rafmacalaba/datause-encoder-data huggingface dataset:
variables are best represented as trend or first-difference stationary (see, for example, Nelson and Plosser, 1982). Although in theory the hypothesis that the series is difference stationary can be tested, it is widely recognized that those tests have very low power, particularly in small samples (Enders, 2003). Also, although the long run properties of the alternative representations are very different, from a practical standpoint the short run forecasts obtained from them tend to be similar. In our particular case, using the Levin et al. (2002) test for unit roots in panel data, we reject the null hypothesis of a unit root for all the panel variables, _GDP , DMTT ,_ and _AID,_ but standard Dickey-Fuller tests cannot reject the hypothesis for the variables capturing the state of the world business cycle ( _GDPH_ and _R_ ), which are common to all countries (the results of the unit root test are reported in Table A1 in the appendix). However, for these common variables we cannot take advantage of the cross-section dimension to increase the power of the tests, so we have only 31 observations. Given the low power of these test, and the small number of observations we prefer to follow the results obtained for the panel variables and estimate the system assuming that the series are trend stationary. Nevertheless, we will also estimate the model in first differences and the results, reported in the robustness section of the paper, will prove qualitatively similar.<sup>7</sup> The model corresponds to a _panel VAR_ in which it is assumed that the dynamics, represented by the _A_ matrices, are common across cross-sectional units. This is a standard assumption in this literature (see Broda, 2001, 2004; Ahmed, 2003; Uribe and Yue, 2003) because, given the length of the time series dimension of the data (around 30 annual observations), it is not possible to estimate country specific dynamics unless we reduce importantly the number of exogenous shocks under consideration, the number of lags, or both. However, as noticed by Pesaran and Smith (1995), this assumption may lead to obtaining coefficients that underestimate (overestimate) the short (long) run impact of exogenous variables if the dynamics differ importantly across countries. There are several reasons that lead us to believe that this concern is unlikely to be important for our results. First, we focus on a subset of relatively homogeneous countries, so the heterogeneity of the parameters is likely to be much smaller than in studies that include countries from different income groups. Second, as Pesaran and Smith (1995) also notice, the asymptotic bias tends to zero if the autoregressive coefficient in the dependent variable (in this case _GDP_ ) is close to one, which

> 7Broda (2001, 2004) uses a similar approach to determine the effect of terms-of-trade fluctuations in countries with different exchange regimes. The main difference between his approach and ours is that he assumes the variables are difference stationary and analyzes the VAR for the changes in the logs.

13
their entry. Although some foreign banks were among the largest in Colombia, none of them compared to the largest banks in Chile or Argentina. Colombia's foreign banks, too, might have been too small to engage in small business lending through credit scoring.

# _C. Macroeconomic environment_

The top panel in Figure 10 provides real growth rates for each of the four countries over this period. Because they exhibit almost the same growth patterns, it is unlikely that this factor can account for the differences in results across countries. One exception to the pattern is Argentina in 1995 and 1996, which, while recovering from the Tequila Crisis, was experiencing slower, but increasing growth, while the others were generally experiencing faster, but declining growth. However, our sample for Argentina starts in 1998. The bottom panel in Figure 10 does indicate, however, that Argentina and Chile both enjoyed lower inflation than either Colombia or Peru during this period. This, too, might have made it easier for large foreign banks to lend to small businesses. In short, it seems plausible that the contracting environment, the structure of the banking sector, and price stability all might have made it easier for large foreign banks in Argentina and Chile to lend to small businesses. However, further work is probably needed to confirm these suppositions.

# **VIII. Conclusions**

As foreign participation increases in the banking sectors of developing countries, many questions remain unanswered. Key among them is the issue of whether foreign banks tend to shy away from lending to small businesses. Using bank level data for Argentina, Chile, Colombia, and Peru over the mid- 1 990s, this paper examines the impact of foreign bank entry on the share and growth rate of lending to small businesses, while controlling for other factors that might affect lending to this sector.

Consistent with evidence from the U.S., we find that medium and large domestic banks in the four countries we study devote less of their lending (as share of total lending) to small businesses than small domestic banks. Also, in most countries, the share of lending devoted to small businesses by domestic banks was dropping in the late 1990s, as evidenced by the negative and significant trend in the share estimations.

20
<!-- Start of picture text -->
1.200<br>1.100<br>1.000<br>0.900<br>0.800<br>0.700<br>0.600<br>0.500<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>Labor Energy Materials<br><!-- End of picture text -->

Figure A.5: Capital Stock Efficiency Indexes, Pulp, Paper, Paper Products, Printing and Publishing, United States, 1991-2005 55
<!-- Start of picture text --> AUSTRALIA 144 ° 146 ° 148 ° 150 ° 152 ° Brisbane 154 °<br>MURRAY-DARLING RIVER BASIN<br>Thargomindah Gold Coast<br>SELECTED CITIES<br>NATIONAL CAPITAL<br>MAJOR ROADS<br>138 ° 140 ° 142 °<br>A U S T R A L I A Grafton<br>Bourke Walgett<br>Leigh Creek<br>Wilcannia Port Maquarie<br>Broken Hill<br>Dubbo<br>Port Augusta<br>Parkes Newcastle Tasman<br>Orange<br>Bathurst<br>Richmond Sea<br>Sydney<br>Mildura<br>Wagga Goulburn Nowra<br>Adelaide Wagga<br>Murray Bridge<br>CANBERRA<br>Albury<br>Echuca Wodonga<br>Wangaratta<br>Horsham Bendigo AUSTRALIA<br>Murray-Darling<br>River Basin<br>Ballarat<br>Melbourne Barnsdale<br>Mount Melton<br>0 50 100 Kilometers Gambier Moe Sale<br>Portland Geelong Morwell Traralgon This map was produced by the Map Design Unit of The World Bank. The boundaries, colors, denominations and any other information<br>0 50 100 Miles Warrnambool Colac shown on this map do not imply, on the part of The World BankGroup, any judgment on the legal status of any territory, or any<br>endorsement or acceptance of such boundaries.<br>138 ° 140 ° 142 ° 144 ° 146 ° 148 ° 150 ° 152 ° 154 ° 156 °<br>e<br>d<br>ar<br>30 °<br>32 °<br>34 °<br>36 °<br>38 °<br>u<br>a<br>D li<br>dg e<br>28 °<br>30 °<br>32 °<br>34 °<br>36 °<br>38 °<br>y<br>m<br>i<br>n<br>bi<br>M<br>N<br>w r<br>m<br>g<br>r<br>o<br>u<br>G<br>i<br>r<br>Edwa<br>D<br>Mur rd<br>Lachlan<br>ra<br>a<br>y<br>lr<br>r<br>i<br>ie<br>n<br>y<br>W<br>rie<br>M<br>qac<br>u<br>a<br>a<br>g<br>r<br>r<br>u<br>M<br>JANUARY 2005 IBRD 33726<br>oL<br>d<br>d<br>o<br>n<br><!-- End of picture text -->
- Li, Wei, Lixin Colin Xu. 2004. “The Impact of Privatization and Competition in the Telecommunications Sector around the World?” _Journal of Law and Economics_ XLVII (2), 395-430.

- Lin, Justin Yifu. 1992. “Rural Reforms and Agricultural Growth in China.” _American Economic Review_ 82(1), 34-51.

- Lin, Justin Yifu, Fang Cai, Zhou Li. 2003. _The China Miracle: Development Strategy and Economic Reform._ Hong Kong: The Chinese University Press.

- Lin, Justin Yifu. 2009. _Economic Development and Transition: Thought, Strategy, and Viability_ . New York: Cambridge University Press.

- Loayza, N., A. Oviedo and L. Serven. 2005a. "Regulation and Macroeconomic Performance." _World Bank Working Paper 3469._

- Loayza, N., A. Oviedo and L. Serven. 2005b. "The Impact of Regulation on Growth and Informality: Cross Country Evidence." _World Bank Policy Working Paper 3623._

- Long, Cheryl. 2010. “Does the Rights Hypothesis Apply to China.” _Journal of Law and Economics_ 53(4) _._

- Long, Cheryl & Zhang, Xiaobo, 2009. "Cluster-based industrialization in China: Financing and performance," IFPRI discussion papers 937, International Food Policy Research Institute (IFPRI).

- Lu, Yi, Tao Zhigang. 2009. “Contract Enforcement and Family Control of Business: Evidence from China.” _Journal of Comparative Economics_ 37, 597-609.

- Lu, Yi, Ivan P.L. Png, Zhigang Tao. 2008. “Do Institutions not Matter in China? Evidence from Enterprise-Level Productivity Growth.” Working paper, University of Hong Kong.

- Lui, Francis. 1985. “An Equilibrium Queuing Model of Bribery.” _Journal of Political Economy_ 93, 76081.

Maloney, William. 2004. "Informality Revisited." _World Development_ 32(7), 1159-78.

- McMillan, John, Christopher Woodruff. 1999a. "Dispute Prevention Without Courts in Vietnam." _Journal of Law, Economics and Organization_ , 15(3), 637-658.

- McMillan, John, Christopher Woodruff. 1999b. "Interfirm Relationships and Informal Credit in Vietnam." _Quarterly Journal of Economics_ , 114(4), 1285-1320.

- McMillan, John, Christopher Woodruff. 2002. "The Central Role of Entrepreneurs in Transition Economies." _Journal of Economic Perspectives_ 16(3), 153-170.

- Megginson, W., and Netter, JR. 2001. “From State to Market: A Survey of Empirical Studies on Privatization.” _Journal of Economic Literature_ 39, 321-389.

Myrdal, G. 1968. _Asian Drama: An Inquiry into the Poverty of Nations_ . Pantheon Books, New York.

- Nickell, Stephen. 1997. “Unemployment and Labor Market Regidities: Europe versus North America.” _Journal of Economic Perspectives_ 3, 55-74.

- North, Douglas. 1990. _Institutions, Institutional Change and Economic Performance._ New York: Cambridge University Press.

- <mark>Petrin, Amil, Jagadeesh Sivadasan. 2006. "Job Security Does Affect Economic Efficiency: Theory, A New Statistic, and Evidence from Chile," University of Minnesota.</mark>

32
# **5. Trade policy uncertainty, trade, and GVCs**

We next use the World Trade Uncertainty (WTU) Index instead of the Economic Policy Uncertainty Index as a measure of policy uncertainty. The behavior of the WTU Index differs from the broader indicator of economic policy uncertainty, as shown in Figure 3. The different pattern captures the large increase in trade policy uncertainty in the post-2017 period, although the limited (in some cases no) variability of the WTU Index in previous years may also reflect the lack of attention to trade policy uncertainty in media reports in the pre-2017 period as discussed in Section 3.

**Figure 3.** **_The Economic Policy Uncertainty (EPU) Index versus the Trade Policy Uncertainty Index._**

<!-- Start of picture text --> 1100<br>1000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>World Economic Policy Uncertainty (EPU) Index<br>World Trade Policy Uncertainty Index<br>Economic Policy Uncertainty Index, Trade Policy Uncertainty Index,<br>by country selected countries<br>800 40<br>600 30<br>400 20<br>200 10<br>0 0<br>AUS BRA CAN CHL CHN AUS BRA CAN CHL CHN<br>COL DEU ESP FRA GBR COL DEU ESP FRA GBR<br>GRC IND IRL ITA JPN GRC IND IRL ITA JPN<br>KOR MEX NLD RUS SGP KOR MEX NLD RUS SGP<br>SWE USA SWE USA<br>1997q1=100<br>1997q1 1998q4 2000q3 2002q2 2004q1 2005q4 2007q3 2009q2 2011q1 2012q4 2014q3 2016q2 2018q1<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Sources: www.PolicyUncertainty.com, Baker et al. 2016), Ahir et al (2019) The regression results in Tables 5 and 6 show no statistically significant impact of trade policy uncertainty on overall trade growth (Table 5) or on GVC trade growth (Table 6). In addition to the low variability over time of the indicator, two other factors might help explain this result. First, 12
With respect to measurement, a promising area of work relates to producing more reliable FDI statistics that control for profit shifting motives and the role of tax havens. For instance, Damgaard et al. (2019) and Damgaard and Elkjaer (2017) build an alternative measure of FDI which does not include special purpose entities, Casella (2019) develops a method aimed at uncovering the identity of ultimate investors, and Coppola et al. (2021) build a new dataset for cross-border investment by both residency and nationality.

# **3 FDI and Growth since the 1970s**

This section describes how the relationship between FDI and growth has evolved over time. It documents that local conditions (financial depth and education) played an important role for the link between FDI and economic growth in the early years of the sample. However, this result no longer applies for more recent growth spells.

The first part of the section focuses on cross-country regressions similar to those of Borensztein et al. (1998) and Alfaro et al. (2004) but estimated over different time periods. The second part of the section explores the evolution of the relationship between FDI and growth using panel data which allow controlling for country-specific and time-invariant unobserved heterogeneity.

Our focus is on FDI received by emerging market and developing countries. Our main dependent variable is real per capita GDP growth (sourced from the World Bank Development Indicators) averaged over either 10 or 20-year periods and our key explanatory variable is net FDI inflows over GDP, also sourced from the World Bank Development Indicators. The number of countries in our regressions ranges between 72 and 96 and varies depending on the time period and the estimation strategy. The number of observations in the panel regressions ranges between 1091 and 3098. Appendix Table A.1 reports summary statistics for all variables used in the analysis and Appendix Table A.2 shows the countries included in each sample.<sup>4</sup> The data show that average net FDI were just above 1% of GDP and ranged between -0.6% of GDP and 15.5% of GDP, at the beginning of the period we study (Panel A of Appendix Table A.1). Over 1995-2014, average FDI flows had increased to 5% of GDP and ranged between less than 1% of GDP and nearly 90% of GDP. Figure A.1 in the appendix shows the cross-country dispersion of average net FDI inflows in 1975-1994 and 1995-2014. Figure A.2 in the Appendix shows the year-by-year cross-country distribution of net FDI inflows. It indicates that average net inflows peaked in 2006-07.

## **3.1 Baseline Cross-Country Regressions**

To assess the presence of a long-run link between FDI and economic growth, we start by regressing the growth rate of real GDP per capita averaged over a 20-year period ( _GRi_ ) on FDI inflows and a set of controls. Formally, we estimate the following model:

> 4 For variable definitions and sources see Appendix Table A.3.

5
# **References** :

Acemoglu Daron, Simon Johnson and James Robinson (2001). “The Colonial Origins of Comparative Development: An Empirical Investigation.” _American Economic Review_ , 91:1369-1401.

Acemoglu Daron, Simon Johnson and James Robinson (2002). “An African Success Story: Botswana”. Centre for Economic Policy Research. Discussion Paper Series No. 3219. MIT.

Akyuz Yilmaz and Charles Gore (2001). “African Economic Development in Comparative Perspectives.” _Cambridge Journal of Economics_ , Vol. 25, 265—288.

Alpers Edward (1967). The East African Slave Trade. Oxford University Press, UK.

Artadi V. Elsa and Xavier Sala-i-Martin (2003). “The Economic Tragedy of the 20th Century: Growth in Africa.” _NBER Working Paper_ No. 9865. The National Bureau of Economic Research.

Aslund Anders and Marek Dabrowski (2008). Challenges of Globalization: Imbalances and Growth. Peterson Institute for International Economics, Center for Social and Economic Research, Washington DC.

Australian Government (2005). “More than Oil: Economic Development in Bahrain, Kuwait, Oman, Qatar and the United Arab Emirates”, Australian Government, Department of Foreign Affairs and Trade, Economic Analysis Unit.

Ayittey George (2006). Indigenous African Institutions. Hotei Publishing, Second edition, USA.

Becker Torbjorn and Paulo Mauro (2006). “Output Drops and the Shocks that Matter.” _IMF Working Paper WP/06/172_ , The International Monetary Fund.

Berg Andy and Zia Qureshi (2005). “The MDGs: Building a Momentum.” _Finance and Development_ , Vol. 42. Number 3. The International Monetary Fund.

Bhacker Reda (1992). Trade and Empire in Muscat and Zanzibar: Roots of British Domination. Routledge, New York, USA.

BP (2007). “BP’s Statistical Review of World Energy.” British Petroleum, The United Kingdom.

Chatterjee Santanu (2005). “Capital Utilization, Economic Growth and Convergence.” _Journal of Economic Dynamic and Control_ , Vol. 29 (2005) 2093-2124.

Collier Paul, Anke Hoeffler and Catherine Pattillo (2001). “Flight Capital as a Portfolio Choice.” _The World Bank Economic Review_ , Vol. 15, No. 1, 55-80.

Collier Paul and Anke. Hoffler (2004). “Greed and Grievance in African Civil Wars.” _Oxford Economic Papers,_ Vol.56, No. 4, 563-595.

Cooper Richard (2008). “Global Imbalances: Globalization, Demography, and Sustainability.” _Journal of Economic Perspectives_ , Volume 22, Number 3, Pages 93-112.

Deaton Angus (2001). “Counting the World’s Poor.” _The World Bank Research Observer_ , Vol. 1, No. 1, pp. 157-168.

Easterly William and Ross Levine (1997). “Africa Growth Tragedy: Policies and Ethnic Divisions.” _Quarterly Journal of Economics,_ Vol. 112, No. 4.

Easterly William (2002). “How did Heavily Indebted Poor Countries Become Heavily Indebted? Reviewing two Decades of Debt Relief.” _World Development_ , 30 (10), 1677-1696.

EIA (Energy Information Administration) (2008). Annual Energy Review 2007. Washington DC: Energy Information Administration.

24
Table S2.4 The Evolution of the Experience Premium

|||||Log re<br>|al wage<br>|s ratio<br>||||
|---|---|---|---|---|---|---|---|---|---|
||1<br>|0-19/0-<br>|9<br>|2<br>|0-29/0-<br>|9<br>|30 o<br>|r more<br>|/0-9<br>|
|Country|1995|2002|2015|1995|2002|2015|1995|2002|2015|
|Argentina|0.20|0.23|0.14|0.30|0.39|0.20|0.31|0.40|0.19|
|Bolivia|0.09|0.17|0.09|0.27|0.24|0.13|0.14|0.07|0.05|
|Brazil|0.27|0.30|0.21|0.44|0.46|0.30|0.42|0.48|0.40|
|Chile|0.15|0.09|0.14|0.27|0.18|0.17|0.33|0.21|0.15|
|Colombia|n.a.|0.14|0.12|n.a.|0.26|0.17|n.a.|0.33|0.14|
|Costa Rica|0.17|0.19|0.20|0.28|0.19|0.28|0.16|0.17|0.22|
|Ecuador|n.a.|0.12|0.07|n.a.|0.24|0.10|n.a.|0.24|0.04|
|Guatemala|n.a.|0.15|0.18|n.a.|0.18|0.27|n.a.|-0.02|0.26|
|Honduras|0.18|0.19|0.14|0.31|0.29|0.35|0.45|0.28|0.29|
|Mexico|0.17|0.19|0.21|0.35|0.32|0.25|0.25|0.24|0.24|
|Nicaragua|0.15|0.12|0.11|0.23|0.22|0.16|0.26|0.18|0.17|
|Panama|0.25|0.14|0.12|0.43|0.27|0.16|0.48|0.34|0.10|
|Peru|0.07|0.08|0.06|0.13|0.13|0.10|0.13|0.15|0.05|
|Paraguay|0.11|0.18|0.15|0.18|0.23|0.26|0.09|0.21|0.19|
|El Salvador|0.14|0.20|0.15|0.23|0.28|0.23|0.15|0.28|0.12|
|Uruguay|0.26|0.26|0.21|0.32|0.42|0.29|0.32|0.41|0.32|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Each data point is determined considering circa concepts (see Table A.1). The log of real hourly wages in the main occupation (labor income in 2005 purchasing power parity) was regressed on dummies of level of education (five levels), five-year intervals of experience, and all possible interactions. The sample was restricted to individuals between ages 18 and 65 years who were employees or self-employed and between the 1st and 99th percentiles of the wage distribution.

55
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

# **APPENDIX D OVERVIEW OF PROST 12**

The model consists of an input workbook and five output modules. On the input side, the user provides country specific data on demographic, economic and pension system related parameters and assumptions about their behavior in the future. This information is entered in the **input file** with six embedded worksheets:

- _General_ Economic variables (GDP and wage growth, inflation, interest rate), non age-specific pension system parameters (pension fund balance and benefit expenditure in the base year, retirement age, contribution rate, pension indexation rules, etc.) and some demographic variables;

- _Population_ Base year population by age and gender along with age-specific fertility and mortality rates and immigration information.

- _Labor_ Age and gender specific labor force participation and unemployment rates as well as distribution of wages and old age pensions across age and gender cohorts.

- _Pension_ Age and gender specific information about pension system contributors, beneficiaries, coverage and retirement rates, average years of service at retirement and replacement rates for new beneficiaries.

- _Profiles_ Information on representative individuals, such as gender, career path, individual wages, life expectancy, etc.

- _Reform_ Parameters relevant to systemic reforms to be simulated (any combination of conventional PAYG, fully funded DC and notional DC pillars), including switching pattern, how the acquired rights will be paid, contribution rates, rules for annuitization and pension payout under DC schemes and replacement rates/benefit formula in a PAYG pillar, indexation, etc.

In the most simplified way the **general calculation scheme** can be summarized as follows:

- 97 -
We test the sensitivity of our estimates to inclusion of treatment interaction with participation rates at the village meeting.<sup>37</sup> ANCOVA is the preferred estimator, as the standard difference-in-difference (DiD) estimator would require twice as much sample to yield the same power when baseline is taken, and autocorrelations are low (McKenzie, 2012).<sup>38</sup> To measure the effect of treatment status on student test scores we fit the following ANCOVA model: where, is the test score for student _k_ in subject _m_ in household _t_ in village _v_ at the kk,mm,t,v,e ddeeiie follow-up. SS is the indicator variable for treatment assignment. controls for the vv kk,m,tt,v,b bbe iie baseline value of the same students’ test scores. The term TT denotes district-level fixed effects. XX dd We adjust the standard errors to account for within-village correlations across students in uu outcomes. We test the sensitivity of our estimates to inclusion of treatment interaction with participation rates at the village meeting.

# **3.6 Attrition**

There is some attrition in each round of data collection. We used the sample of 287 villages and 489 schools for randomization. For a handful of villages, the data quality was poor, with

> 37 We are unable to find any significant interaction effects with participation rates and any outcome of interest. Results are omitted for brevity, but available on request.

> 38 The study is underpowered to detect small effect sizes for a standard DiD estimator.

34