rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

One of most striking empirical differences between advanced economies and middle and lowincome countries in terms of financial crisis is the vulnerability to sovereign debt crisis. The average external default probability of high-income countries is 0.07, less than half of middle and low-income countries, 0.17, and almost one-fifth of Latin American countries, 0.34.<sup>2</sup> Standard theoretical models like Eaton and Gersovitz (1981) do not offer explanations about why rich countries borrow less externally and why they have less incentive to default than middle and low-income countries. Among middle and low-income countries, the fact that Latin American countries defaulted more often than other countries, who presumably face similar external shocks and are in a similar stage of development, suggests that the causes are underlying political factors in addition to macroeconomic fundamentals.

The idea that political factors and institutions might be part of the reason some countries’ default risk is higher is not new. Empirical papers such as Reinhart, Rogoff and Savastano (2003) and Kraay and Nehru (2004) show how countries’ history of non-repayment, weak policies and institutions affect the likelihood of debt distress. My contribution is to find a particular mechanism through which institutions matter for countries’ default risk.

The objective of this paper is twofold. First it shows empirically: 1) a negative correlation between institutional quality and frequency of sovereign defaults; and 2) conditional on having weak institutions, a polarized government is more likely to default. The second objective is to provide a theory of sovereign default that takes into account institutions. Countries with good institutions are characterized by well-developed legal and other institutional structures that make it hard for powerful groups to influence central government policies. The central planner maximizes collective welfare by setting economy-wide expenditure and debt. In contrast, countries with bad institutions are characterized by lack of institutional barriers to limit the influence of powerful groups in the government decision-making process. Powerful groups act in non-cooperative ways;<sup>3</sup> they behave in ways that are optimal from their own point of view,

> 2 Default probability is defined as number of episodes over number of years. For the majority of the countries, the period of analysis is 1960 to 2008. For some recent independent countries, the analysis starts at the year of its independence.  It can be interpreted as incidence of default crisis as well. Source:  Qian, Reinhart & Rogoff (2010).

> 3 Argentina during the seventies and eighties is a clear example of uncoordinated behavior among several public agencies, particularly, between the Provinces and the Federal government. Sanguinetti (1994)

2
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multiplier also appears in the error term at time t-1 and at time t+1.

Using the criteria above, 31 observations had to be eliminated on the basis of wealth alone and the unconstrained Euler equation data set which can a priori be used for estimation has 31 observations.

The model is specified as follows.

# Both utilities are assumed to be quadratic with

_U(GCIKc) =_ A _- " (Gt; - 27)2_ - _(Kt__ - z (1S) where G arnd K are country specific"l.

Alternatively, we could assume the instantaneous utilities to be of the constant elasticity of substitution form. However, meaningful convergence results were difficult to obtain wit, this specification.

The costs are also assumed to be quadratic with

> 11Note that G and K require time subscripts if it is deviations from the respective permanent components which are penalized. However, a meaningful value of G (and of K constructed, for example, by using a permanent decomposition of public investment) can only be obtained if we have enough observations. For simplicity, we ignore that differences in the permanent components from one period to the next appear in the first-order condition.

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Policy Research Working Paper 8628

# **Abstract**

This paper develops a tractable method to identify urban areas and applies it to India, where urbanization is messy. Google Earth images are assessed subjectively to determine whether a stratified large sample of Indian cities, towns and villages, as officially defined, are urban or rural in practice. Based on these assessments, a regression analysis combines two sources of information—data from georeferenced population censuses and data from satellite imagery—to identify the correlates of units in the sample being urban.

The resulting model is used to predict whether the other units in the country are urban or rural in practice. Contrary to frequent claims, India is not substantially more urban than implied by census data. And the speed of urbanization is only marginally higher than official statistics suggest. But a considerable number of locations are misclassified in the midrange between villages and state capitals. The results confirm the value of combining subjective assessments with data from these different sources.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at yli7@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
PWI has consistently resulted around 75% in Australia and other countries. This stability of the index has been explained with the theory of Subjective well-being homeostasis (Lau, et.al.,2005) which proposes that “SWB under normal circumstances is actively controlled and maintained within a limited positive range by a set of psychological devices and personality” (Lau et.al., 205,page 406).

# **5. An Application in Bogotá- Colombia**

# **5.1. Sample Size and Demographics**

A Telephonic Survey was held in Bogotá in April 2006. The total population of Bogotá is of 7 ́056.219 inhabitants. The sample size was of 830 subjects which is representative for “zonas” of the city (local parishes’ level). The main demographics of the survey are as follows:

|||**Table 1.1-Sa**<br>|**mple Size a**|**nd Demograph**|**ics**<br>||
|---|---|---|---|---|---|---|
|**Gd**||**Men**|||**Women**||
|**ener**||44,1%<br>|||55,9%<br>||
|**Age**||**18-25**|**26-35**|**36-45 years**|**46-55**|**>55 years**|
|**(years)**||20,4%|19,7%|21,2%|16,0%|22,8%|
|**Civil Status**||**Married**|**Single**|**De Facto Union**|**Divorced**|**Widowed**|
|||36,9%|29,1%|24,4%|5,8%|3,9%|
|**Number of**||**1 y 2**|**3 y 4**|**5 y 6**|**> 6**|**Without**|
|**children**||37,8%|27,5%|7,8%|4,1%|22,8%|
|||||**Works and**|||
|**Occupation**<br>**Ll**|**Student**|**Worker**|**Home**|**studies**|**Unemployed**|**Pensioned**|
|**eve**|6,9%|37,9%|34,7%|2,3%|12,7%|5,5%|
|**Education**<br>**ll**||**Elementary**|**Secondary**|**University**|**Technical**|**Without**<br>**Studies**|
|**eve**||29,7%|46,8%|15,8%|6,9%|0,8%|
|**Socio-**<br>||**1**|**2**|**3**|**4**|**5**|
|**economical**<br>**Level**||9,40%|47,50%|39,20%|2,70%|1,20%|

64
is likely to be biased due to the possibility of heterogeneity in the treatment effects across groups (De Chaisemartin and D’Haultfoeuille, 2018, 2020a,b; Goodman-Bacon, 2021; Callaway and Sant’Anna, 2021). De Chaisemartin and D’Haultfoeuille (2020a) and De Chaisemartin and D’Haultfoeuille (2020b) for instance, show that in a staggered treatment design with multiple groups and treatment periods, the TWFE estimate is a weighted average of the average treatment effect (ATE) across the various groups and time periods. They show that these weights may even be negative. As a result, the overall ATE can be negative even though the ATE of all the groups is positive, thereby leading to biased estimates. To ensure the robustness of the TWFE estimates in this paper, I present additional estimates using the De Chaisemartin and D’Haultfoeuille (2020a) estimator for estimating robust treatment effect in the presence of heterogeneous treatment using a continuous measure of treatment as in the case of this paper where mobile network coverage rates vary between 0 and 1.

# **3.1.1 Results**

Table 1 presents estimates from the TWFE regressions. Panel A presents estimates from the conventional TWFE estimator with continuous treatment. Panel B however, presents estimates from the De Chaisemartin and D’Haultfoeuille (2020a) TWFE estimator (hereafter referred to as D&G estimator) that is robust to heterogeneous treatment effects.

Starting with panel A, the results show a positive and statistically significant association between expansion in mobile network coverage and growth in nightlight intensity. In Column 1 for instance, the results suggest that a 10 pp increase in mobile network coverage is associated with a 0.18 pp increase in the growth rate of nightlight intensity. Accounting for country controls, the effect reduces slightly to 0.14 pp growth in nightlight intensity for every 10 pp increase in mobile penetration. To what extent does the effect of mobile coverage vary across the various technologies? As highlighted earlier, the 2G mobile technology only allows voice calls and SMS, while 3G and 4G technologies facilitate mobile broadband internet, in addition to the functionalities of the 2G. The results again show a positive association between the 2G and 3G/4G penetration and local economic growth. Specifically, a 10 pp increase in 2G and 3G/4G penetration are associated with a 0.2 pp (column 4) and 0.06 pp (column 6) growth in nightlight intensity respectively.

However as highlighted in Section 3, the TWFE estimates are likely to be biased even under a strict assumption of exogenous variation in mobile network coverage across districts and time, due to the possibility of heterogeneous effects across groups and time, and the associated negative weighting of some groups. To explore this, I first estimate the weights attached to the average treatment effects of the treated (ATT) associated with the TWFE estimates in panel A us9
higher than the rate introduced in some emerging markets and developing economies (EMDEs; World Bank, 2022c). This scenario illustrates a very gradual phase-in of the tax and would only be beneficial if, for example, economies are able to access a new technology in the outer years that would, at the same time, significantly lower costs from inaction in the early years (in net present value terms).<sup>42</sup> Still, risks from waiting to introduce carbon pricing reform may be quite serious due to threshold effects.<sup>43</sup> The second row for each economy in Table 4 shows the results of phasing out fossil fuel subsidies, price controls and exemptions and, contemporaneously, introducing a US$12.50 carbon tax. For most economies, the revenues are only about 75-80 percent of what they would have been under the CEPR, though the benefits of a higher carbon tax also increase beyond 2030 (Table 4 only reports values in 2030). Additionally, monetized welfare co-benefits are more than three-quarters the value of the CEPR package, in large part because mitigation action in India has an important impact on results for the region. It is worth noting that the GDP estimate in 2030 under this scenario is about the same as in the CEPR scenario for most economies, except for Sri Lanka (where the US$12.50/ton CO2e carbon tax yields a 37 percent higher GDP compared to the original CEPR scenario). Comparing the outcomes for income distribution, inequality falls but by much less (about 70-80 percent compared to the CEPR scenario). This is true pre- and post-recycling of revenues (last two columns of Table 4). Because Nepal does not have fossil fuel subsidies, inequality improves by only around half compared to the CEPR scenario.

In sum, a lower assumed carbon tax rate slows the energy transition, and, thus, entails less favorable distributional impacts. In contrast, lower assumed energy prices between 2023 and 2030 lead to higher government revenues, a greater amount of redistribution and, thus, a larger improvement in the postrecycling Gini compared to the BAU and the CEPR (Figure 13). This last sensitivity scenario also helps explain why high global fossil fuel prices are detrimental to South Asian economies: they adversely impact economies’ terms of trade (as the region is a net energy importer), reducing real incomes as a result.

> 42 That said, South Asian economies are already embracing innovative programs like battery exchanges in India, offgrid solar electrification in the rural areas of Bangladesh, and floating solar panels across the region.

> 43 See: https://news.un.org/en/story/2021/09/1099992

26
respectively. The mean number of new leases per village concluded over the entire period was close to 170 and 67 for commercial and personal land, respectively.

For the subset of transactions (12% of commercial land) for which we obtained registry information, mean field area and tenant size are, with 95 and 515 hectares, respectively, are more than a third and nearly half above the means for the sample with tenant characteristics reported in column 4. For this sample, the tenant’s share of village land (36%) and mean and median prices (US$ 138 and US$ 116) are higher and the share of unmapped land lower (3.9%) than in the sample with tenant characteristics. Registry data show that, although use rights to some 80% of field area are registered by the field’s largest user, the mean number of users with registered rights in a field is 2.5 with 10% of fields having 5 or more registered users.

Moreover, 81% of fields include land (8.6% of cultivated field area on average) that, albeit mapped in the cadaster, is not registered. For 16% of cases, the area involved is minuscule (<1% of area) whereas for 47% and 18%, it is either non-negligible (1%-10% of area) or significant (>10% of area). Also, 29% of fields, while having use rights to most of their area registered by a legal entity, include land that is registered in the name of the owner only, possibly indicating presence of an informal lease contract. In 6%, 20%, and 3% of cases, the share of owner-registered area is minuscule (<1%), not negligible (1%-10%), and significant (>10%), respectively.

_Sales markets:_ Parcels transacted in sales markets are slightly larger than those in the lease market, with a mean sales price of US$ 1,132 for commercial and US$ 1,986 for personal land that in the latter case is driven by some outliers, as evidenced by a median price that is almost equal to that for commercial land. Parcel attributes are not too different from those for leased parcels except for a higher share of grassland for personal land and vice-versa for commercial use (appendix table 4). While 93% of commercial land transacted in land sales markets was part of a field, this is true to less than 70% of land for personal use. The share of field area cultivated but not mapped in the cadaster was, with 5%, similar to what is observed for rental markets. With 7.5% as compared to 3.7%, the incidence of this type of informality was much higher for personal than commercial land.

# **3.2 Econometric approach and key issues to be discussed**

Denote transacted parcels, which are located within a field that in turn is located in a village, by _i_ and years by _t_ . We regress sale or lease prices for parcels of commercial or personal use on a set of parcel, field, and village characteristics, along with policy regime dummies, and _hromada_ as well as year fixed effects. Formally, the equation to be estimated is 12
# **5.2 Heterogeneity results**

With these considerations, we now specify a mapping from the land distribution at position _j_ of channel _i_ , summarized by the statistic _Lij_ , to political influence of the form _ιij ∝_ exp( _Lij_ ) _._ If, for example, _G_ is chosen to be the summation operator, then _Lij_ = _L_<sup> ̄</sup> _ij_ (mean landownership), and we would have log _θi_ = log _ιiH −_ log _ιiT_ = _L_<sup> ̄</sup> _iH − L_<sup> ̄</sup> _iT_ , the mean difference in landownership between head and tail. The augmented spatial matching specification (dropping second FO tenure effects) is where _Zi_ is a vector of channel level characteristics—including its length, number of outlets, and position on parent channel—that might influence FO performance. Under the symmetry restriction _δH_ = _−δT_ (see Proposition 3), this regression is equivalent to interacting the treatment dummy _τit_<sup>1withlog</sup><sup>_θi._Symmetry,recall,impliesthatheadoutletsobtainjustas</sup> much additional influence over allocations at the margin from (say) one acre higher mean landownership at the head as from one acre _lower_ mean landownership at the tail.

Table 4 reports eight spatial matching specifications, crossing four versions of _G_ with the two census populations. Even as the coefficients of interest in Table 4, the _δj_ , vary in magnitude across specifications due to the different scaling of the _Lij_ , a consistent pattern emerges: In each case, _δ_<sup>ˆ</sup> _H >_ 0 and _δ_<sup>ˆ</sup> _T <_ 0 and we fail to reject the null of symmetry. That this test has power is supported by the fact that we _can_ reject (in all but one case) the joint null hypothesis that _δH_ = _δT_ = 0. Finally, in the restricted models (i.e., with _δH_ = _−δT_ ), we can strongly reject the null that relative political influence has no effect on water allocation along a channel.

31
The bottom panel in Table 5 shows the correlations between the investment shares predicted by the model and the ones in the data. They are positive and substantially stronger for roads than for electricity. This, in principle, implies a larger degree of misallocation in the electricity infrastructure investments conditional on the available budget.

Looking at roads in more detail, many locations in the data are almost isolated (i.e., have few low-quality connections). When the government connects these locations via additional road segments, trade costs for all locations trading through that road segment fall. Figure 7a shows the degree centrality ( _I_<sup> ̄</sup> _i_ ) for the Fully Optimal scenario against the data.<sup>20</sup> We see that the correlation is positive, but the optimal solution requires substantial reallocation. Because the share of the government budget allocated to roads increases, most locations are above the 45° line. This suggests that the planner reduces investment in roads with less central roles.

Figure 7: Equilibrium Allocations <!-- Start of picture text --> 35<br>-3<br>30<br>-4<br>25<br>-5<br>20<br>-6<br>15<br>-7<br>10<br>-8<br>5<br>-9<br>5 10 15 20 25 30 35 -9 -8 -7 -6 -5 -4 -3<br>Data Data<br>(a) Quality of Roads I ̄ i (b) Log of Population<br>Model Model<br><!-- End of picture text --> <!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>10 20 30 40 50 60 70<br>Data<br>(c) Aggregate Price Index<br>Model<br><!-- End of picture text --> The equilibrium investment allocations induce movements in the population. The relationship between the population predicted by the model and the data is shown in Figure 7b. 20 Degree centrality _I_ ̄ _i_ measures the quality of the roads connected to location _i_ as _I_ ̄ _i_ = ∑ _j∈Ni Iij_ .

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cumulative improvement in real value-added per worker of nearly 90 percent!

Presumably this reflected the exit of relatively inefficient producers, the exploitation of scale economies, product and process innovation, and labor-shedding among those firms that continued to operate.

**Figure 3**

**Real value-added index: Total chemicals versus SDS and cosmetics**

<!-- Start of picture text --> 200<br>150<br>total chem.<br>100<br>SDS & C<br>50<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text -->

**Figure 4**

# **Employment index: Total chemicals versus SDS and cosmetics**

<!-- Start of picture text -->
1.2<br>1<br>0.8<br>total chem.<br>0.6<br>SDS & C<br>0.4<br>0.2<br>0<br>1993  1994  1995  1996 1997  1998 1999 2000 2001  2002 2003 2004<br><!-- End of picture text -->

12