rafmacalaba/datause-encoder-data huggingface dataset:
which relates the (potentially transformed) consumption variable linearly to a vector **x** _c_ **_._** _,t−_ 1 containing dated census means of covariates over the cluster _c_ from time point _t −_ 1.<sup>3</sup> The two error components are the cluster effects _ηct_ and the household errors _echt_ which follow the distributions _F_ 1 and _F_ 2 _,_ respectively, and are assumed to be independent of each other. It is possible to allow for heteroscedasticity in the household error by modeling its variance to covariates. Such covariates may include the census means used in the main regression, but also higher moments such as the variance. Furthermore, geographic information and the fitted values of the first-stage regression may be used. The ELL method describes one option to model heteroscedasticity within the framework discussed here, while Pinheiro and Bates (2000, ch. 5) provide a more comprehensive discussion.

# **2.2 Model estimation based on survey consumption values**

In the first stage, model (2) is estimated using all household consumption values which are available for the village of interest in the survey. The estimation can be done within the maximum likelihood framework or by weighted or (feasible) generalized least squares.<sup>4</sup> As the estimates are used to predict consumption values for the census, the aim is to find a model with high predictive power. Thus, one should find a parsimonious model containing only covariates which explain a substantial share of the variation in the dependent variable. Due to averaging over the cluster, means over candidate variables should exhibit variation across clusters.

# **2.3 Bootstrapping census consumption data**

In the second stage, model (2) is used to predict consumption values for each household in the village of interest based on the census. Note that, to be consistent with the first-stage model using the consumption values from the survey, the explanatory variables in the second stage are also averaged within clusters, i.e., all households in the same cluster have the same value for each explanatory variable. Using the ˆ _′_ estimated regression coefficients **_β_**<sup>�</sup> from model (2) yields predictions _ycht_ = **_x_** _c_ **_._** _,t−_ 1<sup>**_β_**�,i.e.</sup> predicted

> 3In practice, one could use additional secondary information to explain consumption, e.g. geographic information which is typically available in poverty mapping exercises. Besides, fixed effects on higher aggregation levels such as counties and time-invariant explanatory variables on the household level **x** _cht_ could be, in principle, added to the consumption model. As discussed in Section 1, we do not assume many time-invariant variables to be available in practice and it is difficult to test if there are any. In this paper, we restrict ourselves to information that is available in the census.

> 4The chosen estimation method depends on whether and how the survey design, potential heteroscedasticity and the clustering nature of the data are taken into account.

6
Figure 3 shows the distribution of the percentage of the contract period elapsed within the estimation sample. It includes 3,976 projects, of which 167 were canceled. The vast majority of projects had not passed half of their contract periods. One reason for this finding is that PPPs are a relatively new phenomenon compared with the median contract period of a PPP in the sample, which is 25 years. Another is that crucial information on some of the older projects in the PPI database is missing, such as the contract period and the level of contracting government, forcing us to drop them from the sample used for the econometric estimation.

**Figure 3 Distribution of percentage of contract period elapsed**

<!-- Start of picture text --> 700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Percent of contract period elapsed<br>Number of projects<br><!-- End of picture text --> _Source_ : PPI database.

# **_4.3 Estimation Results_**

Nonparametric estimates using all PPP projects in low- and middle-income countries show that PPP projects are more likely to fail during early in their contract periods and that the risks accumulated during their contract periods are not trivial. The smoothed hazard rate estimate using Gaussian kernel and optimal bandwidth indicates a unimodal hazard profile for a PPP project (figure 4). The risk of early termination for a project increases rapidly until about 20 percent of the project’s contract period elapses. It plateaus at this level before declining slightly until it reaches 50 percent. During the second half of the contract period, the risk of early termination decreases until the project approaches the end of the contract period, except for a small increase at about the 80 percent mark.

The cumulative hazard curve shows that the accumulated probability of early termination increases steadily but that its pace decreases after reaching about 50 percent of the contract period (figure 5, panel a.). The Kaplan-Meier survival curve estimate mirrors the profile and 11
- 30 -

Table 2. Indicators by Category (concluded)

|**Sector**|**Ind**|**icators**|
|---|---|---|
|**Financial**<br>Financial liberalization|(1) real interest rates<br>(2) credit growth<br>(3) growth in credit/GDP<br>(4) lending-deposit interest rate spread|(5) growth in M2 multiplier<br>(6) growth of credit/reserve money<br>relative to the United States|
|Other fmancial|(1) "shadow" exchange rate<br>(2) parallel market premium<br>(3) central parity<br>(4) position within the band<br>(5) central bank credit to the banking<br>system<br>(6) money demand-supply gap<br>(7) Ml growth<br>(8) Ml level|(9) MI growth relative to the<br>United States<br>(10) broad money growth relative to the<br>United States<br>(11) change in bank deposits<br>(12) bond-yields<br>(13) inflation<br>(14) inflation relative to the US<br>_(15)_M2/international reserves|
|**Real sector**|(1) real GDP growth<br>(2) per-capita growth<br>(3) output level<br>(4) output gap<br>(5) manufacturing real wages|(6) wage growth<br>(7) unemployment rate<br>(8) employment growth<br>(9) changes in stock prices|
|**Fiscal**|(1) fiscal deficit/GDP<br>(2) fiscal deficit/government spending<br>relative to the United States<br>(3) government consumption/GDP|(4) domestic credit to public sector/total<br>credit<br>(5) growth in public sector credit<br>(6) public sector credit growth/GDP|
|**Institutional/structural**|(1) multiple exchange rate dummy<br>(2) exchange controls dummy<br>(3) relative GDP per capita<br>(4) financial liberalization dummy<br>(5) banking crisis dummy|(6) openness<br>(7) trade concentration<br>(8) months spent on peg<br>(9) past foreign exchange market crisis<br>(10) past foreign exchange market event|
|**Political**|(1) govemment victory dummy<br>(2) government loss dummy<br>(3) elections<br>(4) change in government<br>(5) legal executive transfers|(6) illegal executive transfers<br>(7) degree of political instability<br>(8) left-wing government<br>(9) new finance minister|
<!-- Start of picture text --> 3<br>2<br>r=<br>22 1<br>5<br>8<br>Co<br>“1<br>-.2<br>Aware of Government Benefit from Government<br>Program or Subsidy Program or Subsidy<br>C) cbr ext [5 car int [7] SSBF EXT [EG SSBF INT<br><!-- End of picture text --> <!-- Start of picture text --> AwareProgramof Governmentor Subsidy BenefitProgram fromor GovernmentSubsidy<br>40<br>> 30<br>=<br>g<br>2<br>s3oy 20<br>mo]2<br>a<br>10<br>0 Small Medium Large oSSmall Medium 31Large<br><!-- End of picture text -->
Table I: Income Tax Schedule

||2006|2007|2008|2009|2010|2011|2012|2013|2014|2015|
|---|---|---|---|---|---|---|---|---|---|---|
|||||Pan|el A: S|elf-Emp|loyed||||
|Kink 1|1,858|2,074|2,252|2,599|2,747|2,890|3,042|3,171|3,339|3,522|
|Kink 2|2,775|3,097|3,362|3,880|4,102|4,316|4,543|4,735|4,986|5,259|
|Kink 3|4,629|5,167|5,609|6,473|6,843|7,199|7,577|7,898|8,317|8,773|
|Kink 4|9,276|10,354|11,241|12,972|13,713|14,427|15,185|15,827|16,667|17,581|
|||||Pa|nel B: C|orpora|tions||||
|Notch 1|27,811|31,043|33,701|38,891|41,112|43,253|45,525|47,451|49,969|52,710|
|Notch 2|55,943|62,444|67,791|78,231|82,698|87,004|91,573|95,447|100,513|106,026|

Notes: The table shows the income tax schedule for the years 2006 to 2015. Amounts are in tousands of Costa Rican colones (CRC, ). Panel A shows the location of the kinks on taxable income that separate the ve tax brackets for the self-employed. The tax is applied to taxable income at marginales rates of 0, 10, 15, 20 and 25% respectively for the rst to fth tax bracket. Panel B shows the location of the notches on revenue that separate the three tax brackets for corporations. The tax is applied to taxable income at average rates of 10, 20 and 30% respectively for the rst to third tax bracket. For more information on the tax base, tax schedule and the ling procedure, see http://www.hacienda.go.cr/contenido/12994-regimen-tradicional.

# Table II: Withholding Rate Schedule for Sales Tax

|||W|ithh|oldi|ng|Rate||
|---|---|---|---|---|---|---|---|
||0|1|2|3|4|5|6|
|Base before 08/2011: Value-Added Rate|_≤_<br>5|20|30|40|55|75|_∞_|
|Base since 08/2011: Share of Local Sales|_≤_<br>0|-|-|50|-|-|100|

Notes: The table shows the withholding rate which credit/debit card companies apply to the sales of their sales-tax-liable clients. Prior to August 2011, the average withholding rate was determined by a notched schedule on value-added, with notches at value-added rates of 5, 20, 30, 40, 55 and 75%, and resulting withholding rates between 0 and 6%. Since August 2011, the schedule has been consolidated to three withholding rates of 0, 3 and 6%. The rates are determined by a notched schedule on the share of local sales, with a notch at 50%.

32
# **Figure  1**

<!-- Start of picture text --> Judicial System Indicators<br>(averages in income groups)<br>10<br>8 Low<br>6<br>Medium<br>4<br>2 High<br>0<br>Judicial Citizen's Enforcement Control of<br>Efficiency Access to of Contracts Corruption<br>Index Justice Index Index<br><!-- End of picture text -->

**Source:** Judicial Efficiency from ICRG; Citizen’s Access to Justice from World Business Environment Survey; Control of Corruption from ICRG; Enforcement from Business Environmental Risk Intelligence; GNP from World Bank.

# <u>The Value of a Comparative Approach</u>

How can governments build legal and judicial systems that serve the population while minimizing the costs of such an undertaking for society? In which direction should court reform go- which institutional structure / design should be adopted? The manner in which the judiciary is organized and the rules and regulations that different individuals / entities in an economy follow vary tremendously from country to country. The diversity jumps out by just considering simple numbers on how may lawyers and judges there are in a given country (see Table 1). It is clear when one considers the different ways that judges’ benefits and tenure (La Porta et al 2001) may be determined, and how lawyers may be rewarded. For example lawyers may be paid by the hour regardless of the outcome of the case or they may be paid only if the outcome settles in the client’s favour. Or they may have fixed fees depending on the nature of the transactions. The market for lawyers’ fees may or may not be competitive.

14
# Potential Growth: A Global Database

Sinem Kilic Celik, M. Ayhan Kose, F. Ohnsorge, and F. Ulrich Ruch<sup>1</sup> Keywords : production function; filters; growth expectations; developing economies. JEL Classification : E30, E32, E37; O20 1 Kilic Celik (IMF; skiliccelik@imf.org), Kose (Prospects Group, World Bank; Brookings Institution; CEPR; CAMA; akose@worldbank.org); Ohnsorge (Prospects Group, World Bank; CEPR; CAMA; fohnsorge@worldbank.org); Ruch (Prospects Group, World Bank; fruch@worldbank.org). Kaltrina Temaj provided outstanding research assistance. This paper will appear as a chapter in the forthcoming World Bank book Falling Long-Term Growth Prospects: Trends, Expectations, and Policies . We thank Amat Adarov, Carlos Arteta, Martin Bailey, Eduardo Borensztein, Ajai Chopra, Kevin Clinton, Brahima Coulibaly, Antonio Fatas, Erik Feyen, Jakob de Haan, Graham Hacche, Elena Ianchovichina, Ergys Islamaj, Gerard Kambou, Jean Pierre Lacombe, Valerie Mercer Blackman, Joseph Mawejje, Ugo Panizza, Zia Qureshi, David Robinson, Naotaka Sugawara, Jonathan Temple, Christopher Towe, and Garima Vasishtha, as well as participants of many seminars for excellent feedback and comments. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors and should not be attributed to the institutions they are affiliated with.
Table 2: Production functions using modified data.

|Variable|Model I|Model**H**|Model Inl|
|---|---|---|---|
|Intercept|3.207**<br>(0.717)|2.953**<br>(0.790)|3.947**<br>(0.868)|
|Log of Area|0.384**<br>(0.041)|0.411**<br>(0.040)|0.422**<br>(0.040)|
|Log of family size|0.226**<br>(0.068)|0.172**<br>(0.064)|0.161 *<br>(0.064)|
|Log of hired labor|0.018*|0.014*|0.013|
||(0.007)|(0.007)|(0.007)|
|Log of cash inputs|0.024<br>(0.016)|0.001<br>(0.016)|0.008<br>(0.016)|
|Log of fixed farm capital|0.013<br>(0.048)|0.089<br>(0.047)|0.068<br>(0.047)|
|Log of extension staff-farm<br>ratio (Sfratio)|-0.077<br>(0.069)|-0.110<br>(0.086)||
|Sfratio x Dummy variable|||0.432*|
|for Machakos|||(0.191)|
|Sfratio x Dummy variable|||-0.201|
|for Murang'a|||(0.309)|
|Sfratio x Dummy variable|||-0.261|
|for Kisumu|||(0.184)|
|**Sfratio**x**Dummy variable**|||-0.493**|
|for Kericho|||(0.188)|
|Sfratio x Dummy variable|||-0.106|
|<br>for Taita Taveta|||(0.263)|
|Sfratio x Dummy variable|||-0.235|
|for Trans Nzoia|||(0.349)|
|Sfratio x Dummy variable|||-0.036|
|for Bungoma|||(0.181)|
|Farm output,**1982**|0.040**|0.037**|0.038**|
|(000 Ksh)|(0.009)|(0.008)|(0.008)|
|Farm area,**1982**|0.005|0.004|0.004|
|(acres)|(0.013)|(0.013)|(0.012)|
|Age of head|-0.001|-0.002|-0.003|
|<br>(years)|(0.003)|(0.002)|(0.002)|
|Sex of head|0.097|0.134|0.107|
|(Female=1)|(0.079)|(0.076)|(0.075)|
|Education of head|0.064|-0.041|-0.033|
|(above primary=1)|(0.141)|(0.135)|(0.135)|
|Normal crop|0.631**|0.562**|0.516**|
|<br>(Normal=1)|<br>(0.126)|<br>(0.127)|(0.135)|
|<br>Failed crop|<br>0.521**|<br>0.510**|0.656**|
|<br>(Failed=1)|(0.201)|(0.194)|(0.202)|
|**1990**Cluster mean for|0.331**|0.413**|0.418**|
|normal crop|(0.111)|(0.108)|(0.108)|
|**1990**Cluster mean for|-0.611*|-0.625**|-0.617**|
|failed crop|(0.243)|(0.230)|(0.230)|

|_Continued_|
|---|

_15_
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policies, implying that national policies cannot be ignored. This has also been a factor impeding progress in liberalizing mode 4 trade in the ASEAN context (Bhatnagar and Manning, 2005).

To make temporariness credible, specific measures need to be put in place that address the incentives of EU firms and MENA workers to resist the turnover costs associated with regular mobility. An effective expansion of temporary movement of people supplying services will imply lower entry costs and may reduce adverse selection effects for jobs that can be provided on a temporary but rolling basis. There will be extra costs for firms associated with transport and recurrent training. To make temporariness feasible is also likely to require financial transfers in addition to more access to EU markets. One reason is to induce South Mediterranean countries to manage illegal migration pressures, including transit migrants. Against this there is a presumptive “social” payoff of not having lower skilled people move permanently – something that appears to be becoming part of the objective function of many governments in Europe. Less ambiguous are the likely economic payoffs to the exporting countries – an income flow/remittance transfer and a brain drain reduction benefit associated with temporary movement. Finally, it is important to note that the diverging demographic trends are relatively short term, spanning only the next 20 years or so. Therefore the opportunity to take advantage of demographic differences to ease social and economic pressures will not be there for very long.

# **References**

Amin, Mohammad and Aaditya Mattoo. 2005. “Does Temporary Migration Have to be Permanent?,” World Bank, mimeo.

Amin, Mohammad and Aaditya Mattoo. 2006. “Can Guest Worker Schemes Reduce Illegal Migration?,” World Bank, mimeo.

Bhatnagar, Pradip and Chris Manning. 2005. “Regional arrangements for mode 4 in the services trade: lessons from the ASEAN experience,” _World Trade Review,_ 4(2): 171-99. Blouin, Chantal. 2005. “Liberalizing the Movement of Services Suppliers: Lessons from the Canadian Experience with Temporary Worker Programs,” _Journal of World Trade_ 39(5): 881-94.

Boeri, Tito and Herbert Brückner. 2005. “Why are Europeans so Tough on Migrants?” _Economic Policy_ , 20(44): 629-703.

Cassarino, Jean Pierre. 2005. “Web Site Guide on Europe’s Migration Policy in the Mediterranean: An Overview,” Analytic and Synthetic Notes 2005/10, Euro-Mediterranean Consortium for Applied Research in International Migration (CARIM).

22
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1.60<br>1.40 m 2003 = 2007 m 2009<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00 . . . .<br>basic general specialized tertiary<br>education secondary secondary<br><!-- End of picture text -->

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Policy ReseaRch WoRking PaPeR 4712

# **Abstract**

An adequate supply of infrastructure services has long been viewed by both academics and policy makers as a key ingredient for economic development. Sub-Saharan Africa ranks consistently at the bottom of all developing regions in terms of infrastructure performance, and an increasing number of observers point to deficient infrastructure as a major obstacle for growth and poverty reduction across the region. This paper offers an empirical assessment of the impact of infrastructure development on growth and inequality, with a focus on Sub-Saharan Africa. The paper uses a comparative cross-regional perspective to place Africa’s experience in the international context. Drawing from an updated data set of infrastructure quantity and quality indicators covering more than 100 countries and spanning the years 1960-2005, the paper estimates empirical growth and inequality equations including a standard set of control variables augmented by infrastructure quantity and quality measures, and controlling for the potential endogeneity of the latter. The estimates illustrate the potential contribution of infrastructure development to growth and equity across Africa.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to understand the determinants of growth in Africa. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lserven@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
entrepreneur faces to start a business, from a legal perspective as well as in practice. The index of bankruptcy regulation should reflect the speed and efficiency of the bankruptcy process; in particular how well the justice system establishes priorities for creditors and enforces compensation. With the labor market regulation index, we want to measure how difficult it is for a firm to adjust its labor force. The measure should also include information about the wage setting system of the country, and the power of organized labor. The index of fiscal burden aims at measuring the burden to firms imposed by taxation and fiscal spending, an element that determines in many cases a firm’s choice of location. With the trade regulation index we look at how much countries protect domestic producers; specifically we are interested in measuring the cost for the entire economy of protecting a selected group of producers. The financial markets regulation index should capture how easy a firm’s access to capital markets is. For instance, special credit conditions for some industries, or interest rate controls can reduce the availability of credit to more deserving firms and distort incentives for investment. Finally, the contract enforcement index is a general measure of how easily firms can turn to the justice system to resolve legal disputes.

# **Measuring regulation**

We use six data sources for the construction of our indices: Doing Business (The World Bank Group), Index of Economic Freedom (The Heritage Foundation), Economic Freedom of the World (The Fraser Institute), Labor Market Indicators Database (M. Rama and R. Artecona, 2000), The Corporate Tax Rates Survey (KPMG), and International Country Risk Guide (The PRS Group). These sources cover the largest number of countries and areas under regulation, and their measures use a clear methodology and are straightforward. Except for the Labor Market Indicators Database, all sources are public. Our sample covers 76 countries.<sup>8</sup> In most cases, data are based on surveys conducted in a single year (in the late 1990’s) in a large group of countries; for components with observations for more than one

> 8 We do not consider here the heterogeneity of regulation within countries.  This can be important in some cases, such as the added burden of local taxation in federal systems.  At present, however, very few data sources provide information on regulation at the regional level.  One of these sources is the Investment Climate Survey of The World Bank, which records data on investment climate and activities of over 14,000 firms in about 30 countries.

7