rafmacalaba/datause-encoder-data huggingface dataset:
lower.

As with an altruistic buyer, the wider range of discount factors reflects the increase in the per period payoff from altruism that an altruistic seller gets with respect to the per period payoff of a self-interested seller. Then, the more altruistic the seller is, the lower the payment she is willing to accept. The lower payment increases the buyer’s monetary payoff, resulting in a lower discount factor needed for cooperation. Even with a lower discount factor, the discounted stream of benefits from cooperation is more attractive than the benefits from deviation. That is, the increase in the per-period payoff overcomes a lower valuation of the future, reflected in a lower discount factor, and therefore cooperation can be sustained for a wider range of discount factors.

If parties cooperate, the seller gets a lower material payoff than if she were purely selfinterested. However, the seller receives the discounted value of her outside opportunity (37) because the utility derived from altruism compensates for the lower payment. The buyer gets the discounted value of the net social surplus adjusted for the seller’s altruism (36). The buyer receives a higher material payoff than if the seller were self-interested because he is able the pay less.

# **_Case 4: Spiteful seller and self-interested buyer_**

Let’s assume that the seller gets disutility from the payoffs that the buyer gets, then _as <_ 0, which reflects a spiteful seller. This case can reflect when the buyer is a corrupted government that traditionally has given rents to an elite group. Therefore, the seller dislikes the profits the buyer makes from carbon sequestration. The maximization program is the same as (26) but now _as_ is negative. The next proposition states the optimal contract in the presence of a spiteful seller and a self-interested buyer.

**Proposition 8.** _If contract enforcement is incomplete and the seller is spiteful, assuming δ ≥_ _<u>δs</u>_<sup>_s_</sup> _, a self-interested buyer offers an optimal stationary REDD contract ⟨p∗, b∗_ ( _q∗_ ) _⟩ that implements conservation of the forest land_ _<u>q,</u> which satisfies IRCs_<sup>_s,DICC_</sup> _s_<sup>_s,IRS_</sup> _b_<sup>_m,and_</sup>

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clothing and textile sectors 61-63 and 52 account for more than 55 percent of total exports over our period of study. Eighty-six percent of LTFF loans are accounted for by exporters in these sectors, while EFS is only slightly less concentrated across sectors with 63.5 percent of loans granted to exporters in the aforementioned sectors. Two specific sectors stand out. Cotton (52) receives more than half of the LTFF funds while capturing a disproportionately lower share of EFS loans. Textiles (63) obtains a larger share of both EFS and LTFF loans relative to its contribution to aggregate exports. Interestingly, while most cereals are included in the negative list for EFS, rice is a notable exception and accounts for 17 percent of all loans granted to exporters under this scheme.

Table 3: Sectoral Export Patterns and Loan Disbursements, Top-10 Export Sectors, 20152017

|Sector|% of total|%|of loans|
|---|---|---|---|
|HS 2-digit|exports|EFS|LTFF|
|Cotton (52)|17.4|10.8|48.3|
|Textiles (63)|17.3|27.0|26.3|
|Apparel and Clothing (61)|10.9|13.7|5.7|
|Apparel and Clothing (62)|10.1|11.8|6.1|
|Cereals (10)|8.5|17.1|1.0|
|Minerals (27)|3.3|0.0|0.0|
|Leather (42)|3.1|2.4|0.0|
|Salt, Stone, Cement (25)|2.3|0.3|0.0|
|Fruit and Nuts (08)|1.8|0.3|0.1|
|Raw Hides and Skins (41)|1.8|1.9|0.0|

Summing up, EFS and LTFF finance a substantial share of Pakistan’s exports, although EFS is quantitatively more important. The firms that take advantage of the subsidized loans are much larger than other exporters, and the bulk of the loans outstanding are accounted for by the largest exporting firms in the country. The usage of both schemes is also highly concentrated across sectors with clothing, textiles and apparel absorbing the vast majority of loans outstanding. In the next section we describe the empirical strategy we employ to investigate the impact that EFS and LTFF have had on firms’ export performance.

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England was the prototypical and the most advanced capitalist economy. But we have the data for Chile in 1861 which reveal the same, or even sharper, polarization.<sup>4</sup> Working classes, from the poorest (female fisherman) to the richest (shoe-makers), covered the range of income that goes from less than one-seventh of the national mean to two-thirds of the mean. Only the artisans, who mixed ownership of some capital with their own labor, and possibly even hired a few workers, achieved an income higher than the national average. At the high end of the income distribution were owners of manufactures and mines with incomes between 20 and 55 times the mean, and land owners and large-scale merchants with respectively 35 and 9 times the mean. These top groups comprised only 2 percent of the population while working classes (including peasants) accounted for more than 90 percent.

But if workers filled in most (or perhaps in all) countries, that part of the income pyramid which ran from the subsistence to just somewhat under the mean, their real incomes in various countries could still have differed a lot if country means were very different. But, as we shall see, around the 1850s, they were not. Angus Maddison has estimated that around 1850, the mean income in the poorest countries in the world (Ceylon and China) was around $PPP 600.<sup>5</sup> At the top were the Netherlands and the United Kingdom with a GDP per capita of about $PPP 2,300. Thus, the ratio between the top and the bottom (of country mean incomes) was less than 4 to 1. Consequently, the better-off workers who earned incomes close to the national means, could not, in terms of their standard of living, differ from each other by more than the ratio of 4 to 1. And the bulk of workers who lived at less than their countries’ average income and closer to the subsistence, could not have incomes that differed by more than 2 to 1—with many of them living at approximately the same subsistence level. Indeed, Broadberry and Gupta (2006, Table 6, p. 17) show that in the period 1800-1849, the wheat-wage of an unskilled daily laborer in India (among the poorest countries in the world then) was about 30% of the wage of a similar worker in England. And comparing the Netherlands with the Yangtze valley, two relatively developed areas sharing a number of similar geographic features, Li and van Zanden (2010, p. 21) conclude

> 4 The data are derived from an Occupational Census, were consolidated by Javier Rodriguez Weber, and can be found on Peter Lindert’s Website ―Global Price and Income Histrory group‖ (see the previous footnote).

> 5 In  1990 international prices.

4
schooling were 8.7. Post 2000, average schooling rose to 8.6 years and the returns edged up to 9.1 percent.

<!-- Start of picture text --> Figure 2: Returns to Schooling and Average Years of Schooling by Period<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1980-1985 1986-1990 1991-1995 1996-2000 2001-2005 2006-2010 2011-2013<br>Returns to Schooling Average years of schooling<br><!-- End of picture text --> Source: Montenegro and Patrinos 2014 In fact, the returns to schooling were decreasing over time as the level of education rose (Figure 2). From 1980 to 2010, the returns to schooling declined significantly while schooling levels increased. While schooling levels increased on average almost 11 percent every five years, the returns to schooling fell by 6 percent. This would be in line with the laws of supply and demand. But between 2011 and 2013, schooling levels went up by 6 percent, and the returns to schooling increased by more than 4 percent. This is based on a significant number of economies (149) undertaken with a consistent methodology (Montenegro and Patrinos 2014).

Rising or stagnant returns, or even slowly declining returns, while schooling levels are rising goes against theory. The evidence from estimated returns gives us a hint of possible growing inequality (Figure 3). The variance in the returns to schooling is increasing. Just as the returns to schooling are increasing, so is the variance. We see this in the standard deviation of the returns to schooling, which is increasing over time. Since the standard deviation is a measure of the 8
- xiv -

management and capital improvement budgeting thus become essential components of the urban development programs.

The regional authorities should develop their own institutional capabilities for efficient financial intermediation and for the planning and supervision of programs involving numerous cities and projects at the same time. Since expenditure decisions would be brought closer to the beneficiaries, one would require also some tightening of budgetary and auditing controls at both the regional and the local levels.

# xiii. A Gradualistic Strategy for Urban Lending.

The above considerations suggest adoption of a gradualistic strategy, based on the establishment of a line of credit for municipalities, in the form of a revolving fund managed by regional authorities. Subloans would be assigned first to those communities which show greater potential for fiscal improvement and progressively extended to other jurisdictions which, although initially reluctant to assume the corresponding obligations, may be induced to do so by the demonstration effects of the use of borrowing in more enterprising communities. Over time, it would become possible to restrict grant funding to the truly needy jurisdictions, while borrowing is emphasized in those with a more buoyant economic base.

The proposed approach implies also a different perspective on city size. As long as population size reflects economic opportunities, the larger cities would receive more credit funds but less grants per capita not because their expenditure needs are less visible than those of medium size or smaller cities, but because their greater capacity should be better exploited to raise revenues for self-financing.

One positive feature of this approach is that it may support the formulation of an integrated policy of urban development: at the national level, in implemerting a broad redistribution of fiscal resources among regions; at the regional level, in considering the comparative performance of different cities as parts of an interdependent regional system.

# xiv. Conclusion.

Reviewing the World Bank experience of urban lending in LAC countries, one can see a progressive move from discrete project loans toward comprehensive operations, which combine a concern with the replicability of initiatives with increasing attention to the regular flows of intergovernmental transfers. This trend shows also a sharpening focus on the macro-economic issues involved in financing urban infrastructure and on the need to leverage the impact of Bank lending with some policy revisions on the use of domestic resources.

Particularly in the fast urbanizing LAC countries, the economic and fiscal contribution of cities are an important component of the adjustment process. Urban development lending should therefore be based on a review of those stabilization, allocation and distribution policies of the central governments which directly affect the performance of cities. The Bank needs to clarify its approach to this sector of investment, developing with LAC Borrowers
Uslaner 2005).<sup>2</sup> More recently a widely noted contribution by Acemoglu and collaborators (2012) describes variations in social cohesion and labor relations as equilibrium outcomes of the international division of labor. According to the authors Scandinavian societies are able to maintain their more ‘cuddly’ forms of capitalism by free-riding on technological innovations generated by nations (like the US or UK) with more liberal and competitive labor markets.

The flip side of these more collaborative arrangements is that also advanced levels of welfare state development and worker protection can go along with very deep divides within the working-age population. On the one hand collective bargaining over work conditions and social policies can lead to higher levels of social cohesion if institutions and policies are put in place to share the benefits of economic growth, even with groups with weaker links to the labor market. On the other hand, the negotiation of employment conditions can result in deep divides if those in protected positions prioritize their interests in job security over the broader goal of ensuring wide participation in the labor market. The consequences can be labor market and social protection policies that favor interests of labor market ‘insiders’ at the price of very high barriers of entry into the labor market (Lindbeck and Snower 2002, King and Rueda 2008, Rueda 2005, 2006).<sup>3</sup>

# **III. Links between jobs and social cohesion discussed in this paper**

Even though the literature has documented many potential connections between labor markets and social cohesion systematic evidence on these interactions is often surprisingly scarce. Detailed links between individual level work histories and macro-economic labor market trends are obviously difficult to establish. Most studies of social development have thus treated labor market dynamics as an influence that operates in the background, without rigorous exploration of their precise effects on social outcomes. Likewise, at the micro level, many recent advances in research design that enable analysts to identify causal effects of work on household economic welfare have not yet translated into the dimensions of interest in the context of social cohesion. For example, panel surveys, which provide increasingly

> 2 Theoretical models have variably explained these outcomes to partisan politics and class-related distributive bargaining between capital and labor (Esping-Andersen 1990, Huber and Stephens 2012, Korpi 1983, Lindert 2004), or to functional responses of employers to changing demands for skills and security (Hall and Soskice 2001).

> 3 Workers and employees in protected long-term contracts are typically defined as ‘insiders’; individuals in nonpermanent jobs as ‘outsiders’. However, the literature is divided over the precise definitions (Lindbeck and Snower 2002).

7
Thus their study using BEEPS 1999 showed that there was an impact of competition in CIS countries, where market structure had not yet adjusted, and not in CEE countries, where they hypothesize is had. We are looking at 2005, and the null result of the impact of market structure may be the result of market structure in CIS countries adjusting – another sign of convergence. We note here that our results are robust to formulation in terms of lagged or current market structure.

The impact of the elasticity of demand on restructuring is a priori ambiguous. On the one hand, facing highly elastic demand is associated with a very competitive market environment, and if competition promotes restructuring, then we should observe a correlation between restructuring activity and elastic demand. On the other hand, inelastic demand may be driven by temporary monopoly that is the result of successful innovation, or the profits that result from monopoly can be used to finance restructuring. What we observe in both TEs and the developed market economies is that the latter channels predominate: highly elastic demand (the omitted category) is associated with less deep restructuring. (There is also a hint of a U-shape relationship in some of the results, with less restructuring found in monopolistic environments, but these results rely on the small number of firms reporting such a market structure and are not very robust.) Finally, deep restructuring is clearly associated with perceived pressures to innovate. This is true across all regions, and in both the developed market economies and the TEs. Interestingly, the source of pressure apparently varies systematically across groups of countries. In particular, in both Germany and the cohesion countries, competitive pressure from domestic competitors is a spur to deep restructuring, whereas it has generally no such impact in the TEs, where the pressure comes exclusively from either foreign competition or customers. This is consistent with TEs as followers: in the developed market economies, the domestic competition is perceived as more of a competitive threat than it is in the less developed TEs.

We conclude this section with an examination of whether deep restructuring is associated with external financing. Simple correlations suggest a connection: measures of external 41
9 the supply chain (excluding processing, and consumption or food waste), between 14 (Tanzania) and 18 percent (Malawi) when only looking at estimated losses before marketing, and between 6 (Tanzania) and 11 percent (Malawi) when only considering platform drying and farm storage.

Farmers who report a loss typically report substantial losses—on average between 21 to 27 percent of total production in Malawi and Uganda respectively (Table 1), though only a minority reports a loss (between 7 and 22 percent of the sample in Malawi and Uganda, with one in 5 to one in 7 maize farmers reporting a loss in Tanzania, depending on the survey year). Both the relatively low incidence of PHL as well as the relatively high losses incurred among households reporting PHL, highlight the need for proper targeting of PHL interventions<sup>14</sup> as well as the importance of using nationally-representative samples in quantifying PHL to avoid sample selection bias. PHL estimates based on non-random samples are likely biased upwards if non-randomness in the sample selection leads to overrepresentation of areas or groups with higher likelihood of incurring PHL, such as hot, humid areas, which experience higher pest infestations, or poorer households, which are likely to use less effective storage technologies.<sup>15</sup> Further inspection of the PHL survey data suggests that the self-reported national PHL estimates presented here are internally consistent, providing confidence in the approach. From an agronomic perspective, the occurrence of PHL is more likely and the reported losses are likely higher in hot and humid areas (Stathers, Lamboll, and Mvumi, 2013). Consistently, in the equatorial and sub-humid tropical climate of Uganda, which has warmer and more humid agro-ecological zones (AEZ), self-reported PHLs are highest, followed by Tanzania, which is largely sub-humid with some semi-arid areas, and they are lowest in Malawi, where semi-arid and sub-humid AEZs are equally represented.

> 14 In each country, among those reporting a loss, between 60 and 70 percent report a loss under 20 percent, suggesting a high concentration of the incidence of high PHL, further reinforcing the case for targeting. 15 Alternatively, small losses may not be sufficiently accounted for in the self-reported estimates presented here. Yet if so, they would likely not be considered economically or nutritionally meaningful either by farmers, resulting in lesser likelihood of adopting PHL prevention measures. Some small losses are likely also inevitable.
|**Table 8: Links between for**|**mal land rights**<br>|**and documentation**<br>|**using Prindex da**<br>|**ta**<br>||
|---|---|---|---|---|---|
||**Bequeath**|**Transfer**<br>**within hh**|**Sell**|**Rent**|**Use as**<br>**collateral**|
|Has formal document|0.004|0.029|0.054|0.022|0.012|
||(0.048)|(0.044)|(0.059)|(0.048)|(0.050)|
|Has informal document|0.049|0.017|0.024|0.029|-0.123**|
||(0.045)|(0.056)|(0.057)|(0.046)|(0.060)|
|Residential land only|-0.031|0.033|0.019|0.012|0.062|
||(0.045)|(0.044)|(0.054)|(0.048)|(0.054)|
|R-squared|0.106|0.121|0.126|0.124|0.210|
|Dep. var. Mean|0.868|0.856|0.776|0.850|0.762|
|Observations|575|575|575|575|575|

_Notes_ : Each column reports results from a separate regression using a linear probability model where the dependent variable is a zero-one indicator on whether or not the owner can decide on bequest of land, its _inter vivos_ transfer to other household members, sale, rent, or use as collateral. All columns include respondent and household characteristics as in other tables, regional and provincial dummies, as well as parcel dummy of being a second property. Constant term is not reported, and standard errors clustered at local area level. Statistical significance is denoted by *** for p<0.01, ** for p<0.05, and * for p<0.1.

28
benefits associated with energy subsidies, 14.9%, while the richest quintile captured 25% (Table 7, right panel).

Table 7: Composition of Subsidies Received by Residential Consumers (by source and quintiles of consumption)

||Gasoline|Distributio<br>Diesel|n across eac<br>LPG|h quintile (%)<br>Electricity|Total|Distribution over a<br>Total (millimes<br>TND)|ll quintiles<br>Total<br>(%)|
|---|---|---|---|---|---|---|---|
|Quintile 1|0.1|0.2|54.5|42.2|100|178|14.9|
|Quintile 2|0.4|0.7|56.6|42.3|100|213|17.9|
|Quintile 3|1.4|1.1|55.1|42.4|100|237|19.9|
|Quintile 4|3.8|1.5|55.7|39.0|100|267|22.4|
|Quintile 5|13.3|4.2|46.8|35.8|100|298|25.0|
|Total|4.5|1.8|53.3|40.4|100|1192|100|

_Source:_ WB staff calculations using SUBSIM.

When analyzed in per capita terms, all energy subsidies are found to be regressive. The absolute amount of subsidy benefits increases as individuals and households become richer. Table 8 reports the distribution of subsidies benefiting each socioeconomic group. By and large, results reflect inequalities in the consumption of energy sources across quintiles. In fact, differences may not be attributed to the different—universal versus targeted—nature of subsidies. Even though gasoline, diesel, and LPG subsidies are all universal, their distributional effects vary. LPG is the energy source with the largest subsidy benefits in absolute and relative terms for the poorest quintile and the bottom 40%: they represent close to 60% of all benefits obtained from energy subsidies. But that is also true for the richer quintiles. So, even LPG is the most pro-poor, or rather, the least pro-rich energy source; it is not particularly progressive in terms of subsidy benefits. On the other hand, electricity subsidies, with their complex interplay of progressive and regressive features, do not perform very differently from the LPG universal subsidy. Electricity subsidies constitute the second largest source of subsidy benefits for Tunisians, around 35–45%, with shares decreasing as individuals become richer.

Table 8: Per Capita Energy Subsidy Benefits (in TND, by source and consumption quintile)

||Gasoline|Diesel|LPG|Electricity|Total|
|---|---|---|---|---|---|
|Quintile 1|0.09|0.15|44.53|36.99|81.76|
|Quintile 2|0.43|0.65|55.33|41.42|97.82|
|Quintile 3|1.57|1.24|59.88|46.06|108.74|
|Quintile 4|4.65|1.86|68.30|47.85|122.67|
|Quintile 5|18.18|5.70|63.91|48.89|136.69|
|Total|4.98|1.92|58.39|44.24|109.53|

_Source:_ WB staff calculations using SUBSIM.

15