rafmacalaba/datause-encoder-data huggingface dataset:
of 2.67 for use in this work. Thus, a 1% decrease in the return to farming, relative to the market wage, results in a 2.67% increase in off-farm labor supply.

**_Modeling Production, Exports and Imports:_** When it comes to modeling trade liberalization, an important characteristic of our CGE model is the explicit treatment of two separate foreign trading regimes.  One is the export processing regime, which receives duty-free imports and is therefore extremely open, with considerable foreign-investment.  The other sector is the ordinary trade regime.  Since the 1990s, processing exports have grown rapidly as a result of their preferential treatment. They now account for more than half of China’s total exports. Obviously, any analysis of external trade and the impact of changes in trade policy must have an explicit treatment of this dualistic foreign trading regime in the model.

Trade is modeled using the Armington assumption for import demand, and a constant elasticity of transformation for export supply. Thus, Chinese products are assumed to be differentiated from foreign products, and exports from China are treated as different products from those sold on the domestic market. The small country assumption is assumed for imports and so world import prices are exogenous in terms of foreign currency. Exports are demanded according to constant-elasticity demand curves, the price-elasticities of which are high but less than infinite. Therefore the terms of trade for China are endogenous in our simulation.

Production in each of the sectors of the economy is modeled using nested constant elasticity of substitution functions, and constant returns to scale is assumed. Sectors differentiate between rural and urban labor that substitute imperfectly between them. This is an indirect means of building into the model a geographic flavor – since some sectors will be located largely in urban

> China’s WTO accession (Hertel, Zhai and Wang, 2004), a sensitivity analysis was conducted with low labor transfer elasticity of 0.6. The most important consequence of this alternative simulation is that the poorest agricultural households no longer gain

14
# **1. Introduction**

During the last decade, the ownership structure of banking sectors in developing countries changed substantially: most developing countries witnessed a sharp increase in foreign bank participation and a decline in government bank ownership. Between 1999 and 2009, on average, the share of bank assets held by foreign banks in developing countries rose from 26 percent to 46 percent, while government bank ownership declined from 28 percent to 19 percent.<sup>1</sup> These changes in banking structure were in part motivated by increasing evidence that while foreign bank participation brought many benefits to developing countries, especially in terms of competition and banking sector efficiency<sup>2</sup> , government bank ownership was often detrimental to the financial sector.<sup>3</sup> The recent global financial crisis reignited the debate on the ownership structure of the banking sector and its consequences for financial intermediation. Some have pointed to the presence of foreign banks in developing countries as a key mechanism for transmitting the 20082009 crisis from advanced to developing countries (e.g., IMF, 2009). At the same time, developing countries like Brazil, China, and India, where government-owned banks are systemically important, recovered quickly from the crisis, generating interest in the potential mitigating role that these banks can play during periods of financial distress.<sup>4</sup> Using bank-level data from 2004 to 2009, this paper examines the impact of bank ownership on credit growth before and during the recent crisis. We analyze the growth of banks’

> 1 These data come from the World Bank Regulation and Supervision Surveys. See http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037~pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html. 2 See Cull and Martinez Peria (2010) for a review of the literature on the drivers and the impact of foreign bank participation.

> 3 Arguably, the seminal paper on the negative implications of government bank ownership is La Porta et al. (2002). Also, see literature cited in footnote 10.

> 4 See for example the discussion in the following articles: “They Must Be Giants,” _The Economist_ , May 15, 2010. “Falling in Love with the State Again,” _The Economist_ , April 3, 2010. “Not Just Straw Men,” _The Economist_ , June 20, 2009.

2
measures some of the legislative and regulatory inputs into institution building. While some countries, most notably Hungary and Poland started earlier and thus with more reformed institutions in 1991, the gap between these early reformers and others has widened, with Belarus, Tajikistan and Turkmenistan having undertaken little reforms or even reversed reforms.

While the EBRD index is an important measure of legislative and regulatory reforms that may foster institutional development, we would like to have a broad indicator of institutional development across countries.<sup>11</sup> Unfortunately, no such indicator exists for all 24 countries in our sample before 1996. We therefore focus on a broad indicator of institutional development, as computed by Kaufman, Kraay and Mastruzzi (2003, henceforth KKM). Drawing on 25 different data sources constructed by 18 different institutions, KKM estimate six different dimensions of institutional development: voice and accountability, government effectiveness, rule of law, regulatory quality, absence of corruption and political stability.<sup>12</sup> Each of the six measures is a principal component indicator with a mean of zero and a standard deviation of one, estimated with an unobserved component model to control for missing observations across the different variables. While KKM present estimates of these six indicators for 1996, 1998, 2000 and 2002, we will focus on the indicators for 1996, the earliest available time period.

Our main variable Institutional Development is the average of these six variables and varies between –1.68 in Tajikistan and 0.78 in the Czech Republic. Table 1 presents Institutional

> 11 While a large literature has discussed historical and political determinants of institutional development, there is little if any cross-country evidence linking specific reforms to institutional development.

> 12 These sources include, among others, the Business Environment Risk Intelligence (BERI), Freedom House, Gallup International, the World Economic Forum, the Heritage Foundation, and the International Country Risk Guide (ICRG) compiled by the Political Risk Services group. These institutions collect and construct similar variables of institutional quality, such as corruption, that have been widely used in the literature. KKM apply principal components techniques to this set of variables from various sources to construct broad measures of institutional development along six dimensions. By using the KKM measures as broad indicators of institutional development we avoid having to choose among these different but closely related variables (the correlation between these variables is generally high). Each of the KKM components is highly correlated with most of the underlying measures. For example, the index of corruption is highly correlated with the ICRG index of corruption.

16
through higher destination wealth in cities, which we know, from Table 3, is 0.69 in favor of cities (0.038*0.69=2.6). The first panel (‘ALL’) in Figure 4 visualizes this horse race between distance and wealth for urban migrants. We can also conduct this exercise using all migrant-destination pairs in Table 4. Here towns have a 6.5 percentage point higher likelihood of being chosen (0.025*2.58) and the wealth effect does little in favor of cities reducing that number by only 0.4 percentage points (0.006*0.69). Clearly, the pull effect of higher wealth in cities is much lower than the discouragement effect of the corresponding increase in distance. We conjecture that that is the reason many people prefer towns over cities.

Who does end up going to the city? Our heterogeneity analysis in Table 5 shows how wealth at destination has a larger pull effect for the non-poor and the educated and how the negative effect of distance on destination choice is attenuated by schooling, but exacerbated by poverty. Our simulations depicted in Figure 4 show that the distance effect becomes very attenuated for high levels of education. The deterrent effect of distance is over five times stronger for those without education compared to those with higher secondary education. In fact only for this latter group is the distance effect is so attenuated that the wealth effect (which increases with education, although much more modestly) becomes stronger and cities become more attractive than towns. As the regression results do not simultaneously interact with other individual characteristics, we do not conclude from this analysis that raising education levels would increase migration to cities. Rather the results indicate that there exists a type of respondent for whom the trade-off between distance and wealth no longer favors towns as a destination choice. Such differential frictions would lead to the kind of sorting documented by Hamory et al. (2020) and Young (2013). This may result from education itself as well as other characteristics of the household where the more educated person grew up, such as wealth levels, access to networks, as well as mindset which may all help overcome migration costs.

15
# **2.3 Joint life annuities and guaranteed periods of payment**

One of the shortcomings of life annuities is that they do not provide for bequests. Joint life annuities and annuities with guaranteed periods of payment provide some protection to dependents but entail lower payments than single annuities with no guaranteed periods.

## **Joint life annuities**

Joint life annuities protect nonworking spouses after the death of the primary beneficiary. Their lower payments depend on the differences in age and life expectancy between spouses. The analysis of annuity quotations obtained from the website of the UK FSA shows that monthly payments for joint life annuities covering a male aged 65 and a female aged 60 (with a 67 percent reversion rate for the surviving spouse) are lower by about 13 percent compared with those of single life annuities in the case of fixed nominal annuities with a 10-year guaranteed period of payment. The gap widens to 19 percent for an escalating annuity and to 20 percent for a fixed real annuity (see Annex A).

Traditionally, most countries did not mandate the use of joint life annuities. Chile used to require men to purchase joint life annuities but allowed working women the freedom to choose between single or joint annuities. This reflected the traditionally lower labor force participation of women and their lower earnings. However, a recent change in the rules mandates the purchase of joint life annuities by both spouses.

A useful contribution of joint life annuities is that they mitigate the distorting effects of adopting unisex mortality tables.<sup>17</sup> The distortions emanate from the significant difference in the life expectancy of men and women and the tendency of annuity providers to target male retirees. It is advisable that countries in which the use of unisex mortality tables is compulsory should also impose the requirement to use joint life annuities on both working spouses.

## **Guaranteed periods of payment**

Annuities with guaranteed periods of payment provide protection to the dependents and heirs of primary beneficiaries if death occurs during the guaranteed period. The protection for a nonworking wife or other secondary beneficiary is not as effective as in the case of a joint life annuity but, where they are allowed, single or joint life annuities with guaranteed periods of 10 or 15 years prove very popular. Annuities with guaranteed periods of payment entail very small decreases in monthly payments, at least for periods

> 17 According to the EU Directive 2004/113/EC, men and women should receive equal access to goods and services. The implementation of such directive on the life insurance market implies that insurers are not entitled to use gender risk as a factor for pricing annuities.  Since women have a longer life expectancy than men, in the presence of unisex mortality tables the latter subsidize the former.

25
- [14] Fernandes, Ana and Heiwai Tang (2012). “Determinants of Vertical Integration in Export Processing: Theory and Evidence from China,” Journal of Development Economics, 99(2), 396-414.

- [15] Feenstra, Robert (1998) “Integration of Trade and Disintegration of Production in the Global Economy,”Journal of Economic Perspectives, 12(4): 31-50.

- [16] Feenstra, Robert and Hiau Looi Kee (2008). “Export Variety and Country Productivity: Estimating the Monopolistic Competition Model with Endogenous Productivity,” Journal of International Economics, 74(2), 500–518.

- [17] Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova (2010). “Imported Intermediate Inputs and Domestic Product Growth: Evidence from India,” Quarterly Journal of Economics, 125 (4), 1727-1767.

- [18] Hummels, David, Jun Ishii and Kei-mu Yi (2001). “The Nature and Growth of Vertical Specialization in World Trade,”Journal of International Economics, 54:75-96.

- [19] Johnson, Robert (2014). Five Facts about Value-Added Exports and Implications for Macroeconomics and Trade Research, Journal of Economics Perspectives, 28 (2).

- [20] Johnson, Robert and Guillermo Noguera (2012). “Accounting for Intermediates: Production Sharing and Trade in Value Added,” Journal of International Economics, 86 (2), 224-236.

- [21] Johnson, Robert and Guillermo Noguera (2014). “A Portrait of Trade in Value Added over Four Decades.”Dartmouth College Working Paper.

- [22] Kee, Hiau Looi (2015). “Local intermediate inputs and the shared supplier spillovers of foreign direct investment.”Journal of Development Economics, 112, 56-71.

41
Economic Parameters of Deforestation 12

# 3. Policy Implications

The analysis in sections 4 and 5 provides the methodology for examining the effect of policy choices on land use pattems and deforestation. The paper shows that it is possible to disentangle the complex economic interactions that lead to changes in land use and deforestation. The main instrument for obtaining some dear comparative statics results is the distinction between mining unmanaged forests that are biologically mature and managing production forests for periodic replanting. The results are discussed below in the context of specific policy questions such as timber trade restrictions and agricultural policies.

Log Expor Bans and Other Trade Restictons Many timber exporting countries have imposed log-export bans (s) or high log export taxes at one stage or another (see Crossley, 1993). LEBs were imposed primarily with the objective of promoting domestic processing and the export of higher valued sawnwood or manufaured goods. Even though LEEs were conceived as instruments of infant industry protection, they have implications for logging rates and a lively debate menters on the environmental effects of LEBs (see Goodland and Daly, 1994). LEBs or other trade restrictons as well as import boycotts reduce exxernal log demand and have the effect of lowering log prices in the exporting country. Following a log-export ban in Costa Rica, for example, domestic log prices have fallen to 20-60 percent of international prices (Kishor and Constantino, 1993, p. 12). The view that lower timber prices increase deforsation is reflected in many World Bank documents (see, for example, 3randon and Ramankutty, 1993, p3<sup>7</sup> , and World Bank, 1993).

_flispaperprovides a djfferentiated answru to tke question wittier lower timber prices increase deforestation: A lower tin ber price patd reduces logging of unmanaged forests. Mis resul keolds under open access as well as under secure properfy glits witl_
other measures of financial inclusion such as those from the Global Findex.

(iii) To measure financial _efficiency_ , we use the logarithm of the ratio of overhead costs to income (Bikker 2010), and a profitability indicator—the return on assets (ROA) ratio.<sup>6</sup> (iv) To measure financial stability, we use the volatility of the change in the logarithm of the deposits-to-GDP ratio and the credit-to-GDP ratio (volatility is measured by a moving five-year standard deviation for each of the two variables).<sup>7</sup> In addition, we use an alternative measure of financial stability, the output loss during banking crises computed by Laeven and Valencia (2013), to determine if our results are greatly affected by the particular measure used to represent stability.<sup>8</sup> Figure 5 illustrates the evolution of financial sector development indicators.

# **3.b. Describing the regression model and estimation methodology**

The estimations of whether financial sector strategies have had a meaningful effect on financial development are based on annual data for 150 countries over 1985-2014. Figures 6 (deposits to GDP ratio), 7 (overhead costs to income ratio), and 8 (volatility of the change in the private credit to GDP ratio) show that initiation of a strategy was associated with an improvement in financial development variables in several countries.

To determine whether the apparent improvement in financial development is significantly related to initiation of a strategy, we use FE regression analysis to estimate the relationship between financial sector development and indicators of the existence and quality of a strategy,

> 6 We prefer these two measures over the net-interest-margin and lending-deposit rate spread because they are less controversial. An increase/decrease in the net interest margin can signal a rise or a fall in efficiency, depending on country circumstances. The same applies for the lending-deposit rate spread, as its evolution can be heavily influenced by changes in monetary policy.

> 7 The volatility at time _t_ is measured as the standard deviation of the variable for the [t-2, t+2] time period.

> 8 This is computed as the cumulative sum of the differences between actual and trend real GDP, expressed as a percentage of trend real GDP, over the period [T, t+3] where T is the start of the crisis.

15
-4-

Within **the** limitations implied by **much** comments it does appear that the econometric approach is well established and capable of glving reliable elasticities of substitutLon at an aggregate level.

- (vii) The issue of whether structural parameters are simLlar for different countries has received rather little attention. Models which have pooled data and provided different elasticities have done so on the baLsi of evaluations at different market shares, rather than on allowlng the cost functions to dlffer. Problems of degrees of freedom have previously made thli an impractlcal task, but with the accumulatLon of larger time series Lt should be possible to attempt tests for equalLty of cost structures, and hence for implied equallty of elasticitile at the same fuel Phares. A major gap in evidence is the lack of such studies applied to countries outside **the** O.R.C.D. At present if a model is to be used it would have to be based on highly industrialized countries.

- (viii) Econometric studies of the power sector are much less common so that there **is** less experience on their performance even in advanced economLes. Here there are difficulties, even at an economy wide leva! caused by existing patterns of fuel uses

- (a) many countries, as documented in sectLon 2, only uoe a subset of the three bauic fuels for power gener'ation. This leads to modelling difficulties thep have largely been ignored ln the use of models which are based on the use of all the fuels. This is of particular importance when the aim might be to induce the choice of a fuel not previously utillsed.

- (b) the use of hydro, nuclear and bLomass for power generation has not been explored in econometric modelling of power generation. Studles so far have confined themselves to shares of gas, coal and oil. The nuclear decision may have had a large political element in industrial countries, although it is irrelevant to most developing countries at this stage. Hydro power is seen as being so attractive that it is automatically used first where available and there is no question of price driven substitution. Biomass Li unimportant in the industrialized world, but plays a role Ln fuel choice in many developing countries. Here the problem may be to identify the price of the fuel itself.

- (lx) It would appear that there is certainly insufficient evidence on the quantitatatlve aspects of fuel switchlng to assess the potentlal for government price based policies wlthout further research. The nature of the research needed depends on the nature of the policy envisaged. A specific power plant oriented policy would be best served by an engineeri:j study where an initial assessment might well be based on exLsting calculations whlch were not system specific. If the initial asseessment appeared to indicate a viable program then a more deta±led on the spot study would be needed. For individual industrial plants it
budget. Because social assistance represents about three-quarters of income protection spending, we refer to this variable as the share of social assistance spending. The share of job protection measures in the total social protection budget is the complement of this variable. The pandemic response budget corresponds to governments’ COVID-19-related spending between March 2020 and June 2021.

A second data set, compiled for this paper, provides information on national elections in 205 countries. We collected data on the date the authorities in place as of March 2020 were elected, the terms of office, the anticipated election date after March 2020, the date of legislative elections after March 2020, whether elections were postponed, and whether 2020 was an election year in each country. The information for this data set comes from the National Democratic Institute <u>(NDI n.d.),</u> the Association of World Election Bodies (AWEB n.d.), Freedom House (Freedom House <u>n.d.), the International Institute for Democratic and Electoral Assistance (IDEA n.d), government</u> websites, news sources, and Wikipedia.

The main variable we use to measure the electoral cycle is the number of months that have _elapsed_ since the last election as of March 2020. The theory of political budget cycles tends to focus on the electoral cycle for upcoming elections, which would suggest using the number of months _until_ the next election. However, many political regimes allow the incumbent to alter the electoral schedule under extraordinary circumstances <u>(James and Alihodzic 2020). Indeed, during the</u> pandemic, 24 countries in our sample changed the national election schedule. The number of months until the next election could thus be endogenous to the development of the pandemic. On the other hand, the number of months elapsed since the last election is completely orthogonal to the start of the pandemic. Therefore, this measure provides us with an exogenous, cross-country variation in the period of the electoral cycle, which is key for the unbiased identification of our parameters of interest.<sup>2</sup>

> 2 One of the problems in analyzing the relationship between electoral cycles and social budget allocations is the potential endogeneity of these processes. Competing political parties and the public may anticipate attempts by elected officials to influence the electorate through social spending and either try to counter them or push for measures even if they were not initially planned. Another issue could be the loss-aversion bias or the “long-term movements” of policies, in which risk-averse voters prefer the status quo and the behavior of politicians during the current election

8