ESTUDIO DE LAS NORMAS INTERNACIONALES DE INFORMACIÓN FINANCIERA (NIIF)

While highly informative about the empirical regularities regarding infrastructure provision across income groups, the analytical approach used to this point does not take into account the role of other factors, nor does it make any formal inferences about the observed empirical relationships. To deal with these shortcomings, more rigorous methods

2.4.4 Total infrastructure investment

0 2 4 6 8 10 Top25 Mid50 Low25 Top25 Mid50 Low25 Top25 Mid50 Low25 % of GDP Private infrastructure investment

Public infrastructure investment Low income

Lower-middle income Upper-middle income

Note: Horizontal bars show median infrastructure investment in each quartile of each income group. Vertical lines show median infrastructure investment for a whole income group.

are adopted in this subsection to better examine the relationship

between infrastructure, stage of development, and growth performance. The methodology is outlined in Box 2.4.1.

What do the results of this exercise reveal? The regressions confirm that lower-middle-income economies tend to have greater infrastructure stock than low-income economies, and that upper middle-income economies tend to have higher infrastructure stock than both groups (Table 2.4.2). This is true for most types of infrastructure.

A few results stand out that add nuance to the previous observations on infrastructure provision across development stages. One is the continued accumulation of mobile, internet, and energy infrastructure throughout and beyond the middle-income stage, as evidenced by the increasing size of the country income dummy coefficients.20 _The other, also based on these coefficients, is the tendency for telephone line, transport, and water supply and sanitation provision to level off following a run-up during the upper middle-income phase.

Regarding roads, a possible explanation is that during early stages of development, the focus is on expanding the road network, or building new roads where none existed. At later stages of development, however, the focus shifts to improving the quality and capacity of existing roads by widening and by converting provincial into national roads then into limited-access highways. This is not captured in the indicator, which measures only the length of the road network. Access to water supply and sanitation also tends to rise during early middle-income stages, and expansion in these services naturally tapers as access becomes nearly universal by the time an economy reaches high income or even upper- middle income.

2.4.1 Determinants of infrastructure, empirical methodology Panel regressions can control for factors other than level of development that may influence infrastructure provision in an economy. In the various specifications, the dependent variables are the different types of infrastructure stock. The key explanatory variables enter the regressions as dummy variables, representing income groups (with low- income economies serving as the omitted group) and growth performance (with the slowest-growing economies serving as the omitted group).a _{Following papers on the}

determinants of infrastructure provision such as Fay and Yepes (2003) and Ruiz-Nuñez and Wei (2015), the following equation is estimated (inclusive of a standard set of

controls):

, 0 1 , 2 , 3 , 1 50, 2 25, 1 , 2 , 3 , 4 5 ,

i t i t i t i t i t i t i t i t i t i t i t

I   LMIC  UMIC  HIC mid top  A  P U  D  D 

, 0 1 , 2 , 3 , 1 50, 2 25, 1 , 2 , 3 , 4 5 ,

i t i t i t i t i t i t i t i t i t i t i t

I   LMIC  UMIC  HIC mid top  A  P U  D  D  In this specification, I_i,t is the log of the level of

infrastructure stock in country i at time t (except for water and sanitation access, which are in percent of population); LMIC_i,t, UMIC_i,t, and HIC_i,t which are the income group dummies corresponding to lower-middle income, upper-

middle income, and high income, respectively; mid50_i,t and top25_i,t are the growth performance dummies; A_i,t is the percent share of agriculture in GDP; P_i,t is log of population density, defined as population per square kilometer of land area; U_i,t is the degree of urbanization, defined as urban population as a percent of total population; and D_i and D_t are respectively the economy and time fixed effects.b _{Measures of economic structure (percent share}

of agriculture in GDP), population density, and degree of urbanization are included as more industrialized, more densely populated, and more urbanized economies can be expected to have more infrastructure. Country and time fixed effects are included to control for systematic unobserved heterogeneity across economies and over time. a _{For completeness, dummies for high-income economies}

with GDP per capita in 2011 in PPP terms ≥ $17,600 are also introduced. Results are similar when regressions are estimated on a sample that excludes high-income economies.

b _{The results are similar when GDP per capita and its square are}

used in place of income group dummies. See Abiad, Debuque- Gonzales, and Sy (forthcoming) for details.

2.4.2 Sectoral infrastructure regressions using income group dummies Variables Telephone mainlines (1) Mobile phones (2) Internet users (3) Total roads (4) Rails (5) Electricity (6) Water access (7) Sanitation access (8) Lower-middle income 0.518*** 0.892*** 0.760*** 0.300*** –0.013 0.327*** 3.540*** 5.007*** (0.049) (0.125) (0.162) (0.023) (0.016) (0.031) (1.363) (0.798) Upper-middle income 0.900*** 1.390*** 1.602*** 0.339*** 0.067** 0.485*** 4.917*** 7.665*** (0.064) (0.164) (0.213) (0.042) (0.027) (0.046) (1.753) (0.885) High income 0.817*** 1.845*** 1.880*** 0.277*** 0.069** 0.600*** 4.027* 6.390*** (0.072) (0.244) (0.268) (0.046) (0.031) (0.054) (2.163) (1.011)

Mid 50% for growth 0.030 0.017 0.336*** –0.037* 0.033* –0.079*** 1.994 1.498**

(0.039) (0.129) (0.124) (0.020) (0.017) (0.026) (1.470) (0.684) Top 25% for growth 0.231*** 0.500*** 0.268* –0.138*** –0.011 –0.081** 3.861** 1.953*** (0.053) (0.184) (0.161) (0.033) (0.020) (0.040) (1.578) (0.632) Agriculture, share of GDP –0.033*** –0.081*** –0.071*** –0.011*** 0.000 –0.012*** –0.095 –0.148*** (0.003) (0.009) (0.010) (0.001) (0.001) (0.0020) (0.081) (0.027) Population density 0.164** 6.941*** 5.482*** 0.274*** 0.045 –0.302*** 21.519*** 6.401*** (0.076) (0.515) (0.447) (0.042) (0.038) (0.049) (3.094) (1.017) Urbanization 0.027*** –0.003 0.044*** 0.005*** 0.002* 0.020*** 0.308*** 0.440*** (0.003) (0.012) (0.014) (0.002) (0.001) (0.002) (0.102) (0.043) Constant –1.161*** –35.147*** –32.377*** –2.219*** –4.120*** 3.009*** –24.508* 31.082*** (0.320) (2.480) (2.166) (0.198) (0.127) (0.270) (13.818) (4.790) Observations 3,249 2,016 1,779 3,291 3,225 3,713 525 2,045 R-squared 0.968 0.915 0.931 0.978 0.981 0.978 0.965 0.993

Formal test of differences in coefficients:

Upper middle > Lower middle Yes Yes Yes Yes Yes Yes No Yes

High income > Upper middle No Yes Yes No No Yes No No

Top 25% > Mid 50% Yes Yes No No Yes No Yes No

*** = p < 0.01, ** = p < 0.05, * = p < 0.1.

Note: All regressions include economy and year fixed effects.

Source: Abiad, Debuque-Gonzales, and Sy, forthcoming.

The regressions confirm the positive association between growth performance and certain types of infrastructure stock. Most notably, better performers in the top 25% tend to have more ICT infrastructure. Economies in the top quartile for growth tend to have 25%–50% more telephone mainlines, mobile subscriptions, and internet usage than those in the bottom quartile. Fast-growing economies also seem to have 2%–4% more access to water supply and sanitation. Exceptions to the positive relationship are rail provision, which seems unrelated to growth performance, and energy and roads, where fast-growers seem to have 8%–14% less provision. Coefficients on the control variables generally hold the correct signs in both sets of regressions, with higher population density and higher degree of urbanization associated with significantly more infrastructure and greater share of agriculture with significantly less.

To see which set of economies is responsible for the relationship between good growth performance and the accumulation of certain types of infrastructure, a similar set of regressions is estimated using

subsamples representing the different income groups.21 _Consistent with the earlier assumed facts, the relationship appears to be primarily driven by the faster-growing middle-income economies in the case of ICT (Table 2.4.3). This group of economies also seems to be largely behind the positive association between growth performance and access to water supply and sanitation.

All things considered, the stylized facts and regression results suggest a hierarchy of needs where economies are more likely to invest in basic infrastructure such as water supply and sanitation, roads, power, and telephone mainlines during early stages of development. Economies then seem to turn their attention to advanced infrastructure such as mobile and internet connections when they reach the upper- middle income, with power continuing to be a priority. Fast-growing economies within each income group tend to have higher infrastructure stock than their slower-growing counterparts for certain types of infrastructure, notably ICT.

The macroeconomics effects