rafmacalaba/datause-encoder-data huggingface dataset:
23 also reduces the growth rate of wages for the rich, it also increases the incentives for the rich to save when young. The net effects on aggregate savings may not cancel out exactly.

The results with a--I are reported in Table 6. They are extremely similar to the base case. In any case, the net effect of raising a from 0.8 to 1 is to reduce slightly the advantages of the redistributive PAYG pension system for the poor, and hence to increase slightly their welfare gains from the regime change.

Other sensitivity analyses not reported here show that other parameter changes do not affect in any significant way the base-case results.

# 4.3 The Cost of Supporting the Poor in the Short Run

This section offers a back-of-the-envelope estimation of the feasibility of compensating the poor in the short run, using the 40 percentage points of GDP that are available from the increase in the netional debt The first point to be made is that the increase in the national debt has macroeconomic effects that have already been taken into consideration to find the final steady state. The factor prices achieved in the long run will be no different if the resources obtained by the govenmment with this increase in debt were thrown to the sea or were spent in a program that helps the poor alive during the transition, given that those poor do not leave bequests to their descendants.

The starting point of our estimate is that the wage bill earned by the poor as a percentage of GDP is Bzsp, for the case of ca = 1, and is even less if _a_ = 0.8.

In second place, the transfers lost by the transition poor due to the dissapearance of the redistributive feature of the social security system are at most equal to the basic pension. In tum, the basic pension is at most equal to LW(652,z, a), the last labor earnings of the poor.

In one year, the fiscal cost of the basic pension received by the poor - excluding those received by the middle and high income classes - is bounded above by the following expression: Fisal C c• Wage Bill of all Poor x M65.Pz.cr) N poor old GNP GNP Wage Bill of aLl Poor The first ratio is in turn bounded above by B2zsp. The second ratio is the following function of demographic, experience and income distribution variables:

> <sup>0</sup> LW NLpQQoor old. = w.r **Ws- 65.-** f f<sup>1</sup> 65 Wage Bill of the Poor w- z- a = 21 to 65 epal (+n) a
<!-- Start of picture text --> Figure 5<br>China<br>CO2 Intensity of Cement<br>(Metric Tons of CO2 per metric Ton of Cement)<br>.90<br>.85<br>.80<br>.75<br>.70<br>.65<br>.60<br>.55<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>Source: Rock and Cui (2011)<br>Figure 6<br>China<br>Cement Production and CO2 Emissions<br>(Million Metric Tons)<br>2,000<br>1,600<br>1,200<br>CO2 from Cement<br>Cement Production<br>800<br>400<br>0<br>80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10<br>Source: Rock and Cui (2011)<br>CO2 Intensity<br><!-- End of picture text --> In iron and steel, figure 7 shows that following the large scale technological changes in China’s iron and steel industry (Rock and Jiang 2013), the CO2 intensity of production fell from 6.2 metric tons of CO2 per metric ton of steel in 1985 to 2.3 metric tons of CO2 per metric ton in 2009.<sup>94</sup> Given the very large increase in steel production, this technological shift “saved” 2.25 billion metric tons of CO2 in 2009 relative to our counterfactual point of comparison (figure 8).

> 94 At the same time, energy efficiency in iron and steel production improved from 1,662 kgce per metric ton of steel in 1985 to 1,016 kgce per metric ton of steel in 2007 (Rock and Jiang 2013).

23
Second, an often overlooked implication of the “grease the wheels” hypothesis is heterogeneity across firms in the relationship between bribe demands and policy implementation times, with speed of implementation increasing with firms’ willingness to pay. While the association between bribe demands and policy implementation times is heterogeneous across firms, with larger firms facing longer delays, the data do not seem to support the hypothesis that some firms benefit from being asked for bribes. To start with, even firms that are likely to pay a substantial amount in bribes tend to face delays when confronted with bribe requests. Second, the distribution of policy implementation times for firms that indicated being asked for bribes is not bimodal, but unimodal instead. Last but not least, quantile regressions suggest that, when confronted with bribe demands, firms at the bottom end of the waiting time distribution face delays which are as long as those faced by firms at the upper end of this distribution.

Third, the data also reject the hypothesis that when regulation is more burdensome, opportunities to bribe are especially beneficial in accelerating policy implementation. We do not find evidence that the association between bribe demands and delays varies with how burdensome _de jure_ regulation as proxied by the Doing Business indicators is.

To sum up, although we cannot establish causality and have to be cognizant of the limitations of self-reported subjective cross-sectional data, our results are at odds with the proposition that corruption helps mitigate bureaucratically induced inefficiency. Instead, in the World Bank Enterprise Surveys data corruption is consistently associated with protracted policy implementation.

26
a dominant position in the output market and a firm's costs. In producer theory the cost function of a competitive firm is assumed to be the minimum cost of producing a level of output with given input prices.52/ When moving beyond the competitive market, all firms are still assumed to be profit maximizers, which justifies borrowing the ost function from the competitive market and attributing cost minimizing behavior to the monopolistic firm (or imperfectly competitive firm) as well.53/ Under these assumptions traditional microeonomic analys.s cannot provide any understanding of why monopolistic firms, or other firms enjoyiag a certain degree of market power, may not be operating at minimum cost. As a consequence, little theoretical work has been done to describe how output markets can affect a firm's cost function.54/ 30. Leibenstein first coined the term X-inefficiency to describe the phenomenon that a firm may not be operating at minimum cost.55/ Although X-inefficiency described the lack of cost minimization in private firms, the concept is also appropriate for studying the inefficiency of public firms. Many public enterprises are natural monopolies, or monopolies protected by regulation. Fxamples of natural monopolies in transport include railroads or bridges; an example of monopoly by regulation could be urban transport. The lack of competitors or potential competitors in the output market, or the lack of substitutes can be a cause of X-inefficiency in these firms.56/ One explanation of X52/ Hal Varian, Microeconomic Analysis, W.W. Norton & Company, New York, 1984, p.<sup>21</sup> .

53/ Ibid, p. 80.

54/ J. Tirole, The Theory of Industrial _4anization, MIT Press, Cambridge MA, 1988, p.78.

55/ H. Leibenstein, 'Allocative Efficiency vs. 'X-Efficiency', " American Economic Review, Vol. 56, 1966, p. 392-415.

56/ See Op Cit, Vickers and Yarrow, p. 53-76 for possible limitations of the effectiveness of competition to provide incentives for internal efficiency in private firms as well: in-contestable markets, strategic entry deterrence, and predatory behavior.

22
**Table 3: Contribution to Non - Oil GDP (percent)**

||**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|
|Agriculture|55.3|54.5|53.5|52.3|51.1|49.9|
|Solid Mineral|0.35|0.36|0.36|0.37|0.44|0.40|
|Manufacturing|4.97|5.01|5.01|5.02|5.01|4.99|
|Telecommunication|1.61|1.92|2.35|2.87|3.53|4.38|
|Finance & Insurance|5.50|5.21|5.00|4.80|4.61|4.43|
|Wholesale and Retail Trade|17.40|18.19|19.16|20.16|21.1|21.71|
|Buildingand Construction|1.95|2.01|2.08|2.14|2.22|2.30|
|Others|13.12|12.98|12.72|12.47|12.25|12.06|

_Source: WDI/IMF Reports_ 11. Since 2001, changes in the services sector have led a structural change in Nigeria’s economy, manifested in substantial growth of the telecommunications, transportation, hotel and restaurants, construction and real estate, and financial sectors.

12. The fastest-growing sector has been telecommunications (at an average rate of over 30 percent), followed by the wholesale and retail sectors (about 15 percent) and construction (about 13 percent). Solid minerals grew by over 10 percent on average and manufacturing by about 8-9 percent. Agriculture grew by 6-7 percent on average, the strongest sustained growth performance in more than a decade.

13. Notwithstanding Nigeria’s strong economic performance over the past 10 years, its export and production structure has shown little diversification. Nigeria’s exports are concentrated in oil and gas (98 percent), while the structure of the non-oil economy is dominated by the agriculture, wholesale, and retail sectors that serve the domestic market.

8
Roldós (1997). For the case of Guatemala, this means that changes in the index of quality of capital, _zq_ , are computed as a weighted average of investment of the four broad asset groups. These are (1) public and private construction, (2) imported capital goods and investment in machinery and equipment, (3) domestically produced capital goods, and (4) cultivated assets and major improvements to land. The formula used is: where _Ki_ is the respective capital stock and the weights _vi_ are the relative capital rental rates. The index reflects changes in the composition of capital. If _all_ components of the capital stock are growing at the same rate, quality remains unchanged. If _components_ of the capital stock with higher capital rents are growing more rapidly, quality increases. Since data on the rental rates _vi_ is not readily available for Guatemala, estimates of these are, following Roldós (1997), based on the arbitrage relation: where _Pi_ is a price index, _δ Z_ , _i_ the depreciation rate, and _rt_ is the economy-wide real interest rate. The price indices for the respective asset groups are taken from the Morán and Valle (2002) database. In order to take into account the volatility of the real exchange rate, which affects directly the relative price of the four types of capital, and to correct for measurement bias, the final series are smoothed by a 3-year moving average.

# **_Quality of Labor_**

To quantify labor quality, an index _hq_ is computed as a weighted average of labor within different levels of education. This formulation is consistent with the growth accounting literature that makes adjustments for education. It allows a more accurate indication of the contribution of labor to production. The index _hq_ is defined as follows: where _Lj_ is the labor force with education level _j_ (primary, secondary and tertiary) and _wj_ are the weights for the respective schooling level. The weights measure how the productivity effect of schooling varies with the level of education and are taken from the later regression analysis (Table 6). Interestingly, they correspond approximately with the private returns to schooling at each education level, as presented by Psacharopoulos and Patrinos (2002) for Guatemala.

Following OECD (2001a) another possibility to compute an implicit labor quality index would be to assume direct relations between skills and occupations, to rank

-26-
# **Table 4. Investor Protection and the Effect of Governance on Short-Term Debt**

This table presents the estimates of the following regression.

_stditjk = β0 + β1 x govit + β3 x govit x investorjt + β2 x investorjt + Ω x controlsi,t-1 + αj + λt + θk + εitjk._ The dependent variable is short-term debt and leverage is instrumented by lagged short-term debt. Each column reports the results for a different governance measure. The definition of the variables is in Appendix. We winsorize all variables at the 1% level except the governance variables. We use one-year lagged values of firm and country-level control variables. All specifications include country, year and industry fixed effects. The z-values from 2SLS approach are reported in parenthesis. Robust standard errors are estimated by clustering at the firm level.<sup>***</sup> ,<sup>**</sup> , and<sup>*</sup> indicate significance at 1%, 5%, and 10% levels, respectively.

||(1)|(2)<br>D|(3)<br>ependent variable|(4)<br>:_std_|(5)|
|---|---|---|---|---|---|
|governance measure(gov):|_gov44_|_gov_board_|_gov_indep_|_size_eff_|_ceo_chair_|
|gov|0.424|0.798|0.208|0.133|-0.074|
||(0.662)|(1.461)|(0.910)|(1.541)|(-0.950)|
|gov x_investor_|-0.192**|-0.205***|-0.085***|-0.026**|0.005|
||(-2.200)|(-2.792)|(-2.621)|(-2.265)|(0.506)|
|_investor_|0.107**|0.105**|0.043|0.073*|-0.061|
||(1.969)|(2.282)|(1.418)|(1.909)|(-1.385)|
|_leverage_|<br>-4.820***|<br>-4.798***|<br>-4.778***|<br>-4.763***|<br>-4.737***|
||(-17.136)|(-17.220)|(-17.292)|(-17.197)|(-16.309)|
|_size_|<br>0.041***|<br>0.044***|<br>0.036***|<br>0.032***|<br>0.020***|
||(4.929)|(5.228)|(4.578)|(4.082)|(2.621)|
|_asset_tangibility_|0.454***|0.450***|0.443***|0.453***|0.403***|
||(5.178)|(5.158)|(5.096)|(5.199)|(4.492)|
|_asset_turnover_|0.015|0.015|0.012|0.013|0.016|
||(0.746)|(0.766)|(0.586)|(0.665)|(0.810)|
|_roa_|<br>-1.185***|<br>-1.179***|<br>-1.173***|<br>-1.174***|<br>-1.145***|
||(-13.360)|(-13.398)|(-13.394)|(-13.391)|(-12.681)|
|_mb_|<br>0.034***|<br>0.032***|<br>0.031***|<br>0.030***|<br>0.027**|
||(2.980)|(2.870)|(2.747)|(2.664)|(2.348)|
|_gdp_growth_|0.002|0.004|0.004|0.009|0.010|
||(0.193)|(0.536)|(0.509)|(1.031)|(1.160)|
|_inflation_|0.036***|0.040***|0.039***|0.035***|-0.003|
||(2.842)|(3.172)|(3.146)|(2.903)|(-0.205)|
|_deposits_|<br>0.005***|<br>0.005***|<br>0.005***|<br>0.005***|<br>0.005***|
||(3.072)|(3.073)|(3.227)|(2.956)|(3.275)|
|_bonds_|-0.002|-0.002|-0.002|-0.001|-0.002|
||(-0.876)|(-0.728)|(-0.873)|(-0.345)|(-1.187)|
|_stock_market_cap_|-0.004***|-0.003***|-0.004***|-0.003***|0.001|
||(-4.170)|(-3.845)|(-4.675)|(-3.286)|(1.223)|
|_stockmarketturnover_|-0.000|-0.000|-0.000|-0.001|0.000|
|____|(-0.112)|(-0.443)|(-0.738)|(-0.939)|(0.785)|
|Constant|<br>1.627***|<br>1.328***|<br>1.892***|<br>1.207***|<br>1.767***|
||(3.008)|(2.835)|(4.834)|(2.802)|(3.537)|
|Observations|24,409|24,409|24,409|24,388|21,715|
|R-squared|-7.407|-7.333|-7.269|-7.260|-7.354|
|Industry, Country and Year FE|Yes|Yes|Yes|Yes|Yes|

23
48 Table 6. Annual NACs for all 75 covered agricultural products by country group, 1972–1976, 1984–1988, and 2005–2009

||**1972**|**1973**|**1974**|**1975**|**1976**|**1984**|**1985**|**1986**|**1987**|**1988**|**2005**|**2006**|**2007**|**2008**|**2009**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**World importers**|1.51|1.31|1.23|1.40|1.60|1.73|1.88|2.06|2.24|1.88|1.56|1.43|1.37|1.28|1.30|
|**World exporters**|0.95|0.83|0.81|0.87|0.80|0.86|1.00|1.17|1.16|1.05|1.15|1.12|1.14|1.08|1.07|
|High-income countries|1.46|1.34|1.26|1.40|1.61|1.77|1.91|2.19|2.37|1.97|1.41|1.32|1.28|1.21|1.19|
|Developing countries|1.02|0.88|0.86|0.95|0.93|0.99|1.09|1.10|1.11|1.05|1.23|1.18|1.17|1.11|1.15|
|Asia|1.30|1.04|0.98|1.09|1.16|1.29|1.45|1.46|1.42|1.36|1.45|1.50|1.37|1.21|1.38|
|Africa|0.90|0.80|0.78|0.83|0.80|0.80|0.85|0.90|0.97|0.91|1.16|1.08|1.10|1.07|1.04|
|Latin America|1.01|0.92|0.92|1.08|1.06|1.10|1.28|1.16|1.03|0.99|1.04|1.03|0.93|0.91|1.03|

Source: Updated from Anderson and Nelgen 2012a, based on NRA estimates from Anderson and Nelgen 2012b.

Note: NAC = 1 + NRA/100. The national NACs are unweighted averages. International agricultural prices spiked upwards in 1974 and 2008 and downwards in 1986.
# **2. Ex Ante Governance Criteria for Utility Regulators**

There is an extensive literature on regulatory governance both in general and as it should be applied to utility regulation.

The modern literature on the latter effectively starts with the seminal 1994 paper by Levy and Spiller in which they emphasize the distinction between (a) regulatory governance arrangements and (b) regulatory content (ie what was regulated and how)<sup>8</sup> . On regulatory governance, Levy and Spiller drew attention _both_ to the need for governance arrangements that were sufficiently robust to provide adequate protection for private investors as well as consumers _and_ were securely located in the economic, political, constitutional and legal arrangements of individual countries.

This view became (and remains) the dominant paradigm within which thinking about regulatory arrangements for electricity and other infrastructure industry has developed. However, since then the literature on governance has developed considerably both conceptually and empirically.

In this section, we consider what governance features have been considered most important for effective utility regulation.

## **2.1 Levy and Spiller 1994**

Levy and Spiller (1994) define the governance structure as “the mechanisms that societies use to constrain regulatory discretion and to resolve conflicts that arise in relation to these constraints.”<sup>9</sup> Levy and Spiller do not, however, make substantive suggestions about the required nature of these mechanisms other than to emphasise the need for them to correspond to the institutional endowment of the countries in question. Indeed, they take the view that there may be a variety of types of regulatory arrangement that meet the requirements set out above and which will support private investment. This is shown in the following quotation from the introduction to their paper: “...we argue that performance can be satisfactory with a wide range of regulatory procedures as long as three complementary mechanisms restraining arbitrary administrative action are all in place: (a) substantive restraints on the discretion of the regulator, (b) formal or informal constraints on changing the regulatory system and (c) institutions that enforce the above formal – substantive or procedural – constraints”<sup>10</sup> .

In the empirical section of their paper (which concentrates on telecom regulation), Levy and Spiller concentrate on examining issues such as how easy or difficult it was in the five countries examined (a) to change primary legislation; (b) to change licences (or franchise/concession contract) terms; (c) the quality of the regulatory

> 8 The Levy and Spiller paper (and the regulatory governance-content distinction) drew directly on the work of Douglass North and the new institutional economics of Oliver Williamson.

> 9 See Levy and Spiller  (1994), p 463.  All page references are to the reprint of the original Journal of Law, Economics and Organisation article in the Oliver Williamson and Scott Masten 1999 book of reprinted papers ‘The Economics of Transaction Costs’.

> 10 See Levy and Spiller op cit, pp 461-2.

6
In this paper we take advantage of the 2016 HCES/WMS data which is representative for the droughtprone lowlands unlike earlier rounds, and provide estimates of the vulnerability to poverty for the drought-prone lowlands relative to the other ecological zones of Ethiopia using a two-level hierarchical regression model first proposed by Guenther and Harttgen (2009). As in Demissie and Kassie (2017), the Gunther and Harttgen model uses cross sectional data to get _ex ante_ estimates of the mean and variance of consumption, but it also allows the distinction between idiosyncratic (household level) and covariate (community level) shocks. In addition, the model also allows the decomposition of vulnerability into poverty-induced and risk-induced vulnerability.

To the best of our knowledge, this constitutes the first study on vulnerability focusing on the drought-prone lowlands. We find that the incidence of vulnerably in the drought-prone lowlands is two times the incidence of poverty– 56 percent versus 27 percent. Although the poverty rate is relatively higher in the drought-prone lowlands than any of the other ecological zones, the prevalence of vulnerability is remarkably higher. The incidence of poverty and vulnerability for the nation as a whole is 23 percent and 38 percent, respectively. While both poverty induced (vulnerability due to low average consumption) and risk induced (vulnerability due to high consumption variability) vulnerabilities are higher in the drought-prone lowlands, the relative importance of poverty induced vulnerability is higher in the drought-prone lowlands than in other ecological zones – the ratio of poverty induced vulnerability to risk induced vulnerability is 1.5 in the drought prone low lands as opposed to 0.9 for the whole country. Similarly, although vulnerability rates due to both covariate and idiosyncratic shocks are higher in the drought-prone lowlands, the relative importance of vulnerability associated with covariate shocks is higher in the drought-prone lowlands than in the rest of the country.

The remainder of the paper is organized as follows. In section 2, we present an intuitive explanation of the distinction between poverty and vulnerability and in section 3, we discuss the estimation technique applied. Section 4 describes the data, and section 5 presents the estimation results and discussion. Section 6 concludes.

# 2. Distinction between poverty and vulnerability to poverty

The “vulnerability to poverty” measure employed in this paper rests on the characterization of welfare (measured by income or consumption) by its mean and variance. Figure 1 summarizes these 4
<!-- Start of picture text --> Sheps ro Oe> < OOP<br>ra 0 oo) ° 5 ~~ . 7<br>Y chy 8 4 a7 { Da|<br>> Bred bv \ J<br>Legend M . | Pe LSS<br>© Settlements [J Aragatsotn [| Shirak<br>Roads [| Ararat [| syunik ,<br>[__] Armenia_Border_poly | | Armavir [| Tavush on ed<br>ArmeniaMajor_LakesMarzes {||__| GegharkunikKotayk [| VayotsYerevan DzorCi R,<br>0 15 30 60 90 1 20<br>es Kn a x<br><!-- End of picture text -->