rafmacalaba/datause-encoder-data huggingface dataset:
statistics for each variable. The sample mean for the dependent variable electricity production per capita is 2,639 kwh per capita, and for the telephone lines per capita it is 195. For the purpose of estimation, per capita infrastructure and income measures were transformed into natural logarithmic form.

**Table 1: Summary Statistics of the Panel Data, 1980-2002, 52 low- and middle-income countries**

||**gdppc**|**yag**|**yman**|**yserv**|**eleconpc**|**elepropc**|**telpc**|
|---|---|---|---|---|---|---|---|
|**mean**|8336.2|15.2|17.6|53.1|2343.6|2638.6|195.4|
|**median**|4788.9|12.9|17.1|53.2|784.6|955.3|57.8|
|**max**|57740.8|62.6|40.5|87.5|24858.4|31086.2|1857.4|
|**min**|644.4|0.1|3.5|17.2|11.4|1.9|0.4|
|**stddev**|8641.5|11.7|6.7|11.3|3920.4|4364.8|297.0|
|**skewness**|1.6|1.1|0.4|-0.2|3.1|3.4|2.3|
|**kurtosis**|2.5|1.3|0.1|0.2|11.3|13.6|5.8|
|**observations**|1472|1472|1472|1472|1472|1472|1472|

Source: World Development Indicators data base, World Bank (www.worldbank.org).

# **Results**

We base our prediction of the required stocks and flows of infrastructure on an estimated long-run demand using a 52-country panel. The list of countries in the panel is listed in Appendix A. Justification for the use of international data is in the spirit of providing a benchmark against comparable country experiences. Advantage of the PMGE methodology is that the implicit assumption of homogeneity across the panel is explicitly tested for. The panel of countries was also limited to low- and middle-income countries, in order not to bias estimated infrastructure demand upward through the inclusion of high-income countries.

We estimated the following empirical specification of equation (3):

|ln<br><br><br>|_I_<br>_P_<br>⎛<br>⎞<br>⎜<br>⎟<br>⎝<br>⎠|,<br>,<br>ln<br>_y_<br>_i t_<br>_i t_<br>_YPC_<br>α<br>β<br>=<br>+|3<br>,<br>,<br>,<br>1<br>_k_<br>_i t_<br>_i t_<br>_i t_<br>_k_<br>_YS_<br>_Z_<br>β<br>δ<br>ε<br>=<br>+<br>+<br>+<br>∑|(7)|
|---|---|---|---|---|

where I denotes the relevant infrastructural measure (electricity, total telephone lines),<sup>4</sup> P population, YPC per capita real GDP, YS sectoral shares in GDP, where we consider agriculture (YAG), manufacturing (YMAN), and services (YSERV), and Z denotes a vector of additional explanatory variables.

Table 2 reports estimation results for the two long-run relationships. Results confirm adjustment to equilibrium (see the ECM-parameters, which correspond to the δ - parameters of equation 4), though unsurprisingly for infrastructure the adjustment to equilibrium is relatively slow, particularly in the case of telephones. The Hausman tests (denoted h-tests) confirm the legitimacy of the PMG estimator by failing to reject the homogeneity restriction on the long-run coefficients across our panel of 52 countries. The log likelihood statistics suggest a relatively high level of fit.

> 4 Total telephone lines = fixed + mobile telephone lines.

11
treated as missing and reduces the sample by about 100 firms. Ignoring refusals yields a stronger negative effect of bribery on firm growth. One explanation for the increase in the effect of bribery is that firms who refuse to answer to bribery questions are those that are not the most adversely affected. Perhaps firms who use bribery to their own benefit or even offer bribes to officials are the least likely to reveal their activity. By ignoring firms like these, the remaining set of firms who admit to facing a bribe are those that were solicited forcibly, and may also be the most negatively affected.

# <u>C. Clusters</u>

As a fourth robustness test, we performed the regressions using clusters computed two different ways, one at a finer level and one at a broader level. We originally chose sector-location cells to accommodate small sample size in some countries. The two sectors used to construct the clusters were defined as manufacturing and services.

In the finer grouping, we create clusters at the location- _industry_ level. There are ten industries versus two sectors (Table 1). Due to the finer grouping, the minimum number of firms in a location-industry cluster is set at 20 instead of 30. This clustering allows for an even more homogenous group of firms in comparison to those using sector groupings. There are a larger number of clusters using industry groupings instead of sectors (138 vs. 101), but about 1,500 fewer observations in the regressions. The result of this exercise presented in Table 11 also yield significant estimates of the effect from bribery. The results are quite comparable to those obtained in Table 7 (clusters at the sector level) with slightly higher coefficients. Moreover, the coefficient of interaction of age with bribery is significant with this finer cluster definition.

Table 12 shows results using clusters at the location stratification level. The number of observations increased to 7,003 and reduced the number of clusters to 64. The interaction term between sales and bribery indicator continues to be significant. Table 13 includes results over country groupings and for SMEs only and excluding small economies; this table is comparable to results in Table 9.

# <u>D. Panel Estimation</u>

Lastly, we repeat our analysis over a set of firms that were surveyed in both 2010 and 2006 (Table 14). The panel data is available for fourteen countries as Caribbean and several Central 21
# **INTRODUCTION**

Indonesia, the fourth most populous country and tenth largest economy globally (World Bank, 2022), has made remarkable economic progress since the Asian Financial Crisis. <mark>Between 2000 and 2019, GDP grew by a little over 5 percent per year, propelling the country to upper middleincome status. S</mark> ustained economic growth has lifted millions of Indonesians out of poverty, with an estimated 10 percent of individuals living below the national poverty line in 2019 (World Bank, 2022). While the COVID-19 pandemic reversed some of the country’s economic gains, Indonesia remains committed to achieving high-income status by 2045.

To accomplish its objective, Indonesia needs to address its long-standing development challenges. This includes the informal economy, defined as ” _all economic activities by workers and economic units that are – in law or in practice – not covered or insufficiently covered by formal arrangements_ ” (International Labour Conference, 2002, p. 53). Based on estimates from SAKERNAS, Indonesia’s informal economy accounted for an average of 36 percent of GDP between 2011 and 2019 (Hapsari, Yu, Pape, & Mansour, 2022) and nearly 75 percent of total employment in 2019 (Wihardja & Cunningham, 2021).<sup>2</sup> The latter figure is considerably higher than the share of informal employment in neighboring Southeast Asian countries and other countries at a similar level of economic development.

The key to addressing the challenge of informality in Indonesia and elsewhere is understanding its drivers. As Chen (2005) argued, the underlying causes of informality determine the kind of policies needed – if indeed they are needed at all. Dualism, for example, implies that the informal economy would naturally disappear with economic development (Kanbur, 2017), while other theories call for greater government intervention such as stricter enforcement of taxes and labor laws. Policies may also contradict rather than complement each other. As a case in point,

> 2 Wihardja and Cunningham's (2021) estimate is based on the ICLS-17 definition of informal employment, which accounts for employment status, type of accounts kept, social benefits, and type of institution where the worker is employed. This definition is more comprehensive than other measures, such as self-employment, which yield lower estimates (approximately 50 percent) of informal employment.

2
# **2. Background on the Growth of Mortgage Loans**

## **2.1. Strong Housing Markets**

Compared to other cities in Korea, housing prices in the Seoul metropolitan area have been high. Housing prices, including those in Seoul, dropped precipitously after the Asian financial crisis of 1997. By 2000, housing prices had recovered somewhat.

Prices began to increase steadily in 2001. There was widespread worry that the price rise for properties in the southern areas of Seoul, with their well-planned urban infrastructure and good schools, could cause a real estate bubble. Furthermore, possible ripple effects from such a real estate bubble increased government concerns about the growing gap in income (Jones and Yokoyama, 2008).

In August 2005, the government began to set up new real estate policy packages to steady volatile housing costs. The packages were aimed at increasing the supply of housing, lowering housing prices, and restructuring the Korean property tax system. This last measure was intended to curb speculative demand by increasing the tax burden on high-priced houses and on owners of multiple houses (Jones and Yokoyama, 2008).

At that time, the supervisory authority’s prudential measure for mortgage lending was also included and considered one of the important measures to be used to reduce demand for housing.

## **2.2. Deregulation in Housing Finance**<sup>9</sup>

Housing finance in Korea can be separated into two eras: pre-deregulation and post-deregulation. Before housing finance was deregulated in 1996, over four-fifths of the housing finance market was public.

These loans were provided by the National Housing Fund (NHF). Lower-income households were thus able to receive rates below the going market rate. The Korea Housing Bank (KHB), in turn, provided housing loans to those with higher incomes.

In 1996, the Korean housing market structure was changed drastically: commercial banks entered into the long-term mortgage business. This followed in 1997 by the privatization of KHB. The Asian crisis of 1998 rocked the finance industry and spurred Korea to relax the governmental control over the housing market. A major element in this change was removing the mandatory sub-market housing prices in February 1998.

In 2004, these changes were continued with the establishment of the Korean Housing Finance Corporation (KHFC), which purchases long-term mortgages from commercial banks. This increased both the available liquidity and the length of maturity for mortgage loans.

> 9  The factual details presented in this section were obtained from “Determinants of house prices in nine Asia-Pacific economies” by Glindro and others (2008, p. 27-28).

4
2 years for GT, 6 years for nuclear, 1.5 for wind and PV and 4 years for hydro are assumed. Thermal efficiencies of power plants are assumed to be 37 to 40% for diesel engines, 38 to 39% for gas turbines, 36 to 40% for steam turbines, 54 to 56% for CCGT and 33% for nuclear. Plant availability factors are 90% for CCGT, diesel and gas turbine, 40 to 60% for hydro, 80% for biomass, 18 to 20% for solar, 27 to 30% for wind and 87 to 90% for nuclear.

# **3.2 Economic costs of generation capacity**

Economic cost information for generation plants includes capital costs ($/kW) of each type of candidate power plants, non-fuel O&M costs, and economic lives of plant investments. While this type information ideally should be plant-specific, especially for large scale plants such as large hydropower projects, generalized information based on averages for a technology (e.g., capacity cost of combined cycle gas turbine) is used here. The summary of plant cost data is presented in Table 1. Differences across countries in these costs result in part from detailed consultations with technical experts in Bangladesh, India, Nepal, and Pakistan. For non-hydro renewable energy (i.e., solar and wind), we have carried out sensitivity analysis reducing their future capital costs. The economic life of a plant is assumed to be 30 years for steam turbine technology fired with coal or biomass; 20 years for solar, wind, gas turbine technologies and diesel fired internal combustion engines; 25 years for combined cycle gas turbine technology; 40 years for nuclear technology; and 50 years for hydropower plants.

# **3.3 Fuel prices**

Projections of fuel prices were determined using various sources of information. For coal, we collected domestic coal prices in India and Pakistan and assumed that they would increase at the same rate the imported coal price increases. International price projections are available from International Energy Agency’s 2013 World Energy Outlook (IEA, 2013), which we used to set the trend for imported coal prices in South Asian countries.<sup>11</sup> One important factor, often neglected in regional power system generation expansion planning, is the cost of transporting coal over a long distance from the mine or port. We account for transportation costs for all power plants considered in the study, albeit roughly. This is extremely important in a large country like India where some electricity grids (e.g., Eastern Electricity Grid of India) are endowed with fossil fuel reserves whereas others are not.

> 11 The World Energy Outlook incorporates different scenarios.  We have taken all of our fuel price projections from the current policy scenario, which reflects continuation of existing technology trends but no fundamental technological or policy transformation.

8
29 From 1996 to 2001 (yearly averages), the dollar rose 44.2 percent against the Euro (IMF, 2003a).<sup>22</sup> Euro-area countries accounted for 18.3 percent of Argentina’s trade turnover in 2000 (IMF, 2001). So the rise of the dollar imposed on Argentina what was equivalent to a tax of 44 percent on all exports to the Euro area and a subsidy of 31 percent on all imports from the Euro area, and this disadvantage affected about one-fifth of total trade (or even more considering several Eastern European and other non-Euro economies geared their exchange rates to the Euro).<sup>23</sup> Figure 5 Trade prices and terms of trade (1993=100) <!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br> Exp<br>20<br> Imp<br>0<br> TOT<br> Fuels<br> Agric<br>1993 1995 1997 1999 2001<br><!-- End of picture text --> For its part, Brazil had been a source of buoyancy for Argentine exports in the mid-1990s when the Real Plan caused an overvaluation in the new Brazilian currency, stimulating Argentine exports. The Mercosur free trade area further heightened the importance of Brazil in Argentina’s trade, and the bilateral trade turnover rose from 20.5 percent in 1993 to 26.3 percent in 2000 (IMF, 2001). The peso’s advantage from the overvalued real in the mid-1990s came back to haunt Argentina in 1999 when Brazil was forced to devalue, however. The real fell from 1.16 per dollar in 1998 to 2.36 in 2001, while domestic Brazilian prices rose only 20 percent over the same period. The effect was equivalent to imposing a tax of 70 percent on Argentine exports to Brazil and an import subsidy of 40 percent on Argentine imports from Brazil. Even worse, there was a decline in Brazilian demand volume associated with Brazil’s recession in 1998-99. As in the case of the dollar shock, Argentina could not respond by depreciating its own currency because of the straitjacket of the currency board.

> 22 The simple average for Germany and France.  The Euro equivalents for 1996 are obtained by converting the Deutschemark and Franc by their respective conversion rates to the Euro when it was inaugurated in 1999.

> 23 The 31 percent subsidy-equivalent to imports is from:  1 - 1/1.44 = 1 - 0.69.  Note that the dollar also rose against the yen in this period but by less (11.7 percent) and Japan constitutes a surprisingly small share of Argentina’s trade turnover (2.4 percent in 2000).
# **Oil Intensities and Oil Prices: Evidence for Latin America**

## **Veronica Alaimo and Humberto Lopez**

**_The World Bank_**<sup>*****</sup>

> *The authors appreciate the valuable comments of Cesar Calderon and the assistance of Joana Naritomi in the earliest versions of the paper. Views expressed in this paper are those of the authors and should not be attributed to the World Bank, its Executive Directors or the countries they represent. Address for correspondence: MS I8-801, The World Bank, 1818 H Street, Washington, DC 20433. Email: <u>Hlopez@worldbank.org. Tel: +1 (202) 473-4909. Fax: +1 (202) 522-7528</u>
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21
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

**Figure 7: Côte d’Ivoire - Macroeconomic Impact of the Crisis, 2003**

<!-- Start of picture text --> 8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>-4.0<br>Growth (%) Inflat. (%) Budg. (% GDP) CAB (% GDP)<br>Pre-Crisis 4.0 2.5 0.2 -0.2<br>Post-Crisis -3.0 3.5 -1.9 7.5<br><!-- End of picture text --> _Source_ : IMF and World Bank staff estimates.

**While post-1994 gains in competitiveness have been largely preserved, since end2000 there was a real appreciation of the CFA franc.** (Figure 5). Since the initial overshooting after 1994, the real effective exchange rate remained relatively stable, hovering at about 30 percent below the pre-1994 level. However, since October 2000 until end 2003, there has been a real appreciation of about 20 percent. Part of the real appreciation reflects the appreciation of the euro vis-à-vis the dollar. But at the sectoral level, decline in productivity, and increased cost of production, transportation, and trade in industries located in the war affected areas indicate loss of competitiveness. Part of the regional trade and transshipment has been diverted to neighboring countries (e.g., Ghana).

# **The Social Impact**

**The social situation before the outbreak of the civil war in September 2002 had already been very difficult, as reflected in dramatic deterioration in the U.N. Human Development Index (HDI) and per capita income rank in recent years.** Even before the 2002-03 crisis, Côte d’Ivoire experienced a major deterioration in social indicators and difficulties in meeting the Millennium Development Goals (MDGs). According to the 2003 UN Human Development report, life expectancy was lower and infant mortality higher in 2001 compared with 1995, despite strong growth in the 199598 period. After temporarily declining in 1998, poverty rate is estimated to have exceeded in 2002 its 1995 level––about 38 1⁄2 percent of the population––erasing the gains in the post-devaluation years.<sup>5</sup> According to the latest UNDP HDI,  Côte d’Ivoire was ranked 161 among 173 countries, _16 ranks lower than in 1995_ ; its income rank in PPP terms was 148, reflecting an equally dramatic decline from the rank of 130 in 1995. The highly unequal income distribution, measured by the Gini coefficient, is likely to

> 5 Institut National de la Statistique (2002). _Profil de Pauvreté en Côte d’Ivoire en 2002_ , Abidjan (Auguste).

6
# Domestic factors

Because there are many potential domestic factors explaining the determination of the desired stock of foreign liabilities, the impact of these variables will be related through their effect on the investment climate.

- Investment rate: Higher investment rates can improve the productive capacity of a country, which in turn can increase its repayment capacity as it achieves higher economic growth. Therefore, the expected sign is positive. However, investment can be financed by foreign savings, meaning that a positive correlation between investment rates and capital inflows may occur if foreign savings is the major source of investment financing. Two caveats are in order here. First, empirical evidence suggests that this problem may not be very important, as the bulk of investment in developing countries is financed by domestic savings.'<sup>5</sup> Second, the appropriate use of lags minimize this simultaneity or exogeneity problem.

- Domestic savings rate: Complementarity among different types of savings, foreign and domestic, would reinforce the commitment to increases in the productive capacity and creditworthiness of a country.16 Private savings is intrinsically related to private consumption. Two alternative approaches should be tested. One implies that higher consumption levels are associated with individual perceptions of higher permanent income, which results in a higher desired stock of foreign liabilities today. Under this interpretation, individuals that want to smooth their consumption levels across time-if not liquidity constrained-would borrow to increase their current consumption levels, and this borrowing will lead to a positive relationship between consumption and capital flows. The second view implies more conservative foreign investors, who will be tempted to invest in those countries in which private savings are complementary to external savings. Under this interpretation, in which foreign investors see private savings as a signal of confidence in the prospects of the domestic

I About 95 percent oftotal investment in developing countries during 1988-93 was financed by domestic savings. IMF (1994). 16 Feldstein and Horioka (1980) and Dooley, and others (1987) find strong positive correlations between national savings and domestic investment rates. This finding has been interpreted as evidence of capital immobility. If capital were mobile, there would be no reason to predict that countries with relatively high saving ratios over a given period would also have relatively high investment ratios, since savings would be redistributed to countries that offered relatively high rates of return on physical capital.

12