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Bobonis, G. J. and Finan, F. 2009. “Neighborhood peer effects in secondary school enrollment Decisions.” _Review of Economics and Statistics_ 91(4), 695–716.

<mark>Bourguignon, F., Ferreira, F.H. and Leite, P.G. 2003. “Conditional cash transfers, schooling, and child labor: Micro-simulating Brazil's</mark> _<mark>Bolsa Escola</mark>_ <mark>program.”</mark> _<mark>The World Bank Economic Review</mark>_ <mark>, 17(2): 229-254.</mark> <mark>Chevalier, Arnaud. 2004. “Parental Education and Child’s Education: A Natural Experiment.” IZA Discussion Paper No. 1153.</mark> <mark>Contreras, Diana and Pushkar Maitra .2013.</mark> _<mark>Health Spillover Effects of a Conditional Cash Transfer Program</mark>_ <mark>. No. 44-13. Monash University, Department of Economics.</mark> <mark>Dammert, Ana C. 2008. "Heterogeneous impacts of conditional cash transfers: Evidence from Nicaragua."</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>58(1): 53-83.</mark> <mark>Das, Jishnu, Quy-Toan Do, and Berk Özler (2005). "Reassessing conditional cash transfer programs."</mark> _<mark>World Bank Research Observer</mark>_ <mark>20(1): 57-80.</mark> de Hoop, Jacobus and Furio. C. Rosati (2014). “Cash Transfers and Child Labor”, _World Bank Research Observer_ , 29(2): 202-234.

<mark>Department of Social Welfare and Development.</mark> _<mark>Pantawid Pamilya Financials.</mark>_ <mark>URL:</mark> <u><mark>http://pantawid.dswd.gov.ph/index.php/pantawid-pamilya-financials. Accessed on May 4,</mark></u> <mark>2016.</mark> <mark>Duryea, Suzanne, and Andrew Morrison. 2004. "The effect of conditional transfers on school performance and child labor: Evidence from an ex-post impact evaluation in Costa Rica." Working Paper.</mark> Edmonds, Eric. V. 2008. “Child Labor”, In Schultz, T. and J. Strauss (Eds.) _Handbook of Development Economics Volume 4._ 29
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al. (2017) find that cash transfer beneficiaries are able to protect their consumption and food security from rainfall deviations in Zambia, but the mechanisms through which this occurs are not studied. Overall, the evidence is limited and mixed, indicating that the impact of cash transfers on resilience may work through different channels, which need to be better understood.

We contribute to this literature by studying a government-led cash transfer program that delivers regular, small, unconditional cash transfers to rural Nigerien households for two years. Niger is a particularly relevant setting given it has high poverty levels, was ranked last in 2018 in terms of human development indicators, faces frequent weather shocks, and has rolled out a safety net program as a flagship government initiative.<sup>5</sup> The expectation was that regular cash transfers delivered year-round would help households protect themselves against shocks and promote resilience. We analyze household data collected for the randomized evaluation of the project between 2012 (baseline) and 2015 (follow-up) to measure the impact of the transfers.<sup>6</sup> We combine household surveys with satellite data on rainfall to identify whether cash transfers mitigate the welfare effects of exogenous climatic shocks on household consumption and food security. We also measure impacts on income-generating activities, savings and assets to assess pathways to enhanced resilience.

In doing so, we make three main contributions to the literature on resilience and cash transfers. First, we document the impact of a government-led, multi-year unconditional cash transfer program in an extremely poor context where households face high exposure to climatic shocks.<sup>7</sup> Second, we focus explicitly on household resilience by measuring how cash transfers protect consumption and food security from climatic shocks. By considering intermediary impacts on savings, income smoothing through household economic activities and asset accumulation, we contribute to a better understanding of the mechanisms through which cash transfers foster resilience. Finally, we add to the existing literature by analyzing the mitigation role of cash

> 5 Despite its low development indicators, Niger is also under-studied (Porteous, 2020).

> 6 Specifically, the paper extends the analysis of an RCT embedded in the first phase of the national safety net program and presented in Premand and Barry (2020), who disentangle the relative effects of cash transfers and behavioral change accompanying measures on young children’s human capital. Premand and Barry (2020) also provide more information on the study background, implementation, and collaboration with government. We use the baseline and follow-up data collected as part of the RCT.

> 7 We also compare our results with those found in the study by Stoeffler et al. (2019) of a prior, smaller-scale cash transfer pilot project in Niger.

5
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Next, we perform an analogous analysis for each IEA sub-region. The results from these exercises are presented in panels B to F. Nearly all sub-regions are expected to see an increase in FHD, with sub-regions like the Caribbean and Southern South America particularly hard hit. One notable exception is the Andean Region (Colombia, Ecuador, and Peru), which is expected to see a reduction in the FHD of roughly 25 percent. Given the location of the Andean Region and the anticipated evolution of hydrological droughts, this result highlights the potential that cross-border trade may hold in mitigating drought-induced air pollution.

In the second exercise, we project the evolution of premature deaths in LAC up to 2059. Specifically, we extend the premature deaths counterfactual calculation presented in Section 4.1 but compute excess air pollution using the runoff projections from the climate models instead of the observed runoff. We also allow for demographic changes using the demographic projections of the SSPs. The SSPs do not include projections on the evolution of electricity generation. However, because we expect the retirement schedule of combustion power plants to have significant implications for this exercise, we include information on possible retirements of combustion power plants by taking advantage of the region’s IEA electricity generation scenarios and attaching the most closely aligned scenario to each SSP.

For SSP1-2.6, we use the Announced Pledges Scenario (APS). This scenario is described in IEA (2023) and is consistent with an expected temperature rise of 1.7 _◦_ C by 2100. This scenario outlines how total electricity generation by fuel type will evolve if countries fully honor existing pledges. This scenario is optimistic as it does not mirror what governments are doing but rather governments’ aspirations. In this scenario, gas, coal, and oil power generation are expected to decline, while bioenergy generation is expected to increase. For the exercise, we omit the increase in bioenergy generation (no new plants are included) and use the information on the decline in generation from coal, gas, and oil to model the phase-out of combustion power plants. To present the most optimistic scenario for PM2 _._ 5 reduction, we assume that plants with the largest exposed population are retired first to meet the pledges.

For the SSP2-4.5, we use the Stated Policies Scenario (STEPS). This scenario is described in IEA (2023) and is consistent with an expected temperature rise of 2.4<sup>_◦_</sup> C by 2100. This scenario does not assume the automatic attainment of aspirational energy targets but is built on the IEA’s current assessment of the re25
-6-

Gini cooffLcient for gross houaehold income at the end of the period fell from 0.29 for main wage earnings to 0.23 when including social inco and taxes. The mergence of more cor.plex bargaining behavior in the PRB turned around the interactlon kitween managers, workers and government. Enterprise managers, were motivated through bonus payments associating their wages with performance or synthetic lndLcators. By lmpooirg an approprLate Lneentive structure the goveranmnt hoped t2 engage workers and managers in a cooperative bargain over wages and effort. For the most part, thio structure was given through tax policy conditioned on tho wage, elther on the **level** or, **at** timos, on the rate of growth.<sup>9</sup> Wages paLd out above an announced norm were taxed by the government. Most comonly a tax (t) was levied on that share of the wage bill above the warranted amount (wP), This Lmplied in terms of cost per employee; w+t(w **-wP)** , for w3wP This tax fcrmula wo%Ald have only been fiscally neutral Lf a rebate or subsidy per employee had been offered.10 A rebate or subsLdy was obvLously not applied ln the full employment regime as an explicLt objectLve of tax based wage policy was to motivate labour sheding by enterprLsoe.

Experience with applying an exogenous incentlve structure and regulating wage development by tax based wage policies was very mixed and generally dLappoLnting. In general, It would be appropriate to see workers and managers playing cooperatLvely against the goveranment. Non-cooperative settings invariably prevaLled when, as In Poland, there was no underlying agreement over the dLitrLbution of rescurces Ln the economy. While the broad 0 The dLfference for personal not Lncome wLth and wlthout<sup>social income</sup> was 0.18 and 0.23. See World Bank (1991).

**9** A bewllderLng range of wage tax formulas have been applied; for further details, see Adam (1979)1 IMF (1989a and 1989b;.

'° A rebate, such as rw-t(w-w"), would have net the cost per worker at; w+t(w-w0<sup>)-rw,thereby</sup> being neutral wLth regard to employment.
**Figure 5:** Evolution of _PALE_ and Life Expectancy, 1990-2019

<!-- Start of picture text --> 70 60 70 60<br>60 50 60 50<br>50 50<br>40 40<br>40 40<br>30 % 30 %<br>30 30<br>20 20<br>20 20<br>10 10 10 10<br>0 0 0 0<br>Year Year<br>Life Expectancy PALE Life Expectancy PALE<br>Headcount ratio (right axis) Headcount ratio (right axis)<br>(a) PALE 1 (b) PALE 0 . 5<br>Years Years<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Reading: in 1990, Poverty-Adjusted Life Expectancy was about 30 years according to _PALE_ 1 and 48 years according to _PALE_ 0 _._ 5.

increased from 62 to 71 but the decrease in poverty expectancy is even more spectacular, from 30 years in 1990 to only 7 years in 2019. This decrease in poverty combined with an increase in life expectancy resulted in a large increase in _PALE_ 1, from 32 in 1990 to 64 years in 2019. For _θ <_ 1, the corresponding _PALEθ_ curves all lie between life expectancy and the _PALE_ 1 curve. We show _PALE_ 0 _._ 5 in the right hand panel of Figure 5. By construction, _PALE_ 0 _._ 5 is higher in absolute value (47 years in 1990). However, its evolution is much slower than that of _PALE_ 1: from 1990 to 2019, _PALE_ 0 _._ 5 increased by 44% as opposed to 101% for _PALE_ 1. Indeed _θ_ = 1 implies that one year spent in poverty is equivalent to one year spent dead. When instead one assumes that a year spent in poverty is equivalent to half a year lost to death, life expectancy has more weight in PALE. As the progress on life expectancy have been much slower than those against poverty, _PALE_ 0 _._ 5 growth is slower.

Note that PALE can not be directly decomposed into each of its components. However, it is possible to decompose its growth into the contribution of that of each of its component. Indeed, the growth rate of _PALEθ_ can be decomposed as follows: where _εLE_ = 1 and _εH_ = (1 _−−θHθH_ )<sup>representtheelasticitiesof</sup><sup>_PALEθ_tolifeex-</sup> pectancy and poverty, respectively. Figure 6 shows the share of the growth of _PALEθ_ explained by changes in life expectancy, from 1991 to 2019. First, note that the choice of _θ_ = 1 is conservative: the contribution of life expectancy to PALE is on average 16 percentage point smaller when _θ_ = 1 than when _θ_ = 0 _._ 5. Second, irrespective of the precise value given to _θ_ , the contribution of life expectancy is growing over time. Life expectancy contributes on average to 17 (resp. 32) percent of PALE’s growth from 1991 to 2004, as opposed to 34% (resp. 51%) from 2005 onwards. Even though mortality has not decreased as much as poverty, the changes in mortality still play a substantial role in the trend of _PALEθ_ .

17
# **7. References**

Aisbett, E. and L. Pearson (2012), “Environmental and Health Protections, or New Protectionism? Determinants of SPS Notifications by WTO Members”, Crawford School Research Paper No. 12-13, December Akerlof, G. (1970), “The Market for “Lemons”: Quality Uncertainty and the Market Mechanism”, The Quarterly Journal of Economics, 84 (3), pp 488-500 Andriamananjara, S., J. M. Dean, R. Feinberg, M. Ferrantino, R. Ludema, M. Tsigas (2004), “The Effects of Non-Tariff Measures on Prices, Trade, and Welfare: CGE Implementation of Policy-Based Price Comparisons”, USITC Economics Working Paper No. 2004-04-A Bacchetta, M., J. Richtering and R. Santana (2012), “How Much Light Do WTO Notifications Shed on NTMs?” in Non-Tariff Measures. A Fresh Look at Trade Policy’s New Frontier (eds) Cadot O. and M. Malouche, The World Bank Beghin, J. and J-C Bureau (2001), “Quantitative Policy Analysis of Sanitary, Phytosanitary and Technical Barriers to Trade”, Economic Internationale 87, pp 107-130 Beghin, J., A-C Disdier, and S. Marette (2013), “Trade Restrictiveness Indices in Presence of Externalities: An Application to Non-Tariff Measures”. Working Paper No. 12023, Department of Economics, Iowa State UniversityBerthou, A. and C. Emlinger (2011), “The Trade Unit Values Database”, CEPII Working Paper 2011-10, CEPII Research Center Bradford, S. (2003), “Paying the Price: Final Goods Protection in OECD Countries”, The Review of Economics and Statistics, 85(1), pp 24–37 Cadot, O. and J. Gourdon (2012), “Assessing the Price-Raising Effect of Non-Tariff Measures in Africa”, CEPII Working Paper 2012-16, CEPII Research Center Cadot, O. and M. Malouche (eds), (2012), Non-Tariff Measures. A Fresh Look at Trade Policy’s New Frontier, The World Bank Calvin, L. and B. Krissoff (1998), “Technical Barriers to Trade: A Case Study of Phytosanitary Barriers and U.S.-Japanese Apple Trade”, Journal of Agricultural and Resource Economics, 23(2), pp 351-366 Cato J. C., W. S. Otwell, and A. S. Coze (2005), “Nicaragua’s Shrimp Subsector: Developing a Production Capacity and Export Market during Rapidly Changing Worldwide Safety and Quality Regulations”, Agriculture and Rural Development Discussion Paper, The World Bank ‐ Chemingui, M. and S. Dessus, (2008), “Assessing Non Tariff Barriers in Syria”, Journal of Policy Modeling, 30(5), pp 917-928 Chemnitz, C. and N. Künkel (2006), “Standards, a Catalyst for the Winners – a Barrier for the Losers? An Empirical Analysis of the Impact of Higher SPS Measures on the Trade Performance of Developing Countries”, Paper Prepared for Presentation at the 98th EAAE Seminar ‘Marketing Dynamics within the Global Trading System: New Perspectives’, Chania, Crete, Greece on 29 June – 2 July, 2006 32
limited because they may reflect erratic changes in expectations. To what extent this is actually the case may be difficult to gauge, because existing asset price models are based on unobserved variables; their empirical predictions are subject to wide margins of error. This makes it difficult to identify the "right" price (reflecting, say, future profit growth rates or productivity shocks) and therefore what is an erratic movement or speculative bubble. In such conditions, incorporating asset prices systematically in monetary policy feedback rules may be unwarranted (see Fuhrer and Moore (1992)). Moreover, the risk premium that is typically embedded in asset prices tends to vary over time. Basing monetary policy on a broader, asset-based measure of prices or monetary conditions may actually lead to greater variability in current and future output and inflation.

# 6 Recent Experiences

As noted in the introduction, in recent years a number of industrial and developing countries have adopted an inflation targeting regime. To put matters in perspective, consider the results of the survey of monetary policy frameworks conducted in late 1998 by the Bank of England and summarized by Sterne (1999). 91 countries, of which 43 were considered developing countries, were covered. Almost all the central banks surveyed targeted either the money supply, the exchange rate, or inflation directly. Several of them (in both industrial and developing countries) stated that instability in the velocity of money (induced in many cases by financial liberalization) led them to abandon money targeting. In 1998 there were 54 countries with an explicit inflation target (compared to 8 in 1990), 37 with an explicit monetary aggregate target, and 47 with an explicit exchange rate target. Between 1990 and 1998, explicit targets (whether for money, inflation, or the exchange rate) became more widespread. Of the 54 countries that had inflation targets, 13 (or 14 percent of the sample) had inflation targets only; thus, many countries had several explicit targets.<sup>41</sup> The majority of countries that abandoned their exchange rate target (including Mexico in 1995 and Russia in 1998) did so in response to a currency crisis. The fastest growing regime was the combined use of explicit money and inflation targets (24 percent of the countries in the 4"The use of multiple explicit targets may reflect the fact that, in practice, targets are viewed more as benchmarks (against which outcomes are measured) by monetary authorities as opposed to objectives that must be achieved.

40
Croke et al. (2016) school feeding. As noted, the estimated weight gain in kilograms per dollar spent from school feeding is less than 0.01 (from RCT studies; Galloway et al. (2009)). Given the $0.60 per year treatment cost of deworming in environments where two annual doses are required, an effect of just 0.006 kg would make deworming cost effective relative to school feeding (setting aside, for the time being, the issue of other outcomes).

Examining the random effects meta-analysis estimator that is the focus in TMSDG and taking as given the variance of the effect across studies that TMSDG estimate, and the standard errors TMSDG report for the underlying studies, the implied Minimum Detectable Effect (MDE) size at 95% confidence and 80% power is 0.28 kg.<sup>11</sup> As illustrated in Figure 3, a wide range of possible effect sizes lies above the magnitude at which deworming would be cost effective relative to other common interventions for similar populations (~0.006 kg), yet below the magnitude which TMSDG would be adequately powered to detect (~0.28 kg). For example, this range includes TMSDG's random effects estimate of 0.08 kg, for which there is only 13% statistical power to detect a significant effect at 95% confidence.

TMSDG also appears to be underpowered to detect effects that might be reasonable to expect from MDA given the estimated effects TMSDG report in studies of those known to be infected. This is notable because the suggestion that there are positive effects from treating individuals known to be infected, yet no impact from treating populations

> 11 These estimates were obtained using the method of Hedges and Pigott (2001). In particular, for a given E <u>E</u> E E E E <u>E</u> E E E effect size, we estimate power as: P P Φ �1.96 − E S S S S �+ Φ(−1.96 − E S S S S ~~)~~ , where Φ is the cumulative distribution function for a standard normal random variable, and S S S P P is the standard error for the average effect size under the random effects model. Reported PP= 1 − EES EES power for a given effect size is the probability that the null hypothesis that the average effect size is zero is rejected at the 0.05 level of significance. The reported MDE is an estimate of the effect size that would PPS deliver a test with 80% power.

12