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41
limited: with a score A found in only 5 percent of the dimensions assessed and score B in 4 percent. The three small states in the group with the best all round scores also displayed the greatest strengths in debt management capacity. They met the minimum requirement for an average of 54 percent of the dimensions assessed and surpassed it in 22 percent of which score B accounted for 13 percent and score A for 9 percent. In some instances the assessments revealed wide disparity in capacity across different aspects of debt management. As an example, one small state failed to meet the minimum requirement in 78 percent of the dimensions assessed. But, for the remaining 22 percent half were scored A.

Differences in debt management performance of small states due to geographic location were negligible: on the contrary, the assessments found striking similarities in debt management capacity of small states in all parts of the globe. Small states in the Sub-Saharan Africa were found to have the strongest debt management capacity: they met the minimum requirement for, on average, 36 percent of the dimensions assessed. In South Asia small states, where debt management capacity was assessed as weakest, 29 percent of assessed dimensions met the minimum requirement on average. The Caribbean also had the highest percentage of A and B scores, closely followed by Sub-Saharan Africa. Overall however, the difference in scores across regions was limited (Figure 3).

# **Figure 3: Debt Management Capacity – Comparison by Region**

<!-- Start of picture text --> 0 20 40 60 80<br>30<br>East Asia<br>25<br>and Pacific<br>20<br>Latin America<br>A<br>and Caribbean 15<br>B<br>South Asia 10 C<br>5<br>Sub-Saharan<br>Africa 0<br>East Asia Latin America South Asia Sub-Saharan<br>D C and above and Pacific and Caribbean Africa<br><!-- End of picture text --> Source: DeMPA reports for 17 sample small states Note: For purposes of analysis Djibouti is included in Sub-Saharan Africa As a general rule higher per capita income is found to correlate closely with better policies and more robust institutional capacity. On that basis, richer small states, i.e. those with a higher level of GNI per capita, could be expected to have a stronger debt management performance than poorer ones. However, for the small states in the sample group this did not hold true. The 17 sample small states divide into 3 classified as low income, with an average per capita income of US$660; 8 classified as lower middle income, with an average per capita income of US$2,390 and 6 classified as upper middle income, with an average per capita income of US$8,495. A 13
# **1. Introduction**

The macroeconomic literature has long been interested in the contribution of infrastructure capital to aggregate productivity and output. Numerous theoretical papers have approached it using an aggregate production function including public capital as an additional input, first in the context of Ramsey-type exogenous growth models (e.g., Arrow and Kurz 1970) and later in endogenous growth models (Barro 1990, Futagami, Morita and Shibata 1993). This analytical literature has grown enormously in the last fifteen years, exploring a multitude of variants of the basic models, such as alternative financing schemes, simultaneous consideration of public capital and productive current spending flows, utility-yielding public capital, or public infrastructure congestion.<sup>1</sup> Quantitative assessments of the contribution of infrastructure are critical for many policy questions – such as the output effects of fiscal policy shocks instrumented through public investment changes (e.g., Leeper, Walker and Yang 2010; Ilzetzki, Mendoza and Végh 2010), or the extent to which public infrastructure investment can be self-financing (Perotti 2004). The empirical literature offering such quantitative assessments took off with the seminal work of Aschauer (1989) on the effects of public infrastructure capital on U.S. total factor productivity. The literature has boomed over the last two decades, with dozens of papers using a large variety of data and empirical methodologies, and with widely contrasting empirical results.<sup>2</sup> For example, Bom and Ligthart (2008) report that in a large set of empirical studies using industrialcountry data in a production function setting, estimates of the output elasticity of public capital range from -0.175 to +0.917.

However, much of the empirical literature on the contribution of infrastructure to aggregate output is subject to major caveats. Studies based on time-series have often ignored the non-stationarity of aggregate output and infrastructure capital, which typically display stochastic trends. This has sometimes led to implausibly high estimates of the productivity of infrastructure, owing to spurious correlation between both variables (Gramlich 1994).<sup>3</sup> In addition, empirical

> 1 See for example Turnovsky (1997), Glomm and Ravikumar (1997), Baier and Glomm (2001), and Ghosh and Roy (2004).

> 2 See for example Sánchez-Robles (1998); Canning (1999); Demetriades and Mamuneas (2000); Röller and Waverman (2001); Esfahani and Ramirez (2003); Calderón and Servén (2004). A recent overview of relevant empirical literature is provided by Romp and de Haan (2007).

> 3 One example is Aschauer‘s original estimate of the output elasticity of public capital, which was so high that the implied marginal product of infrastructure capital was close to 100% per year.

2
**Graph 1: Trends in road allocation and RDA staff (2008- 2012)**

<!-- Start of picture text --> 1000<br>900 890<br>800<br>700<br>648<br>600<br>500<br>400<br>300 323 338 347 337 345<br>225 232 243<br>200<br>100<br>0<br>2008 2009 2010 2011 2012<br><!-- End of picture text --> _Data source:_ NRFA and donors and RDA for staffing. In blue, total annual road commitments in billion USD and in red, the total number of staff in RDA.

**High turnover:** Pressures on staff are exacerbated by the fact that several dismissals and non-renewal of contracts have occurred since the 2009 audit (see Graph 2). The peak in staff exits occurred in December 2011 with the dismissal of all 12 staff from the procurement department.  This, together with the increase in the allocations to the sector lowered RDA’s capacity to absorb funding for new projects (approx. ZMK 1.6 trillion).

**Graph 2: Trends in RDA staffing (2008- 2012)**

<!-- Start of picture text --> 400<br>350 347 337 345<br>323 338<br>300 307<br>250<br>Total number of staff<br>200<br>Number of entries<br>150<br>Number of Exits<br>100 103<br>50 20 23 24<br>10<br>16<br>0<br>2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> _Data source_ : RDA.

**Loss of institutional memory:** Staff turnover at the decision-making level has led to a loss of institutional memory and a constantly changing work environment. In the past three years, the RDA has cycled through four different CEOs (including acting) and three senior management teams. The departure of

7
stronger prudential framework<sup>35</sup> - this is a necessary precondition for the successful implementation of this recommendation. With respect to extending the safety net, the lingering public perception (confirmed in practice during the 2002 mini-crisis) of the dependence of fiduciarias on their banks, renders unclear the validity of such a concern<sup>36</sup> .

**Table 11: Size and Ownership of Five Largest Fiduciarias Excluding Investments (December 2005)**

||**Assets under Management**<br>**(Col$ billion)**|**Market Share**|**Ownership**|
|---|---|---|---|
|Fiducolombia|15,926|26%|Grupo Sindicato|
|Previsora|9,120|15%|State-owned|
|Fidupopular|4,013|6%|Grupo Aval|
|Cititrust|3,999|6%|Citigroup|
|Fiduoccidente|3,823|6%|Grupo Aval|

_Source: Superintendencia Financiera, own analysis._

**Implementation of this recommendation should not be very difficult for the regulator or the industry** . On the regulatory side, both bank permissible activities and the prudential framework need to be amended appropriately. On the industry side, much will depend on how different banks – especially those that belong to the large financial groups dominating this market (see Table above) – perceive the relative benefits and costs of absorbing their fiduciaria subsidiary<sup>37</sup> . However, given the dependence of many fiduciarias on their bank parents for many of their activities (e.g. sales, collections, back office), it is unlikely that the overall impact on the financial system will be significant.

# **_E. Specific Reform 4: Investment Banking – Banks, CFs and Comisionistas de Bolsa_**

**Recommendation A: Allow banks to offer all types of investment banking services, with the exception of long-term equity investments in the real sector.** Banks are currently prohibited from offering such services – i.e. advisory/structuring, securities underwriting (except for sovereign debt) and brokerage, and equities trading – which are currently provided by CFs and comisionistas de bolsa. This recommendation would allow banks to also offer investment banking directly (with the exception of long-term equity investments in non-financial companies), but it would let them choose whether to provide such services in-house or via a specialized vehicle instead.

> fiducias en garantía for loans originated by its group bank, while its ‘sweep account’ services (i.e. overnight placement of excess liquidity) for the trusts it manages are often invested in related funds.

> 35 These include, for example, stronger corporate governance standards and greater transparency, or even the outright prohibition of certain transactions with other financial group entities.

> 36 An additional, albeit questionable, risk stems from legal complications (i.e. confusion between third party and own funds) that could arise in case of bank insolvency; although this should not be a preoccupation in theory, it has been corroborated in practice in some countries.

> 37 21 out of 28 fiduciarias, accounting for 80 percent of total fiduciary assets, were controlled by banks as of end-2005.

26
INEI selected a new food basket and computed new poverty lines. In this paper, we conduct our simulations for welfare measures during 2010.<sup>17</sup> Peru computes seven poverty lines: one for Metropolitan Lima, and one for each of the three geographic regions that divide the rest of the country, differentiating by rural and urban sectors. From 2010 onwards, the poverty lines are updated only to account for change in prices, but the composition of the baskets remains the same. The national poverty line is then calculated as a weighted average of the seven regional poverty lines.

The selection of the food baskets is based on household and individual consumption reported in the ENAHO. Information on the caloric composition of each food item comes from a nutritional table computed by the National Center for Food and Nutrition (CENAN) under the direction of the National Institute of Health. This table consists of 941 food items that correspond to the items reported by households in the main household consumption module in ENAHO, and 12 food items that correspond to the FAFH module applied to each adult. The 12 food items correspond to each meal occasion – breakfast, lunch, dinner, and snack - differentiated by three food sources – street vendors, restaurants, and work. Two necessary pieces of information that are not collected in this module are the content and quantities of food consumed. To assign calorie content, CENAN provides an estimate for each of these items by selecting representative food items. Quantity consumed is calculated indirectly based on the unit calorie cost derived from take-out food and food consumed away by children, i.e. food away from home items reported in the main household consumption module.

As mentioned in the previous section, the baskets are selected based on the consumption patterns and calorie requirement of the reference population. In Peru, this population corresponds to the individuals between the 20<sup>th</sup> and 40<sup>th</sup> percentile of the per capita expenditure distribution, an interval that is centered around the moderate poverty rate. Once the cost of the food basket is established, the Orshansky coefficient is used to determine the moderate poverty line.

> 17 We also conducted robustness checks of our estimates for a series of years and find similar results. Those results are available upon request.

16
investment, whereas the level of exports has returned to its pre-crisis level and public expenditure never dropped. Public expenditure, however, slightly increased even during the economic crisis. Recovery mostly seems to have come from the export increase.

**Figure 2.2: GDP and Components of Aggregate Demand during the Crisis**

<!-- Start of picture text --> Mexico: GDP and Aggregate demand (s.a., Q2-2008=100)<br>115<br>110<br>105<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>GDP Private Investment Exports<br>Private Consumption Public Expenditure<br>2008:II 2008:III 2008:IV 2009:I 2009:II 2009:III 2009:IV 2010:I 2010:II<br><!-- End of picture text --> _Source:_ World Bank (2010).

The large contraction of economic activity by 6.5 percent in 2009 created a particularly large output gap. This could mean that the economy will grow for some years at a level moderately above its potential rate of growth; that is, absorbing excess capacity instead of creating inflationary pressures or absorbing new employment. In 2009, the government opted to maintain the same level of expenditures as in the previous year despite large drops in public revenue. Then the government initiated a process of fiscal consolidation by increasing taxes and containing public expenditures. The increase in taxes and public sector prices may have led to consumer price inflation by the end of 2010 compared to 2009. The government also opted to increase the level of international reserves to mitigate potential further financial shocks.

During the crisis, the central bank intervened in foreign exchange markets by providing foreign currency liquidity to the private sector. The Mexican peso increased in the last quarter of 2008 and has remained steady since then. Monetary policy has taken place within a medium-term inflation targeting framework of 3 percent. The central bank eased monetary policy in January 2009. In retrospect, the crisis did not have severe exchange rate, monetary, or financial consequences in 4
described in detail in Chen and Ravallion, 1996). This is a good quality budget and income survey, notable in the care that goes into reducing both sampling and nonsampling errors. Similarly to the RHS, sampled households maintain a daily record on all transactions plus log books on production. Local interviewing assistants visited each household at two-three weekly intervals, at which time inconsistencies found at the local (county-level) NBS office are checked. Other trained interviewers also visited at regular intervals to collect additional data. This intensive interviewing method is a marked contrast to most surveys in which the respondent is visited only once or twice.

The consumption aggregate is built up from very detailed data on cash spending on all commodities and imputed values of in-kind spending, which is mainly consumption from household production, valued at local selling prices. Living expenditures exclude spending on production inputs (which are accounted for in net income from own-production activities). They also exclude transfer payments, though these only account for a small share of total spending (3.7% over the whole sample in 1996). The income aggregate includes cash income from all sources and imputed values for in-kind income. Income is measured net of all production costs, including interest on debt (including loans from the SWP). The migrant workers were not tracked, although the income aggregate includes remittances received from family members who migrated, including those supported by the SWP. Remittances are expected to be the main means by which the out-migration component reduced poverty in the short run.

Given the unusual effort that went into data collecting and checking the consumption and income data, we expect that subtracting consumption from income will give reasonably accurate estimates of savings. We also look into what forms the savings took. There are many forms of saving in this setting, including money balances and investment in own-production activities. The survey was not designed to allow a complete independent accounting of all forms of saving. Some data were collected on assets and liabilities, although the reliability of the reported values is questionable. We also study impacts on holdings of specific assets.

For the 2004/05 follow-up survey we used exactly the same survey instrument as for the 1996-2000 surveys, augmented with a module to elicit perceptions of both welfare and the project’s impacts. The module asked respondents to assess whether various 10
relationship for Colombia. For India, we do not see this pattern, and this held true when just considering manufacturing in Ghani, Kerr and Tewari (2013) too. These null patterns are also evident with population density, although the point estimate starts to become larger in size. It is also striking that infrastructure metrics and banking conditions tend to have limited correlation with the specialization and diversity metrics. Finally, overall urbanization levels of districts are not clearly linked. Further inquiry about the lack of predictive power for these metrics is warranted given their observation in other settings.

# **4. Employment Growth with Specialization and Diversity**

This section describes our empirical exercises regarding how employment growth for a district links to its initial specialization or diversity. The topic of specialization/diversity and city growth has been often been studied over the past two decades (Duranton, 2013a), and we seek to provide evidence on this link across Indian manufacturing and services. We particularly study the inclusive nature of local growth given its pressing importance for India.

We estimate a cross-sectional growth equation at the district level of the form: _Growthd_ = β⋅ _Specializationd_ , _t_ 0 + δ⋅ _Diversityd_ , _t_ 0 + χ _s_ +φ⋅ _Z d_ + ε _d_ .

Our core regressors are again the specialization and diversity measures that utilize a ten-point scale. The _t0_ subscript signifies that we measure these attributes at the start of the sample using the 2000 data. The outcome variable is the log employment growth in manufacturing and services for the district from 2000 to 2010. Regressions include a vector of state fixed effects χ _s_ to account for systematic differences in regional specialization and diversity evident in Figures 3 and 4, differences in regional growth rates in these sectors for India across the period, and similar. The vector _Zd_ contains the additional district-level controls that we modeled in Table 7. These controls, among other things, include measures like initial population size and income levels to capture convergence processes. We weight districts by their log population in 2001 and report robust standard errors.

The first column of Table 8 shows a lack of correlation for the specialization index, while Column 2 finds a strong and significant role for the diversity index. An increase of one decile in the diversity index is associated with 7% higher employment growth (e.g., moving from a 18