rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8571

# **Abstract**

Despite their importance, data on the structure of bank credit by maturity are scarce. For Latin America and the Caribbean, data are particularly difficult to obtain, as few banks report loan maturity data in commercial data sets such as Bankscope. With support from the Association of Supervisors of Banks of the Americas, this study assembled a novel data set on the structure of bank credit allocation in Latin America and the Caribbean covering 21 countries during 2004–14. This paper uses Bankscope and International Financial Statistics data to extended the coverage to more than 100 countries, creating the largest data set so far on credit by maturity. Benchmarking credit structure in Latin America and the Caribbean, the paper finds that the region is financially underdeveloped, because the ratio of short-term credit to gross domestic product is lower than in peers; long-term credit is at par; and consumer and commercial loans are lower. The paper also explores patterns of credit growth through nonparametric regressions. The results indicate that short-term credit grows faster than long-term credit as income grows in low-income countries, but the situation reverses when countries reach high- or middle-income status. Reflecting this trend, the share of mortgage loans rises with income.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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/research. The authors may be contacted at egutierrez2@worldbank.org, nkarmali@worldbank.org or dsourrouille@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
87 payments) commitments towards members. Since old–age insurance is mainly of long–term nature, investment portfolios consist therefore of a large share of fixed-income instruments (c.f. section 5.3). The second objective is to follow an absolute return strategy, to generate positive returns from which the insured can profit in the form of higher benefits (such as inflation indexing) or lower contribution rates for both employers and employees. Positive returns can be used to increase the funding status of the pension fund. Most pension funds follow a dynamic approach on the basis of existing commitments and risk capacity.

**<u>Table 36  Valuation Rules for Pension Funds</u>**

|**Valuation Rules:**|Comments|
|---|---|
|Fixed-Interest Securities with fixed currency and<br>maturity:<br>- at most at face value|with a conversion right: at most at market value|
|Securities with a market value:<br>- maximal at market value|stocks, participation certificates and warrants|
|Derivatives:<br>- maximal at market value<br>other securities:<br>- at most at face value|real securities, fixed-term deposits|
|Real estate and investments in real estate companies:<br>- at most at market value||

Source: BVV Table 37 presents the strategic asset allocation of some selected pension funds. (All the listed pension funds are also presented in the Appendix.) Additionally, the funding ratio, the technical interest rates (to allow for a comparison of funding ratios) and the number of total insured are given. The presented strategies differ from each other, but each pension fund invests a high fraction of its portfolio in at least one asset class (either Swiss currency bonds, foreign shares or real estate). It is likely that the chosen class represents the risk capacity of the pension fund. Almost all investment strategies are focused on global diversification and have a long–term focus. Furthermore, there are no explicit investment strategies to hedge mortality risks directly.
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# **Section IV - Size, Ownership and Productivity**

In this section we further analyze the extent of enterprise adjustment and, in terms of their labor productivity growth, the response of public and private firms to the reforms implemented in the 1990s. For this purpose we employ a different data set, with data covering 9 industrial sectors at one-digit level over the period 1990-1993.14 Compared to the data used in section III, this data set allows to control for the size of enterprises. In addition, we kept cooperative enterprises as a separate entity. Specifically, we can identify 5 different groups: (1) enterprises with 6-20 employees; (2) enterprises with 2150 employees; (3) enterprises with 51-300 employees; (4) enterprises with 301-500 employees and (5) enterprises with more than 500 employees It is also worth noting that in this new setting we are able to include in the analysis small firms with 6-20 workers whereas in the previous data set these where excluded. Basic statistics have been already shown in table 3.

Consistent with the previous section, we decompose sectoral productivity growth into aggregate cyclical components, structural industry-specific components and size specific ones (Eq. (1)). Given the results discussed in the previous section concerning the heterogeneous behavior of private and public firms with respect to the adjustment process, we initially controlled for model homogeneity across the different ownership groups. In order to do so, we fitted the following model'5: AY(k,i,t,o) = 1. + n(k) +s(i,t) +m(t) +z(o)+ z(o)*n(k) +z(o)*s(j,t) +z(o)*m(t) + u(k,i,t,o)(3) where AY is labor productivity growth, t is time and i is the one-digit sector, z(o) is a dummy variable with levels 0 for public enterprises data and 1 for private and cooperative ones; n(k) is a dummy variable representing the class size. By testing the significant regressors which include z(o) -- alone or interacting with the other variables -- we can 14 The sectors are: metallurgy, electro-engineering, chemical, mineral, wood and paper, light industry, food industry, other industries (i.e. animal feed and utilization industry, polygraphy and other like toy industry, music instruments and rub materials production). For a description of the data set, see Jackson et al (1995).

15 See for example Draper N.R. and Smith S. (1981).
unemployment stock has started in 1996,<sup>when inflowinto hasbeguntoexceed</sup> outflow from unemployment, and has accelerated<sup>after1998(Figure1).1Majorityof</sup> the unemployed (55 percent) are jobless<sup>for overoneyear,i.e.arelong-term</sup> unemployed. High unemployment combined<sup>with a lowlabor force participationrate</sup> (about 50 percent) implies low employment-to-population<sup>ratio.Only42 percent of</sup> persons of working age (aged 15 or more) are<sup>employedin Croatia.This entailsa low</sup> level of the utilization of labor resources<sup>andtranslatesinthelower levelof output</sup> and, eventually, lower economic welfare.

**Table 1 Key labor force indicators**

<!-- Start of picture text --> 2000.1 2000.11 2001.1 2001.11<br>Labor force participation rate 50.4 51.1 49.0 50.3<br>Employment rate 42.8 42.4 41.5 42.1<br>Unemployment rate 15.1 17.0 15.3. 16.3<br>Share of LTU 52.1 53.6 56.9 55.3<br>Notes:<br>Labor force participation rate = (Employed + Unemployed)/Population aged 15+<br>Employment rate = Employed/Population aged 15+<br>Unemployment rate = Unemployment/Labor force<br>Source: Labour Force Force in the Republic of of Croatia, Second Half-year of 2001, of 2001, 2001, Central Bureau of Statistics.of Statistics.Statistics.<br>Figure 1 The evolution of registered unemployment, 1992 - 2002<br>450<br>400 ._ _ _-- . - L - ,<br>350<br>300 .- -- -<br>o 250 - ~ v -- . {. -Unemployment<br>° 200- ~-7.. ;- nfo<br>250 15 - <-- . . Outflow<br>100 - - . . --, C<br>am a) a)C0) 0C 0) 0 ) 0 0 0<br>0) 0) 0 0) 0) 0) 0) 0) 0 0 0<br>_ _- _ - _- _- _ _ Ca N N<br>Source: Croatian Employment Service.<br><!-- End of picture text --> Source: Labour Force Force in the Republic of of<sup>Croatia,SecondHalf-year of 2001, of 2001, 2001,CentralBureau of Statistics.of Statistics.Statistics.</sup> Data used to calculate the unemployment rate (shown<sup>in Table1) come from the Labor Force</sup> Survey (LFS), which applies the standard ILO<sup>definitionof unemployment.Data on the dynamics of</sup> unemployment (show in Figure 1) come from<sup>theunemploymentregister and are not comparable with</sup> the LFS data. The description of both sources<sup>of unemploymentdata is providedin Box1.</sup> _-2-_
12 countries of Europe and North<sup>America. South Asia and Africa are still</sup> lagging behind but progress,<sup>although slower, has also been registered there.</sup> Declines in infant and child mortality<sup>rates (which mean that more</sup> children get to live beyond age<sup>1 and beyond age 5) are considered the one</sup> main reason for increases<sup>in life expectancy. While this generally holds true</sup> at the global level, such declines<sup>have, like life expectancy increases, been</sup> uneven among different regions.<sup>The data on child mortality rate from HIl</sup> and Pebley reflect this<sup>very woll (Table Health 1).</sup> In general, African countries appear<sup>to have higher child mortality</sup> rates and a lower pace of decline<sup>than other regions, except maybe South Asia.</sup> only five African countries offer<sup>observations for every quinquennium since</sup> 1960: Botswana, Ghana, Liberia,<sup>Senegal, and Uganda. Botswana offers by far</sup> the most successful story with<sup>an under five mortality (5qO) of 70 per</sup> thousand in 1980-85, and a more<sup>rapid rate of decline in the last years than</sup> earlier. Senegal, too, fastens<sup>its rate of decline in the 1980s, although</sup> moderately. On the other<sup>hand, Liberia's rate of decline slows down despite</sup> the still high level of its<sup>5qO (220). Ghana and Uganda see their child</sup> mortality actually increase in<sup>the 1980s, after two decades of decline. The</sup> civil war situation of Uganda<sup>in the last 10 to 15 years is a plausible</sup> explanation of its poor<sup>performance. Ghana's is more puzzling, and some have</sup> pointed towards its repeated<sup>and radical adjustment policies of the 1980s for</sup> an explanation.
and quality are all important. Women’s pre-subsidy employment statuses, average wages, childcare arrangements, and other factors not directly associated with childcare costs and availability are also important. These indirect factors include childcare regulations, welfare systems, and social norms. All of these factors might strengthen or reduce the effectiveness of ” family policies _._ With current substantial changes to the welfare state structure in Poland, the link between female labor market participation and fertility needs to be assessed. With the introduction of the 500+ benefit for the second and every subsequent child, the structure of this system changed considerably and needs to be reexamined for its effects on incentives to enter or remain in the labor market and fertility decisions. This paper will aim to contribute to this intensive debate by examining the externalities caused by the design of the 500+ benefit in Poland. For this, we will focus on the disincentive effects of entering the labor market for women and point to new developments in the childcare provision and its effect on childcare in Poland that need further research to fully comprehend the issue.

# **III. Method and Approach**

- **a. Tax-Benefit Model and Its Assumptions**

The OECD Tax-Benefit Model (TBM) is a tool to simulate how multiple tax and benefit rules and their interplay affect the net income of households. This de jure model seeks to visualize the net returns from employment at different levels of total household income and to let this vary according to different family types. It aims to incorporate all taxes and contributions (for example, income tax, social security contributions, and health insurance payments) and any benefits (social assistance, child benefit, family benefits, and so on) that are in the law of the examined country. Its strength lies in the ability to incorporate the complex social policy system with all the different eligibility criteria and thresholds into a unifying framework and statistical program. The model is particularly useful to synthetize the effect of different benefits in a complex social assistance system, as in Poland. Figure 2 illustrates a summary of the different eligibility thresholds for different noncontributory programs in Poland.

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WPS3192

# **GREENFIELD FOREIGN DIRECT INVESTMENT AND MERGERS AND ACQUISITIONS: FEEDBACK AND MACROECONOMIC EFFECTS***

César Calderón Central Bank of Chile Norman Loayza The World Bank Luis Servén The World Bank

## **Abstract**

FDI flows to developing countries surged in the 1990s, to become their leading source of external financing. This rise in FDI volume was accompanied by a marked change in its composition: investment taking the form of acquisition of existing assets (M&A) grew much more rapidly than investment in new assets (“greenfield” FDI), particularly in countries undertaking extensive privatization of public enterprises. This raises two issues. First, is the M&A boom a one-time effect of privatization, or is it likely to be followed by a rise in greenfield investment? Second, do these two types of FDI have different macroeconomic causes and consequences – in relation to aggregate investment and growth? This paper focuses on establishing the stylized facts in terms of time precedence between both types of FDI, investment and growth, using annual data for the period 1987-2001 and a large sample of industrial and developing countries. We find that in all samples higher M&A is typically followed by higher greenfield investment, while the reverse is true only for developing countries. In industrial and developing countries alike, both types of FDI lead domestic investment, but not the reverse. Finally, neither type of FDI appears to precede economic growth in either developing or industrial countries, but FDI does respond positively to increases in the growth rate.

## _JEL classification codes_ : F43, F37, O16

## World Bank Policy Research Working Paper 3192, January 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * This research was supported by the World Bank’s Latin American Regional Studies program. We thank Linda Kaltani for able research assistance.
<!-- Start of picture text --> toon<br>ooSoS | ome<br>Lo<br>$ ‘AUC: 65.5%<br>9°BE AUC:. 50.8%<br>o<br>rN AUC:AUC: 61.7958.1%<br>50 100<br>False Positive Percentage<br><!-- End of picture text -->

<!-- Start of picture text --> S toon<br>raooSoSLo |mom<br>a<br>£ ‘AUC: 74.0%<br>fo}Be ; AUC:. 55.5%9<br>mS3 o AUC:AUC: 61.4%55.3%<br>0 50 100<br>False Positive Percentage<br><!-- End of picture text -->

<!-- Start of picture text --> S toon<br>ooSoraSLo |mom<br>a<br>£ ‘AUC: 68.9%<br>fo}Be : AUC:sep 56.1%40<br>mS3 o AUC:AUC: 61.4%58.1%<br>0 50 100<br>False Positive Percentage<br><!-- End of picture text -->
where u<sup>0</sup> cg<sup>,u1</sup> cg<sup>,andu2</sup> c<sup>areaverageerrortermsinclusterc.</sup> Whereas in the simplified model I was interested in three parameters, ψ, θ and λ, here I need to estimate two matrices and one vector of parameters. The matrices are Ψ and Θ, with gth row denoted by ψg = (ψg1, ..., ψgg, ..., ψgG) and θg = (θg1, ..., θgg, ..., θgG), respectively. The vector of wage elasticities is λ = (λ1, ..., λG)<sup>0</sup> .

To solve for the matrices of parameters, Θ, Ψ, and λ, I need to manipulate the model and introduce some new notation. Let pc be a G × 1 vector of the logarithm of (unobserved) prices in cluster c. Stacking the vectors p<sup>0</sup> c<sup>forallclusters,IgetaC×GmatrixPof</sup> prices. Next, I stack observations on average unit values for good g, (17), into a C × 1 vector ybg<sup>1=1Cα</sup> 1<sup>g+ Pψ0</sup> g<sup>+u1</sup> g<sup>,where1CisaC× 1vectorofones,ψgisthegthrowofmatrixΨ</sup> and u<sup>1</sup> g<sup>isavectorofresiduals.Itfollowsthat</sup> where Π is the variance-covariance matrix of the vector of good prices (across clusters). Next, I construct a G × G matrix V1 with element gk given by (19) where Ω11 is the matrix with gk element E[u<sup>1</sup> g<sup>0u1</sup> k<sup>].</sup> Following the same procedure, I generate the vector byg<sup>0by stacking the estimated average</sup> budget shares spent on good g, equation (16), by clusters. This vector is ybg<sup>0= 1Cα</sup> 0<sup>g+Pθ0</sup> g<sup>+</sup> f<sup>c</sup> +u<sup>0</sup> g<sup>,whereθgisthegthrowofmatrixΘ,andu0</sup> g<sup>isavectorofresiduals.Itfollowsthat</sup> Next, I build a G × G matrix V10 with element gk given by (21) where Ω10 is the matrix with gk element E[u<sup>1</sup> g<sup>0u0</sup> k<sup>].</sup> 13
**Table 1b: T‐Statistics for the Difference in Means ‐between Full and Restricted**<sup>**1**</sup> **<u>(ERR in PAD and ICR only) sample</u>**

||**1975‐2015**|**1985‐2015**|**1995‐2015**|
|---|---|---|---|
|IEG 6‐point Rating|||6.50***|
|**_Evaluation Characteristics_**||||
|Y<br>b<br>Cli<br>d||||
|ears<br>etween<br>ompeton<br>an<br>Evaluation|1.00|1.10|‐4.20***|
|**_Macro_**<br>**_Variables_**<br>**_(averaged_**<br>**_over_**<br>**_project life)_**||||
|Log of Real GDP per capita growth,<br>averaged overproject life|2.30**|2.50***|3.80***|
|LogCPI averaged overproject life|0.80|‐0.40|‐0.04|
|CPIA score(1=bad,6=good)||1.00|2.80***|
|**_Basic Project Characteristics_**||||
|Logarithm of project net commitment<br>(US m$)|9.10***|12.60***|13.20***|
|Years from Approval to Completion|0.20|2.50***|2.90***|
|Preparation Costs/Commitment, (%)|‐9.50***|‐7.70***|‐6.50***|
|Supervision Costs/Commitment, (%)|‐13.20***|‐11.10|‐9.30***|

Source: Staff calculation. *** represents significant level and 1% and ** at 5%. Note: (1) The restricted sample contains projects that have reported ERRs in both the PAD and ICR.

# 5.  Overview of Results

Both OLS and logit regressions are employed to examine the impact of the quality of economic analysis (correcting for both country and project level factors). Regression results are reported in the Annex and show that country level correlates alone explain around 10 percent of the variation in project performance. Project level factors explain an additional 10 percent of the variation in project performance, similar to results estimated by Denizer et al. (see Tables A.6 to A.9, regression 1).

**_Country level correlates of project performance_** . For each project, the average over the life of the project of the CPIA and GDP growth per capita are the key variables used in the regressions.  We also include a dummy for when the CPIA rating schedule was modified in 1998 (the scale was changed from 5 to a 6 point scale). Regression results show that this change has no significant impact.  Overall, the country level correlates all have the expected signs.  A higher level of institutional development and growth is correlated with better project performance.

**_Project level correlates of performance_** .   This paper focuses primarily on the impact of project economic analysis on project performance.  However, the analysis also corrects for other project level effects which might have a bearing on project performance.  Specifically, the regressions correct for the size of the project, its duration (the time between project appraisal and completion), the track record of the task team leader (in terms of ratings for previous projects managed by the same team leader), cost of

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# **5.2. Fixed Effects Results**

The within groups estimations under all specifications show a positive and statistically significant association between trade liberalization and GDP growth. The first column in table 2 includes the entire period from 12 years before to 9 years after the reform under the basic specification of equation (5). The apparent impact of trade opening is an increase in income per capita growth of 1.7 percent, a considerable acceleration with high statistical significance. As expected, this impact is lower when the years of crisis [-4,1] are excluded but it is still above 1 percent p.a. and statistically significant at 1%.

**<u>Table 2: Trade Liberalization and GDP Per Capita Growth</u>**

||Basic Equation<br>Using Entire<br>Period [-12,9]<br>Ind|E<br>Basic<br>Equation<br> <br>ependentV|xcluding [-4<br>With Time<br>Trend<br>ariable:GD|,1]<br>With Year<br>Dummies<br>P PerCapita|Excluding<br>[-4,4]<br>Growth|Excluding<br>[-5,5]|
|---|---|---|---|---|---|---|
|postlib|1.686|1.23|2.096|3.571|1.049|0.993|
||(6.10)**|(4.16)**|(3.49)**|(3.66)**|(2.91)**|(2.47)*|
|trend|||-0.105||||
||||(1.65)||||
|Constant|0.356|0.926|0.584|0.737|0.926|0.993|
||(1.92)|(4.32)**|(1.96)|(0.27)|(4.16)**|<br>(4.11)**|
|Observations|856|661|661|661|505|427|
|Number of countries|39|39|39|39|39|<br>39|
|R-squared|0.04|0.03|0.03|0.11|0.02|<br>0.02|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% We noted earlier though that the coefficient of POSTLIB in the basic specification of equation (5) will underestimate the impact of trade liberalization, if the expansionary macroeconomic policies and economic context in the pre-liberalization years are taken into account. Equation (5) is reestimated adding a time trend and year dummies. These variables should capture the impact of the negative trend in the macroeconomic and external context. The third column of Table 2 shows that the time trend has a negative coefficient, although it is not statistically significant. Inserting a time trend has the effect of increasing the coefficient to above 2 percent while using year dummies raises it to almost 4 percent.