0|1.00<br>(4)|
|Teli|0.375|0.063|0.250|0.250|0.063|1.00<br>(16)|
|Passi|0.267|0.133|0.067|0.067|0.467|1.00<br>(14)|
|Jatab|0.632|0.263|0.105|0.00|0.00|1.00<br>(19)|
|Other|0.143|0.143|0.286|0.286|0.143|1.00<br>(8)|
|% of<br>households|22%|19%|20%|19%|20%|(143)<br>100%|

19
WPS 3202

# **Governance and Bank Valuation**

## Gerard Caprio, Luc Laeven and Ross Levine*

**Abstract:** Which public policies and ownership structures enhance the governance of banks?  This paper constructs a new database on the ownership of banks internationally and then assesses the ramifications of ownership, shareholder protection laws, and supervisory/regulatory policies on bank valuations. Except in a few countries with very strong shareholder protection laws, banks are not widely held, but rather families or the State tend to control banks. We find that (i) larger cash-flow rights by the controlling owner boosts valuations, (ii) stronger shareholder protection laws increase valuations, and (iii) greater cash-flow rights mitigate the adverse effects of weak shareholder protection laws on bank valuations. These results are consistent with the views that expropriation of minority shareholders is important internationally, that laws can restrain this expropriation, and concentrated cash-flow rights represent an important mechanism for governing banks. Finally, the evidence does not support the view that empowering official supervisory and regulatory agencies will increase the market valuation of banks.

**JEL Numbers** : G21, G34, K22, G28

**Key words** : Corporate Governance, Securities Law, Supervision, Regulation

### World Bank Policy Research Working Paper 3202, February 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Caprio and Laeven: World Bank; Levine: University of Minnesota and the NBER. We thank Thorsten Beck, George Benston, Sugato Bhattacharyya, Stijn Claessens, Patrick Honohan, Jack Kareken, Rafael La Porta, Phil Strahan, Anjan Thakor and seminar participants at Emory University, the Federal Reserve Bank of New York, and the University of Minnesota’s Carlson School of Management for helpful comments.