rafmacalaba/datause-encoder-data huggingface dataset:
# 4.2. Impacts on Welfare

Results reported in columns 1-3 of Table 3 indicate that per capita food consumption and, to a somewhat lesser extent, per capita total consumption fell by more between 2014 and 2016 in areas with higher EVD prevalence, indicating that households did not have the means to completely self-insure against this (income) shock. Recall that our estimation sample consists of two repeated cross-sections of households, which form a panel at the level of the enumeration area. Thus, fixed household characteristics can be included in the EA fixed effect regression. Moreover, the burials variable is assumed to be zero in 2014 (prior to the Ebola outbreak), which is equivalent to interacting burials with a dummy for 2016.

Next, we investigate whether households in districts more involved in rice cultivation, and hence more subject to on-farm labor disruptions, might have experienced larger consumption declines. We do so by adding an interaction term between the share of district land under rice (in 2014) and Ebola mortality (columns 4-6). This term attracts a statistically significant negative coefficient, at least in the case of food consumption, suggesting that rice cultivating areas were indeed more vulnerable to the Ebola outbreak.

# 5. Conclusions

Consumption growth in rural Liberia was flat between the 2014 and 2016 waves of the HIES (Appendix 1 Table A1.2). Moreover, rural poverty increased from 70% in the first half of 2014 (just before the Ebola crisis) to 82% in the first half of 2016 following the Ebola crisis (LISGIS, 2017). The evidence presented in this paper is consistent with the Ebola outbreak being partially responsible for higher poverty in rural Liberia. The 19
flows requirement which is our proxy used for the sustainability of water withdrawals (SDG6), though the local impacts in other regions such as East Asia and Pacific may be significant.

Consideration of these multiple trade‐offs with SDGs helps to determine the regions where investments could be targeted with limited adverse impacts. According to our findings (Table 1), the most relevant regions for such large‐scale irrigation investments would be Europe and Central Asia, Middle East and North Africa, and South Asia where substantial benefits are to be expected with limited negative effects.

Table 1. Summary of the difference in impacts by indicator for Investment Scenarios relative to ZeroInvest in 2050 with color‐ coded SDG scoring

||Irrig.<br>Area|Investment<br>Cost|Crop<br>prices|Food<br>availability|GHG<br>AFOLU|Cropland|Other Nat<br>Land|Forest|Env. Flow<br>Requirem<br>ent|
|---|---|---|---|---|---|---|---|---|---|
|**Invest**|Mha|$ Billion<sup>1</sup>/<br>year|%<br>change|kcal/cap/<br>day|MtCO2eq|Mha|Mha|Mha|% of EFRs<br>at risk<sup>2</sup>|
|AFR|8.8|3.7|‐2.0|9.9|‐4.6|1.5|0.1|‐0.3|0.7|
|EAP|36.7|6.4|‐2.3|13.5|68.6|1.5|‐0.8|‐1.2|0.8|
|ECA|5.4|0.8|‐0.5|2.3|5.1|‐0.7|0.6|0.0|0.4|
|LCR|12.4|2.0|‐0.5|7.3|7.9|‐4.0|2.0|0.4|0.3|
|MNA|4.4|1.1|‐5.1|18.0|8.3|1.0|‐0.7|0.0|7.1|
|SAR|38.2|3.4|‐2.9|51.0|72.4|3.2|‐2.2|0.0|6.7|
|WLD|104.3|17.2|‐1.8|20.2|143.7|1.0|0.4|‐1.1|0.8|
|**MaxInvest**||||||||||
|AFR|22.7|10.1|‐2.2|7.7|‐10.9|‐1.3|2.3|‐0.5|2.0|
|EAP|49.4|11.3|‐3.3|34.9|67.9|1.6|‐1.9|‐1.1|2.0|
|ECA|18.5|4.7|‐1.5|8.0|7.0|‐3.7|2.8|0.0|2.6|
|LCR|43.5|8.0|‐7.3|54.1|99.0|‐5.4|8.1|‐4.9|1.6|
|MNA|5.9|1.7|‐6.5|19.7|6.9|1.0|‐0.7|0.0|7.4|
|SAR|49.6|4.8|‐5.1|71.0|71.5|5.6|‐3.1|0.0|12.2|
|WLD|187.7|40.3|‐3.8|34.2|221.4|‐4.9|10.1|‐6.5|2.1|

This working paper is structured as follows. Section 2 describes the methods we used for this analysis including the modeling framework, the irrigation investment scenarios, and the sensitivity analysis. Section 3 presents the investment needs and the composition of irrigation systems under the scenarios. Section 4 presents the impacts of the irrigation investments on the SDGs. Section 5 provides the sensitivity analysis. Finally, Section 6 concludes the working paper.

Additional results and figures are available in the supplementary information<sup>3</sup> with more details on the modeling framework, regional results, and sensitivity analysis. Figures from the supplementary information are referenced in this paper with the “SI” prefix.

> 1 Constant 2000 dollars.

> 2The total volume of environmental flow requirement (EFR) at risk to become unsustainable is calculated first by identifying pixels in which at least one month of the volume of surface water withdrawals by irrigation are in excess of the volume that should be left for the environment and then calculating the total volume of EFR in the unsustainable pixels over the entire growing period. The total volume of EFRs for unsustainable pixels is then compared to the total volume of EFRs for the region to calculate the share.

> 3 <u>http://pure.iiasa.ac.at/id/eprint/15748/</u>

6
shocks for the observed patterns of capital flows.

Table 5 offers a summary view of the fraction of the variance explained by global and group factors (additional details are given in A.6). On average, the common factors taken together account for a considerable portion of the variance of gross flows – 45 percent for gross inflows and 42 percent for gross outflows. However, there is a sharp contrast between advanced and emerging countries. Among the former, common factors account for 58 and 62 percent of the variance of inflows and outflows, respectively. Among the latter, the figures are 36 and 28 percent. Thus, common shocks dominate the capital flows of advanced countries, while idiosyncratic shocks dominate the capital flows of emerging countries. Further, the respective roles of global and group factors are quantitatively similar in the case of gross inflows, while the global factor dominates gross outflows – trivially so in the case of emerging countries, given the absence of a group factor affecting their outflows.

One might worry that these results overstate the role of common factors because the advanced-country group includes the world’s leading financial centers, which could be artificially inflating the role of commonality. However, the lower panel of Table 5 shows that excluding the U.S., U.K., Switzerland, Germany, and Japan from the calculations has very little effect on the variance decomposition figures.

These results concerning the quantitative role of common factors might appear to be in contrast with those that Cerutti et al. (2017c) reach using quarterly data on capital flows disaggregated by flow type (FDI, portfolio debt, portfolio equity, bank credit). They find a very modest role for global factors, whether alone or augmented with a handful of variables summarizing world real and financial conditions. Specifically, regressions of flows on two global factors – one estimated from 6 non-central advanced countries, and another one estimated from 15 emerging market economies (those with weight in the MSCI index above 1%) – yield an average (across countries and flow types) adjusted _R_<sup>2</sup> of 0.05 (figure A7 of Cerutti et al. (2017c). If the factors are augmented with selected global variables, the adjusted _R_<sup>2</sup> still averages only 0.12.<sup>25</sup> In Appendix C we show that a large part of the discrepancy with our re-

> 25In turn, Cerutti et al. (2017a) likewise find that an emerging-market inflow group factor, specific to the inflow type, accounts on average for just 12 percent of the variance of portfolio equity and bonds as well as bank inflows to 33 emerging markets, using quarterly data over 2001-2015.

18
**Figure 4: Private credit / GDP in African countries 2007-11, actual vs. predicted values** Predicted values of banking sector development, measured by credit to the private sector extended by deposit money banks/GDP, come from OLS regressions that control for a set of country-level variables including endowment (population and resources), macroeconomics, institutions, banking structure and other variables. Note that negative predicted values are replaced by zero. Source: Allen et al., 2013.

<!-- Start of picture text --> 0.8 MUS<br>ZAF<br>0.7<br>0.6<br>CPV<br>0.5<br>NAM<br>0.4<br>0.3 NGA KEN<br>MRT SWZ SEN BWA<br>0.2 CIV ETHTGO MOZMLIBFABEN<br>0.1 GABAGOLBRCAFSDN BDISLE CMRNERZMBUGACOMTZA MWIGMBMDGLSO GHA<br>COGZAR TCD GIN GNB<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>Predicted Values<br>Private Credit / GDP<br><!-- End of picture text --> Moreover, demand-side evidence from the Global Financial Inclusion Index (Global Findex) indicates that the credit extended by financial institutions is not spread evenly across the population. The predicted share of FINDEX respondents that had a loan from a financial institution (again based on regression models from other developing countries) exceeds the actual share in almost all cases (including South Africa), and by a noticeably wider margin than for Private Credit to GDP in many cases. Though crude tools, the benchmarking regressions indicate that private credit provision in Africa, though improving somewhat, lags behind what fundamentals would predict, and that the allocation of what credit there is does not extend deeply into the population. To a lesser extent, there are also development gaps on the savings side. Predicted liquid liabilities/GDP levels exceed actual levels for most countries, though the gap has narrowed since 2001-6, and the share of individuals age fifteen or over with an account at a formal institution actually exceeds predicted levels in many countries (Allen et al, 2013). Indeed, most African countries are near predicted levels on that metric, and about the same number fall above and below the prediction line.

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policy efforts and some recent improvements in the anchored child poverty rate (Figure 1 panel a)<sup>3</sup> , child poverty continues to be high. In 2021<sup>4</sup> , 25.9% of children (aged less than 18) in Bulgaria were at risk of poverty compared with 22.3% of adults (aged 18 and over). This is also higher than the poverty rate for the whole population (22.9%). This rate is among the highest rates in the EU, together with Spain and Romania, and significantly higher than the EU average (19.3%) (figure 2 panel b). Similar results hold when looking at the risk of poverty and social exclusion<sup>5</sup> , where the rates reached 33.9% of children compared to 31.8% of adults (aged 18 and over) (Eurostat 2022). This is concerning, as global evidence suggests that income-based child poverty has l <mark>ife-long consequences f</mark> or individuals and society, including long-term impacts on key development outcomes, such as deteriorating education and physical and mental health, as well as worse labor market outcomes later in life (Schmidt et al. 2021; Fergusson et al., 2007; Lesner, 2018; Cho & Heshmati, 2015; Duncan & Magnuson, 2013; Hallaert et al, 2023).

# **Figure 2. Child Poverty, Evolution, and EU Comparison**

- _a. Anchored at-risk-of-poverty rate (fixed at 2019), Total and by Age groups, Bulgaria, 2019-2021_

<!-- Start of picture text --> 35.<br>30.<br>25.<br>20.<br>15.<br>10.<br>2018 2019 2020 2021<br>Less than 18 years 18-64 65 or over All population<br>Percent<br><!-- End of picture text -->

- _b. At-risk-of poverty rate, Children and Adults in the EU, 2021_

<!-- Start of picture text --> 30.<br>25.<br>20.<br>15.<br>10.<br>5.<br>0.<br>Child (less than 18) Adults (18 years or over)<br>Percent<br>EU27 Spain Romania Bulgaria Italy Luxembourg Greece Slovakia France Malta Austria Portugal Lithuania Sweden Latvia Croatia Ireland Cyprus Germany Poland Belgium Estonia Netherlands Czechia Denmark Hungary Finland Slovenia<br><!-- End of picture text --> Source: Eurostat, ILC_LI02__custom_6710772 Note: These figures present income year, not survey. The at-risk-of-poverty threshold is set at 60% of the national median equivalized disposable income. For the estimation of the anchored AROP rates, the at-risk-of poverty threshold is calculated in the standard way for the selected base year (2019) and then adjusted for inflation.

> 3 The anchored at-risk of poverty rate (fixed at 2019) among children decreased by 8.3 percentage points between 2019 and 2022 (Eurostat).

> 4 2021 income year, 2022 survey year.

> 5 The at-risk of poverty and social exclusion (AROPE) indicator includes the <mark>persons who are either at risk of poverty, or severely materially and socially deprived, or live in a household with a very low work intensity.</mark>

4
technology transfer and absorption. Even when they are producing for home markets, these industries constantly compete with imported goods, which requires the use of frontier technologies that lead to more homogenous production technologies across countries. This technology transfer does not occur in traditional agriculture and non-tradable services with large informal sectors. Finally, the electricity-value added elasticity decreases in value when using the sample of non-high-income countries, declining from 0.036 to 0.022. One of the explanations for this is that there is generally a larger and cheaper labor force in developing countries, which can substitute some automated tasks with cheap labor, compared to advanced economies.

Table 2. Regressions Results for Manufacturing Value Added.

|||All Countries<br>||All Non-<br>|high-income<br>|Countries<br>|
|---|---|---|---|---|---|---|
||(1)<br>FE|(2)<br>Diff GMM|(3)<br>Sys GMM|(4)<br>FE|(5)<br>Diff GMM|(6)<br>Sys GMM|
|First Lag of Log|0.716***|0.319***|0.974***|0.761***|0.409**|0.986***|
|Manufacturing GVA|(58.21)|(2.68)|(56.20)|(48.90)|(2.56)|(37.44)|
|Log Manufacturing|0.096***|0.051*|0.036***|0.045***|0.014|0.022**|
|Electricity|(13.79)|(1.66)|(3.14)|(4.35)|(0.22)|(2.28)|
|Consumption|||||||
|Observations|1855|1728|1855|1114|1035|1114|
|Countries|123|119|123|76|72|76|
|Hansen-p||0.065|0.151||0.667|0.916|
|AR2-p||0.092|0.816||0.678|0.134|
|AR3-p||0.012|0.243||0.074|0.252|

This table shows the regression results of six regressions: The fixed effects, difference GMM, and system GMM for all countries and non-high-income countries. The dependent variable is the log of contemporary Manufacturing GVA. The independent variable of interest is the log of contemporary sector electricity consumption. t statistics are in parentheses and stars indicate significance: * p < 0.10, ** p < 0.05, *** p < 0.01. We exclude observations (years) with over 100% absolute change in electricity consumption or GVA growth year-on-year. The bottom of the table shows the number of observations (years times number of countries), the number of countries in the panel, the Hansen-p value, and the p-values of the tests of second- and third-order autocorrelation. We instrument for the lag of sector value added using the third and fourth internal lag, and for contemporary electricity consumption using the third and fourth internal lag in the Difference GMM and System GMM regressions. Included control variables are share of imports and exports in GVA, share of manufacturing in GVA, and the log of capital and labor.

In the services sector, the two-step system GMM results also show a significant positive relationship between sectoral electricity consumption and value added, with an estimated elasticity of 0.029 when using data for all countries (see Table 3, column 3). In addition to the two-step system GMM, all other estimations produce positive and significant coefficients with varying magnitudes (see Table 3, columns 1-3). Both the Hansen test and autocorrelation tests indicate the exogeneity of the chosen instruments in the system GMM, while the Hansen test rejects the validity of the instruments for the difference GMM 16
||(0.017)|(0.012)|(0.021)|
|---|---|---|---|
|Log of TFP||0.388<sup>***</sup><br>(0.008)||
|Log of lagged TFP|||-0.010<sup>**</sup><br>(0.005)|
|Firm effects|Yes|Yes|Yes|
|Year effects|134986|134986|93465|
|No. of observations|0.017|0.344|0.016|
|Within R-squared|-0.010<sup>***</sup>|0.002|-0.009<sup>***</sup>|
||(0.002)|(0.001)|(0.002)|

Notes: All models include firm and year fixed effects. Standard errors clustered at the firm level are parentheses. ***, **, and * indicate significance at 1 percent, 5 percent, and 10 percent. The reference categories for the exporter, age, and firm size groups are “Non-exporters”, “Age: 2-5 years”, and “Firm size: 1-5 employees”, respectively.

# Sales share

A simple model of oligopoly competition between firms in prices or quantities would imply a positive relationship between sales share and markup, since the elasticity of demand faced by a firm is decreasing in its market share. Indeed, this relationship is often postulated in South Africa and the results obtained confirm a significant positive relationship between the two variables, i.e. firms with higher sales share being able to charge higher markups. The average change in markup and prices per 1 percentage point change in sales share is around 1 percent and 7 percent respectively.

# Export status

A number of theoretical international trade models have predictions regarding markups and firmlevel export status. Most models suggest that exporting firms would be more productive (either through self-selection of more productive firms into export or through “learning by exporting”) and therefore would have higher markups due to lower costs per unit of production.<sup>16</sup> Another theory could be that firms lower their markups when exporting to more competitive foreign markets. As a result, markups for exporting firms would be lower on average and there would be a clear pattern of markups falling as the share of exports in the firm’s sales increases.<sup>17</sup> In understanding the association between export status and markups, the method we have used here

> 16 See for example Bernard et al. (2003) and Melitz and Ottaviano (2008).

> 17 See for example Hornok and Muraközy (2018).

13
consumption increases, which is just the opposite of the immediate impact effect when the spending is announced. The reason is simply that by the time the spending occurs, the initial increase in investment and decline in consumption have already occurred, and now investment (consumption) are declining (increasing) back to their steady‐state levels.

At first glance, this provides a good candidate explanation for why my estimated multipliers are so small – it could simply be that most of the private sector response to the increase in spending has already occurred at the time that the original World Bank project was approved and announced, and there are only limited further labour supply and output responses when the spending is actually implemented in subsequent years. To investigate this possibility further, in Table 8 I document the effects of changes in government spending on the major expenditure components of GDP. I do this by re‐estimating Equation (1), but replacing the dependent variable in turn with changes in private consumption and investment, government consumption and investment, and net exports. As before, I use the four alternative measures of disbursements on World Bank loans as instruments for total government spending.<sup>11</sup> Not surprisingly, I find that government consumption expenditures and government investment increase when total government spending increases – this is true almost as a matter of arithmetic. Of more interest are the responses of private consumption and private investment. These are much less precisely estimated than the effects on output, and so it is difficult to draw very firm conclusions. However, it is noteworthy than in three out of four specifications, the response of private consumption to changes in government spending is negative, while the response of private investment is positive in three of four cases. This pattern of consumption and investment responses to changes in public spending looks much more like the patterns the theory would predict for an _unanticipated_ change in spending rather than an anticipated change. And this in turn casts some doubt on the importance of anticipation effects in accounting for the small estimated multipliers in my benchmark specifications.<sup>12</sup>

> 11 Data on private and government consumption, total investment, and net exports are taken from the national accounts as reported in the World Development Indicators.  I use data on total public investment from the IMF’s WEO database to separate total investment into public and private investment.  Data on the expenditure components of GDP and/or public investment are missing for a handful of observations in my sample.  However, the fit of the first‐stage regressions of changes in government spending on changes in disbursements on World Bank loans are nearly the same as those reported in Table 3 for the full sample, and so are not repeated for this subsample.

> 12 A possible explanation for this pattern can be found in the work of Leeper, Walker and Yang (2010), who use a calibrated model of the US economy to investigate the short and long‐run effects of productive government

25
# **2. Russia’s Three-year Road to the 1998 Crisis**

By the end of 1997, Russia had more-or-less completed the privatization of its manufacturing and natural resource sectors. And in February 1998, annual inflation at last dipped into the single digits. In spite of these accomplishments, Russia experienced an economic meltdown just six months later. This section explains how this happened, showing that Russia’s crisis was in the making for some three years.<sup>7</sup>

# 2.1 The Stabilization Program

We start with the inflation reduction. In mid-1995, Russia adopted a stabilization program with the goal of achieving single-digit inflation by 1997 and shrinking the fiscal deficit of the federal government to less than 3 percent of GDP by 1998. Its centerpiece was a fixed exchange rate as a nominal anchor for prices, to be supported by reductions in the fiscal deficit, a shift away from printing money to debt-financing of the deficit and restrictions on credit to the private sector.<sup>8</sup> But real interest rates jumped to extraordinarily high levels and the real exchange rate appreciated sharply. The ex-ante short-term real interest rate averaged 56 percent between May 1995 and July 1997 (figure 1) while the ruble appreciated by some 55 percent in real terms against the US dollar over the same period (figure 2).

> are attempting to link Vice President Gore to the failure of economic reform in Russia....”  Of course, it was Russia 1998 which epitomized the ‘failure’.

> 7 This section draws chiefly on Kharas, Pinto and Ulatov (2001); Pinto, Gurvich and Ulatov (2005); Pinto, Drebentsov and Morozov (2000a, b);  and Commander and Mumssen (1999).

> 8 The exchange rate was fixed in the sense that it was managed within tight, pre-announced bounds relative to the level of prevailing inflation.

5