rafmacalaba/datause-encoder-data huggingface dataset:
9 for all national debt. irrespective whether it was incurred by the private sector. The Chilean crisis of 1982-85, for example, required the Government to fully guarantee all domestic deposits as well as the external debt of the private sector (Velasco, 1991). This was most dangerous for fiscal and monetary stability, because in a recession, while government revenue falls sharply, larger resources are required to service not only the government's growing domestic and external debt, but also to cover private sector external liabilities, as well as losses in a banking system that is subject to a government (implicit or explicit) deposit guarantee.

# National balance sheet

Accordingly, it would be useful to develop a notion of a **national balance sheet** for the effective risk management of an economy. Just as corporate managers manage their risk by changing the structure of their assets and liabilities, policy makers may have to manage domestic and systemic risks through appropriate rules and regulations that shift the national asset-liability mix.

At a minimum, the national balance sheet should distinguish between four domestic sectors: government, enterprises, households and the financial sector, and an external sector.' The importance of identifying sectoral balance sheets is to distinguish how different sectors finance their asset base, and how financial imbalances in one sector could trigger off imbalances in other sectors. Indeed, the building of stable financing structure (the liabilities side of the national balance sheet) has been a major pre-occupation of financial sector reform efforts in recent years.

As a result of recent experiences in market shocks in the process of internationalization of banking, as well as the global harmonization of regulation of banking and financial markets, some of the risks in national asset-liability management have become better (but not yet fully) understood. These risks may be categorized into the following:

- Market risks (interest rates, exchange rates, prices etc);

- Credit or default risks

- Regulatory risks

- Liquidity risks

- Fraud risks

- * Systemic risks

Clearly, the government has a major role in all of these risk categories, either as a market participant or regulator. For example, macro-economic policies of the government will I From a national point of view, the national net wealth may be defined as the sum of domestic **assets** (land, equipment and intellectual property) plus financial claims on foreigners (including foreign exchange reserves), less external claims on the economy, including extemal short-term and long-term debt, as well as foreign direct and portfolio investment in domestic corporations. Such a 'net' national balanc^ sheet would unfortunately not be able to distinguish the large intemal sectoral imbalances that could jeopardize growth within an economy.
# Electricity Transition in MFMod: A Methodological Note with Applications<sup>∗</sup>

Charl Jooste<sup>1</sup> , Florent McIsaac<sup>1</sup> , Alexander Haider<sup>1</sup>

> 1Macroeconomics, Trade & Investment Global Practice, World Bank, Washington, DC

_JEL Classification:_ C10, C50, Q43 _Keywords:_ Economic modeling, electricity transition, carbon pricing

> ∗We thank Claire Nicolas, Etienne Espagne, Unnada Chewpreecha, Heather Ruberl, Frederico Gil Sander, Stephane Hallegatte, Andrew Burns, and Manaf Touati for their helpful comments. The authors thank the multi-donor Climate Support Facility for funding this work.
The chief reason behind the large increase in the relative value of cotton by‐products during the commodity boom is that while the prices of by‐products went up, like those of most other agricultural commodities, the price of cotton (lint) did not increase much. The divergence between the price of cotton and those of other agricultural commodities began in the early 2000s (Figure 4). Be‐ tween 2000 and 2008, the World Bank index of nominal food prices increased by 147 percent but cotton prices increased by only 21 percent (from US$1.30 to 1.57 per kg).

# **Figure 4**

**Agricultural Price Index and Cotton Prices (Real, MUV‐deflated, 1980=100)**

<!-- Start of picture text --> 160<br>140<br>120<br>100<br>Agriculture<br>80<br>60<br>40<br>20<br>1960 1966 1972 1978 1984 1990 1996 2002 2008<br>Source: World Bank 4<br><!-- End of picture text --> There are three reasons why cotton prices did not join the commodity boom. First, cotton receives more subsidies than other commodities, encouraging more production than would have taken place without the support. Second, the boom in food prices was partly aided by growth in demand for biofuel produc‐ tion. The direct impact of biofuel demand is felt only by maize, sugarcane, and some edible oils, but the indirect impact is felt by almost all food crops, because of the strong substitutability both on the input side (mainly shifting of land from one crop to another) and on the output side (especially in edible oils, some of which are highly substitutable). In cotton, however, the indirect impact is very limited: cotton is not a substitute for any other commodity on the output side,

**_Page 6_**
of public investment, , are set equal their historical averages over 2000-2017, gg reflecting “business as usual” investment rates. tt ıı̅ In the External Balance submodel, private savings, , are set to match the pp observed average of private savings (% of GDP) in 2000-2019, computed using tt ss̅ data from the IMF-WEO and IMF-FAD. Similarly, the share of public investment in total expenditure, , is set to match the average ratio of public investment to total expenditure over 2000-2017 (also IMF- WEO & IMF-FAD). For simplicity, tt ηη̅ the default fiscal rule is the BBR-Default, with a zero target for the primary balance ( = 0) and stable external public debt ( 2020), though the user can refer to gg the IMF-FAD for actual targets for the fiscal balance and debt in specific countries. tt bb<sup>�</sup> dd<sup>gg</sup> = dd Finally, the exogenous component of the private debt, , is set to zero by default. pp tt dd̅

# **IV. Application of the LTGM-NR to Angola**

In this section, we illustrate how to apply the LTGM-NR in practice with three exercises for Angola. After a brief discussion of the calibration, we assess the baseline business-as-usual growth path over the next three decades (subsection A), how the long-term growth would be affected by higher oil prices (subsection B) and higher oil discoveries (subsection C). Subsection A compares the LTGM-NRDefault with the standard LTGM (submodel 1). Subsections B and C consider all fiscal rules (BBR, SSR, BBR, SSR-HR) and submodels (Default & External Balance). As before, the LTGM-NR only capture supply-side of the economy, and not the short-run response of the economy to shocks via aggregate demand.

**Calibration to Angola (Default submodel).** The calibration to Angola follows the logic of the generic calibration presented in section III. To keep the description brief, we discuss here only the most important parameters and variables of the Default submodel (for details, see Table 3). We calibrate the model with data up to 2020 but the simulation runs from 2023 to 2050. The years 2021-2022 are excluded from the simulation because the LTGM-NR is not suited to account for the shortterm volatility induced by the COVID-19 pandemic. Instead, we refer to the IMF’s official forecasts for 2021-2022 (see IMF 2021).

24
40

|**Bank**|**Changes in business strategies**|
|---|---|
|Intesa Sanpaolo (Italy)| Changing asset-liability management (subordinated debt buyback scheme); Selling<br>assets(in Persian Gulfproject finance and corporate loan books)to Arab banks|
|Monte dei Paschi<br>(Italy)| Changing RWA management (by applying its "internal model" to reevaluate<br>RWAs); retaining earnings; partial deleveraging (sale of stakes)—considering<br>possible ways to raise the capital ratio. Some analysts expect the bank to issue<br>CoCos to raise its core Tier 1 capital ratio to 9percent.|
|Natixis (France)| Overhauling its investment bank; Cutting jobs|
|Portuguese banks (5<br>largest) (Portugal)| Reducing loans to meet the 120% L/D ratio target by 2014 (commitment in the<br>official bailoutpackage)|
|Royal Bank of<br>Scotland (RBS) (UK)| Refocusing on its UK High Street and corporate banking operations ; Running off its<br>non-core business (deleveraging and derisking–investment banking, global banking<br>and markets business); Reducing RWAs; Pulling back from certain markets<br>(particularly in Asia) and products (selling aviation leasing business to Sumitamo<br>Mitsui)|
|Sabadel (Spain)| Raising capital|
|Société Générale<br>(France)| Planning to offload assets; Planning cuts to its networks in Russia, Romania, the<br>Czech Republic, and Egypt; Restructuring its corporate and investment bank;<br>Cutting jobs in the corporate and investment division|
|UniCredit SpA (Italy)| Restructuring its investment banking operations, and reorganizing its CEE<br>businesses; Launching a Є7.5 billion capital increase; Planning to narrow its<br>geographic focus in Eastern Europe, retreating from less profitable markets and<br>focusing on its profitable markets (Poland, Turkey, Russia and Croatia); Cutting its<br>reliance on funding from abroad at their units in CEE countries and focusing more<br>on local funding|
|Volksbank (Austria)| Selling its subsidiaries from its diversified presence in nine CEE countries (except<br>for its Romanian operation) to a Russian bank (Sberbank), reflecting the reduced<br>ability of diversified banks to move resources across their operations in different<br>jurisdictions as national regulators try to prevent outflows of capital and liquidity<br>(through ring-fencingof their subsidiaries)|
|Wells Fargo (USA)| Acquiring loan and securities portfolios being sold off by ailing European lenders<br>Purchased$2.6 billion worth of loans from Irish banks|

Sources: ECB and IIF Survey of Lending Standards; JP Morgan Analyst Reports, various news sources (including the _Financial Times_ , the _Wall Street Journal_ , _The Economist_ , Reuters.
|**Table 6. US Non-Agricu**|**ltural Impo**<br>**Incorpor**<br>|**rts: Value of Pre**<br>**ating Actual Utiliz**<br>|**ferences, 2003**<br>**ation **<br>|
|---|---|---|---|
||**Value**|**_Non-Apparel_**|**_Apparel_**|
|**_Beneficiaries of:_**|_(% of Total_|_Dutiable Non-Ag Export_|_s to US)_|
|**CBERA-only & GSP**||||
|**_St Vinc & Gren_**|5.5|5.5|0.0|
|**_Dominica Is_**|5.3|5.3|0.0|
|**CBTPA & GSP**||||
|**_Guyana_**|13.6|2.8|10.8|
|**_Haiti_**|13.0|0.2|12.8|
|**_Honduras_**|12.8|0.1|12.7|
|**_Jamaica_**|12.7|0.1|12.6|
|**_El Salvador_**|10.9|0.1|10.8|
|**_Dominican Rep_**|10.6|1.1|9.5|
|**_Belize_**|9.9|0.1|9.8|
|**_Costa Rica_**|8.2|1.2|7.0|
|**_Nicaragua_**|6.1|0.1|6.0|
|**_Guatemala_**|5.8|0.2|5.6|
|**ATPA & GSP**||||
|**_Bolivia_**|7.6|4.1|3.5|
|**_Peru_**|5.2|1.0|4.2|
|**AGOA & GSP**||||
|**_Botswana_**|19.9|0.0|19.9|
|**_Swaziland_**|17.2|0.0|17.2|
|**_Kenya_**|16.9|0.0|16.9|
|**_Namibia_**|10.3|0.4|9.9|
|**_Mauritius_**|8.6|0.1|8.5|
|**AGOA-LDC & GSP**||||
|**_Ethiopia_**|22.0|0.3|21.7|
|**_Malawi_**|19.9|0.0|19.9|
|**_Cape Verde_**|19.2|0.6|18.6|
|**_Lesotho_**|17.9|0.0|17.9|
|**_Mozambique_**|17.8|0.0|17.8|
|**_Uganda_**|17.1|0.1|17|
|**_Madagascar_**|15.9|0.1|15.8|
|**_Tanzania_**|7.3|0.6|6.7|

37
_Macro and micro economic implications of competition under Water-Restricted (scenario III) and Water-Liberalized Urbanization (scenario IV)_ Agriculture is an important sector, especially as an employer for many rural households. However, it is industry and services that dominate the South African economy, and which have outperformed agriculture over the 15 years following the end of Apartheid. Agricultural GDP grew at 0.4 percent per year during 1994-2007, while industry and services grew at 2.6 and 4.3 percent, respectively (StatsSA, 2008). These transitional forces pulled labor from agriculture as per capita incomes grew. This reflects SA’s accelerating shift away from primary sector production (including mining) towards greater industrialization and a more prominent role for services (e.g., transport, communication and finance). These structural changes have been at least partly facilitated by the removal of agricultural subsidies and trade protection for many agricultural products, and by the greater openness of the economy, which has fostered capital deepening that contributed to the rise in real wages and nonagricultural export growth (Hérault and Thurlow, forthcoming).

The sectoral pattern of growth and the lifting of restrictions on internal migration, has also favored urban centers, which in turn has prompted rapid out-migration from rural areas. While SA has long been undergoing an urbanization of its population, the rate at which the rural population has migrated to larger metropolitan areas has risen sharply since the mid-1980s. During 1960-1985, the rural and urban populations grew at similar rates of 2.2 and 2.6 percent per year, respectively (World Bank, 2008). However, towards the end of Apartheid, there was a rapid divergence in population growth, with rural and urban populations growing at 0.9 and 3.0 percent, respectively during 1985-2005. As a result, the urban population share rose by 9.9 percentage points between 1985-05 (compared to 2.8 percentage points during 1960-1985), such that by 2005 about 60% of the population live in urban centers (compared to 49.4 percent in 1985).

While some of the ‘urbanization’ of the population may be attributed to higher HIV/AIDS-related mortality in rural areas, there is evidence that workers and their families are leaving rural areas and moving to major metropolitan centers (Posel and Casale, 2003 and 2006). Furthermore, many migrants are moving into informal 31
# 1. Introduction

Global remittance flows to developing countries have experienced accelerated growth since the early 1990s, increasing from less than $50 billion around 1990 to a whopping $328 billion in 2008 (Ratha et al, 2009). Statistics by country indicate India, China, and Mexico as the largest recipients of money from migrants abroad. In contrast, Tajikistan, Tonga and Moldova, and other small states lead in terms of the share of remittances in GDP. Among factors considered to have triggered this rapid growth are the increase in international migration, the cost reductions as well as convenience of transferring money through formal channels, and the better measurement and reporting of remittances in the Balance of Payments statistics.

The evolution of remittances flows prompted researchers to evaluate their role as an external foreign exchange inflow and their ability to neutralize macroeconomic shocks and reduce output volatility. We add to this literature by examining the remittances’ (REM’s) stability, cyclicality and stabilizing impact, in comparison to the same three features of two other external inflows, namely foreign direct investment (FDI), and official development aid (ODA). Our methodology relies on coefficients of variation to assess the stability and stabilizing impact, whereas cyclicality is evaluated using correlations between GDP on the one hand and the original measures as well as the cyclical components of REM, FDI and ODA on the other.

The analysis covers 116 countries over the period 1980-2007 and reveals that ODA is more stable than REM in 73% of the countries examined and REM more stable than FDI in 72% of them. The coefficients of variation (CV) for 1980-2007 averaged over the sample countries confirm that ODA is the most stable (CV = .47), followed by REM (.75) and FDI (1.47). In terms of cyclicality, the results indicate that REM is pro-cyclical, FDI more pro-cyclical, and ODA counter-cyclical. Finally, ODA has a stabilizing impact in 56% of the countries examined, while REM and FDI are destabilizing or have no effect in most of the countries (around 80% and 90%, respectively).

Our strategy of comparing the behavior of remittances and other capital flows has been previously employed by other studies in the literature (Ratha, 2003, IMF 2005, Lueth and RuizArranz, 2007). The motivation for this approach relates to the fact that, as explained in Frankel (2009), capital flows are in theory expected to deliver the benefits of smoothing, diversification, 2
Smith 1998; Huybens and Smith 19991.5 According to the financial services view, it is not bankbased or market-based systems per se, but rather the overall level of financial developmen **t** hat stimulates the flow of external funds to worthy firms.

The _legal-based view_ ILaPorta, Lopez-de-Silanes, Shleifer, and Vishny 1999b] rejects the analytical relevance of the bank-based versus market-base **d** ebate altogether. This view instead argues that countries with legal codes that protect outside investors and legal systems that enforce those codes will have financial systems that facilitate extemal finance. Thus, the legalbased view predicts that the component of overall financial development defined by the legal system critically influences the expansion of existing firms and the formation of new ones.

To evaluate the competing predictions of the bank-based, market-based, financial services, and legal-based views, we use a panel of 42 countries and 36 industries over the 1980s. As in Rajan and Zingales (1998) and Cetorelli and Gamberra (2000), we focus on industries and decompose industrial growth into that part due to the creation of new firms and that part due to the expansion of existing firms. Rajan and Zingales (1998, henceforth RZ) show that industries that are externally dependent - _industries that are naturally heavy users of external finance -_ grow relatively faster in economies with higher levels of financial development. Thus, as a first step, we examine whether externally dependent industries grow faster in market- or bank-based financial systems, or whether it is the overall level of financial developmen **t** hat enhances the growth of externally dependent industries. We also assess whether the legal system plays a critical role in producing a financial system that fosters the growth of externally dependent firms. In a second step, we decompose industry growth into the growth of the number of establishments

> 5 Also, see Stulz (2000) who notes that stock markets offer an exit option for entrepreneurs and therefore decreases banks' market power. Stock markets also enable an entrepreneur who has obtained bank financing to realize profits from a successful project by selling it. The existence of markets increases the return for the entrepreneurs and thus the incentives to undertake risky, innovative projects. Together, banks and markets work to promote growth.

5
# **_3.2 Social impacts on the destination countries in the South_**

Apart from the increased competition at the labor markets, **increasing inflows of migrants impose an integration challenge** in all areas of social life. In many of the developing countries, however, policies to manage immigration are lacking while control of the same is failing to curtain the inflow of migrants due to scarce resources, weak administrative capacity, and porous borders. Some of the cross-border migration is often widely accepted (Adepoju 2005), but sometimes immigrants even from neighboring countries are treated as unwanted foreigners (Crush 2000). This inability to control migration and to integrate the newcomers has at times led to dramatic actions and great human suffering. When the economy is already under pressure, failure of integration has sometimes led to massive expulsions of migrants mostly in the South. The Nigerian Government, for instance, expelled over 2 million immigrants mainly from Ghana in 1983 due to a domestic economic crisis, for which the aliens became scapegoats (Lassailly-Jacob _et al._ 2006). More recent examples of forced repatriation can be found both in the North as well as in the South: the United States deported more than 350,000 immigrants and South Africa 300,000 in 2008 alone (UNDP 2009, p. 99). The case of Cote d’Ivoire, where striping of immigrants of some of their rights sparked a chaos that has led the once stable country to the verge of an internal conflict, illustrates the growing intolerance to foreigners in the South (Adepoju 2003, UNDP 2009). These instances highlight the importance of strengthening migration and integration policies in developing countries as they host large numbers of immigrants.

**The challenge of integration is most prominent in urban areas.** Most internal

as well as international migrants end up in the cities of developing countries because of employment opportunities, with many working in the informal sector of business, transport, crafts and services. For example, a quarter of the population of Libreville in Gabon is from another country from Central Africa and West Africa (Lassailly-Jacob _et al._ 2006). Sometime the movement is driven by the falling living standards and weak support services in the source community (World Bank 2009). If the excess supply of labor is combined with poor ability of the local authorities to manage immigration, the result is commonly increased disparities and expansion of slum areas in the cities (UNDP 14
The OLS regressions in Table 1 (columns (2) and (3)) identify a number of salient correlates of moneylender interest rates. Interest rates are lower in villages with higher irrigation coverage. More irrigation means lower risk and higher productivity (through green revolution). Though higher productivity may allow moneylenders in a segmented market to charge higher interest rates, the OLS results suggest that the lower risk premium predominates over the productivity effect. Interest rates are higher in more isolated villages (far from market centers). As the market segmentation is likely to be more severe in remote villages, moneylenders can, ceteris paribus, extract more rent by charging higher interest rates. Interest rates are also higher in poorer villages, which may partly reflect higher risk premium, and is lower in places where moneylenders face greater competition from better access to formal banks.

The last three columns in Table (1) report estimates from matching and two propensity score reweighting estimators: Normalized IPW and MB. The confidence intervals for IPW and MB are generated using bootstrapping procedure with 250 replications, following Millimet and Tchernis (forthcoming). The matching estimate (Caliper with a radius of 0.25) is 8.185, larger than the OLS estimate in column (4), 6.054.<sup>27</sup> The normalized IPW estimate is marginally larger in magnitude than the matching estimate for comparable specifications, and the MB estimate is even larger. In fact, the lower cut-off estimates of 95 percent confidence intervals for IPW and MB are larger in magnitudes than the point estimate from OLS in column (1). Recall that matching and IPW reduces the bias in OLS estimate by making the treatment and comparison groups more comparable, and the MB estimator, in addition, minimizes the bias due to the failure of CIA (possibly due to dynamic learning effects) in the normalized IPW by trimming the sample around the bias minimizing propensity score. The magnitudes of the estimates, i.e., MB > IPW > Matching > OLS, strengthens substantially the argument that the direction of omitted variables bias is _downward_ . The results in Table (1) thus suggest strongly that the effect of MFI coverage on moneylender interest rate is most likely to be positive and significant in magnitude.

# **2.5. ESTIMATES FROM HETEROSKEDASTICITY BASED IDENTIFICATION**

The specification of the estimating equation used for the Klein and Vella (2009a) approach is the same as in column (4) in Table 1. The implementation of the K-V estimator involves the following steps. First, a heteroskedastic probit is estimated to generate the predicted probability of participation in MFI programs. For heteroskedastic probit regression, we follow Farre et al. (2012, 2013) and assume that the heteroskedasticity function Sv�X<sup>�</sup> i′π� has the following parametric form due to Harvey (1976):

> 27 The matching estimates do not vary across alternative matching algorithms such as nearest neighborhood and Kernel.  More extensive matching estimates are available from the authors.

15