rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9435

# **Abstract**

Severe weather shocks recurrently hit Malawi, and they adversely affect the incomes of many farm households as well as small businesses. With climate change, the frequency of extreme weather events is expected to increase further. A clear understanding of households’ vulnerability to shock-induced poverty is critical for disaster risk management and the design of scalable social safety net programs. Standard poverty measures rely on static snapshots that are suitable for quantifying structural poverty but not for assessing the vulnerability of non-poor households to fall below the poverty line when they experience shocks. This study uses a nationally representative household survey and exogenously measured weather shocks to assess households’ vulnerability to poverty in Malawi. To accurately estimate the impacts of shocks on consumption and vulnerability, the study excludes any kind of assistance (aid and food or cash transfers) that households might have received after major disasters. The key findings of the study are as follows: (1) drought during the growing season decreases non-assistance consumption per capita by 5–12 percent, depending on its intensity; (2) excess rainfall at the onset of the growing season reduces food consumption by 1.8 percent, while excess rainfall later in the growing season appears to increase consumption; (3) vulnerability to poverty is generally higher than static poverty, especially compared to static poverty measured during a good weather year; and (4) in years of extreme droughts, such as 2016, recorded poverty rates are higher than vulnerability, which indicates that the magnitude of drought in 2016 was so large that the chance of falling below the poverty line as a result of an even higher magnitude shock was low. These results suggest that identifying vulnerable households is key in designing adaptive social safety net programs that can be scaled up to cover those who become eligible for such programs after experiencing shocks.

This paper is a product of the Poverty and Equity Global Practice. 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/prwp. The authors may be contacted at HFuje@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
activities. In addition, revenues from carbon pricing can be used to lower existing distortionary taxes with the aim of promoting both greater efficiency and equity in the tax system. To evaluate the economic appeal of alternative reforms, we start from a reference scenario (Baseline) that represents future demographic, energy and emissions evolution under current policy legislation. Then a set of policy scenarios are evaluated which implement the climate mitigation measures to achieve the fitfor55 emissions reductions. The policy measures are represented through the introduction of carbon prices and are compared with respect to the baseline scenario to estimate the policy impacts. Scenarios cover the 2021-2030 timeframe.

|**Scenarios**|**Description**|
|---|---|
|Baseline|Continuation of current policies: 40% reduction in GHGs compared to 1990|
|Fit55|Increased climate ambition to achieve 55% emissions reduction target|
|Fit55 – Wage<br>tax|Fit for 55 with additional carbon revenues recycling to reduce Labour taxes (i.e. Social Security<br>Contributions paid by the employer, SSCer) – revenue-neutral reform|
|Fit55 – CIT|Fit for 55 with additional carbon revenues recycling to reduce corporate income taxes (IRAP and IRES)<br>– revenue-neutral reform|
|Fit55 – VAT|Fit for 55 with additional carbon revenues recycling to reduce VAT – revenue-neutral reform|
|Fit55 – Excises|Fit for 55 with additional carbon revenues recycling to reduce Excises – revenue-neutral reform|
|Fit55 – Income<br>tax|Fit for 55 with additional carbon revenues recycling to reduce households' income taxes - revenue-<br>neutral reform|

The baseline scenario replicates trends of the Gross Domestic Product (GDP) from the International Monetary Fund’s (IMF, 2021). To capture the expected energy and emission trends, some assumptions on energy-related variables are assumed: costs of renewable electricity generation decline over time, non-price related changes in preferences in favor of renewables, increases in electricity shares for the final and intermediate consumers, improvements in energy efficiency. In addition to these assumptions, in the baseline scenarios we also introduce carbon prices to replicate the current climate targets ambition (see Figure 2). Under these assumptions, GHGs emissions are declining considerably in the baseline scenario to achieve a reduction of at least 40% (from 1990 levels). The fitfor55 policy scenarios raise the 2030 greenhouse gas emission reduction target, 10
Klenow, 2013) suggest that India has a flat life cycle where 40-year-old plants are no larger than young plants.

We have the following main findings: First, our cross-country analysis shows that in the formal sector in developing countries, older firms are substantially larger than younger firms. While the average mid-aged firm between 10 and 19 years of age is only twice the size of the average plant under the age of 5 years, the average plant that is 40 years and older is 4.65 times the size of the average plant under the age of 5 years in terms of employment.<sup>4</sup> The significantly upward sloping age-size profile is pervasive in the vast majority of countries and in only less than 10% of the countries, the average 40+ year plant is not at least as big as plants younger than 5 years. One possible explanation for our findings is selection effects due to firm exits since we have data only on surviving firms. To improve on our cross-sectional evidence, we look at employment at two points in time – last year and three years back. We find that on average, firms are approximately the same size as two years ago suggesting that the surviving plants we examine are not the fastest growing in the distribution and that our results are unlikely to be driven mostly by plant exits.

Second, when we study the life cycles of Indian plants, we find that the average 40-yearold plant in the formal sector in India is two to four times the size of plants less than five years of age, both when compared contemporaneously and when comparing older and younger firms within the same cohort. Our results hold true when we take sampling weights into account and look at the entire population of firms, and also when we study just the Census sector firms that

> 4 Our results are robust to considering only manufacturing plants and taking into account survey sampling weights. We obtain similar patterns when we look across country income groups. Our results are also robust to dropping firms with government ownership, dropping former Socialist economies where they may a discrepancy in age reported due to large-scale privatization, and also to dropping firms that started operations before countries’ independence. This finding also holds controlling for industry and country fixed effects in a multivariate analysis.

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**Table 3 : External Debt and Growth: 5-year average panel**

||1<br>[OLS]|2<br>[FE]|3<br>[GMM system]|
|---|---|---|---|
|Present Value of Debt [PVY]/GDP (initial)|-0.208|0.561|-0.323|
||[0.99]|[2.26]*|[0.54]|
|Present Value of Debt [PVE]/GDP (initial)|-0.118|0.28|0.215|
||[0.47]|[0.89]|[0.39]|
|Total Outstanding Debt/GDP (initial)|-0.269|0.053|-0.236|
||[1.19]|[0.18]|[0.41]|
|Present Value of Debt [PVY]/Exports (initial)|-0.011|0.267|0.051|
||[0.05]|[1.02]|[0.08]|
|Present Value of Debt [PVE]/Exports (initial)|0.298|0.067|0.147|
||[1.12]|[0.20]|[0.25]|
|Total Outstanding Debt/Exports (initial)|0.007|-0.437|0.168|
||[0.03]|[1.42]|[0.26]|

* significant at 10%; ** significant at 5%; *** significant at 1% Absolute value of z statistics in brackets Note: [GMM System]: Two System Estimator with Small Sample Windmejer  (2005) Robust Correction [PVE]= PV data from B.Easterly; [PVY]: PV data from Yuri Dikhanov Set of Control Variable identical to Table 1 All regressions include time effects

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areas in 1985 (19.1 % in 1986). After correcting for the over-representation of larger households in the sample in 1985, the differential is reduced to 27.7 percent (13.5% in 1986).

Somewhat puzzling is the influence of the H-S weights on West Forest in 1987, where mean per capita expenditure is marginally reduced. This occurred despite a decrease in mean household size of 11 percent (Table 7). It seems that for 1987 at least, the larger households in West Forest enjoyed above-average expenditure levels compared with smaller households, so that reducing their weight reduces the overall mean, albeit marginally. In all other cases, mean expenditures are adjusted upward.

How do these corrections influence the socio-economic group data? The evidence of Table 10 is that the sampling bias also has uneven effects on socio-economic groups. Not surprisingly, mean expenditures of socio-economic groups based largely in Abidjan (mainly formal private sector employees) are affected little by the H-S weights. Similarly, those based predominantly in Savannah (food-crop farmers) are also affected less than other groups. On the other hand, mean expenditure of export-crop farmers (especially those located in West Forest) increases noticeably.

There are some minor changes in the rank ordering of the groups (for example, informal sector employees and the self-employed switch rank order in 1985), but the main effect of the H-S weights is to change differentials in the welfare measure. For example, per capita expenditure among export crop farmers is recorded as 4.9 percent higher than food-crop farmers in 1985 based on the original biased data. This differential increases to 11.6 percent as a result of the weighting. Similarly, government employees appeared to enjoy expenditure levels which were 11.8 percent higher than their counterparts in the formal private sector in 1986, whilst in reality, i.e. when the sampling bias is corrected, the difference was 18.4 percent. Such adjustments in measured differentials between the groups are obviously important, and underline the significance of correcting for the over-estimation of household size in the early years of the CILSS. Indeed, the differences are large enough to be of more than mere academic interest, and to have a potential effect on policy 19
# **Figure 16. LAC: Correlates of investment growth slowdown**

_The investment slowdown has coincided with severe terms-of-trade deteriorations, sharp output growth slowdowns, slowing FDI inflows, political tensions, and domestic policy tightening. Over the medium term, investment growth is expected to remain low._

# **A. Regional output growth**

# **B. Long-term investment growth forecasts**

<!-- Start of picture text --> Percent8 1990-2008 avg 2003-08 avg 11Percent LACBrazil ArgentinaChile<br>Colombia Mexico<br>6 10 Peru<br>9<br>4<br>8<br>2<br>7<br>0<br>6<br>-2<br>5<br>4<br>SA MCC LAC EMDE 2010 2011 2012 2013 2014 2015<br>C. Terms of trade changes D. Political stability<br>Percent ICRG Index (0-100, 100=best)<br>15 70<br>10<br>68<br>5<br>66<br>0<br>-5 64<br>Energy exporters<br>-10 Non-energy commodity exporters 62<br>-15 Commodity importers 60 Commodity exporters<br>-20 Commodity importers<br>2010 2011 2012 2013 2014 2015 58<br>2010 2011 2012 2013 2014 2015<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br><!-- End of picture text --> Sources: Haver Analytics, Consensus Economics, World Economic Forum (2016), World Bank. A. GDP-weighted averages. SA is South America. MCC is Mexico, Central America, and the Caribbean. B. Five-year ahead consensus forecasts for investment growth.

C. GDP-weighted average annual change in terms of trade. Negative value indicates deterioration. Energy exporters include Bolivia, Colombia and Ecuador. Non-energy commodity exporters include Argentina, Brazil, Chile, Costa Rica, Guatemala, Honduras, Nicaragua, Panama, Paraguay, Peru, and Uruguay. Commodity importers include Dominican Republic, El Salvador, Haiti, and Mexico. D. Investment-weighted averages. A decline indicates greater political instability.

_Infrastructure investment_ . On average across the 16 EMDEs in LAC over 2008-13, infrastructure investment amounted to just 3.7 percent of GDP, well below the 5-6 percent of GDP required just to sustain current economic growth rates (Bhattacharya, Romani, and Stern 2012; Kohli and Basil 2010; Fay and Yepes 2003; Calderón and Servén 2003; and Perrotti and Sánchez 2011). Apart from low investment levels, the quality of infrastructure in the LAC region is poor relative to that of advanced economies and Asian emerging markets. The average LAC economy ranked 82nd out of 138 economies (around the 40th percentile) on quality of infrastructure (World Economic Forum 2016; Figure 17). Priority infrastructure needs in the region include improving road conditions through maintenance and rehabilitation (Uruguay), upgrading infrastructure relating to 31
Exposure to new exemplars can change the meaning of a category and the self-image of its members; law has a _schematizing function_ .<sup>5</sup> _3. The cultural categories learned in a society in which ascriptive groups have unequal rights promote behaviors that make group inequality persist long after the abolition of the unequal formal rights._ The historically relevant forms of thought (Berger and Luckman 1967; Boyd and Richerson 1988; Bruner 1990; Sen 1992; Nisbett and Cohen 1996; DiMaggio 1997; Nunn 2012; Hoff and Stiglitz 2010) and the accessible exemplars (Kahneman and Tversky 1973; Tversky and Kahneman 1973) conditions cultural categories.

Institutional practices constitute people into categories with particular meanings, and the categories influence how people see themselves, how they behave, and how they are treated. So pernicious and stigmatizing are some group stereotypes that they “get inside the heads” of members of the stigmatized groups and undermine their performance and their efforts to improve their lives (a survey is Hoff and Walsh 2018). Negative stereotypes impair performance in school settings (Steele and Aronson 1995; Hoff and Pandey 2006, 2014), the job application process (Linos, Reinhard, Ruda 2017), and the workplace (Davies, Spencer, Steele 2005). Some discrimination against groups is based on rational calculation ( _i.e.,_ statistical discrimination; see 5 The schematizing power of institutions is emphasized by the sociologists Berger and Luckmann (1966), Friedland and Alford (1991), and DiMaggio (1997); the anthropologist Douglas (1973, 1986); and the psychologists Bruner (1990, 58), Nisbett and Cohen (1996), and Markus and Kitayama (2010). These scholars showed that institutions influence cognition. We are indebted to their work.

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**_Energy demand models for policy_** **_<u>formulation</u>_**

ApsXs + ApiXi + Fp = Xp AisXs + AiiXi + Fi = Xi (6) Where Xs = output vector for energy supply, Xp = output vector for energy products, Xi = output vector for non-energy sectors, Fs = final demand for energy supply, Fp = final demand for energy products, Fi = final demand for non-energy sectors. Ass = I/O coefficients describing sales of the output of one energy/ supply conversion sector to another energy conversion sector. Asp = I/O coefficients describing how distributed energy products are converted to enduse forms. Asi = 0 implying that energy supplies are not used by non-energy producing sectors. Energy is distributed to the non-energy producing sectors via energy product sectors. Aps = I/O coefficients describing how energy products – final energy forms – are used by the energy supplying industries. App = 0 implying that energy products are not used to produce energy products Api = I/O coefficients describing how energy products – final energy forms are used by non-energy producing sectors.

Ais = I/O coefficients describing the uses of non-energy materials and services by the energy industry.

Aip = 0 implying that energy product sectors equipment require no material or service inputs. This is because they are pseudo sectors and not real producing sectors.

Aii = I/O coefficients describing how non-energy products are used in the non-energy producing sectors.

If we rewrite Eq. 6 in the summary form, X<sup>E</sup> = A<sup>E</sup> X<sup>E</sup> + F<sup>E</sup> , where the superscript E indicates energy input-output matrices, the equivalent equation of equation 5 is X<sup>E</sup> = (I-A<sup>E</sup> )<sup>-1</sup> F<sup>E</sup> (7) We could then calculate the various alternative final energy demand scenarios, the corresponding new total output requirements for non-energy industry and energy supply industry and energy services output and their respective intermediate outputs. Some perspective on inter-fuel substitution could also be gained, if one were satisfied that prices would not significantly influence the substitution process and if one were satisfied that the assumption of constant input output relationships would be true in practice.

Source: Based on Chapter 7, Macro-Demand Analysis, of Codoni et al (1985). See also Miller and Blair (1985).

# **3.2.2.4 Scenario approach**

The scenario approach has been widely used in climate change and energy efficiency policy making (Ghanadan and Koomey, 2005). The scenario approach has its origin in 38
the types of self-employed occupations that are typically promoted by policy efforts to create more jobs in sending regions, and our experimental studies are all either in urban settings or are trying to foster off-farm work. Secondly, both the migrant selection effects and causal impacts of working in agricultural work may be different from non-farm self-employment: for example, the idea of being able to take your business with you as you migrate is less viable for a farmer than for someone selling clothing or cooking food. After these restrictions, our total sample is almost 140,000 respondents.

Each of the panel surveys attempts to track individuals who move,<sup>9</sup> and migration is defined as having moved from one administrative area to another between survey rounds. The distance that constitutes ”migration” varies across studies (see online appendix B), so we further split migration into internal and international migration, and then examine the robustness of our results to different distances of internal migration. The samples we have are large, and allow tracking individuals over periods ranging from 3-4 years (Mexico), up to 14-15 years (Egypt, Indonesia). However, even with and a international is a rare long panels large sample, migration phenomenon, accounting for only 1 percent of our observations.

# _Does self-employed at home mean self-employed after migration?_

In their review of immigrant entrepreneurship, Fairlie and Lofstrom (2015) note that the “relationship between home country self-employment experiences and host country self-employment is not well understood”, and that a key reason is “due to a lack of suitable individual level data that includes pre-migration self-employment information”. Our panel data enable us to provide additional evidence on this question, by examining the extent to which self-employment at home makes it more likely to be also self-employed after moving. This also helps address the question of whether the self-employed can simply move their job with them.

Figure 1 puts together evidence from six countries for which we have information on type of occupation at destination for the majority of migrants.<sup>10</sup> After dropping respondents who do not move between survey waves, we are left with a sample of more than 15,000 distinct migrants aged 18 to 65 who were self-employed, wage workers, or not working at home.

> somewhat similar migration rates to the self-employed in most countries.

> 9The CFPS and ELMPS do not track international migrants, and it is likely that many of the attritors from the different panels may be migrants. In the absence of alternative information, our implicit assumption is that the self-employed are not more likely to attrit when they migrate than other occupations. Under this assumption, we may understate the magnitudes of migration, but attrition should not bias the signs of associations in the data.

> 10Occupation type after move is missing for a large part of the sample in the Mexican data and in all the Nigerian data.

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# _A. Panel VAR Variables_

The variables in our PVAR model are taken from the IMF’s International Financial Statistics (IFS): PRIVATE CREDIT, FOREIGN LIABILITIES, GDP, and DEPOSITS. We construct our variables as follows. First, to ensure stationary, we calculate quarter-on-quarter nominal growth rates in local currencies for all variables in the system. Second, we construct real growth rates by subtracting the quarter-on-quarter percent change in the CPI index.<sup>2</sup> Third, to remove significant country-specific seasonality in GDP growth rates, we regress them on country-specific quarter dummies and subtract the predicted values. Table 2A provides the key descriptive statistics of the variables in the PVAR model.

PRIVATE CREDIT is the quarter-on-quarter real growth rate of private credit denominated in local currency. Private credit equals deposit money bank credit to the domestic private sector. FOREIGN LIABILITIES is the quarter-on-quarter real growth rate of foreign liabilities of deposit money banks denominated in local currency. These liabilities consist of claims of nonresidents (both financial and non-financial) on the domestic banking system, and include deposits, securities, loans, financial derivatives and other liabilities. Note that these liabilities include the claims of foreign banks on their subsidiaries, in the form of loans, deposits, or other instruments. These foreign liabilities constitute the cross-border linkages between home and host countries and the channels through which financial shocks are transmitted. GDP is the quarteron-quarter, seasonality-adjusted growth rate of real GDP in local currency. DEPOSITS is the quarter-on-quarter real growth rate of bank deposits. Bank deposits consist of the sum of domestic demand, time, and savings deposits of deposit money banks.

Table 2A shows that the mean real quarterly growth rates of FOREIGN LIABILITIES were higher than those of PRIVATE CREDIT AND DEPOSITS, and its standard deviation was considerably higher, by a factor of three or larger. This result reveals the major weakness of

> 2 We also used GDP deflators but this variable limited the sample. All our results hold using the GDP deflator although they become slightly less significant.

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