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35
national accounts data. Second, the vast majority of papers in the empirical growth literature examine growth over regularly-spaced spells of fixed length, typically five or ten years long, and with a much larger set of observations. This contrasts with our sample, which is much smaller and irregularly-spaced, as dictated by the limited availability of household survey data in particular years.

One way to assess the importance of these considerations is to re-estimate the results in Table 7, but replacing growth in household survey-based measures of average income with corresponding data from the national accounts. In particular, we use real per capita GDP growth from the national accounts, in order to be consistent with the empirical growth literature. However, we keep the structure of our irregularly-spaced panel of observations determined by the availability of survey data. For most variables, our findings are broadly similar to when we use household survey mean growth rates. In addition, for some of our social welfare functions, we find that the budget balance has a posterior inclusion probability greater than 0.5, and enters positively in the growth regressions, suggesting that higher budget deficits are correlated with slower growth. One further notable finding is that when we switch to national accounts growth rates, initial equality enters positively and significantly in roughly three-quarters of all specifications for the growth regression, suggesting that initial inequality is bad for subsequent growth. However, this does not imply that higher initial equality is good for growth in social welfare. This is because, as we have already seen in Table 7, higher initial equality is significantly correlated with subsequent declines in equality, and this effect is quantitatively larger than the positive effect on average income growth. As a result, the posterior probability-weighted slope coefficient on initial equality in the social welfare regression is still negative.

# **Conclusions**

This paper is motivated by the widespread concerns about inequality and its consequences that are often heard in current policy discussions. Our objective in this paper is to provide some simple descriptive evidence on the implications of observed inequality for changes in social welfare. By specifying a social welfare function, it is possible to quantify the welfare effects of changes in inequality, and compare them with the welfare effects of growth in average incomes. In particular, we exploit the fact that growth in a number of standard welfare functions can be decomposed into growth in average incomes, and growth in a particular measure of inequality specific to each social welfare function.

We implement these decompositions in a large dataset of high-quality data on income and its distribution across individuals, covering 117 countries over the past four decades. Using this data, we 26
# **Transport Policies and Development**

# **1. Introduction**

In developed economies, transport investments and improved transport technology over the last century have resulted in a continuous decline in transport costs, which in turn stimulated growth and economic development. In low- and middle-income countries, the current potential for transport investments and policies to boost sustainable and inclusive growth through declining transport costs also appears to be large. This is especially the case given significant backlogs of transport infrastructure investment in both rural and urban areas, weak governance and inadequate regulations in the transport sector, and rising social costs in terms of congestion, pollution and accidents, especially in emerging large cities. Transport investments can be very large and transformative in their nature, and their success depends on a variety of factors. Because these factors are not well understood and may not be taken into account by policy makers, there is often a risk that transport investments are not cost-effective and do not produce the range of expected outcomes. Thus, understanding and assessing how transport policies can produce growth-inducing effects and reduce social costs will be important for setting priorities in the strategic use of scarce resources.

The objective of this paper is to review the broader direct and indirect benefits and costs of transport investments and policies in developing countries. The literature on transport impacts is large and covers a variety of issues, each study shedding light on a specific aspect. The largest share of papers looks at road and, to a lesser extent, at rail transport. Air and sea transport tends to be much less studied and this review reflects this bias in the literature. Recent surveys on this topic focus on the impact of transport infrastructure on the spatial distribution of economic activity, productivity and economic growth (see Redding and Turner, 2014, and Deng, 2013) but do not systematically review the existing evidence from developing countries. They also tend to overlook issues relevant to developing countries which have implications for transport policies. Policy makers may, for instance, wish to guide or accompany the ongoing process of structural transformation (i.e. the movement out of agriculture into industries and services) with interventions in the transport sector. Another such issue is the fast pace of urban growth currently occurring in 2
sector general equilibrium model of an open economy.

We then consider two implementations of the PAMS framework. The first interpretation of this framework uses the ERH approach based on unit records to simulate the poverty impact of macroeconomic shocks and policies analyzed within a macroconsistency model such as the Revised Minimum Standard Model Extended (RMSM-X). The second interpretation is a reduced-form of the first whereby a CGE model is linked recursively (in a top-down fashion) to a poverty module built upon a parameterization of the Lorenz curve _à la_ POVCAL.

We then review a different macro-micro simulation model whereby the macroeconomy is represented by an IS-LM model.

Finally, we describe briefly the IMMPA framework, which links a dynamic CGE model including a financial sector to a distributional module that is analogous to PovStat.

_Given our distributional concerns, we must judge these models ultimately on the basis of how well they use the wealth of available information (from national accounts and household surveys) to explain the interdependence among sectors and institutions and the heterogeneity of actors_ . Naturally, in a particular environment, additional criteria come into play, such as the skills required of the analyst to implement a given approach.

# **_4.1. The 123PRSP Model_**

The 123PRSP model is based on the idea that the impact on poverty of macroeconomic shocks and policies is transmitted through two basic channels. The first channel involves distributional-neutral _changes in the average income_ , while the second is the change in the sectoral pattern of growth induced by _changes in relative prices_ (for example, changes in the real exchange rate). The overall impact is thus equal to the pure growth impact, plus the distributional impact.

This idea is implemented within a three-layer model. The _macro_ -layer has three components: (1) a financial programming model, (2) a trivariate vector autoregressive (VAR) model, and (3) a medium-term growth model based on cross-country regression analysis. The _meso_ - (or intermediate) layer is represented by a two-sector general equilibrium model of an open economy. Finally, the _micro_ -layer relies on the envelope theorem to model the distribution of welfare across households.

32
pull the consistency limit toward the result that induces the optimal conditional distribution implied by the weighted kernel across all penalties and weights, as judged by the out-of-sample criterion.<sup>1</sup> We use the framework together with out-of-sample selection of fixed effects, to model remotely sensed and reported environmental data for 95 countries that include 85% of the world’s population, 83% of global carbon output and 72% of all forest cover. We find that the production gradually favors conserving the earth’s finite resources as GDP increases, but our results are not supportive of a single Environmental Kuznets Curve. Rather, countries follows individual curves with heterogeneity in the location, shape, and height of tipping points, depending on the composition of the economy. Focusing the policy dialog on a single average curve may thus result in harmful economic outcomes, as the traditional EKC may provide a poor estimate of local conditions. Across different possible paths of development, we do observe that average development is associated with an inverted _U_ -shape in deforestation, pollution and carbon intensities of production units. Per capita carbon emissions, follow a _J_ -curve. We use the models to extrapolate environmental output forward to 2030 under alternative growth scenarios, allowing each country to progress further on it’s specific development path. These results highlight the daunting implications of continued development under the current standards of practice and remind us once more that immediate and sustained global efforts are required on multiple fronts to mitigate forest loss, improve air quality, and shift the global economy to a 2<sup>_◦_</sup> pathway.

The remainder of this paper is as follows. We discuss estimation methods in Section 2. Additional discussion on the role of the penalty in establishing identifiable uniqueness, and the interpretation of the estimation results are provided in our appendix. Section 3 details the data used for our empirical analysis in section 4. Finally in section 5, we use our empirical descriptions to explore the implications of continuation of growth on environmental output. Section 6 concludes.

# **2 Methods**

We use the Kernel Regularized Least Squares estimator developed in Hainmueller and Hazlett (2014). We aim to summarize the key features of non-parametric kernel estimation.

> 1That result is the same as the standard convergence toward the minimizer of the expected KullbackLeibler divergence Kullback and Leibler (1951) between the true conditional density of the data and the conditional density implied by the estimated model White (1994). Under that result, the estimator converges to the result that delivers the best approximation to the true distribution of the data.

3
# **Table 3b. Estimation results for Model 2 (household expenditures) with perceptions as conflict indicator**

||||**Bad Sec**|**urity Conditions**|**in Village/Neigh**|**borhood**|||
|---|---|---|---|---|---|---|---|---|
||(1)<br>HH Weighted ‐|(2)<br> <br>HH Weighted ‐|(3)<br> <br>HH Weighted ‐|(4)<br>HH Weighted ‐|(5)<br> <br>HH Weighted ‐|(6)<br> <br>HH Weighted ‐|(7)<br>HH Weighted ‐|(8)<br> <br>HH Weighted ‐|
||OLS|OLS|IV 2sls|IV 2sls|OLS|OLS|IV 2sls|IV 2sls|
|VARIABLES|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|Log per capita<br>Consumption|<br>Log per capita<br>Consumption|
|Demographics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Education|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Location|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Opium Income District|No|Yes|No|Yes|No|Yes|No|Yes|
|Poverty Headcount District (2007)|No|No|No|No|Yes|Yes|Yes|Yes|
|Season Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Foreign Aid (USD per 1,000 inhabitant)|‐0.00439|‐0.00870|‐0.00276|‐0.00707|0.0331|0.0285|0.0344|0.0298|
||(0.0375)|(0.0368)|(0.0388)|(0.0381)|(0.0459)|(0.0468)|(0.0467)|(0.0476)|
|Casualties (per 1,000 inhabitant)|‐0.165|‐0.170|‐0.162|‐0.167|‐0.114|‐0.120|‐0.112|‐0.118|
||(0.177)|(0.178)|(0.176)|(0.176)|(0.158)|(0.157)|(0.157)|(0.156)|
|International Troops (per 1,000 inhabitant)|0.00291|0.00277|0.00290|0.00276|0.00293|0.00284|0.00292|0.00283|
||(0.00473)|(0.00470)|(0.00464)|(0.00461)|(0.00438)|(0.00438)|(0.00430)|(0.00430)|
|Afghan Troops (per 1,000 inhabitant)|0.00533***|0.00543***|0.00534***|0.00544***|0.00667***|0.00666***|0.00668***|0.00668***|
||(0.00178)|(0.00175)|(0.00175)|(0.00173)|(0.00187)|(0.00186)|(0.00185)|(0.00183)|
|Observations|18,221|18,221|18,221|18,221|18,211|18,211|18,211|18,211|
|Adjusted R‐squared|0.366|0.369|0.366|0.369|0.379|0.380|0.379|0.380|
|**Joint Significance (Prob > Test)**|**0.0158**|**0.0113**|**0.00454**|**0.00270**|**0.0229**|**0.0207**|**0.00790**|**0.00676**|
|**Joint Significance(%)**|**98.4%**|**98.9%**|**99.5%**|**99.7%**|**97.7%**|**97.9%**|**99.2%**|**99.3%**|

Robust standard errors (Province) in parentheses *** p<0.01, ** p<0.05, * p<0.1 _Note:_ Compared to the previous specification, casualties are substituted by the share of households, at the province level, who raise concerns about bad security conditions in their village. Standard errors are clustered at the province level and are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks, respectively. Joint significance tests are performed for the four conflict indicators. That is an F test for the OLS specifications and chi2 for the IV 2LS. The preferred estimation is highlighted in yellow. HH stands for households, OLS for ordinary least squares, and IV 2SLS for instrumental variables two-stage least squares.

18
This record is impressive, but several factors suggest that the next decade will pose major challenges for NEPA and the EPBs. Continued rapid decline in pollution intensity will be necessary just to stay even with the pace of industrial growth. Moreover, recent findings on pollution-related health damage suggest that considerable _improvement_ in ambient quality would be appropriate. Faced with the simultaneous need to reduce pollution and increase industrial output and employment, the Chinese government has become very interested in cost-effective regulation. The current pollution control system is under scrutiny, because its peculiar mix of regulations and economic incentives bears little resemblance to a conventional emissions charge system.

NEPA regulations specify effluent standards by sector, and a schedule of fees (the pollution levy) to be paid by any enterprise whose effluent discharge exceeds the mandated standard. With the approval of NEPA, local areas may raise both standards and fees above national levels. Levies are charged only on the 'worst case' pollutant from each source.<sup>3</sup> This incentive system is supplemented by more traditional pollution control measures. Under the 'Three Simultaneous Steps' system, new enterprises are required to construct abatement facilities with capacity sufficient to meet the relevant effluent standards.

Chinese regulators are debating whether this mixed-instrument regime should be changed to a conventional emissions charge system. Since transition costs are likely to be high, there is strong interest in estimating the potential net gain from such a change. Theoretically, the gain could be substantial. Commonly-cited simulation results from the OECD economies suggest that total costs in a standards-based system can be several times those in a charge-based system.<sup>4</sup> For China, a well-informed judgment should be based on much better knowledge about actual abatement costs.

# 3. **THE** DATA

Our data are drawn from two sources: (1) the China Monitoring Station in Beijing, which monitors the 3000 factories currently rated as China's largest potential polluters; (2) the Tianjin Environmental Protection Bureau, which monitors industrial facilities in the Tianjin urban region. The available data bases include information on production, emissions by pollutant, abatement, abatement costs, and pollution-related penalties such as pollution levies, fines and compensation paid for damage. Relevant variables for the present study are summarized in Table 3.1.

For this exercise, NEPA has provided us with 1994 data for 200 factories scattered across China's urban/industrial areas. The Tianjin EPB has supplied data for 60 additional plants. The data base provides separate information for each emissions source. Many facilities have multiple sources, yielding a total sample size of 370 obsrvations. Although the data base is exceptionally complete, it records only three pollutants abated for each source. This is probably sufficient for most actual

- 3 For more extensive discussion of the pollution levy svstem. see Wang and Wheeler (1996).

4 See Wheeler (1992) 2
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- ti - Share of agriculture (farming, fishing and frestry),

- ta Share ef services (personal and business services, health care,

hotels, tourism, and so on),

- 3 Share of industry (manufacturing, mining, construction, and so

on), averaged over 1979-85. Ideally, I would also have experimented with the share of each sector in department _employment,_ but data were not available. It may be more appropriate to use sector shares in income, since it is the earning power<sup>of children</sup> that parents are concerned about, not hours worked. Using<sup>sectoral</sup> shares in departmental GDP as proxies for ti assumes that within<sup>each sector,</sup> the labor intensity of production does not change as sectoral output changes.

p- Changes in P5 are proxied by a _rural-urban dutwi_ variable. I assume that the price of schooling is lower .n urban than in rural<sup>regions.</sup> This could be because the average distance to school in rural regions is greater than that in urban regions.<sup>Thuswithin each department,</sup> the parameter P8 varies with region of residence.

Z2 The estimation in this section uses household data from the PLSS for subset Z2 variables, as follows: is 15Alternatively, it could be because rural areas are generally agricultural, and urban areas are more industrial. If children's labor is more valuable in agrarian economies, then the price of schooling would include the higher opportunity cost in rural areas. This effect<sup>confounds</sup> the structure of the economy with the rural-urban decomposition, and is ignored here.

21