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39
NO2 (LHS) Stringency index (RHS) <!-- Start of picture text --> China Italy<br>60 Yearly NO2 percent change Stringency index 100 60 Yearly NO2 percent change Stringency index 100<br>Wuhan lockdown Northern Italy<br>lockdown<br>40 80 40 80<br>20 20<br>60 60<br>0 0<br>40 40<br>−20 −20<br>−40 20 −40 20<br>−60 0 −60 0<br>Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr<br>2019 2020 2021 2019 2020 2021<br>United States of America France<br>60 Yearly NO2 percent change Stringency index 100 60 Yearly NO2 percent change Stringency index 100<br>40 80 40 80<br>20 20<br>60 60<br>0 0<br>40 40<br>−20 −20<br>−40 20 −40 20<br>−60 0 −60 0<br>Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr Oct Dec Feb Apr Jun Aug Oct Dec Feb Apr<br>2019 2020 2021 2019 2020 2021<br><!-- End of picture text --> Figure S5: COVID-19 Lockdown Stringency Index and NO2, by-Country. _Notes._ NO2: percentage change in the 91 day rolling. Stringency Index: World Bank, Our World in Data.

_N_ = 3 _,_ 073 and _R_<sup>2</sup> = 0 _._ 01. The constant is negative both due to overall downward trend in NO2, and overall nationwide decline in NO2 in 2020 due to the pandemic. The effect of the Democratic margin is negative, consistent with the collective inaction thesis. While this result is interesting, there are important robustness checks to this incomplete claim. For instance, we make no effort to disentangle whether these differences are due to more lax orders, or more resistance to orders. These questions are beyond the scope of our analysis.

# **B Other Results**

## **B.1 Night Lights Baseline**

In this subsection we show how the incremental introduction of updated data, satellite instruments, and technical adjustments (including removal of stray lights and inter-calibration) takes us from Henderson et al. (2012)’s results to the night lights baseline we present in this paper (Table 2, Column 2).

Before presenting this correspondence, we describe the difference between instruments and satellites, which is sometimes confusing. We make use of two separate night lights data sets. First, we use data derived from the US Air Force Defense Meteorological Satellite Program’s S7
quantities, with a resulting disappearance of fuel exports. Such a poor performance in the energy sector is a result of severe cuts in the amount of gas exported to Chile and Uruguay and falling oil production.

**Figure 5. Growth Rate of Exports, Volumes and Prices by Category (index year 1993=100)**

<!-- Start of picture text --> Total Exports<br>Value<br>35<br>25 Price<br>15 Quantity<br>5<br>-5<br>-15<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Primary Products Agriculture Manufacture<br>Value Value<br>75 40<br>Price Price<br>55 30<br>35 Quantity 20 Quantity<br>10<br>15 0<br>-5 -10<br>-25 -20<br>Growth Rate<br>Growth Rate Growth Rate<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source_ : Instituto Nacional de Estadísticas y Censos (INDEC).

**Economic recovery since the crisis has also led to an upturn in imports, despite a weak exchange rate.** In 2006 the import bill exceeded the previous peak in 1998, and it grew even more steeply in 2007. The value of Argentina’s imports reached US$46.6 billion during 2007, a jump of 45.6 percent on 2006 (Figure 6). Import prices remained flat over the period under consideration, while both value and quantity fluctuated in tandem. An important decrease in 2001-2002 was followed by a sharp increase in 2003 and then a decline again. This decline is associated with the fact that many local firms have shifted from imports to local equivalents as a response to the devaluation of the peso and to the import substitution strategy.

5
_Family Planning Sucas Storis in Bangladesh and India_ _30_ Table 6. Contraceptive prevalence **rates** (at the end of calendar year) In the Project Area, Control Area, Kundam block, Madhya Pradesh, and India: 1980-85

|_Year_|_Project_<br>_Area_|_Control_<br>_Area_|_Kundam_<br>_Area_|_Madhya_<br>_Pradesh_|_India_|
|---|---|---|---|---|---|
|1980|19.9|20.0|19.9|21.3|22.8|
|1981|27.3|21.5|23.7|21.8|23.7|
|1982|36.6|25.8|29.2|23.6|25.9|
|1983|47.0|32.4|37.2|27.2|29.5|
|1984|66.2|39.0|49.5|29.4|32.1|
|1985|68.4|41.5|51.7|31.9|34.9|

Soumo: **Cited** by Khan **and Gupta** (1988) **from the** 1986 FPAI Annual Report on Kundan **Pjecot;** _Faniy Wcre Programnwm in bIia_ **_Yearbooks_** pubtishod by **the Minimty** of **Halth** and **Family** Welfam, **Goverment** of **nd, Delhi.** was quite low because most of the contraceptive acceptors were high parity elderly couples, a more plausible explanation is that the birth rate 36 in 1980-81 was an underestimate since the rate for Madhya Pradesh state (including urban areas) was 39 in 1980-81. However, the birth rate estimate of 35 per 1000 population in 1986-87 is too high for contraceptive prevalence of more than 60 percent and hence, if reliable, casts doubts about the reliability of contraceptive prevalence figures.

According to the Project and PHC records, the infant mortality rate in the Project Area declined form 156 per 1000 births in 1980-81 to 68 in 1985-86. However, the estimates derived from the 1986-87 surveys of the ICMR and ORG are respectively 110 and 130. Hence, according to the Project and PHC records, the Project had a significant impact on infant mortality but according to the data collected in the two surveys, the impact was not great.

# _Costs of the Pmjiect_

There are two kinds of Project costs: (i) expenditures incurred by the FPAI for maintaining its own staff and office at Kundam block; (ii) contributions made by the government, community, and the Family Planning Association of India for its development and family planning activities. The Project was initiated with funding to the FPAI from Population Concern, a U.K.-based aid agency. Additional funding for the period 1982-85 and full funding thereafter to the FPAI for the Project came from the IPPF. In 1985-86, the FPAI incurred a total expenditure of about Rs. 200,000 (about US $13,000) for maintaining its staff and office and for sharing one percent of the Project's development and family planning activities. The share of staff salary accounted for 54 to 70 percent expenditure and the share of transportation cost accounted for 15 to 23 percent during the years 1981-82 to 1985-86.

Ile cost of the Project's development and family planning activities increased from about Rs. 9,000 (about US $750) in 1981 to Rs. 1,4000,000 (about US $94,000) in 1985. In 1981, 27 percent of this cost was shared by the Project and 73 percent by the community, mostly in the form of voluntary labor. As the government's share in this cost increased from 42 percent in
_Figure A5: Composition of automatic stabilizers by income groups_ <!-- Start of picture text --> AT BE BG CY CZ DE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>DK EE EL ES FI FR<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>HR HU IE IT LT LU<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>LV MT NL PL PT RO<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>SE SI SK<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>Unemployment Social assistance Other Taxes SIC<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br><!-- End of picture text --> Note: Income groups defined based on Scenario D. Other includes housing, family and education, health as well as old-age and survivor benefits.

Source: Own calculations based on EUROMOD I4.0+.

26
||Control mean|(1)|(2)|(3)|
|---|---|---|---|---|
|**Child health**(children < 5years old)|||||
|Diarrhea prevalence 48 hours|0.04|0.014|0.013|0.01|
|||(0.014)|(0.013)|(0.013)|
|Diarrhea prevalence 7 days|0.06|0.019|0.017|0.011|
|||(0.019)|(0.018)|(0.018)|
|ALRI prevalence 48 hours|0.041|-0.035***<br>(0.013)|-0.035**<br>(0.014)|-0.039***<br>(0.014)|
|||[-96%]|[-96%]|[-96.5%]|
|ALRI prevalence 7 days|0.051|-0.042**<br>(0.017)|-0.041**<br>(0.016)|-0.047**<br>(0.018)|
|||[-82.3%]|[-80.3%]|[-89%]|
|Weight-for-age z-score|-0.69|0.0616|0.037|0.106|
|||(0.098)|(0.092)|(0.094)|
|Length/Height-for-age z-score|-1.453|-0.0328|-0.034|0.0371|
|||(0.106)|(0.091)|(0.080)|
|Weight-for-length/height z-score|0.203|0.119|0.12|0.140*|
|||(0.079)|(0.078)|(0.077)|
|Anemia (Hb < 110 g/L)|0.283|-0.0321|-0.0374|-0.046|
|||(0.036)|(0.032)|(0.033)|
|Controls||NO|YES|YES|
|District dummies<br>Clustered standard errors in parentheses; *** p<0|.01 ** p<0.05 * p<0.1|NO|NO|YES|

60
An alternative method of checking for aggregation bias is to use simulation methods that apply country-level growth projections to baseline distributions at country level and then aggregate up to the global level. This calculation is more complicated. (Note that the base-year distribution is projected forward for each country separately. A constant elasticity is not assumed, as this is unlikely to hold.) However, reassuringly, the calculation is in close accord with the prior calculation based on aggregate trends. Trajectories for poverty in 2015 by this method are reported in the 2012 Global Monitoring Report, drawing on the Bank’s countryspecific economic growth projections. These projections give a poverty rate of 16.3 percent for 2015 (World Bank 2012a),<sup>14</sup> which is very close to that implied by the first method, namely, 15.9 percent. Table 1 includes the forecasts by region for 2015 from the Global Monitoring Report. As is clear from table 1, success in reducing poverty in South Asia and sub-Saharan Africa will be crucial to overall progress in poverty reduction. Consistent with the more optimistic scenario above, 80 percent of the projected extreme poverty count for 2015 is in these two regions (about equally).

A further concern is that a constant annual-percentage-point decline cannot continue indefinitely. The trajectory can be expected to slow, becoming nonlinear in time. What is much less clear is when the trajectory is likely to slow. The next section will use a version of the simulation method (as described above for addressing aggregation bias) to determine whether the linear trajectory for the overall poverty-reduction rate is consistent with the expected economic growth rates associated with the optimistic path under explicit assumptions about income distribution.

# **An alternative approach using simulations**

We have observed that the linear projection of the time series of poverty measures for the developing world suggests that one billion people could be lifted out of poverty by 2027. However, the above discussion has also pointed to some concerns about whether the linearity will hold that long. This section approaches the problem of quantifying an optimistic trajectory in a rather different way, without relying on the time-series evidence.

> 14 These forecasts factor in historical gaps between growth rates in the survey means and growth rates in private consumption per capita in the NAS. For most countries, approximately 90 percent of the NAS growth rate is passed onto the survey means, but for India, it was only approximately half, consistent with the larger gap between the two growth rates for India. In addition, an allowance is made for rising inequality in both China and India. For further details, see World Bank (2008).

13
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20
|e|e<br>Lesotho|e<br>LowIncome|SSA|World||performancevs.<br>Benchmark|
|---|---|---|---|---|---|
|Deviation of-10%o or more from Benchmark||||||
|Energy: Access to ElectricityNetwork(6 ofPopulation)|6|ar|15|60|Underperform|
|ElectricityAverage End-UserPrices (US cents/k¥'h) - (Residential‘Mon-Energy: Residential|ays|ore|6/3|of6|Underperform|
|ICT: Mainlines Teledensity (mainlines subscribers/1000 people)|16|29|31|213|Underperform|
|ICT: Cost of LocalPhone Call(US cents/S minutes)|22|&|10|3|Underperform|
|ICT:Phone Faults (reported faults/100 mainlines)|75|ef|37|a7|Underperform|
|Transport: Paved Roads(% of totalroads)|18|a¢|25|50|Underperform|
|Water: Access to ImprovedSanitation(% ofpopulation)|ay|4t|ay|64|Underperform|
|Deviation ofbetween -10%> and +10% from Benchimarl:||||||
|Deviation of +10%> or more from Benchmark||||||
|ICT: Cellular Teledensity (celhalar subscribers/1000 people)|47|a7|73|236|Cuatperform|
|ICT: Cost of Phone Call to the US CUS cents/3 minutes)|231|Bol<br>3|407|335|Outperform|
|ICT: Cost ofCellularLocalCall(US cents/3S offpeak minutes)|26|ae|42|49|Outperform|
|Water: Access toImproved Water Sources (% ofpopulation)|76|6s|64|20|Outperform|
|Transport: RoadDensity inTerms ofPopulation (road kn/‘1000 people)|3.4|a.|3.3|67|Outperform|
|Transport: RoadDensity<br>in Terms of Land (road-kmv/‘1000 sq kn)|196|rst|155.7|340.6|Outperform|
|Transport:TravelTimetoWorkinMainCities(minutes‘one-wayworktrip)|15|a3|34|31|Outperform|
identify the approximate bounds of the price shock of interest based on the monthly time series data on average maize prices, the geo-spatial time series data on rainfall, and the data on annual national maize production dynamics. The nationally-representative, multi-topic household survey data that include child anthropometric measures and detailed agricultural information, among other topics, underlie our distinct contribution to the relevant literature that has been dominated by studies using non-representative samples and exploring the welfare effects of commercialization during times of relative staple food price stability.

We find that household tobacco production in the year of or the year after child birth and exposure to a domestic staple price shock lowers child height for age z-scores by 1.27, implying a 70 percent drop in z-score. These results put renewed emphasis on the food insecurity and malnutrition risks that materialize at times of high food prices and that might have disproportionate adverse effects on uninsured cash crop producers who limit their staple crop production.

The paper is organized as follows. Section 2 presents a succinct review of the vast empirical literature on the links between agricultural commercialization and nutrition, followed by the history of tobacco commercialization in Malawi. Section 3 describes the data and Section 4 details the identification strategy. The results are presented in Section 5. Finally, the paper concludes with a summary of our results and their implications for policy and future research.

# **2 The Nexus between Agricultural Commercialization and Nutrition**

Exploration of the comparative advantages of developing countries in labor-intensive cash crops is rooted in the seminal development article by Johnston and Mellor (1961). Although an extensive literature exists surrounding agricultural commercialization, much of it was undertaken during times of relative food price stability and has focused on the documentation of short-term effects. The majority of these articles document large, positive and statistically significant effects of agricultural commercialization on household and child welfare outcomes.<sup>5</sup>

> et al., 2008).

> 5For more information on agricultural commercialization in the literature see (Côte d’Ivoire: Sahn (1990); The Gambia: von Braun, Puetz, and Webb (1989); Guatemala: von Braun, Hotchkiss, and Immink (1989); Katz (1994); Barham, Carter, and Sigelko (1995); Kenya: Kennedy and Cogill (1987); Philippines: Bouis and Haddad (1990); India: Birthal, Joshi, and Gulati (2005); Senegal: Maertens and Swinnen (2009); Madagascar: Minten, Randrianarison, and Swinnen (2007); Maertens and Swinnen (2009) provides a review of evidence from various sub-Saharan African settings).

4