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

Climate shocks are expected to become more frequent and more intense as climate change continues (United Nations Framework Convention on Climate Change 2007). Those shocks will cause widespread economic and social damage. Among those who could be hurt the most are the poor. Studies are increasingly focusing on how climate change affects those in poverty. The objective of this study is to systematically review and synthesize the existing literature on the distributional impact of climate change, focusing on poor households. Specifically, this study reviews the literature to examine two questions: Are the poor more exposed to climate shocks and are they more adversely affected by climate shocks.

Poor households may be more exposed to climate shocks than non-poor households for several reasons: First, poor households often reside or work in locations that are prone to climate shocks— in part because they have fewer options than better-off households. Second, poor households may face a more difficult tradeoff between locational amenities, including climate risks and proximity to income-earning opportunities (Kim 2012; Hallegatte et al. 2016).

Even if poor and non-poor households are equally exposed to climate shocks, poor households could be disproportionately harmed by climate shocks because they may have fewer resources to invest in protection against climate risks, resulting in a larger loss of their income or assets; have lower-quality housing and infrastructure; be less able to respond to shocks after they occur; or have less access to post-disaster relief mechanisms than non-poor households (Anttila-Hughes and Hsiang 2013; Hallegatte, Fay, and Barbier 2018). As a result, climate shocks can keep households in poverty for prolonged periods (Carter et al. 2007). A better understanding of exposure to these environmental shocks and their effects on the poor can shed light on the burden of climate change and how policies can help.

There is a large and rapidly growing literature on the relationship between climate change and poverty. This study updates previous literature reviews (Hallegatte et al. 2020; Hallegatte et al. 2016), and incorporates quantitative estimates of all 37 recently published _Country Climate and Development Reports_ (CCDRs) from the World Bank. Previous literature reviews have described groups of studies in detail (Hallegatte, Fay, and Barbier 2018), or examined specific channels through which poor households are more affected, such as physical infrastructure (Hallegatte, 2
Table 7: Second stage: Effects of refugee presence on household welfare disaggregated by initial land ownership, with - <u>analysis at cluster level and the distance weighted refugee index at 50km from the clusters (Panel 2009 2012)</u>

||Log of welf|are (consumpti<br>equival|on aggregate<br>ent)|per adult|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||<br>Panel A (O<br>|<br>wn<=median<br>|<br>land size“2.<br>|<br>5 acres”)<br>|
|Log of refugees (50km)|0.07818|0.07681|0.08618|0.09122*|
||(0.05174)|(0.05145)|(0.05350)|(0.05023)|
|Observations|2,124|2,124|1,981|1,980|
|Kleibergen-Paap rk Wald F|11.99|11.96|12.08|11.94|
|Root MSE|0.526|0.526|0.506|0.378|
||Panel B (O|wn>median l|and size“2.5|acres”)|
|Log of refugees (50km)|0.01440<br>(0.05408)|0.02057<br>(0.05455)|0.02718<br>(0.05567)|0.03440<br>(0.05698)|
|Observations|2,170|2,168|2,124|2,122|
|Kleibergen-Paap rk Wald F|13.41|13.38|13.09|13.08|
|Root MSE|0.508|0.505|0.493|0.340|
|T-stat (_comparing corresponding coeffs in Panels A & B_)|0.8517|0.7495|0.7622|0.7439|
|_t_0.05,∞|1.96|1.96|1.96|1.96|
|_t_0.1,∞|1.645|1.645|1.645|1.645|
|**_Included controls_**|||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education,<br>occupation, other income sources, land ownership)|_No_|_No_|_Yes_|_Yes_|
|<br>Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|

_Apart from HH size, the more endogenous controls added are as at the base year of the study. In Regression (4) where household fixed effects replace the cluster fixed effects, all the time invariant variables drop off (only age, agesq & male remain). Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1._ Overall, the heterogeneous analysis presents somehow surprising results. There is a relatively large consensus that hosting refugees creates relative winners and losers within the hosting population. Although research on the distributional impact of forced displacement remains scarce (Verme & Schuettler, 2021), quantitative and qualitative studies usually converge in identifying households with access to human and physical capital as those most likely to adapt to the refugee shock and respond optimally to changing economic opportunities (Maystadt et al., 2019). Our results are sufficiently puzzling to call for further investigation of possible mechanisms behind these redistributive effects of benefits/losses.

27
# **2. A model of long‐term growth with public capital**

In this section, we provide an overview of the model structure (Sections 2.1‐2.3) and some intuition on growth drivers (Section 2.4). Section 3 describes how these model equations enter the LTGM‐ PC Excel‐based tool which enables users to run policy simulations.

# _2.1 The production function_

To analyze the effects of public capital on growth, we adapt the Standard LTGM by splitting aggregate capital stock into public and private portions. We assume a Cobb‐Douglas specification, where the two capital stocks have unitary elasticity of substitution, in contrast to being perfect substitutes in the Standard LTGM.<sup>8</sup> Based on the models in Eicher and Turnovsky (2000) and Agenor (2013), we first consider the following production function at time, t : Each firm takes technology (TFP), A� and public services S� as given, that is, these are externalities to the firm. K�� is the private capital stock, h�L� is effective labor, which can be further decomposed into h� , human capital per worker and L� , the number of workers. 1 �β and β are private capital and labor income shares. Next, we consider the following specification for public services S� : G� is the efficient physical public capital stock – the public capital that is actually used in production. ζ captures whether public capital is subject to congestion (or not) – discussed further below. φ is the _usefulness_ of public capital (more technically the elasticity of output to efficient public capital).

Due to corruption, mismanagement or pork‐barreling, only a fraction θ� �1 of measured public capital is useful for production. The measured capital stock K��� is what is recorded in international statistical databases, constructed using the perpetual inventory method. θ� is the average _efficiency/quality_ of the public capital stock. Equations (1), (2A) and (2B) can be written in a more conventional production function as:

# <u>Congestion ( ζ ∈�0,1� )</u>

In principle, the congestion parameter in Equations (2A) and (3) can take values between: ζ�1 (full congestion) and ζ�0 (no congestion). As long as ζ�0 , it is the _ratio_ of public capital to private capital that provides public services, rather than the absolute amount of public capital (Barro and Sala‐i‐ Martin 1992).<sup>9</sup> When there is a large amount of private capital relative to public capital, the public capital

> 8 See Appendix A1.5 for a general idea of how the Standard LTGM differs from the LTGM‐PC.

> 9 Congestion can also be measured in terms of both private capital and labor supply (see for instance Glomm and Ravikumar (1997)) or aggregate output, but these do not result in substantial changes to the analysis (Eicher and Turnovsky 2000). Aside from absolute congestion, there can also be relative congestion, in which case, congestion increases only if aggregate usage increases relative to individual usage (see Eicher and Turnovsky (2000) for further details on how relative and absolute congestion affect growth analysis).

5
## 4.4 Democracy and Development

Finally, to rule out the possibility that the results are driven by subgroups of countries with extreme characteristics, we restrict the sample in two ways. First, we exclude highly autocratic countries, de...ned as those whose Polity IV score in 1990 takes the extreme value –10 ("strongly autocratic" in the language of the score producers). Second, we exclude highly developed countries, de...ned as those that are classi...ed by UNDP in 1990 as having “very high human development”based on the country’s Human Development Index (HDI). The two subgroups of countries are listed in Table 14. Note that the most developed countries are also the most democratic.

Tables 12 and 13 present the results with the restricted samples. The tables report the two early D-GMM baseline speci...cations for each of the three measures of ...scal performance. Overall, the prior patterns are preserved, strengthening the support for hypotheses (H1)(H3). The government balance regression in Table 12 column (2) shows stronger MTEF e¤ects both in magnitude and in statistical signi...cance. This result seems consistent with the notion that MTEFs as commitment devices are particularly well suited for democratic settings which Mueller and Stratmann (2003) argue are more prone to ...scal indiscipline.<sup>34</sup> The results in Table 13 excluding the most developed countries are comparable to the baseline results in Tables 6, 8, and 10, both in magnitude and statistical signi...cance, providing evidence that the MTEFs’ positive e¤ects on ...scal performance are not a phenomenon speci...c only to the most developed countries.<sup>35</sup>

# 5 Conclusion

In the last two decades more than 120 countries have moved toward a multiyear budget process. Although there has been much debate in the literature as to whether MTEFs are a worthwhile budget institution, a systematic empirical analysis of their impacts has been lacking due to insu¢ cient data on MTEF adoption around the world. This paper is the ...rst to empirically investigate the MTEFs’impacts on ...scal performance in a large sample of

> 34Ideally, to study how democracy and development condition MTEF impacts we would have interacted each MTEF indicator with measures of democracy and development. However, that would introduce three new endogenous variables, increasing the number of internal instruments above the number of available countries, and biasing the estimates toward their OLS counterparts.

> 35We also explored how the results change when we exclude more countries. Generally, the results are more sensitive when excluding more autocratic countries than when excluding more developed countries. The estimation strategy limits the degree to which we can restrict the sample through the requirement that the number of instruments times the number of periods remain below the number of panel units. See Roodman (2009).

20
<!-- Start of picture text --> a)<br>o= “§ ae = é re” 3<br>oo 474 (OQO0KZT)<br>w<br>ai)wo a” % te oo,<br>i) ee ‘em feet 3 So eet - :<br>a=)= = - tee ee<br>=<br>“| 1772 (Q00KZT)<br>oe<br>x ee et eo . “oe oo oo<br>Rd Si FA Ne : : * tt . - _ _ .<br>is) 500 1000 1500 2000 2500 3000<br>Annual per capita income (OO0K2T)<br><!-- End of picture text -->
and the projected distribution in 2030. From the figure it appears that the largest changes in income distribution between 2000 and 2030 are found around the middle of the distribution rather than towards the upper or lower tails of the distribution. In fact, because the two Lorenz curves intersect in these tails, it is not possible to say that the 2030 distribution Lorenz-dominates that of 2000, i.e. we _cannot_ claim that inequality in 2030 is lower as compared with 2000 regardless of the inequality measured being used. However, using standard inequality statistics such as the Gini, the Theil, and the mean logarithmic deviation – i.e. indicators that do not give too much weight to the extreme parts of the distribution – a marked reduction of inequality, as shown in Table **1** , is recorded during the period considered here. These expected trends in global income distribution suggest that the mid-1990 could be seen as a turning point after which global inequality began showing a negative tendency.

Table 1: Global Income Inequality Indices

||||Dispersion|Convergence|
|---|---|---|---|---|
|Index|2000|2030|Only|Only|
|Gini|0.672|0.626|0.673|0.625|
|Theil|0.905|0.749|0.904|0.749|
|Mean Log Deviation|0.884|0.764|0.893|0.759|

Data source: Authors' own calculations using data from GIDD The remainder of this section analyzes the factors driving these expected distributional changes by means of two complementary approaches. First, we conduct the analysis in terms of the convergence and dispersion components, i.e. changes in income disparities between and within countries. This is taken up in the next two sub-sections, which show that the reduction in global income inequality between 2000 and 2030 is the outcome of two opposing forces: the inequality-reducing convergence effect and the inequalityenhancing dispersion effect ( Table **1** ). In other words, poor countries will catch up but it will come at a cost in terms of higher within-country income inequality. These results suggest the continuation of recent trends documented in Ferreira and Ravallion (2008) using a large sample of countries covering 93 percent of the population in the developing world. Second, we develop an alternative way of analyzing the evolution of the global income distribution 11
-47-

estimates of "non-accelerating inflation rates of unemployment" (NAIRUs) suggest that these have drifted upwards in the 1980s, closely<sup>mirroring</sup> actual rates of unemployment [Reichlin, 19871. If such estimates are to be believed, a non-inflationary recovery may be problematic (unless it were accompanied by some form or other of incomes policy).

While a consensus on the reasons for the rise in European NAIRUs has not yet ben achieved, much of the recent literature tends to stress the concept of "hysteresis" or persistence, in other words, the idea that present unemployment levels may be closely linked to past unemployment levels [Blanchard and Summers, 19861. In its simplest form, this approach assumes that wage bargaining is determined entirely by so-called "insiders", who set their claims at a rate just sufficien **t** o ensure their continued employment. "Outsiders", i.e. the unemployed, have no effect whatsoever on the wage bargaining process. If unemployment rises, because of adverse shocks, some insiders lose their status, but the<sup>new and</sup> smaller group of insiders pursues its earlier policy. As for companies, these may have little incentive to hire outside their labour force at lower wages, if they can obtain greater loyalty, a better productivity performance, or a lower incidence of strikes by granting pay levels above those which would have been determined by more atomistic market forces.

In this view, high unemployment just feeds on itself, and any upward shift in its level is self-perpetuating. The converse, however, should also be true, in which case the solution to the problem would seem to be that proposed by Solow: "To bring down unemployment, just have low unemployment for a while" [Solow, 1986, p.5331. Indeed, "enfranchising" additional workers may actually reduce wage demands if it raises the number of insiders [Blanchard and Summers, 1986]. The appropriate policy
**Figure 1:** _Global competitiveness and supply of second-generation infrastructure_

<!-- Start of picture text --> 6<br>5.5<br>5 CHN<br>4.5 AZE<br>4 RUS UKR<br>KAZ<br>ARM GEO<br>3.5<br>KYG MOL<br>TAJ<br>3<br>2.5 World EU12 EAP12 Eurasia<br>2<br>2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Impact of ICT on Access to Basic Services (1-7 )<br>Global Competitiveness Index (1-7)<br><!-- End of picture text --> Source: World Economic Forum (2012) Note: GCI index based on successive aggregations of scores from the indicator level all the way up to the overall GCI score. Uses annual Executive Opinion Survey, ranking out of 144 economies and scores measured on a 1-7 scale.

As income grows, the demand for infrastructure services increases, but for a different type of assets—second-generation-type assets that help in accelerating growth and even shift countries towards a more sustainable development trajectory (World Bank 2011a). Specifically, second-generation infrastructure includes projects that seek to maximize co-benefits among infrastructure sectors, or between these sectors and the environment. An example of first-generation infrastructure is a pipe system that connects households to an improved water source. A pipe system that is enhanced with an ICT solution to optimize water use is an example of second-generation infrastructure (e.g., ICT-based water metering). We find that economies that are most advanced in exploiting complementarities between sectors are also those that are the most competitive (Figure 1).

Is Eurasia well-endowed with second-generation infrastructure assets? We find that Eurasia ranks low or average relative to the rest of the world. For example, ICT solutions are only moderately used to enhance the access to basic infrastructure services (Figure 1). As another example, much of the core infrastructure across sectors is being developed alongside national fiber optic infrastructure. The region is not yet fully exploiting the benefit of sharing the existing infrastructure across the energy, transport and telecom sectors (ducts, towers) or coordinating the linear rights of way across sectors to get more out of the existing infrastructure. More broadly, we find that the region is lagging in terms of the following components (World Bank 2011a): 8
49

# **Table 2a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Innovation Pillar Indicators**

|**Years : 1960 - 2000**|**Reg 2a.1**|**Reg 2a.2**|**Reg 2a.3**|**Reg 2a.4**|**Reg 2a.5**|**Reg 2a.6**|**Reg 2a.7**|
|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**Per Worker**|**0.2119*****<br>**(0.0424)**|**0.2147*****<br>**(0.0434)**|**0.2251*****<br>**(0.0385)**|**0.2118*****<br>**(0.0499)**|**0.2020*****<br>**(0.0473)**|**0.2136*****<br>**(0.0492)**|**0.2150*****<br>**(0.0492)**|
|**Initial GDP (1960)**|**-3.7103*****<br>**(0.8717)**|**-3.7533*****<br>**(0.8494)**|**-3.5395*****<br>**(0.8143)**|**-3.4598*****<br>**(0.7319)**|**-3.6375*****<br>**(0.7667)**|**-3.4116*****<br>**(0.7518)**|**-3.4059*****<br>**(0.7565)**|
|**HC15**|**4.4661****<br>**(1.9854)**|**4.6428****<br>**(1.9699)**|**4.0179****<br>**(1.9165)**|**2.9314***<br>**(1.6963)**|**2.2785**<br>**(1.7155)**|**2.8552***<br>**(1.5822)**|**2.9533***<br>**(1.5961)**|
|**Sq of HC15**|**-1.0965****<br>**(0.4726)**|**-1.1303****<br>**(0.4686)**|**-0.9878****<br>**(0.4544)**|**-0.7006***<br>**(0.3843)**|**-0.5215**<br>**(0.3967)**|**-0.6638***<br>**(0.3805)**|**-0.6932***<br>**(0.3846)**|
|**Log (Educational**<br>**Quality)**|**0.3719**<br>**(0.5412)**|**0.4121**<br>**(0.5533)**|**0.5337**<br>**(0.5009)**|**0.3454**<br>**(0.5542)**|**0.5610**<br>**(0.5460)**|**0.4735**<br>**(0.5881)**|**0.4750**<br>**(0.5830)**|
|**Log (Patents)**|**0.1956*****<br>**(0.0701)**|||||||
|**Log (Utility Patents)**||**0.1839****<br>**(0.0706)**||||||
|**Log (Journals)**|||**0.2295*****<br>**(0.0494)**|||||
|**Log (Royalty Payments)**||||**-0.0047**<br>**(0.0195)**||||
|**Log (Royalty Receipts)**||||**0.0369****<br>**(0.0175)**||||
|**Log (Royalties)**|||||**0.0268****<br>**(0.0134)**|||
|**Royalty Payments**<br>**per capita**||||||**0.000005*****<br>**(0.000001)**||
|**Royalty Receipts**<br>**per capita**||||||**0.000002**<br>**(0.000006)**||
|**Royalties per capita**|||||||**0.000005*****<br>**(0.000001)**|
|**Phones**|**0.0040*****<br>**(0.0011)**|**0.0041*****<br>**(0.0011)**|**0.0037*****<br>**(0.0009)**|**0.0051*****<br>**(0.0011)**|**0.0052*****<br>**(0.0011)**|**0.0049*****<br>**(0.0011)**|**0.0049*****<br>**(0.0011)**|
|**Constant**|**-4.5304****|**-4.8463****|**-5.5338****|**-3.1130**|**-3.3607**|**-3.3407**|**-3.4270**|
||**(2.2424)**|**(2.2722)**|**(2.1996)**|**(2.0799)**|**(2.0756)**|**(2.2176)**|**(2.2023)**|
|**R squared**|**0.5912**|**0.5854**|**0.6248**|**0.6126**|**0.5999**|**0.6045**|**0.6042**|
|**Number of Countries**<br>*, **, *** denotes significance at the<br>Robust standard errors obtained usi|**85**<br>90, 95 and 99 per<br>ngWhite's correct|**85**<br>cent confidence<br>ion for heterosc|**85**<br>level respectively<br>edasticity.|**84**|**84**|**84**|**84**|
account for these effects, we have also simulated the effect of universal primary education of mothers using the coefficients estimated in the survival model that includes only child characteristics and mother’s education (Table A7). As we have argued above, this model can be considered to capture the ‘gross’ effect of education on child mortality. In addition, we provide the results of a simulation that implements the three policies from above, universal mothers’ education, elimination of short birth intervals and BCG-vaccination simultaneously. These two simulations should yield similar results if mothers with primary education in the underlying sample also tend to have adequate birth spacing and vaccinated children. The results are reported in Table 3.

**Table 3: Comprehensive policy interventions**

|Country|U5M Base<br>values|Universal primary<br>education (gross<br>effect)|All 3policies|Needed to reach<br>target|
|---|---|---|---|---|
|Guinea|155.2|-25.7|-39.2|-77.9|
|Senegal|107.2|-31.2|-33.6|-63.4|
|Mali|211.4|-96.9|-96.8|-129.3|
|Niger|169.6|-76.9|-76.4|-64.4|
|Kenya|116.6|-20.5|-44.8|-85.7|
|Uganda|111.7|-23.9|-38.0|-53.8|
|Madagascar|89.2|-10.1|-27.7|-34.6|
|Mozambique|144.4|-13.2|-23.6|-79.0|

Source: Authors’ calculation from DHS surveys and simulations.

In Mali, Niger, and Senegal, the two simulations indeed yield very similar impacts. This hence reflects that the small share of mothers with primary (or higher) education in these countries is indeed much more likely to have short birth intervals and their children vaccinated, as also indicated in the above analysis of these proximate determinants. In all the other countries, the simultaneous implementation of all three policies has a considerably larger effect. Here, levels of primary education are typically higher (Kenya, Uganda, Madagascar) and less strongly associated with the other policies (in particular Guinea and Mozambique where levels of primary education are also low).

The most striking result from these simulations, however, is that simultaneously achieving universal primary schooling completion for mothers, universal BCG vaccination and eliminating short birth intervals would not suffice to achieve MDG4 in 7 out of 8 countries of our sample. In Table 4, we summarize our assessments based on the simulations and compare then with the official UNDP judgment on the feasibility of achieving MDG4 by 2015.

38