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64. Agarwal A, McMorrow M, Onyango P, Otieno K, Odero C, et al. (2013) A randomized trial of artemether-lumefantrine and dihydroartemisinin-piperaquine in the treatment of uncomplicated malaria among children in western Kenya. Malaria J 12: 254.

65. National Coordination Agency for Population and Development (2005) Kwale District Strategic Plan 2005-2010 for implementation of the National Population Poilcy for Sustainable Development. Nairobi: National Coordination Agency for Population and Development. 1-55p 66. Kenya Food Security Steering Group (2012) The 2011/12 Short Rains Season Assessment Report. Nairobi: Kenya Food Security Steering Group. 1-42 p.

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71. Bhargava A, Jukes M, Ngorosho D, Khilma C, Bundy DA (2005) Modeling the effects of health status and the educational infrastructure on the cognitive development of Tanzanian schoolchildren. American journal of human biology : the official journal of the Human Biology Council 17: 280292.

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# **Tables and figures**

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# **Is infrastructure capital productive?**

# * **A dynamic heterogeneous approach**

**César Calderón**<sup>a</sup> , **Enrique Moral-Benito**<sup>b</sup> , **Luis Servén**<sup>a</sup> a _The World Bank, 1818 H Street NW, Washington DC 20433, USA_ b _Bank of Spain, Alcalá 48, 28014, Madrid, Spain_

## **JEL Classification:** H54, E23, O40

**Keywords:** Infrastructure, panel cointegration, parameter heterogeneity.

***** We are grateful to Nicola Limodio and Stephane Straub for useful comments and suggestions. We also thank participants at the Toulouse conference on  ̳Infrastructure Economics and Development‘, the Australian National University conference on  ̳The Economics of Infrastructure‘, the IMF conference on  ̳Investment Scaling-up in Low-Income Countries‘, the 2010 Symposium of Economic Analysis, and seminars at Brookings and the Bank of Spain. However, any remaining errors are ours only. Rei Odawara and Junko Sekine provided excellent research assistance. This work was partly supported by the Knowledge for Change Program as well as the Government of Japan through the Consultancy Trust Fund Program (CTF) of the World Bank. The views expressed in this paper are only ours and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.
<u>Table 10: Model results for the low income sample, sudden hydrometeorological events (N=35)</u>

|<br>Model|<br>Gross savings<br>PV dCons|<br>Gross<br>disaster<br>savings<br>umption inpe|<br>Net savings<br>r cent ofper capi|<br>Net disaster<br>savings<br>ta income of the|<br>Genuine<br>savings<br> year before th|<br>Genuine<br>disaster<br>savings<br>e event|
|---|---|---|---|---|---|---|
|Constant|-0.094|-0.043|-0.001|0.046|0.042|0.073|
||(1.522)|0.817|0.027|(1.043)|(0.741)|(1.500)|
|Savings<br>measures|1.414***|1.403***|1.434***|1.471***|1.148***|1.323***|
||4.894|(5.028)|(4.474)|(4.736)|(2.827)|(3.228)|
|R Square|0.421|0.434|0.378|0.405|0.195|0.240|

Note: Significance at the * 10% level;** 5% level; *** 1% level To start with, while the savings measures are all highly significant, coefficients increase to above 1. Overall, the statistically explained variation also increases substantially to, e.g., a R square of 0.42 for gross savings. Then, most interestingly, the disaster adjustment finally makes a difference and the explained variation increases by about 1%, 2.5% and 4.5% for gross savings, net savings and genuine savings, respectively. Best results are still obtained when using the conventional gross savings measure. Further revising for aid inflows improves regression results slightly and increments in explanatory power are 3%, 4% and 5.5% respectively (see table II-4). It is important to remember that due to a lack of genuine savings and consumption data, a number of highly vulnerable countries, such as disaster-prone Caribbean countries (e.g., St. Lucia, which in 1988 experienced the largest ever loss as compared to national income) are not considered in this data set, which may improve the results in terms of explained variation with and without accounting for disaster shocks.

We tentatively conclude that for this group of low-income countries and events, produced capital, and thus losses therein, play a stronger role in explaining consumption changes; furthermore, disasters losses seem to have a small adverse impact on consumption streams, although the disaster loss variable is again highly non-significant. To provide a graphical impression of these relationships, on Figure 6, we chart out gross disaster savings vs. the present value of changes in consumption for this sample.

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|Household per capita|**Table 2. Value Add**<br>VAT paid<br>total VA|**ed Taxation: Inciden**<br>as a share of<br>T revenues<br>|**ce Analysis**<br>VAT paid<br>househo|as a share of<br>ld income<br>|
|---|---|---|---|---|
|<br>income decile|Actual VAT|VAT<br>w/o exemptions/<br>exclusions|Actual VAT|VAT<br>w/o exemptions/<br>exclusions|
|1|3.75|3.98|17.27|24.27|
|2|4.46|4.69|10.02|13.94|
|3|5.58|5.65|9.08|12.17|
|4|6.30|6.25|8.29|10.89|
|5|7.06|6.91|7.45|9.66|
|6|8.46|8.23|7.43|9.57|
|7|10.11|9.69|7.23|9.17|
|8|12.33|11.99|6.96|8.95|
|9|14.93|14.75|6.24|8.16|
|10|27.01|27.87|4.74|6.48|
|Total|100.00|100.00|6.50|8.61|
|Gini coefficient before tax|es||0.5768|0.5768|
|Gini coefficient after taxes|||0.5864|0.5909|
|Effect of VAT exemptions|/exclusions on Gini|coefficient|0.0045||

Source: World Bank (2012) using _Encuesta Nacional de Ingresos y Gastos, 2006-07._ 27. **A policy to reduce the price of food may also seem sensible given that the cost of food is relatively high in Colombia.** Food prices in Colombia are higher than the regional average in 23 out of 29 categories, according to the International Comparison Program. Prices of basic commodities, such as rice or flour, are relatively high in Colombia (FAO, 2010). This may be related to the high cost of logistics (Colombia ranks 82 in the WDI Logistic Cost Index). For instance, in some remote areas in Colombia the transport cost represents half of the price of food. Protectionism in the agricultural sector also contributes to higher food prices (Anderson and Valdes, 2008). An important justification for VAT exemptions is to exclude the poorest segments of the population from paying this tax in their consumption of basic commodities. The problem is that VAT exemptions benefit all consumers, and not only the poor, making it an unsuitable targeting mechanism to reach the poor.

28. **The VAT policy of exemptions and exclusions has unintended consequences, as the rich benefit disproportionally.**<sup>17</sup> Because they account for such a large proportion of total consumption, the rich also account for the largest proportion of total VAT excluded/ exempted consumption. In fact, half of the value of excluded/exempted goods is consumed by the richest quintile of the population while the poorest quintile consumes approximately 5 percent (Table 3). While the poor benefit more in relative terms compared to their low incomes, this does not change the fact that the richest 20 percent of the population capture a large part of the monetary value of this indirect subsidy.

> 17 The distinction between exemptions and exclusions in Colombia is the following: VAT exempt goods (or services) effectively have a zero rate.  The producer of VAT exempted goods can claim VAT reimbursements for the inputs used to produce the good.  A producer of VAT excluded goods, by contrast, cannot claim such reimbursement, implying that the VAT of inputs becomes a cost of production.

11
compared. Typical temperatures are much higher at the western and eastern grid points (between 25° and 30° C.) than at the central (highland) point (10° - 15° C.). Both levels and seasonal variations in rainfall are also quite different. The central point has high average rainfall, with a smooth pattern of rise and fall that is approximately centered in July (month 7). The southern and eastern points have very low rainfall, distributed in a bimodal pattern with peaks in the spring and fall. The other two points have sharply single-peaked distributions, peaking in July in the west and August in the north.

Figure 2 presents our benchmark results for weighting GCMs in this exercise. The table presents average R<sup>2</sup> scores for the bivariate relationships between the CRU (historical) rainfall and temperature observations and the corresponding observations from the 8 GCMs (documented in Appendix 1). Each score is computed from monthly observations for 40 years, over 372 Ethiopian grid squares. The best fit (R<sup>2</sup> = .50) is provided by GISSA, the AOM model of NASA’s Goddard Institute for Space Studies. The worst fit (R<sup>2</sup> = .10) is provided by IPSL, the CM4 model of the Institute Pierre Simon Laplace, France. We should emphasize that these results are for Ethiopia only. It is quite possible that the pattern of fit across GCMs is very different for other countries. In any case, we observe stark differences for Ethiopia: The ratio of R<sup>2</sup> -scores for best and worst fit is 5:1. We use relative weights computed from the results displayed in Figure 1 in the stepwise “best-fit” exercise described in Section 2.

Figures 3-5 illustrate our projection results for three grid points in Figure 1. As we note in Section 2, our methodology condenses all the GCM information into “best-fit” patterns for three periods: historical (1960-2000), 2001-2050 and 2051-2100. For Figures 3-5, we juxtapose the projections for 2051-2100 (in red) with historical data 10
**Figure 5: Access to Bank Deposits in 2005 (left), and the Use of Bank Deposits in 2011 (right)**

<!-- Start of picture text --> Median access to deposits (Index 0-100)<br>100<br>80<br>60<br>40<br>20<br>0<br>All HIC MIC LIC<br><!-- End of picture text --> <!-- Start of picture text --> Median use of deposits (% of population)<br>50<br>40<br>30<br>20<br>10<br>0<br>All HIC MIC LIC<br><!-- End of picture text --> Source: Honohan (2008) and Demirguc-Kunt & Klapper (2012).

In sum, countries within the MIC group show the greatest variation in both the maximum drop in deposit growth and the pre-crisis access to deposits. We can thus expect that most of the interaction between the access to deposits before the global financial crisis and the changing deposit growth in the immediate aftermath of the crisis could happen within the subsample of MICs. We will formally examine this link next, using a regression model, which controls for other factors that could significantly influence the effect of access to deposits on the drop in deposit growth during the global financial crisis.

# **3. Regression Model**

To formally analyze the link between access to bank deposits prior to the 2008 crisis and the deposit dynamics in the crisis, we use a regression model of the following form: where yi is the maximum drop in bank deposit growth between 2006 and 2010, and atdi is an index of Honohan (2008) measuring access to bank deposits before the 2008 crisis period (Honohan(2008)). As an alternative measure of access to (the actual use of) deposits, we use the Demirguc-Kunt and Klapper (2012) measure of the share of people that use banking deposits in 2011 (D-K&K(2012)). Xi is a vector of control variables, εi a white noise disturbance, and the subscript _i_ stands for countries.

The vector of control variables, Xi, includes a constant, the population size (popsize), GNI per capita (gnipc), CPI inflation (inflation), a dummy variable taking the value of 1 if the country experienced a banking crisis around 2008 and 0 otherwise (bc), GDP growth corresponding to the time period over which the maximum drop in deposits happened (gdpgr), a dummy variable taking the value of 1 if the country had explicit public deposit insurance and 0 8
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inventory as a monetary asset, as does Argentlna, while Chile allows inventory to be included in the revaluation of assets (see box). Israel excluded machinery and assets from the list of 'protected' and therefore adjustable assets while at the same time not indexing depreciation.

Adiustment Rules in Chile: The Chilean rules of inflation adjustment and income estimation are similar to the net worth method discussed above. The relevant balance sheet items are revalued by the inflation factor, depreciation is calculated on the revalued undepreciated basis, and net worth NW1 ' is derived. Taxable income is then calculated as the difference between NW1 ' and NWO. This comprehensive system of adjustment in Chile was arrived at after a number of years of using various partial and irregular methods. In the 1940s firms were allowed to occassionally revalue fixed assets. In the 1950s the revaluation was made more regular and vas sometimes extended to include inventory. A net worth based partial adjustment system was used between 1959 and 1974. Only since 1974 has the comprehansive system of adjustment been used to adjust income for inflation. Indexation has been developed with a view to prevent tax distortions, maintain compliance, and preserve tax elasticity. Unlike most other countries with inflation adjustment systems, balance sheets in Chile are adjusted both for tax purposes and also for financial statement purposes. As the net worth exercises above demonstrated, financial statements are the basis for determining taxable income in Chile. Monetary correction is mandatory for financial statements and the same corrections are valid for tax and financial records. The only exceptions to this are entities such as small taxpayers who do not maintain regular accounts.
**Figure 3: Incidence of extremists by country (% of total respondents)**

<!-- Start of picture text --> Thailand .2<br>Morocco<br>ArgentinaLatvia .3 .3 .4<br>Paraguay .5<br>Lithuania<br>Bolivia .5 .5<br>.7<br>UzbekistanNepal Iraq 1 .9<br>Peru 1.2<br>Ecuador 1.3<br>UruguayIndonesia 1.41.4<br>Ivory Coast 1.6<br>Colombia 1.6<br>Iran 1.7<br>Lebanon 1.8<br>Yemen 1.8<br>Malaysia 1.8<br>SyriaRussia 2.22.2<br>Kazakhstan 2.2<br>Azerbaijan 2.3<br>Bosnia Herzegovina 2.3<br>Kuwait 2.4<br>Albania 2.9<br>Kyrgyzstan 3.0<br>Egypt 3.0<br>Qatar 3.1<br>Tanzania 3.1<br>Tajikistan 3.1<br>Tunisia 3.2<br>Bahrain 3.2<br>Saudi Arabia 3.2<br>Jordan 3.4<br>Burkina Faso 3.7<br>Algeria 3.9<br>United Arab Emirates 5.0<br>Afghanistan 5.5<br>India 5.9<br>Bangladesh 7.4<br>Sudan 8.0<br>Niger 8.2<br>Nigeria 8.2<br>Oman 9.2<br>Pakistan 9.8<br>Kosovo 10.8<br>Comoros 11.2<br>Somaliland 12.1<br>Senegal 12.8<br>Mauritania 13.8<br>Mali 14.8<br>Chad 17.0<br>Guinea 25.7<br>Djibouti 33.7<br>0 10 20 30 40<br><!-- End of picture text --> Note: The number of observations included is 144,200.

**Figure 4: Incidence of extremists by region (% of respondents)**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2006 2007 2008 2009 2010 2011 2012<br>ECA EAP MENA SSA LAC SA<br><!-- End of picture text --> Notes: The number of observations included is 144,200. The regions are defined following the World Bank’s regional definitions.

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Table 2: Intergenerational Persistence in Schooling in Indonesia

|||**LINE**|**AR CEF**|||**QUADR**|**ATIC CEF**||
|---|---|---|---|---|---|---|---|---|
||**Full**|**Rural (R)**|**Urban (U)**|**R–U**|**Full**|**Rural (R)**|**Urban (U)**|**R–U**|
|Father Edu<br>Father Edu Sq<br>Constant|0.412***<br>(0.011)<br>6.836***<br>(0.096)|0.384***<br>(0.012)<br>6.878***<br>(0.104)|0.429***<br>(0.014)<br>6.941***<br>(0.131)|-0.045***<br>(0.017)<br>-0.0627<br>(0.14)|0.267***<br>(0.025)<br>0.010***<br>(0.001)<br>7.214***<br>(0.114)|0.220***<br>(0.027)<br>0.012***<br>(0.002)<br>7.258***<br>(0.119)|0.334***<br>(0.040)<br>0.006***<br>(0.002)<br>7.249***<br>(0.183)|-0.114**<br>(0.045)<br>0.007***<br>(0.003)<br>0.0086<br>(0.18)|
|Observations|15477|10239|5238|15477|15477|10239|5238|15477|
|||**Estimates**|**of Relative a**|**nd Absolute**|**Mobility from**|**Quadratic C**|**EF**||
||**R**|**elative Mob**|**ility**||||**Absolute Mob**|**ility**|
||**Full**|**Rural(R)**|**Urban(U)**|||**Full**|**Rural(R)**|**Urban(U)**|
|**IGME0**|0.267|0.220|0.334||**ES0**|7.214|7.258|7.249|
|**IGME6**|0.387|0.364|0.406||**ES6**|9.176|9.01|9.469|
|**IGME9**|0.447|0.436|0.442||**ES9**|10.427|10.21|10.741|
|**IGME12**|0.507|0.508|0.478||**ES12**|11.858|11.626|12.121|
|**IGME16**|0.587|0.604|0.526||**ES16**|14.046|13.85|14.129|
|**Weighted IGME**|0.3937|0.3602|0.4209||**Weighted ES**|9.4305|9.049|10.1756|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Here, _IGMEk_ = _ψ_<sup>ˆ</sup> 1 _j_ + 2 ˆ _ψ_ 2 _jEkp_<sup>and</sup><sup>_ESk_=</sup><sup>_ψ_ˆ0</sup> _j_ + _ψ_ ˆ1 _jEkp_<sup>+</sup><sup>_ψ_ˆ2</sup> _j_ ( _Ekp_<sup>)2 where</sup><sup>_Ep_</sup> is father’s years of education.

34
# **TABLE 14. Job turnover. Share of unskilled workers**

||FE<br>|<br>|FE‐2<br>|SLS<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|0.0077**|0.0042|0.011|‐0.027|
||(0.0032)|(0.0036)|(0.018)|(0.018)|
|ICT * Informality|‐0.0091***|‐0.010***|‐0.017***|‐0.015**|
||(0.0032)|(0.0037)|(0.0066)|(0.0071)|
|Observations|3,566|3,564|3,566|3,564|

Dependent variable: change in the share of unskilled workers. Regressors: ICT investment dummy, ICT investment dummy interacted with a district‐level indicator of high informality in local labor markets. Instruments defined as in Table 3. Robust standard errors in parenthesis.

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# **1.  Introduction**

This paper examines the relationship between firm performance and growth and the business environment in the countries of the South Asia Region (SAR): Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. The analysis uses data collected for the Enterprise Surveys conducted by the World Bank of formal sector firms in the region, along with surveys of informal sector and rural firms in several countries. The paper serves as an input to the World Bank South Asia Region’s flagship report, “More and Better Jobs in South Asia”.

Surveys of firms across countries can be used to provide a rich description of how managers perceive the costs to them of the business environment in which they operate. Interpreting these surveys requires a conceptual framework, and the analysis in this paper uses the approach set out in Carlin et al. (2006, 2010, 2012) and Carlin and Schaffer (2012). In this framework, the responses of firms to questions about the quality of the business environment can be interpreted as _shadow prices_ : estimations by managers of the cost imposed on the firm by inadequacies of an aspect of the business environment – regulation, physical infrastructure, availability of skilled labor, macroeconomic conditions, rule of law, etc. – for the growth of their firm. Expenditures on mitigation of these costs as reported by firms also fit naturally into this framework. When taken to the data, these predictions inform the policy maker whether, for example, it is the case that improvement in a particular element of the business environment is likely to benefit well- or poorly performing firms; whether public input bottlenecks are more pressing in urban or rural areas; and whether there are important differences across industries in the burden of weaknesses in the business environment. Since the focus of the flagship report is on jobs, we examine in particular how different elements of the business environment affect firms that are expanding employment.

Our modeling framework allows us to interpret clearly the different businessenvironment related measures collected in enterprise surveys and to formulate hypotheses as to how they relate to firm efficiency and growth. It predicts in particular that well-performing firms report _higher_ costs of constraints. This insight 2
common mean across all 14 categories and years has been used to calculate the correlations. Not surprisingly, the correlations are remarkably high. Despite large differences in the level of prices across countries and products, they move together. The lowest correlations are those of Mexico's prices and even in that case, with one exception, they exceed .6. For Bangladesh, all correlations except that with Mexico, are larger than .83.

Finally, we need to confront the issue of whether or not quotas are binding. It will be too much to expect that quotas are binding in all years for all categories for all countries in our sample. All we can offer here is broad evidence in favor of the assumption. Several points may be noted. First, as a minimal defense, we note that a large body of the recent literature on the evaluation of the future impact of the MFA phase out under the Uruguay Round, based on Computable general Equilibrium models, uniformly assumes binding quotas (e.g., Whalley 1996). Second and more directly, Table 8 shows detailed data on quota utilization rates for 1993, the latest year for which we could obtain reliable data. We report quota utilization rates for the various products for all countries in our sample except Hong Kong."<sup>7</sup> These rates are remarkably high for virtually all countries in the majority of categories suggesting binding or near-binding quotas. Third, quota rents in most countries have been found to be positive even when the utilization rate is below 100%. Partly due to group quotas (see the next paragraph) and partly due to the manner in which quotas are administered, quotas seem to bind even when the utilization rate is below 100%. Finally, Dean (1991) has tested econometrically for whether the quotas are binding and concluded in "<sup>7</sup> The reporting year for quota utilization is from April 1, 1992 to March 31, 1993. Data for Hong Kong were not available but utlization rates for Hong Kong are known to be very high.

20