Fiscal Policy, Infrastructure Expenditure and Growth: Sharing experience from Latin America

Luis Serven, Research Manager, DEC

Workshop: Modeling Fiscal Policy, Public Expenditure and Growth linkages, June 14-15,

2006

Background

Work focused on Latin America Motivation: perception that fiscal adjustment has led to a steep decline in productive public

expenditure – specifically infrastructure. Private sector entry insufficient to offset the decline, hence potentially adverse growth effects. Concern with myopic fiscal rules targeting liquidity and ignoring intertemporal dimension of

solvency Research program including

Analytical papers Empirical cross-country work Country studies (Brazil, Colombia…)

Selective summary

1. Assembling infrastructure spending data2. From expenditure flows to asset stocks3. The output contribution of infrastructure4. Country simulation models

Infrastructure expenditure

Very limited data availability: only Central Gov expenditure from GFS – unreliable because of (i) decentralization and (ii) PEs.

Collect data from national sources for 7 countries (ARG, BRA, BOL, CHL, COL, MEX, PER), 1980-2000.

4 different sectors: roads, railways (if applicable), power, telecommunications.

public and private investment No luck with O&M

Infrastructure expenditure

A casualty of fiscal adjustmentLatin America

Infrastructure expenditure

Primary deficit and public infrastructure investment (% GDP)

-5.00%

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

1995 1996 1997 1998 1999 2000 2001 2002 2003

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

Primary deficit (left scale)

Infrastructure investment (right scale)

Brazil

Latin AmericaTotal (public +private) investment in

Infrastructure

(weighted average of 7 countries, percent of GDP)

Infrastructure expenditure

• What matters is the availability / quality of services, not (necessarily) the volume of expenditure.

• Their trends may be very different -- due to changing efficiency, corruption, waste…(Pritchett, Tanzi etc)

Expenditure flows and asset stocks

Capacity change

Investment

Brazil: the power sector

Expenditure flows and asset stocks

• Easterly and Servén (2003): regression approach relating the trajectory of infrastructure asset stocks to investment flows.

• Simple-minded ARDL models (to allow time-to-build)

• Kt = {power generation capacity in Gw; Km of roads (+railways); # of phone lines}

• It = total investment (also private and public separately)

• Panel data for 8 countries / 20 years; fixed effects (+time effects)

Expenditure flows and asset stocks

1( ) ln ( )( / )t t t tA L K B L I K

• Results: can explain very well telecom (R2 = .7-.8), fairly well roads / rail (R2 = .4-.6), power not so well (R2 = .2-.3).

• For roads, some evidence that private investment makes a bigger contribution to stocks (= lower unit cost)

• Other regressions relating measures of service quality (e.g., phone faults, power losses…) to private sector participation – with mixed results: positive for telecom, negative for power…

• Heterogeneity of assets potentially a big problem here (e.g., private sector only does “easy” roads)

Expenditure flows and asset stocks

• Ferreira (2005) does similar exercises for Brazil. He gets very close tracking for telecom, fair for power, poor for roads.

• Unresolved major question: how does the stock / flow link depend on governance and fiscal institutions – or other country features ?

One way to address this would be through country-specific (rather than pooled) estimates -- e.g., a RCM. But that requires broader cross-country data coverage...

Expenditure flows and asset stocks

Standard Cobb-Douglas specification imposing CRS:

Panel data for 90+ countries, 1960s-1990s

h = years of secondary schooling (other measures also)

z = physical infrastructure measures (km of roads, phone lines, power generation capacity Gw)

Endogeneity a major problem. 2 empirical approaches:

-- GMM (large N asymptotics: Arellano-Bond etc) with internal instruments as well as demographic instruments

-- Panel time-series approach (large N and T: Mark-Sul, Philips-Moon) w/ unrestricted cross-country heterogeneity

The output contribution

ititititititittiitit lzlhlkbaly )()()(

The output contributionTable 3.4

Alternative GMM estimates(Dependent variable: log GDP per worker)

1 2 3 4

Model specification Differences Differences Differences SystemInstruments Levels t-2 Levels t-3 Demographics Levels +diffs

Physical capital 0.363 0.361 0.351 0.222(10.832) (11.034) (7.903) (7.867)

Secondary schooling 0.148 0.169 0.159 0.222(3.361) (3.815) (3.443) (5.520)

Electricity generating capacity 0.112 0.123 0.177 0.109(1.809) (2.148) (2.468) (2.970)

Roads 0.119 0.117 0.105 -0.005(2.197) (2.'195) (1.241) (0.084)

Main phone lines 0.151 0.140 0.138 0.147(3.634) (3.236) (3.168) (6.164)

Wald test of joint significance (p-value) 0.000 0.000 0.000 0.000Sargan test (p-value) 0.319 0.312 0.141 0.0021st-order autocorrelation (p-value) 0.111 0.106 0.143 0.5552nd-order autocorrelation (p-value) 0.793 0.794 0.888 0.778Number of observations 3232 3232 3232 3232Number of countries 101 101 101 101

Note: All variables are measured per worker and (except for schooling) expressed in logs. Heteroskedasticity-consistent T-statistics in brackets.

The output contribution

Lag selection criterionImposed

(1,1) SBC PIC

Physical capital 0.299 ** 0.282 ** 0.276 **0.025 0.023 0.023

Years of Secondary Schooling 0.036 * 0.051 ** 0.054 **0.019 0.020 0.019

Electricity Generation Capacity 0.082 ** 0.066 ** 0.074 **0.018 0.019 0.018

Roads 0.043 * 0.047 ** 0.060 **0.025 0.024 0.022

Main Telephone Lines 0.028 * 0.049 ** 0.046 **0.015 0.016 0.015

Cross-section independence test (p-value) 0.010 0.385 0.240Parameter homogeneity test (p-value) 0.000 0.429 0.365

Observations / countries 3382 / 89 3382 / 89 3382 / 89

Notes:The dependent variable is real GDP. All variables are expressed in logs and relative to the labor force.Standard errors under each coefficient computed using the QSPW method of Andrews and Monahan (1992)(**) significant at the 5% level; (*) significant at the 10% level

Table 4. Infrastructure-augmented production functionPanel DOLS estimates controlling for common factors

Lag selection subject to a maximum of 1 lead and 1 lag.

Significant contributions of infrastructure in both cases – but smaller coefficients (as much as 50%) in the panel time-series approach.

Tests of country-specific vs pooled estimates do not show much evidence of cross-country parameter heterogeneity – in general, null of homogeneity cannot be rejected…

The output contribution

Focus on assessing alternative fiscal strategies / fiscal rules

2 applications: Brazil & Colombia

• Brazil (Ferreira and Gonçalves 2005): main concern is the interplay of fiscal rules, investment and growth.

• Colombia (Suescún 2005): upcoming fiscal correction to accommodate pension deficit.

Models of intertemporally-optimizing infinitely-lived agents, with endogenous labor supply.

…but model details differ.

Simulation models

Brazil model

Questions:

(1) Would it make sense to raise public infrastructure investment ?

(2) If so, what form of financing should be used ? Cuts in other expenditure, taxes or debt ?

Main conclusion from simulations: the best strategy for growth and welfare is an investment increase financed by reducing public consumption.

Brazil model

• Three reproducible factors: infrastructure capital, business capital and human capital

• Infrastructure capital can be supplied by the government or the private sector (imperfect substitutes)

• Market-determined user fees on infrastructure services (infrastructure expansion may cost revenues ! [Uruguay])

• Public consumption does not yield utility• Learning-by-doing human capital externality • Solvency assured by tax rates that depend on public debt

Colombia model

Question: should the adjustment be financed via tax hikes or infrastructure investment cuts ?

Simulations show investment cuts yield lower growth in long run (about 1% less per annum ) – i.e., the cost of lower public capital outweighs the gain from lower real interest rates allowed by reduced indebtedness.

The less easily substitutable public infrastructure, the deeper the slowdown.

Colombia model

End

When can public investment pay for itself ?

The marginal impact on net worth of a deficit-financed rise in the public investment ratio:

Tax ratio

User charge on public services

Unit maintenance cost

Purchase cost of capital (including corruption, waste etc…)

Marginal cost of borrowing

Marginal product of public capital

rpmK

Y

id

nwdK

K

Y

r

p

m

K

When can public investment pay for itself ?

Perotti (2004): In most industrial countries, no – marginal return is too low.

But marginal return should be higher with lower stocks.

Ferreira (2005): In Brazil, yes – depending on slope of cost of borrowing.

Estache (2005): In many African countries, yes.