rafmacalaba/datause-encoder-data huggingface dataset:
hypothesis based on the test statistics =0.000. The test results suggest the presence of heteroskedasticity. |ppc pp| > א<sup>2</sup>

A standard assumption in panel-data models is that the error terms are independent across cross sections. This assumption is used for identification purposes rather than descriptive accuracy. We test for cross-sectional dependence following Pesaran (2004). Pesaran's statistic follows a standard normal distribution and can be applied to balanced and unbalanced panels. Pesaran's test = of cross sectional independence identifies the existence of cross sectional dependence ( 3.9 and 2 = 1.48. The fraction of variance is due to ui ) in our data. uu<sup>2</sup> σσ ii σσ , ρρ= 0.87 We also test for serial correlation in the idiosyncratic errors of a linear panel-data model discussed by Wooldridge (2002). Drukker (2003) presents evidence based on simulations that this test has good size and power properties in reasonable sample sizes. Under the null hypothesis of no serial correlation of the residuals from the regression with the first-differenced variables, our panel data shows the existence of autocorrelation (F(1, 88) = 16.48, =0.000). The above test suggests the presence of serial correlation within the series. |ppc pp| > FF Another concern of using panel data is the presence of missing values. One way to deal with it is to delete these observation. However, this can lead to biased estimates and may either reduce or exaggerate statistical power. Each of these distortions can lead to invalid conclusions (Acock, 2005). Baltagi and Wu (1999) propose a feasible generalized least squares procedure as a weighted least squares alternative to handle a wide range of unequally spaced panel data patterns. According to the authors, this procedure is simple to compute and provides natural estimates of the serial correlation and variance components parameters.

Based on the above discussion and test statistics, we use panel Feasible Generalized Least Square ( _FGLS_ ) as the basis of our estimation strategy.<sup>7</sup> Hansen (2007) points out that clustering and policy autocorrelation occur in many cases when using panel data for over a long time period. The clustering problem is caused by the presence of a common unobserved random shock at the group level that will lead to correlation between all observations within each group. The policy autocorrelation problem, on the other hand, arises if the groups are followed over a long time and

> 7 The use of long panel makes the empirical model vulnerable to (i) unbalanced panel with unevenly spaced data, (ii) heteroskedasticity, (iii) multicollinearity and (iv) auto correlation.

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9 advanced countries and countries with a population smaller than 200,000 persons as of 2002<sup>11</sup> , we are left with a set of about 110 emerging countries and developing economies (Table A2).

As pointed out in the introduction, the descriptive evidence one wants to see when dealing with migrants’ transfers relates to three main characteristics: their increasingly large size, their limited volatility and their low correlation with the economic cycle in the receiving economy. In what follows, we mostly focus on workers’ remittances, which is not only the largest item among the three relating to migrations, but also the one with the largest coverage in terms of countries. For a better assessment of the above properties, we compare workers’ remittances to net FDI inflows that, excluding workers’ remittances themselves, have always been the largest and the least volatile item among capital inflows to emerging countries (Lipsey, 1999).

In our sample, workers’ remittances in 2003 totaled 65.2 billion US dollars, compensation of employees a lower $18.3 billion, migrants’ capital transfers only $1.3 billion. Overall, the transfers of funds from migrants reached about $85 billion<sup>12</sup> . In the same year, FDI inflows towards the same set of countries for which workers’ remittances data are available amounted to $111.9 billion.

In the past 30 years workers’ remittances have grown continuously, starting from 3.6 billion in 1976 (fig.1). This increase partly reflects an expansion of the set of countries for which workers’ remittances data are available: 23 in 1976, 47 at the end of the 1980s, up to 77 at the beginning of the current decade. However, it also reflects a widespread increase of the relevance of workers’ remittances towards most of the countries for which long time series are available.

Comparing workers’ remittances and net FDI inflows over time, having again restricted the sample to the country-year observations for which both data are available, it appears that workers’ remittances exceeded net FDI inflows up to 1992 (fig. 2). Since then, FDI gained prominence, although the consistent gap which opened up at the end of the 1990s was partly

> 11 This is a typical cleaning procedure in cross-country analyses aimed at avoiding that volatile behavior of economic indicators in small countries may affect the results.

> 12 This compares with the $91 billion estimate reported by the IMF (2005), which made a special effort to fill the gaps in the database using information received from country desks and national authorities.
**Table 2. Metropolitan areas with the smallest percentage of college graduates**

|**Bottom metro areas**|**Rank**|**Percentage**<br>**with college**<br>**degree**|**Salary of**<br>**college**<br>**graduates ($)**|**Salary of high**<br>**school**<br>**graduates ($)**|
|---|---|---|---|---|
|Mansfield,OH|286|17|53,047|35,815|
|Beaumont-Port Arthur-Orange,TX|287|17|58,234|38,352|
|RockyMount,NC|288|16|52,330|34,329|
|Stockton,CA|289|16|59,651|37,928|
|Fort Smith,AR/OK|290|16|50,937|33,187|
|Ocala,FL|291|16|47,361|32,725|
|Yuba City,CA|292|16|56,403|34,999|
|Modesto,CA|293|15|60,563|36,126|
|Waterbury,CT|294|15|54,651|37,280|
|Brownsville-Harlingen-San Benito,TX|295|15|43,800|22,450|
|McAllen-Edinburg-Pharr-Mission,TX|296|15|44,605|22,845|
|Anniston,AL|297|15|48,928|33,031|
|Yakima,WA|298|15|50,160|29,084|
|Bakersfield,CA|299|14|65,775|34,807|
|Danville,VA|300|14|42,665|28,868|
|Houma-Thibodaux,LA|301|14|56,044|37,395|
|Vineland-Millville-Bridgeton,NJ|302|13|57,668|35,375|
|Flint,MI|303|12|43,866|28,797|
|Visalia-Tulare-Porterville,CA|304|12|55,848|29,335|
|Yuma,AZ|305|11|52,800|28,049|
|Merced,CA|306|11|62,411|29,451|

_Source:_ Author’s calculations based on U.S. Census Bureau (2008).

These two tables have several noteworthy features. First, the sheer size of the geographical differences is staggering, often exceeding the differences between countries. Stamford, Connecticut, the city with the highest percentage of college-educated workers in the United States, has five times the number of college graduates per capita as Merced, California, the city with the lowest. This difference is much larger than the difference in schooling between the United States as a whole and many developing countries, such as India (three times), Peru (three times), and South Africa (four times).

Second, differences in education are associated with huge differences in average nominal salary. College graduates in the top group make $70,000–$80,000 a year, or about 50 percent more than college graduates in the bottom group. Compare San Jose, fifth from the top, with Merced, at the very bottom. Both cities are in California, less than 100 miles apart, but their labor markets belong to different universes. San Jose, in the heart of Silicon Valley, has more than four times as many college graduates per capita as Merced—and salaries 40 percent higher for college graduates and 130 percent higher for workers with a high school diploma.

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Figure 2: Linzer and Staton Measure of Latent Judicial Independence for the Americas (and a few Europeans because we were too lazy to remove them), 1960-2010.
indicators measured at the regional level, as well as the _Global Competitiveness_ indicators at the country level. We find that the three sets of indicators offer independent sources of variations in explaining firm performance. Among them, the _Enterprise Survey_ indicators explain the largest share of variations in firm performance. Our investigation also suggests that the de jure _Doing Business_ indicators are more effective in improving firm performance in countries with better contract enforcement.

By comparing the three sets of estimates, we are able to modify our priors on the effects of the business environment. In particular, we increase our confidence on the four aspects of the business environment with which the multiple sources of data yield more consistent findings: access to finance, electricity, internet, and human capital. For these indicators, results from at least two data sets are robust and the third set does not contradict the other two. We are less sure about the negative effect of corruption. We also find substantial heterogeneity of the effects of the business environment by firm ownership and size. In terms of productivity, foreign-owned firms benefit more from the maintenance of physical safety and from ease in obtaining construction permits, and gain competitive advantages with respect to domestic firms when domestic infrastructure or access to finance is worse. Foreign-owned firms are less burdened in terms of firm growth by difficulty in land access. For relatively small firms, particularly important BE elements are containing corruption, availability of both basic and modern infrastructure, a better human capital environment, and land access. The benefit of access to finance shows in firm growth for smaller firms, but in productivity for large firms. Similar to foreign firms, relatively large firms benefit more from physical safety.

Our research has several policy implictions. First, because using multiple sources of data allows us to alter our priors about the importance of various elements of the business environment, we believe it is important to collect new data, and to exploit under-utilized data to enrich our understanding of the effects of the business environment. Second, we believe it is crucial to consider the interaction of government enforcement and de jure institutions. Government enforcement ability and incentives differ greatly across countries due to differences in the income level, history, and complementary institutions. The same de jure laws and regulations would have vastly different effects in countries with distinct enforcement. Third, due to differences in resoures, technology, and bargaining power, firms of different ownership and size respond quite differently from the same business environment. Targetted policies and regualtions should be considered. Our findings here thus echo the O-ring theory of development that emphasizes distinct binding constraints in different contexts (Kremer 1993).

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Note: This table shows the share of HGFs (according to the 3‐year Birch and OECD definitions, based on employment) in the regression sample. KIS stands for Knowledge Intensive Services, and LKIS for Less Knowledge Intensive Services. Sector types are defined based on the Eurostat definition.

# 2.2 Spillover measures

HGFs may affect the rest of the economy through direct and indirect channels. They may affect firms that buy from and sell to them, but they may also impact firms that are not directly linked to them but operate in the same sector.

Let HS��� be the share of HGFs in (2‐digit) industry j , region (county or microregion) r , and year t , it accounts for the firms with at least 5 employees and an HGF phase starting in year t , t�1 or t�2 :<sup>12</sup> where N��� is the number of firms in industry j in region (county or microregion) r and year t , and HGF�� is an indicator variable that equals 1 if firm _i_ is a HGF.

The upstream (forward) and downstream (backward) measures are based on the average share of HGFs in supplier and buyer industries in the region, weighted by the volume of intermediate‐good flows across other industries. Importantly, when calculating these measures for a given firm, we omit said firm’s 2‐digit industry from the computation to capture only genuine inter‐industry spillovers. Buyer‐ seller connections are identified using information from the 2‐digit (symmetric harmonized) input‐ output matrices for Hungary provided by the OECD:<sup>13</sup> and where α�� are the normalized coefficients from the symmetric input‐output matrix representing (domestic) intermediate good flows from industry m to j .

We also control for intra‐industry spillovers by calculating the share of HGFs in the _firms’ 2‐digit industry_ ; using data on all entities with the same 2‐digit code, save for the ones that belong to the firm’s own 4‐digit industry<sup>14</sup> (denoted by g ). Thus, this measure is calculated as:

> 12 For consistency with the regression sample, we calculate the spillover measures based on the subsample of firms with at least 5 employees.

> 13 Source: http://stats.oecd.org/Index.aspx?DataSetCode=IOTS. These tables are available only between 2000‐ 2011, so we have used the 2011 weights for the years after 2011 and the 2000 weights for the years before 2000.

> 14 The sub‐industry is narrowly defined as it describes one specific economic activity. An example is subindustry 10.11, the processing and preserving of meat. The second measure is the share of HGFs in the firm’s _industry_ defined as the 2‐digit NACE code. The industry is more broadly defined as it captures many economic activities. Using the same example, the processing and preserving of meat will be grouped together with production of meat products under industry 10, manufacture of food products.

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Policy Research Working Paper 10056

# **Abstract**

Public-private partnerships are used to procure public infrastructure. Despite involving private investors and concessionaires, they impact the public budget like traditional provision and create fiscal risks. This paper develops a conceptual framework to assess whether and how public-private partnerships shift risks to concessionaires and financiers. It uses this framework to describe and assess the Chilean public-private partnerships program. The paper identifies renegotiations as the major source of fiscal risk, which involved additional investments, increasing the cost by about one-third over the original project cost estimates. The 2010 law reform on public-private partnerships introduced changes to the renegotiations regime and began the routine use of variable term contracts. Using contractual data, the analysis finds evidence suggesting that renegotiations fell dramatically. The paper also calculates the realized internal rates of return for 50 highway and airport public-private partnerships, using cash flow data for the entire public-private partnerships program, which started in 1991. The average internal rate of return is 6.8 percent, with averages of 9.1 and 3.1 percent for fixed and variable term public-private partnerships, respectively. The returns show a large dispersion, which suggests that infrastructure projects are intrinsically risky and private participation entails significant risk shifting from the budget to concessionaires and financiers.

This paper is a product of the Infrastructure Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at alexander.galetovic@uai.cl.

_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
disaggregate CAPEX by sector. However, evidence from BOOST allows the authors to investigate the evolution of public infrastructure spending by sector over the period 2009–16. During this time, infrastructure spending in the WSS sector began at about 0.3 percent of global GDP, climbing to a peak of about 0.6 percent in 2012, before falling back to 0.3 percent. The average expenditure for this period was around 0.4 percent of global GDP.

When combined with our estimates for OPEX, the use of the limited direct CAPEX data available results in total networked water and sewered sanitation subsidies in low- and middle-income countries in the range of 0.75–0.95 percent of GDP. While these estimates are below our estimate of 1.59–1.95 percent of GDP for low- and middle-income countries, such discrepancy is not unexpected given key differences between the two approaches followed.

First, the use of direct expenditure significantly underestimates the CAPEX subsidies provided to the sector for existing infrastructure due to the deferral of maintenance—a phenomenon especially common in low- and middle-income countries. It has been well-documented that low- and middle-income countries in particular struggle with revenue collection and limited fiscal capacity, and are thus prone to significant deferrals of maintenance, as well as major repairs and replacement of existing infrastructure. Yet, even if these expenditures are not currently being made—either through taxes, transfers, or tariffs—they will need to be covered by future generations to maintain existing WSS services over time. Since our model allocates such expenditure in equal installments across the design life of the asset, its estimates are significantly higher by reflecting these intergenerational subsidies.

Second, while our model accounts for the full costs of required major repairs and replacement of existing infrastructure, it does not account for expenditures towards infrastructure expansion. In a steady-state situation whereby infrastructure expansion is limited, both estimates should be reasonably similar since actual direct CAPEX would be exclusively and comprehensively covering the maintenance and replacement of existing infrastructure. These key differences between the two models are depicted in figure 3.

**Figure 3 Estimating the Magnitude of Subsidies: Two Approaches**

<!-- Start of picture text --> Hybrid direct<br>Actual subsidy of key cost Full model<br>expenditure/ model<br>components approach<br>approach<br>Infrastructure<br>CAPEX from<br>expansion direct<br>C<br>expenditure<br>A<br>extrapolation<br>P<br>E<br>X Infrastructure CAPEX from<br>replacement model<br>OPEX and OPEX and<br>OPEX<br>inefficiencies inefficiencies<br>Inefficiencies from model from model<br><!-- End of picture text --> _Source:_ Authors’ compilation.

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