metadata: authors: 0: author: Adhikari,Samik
1: author: Seetahul,Suneha
count: Sierra Leone
docna: 0: docna: Candle in the Wind ? Insights from COVID-19
            Emergency Cash Transfers to Informal Sector Workers in
            Sierra Leone
docty: Policy Research Working Paper
abstracts: cdata!: This paper takes stock of the insights
            and learnings from a COVID-19 emergency cash transfer
            program that was administered to vulnerable informal sector
            workers in Sierra Leone. It starts by reviewing relevant
            examples of cash transfer programs that were instituted in
            response to the COVID-19 crisis. It then describes the
            context, intervention, and data of the emergency cash
            transfer program, before presenting a quasi-experimental
            analysis of the emergency cash transfer&#x2019;s
            potential impacts on various measures of economic security
            and subjective well-being of households with urban informal
            sector workers. The analysis is conducted by matching
            administrative data to survey data and using program
            eligibility criteria and inverse probability weights to
            identify the short- and medium-term relationship between a
            one-off US$135 cash transfer and various labor market, food
            security, human capital, and subjective well-being outcomes
            for recipient and nonrecipient households of the emergency
            cash transfer. The analysis finds a positive potential
            impact of the transfer and the number of hours worked as
            well as employment in the medium term. It also finds that
            program beneficiaries report higher chances of their main
            income increasing or staying the same compared to
            nonbeneficiaries. The positive correlation between the
            transfer and income disappears over the medium term, perhaps
            suggesting that one-off transfers work best to cushion
            vulnerable self-employed households and informal wage
            workers in the short term but do not impact medium-term
            employment or income.
display_title: Candle in the Wind ? Insights from
            COVID-19 Emergency Cash Transfers to Informal Sector Workers
            in Sierra Leone
listing_relative_url: /research/2023/04/34036748/candle-wind-insights-covid-19-emergency-cash-transfers-informal-sector-workers-sierra-leone
topicv3: Social Protection and Labor m1327194 697,Cash Transfers m1327437 1608,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-04-06T00:00:00Z
dois: 10.1596/1813-9450-10395
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1311263
1: author_key: 1996230
model_extractions: Policy Research Working Paper 10395

# Candle in the Wind?

Insights from COVID-19 Emergency Cash Transfers to Informal Sector Workers in Sierra Leone _Samik Adhikari Suneha Seetahul_ Social Protection and Jobs Global Practice April 2023
Policy Research Working Paper 10395

# **Abstract**

This paper takes stock of the insights and learnings from a COVID-19 emergency cash transfer program that was administered to vulnerable informal sector workers in Sierra Leone. It starts by reviewing relevant examples of cash transfer programs that were instituted in response to the COVID-19 crisis. It then describes the context, intervention, and data of the emergency cash transfer program, before presenting a quasi-experimental analysis of the emergency cash transfer’s potential impacts on various measures of economic security and subjective well-being of households with urban informal sector workers. The analysis is conducted by matching administrative data to survey data and using program eligibility criteria and inverse probability weights to identify the short- and medium-term relationship between a one-off US$135 cash transfer and various labor market, food security, human capital, and subjective well-being outcomes for recipient and nonrecipient households of the emergency cash transfer. The analysis finds a positive potential impact of the transfer and the number of hours worked as well as employment in the medium term. It also finds that program beneficiaries report higher chances of their main income increasing or staying the same compared to nonbeneficiaries. The positive correlation between the transfer and income disappears over the medium term, perhaps suggesting that one-off transfers work best to cushion vulnerable self-employed households and informal wage workers in the short term but do not impact medium-term employment or income.

This paper is a product of the Social Protection and Jobs 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 sadhikari2@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
# **Candle in the Wind? Insights from COVID-19 Emergency Cash Transfers to Informal Sector Workers in**

# **Sierra Leone**<sup>**1**</sup>

# **Samik Adhikari**

# **Suneha Seetahul**

**_Keywords_** _: Unconditional Cash Transfer, Covid-19, Sierra Leone_

# **_JEL codes_** _: I38_

> 1 The authors would like to thank the National Commission for Social Action (NaCSA), Statistics Sierra Leone (Stats SL), and other stakeholders involved in implementing the ECT Program. We would also like to thank Elizabeth Mary Foster for timely collaboration on collecting data for ECT Program Beneficiaries through the CIMS high-frequency phone surveys and Wendy Cunningham and Yuko Okamura for their valuable comments. We are indebted to Shreya Chatterjee for her role in conducting the preparatory data analysis for the design of the ECT and in preliminary data analysis for this paper. Finally, we would like to thank the World Bank Sierra Leone Social Protection and Jobs team: Junko Onishi, Abu Kargbo, Steisianasari Milieva, Judith Sanford, Sumati Rajput, Thomas Vaughn Bowen, Mpumelelo Nxumalo and Hannah Buya Kamara for their input and feedback on various drafts of this paper. Data files available upon request.
# **1. Introduction**

Because of their well-documented, positive short-term effect on welfare (see, for instance, Bastagli et al., 2016), cash transfers have constituted the majority of safety net interventions implemented to mitigate the effects of COVID-19 on households. Since the start of the pandemic, cash transfers have represented 42 percent of total social assistance programs and 24 percent of all global social protection measures to respond to COVID-19 (Gentilini et al. 2021).<sup>2</sup> The initial design of many cash transfer programs reflected an objective to shield vulnerable households and individuals from the negative impacts of the unfolding COVID-19 pandemic. However, and increasingly, cash transfer measures are also being used to support workers in low- and middle-income countries to offset losses of incomes and revenues induced by COVID-19 (De La Flor et al., 2021; Gentilini et al., 2021).

This paper takes stock of the Emergency Cash Transfer (ECT) provided to informal workers in urban Sierra Leone and implemented shortly after the onset of the pandemic, from June to August 2020. Our aim is to contribute to the evolving literature on the relationship between cash transfers and labor market outcomes for informal sector workers. While most studies that assess the impact of cash transfers look at outcomes related to consumption and subjective well-being, the targeting parameters and transfer size of the ECT allow us to also look at the relationship between the transfers and labor market outcomes of self-employed households in the informal sector, thus adding to the emerging strand of literature that explores the linkages between social assistance and employment-related outcomes in low-income settings (Bassier et al., 2021; Bhorat et al., 2021; Gerard et al., 2020).

> 2 Data until May 14, 2021.

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The informal sector accounts for around a third of the GDP and 70 percent of all employment in emerging markets and developing economies (Ohnsorge & Yu, 2022). There are several reasons why workers, firms, and households operating in the informal economy have been impacted more severely by the COVID-19 crisis. First, informal sector workers are largely excluded from work-related social protection measures, which makes it costlier for them to stop working to adhere to social distancing and other restriction measures (Bassier et al., 2021; Gerard et al., 2020). Second, emerging evidence suggests that workers most affected by the COVID-19 crisis are not necessarily at the bottom of the income distribution and therefore have no recourse to safety nets which target the poorest and have limited coverage in low-income settings.<sup>3</sup> Third, informal sector workers are less likely to be able to work from home (Garrote Sanchez et al., 2021).

The evidence on the impact of social assistance measures implemented by governments to face the COVID-19 crisis, namely the ones directed at informal workers, remains to be assessed. Two general lessons can however be observed in the emerging literature on the impact of social assistance. First, the implemented social assistance schemes had important positive impacts. In low- and middle-income countries, 80 percent of people – who would have fallen below the US$1.90 poverty line – did not because of social assistance measures (Fajardo-Gonzalez et al., 2021). The expansion of existing social assistance schemes introduced by the governments in the form of cash transfers had a large offsetting effect on poverty in Brazil and Argentina where the social assistance programs were more ambitious compared to countries like Colombia where smaller effects were observed (Lustig et al., 2020). In India, an expansion of pre-existing social assistance schemes, which consisted in providing cash transfers and

> 3 For example, Lustig et al. (2020) find that in Argentina, Brazil, Colombia and Mexico, the COVID-19 crisis mostly affected those in the middle of the income distribution.

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in-kind support to farmers, alleviated credit constraints, increased agricultural investments, and facilitated the procurement of seeds, fertilizers, and pesticides (Varshney et al., 2021). In Ethiopia, households participating in the Productive Social Safety Net Program experienced an increase in food insecurity of only 2.4 percentage points compared to 11.7 percentage points for other households. While previous research has shown that transfers are an effective means to stop individuals from falling into poverty traps (Balboni et al., 2021), these findings provide evidence that social assistance measures have been successful in shielding households from the economic fallout caused by the COVID-19 pandemic.

Second, because the COVID-19 crisis has impacted households and workers across the income distribution and not only the poorest, the social assistance measures implemented can be considered more as mitigation tools rather than purely a poverty alleviation mechanism (Bhorat et al., 2021).

This paper uses the COVID-19 Impact Monitoring Survey (CIMS, 2020) matched to administrative data to analyze a range of outcomes. The analysis is motivated by the question: does an ECT mitigate the microeconomic impact of COVID-19 on poor households that rely on informal self-employment for subsistence? The existing data allows us to draw short-term and medium-term correlations between the ECT and various objective economic security outcomes pertaining to labor market participation, food consumption and children’s human capital and subjective indicators of psychological wellbeing and satisfaction with the government’s response to the pandemic. Round 1 of data collection overlaps with the rollout of the program, allowing to analyze a cohort of beneficiaries as early as June-August 2020. The Round 2 of data collection (November-December 2020) allows to identify a second cohort of late beneficiaries, thus providing information on their outcomes, but also on the outcomes of the first cohort of beneficiaries. We identify correlations between receiving the ECT and the various outcomes for Round 4
1 and Round 2 cohorts by identifying beneficiary and comparable non-beneficiary households and by applying inverse probability weights which allow a more robust comparison between the two groups.

The results show a positive potential impact of the ECT on labor market participation and income, child nutrition, psychological wellbeing and government satisfaction. However, these results also suggest that timing matters in terms of how long the positive effect of this type of one-off transfer can last and when the transfer is received. The positive relationship between receiving the ECT and income, for instance, is visible in the short-term for the recipients who received the transfer only a few months after the pandemic. These beneficiaries no longer benefit from the positive impact of the ECT 3 to 7 months after the reception date. Moreover, the positive correlation between receiving the ECT and psychological wellbeing also disappeared in the medium-term. These results suggest that for income stability, receiving the ECT early in the pandemic as opposed to a few months later might have proved beneficial, allowing households to maintain labor market activity and income in comparison to control households. While for other factors such as the ability to buy staple foods, children’s nutrition, decreasing worries about children being out of school and government satisfaction, the opposite is true. We provide a discussion on these results and directions for further work.

# **2. Description of the Context and ECT Intervention**

The Government of Sierra Leone declared a public emergency on March 24, 2020, and implemented a series of restrictions including lockdowns and limited inter-district movement. By May 15, 2020, the unfolding COVID-19-related restrictions had immediate impacts on private sector workers with 68 percent of businesses reporting a decrease in weekly income of on average half of their pre-pandemic income (Meriggi et al., 2020). This decline was accompanied by disruptions in employment, with a 5
reduction of 2.5 working hours on average per day for 37 percent of business owners. The main reasons reported by business owners for these economic difficulties are the COVID-19 restrictions leading to a drop in demand and a lack of access to suppliers (Meriggi et al., 2020). In a context where more than 40 percent of the population lived in extreme poverty before the pandemic,<sup>4</sup> the lockdowns and restrictions threatened to further derail the economy and push more Sierra Leoneans into poverty.

To combat the adverse economic impact of the COVID-19 induced lockdowns, the government launched the Quick Action Economic Recovery Program which aimed to maintain macroeconomic and financial stability and mitigate the effect of the pandemic on households and businesses (Showers and Ganson, 2020). The government had prior experience in the provision of cash transfers in responding to natural disasters and health shocks. The Government of Sierra Leone’s National Commission for Social Action (NaCSA) had implemented the Social Safety Net Cash Transfers, locally known as _Ep Fet Po_ , since 2014. This program had already been used as a vehicle to scale up social protection coverage following the Ebola disease outbreak in 2015 and during the flood and mudslide catastrophe in 2017. Building on these previous experiences, and with the support of the World Bank, NaCSA provided a one-off COVID-19 Emergency Cash Transfer in the four provincial capitals of Sierra Leone and in Freetown in June 2020 to support 29,000 vulnerable informal sector households (Sanford, 2022).

The amount of the transfer was Le1,309,000 (approximately US$135), which is equivalent to approximately two months of minimum wage in Freetown. This amount is also equivalent to one month of consumption expenditure of the bottom welfare quantile of Freetown households. This cash transfer was unconditional, and the benefit was provided in a single installment to prevent opportunities of

> 4 In 2018, 3.3 million individuals in Sierra Leone (43 percent of the population) were under the international poverty line of $1.90 per day. Poverty is expected to rise up to 44% following the COVID-19 crisis.

6
COVID-19 transmission. Moreover, households owning a mobile phone were provided the transfer through e-cheques and households without a mobile phone were provided a paper copy of the e-cheque.

The aim of the ECT was to target informal workers and the number of beneficiaries per city was determined by a quota system based on the estimated size of the informal sector in each city. The identification of poverty for informal workers is complicated. In comparison to developed countries where cash transfers are targeted at low-income individuals, the income of a large share of developing country workers is not observable because they work in the informal economy (Hanna & Olken, 2018). Household targeting was therefore implemented in two steps. The first one was a community-based targeting. City councils assisted NaCSA in compiling prelists of potential beneficiary households with the help of market leaders and traders’ associations. A Light Proxy Means Test (LPMT) was then used to identify those needing support the most in the city-specific lists. The LPMT comprised a set of household characteristics, assets, work status and disability status (see Appendix 1). The rationale of a Proxy Means Test is to use observable characteristics to determine a predicted income or consumption level (Hanna & Olken, 2018) and households or individuals who are deemed eligible by the LPMT are provided the transfer.

Figure 1 presents the timeline of ECT implementation and data collection. The transfers were first implemented in four regional headquarter towns of Sierra Leone, namely Bo, Kenema, Makeni, and Port Loko before the bulk of the transfers were provided to informal sector workers in Freetown (see Table 1). Following complaints on the transparency of beneficiary selection in Freetown, the government created a pre-listing review committee composed of public and private sector representatives to review community-based targeting conducted by local councils in association with market traders. Most of the 7
<!-- Start of picture text --> May-August 2020<br>11 March 2020 ECT implementation<br>WHO declares<br>COVID-19 asa May August<br>global pandemic 2020 2020<br>————————SSSSa<br>e O Ld ° o e<br>March 31 2020 June November<br>Sierra Leone 2020 2020<br>pai test COVID June-August 2020 November-December<br>CIMS Round 1 2020 CIMS Round 2<br>data collection data collection<br><!-- End of picture text -->
2020 and the second wave of data (Round 2) was collected between November and December 2020. The CIMS database sampled households through three different sample frames: the Sierra Leone Integrated Household Survey (SLIHS, 2018), a nation-wide random sampling and a random sample of beneficiaries from the ECT. The intent of including the beneficiaries of the ECT in the sampling frame was to allow an analysis of how the ECT recipients fared throughout the pandemic. Using this feature of the CIMS survey, as a secondary step, we matched the CIMS survey data to administrative data with information on the 32,499 individuals enrolled in the ECT. This step allowed to retrieve the LPMT scores of beneficiaries of the ECT, which are not available in the CIMS data.

This paper analyzes two types of socioeconomic outcomes that are likely to be affected by the pandemic and its induced policy responses: (1). Objective economic security outcomes which pertain to the labor market, food security and children’s human capital and include: individual employment and hours worked, household income change, inability to buy staple foods (rice, dried fish and palm oil), ability to support children’s return to school, child identified as malnourished and child given vitamin A supplementation; (2). Subjective indicators of psychological wellbeing and satisfaction with government measures (concern about children being out of school, food shortages, price increases, being sick with COVID-19, quarantine, lack of other health care and satisfaction with government response to COVID19).

Although the CIMS panel data has 7,369 respondents in Round 1 and 5,685 respondents in Round 2, our analysis only includes a small share of this data for the following reasons. First, given our aim to analyze the ECT transfer, we restrict the analysis to the districts in which the transfer was provided. Second, 9
because of a high occurrence of missing data and the data collection design,<sup>8</sup> our analysis is restricted to outcomes for which there are sufficient observations in terms of the ECT treatment. The structure of the survey and the issue of missing data also lead to highly unequal sample sizes across the different outcomes.<sup>9</sup> Therefore, our active sample ranges from 187 to 913 observations depending on the outcome of interest and the specification (i.e., cross-sectional or pooled cross-sectional estimation). Moreover, some of the child human capital outcomes were exclusively collected in Round 1 whereas others in Round 2, limiting the scope of the analysis to only one of the two rounds. These characteristics of the CIMS data limit the scope of the analysis presented in this paper. A discussion on the data and future efforts for data collection that could make the analysis more robust are discussed in Section 6.

The timing of the implementation of the ECT and of the CIMS data collection allow us to analyze two cohorts of beneficiaries: households that received the ECT before the first round of CIMS (hereafter referred to as ECT-R1 beneficiaries) and households that received the ECT after the first round of CIMS (hereafter referred to as ECT-R2 beneficiaries). The timing of the survey and the data collection (see Figure 1) allows us to analyze short-term ([0;3] months lapse between receiving the ECT and the survey) and medium-term ([3;7] months lapse) impacts of the ECT for ECT-R1 beneficiaries. We consider the impact of the ECT on ECT-R2 beneficiaries as a medium-term impact ([3;4] months lapse).

Table 2 shows that the ECT was mostly used for food (76 and 80 percent respectively in Rounds 1 and 2) and investing in an existing business (73 and 86 percent respectively in Rounds 1 and 2). The other

> 8 Part of the questionnaire was administered to all respondents (i.e., Basic information, information about school-aged children and income), but only one of the four following sets of questions was asked to each respondent: “Education, Child Welfare”; “Employment, KAB, Lockdown”; “Health, Access”; “Agriculture, Food Security, Social Protection”.

> 9 As a robustness check, we compare the characteristics of the full sample to a sample restricted to observations for whom we have information on labor market participation (i.e. the sample which corresponds to a large set of the estimations presented in this paper). The results presented in Supplementary materials 1 show no significant differences between the two samples.

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important items of expenditure of the ECT are medical care (32 and 24 percent respectively) and nonfood consumption (14 and 31 percent respectively). The fact that the main use of the ECT transfer was for food consumption is in line with the fact that the COVID-19 pandemic affected households’ food insecurity (Londoño-Vélez & Querubin, 2020), especially in Sub-Saharan Africa (Aborode et al., 2021). Food consumption and investment in businesses being the two main items of expenditure of the ECT also points out the porosity of the household and labor spheres for informal workers who use this type of transfer to respond to immediate consumption needs and to alleviate credit constraints. Moreover, the increase in non-food consumption and investment in an existing business between the two rounds may reflect the fact that by the time of the second round of data collection, some of the governmentmandated COVID-19 restrictions had started to be lifted, allowing individuals to earn labor income and use the transfer for a larger set of expenses.

**_Table 2_** _: Use of ECT-R1 and ECT-R2_

|Transfer used for|Share of ECT-<br>R1<br>beneficiaries|N (ECT-R1)|Share of ECT-<br>R2<br>beneficiaries|N (ECT-R)|Two-sample<br>t-test P-value|
|---|---|---|---|---|---|
|Food|75.56%<br>(0.024)|311|80.20%<br>(0.028)|197|0.224|
|Invest in existing business|72.99%<br>(0.025)|311|86.29%<br>(0.025)|197|0.000|
|Medical care|31.51%<br>(0.026)|311|24.37%<br>(0.031)|197|0.083|
|Non-food consumable items|14.47%<br>(0.020)|311|31.47%<br>(0.033)|197|0.000|
|Paid debt|11.25%<br>(0.018)|311|3.05%<br>(0.030)|197|0.000|
|Start new business|9.97%<br>(0.017)|311|3.55%<br>(0.036)|197|0.007|
|House rent|8.04%<br>(0.015)|311|4.57%<br>(0.015)|197|0.128|
|Home improvement|2.89%<br>(0.009)|311|4.57%<br>(0.015)|197|0.321|

_Source_ : Authors’ calculations from CIMS (2020). Note: Standard errors in parentheses.

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# **4. Methodology**

# _4.1. Defining a Control Group_

In the absence of experimental data which would allow to identify the impact of the ECT, we implement a quasi-experimental approach which consists in defining a control group using the LPMT targeting tool and an Inverse Probability Weight (IPW) estimator. This method allows us to identify a potential impact of the ECT.

The treated households are identified as those matched to the administrative data who declare that they have received a transfer from NaCSA and are residing in one of the five districts in which the program was implemented (Bo, Bombali, Kenema, Port Loko and Western Area Urban). These restrictions eliminate the possibility of including households before they received the ECT (i.e., a household listed as a beneficiary in the administrative data that declares it hasn’t received a cash transfer in the CIMS) and capturing the effect of other cash transfers. The sample restriction to the districts of interest excludes households that migrated outside of these cities or were not captured in Round 2 because of attrition. Moreover, as described in section 2, for a household to be considered as a beneficiary, they must be deemed eligible by the LPMT.

The main challenge in the implementation of the quasi-experimental method is to identify a relevant counterfactual for the treated households. We exploit the fact that beneficiaries of the ECT were chosen based on LPMT but that the scope of the ECT program was not sufficient to cover all informal sector households in need in the five districts in which the program was implemented. We therefore identify the control group as households that passed the LPMT in the CIMS data but did not receive the transfer 12
as they were not in the initial pool of pre-listed beneficiaries to receive the transfer. We assume that these households are comparable to the treated households.

The Proxy Means Test has been used in previous studies as a criteria to identify treated and control groups of a cash transfer (for example see Stoeffler et al. (2020)). The challenge with this methodology of identification is that we do not have the true LPMT score of the control households. Using the CIMS data, it is possible to recreate a proxy LPMT score, but only partly. Indeed, as described in Appendix 1, the LPMT score is calculated as a sum of 10 different characteristics which include household characteristics, assets, work status and disability status. Out of the 10 characteristics, 3 are unavailable in the CIMS data, two of which we are able to proxy. We do not have any information on whether the household uses bottled or sachet water for drinking. We also do not have the information on (1) whether the head of the household is literate in English or Sierra Leonian language and (2) whether the head of the household has an informal contract. For these two characteristics, we use information on the education level and work with a formal contract of the respondent (as opposed to the household head) as proxies. Among the Round 1 beneficiaries, 32 percent are non-household heads and among the R2 beneficiaries, 45 percent are non-household-heads. We are therefore able to recreate an LPMT score that can take a total of 9 instead of 10, but with potential errors because of the two proxies.

The original LPMT was calculated using a simplified tool collected by program administrators.<sup>10</sup> The LPMT threshold used by NaCSA to determine the eligibility of a household for the ECT was 7 out of 10. As described in Figure 2, households that scored 7 or above have necessarily passed the original LPMT because these households have a true score of 7 to 10. However, there are potentially households that

> 10 The data for the LPMT tool was collected by Statistics Sierra Leone (Stats SL) in presence of NaCSA and the Anti-Corruption Commission (ACC). Independent spot checks of the ECT confirm that most participants of the exercise thought the process was fair and transparent (Sanford 2023).

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have a real score of 7 which only have a proxy score of 4 to 6 with our scoring mechanism. To minimize exclusion errors, we set the threshold to LPMT>=4. We assume that households that declared receiving a transfer from NaCSA and score 4 or above in the proxy threshold are likely beneficiaries, as they have little incentive to report receiving a transfer if they are not beneficiaries. The likelihood of the inclusion error in the control group is therefore low. We exclude all households below the threshold of 4 from the analysis because if our proxies are correct, no households should be under the threshold of 4. It is likely that our proxies do not perfectly overlap with the original criteria, but the chances of excluding treated individuals is relatively low and restricting to LPMT>=4 ensures better comparability with control households.

**_Figure 2_** _: Proxy LPMT score calculation and potential errors_

<!-- Start of picture text --> 0 1 2 3 4 5 6 7 8 9 10<br>potential inclusion error<br><!-- End of picture text --> 14
**_Figure 3_** _: Comparison of Proxy LPMT and True LPMT for treated households_

_Source_ : Author’s calculations from NaCSA administrative Data and CIMS (2020) The comparison of true LPMT scores and the proxy LPMT for treated households provides a further justification of the relevance of the chosen threshold of 4. In Figure 3, we match administrative data from NaCSA to the CIMS (2020) data to compare the distribution of the true LPMT and proxy LPMT for treated households. The figure shows that for households’ whose true LPMT are between 7 and 10, we can see that a large majority have a proxy LPMT equal to 4 or more. We argue that this threshold allows us to strike the balance of minimizing exclusion and inclusion errors.

# _4.2. Identification Method_

Since we do not have experimental data and although we implement a cautious identification of the control group, there remain significant differences between the treatment and control groups (see Appendices 3 and 4). For instance, 60.7 percent of ECT-R1 beneficiaries were working the week prior to the Round 1 of survey in comparison to only 50.6 percent of the control group. For this reason, we use an Inverse Probability Weighting method to rebalance treated and control observations along a set of 15
observable characteristics. The probability of treatment (propensity score) is calculated using a logistic model. These scores are then used to compute weights allowing to control for covariate imbalance prior to the treatment. This step combined with a regression analysis ensures a more robust identification of the impact of the ECT on the various outcomes and, as recommended by Imbens & Wooldridge (2009), this combination allows a better identification of treatment effects than matching estimators.

We identify 4 types of results:

- Short-term relationship between ECT-R1 and the outcome. Short-term refers to a lapse of 1 to 3 months between the reception of the ECT and the survey.

- Medium-term relationship betweenECT-R1 and the outcome. Medium-term refers to a lapse of 3 to 7 months between the reception of the ECT and the survey.

- Medium-term relationship between ECT-R2 and the outcome. Medium-term refers to a lapse of

- 3 to 4 months between the reception of the ECT and the survey.

- Pooled relationship between both ECT-R1 and ECT-R2 and the outcome. This overall effect is

detected after a 1 to 4 months lapse between the reception of the ECT and the survey.

The IPW estimator allows to identify Average Treatment Effects as defined in Equation 1: For each Outcomek and period _t_ (t=1 for Round 1 of CIMS and t=2 for Round 2), we estimate an Average Treatment Effect ATEk,t=1,2 of ECTt (t=1 for Round 1 of CIMS and t=2 for Round 2). π̂(Zi) is the estimated propensity score conditional on a set of covariates (gender, age, household dependency ratio, head of household, completion of secondary school, main income source, asset score and location).

16
Outcometki measures the different outcomes _k_ for each household _i_ in t=1 and t=2. ECTti identifies the treated households in each round t=1 and t=2. This method allows to estimate the Average Treatment Effect of the ECT at the two periods 1 and 2. In the pooled estimation, observations from t=1 and t=2 are pooled together.

Despite our efforts to implement a robust identification method by selecting an adequate control group and using the IPW estimator, we cannot verify that there are no unobservable factors affecting the treated and control households differently given the non-experimental nature of the data. We implement balance and overidentification tests to test for comparability of the treated and control groups after reweighting. Although the tests are encouraging, as discussed in Section 6, we interpret the results as correlations ( _i.e._ potential impact) instead of causality ( _i.e._ impact) as a precautionary measure.

# **5. Results**

Figure 4 shows the distribution of control and beneficiary (treatment) households for the two rounds of the ECT. In total, the treatment groups contain 311 households that have received the transfer before the Round 1 of CIMS and 197 households that have received the transfer after the Round 1 of CIMS.

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**Figure 4:** Treated and control groups

_Source_ : Authors’ calculations from CIMS (2020). Note: These shares represent the total number of beneficiaries and control households in the observation. Note that none of the estimations presented in the paper include all treated and control households in either rounds because of the structure of the survey and missing observations. The number of treated and control households for each outcome is presented in 2 to 4.

Among households included in the analysis, 52 percent of respondents worked in Round 1 and 60 percent in Round 2 (see Appendix 3). These statistics suggest that although the share of inactivity/unemployment is high in the sample of analysis, there are signs of employment stability following the onset of the COVID-19 crisis. The number of hours worked suggests that time-related under-employment was not a salient issue following COVID-19. Indeed, excluding those who worked zero hours, eligible households in Round 1 and Round 2 worked respectively 40 and 42 hours weekly. However, further explorations of the distribution of hours show a bimodal distribution in both Rounds 1 and 2 (see Appendix 5). Fourteen percent of the eligible households in Round 1 and 11 percent in Round 2 worked under 10 hours in the week prior to the survey.

18
Table 3 shows results from the IPW estimations of the correlations between the ECT and objective economic security outcomes.<sup>11</sup> The results show that in the short-term and medium-term, receiving the ECT is positively correlated to employment outcomes both at the extensive and intensive margin. At the extensive margin, there is no significant short-term correlation for ECT-R1 beneficiaries, but a significant medium-term positive coefficient of 13% is visible for ECT-R1 beneficiaries and of 18% for ECT-R2 beneficiaries. The pooled specification (Round 1 and 2 beneficiaries) which includes a larger set of observations also shows a significant positive correlation (12%) between receiving the ECT and the probability of working in the week preceding the survey. At the intensive margin, the ECT had a significant positive relationship for ECT-R1 beneficiaries with approximately 6 hours of additional weekly working hours in the short-term and 9 hours in the medium-term. For R2 beneficiaries, the relationship is also significant with 15 additional working hours. The pooled specification shows a significant coefficient of 10 hours, showing that all ECT beneficiaries worked more hours than their non- beneficiary counterparts.

The results in Table 3 also show that ECT-R1 beneficiaries had a significantly higher likelihood of being in a household in which income increased or remained stable and a lower chance of being in a household in which income decreased or stopped in the short-term. In the medium-term, no significant relationship is found for the main income and the reverse relationship is visible for ECT-R1 and ECT-R2 beneficiaries for other sources of income. These results suggest that the ECT allowed households to maintain a stable income at the onset of the pandemic with a visible effect for Round 1 beneficiaries, but that this effect did not last. In the medium-term, although there is a larger potential effect on labor market participation,

> 11 Full table available upon request.

19
the potential effect on main income is no longer visible. Moreover, the negative coefficient of income stability in the medium-term may indicate a relative deprivation in comparison to the months in which they used the ECT and considered it as an “Other source of income”. Given the fact that the ECT was a one-time transfer and that its amount was equivalent to two months minimum wage in Freetown, it is not surprising to find a negative effect on income in the medium-term specifications which represent households’ income 3 to 7 months post-transfer. Treated households, whose other source of income stopped altogether was 22.5 percent in Round 1 and 32.9 percent in Round 2. Among these households, it is possible that some refer to the ECT transfer amount running out and others refer to another source of income stopping or decreasing.

Concerning food consumption, Table 3 shows that the ECT did not have a significant association with the reduction of inability of buying staple foods (rice, dried fish and palm oil) for Round 1 beneficiaries but had a significant relationship for Round 2 beneficiaries. Indeed, Round 2 beneficiaries were 7 percent less likely to be unable to buy staple foods compared to non-beneficiaries. We argue that there are two possible explanations for this result. First, it is possible that in the early months following the pandemic, households may have had stocks of staple foods, which is why ECT-R1 beneficiaries did not need to use the ECT for this type of consumption as opposed to ECT-R2 beneficiaries. Second, 76 percent of treated households declare that they used the ECT to buy food (Table 2), however these types of expenses represent basic needs food consumption which have been found to be relatively more income inelastic, especially in contexts of poverty and urban areas (Melo et al., 2015). It is therefore likely that control households managed to acquire similar quantities of staple foods as treated ones by prioritizing these expenses even if they did not receive cash assistance. Moreover, the data on how the ECT was used does not allow to calculate the share of the transfer used for various purposes. Even if 76 percent of ECT-R1 20
beneficiaries used the ECT to buy staple foods, the estimation result suggests that this share might have been low.

Concerning child human capital outcomes, Table 3 shows that the ECT does not have a significant relationship with the confidence of households in their ability to financially support children’s return to school for ECT-R1 beneficiaries. However, ECT-R2 beneficiaries are less likely to live in a household in which a child was identified as malnourished. The results show no significant association between receiving the ECT in Round 1 and malnourishment in the medium-term. No significant relationship between receiving the ECT in Round 2 and vitamin A supplementation was found which is expected given that it is generally advised to give children Vitamin A supplementation twice per year (World Bank, 2014) and that the short-term nature of the intervention and the context of COVID-19 restrictions may not be suited for this type of health expense.

21
# **_Table 3_** _: Correlations between the ECT and socioeconomic outcomes_

||Worked<br>last week|Hours<br>worked<br>last week|Main<br>source of<br>income<br>increased<br>or stayed<br>same|Main<br>source of<br>income<br>decreased<br>or stopped|Other<br>source of<br>income<br>increased<br>or stayed<br>same|Other<br>source of<br>income<br>decreased<br>or stopped|Inability<br>to buy<br>staple<br>foods|Able to<br>financially<br>support<br>child's<br>return to<br>school|Child<br>identified<br>as mal<br>nourished|Small children<br>given vitamin A<br>supplementation|
|---|---|---|---|---|---|---|---|---|---|---|
|||||||**1. ECT-R1: R**<br>|**ound 1(sho**<br>|<br>**rt-term) outc**<br>|**omes**||
|ECT-R1|0.050|5.72*|0.135**|-0.135**|0.192***|-0.396***|0.000|0.025|||
||(0.058)|(2.947)|(0.060)|(0.060)|(0.066)|(0.061)|(0.050)|(0.053)|||
|Observations|569|569|562|562|392|392|533|512|||
|||||**2.**<br>|**ECT-R1: Roun**<br>|**d 2(medium-**<br>|**term) outco**<br>|**mes(ECT-R2**|**beneficiaries e**<br>|**xcluded)**|
|ECT-R1|0.126*|8.576**|0.025|-0.025|-0.164**|0.064|0.030|-|0.024|-|
||(0.065)|(3.690)|(0.056)|(0.056)|(0.077)|(0.071)|(0.039)||0.071||
|Observations|702|702|682|682|503|503|640||208||
|||||**3.**|**ECT-R2: Roun**|**d 2(medium-**|**term) outco**|**mes(ECT-R1**|**beneficiaries e**|**xcluded)**|
|ECT-R2|0.180***|15.340***|-0.145|0.145|-0.265***|0.290***|-0.068***|-|-0.059*|-0.063|
||(0.060)|(4.461)|(0.094)|(0.094)|(0.077)|(0.092)|(0.021)||(0.034)|0.066|
|Observations|459|459|442|442|328|328|433||187|305|
|||||||**4. Pooled o**|**utcomes of E**|**CT-R1 and EC**|**T-R2**||
|ECT-R1 and<br>ECT-R2|0.121**|10.448***|0.173***|-0.173***|0.156**|-0.294***|-0.031||||
||(0.056)|(3.270)|(0.062)|(0.062)|(0.061)|(0.070)|(0.034)||||
|Observations<br>_Source_: Authors’|908<br>calculations|908<br>from CIMS (20|893<br>20)|893|627|627|810||||

_<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates. Full set of results available in Online supplementary materials S2 to S6.</mark> 22
Table 4 shows results from the IPW estimations of psychological wellbeing and government measures satisfaction outcomes. The results indicate positive relationships for ECT-R1 beneficiaries in the shortterm, who experienced significantly lower levels of concerns about food shortages, being sick with COVID-19, quarantine, and the lack of health care (besides COVID-19). These short-term relationships do not however last in the medium-term for R1 beneficiaries. ECT-R2 beneficiaries report experiencing lower levels of concerns concerning children being out of school and food shortages, however they are significantly more concerned about price increases, which may indicate a sense of relative deprivation. Receiving the ECT is also significantly correlated with the satisfaction with the government response in all estimations except for the medium term ECT-R1 beneficiaries.

**_Table 4_** _: Correlations between the ECT and subjective well-being measures, satisfaction with_ _<u>government response</u>_

||Not<br>Concerned<br>about<br>children|Not<br>Concerned<br>about food<br>shortages|Not<br>Concerned<br>about price<br>increases|Not<br>Concerned<br>about<br>being sick|Not<br>Concerned<br>about<br>quarantine|Not<br>Concerned<br>about lack<br>of other|Satisfaction<br>with<br>government<br>response|
|---|---|---|---|---|---|---|---|
||out of<br>school||**1. ECT-R1: Ro**|with<br>COVID-19<br>**und 1 (short-t**<br>|**erm) outcome**<br>|healthcare<br>**s**<br>||
|ECT-1|0.146|0.466***|0.062|0.414**|0.387***|0.222*|0.041***|
||0.096|0.127|0.045|0.114|0.118|0.120|0.009|
|Observations|568|569|569|569|567|567|555|
||**2.**<br>|**ECT-R1: Round**|**2(medium-te**|**rm) outcomes**|**(ECT-R2 bene**|**ficiaries exclu**|**ded)**|
|ECT-1|-0.305**|-0.064|-0.015|-0.028|0.217|0.122|0.017|
||0.055|0.108|0.040|0.195|0.184|0.199|0.015|
|Observations|648|649|649|649|649|648|647|
||**3.**|**ECT-R2: Round**|**2(medium-te**|**rm) outcomes**|**(ECT-R1 bene**|**ficiaries exclu**|**ded)**|
|ECT-2|0.594**|0.567**|-0.040**|-0.120|-0.045|0.177|0.035***|
||0.244|0.231|0.020|0.140|0.140|0.163|0.009|
|Observations|438|438|438|438|438|437|435|
|||**4**<br>|**. Pooled o**<br>|**utcomes of EC**<br>|**T-R1 and ECT-**<br>|**R2**<br>||
|ECT-1 and ECT-2|0.319***|0.473***|0.012|0.389***|0.389***|0.221**|0.046***|
||0.114|0.123|0.031|0.098|0.100|0.098|0.008|
|Observations<br>_Source_: Authors’ cal|911<br>culations from|913<br>CIMS (2020)|913|913|911|910|892|

_<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates. Psychological wellbeing outcomes are originally in a 5-point Likert scale format and are considered as continuous in the IPW estimation. The coefficients should</mark> 23
<mark>therefore be interpreted based on a score of 1 (largest level of concern) to 5 (lowest level of concern). Full set of results available upon request.</mark>

# **6. Discussion and Next Steps**

Cash transfers have been established as a consumption support measure in traditional social protection programming. The COVID-19 pandemic has shifted that paradigm to a certain degree by introducing cash-based measures to support vulnerable workers in the informal sector on a one-off or punctual basis. This paper assessed the potential impact of one-such measure on subsequent labor market, economic security, and subjective psychological well-being for vulnerable self-employed households in Sierra Leone. While the ECT was found to have positive association with various measures of well-being in the short run, some of the positive relationship dissipated in the medium run. In this section, we discuss four areas that have implications on the analysis conducted in this paper and suggest areas for future research.

Because of lack of experimental data, we use a quasi-experimental approach to create a control group for our analysis. This approach uses two main steps to allow the identification of a potential impact of the ECT on households. First, we construct a proxy LPMT and uses administrative data to choose the threshold that will best allow to identify a relevant control group. The 10 LPMT questions used to assess eligibility for the ECT program, and the questions captured in the CIMS surveys had some notable differences. We were able to proxy for two out three of the questions that were missing from the CIMS survey. Moreover, we use administrative data to determine the eligibility cutoff that strikes the balance between minimizing both the exclusion and inclusion errors. We use the cutoff of 4 out of 10 to identify the treated and the control group and note the margin of inclusion error given this identification design. While we show that the cutoff of 4 successfully identifies program recipients from non-recipients, there 24
could still be notable differences between the two comparison groups at baseline, which we address in a second step, by using an IPW method. The IPW allows to balance the treated and control samples on a set of covariates. A balance check table is presented in Appendix 2 and shows that the inverse probability reweighting improves the covariate balance in most cases. One notable exception is the household dependency ratio which remains different and slightly higher after matching. However, overidentification tests conducted after each estimation confirm that the IPW successfully rebalanced the covariates in each estimation. We therefore consider the matching to be satisfactory. However, we only control for characteristics that are observed in the data and there could be other omitted variables driving the short- and medium-term impact that is not captured in the surveys.

Furthermore, the timing of the ECT implementation and the two rounds of CIMS surveys create both opportunities and challenges for our analysis. While it allows us to estimate impact on beneficiaries who received the ECT before and after the first round of data collection, the long-time lag between the end of the ECT implementation (August 2020) and the start of Round 2 of the CIMS survey (November 2020) presents difficulties in interpreting the results for beneficiaries who received the transfers after the conclusion of the first round. Descriptive data also shows that compared to June-August 2020, the economic recovery was well on-track by November-December 2020 and could introduce sources of bias in the findings, especially on Round 2 beneficiaries.

Finally, while the CIMS data provided a timely opportunity to track the outcomes of ECT beneficiaries, the survey itself had limitations because of the way it was designed. In particular, part of the questionnaire was administered to the full set of respondents available in the sample, but other sections 25
of the survey was only administered to one-third of the sample. This significantly reduces the number of identifiable ECT beneficiaries when analyzing specific outcomes and reduces the overall sample size.

Several recommendations arise from this discussion: (1) collecting experimental data by aligning the purposes of policy and research in future implementations of ECTs would allow to collect stronger evidence; (2) survey data such as the CIMS (2020) are extremely valuable in the context of missing experimental data and they should pursue efforts to minimize data issues (e.g. missing responses, small sample size); (3) qualitative data would be a complementary source of information for our study and would allow us to gain further insight into how households allocate the amount of the ECT across their various needs.

# **7. Conclusion**

This paper took stock of lessons from a COVID-19 ECT program that was administered to vulnerable informal sector workers in Sierra Leone. It discussed how cash transfers are increasingly being instituted as a policy measure to support informal sector workers in the wake of the pandemic and reviewed emerging evidence on the impact of these measures in other countries. It also described the setting and context of the ECT program and presented analysis on the relationship between receiving the ECT and various socioeconomic outcomes and measures of subjective well-being for households with urban informal sector workers in Sierra Leone. While the ECT was found to be positively associated with effectively shielding vulnerable informal sector workers from income and job losses in the short-term, the analysis was inconclusive on the medium-term outcomes related to income, suggesting the need for further experimental data. The ECT was also found to be positively associated with measures of subjective well-being, especially on households receiving the transfers being less concerned about 26
children being out of school, food shortages, being sick with COVID-19, and lack of health care. Receiving the ECT was also associated with positive views of the government response to the pandemic. The paper contributes to the literature on this new form of punctual social protection measures and calls for further research allowing to further understand how emergency cash transfers can have a lasting impact.

27
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www.odi.org/twitter%0Awww.odi.org/twitter%0Awww.odi.org/twitter%0Ahttps://www.odi.org/s ites/odi.org.uk/files/resource-documents/10749.pdf Bhorat, H., Oosthuizen, M., & Stanwix, B. (2021). Social Assistance Amidst the COVID‐19 Epidemic in South Africa: A Policy Assessment. _South African Journal of Economics_ , _89_ (1), 63–81. https://doi.org/10.1111/saje.12277 De La Flor, L., Mujica, I., Fontenez, M. B., Newhouse, D., Rodriguez Alas, C., Sabharwal, G., & Weber, M.

(2021). _Taking Stock of COVID-19 Labor Policy Responses in Developing Countries_ . https://openknowledge.worldbank.org/handle/10986/35331 Fajardo-Gonzalez, J., Gray Molina, G., Mantoya-Aguirre, M., & Ortiz-Juarez, E. (2021). _Mitigating_ _Poverty : Global Estimates of the Impact of Income Support during the Pandemic_ (Development 28
Futures Series Working Papers). https://www.undp.org/publications/mitigating-poverty-globalestimates-impact-income-support-during-pandemic Garrote Sanchez, D., Gomez Parra, N., Ozden, C., Rijkers, B., Viollaz, M., & Winkler, H. (2021). Who on Earth Can Work from Home? _The World Bank Research Observer_ , _36_ (1), 67–100. https://doi.org/10.1093/wbro/lkab002 Gentilini, U., Almenfi, M., Blomquist, J., Dale, P., De, L., Giuffra, F., Desai, V., Fontenez, M. B., Galicia, G., Lopez, V., Marin, G., Mujica, I. V., Newhouse, D., Palacios, R., Quiroz, A. P., Alas, C. R., Sabharwal, G., Weber, M., Acar, A., ... Grun, R. (2021). _Social Protection and Jobs Responses to COVID-19 : A Real-Time Review of Country Measures_ .

https://doi.org/https://openknowledge.worldbank.org/handle/10986/33635 Gerard, F., Imbert, C., & Orkin, K. (2020). Social protection response to the COVID-19 crisis: options for developing countries. _Oxford Review of Economic Policy_ , _36_ (Supplement_1), S281–S296. https://doi.org/10.1093/oxrep/graa026 Hanna, R., & Olken, B. A. (2018). Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries. _Journal of Economic Perspectives_ , _32_ (4), 201–226. https://doi.org/10.1257/jep.32.4.201 Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. _Journal of Economic Literature_ , _47_ (1), 5–86. https://doi.org/10.1257/jel.47.1.5 Londoño-Vélez, J., & Querubin, P. (2020). _The Impact of Emergency Cash Assistance in a Pandemic : Experimental Evidence from Colombia_ .

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https://www.cgdev.org/publication/impact-covid-19-lockdowns-and-expanded-social-assistanceinequality-poverty-and-mobility Melo, P., Abdul-Salam, Y., Roberts, D., Gilbert, A., Matthews, R., Colen, L., Mary, S., Gomez Y Paloma, S.

(2015) ; Income Elasticities of Food Demand in Africa: A Meta-Analysis; EUR 27650 EN; doi:10.2791/661366 Meriggi, N., Humphreys, M., Kamara, A. B., Krupoff, M., Levine, M., Mcleod, H., Mobarak, M., Prichard, W., Shridhar, A., Van Der Windt, P., & Voors, M. (2020). _Tracking the economic consequences and responses to COVID-19 in Sierra Leone_ (Working Paper - IGC, Issue May). https://sldashboard.github.io/corona/ Ohnsorge, F., Yu, S. (2022). _The Long Shadow of Informality : Challenges and Policies_ . World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/35782 Sanford, J. 2023. Expanding safety nets in response to shocks: The Experience of the Government of Sierra Leone’s Ep Fet Po Programme’s COVID-19 Response. Forthcoming.

Stoeffler, Q., Mills, B., & Premand, P. (2020). Poor households’ productive investments of cash transfers: Quasi-experimental evidence from Niger. _Journal of African Economies_ , _29_ (1), 63–89. https://doi.org/10.1093/jae/ejz017 Varshney, D., Kumar, A., Mishra, A. K., Rashid, S., & Joshi, P. K. (2021). India’s COVID-19 social assistance package and its impact on the agriculture sector. _Agricultural Systems_ , _189_ (July 2020). https://doi.org/10.1016/j.agsy.2021.103049 World Bank (2014). Vitamin A at a glance. World Bank, Washington, DC.

http://web.worldbank.org/archive/website01213/WEB/IMAGES/AAGENGVI.PDF. Accessed in March 2023.

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31
# **<u>Appendix</u>**

# **_Appendix 1:_** _LPMT scoring mechanism and CIMS proxy LPMT scoring method_

|**Category**|**LPMT Question**|**LPMT Scoring Mechanism**|**Availability in CIMS survey and**<br>**Proxy variables**|
|---|---|---|---|
|**Household**<br>**characteristics**|What is the gender of the head of the<br>household?|1 if female, 0 if male|Available|
||How many children below 18 years of age<br>are in the household?|1 if above 3, 0 if below 3|Available|
||How many members above 18 years of age<br>are in the household?|1 if above 6, 0 if below 6|Available|
||Can the head of the household read or write<br>in English or Sierra Leonean language?|1 if illiterate, 0 if literate|Not available. Education level (i.e.,<br>below primary dummy) of<br>respondent is used as aproxy|
|**Household Assets**|How many rooms does your house have?|1 if number of rooms divided<br>by household size is less than<br>1, 0<br>if number of rooms divided by<br>household size is more than 1|Available|
||Does your household have cement walls?|1 if no, 0 if yes|Available|
||Does the household use a flush to pit<br>latrine?|1 if the answer is no, 0 if the<br>answer isyes|Available|
||Does the household use a bottle/sachet<br>water for drinking?|1 if the answer is no, 0 if the<br>answer isyes|Not Available|
|**Work Status**|Does the head of the household/main<br>income-earner have a contract with his/her<br>employer?|1 if the answer is no, 0 if the<br>answer is yes)|Not available. We use information<br>on whether respondent has a formal<br>contract as aproxy|
||What is the occupation of the household<br>head/main income-earner of the family?|Validation question|Available|
|**Illness/Disability**|Does anyone in your household suffer from<br>illness or disability?|1 if yes, 0 if no|Available|
|**Total possible score**||10|9|

_Source_ : Authors’ calculations based on administrative data from NaCSA (2020) and CIMS(2020). _Note_ : LPMT variables and threshold were calibrated by analyzing the overlap the LPMT score and poverty level in the SLIHS (2018) household survey data.

32
# **_Appendix 2:_** _Balance check table_

||Standardize<br>|d differences<br>|Varian<br>|ce ratio<br>|
|---|---|---|---|---|
||Raw|Weighted|Raw|Weighted|
|Female|0.111|0.037|0.964|0.986|
|Age|0.051|0.013|0.778|0.695|
|Household dependency ratio|0.100|0.112|0.728|0.715|
|Head of household|0.129|-0.038|0.964|1.008|
|Completed secondary school|-0.019|0.001|0.989|1.000|
|Main income source: Agriculture|-0.033|-0.037|0.989|1.000|
|Main income source: Business|0.283|0.029|0.785|0.980|
|Main income source:|-0.314|0.016|0.544|1.024|
|Wage/Salary|||||
|Main income source: Other|-0.029|-0.055|0.841|0.697|
|Asset score|-0.401|-0.135|0.560|0.818|
|Bo|-0.031|-0.068|0.954|0.877|
|Bombali|-0.176|0.027|0.681|1.052|
|Kenema|-0.336|-0.095|0.356|0.790|
|Port Loko|0.227|-0.007|1.892|0.979|
|Western Area Urban<br>_Source_: Authors’ calculations from CIMS (202|0.199<br>0)._Note_: This Balanc|0.093<br>e check table includes R|0.999<br>ound 1 beneficiaries|0.999<br>in the largest|

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020).</mark> _<mark>Note</mark>_ <mark>: This Balance check table includes Round 1 beneficiaries in the largest estimation sample of our ECT-R1 analysis. “Raw” columns show the values prior to the inverse-probability weighting and “Weighted” columns show the values post weighting. The standardized differences should be equal to zero and the variance ratio should be equal to 1 if the samples are perfectly balanced.</mark> 33
# **_Appendix 3:_** _Objective economic security outcomes: descriptive statistics and sample sizes_

||N|N|Mean|Mean|Mean|Differenc|
|---|---|---|---|---|---|---|
||Control|Treated|Total|Control|Treated|e p-value|
|Round 1 outcomes|**R**|**1 Benefici**|**ary Househ**|**olds(ECT-**|**1)**||
|Worked last week|480|89|0.522|0.506|0.607|0.082|
|Hours worked last week|480|89|21.098|19.907|25.528|0.013|
|Main source of income increased or stayed the same|475|87|0.336|0.314|0.460|0.008|
|Main source of income decreased or stopped|475|87|0.663|0.686|0.540|0.008|
|Other source of income increased or stayed same|303|89|0.349|0.284|0.573|0.000|
|Other source of income decreased or stopped|303|89|0.543|0.637|0.225|0.000|
|Inability to buy staple foods|492|55|0.117|0.116|0.128|0.803|
|Able to financiallysupport child's return to school|446|66|0.863|0.857|0.909|0.246|
|Round 2 outcomes|**R1 Benef**|**iciary (EC**<br>**exclu**|**T-1, R2 Ben**<br>**ded from C**|**eficiary ho**<br>**ontrol)**|**useholds**||
|Worked last week|623|78|0.603|0.588|0.731|0.015|
|Hours worked last week|623|78|25.471|24.416|33.898|0.003|
|Main source of income increased or stayed the same|606|76|0.683|0.690|0.632|0.304|
|Main source of income decreased or stopped|606|76|0.317|0.310|0.368|0.304|
|Other source of income increased or stayed same|450|67|0.603|0.625|0.463|0.011|
|<br>Other source of income decreased or stopped|450|67|0.265|0.256|0.329|0.208|
|<br>Inability to buy staple foods|566|76|0.081|0.077|0.105|0.41|
|Child identified as malnourished|214|24|0.080|0.080|0.084|0.947|
|Childgiven vitamin A supplementation|348|35|0.389|0.394|0.343|0.558|
|Round 2 outcomes|**R2 Benef**|**iciary (EC**<br>**exclu**|**T-2, R1 Ben**<br>**ded from C**|**eficiary ho**<br>**ontrol)**|**useholds**||
|Worked last week|623|40|0.594|0.588|0.700|0.161|
|Hours worked last week|623|40|25.185|24.416|37.175|0.004|
|Main source of income increased or stayed the same|606|38|0.688|0.690|0.658|0.681|
|Main source of income decreased or stopped|606|38|0.312|0.310|0.342|0.681|
|<br>Other source of income increased or stayed same|450|26|0.617|0.625|0.500|0.205|
|Other source of income decreased or stopped|450|26|0.265|0.256|0.423|0.06|
|Inability to buy staple foods|566|40|0.074|0.077|0.025|0.22|
|Child identified as malnourished|214|24|0.080|0.080|0.084|0.947|
|Childgiven vitamin A supplementation|348|38|0.388|0.394|0.342|0.537|
|Round 2 outcomes|**R1 and R**|**2 Beneficia**|**ry Househ**<br>**2)**|**olds (ECT-1**|**and ECT-**||
|Worked last week|623|118|0.609|0.588|0.721|0.007|
|Hours worked last week|623|118|24.416|24.416|35.008|0.000|
|Main source of income increased or stayed the same|606|114|0.682|0.690|0.640|0.299|
|<br>Main source of income decreased or stopped|606|114|0.318|0.310|0.359|0.299|
|<br>Other source of income increased or stayed same|450|93|0.599|0.625|0.473|0.007|
|Other source of income decreased or stopped|450|93|0.273|0.256|0.355|0.051|
|Inability to buy staple foods|566|116|0.078|0.077|0.077|0.996|
|_Source_: Authors’ calculations from CIMS (2020).|||||||

34
# **_Appendix 4:_** _Subjective indicators of psychological wellbeing and satisfaction with government measures: descriptive statistics and sample sizes_

||N|N|Mean|Mean|Mean|Difference<br>|
|---|---|---|---|---|---|---|
||Control|Treated|Total|Control|Treated|p-value|
|Round 1 outcomes|**R1 Benefic**|**iary House**|**holds(ECT-**|**1)**|||
|Concerned about children out of<br>school|479|89|1.445|1.405|1.596|0.039|
|Concerned about food shortages<br>|480|89|1.561|1.486|1.967|0.000<br>|
|Concerned about price increases|480|89|1.103|1.089|1.180|0.041|
|Concerned about being sick with|480|89|1.528|1.438|1.978|0.000|
|COVID-19|||||||
|Concerned about quarantine|480|89|1.606|1.519|2.045|0.000|
|Concerned about lack of other|478|89|1.659|1.619|1.877|0.018|
|healthcare|||||||
|Satisfaction with government|468|87|0.966|0.960|1.000|0.056|
|measures|||||||
|Round 2 outcomes|**R1 Benefic**<br>**excluded f**|**iary (ECT-1**<br>**rom Contro**|**, R2 Benefic**<br>**l)**|**iary househ**|**olds**||
|Concerned about children out of<br>school|572|78|1.432|1.451|1.321|0.206|
|Concerned about food shortages|573|78|1.570|1.571|1.603|0.782|
|Concerned about price increases|573|78|1.065|1.068|1.052|0.663|
|Concerned about being sick with|573|78|1.851|1.850|1.872|0.873|
|COVID-19|||||||
|Concerned about quarantine|573|78|1.948|1.941|2.013|0.61|
|Concerned about lack of other|572|78|1.759|1.752|1.821|0.572|
|healthcare|||||||
|Satisfaction with government|569|77|0.958|0.960|.948|0.636|
|measures|||||||
|Round 2 outcomes|**R2 Benefic**<br>**excluded f**|**iary (ECT-2**<br>**rom Contro**|**, R1 Benefic**<br>**l)**|**iary househ**|**olds**||
|Concerned about children out of<br>school|572|40|1.466|1.451|1.725|0.036|
|Concerned about food shortages|573|40|1.589|1.571|1.900|0.73|
|Concerned about price increases|573|40|1.068|1.068|1.050|0.412|
|Concerned about being sick with|573|40|1.837|1.850|1.700|0.632|
|COVID-19|||||||
|Concerned about quarantine|573|40|1.931|1.941|1.850|0.87|
|Concerned about lack of other|572|40|1.742|1.752|1.725|0.196|
|healthcare|||||||
|Satisfaction with government|569|40|0.962|0.960|1.000|0.036|
|measures|||||||
|Round 2 outcomes|**R1 and R2**|**Beneficiary**|**Household**|**s(ECT-1 an**|**d ECT-2)**||
|Concerned about children out of<br>school|572|118|1.452|1.451|1.458|0.941|

35
|Concerned about food shortages|573|118|1.593|1.571|1.704|0.174|
|---|---|---|---|---|---|---|
|Concerned about price increases|573|118|1.065|1.068|1.051|0.594|
|Concerned about being sick with|573|118|1.844|1.850|1.814|0.748|
|COVID-19|||||||
|Concerned about quarantine|573|118|1.944|1.958|1.958|0.885|
|Concerned about lack of other|572|118|1.758|1.788|1.788|0.719|
|healthcare|||||||
|Satisfaction with government|569|117|0.961|0.966|.966|0.753|
|measures|||||||

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020)</mark> 36
**_Appendix 5:_** _Kernel density plot of the number of hours worked prior to the survey_

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020)</mark> 37
# Supplementary Materials

38
|Supplementarymaterials S1. Comparis<br>|on of full sample and sa<br>|mple with labor ma<br>|rket information<br>|||
|---|---|---|---|---|---|
|Variable|Mean full sample|N full sample|Mean sample<br>with labor market<br>information|Nsample with<br>labor market<br>information|Two-sample t-<br>test P-value|
|Female|0.486<br>(0.009)|3078|0.488<br>(0.018)|770|0.897|
|Age|36.748<br>(0.219)|3082|36.499<br>(0.433)|770|0.609|
|Household dependencyratio|1.090<br>(0.033)|3080|1.090<br>(0.016)|771|0.818|
|Head of household|0.590<br>(0.008)|3080|0.585<br>(0.018)|772|0.825|
|Completed secondaryschool|0.337<br>(0.009)|3080|0.334<br>(0.017)|772|0.896|
|Main source of income: Business|0.583<br>(0.028)|3080|0.588<br>(0.018)|772|0.812|
|Asset score|0.355<br>(0.003)|3080|0.357<br>(0.006)|772|0.853|

_Source_ : Authors’ calculations from CIMS (2020). Note: Standard errors in parentheses.

39
Supplementary materials S2. Full regression table of ECT-R1: Round 1 (short-term) outcomes

||Worked last<br>week|Hours worked<br>last week|Main source<br>of income<br>increased or<br>stayed same|Main source<br>of income<br>decreased or<br>stopped|Other source<br>of income<br>increased or<br>stayed same|Other source<br>of income<br>decreased or<br>stopped|Inability to<br>buy staple<br>foods|Able to<br>financially<br>support<br>child's return<br>to school|
|---|---|---|---|---|---|---|---|---|
|ATE of ECT-R1|0.050|5.724*|0.135**|-0.135**|0.192***|-0.396***|0.001|0.025|
||(0.058)|(2.947)|(0.060)|(0.060)|(0.066)|(0.061)|(0.050)|(0.053)|
|POM (Control)|0.502***|19.598***|0.307***|0.693***|0.284***|0.640***|0.118***|0.856***|
||(0.023)|(1.199)|(0.021)|(0.021)|(0.026)|(0.028)|(0.015)|(0.017)|
|Female|0.260|0.260|0.252|0.252|0.223|0.223|0.008|0.406|
||(0.272)|(0.272)|(0.278)|(0.278)|(0.296)|(0.296)|(0.355)|(0.306)|
|Age|0.002|0.002|0.003|0.003|-0.003|-0.003|0.011|0.013|
||(0.010)|(0.010)|(0.010)|(0.010)|(0.011)|(0.011)|(0.012)|(0.011)|
|Household dependency ratio|0.035|0.035|0.050|0.050|0.223|0.223|0.431***|0.268**|
||(0.101)|(0.101)|(0.101)|(0.101)|(0.145)|(0.145)|(0.138)|(0.132)|
|Head of household|0.354|0.354|0.342|0.342|0.408|0.408|0.119|0.701**|
||(0.294)|(0.294)|(0.297)|(0.297)|(0.321)|(0.321)|(0.369)|(0.353)|
|Completed secondary school|0.087|0.087|0.057|0.057|-0.102|-0.102|0.078|-0.534|
||(0.276)|(0.276)|(0.281)|(0.281)|(0.305)|(0.305)|(0.346)|(0.361)|
|Main income source: Business|0.036|0.036|0.235|0.235|0.268|0.268|0.748*|0.893*|
||(0.412)|(0.412)|(0.447)|(0.447)|(0.432)|(0.432)|(0.444)|(0.541)|
|Main income source: Wage/Salary|-0.758|-0.758|-0.561|-0.561|-0.850|-0.850|-0.694|0.021|
||(0.527)|(0.527)|(0.554)|(0.554)|(0.553)|(0.553)|(0.674)|(0.616)|
|Main income source: Other|-0.362|-0.362|-0.163|-0.163|-0.171|-0.171||0.335|
||(0.831)|(0.831)|(0.846)|(0.846)|(0.924)|(0.924)||(1.181)|
|Asset score|-2.749***|-2.749***|-2.707***|-2.707***|-3.663***|-3.663***|-3.715***|-0.732|
||(0.848)|(0.848)|(0.865)|(0.865)|(0.948)|(0.948)|(1.048)|(0.863)|
|Bombali|-0.517|-0.517|-0.558|-0.558|-0.723|-0.723|0.997*|-0.213|
||(0.470)|(0.470)|(0.488)|(0.488)|(0.513)|(0.513)|(0.554)|(0.484)|
|Kenema|-1.191**|-1.191**|-1.092*|-1.092*|-1.340**|-1.340**|-0.608|-0.206|
||(0.589)|(0.589)|(0.591)|(0.591)|(0.592)|(0.592)|(0.744)|(0.536)|
|Port Loko|0.602|0.602|0.696|0.696|0.481|0.481|0.901|0.593|
||(0.455)|(0.455)|(0.457)|(0.457)|(0.487)|(0.487)|(0.617)|(0.511)|
|Western Area Urban|0.271|0.271|0.311|0.311|0.470|0.470|0.275|-0.054|
||(0.343)|(0.343)|(0.350)|(0.350)|(0.362)|(0.362)|(0.457)|(0.352)|
|Constant|<br>-1.266**|<br>-1.266**|<br>-1.539**|<br>-1.539**|<br>-0.623|<br>-0.623|<br>-2.864***|<br>-3.722***|
||(0.615)|(0.615)|(0.629)|(0.629)|(0.664)|(0.664)|(0.811)|(0.884)|
|Observations<br>_Source_: Authors’ calculations from CIMS (2020)._N_|569<br>_ote_: Standard errors in p|569<br>arentheses: ***|562<br>p<0.01, ** p<0|562<br>.05, * p<0.1. In|392<br>verse probabilit|392<br>y weighted regr|533<br>essions. For e|512<br>ach estimation,|

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020).</mark> _<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates.</mark> 40
Supplementary materials S3. Full regression table of ECT-R1: Round 2 (medium-term) outcomes

||Worked last<br>week|Hours worked<br>last week|Main source of<br>income<br>increased or<br>stayed same|Main source of<br>income<br>decreased or<br>stopped|Other source of<br>income<br>increased or<br>stayed same|Other source of<br>income<br>decreased or<br>stopped|Inability to buy<br>staple foods|Child identified<br>as mal<br>nourished|
|---|---|---|---|---|---|---|---|---|
|ATE of ECT-R1|0.126*|8.576**|0.025|-0.025|-0.165**|0.064|0.030|0.024|
||(0.065)|(3.690)|(0.056)|(0.056)|(0.078)|(0.071)|(0.039)|(0.071)|
|POM (Control)|0.586***|24.315***|0.688***|0.312***|0.624***|0.260***|0.075***|0.082***|
||(0.020)|(1.066)|(0.019)|(0.019)|(0.023)|(0.021)|(0.011)|(0.020)|
|Female|0.432|0.432|0.453|0.453|0.436|0.436|0.677**|-0.142|
||(0.290)|(0.290)|(0.295)|(0.295)|(0.302)|(0.302)|(0.292)|(0.585)|
|Age|-0.012|-0.012|-0.012|-0.012|-0.010|-0.010|-0.013|0.065***|
||(0.011)|(0.011)|(0.012)|(0.012)|(0.013)|(0.013)|(0.012)|(0.024)|
|Household dependency ratio|0.163|0.163|0.185|0.185|0.204|0.204|0.371**|0.470**|
||(0.127)|(0.127)|(0.127)|(0.127)|(0.154)|(0.154)|(0.148)|(0.204)|
|Head of household|0.182|0.182|0.176|0.176|0.431|0.431|0.797**|-0.448|
||(0.306)|(0.306)|(0.309)|(0.309)|(0.337)|(0.337)|(0.339)|(0.575)|
|Completed secondary school|-0.292|-0.292|-0.381|-0.381|-0.439|-0.439|-0.352|-0.145|
||(0.295)|(0.295)|(0.305)|(0.305)|(0.338)|(0.338)|(0.328)|(0.528)|
|Main income source: Business|0.275|0.275|0.221|0.221|0.388|0.388|1.229**|1.220|
||(0.449)|(0.449)|(0.466)|(0.466)|(0.457)|(0.457)|(0.623)|(0.980)|
|Main income source: Wage/Salary|-0.882|-0.882|-1.050*|-1.050*|-0.999*|-0.999*|0.081|-0.557|
||(0.567)|(0.567)|(0.590)|(0.590)|(0.601)|(0.601)|(0.733)|(1.232)|
|Main income source: Other|-0.865|-0.865|-0.918|-0.918|||0.521||
||(1.063)|(1.063)|(1.067)|(1.067)|||(0.933)||
|Asset score|-2.759***|-2.759***|-2.620***|-2.620***|-2.878***|-2.878***|-1.518*|-5.273***|
||(0.790)|(0.790)|(0.803)|(0.803)|(0.899)|(0.899)|(0.833)|(1.822)|
|Bombali|0.460|0.460|0.511|0.511|0.379|0.379|0.460|1.375*|
||(0.516)|(0.516)|(0.523)|(0.523)|(0.554)|(0.554)|(0.584)|(0.830)|
|Kenema|-0.190|-0.190|-0.118|-0.118|-0.518|-0.518|-0.678||
||(0.588)|(0.588)|(0.592)|(0.592)|(0.670)|(0.670)|(0.832)||
|Port Loko|1.410***|1.410***|1.507***|1.507***|1.283**|1.283**|0.995|1.838*|
||(0.485)|(0.485)|(0.495)|(0.495)|(0.508)|(0.508)|(0.705)|(1.070)|
|Western Area Urban|1.231***|1.231***|1.236***|1.236***|1.252***|1.252***|1.299***|0.817|
||(0.376)|(0.376)|(0.386)|(0.386)|(0.415)|(0.415)|(0.441)|(0.718)|
|Constant|<br>-1.980***|<br>-1.980***|<br>-2.019***|<br>-2.019***|<br>-1.980***|<br>-1.980***|<br>-3.979***|<br>-4.514***|
||(0.670)|(0.670)|(0.671)|(0.671)|(0.733)|(0.733)|(0.719)|(1.472)|
|Observations<br>_Source_: Authors’ calculations from CIM|702<br>S (2020)._Note_: Stan|702<br>dard errors in par|682<br>entheses: *** p<0.|682<br>01, ** p<0.05, * p|503<br><0.1. Inverse prob|503<br>ability weighted re|640<br>gressions. For eac|208<br>h estimation,|

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020).</mark> _<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates.</mark> 41
Supplementary materials S4. Full regression table of ECT-R2: Round 2 (medium-term) outcomes

||Worked last<br>week|Hours<br>worked last<br>week|Main source<br>of income<br>increased or<br>stayed same|Main source<br>of income<br>decreased<br>or stopped|Other<br>source of<br>income<br>increased or<br>stayed same|Other<br>source of<br>income<br>decreased<br>or stopped|Inability to<br>buy staple<br>foods|Child<br>identified as<br>mal<br>nourished|Small children<br>given vitamin A<br>supplementation|
|---|---|---|---|---|---|---|---|---|---|
|ATE of ECT-R1|0.180***|15.340***|-0.146|0.146|-0.266***|0.290***|-0.068***|-0.059*|-0.064|
||(0.060)|(4.461)|(0.094)|(0.094)|(0.077)|(0.092)|(0.022)|(0.034)|(0.066)|
|POM (Control)|0.596***|23.998***|0.690***|0.310***|0.625***|0.255***|0.088***|0.095***|0.388|
||(0.024)|(1.269)|(0.023)|(0.023)|(0.028)|(0.025)|(0.014)|(0.023)|(0.030)|
|Female|0.690|0.690|0.827*|0.827*|1.256**|1.256**|0.072|0.062|0.414|
||(0.423)|(0.423)|(0.429)|(0.429)|(0.540)|(0.540)|(0.366)|(0.631)|(0.449)|
|Age|0.003|0.003|0.002|0.002|-0.007|-0.007|0.003|0.031|0.017|
||(0.016)|(0.016)|(0.016)|(0.016)|(0.020)|(0.020)|(0.014)|(0.022)|(0.015)|
|Household dependency ratio|0.114|0.114|0.063|0.063|-0.487|-0.487|0.210|0.087|0.275*|
||(0.150)|(0.150)|(0.169)|(0.169)|(0.308)|(0.308)|(0.160)|(0.249)|(0.167)|
|Head of household|0.218|0.218|0.295|0.295|0.534|0.534|-0.593|-0.700|-0.222|
||(0.390)|(0.390)|(0.384)|(0.384)|(0.507)|(0.507)|(0.380)|(0.504)|(0.419)|
|Completed secondary school|-0.241|-0.241|-0.359|-0.359|-0.571|-0.571|0.009|-0.416|0.248|
||(0.439)|(0.439)|(0.463)|(0.463)|(0.536)|(0.536)|(0.414)|(0.709)|(0.435)|
|Main income source: Business|0.261|0.261|0.471|0.471|0.497|0.497|0.922|0.568|0.734|
||(0.542)|(0.542)|(0.699)|(0.699)|(0.674)|(0.674)|(0.616)|(0.776)|(0.661)|
|Main income source: Wage/Salary|-0.233|-0.233|-0.007|-0.007|-0.084|-0.084|0.268|-0.877|-0.412|
||(0.709)|(0.709)|(0.834)|(0.834)|(0.863)|(0.863)|(0.734)|(1.022)|(0.830)|
|Asset score|-1.961|-1.961|-1.836|-1.836|-0.310|-0.310|-2.823**|-2.866|-1.636|
||(1.243)|(1.243)|(1.253)|(1.253)|(1.408)|(1.408)|(1.366)|(1.918)|(1.551)|
|Bombali|0.133|0.133|-0.162|-0.162|0.740|0.740|-0.253|-0.640|-1.224|
||(0.754)|(0.754)|(0.823)|(0.823)|(1.267)|(1.267)|(0.879)|(1.345)|(1.175)|
|Kenema|-0.066|-0.066|-0.033|-0.033|1.315|1.315|0.179|0.077|0.050|
||(0.815)|(0.815)|(0.819)|(0.819)|(1.188)|(1.188)|(0.767)|(1.233)|(0.809)|
|Port Loko|0.858|0.858|0.878|0.878|1.870|1.870|0.859|1.941*|1.663**|
||(0.790)|(0.790)|(0.792)|(0.792)|(1.150)|(1.150)|(0.872)|(1.137)|(0.815)|
|Western Area Urban|0.761|0.761|0.663|0.663|1.736*|1.736*|0.744|1.049|0.850|
||(0.565)|(0.565)|(0.567)|(0.567)|(1.016)|(1.016)|(0.654)|(0.803)|(0.607)|
|Constant|-3.177***|-3.177***|-3.356***|-3.356***|-4.304***|-4.304***|-2.644***|-2.702|-3.584***|
||(1.001)|(1.001)|(1.078)|(1.078)|(1.509)|(1.509)|(0.869)|(1.923)|(1.241)|
|Observations<br>_Source_: Authors’ calculations from CIMS (20|459<br>20)._Note_: Standard|459<br>errors in pare|442<br>ntheses: *** p|442<br><0.01, ** p<0.|328<br>05, * p<0.1. In|328<br>verse probabili|433<br>ty weighted re|187<br>gressions. Fo|305<br>r each estimation,|

_<mark>Source</mark>_ <mark>: Authors’ calculations from CIMS (2020).</mark> _<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates.</mark> 42
Supplementary materials S5. Full regression table of pooled outcomes of ECT-R1 and ECT-R2

||||Main source of|Main source of|Other source of|Other source of||
|---|---|---|---|---|---|---|---|
||Worked last<br>week|Hours worked<br>last week|income<br>increased or<br>stayed same|income<br>decreased or<br>stopped|income<br>increased or<br>stayed same|income<br>decreased or<br>stopped|Inability to buy<br>staple foods|
|ATE of ECT-R1|0.121**|10.448***|0.173***|-0.173***|0.156**|-0.294***|-0.031|
||(0.056)|(3.270)|(0.062)|(0.062)|(0.061)|(0.070)|(0.034)|
|POM (Control)|0.509***|19.517***|0.347***|0.653***|0.284***|0.614***|0.120***|
||(0.018)|(0.930)|(0.017)|(0.017)|(0.020)|(0.022)|(0.012)|
|Female|0.579**|0.579**|0.605**|0.605**|0.667***|0.667***|0.208|
||(0.238)|(0.238)|(0.241)|(0.241)|(0.259)|(0.259)|(0.274)|
|Age|-0.000|-0.000|0.001|0.001|-0.008|-0.008|0.015*|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.010)|(0.010)|(0.009)|
|Household dependency ratio|0.126|0.126|0.145|0.145|0.194|0.194|0.420***|
||(0.106)|(0.106)|(0.107)|(0.107)|(0.133)|(0.133)|(0.118)|
|Head of household|0.388|0.388|0.424*|0.424*|0.580**|0.580**|-0.007|
||(0.253)|(0.253)|(0.254)|(0.254)|(0.281)|(0.281)|(0.270)|
|Completed secondary school|-0.066|-0.066|-0.143|-0.143|-0.168|-0.168|-0.018|
||(0.240)|(0.240)|(0.245)|(0.245)|(0.265)|(0.265)|(0.276)|
|Main income source: Business|0.539|0.539|0.735*|0.735*|0.626*|0.626*|0.940**|
||(0.356)|(0.356)|(0.386)|(0.386)|(0.376)|(0.376)|(0.378)|
|Main income source: Wage/Salary|-0.458|-0.458|-0.247|-0.247|-0.551|-0.551|-0.263|
||(0.442)|(0.442)|(0.465)|(0.465)|(0.476)|(0.476)|(0.486)|
|Main income source: Other|-0.439|-0.439|-0.247|-0.247|-0.446|-0.446||
||(0.772)|(0.772)|(0.785)|(0.785)|(0.801)|(0.801)||
|Asset score|-2.464***|-2.464***|-2.423***|-2.423***|-2.589***|-2.589***|-3.473***|
||(0.665)|(0.665)|(0.675)|(0.675)|(0.699)|(0.699)|(0.800)|
|Bombali|-0.329|-0.329|-0.442|-0.442|-0.530|-0.530|0.345|
||(0.416)|(0.416)|(0.443)|(0.443)|(0.481)|(0.481)|(0.460)|
|Kenema|-0.915*|-0.915*|-0.824*|-0.824*|-0.867*|-0.867*|-0.104|
||(0.468)|(0.468)|(0.472)|(0.472)|(0.502)|(0.502)|(0.509)|
|Port Loko|0.209|0.209|0.335|0.335|0.053|0.053|0.328|
||(0.385)|(0.385)|(0.386)|(0.386)|(0.430)|(0.430)|(0.504)|
|Western Area Urban|0.518*|0.518*|0.548*|0.548*|0.634*|0.634*|0.622|
||(0.298)|(0.298)|(0.305)|(0.305)|(0.338)|(0.338)|(0.381)|
|Constant|-2.155***|-2.155***|-2.474***|-2.474***|-1.684***|-1.684***|-3.122***|
||(0.533)|(0.533)|(0.533)|(0.533)|(0.604)|(0.604)|(0.623)|
|Observations<br>_Source_: Authors’ calculations from CIMS (2020)<br>_Note_: Standard errors in parentheses: *** p<0.01|908<br>, ** p<0.05, * p<0.1.|908<br>Inverse probabilit|893<br>y weighted regres|893<br>sions. For each es|627<br>timation, overide|627<br>ntification tests s|810<br>hows that the IPW|

_<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates.</mark> 43
Supplementary Materials S6. Balance tests for ECT-R1 short-term, ECT-R1 medium-term, ECT-R2 short-term and pooled estimations

||Short-term<br>|ECT-R1 – Worked last week<br>|||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.110566|0.037604|0.963533|0.985948|
|Age|0.051401|0.013315|0.777923|0.694858|
|Household dependency|||||
|ratio|0.100014|0.112127|0.72764|0.715065|
|Household head|0.129401|-0.03805|0.964385|1.008376|
|Completed Secondary|||||
|School|-0.01866|0.000524|0.989061|1.000697|
|Main income source:|||||
|Business|0.282555|0.028862|0.785406|0.98044|
|Main income source:|||||
|Wage/Salary|-0.31419|0.015886|0.544325|1.024299|
|Main income source:|||||
|Other|-0.02958|-0.05497|0.841382|0.696539|
|Asset score|-0.40076|-0.13508|0.559976|0.817707|
|Bombali|-0.17604|0.02651|0.681147|1.051766|
|Kenema|-0.33648|-0.09471|0.356268|0.790145|
|Port Loko|0.227008|-0.00678|1.892117|0.978709|
|Western Area Urban|0.198976|0.093012|0.999461|0.999398|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.698917||||
|_Source_: Authors’ calculations<br>P-value|from CIMS (2020)<br>0.904415||||

44
||Short-term EC<br>|T-R1 - Hours worked last week<br>|||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.110566|0.037604|0.963533|0.985948|
|Age|0.051401|0.013315|0.777923|0.694858|
|Household dependency|||||
|ratio|0.100014|0.112127|0.72764|0.715065|
|Household head|0.129401|-0.03805|0.964385|1.008376|
|Completed Secondary<br>School|-0.01866|0.000524|0.989061|1.000697|
|Main income source:|||||
|Business|0.282555|0.028862|0.785406|0.98044|
|Main income source:|||||
|Wage/Salary|-0.31419|0.015886|0.544325|1.024299|
|Main income source:|||||
|Other|-0.02958|-0.05497|0.841382|0.696539|
|Asset score|-0.40076|-0.13508|0.559976|0.817707|
|Bombali|-0.17604|0.02651|0.681147|1.051766|
|Kenema|-0.33648|-0.09471|0.356268|0.790145|
|Port Loko|0.227008|-0.00678|1.892117|0.978709|
|Western Area Urban|0.198976|0.093012|0.999461|0.999398|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.698917||||
|P-value<br>_Source_: Authors’ calculations|0.904415<br>from CIMS (2020)||||

45
||Short-term ECT-R1 - Main<br>|source of income increased or st<br>|ayed same<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.112985|0.026034|0.961657|0.990399|
|Age|0.059943|0.010385|0.78753|0.705493|
|Household dependency|||||
|ratio|0.11486|0.114783|0.723936|0.696763|
|Household head|0.127472|-0.02303|0.9636|1.005808|
|Completed Secondary|||||
|School|-0.03293|0.00963|0.97366|1.010351|
|Main income source:|||||
|Business|0.310788|0.026403|0.749839|0.981168|
|Main income source:|||||
|Wage/Salary|-0.3121|0.029918|0.551329|1.044856|
|Main income source:|||||
|Other|-0.02787|-0.05754|0.851761|0.685269|
|Asset score|-0.38692|-0.13456|0.577666|0.844283|
|Bombali|-0.20556|0.022463|0.627169|1.044344|
|Kenema|-0.33023|-0.10418|0.365475|0.769362|
|Port Loko|0.233113|-0.00469|1.910482|0.985425|
|Western Area Urban|0.219652|0.101067|0.991745|0.996824|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.824087||||
|P-value<br>_Source_: Authors’ calculations|0.898288<br>from CIMS (2020)||||

46
||Short-term ECT-R1 - Mai<br>|n source of income decreased or<br>|stopped<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.112985|0.026034|0.961657|0.990399|
|Age|0.059943|0.010385|0.78753|0.705493|
|Household dependency|||||
|ratio|0.11486|0.114783|0.723936|0.696763|
|Household head|0.127472|-0.02303|0.9636|1.005808|
|Completed Secondary|||||
|School|-0.03293|0.00963|0.97366|1.010351|
|Main income source:|||||
|Business|0.310788|0.026403|0.749839|0.981168|
|Main income source:|||||
|Wage/Salary|-0.3121|0.029918|0.551329|1.044856|
|Main income source:|||||
|Other|-0.02787|-0.05754|0.851761|0.685269|
|Asset score|-0.38692|-0.13456|0.577666|0.844283|
|Bombali|-0.20556|0.022463|0.627169|1.044344|
|Kenema|-0.33023|-0.10418|0.365475|0.769362|
|Port Loko|0.233113|-0.00469|1.910482|0.985425|
|Western Area Urban|0.219652|0.101067|0.991745|0.996824|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.824087||||
|P-value<br>_Source_: Authors’ calculations|0.898288<br>from CIMS (2020)||||

47
|S|hort-term ECT-R1 - Other so<br>|urce of income increased or stay<br>|ed the same<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.138687|0.011981|0.954945|0.996544|
|Age|0.033693|-0.02077|0.739206|0.666668|
|Household dependency|||||
|ratio|0.184073|0.056575|1.067173|0.821725|
|Household head|0.181898|0.001425|0.95491|1.00005|
|Completed Secondary|||||
|School|-0.07875|0.060682|0.931204|1.053737|
|Main income source:|||||
|Business|0.410442|-0.0036|0.743303|1.002055|
|Main income source:|||||
|Wage/Salary|-0.39976|0.067388|0.487607|1.090169|
|Main income source:|||||
|Other|-0.00421|-0.05723|0.981152|0.661064|
|Asset score|-0.52842|-0.14462|0.512284|0.853838|
|Bombali|-0.24007|0.078601|0.608169|1.13886|
|Kenema|-0.38125|-0.19002|0.324545|0.599926|
|Port Loko|0.192007|-0.01153|1.676343|0.966917|
|Western Area Urban|0.328335|0.139717|1.034558|1.015501|
||Overidentification test fo|r covariate balance|||
|Chi-square|8.124636||||
|P-value<br>_Source_: Authors’ calculations|0.882732<br>from CIMS (2020)||||

48
||Short-term ECT-R1 - Other source of income decreased or<br> <br>|stopped<br>||
|---|---|---|---|
||Standardized<br>differences- Raw<br>Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.138687<br>0.011981|0.954945|0.996544|
|Age|0.033693<br>-0.02077|0.739206|0.666668|
|Household dependency||||
|ratio|0.184073<br>0.056575|1.067173|0.821725|
|Household head|0.181898<br>0.001425|0.95491|1.00005|
|Completed Secondary<br>School|-0.07875<br>0.060682|0.931204|1.053737|
|Main income source:||||
|Business|0.410442<br>-0.0036|0.743303|1.002055|
|Main income source:||||
|Wage/Salary|-0.39976<br>0.067388|0.487607|1.090169|
|Main income source:||||
|Other|-0.00421<br>-0.05723|0.981152|0.661064|
|Asset score|-0.52842<br>-0.14462|0.512284|0.853838|
|Bombali|-0.24007<br>0.078601|0.608169|1.13886|
|Kenema|-0.38125<br>-0.19002|0.324545|0.599926|
|Port Loko|0.192007<br>-0.01153|1.676343|0.966917|
|Western Area Urban|0.328335<br>0.139717|1.034558|1.015501|
||Overidentification test for covariate balance|||
|Chi-square|8.124636|||
|P-value|0.882732|||
|_Source_: Authors’ calculations|from CIMS (2020)|||

49
||Short-term ECT-|R1 - Inability to buy staple foods||
|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted<br>Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.141445|0.181159<br>0.957962|0.913636|
|Age|0.0605|0.231494<br>0.601474|0.603174|
|Household dependency||||
|ratio|0.486281|0.163569<br>1.670146|0.962864|
|Household head|0.158556|-0.01786<br>0.929527|1.007684|
|Completed Secondary<br>School|-0.06725|-0.17698<br>0.947407|0.806425|
|Main income source:||||
|Business|0.482997|0.029173<br>0.632474|0.98426|
|Main income source:||||
|Wage/Salary|-0.50698|-0.01256<br>0.360587|0.984655|
|Main income source:||||
|Other|-0.60679|-0.14727<br>0.356585|0.578599|
|Asset score|0.200524|0.166385<br>1.53369|1.401642|
|Bombali|-0.23662|-0.103<br>0.491629|0.756605|
|Kenema|0.164866|0.070931<br>1.679337|1.25162|
|Port Loko|0.020986|-0.07167<br>1.0145|1.000133|
|Western A|rea Urban<br>Overidentification test fo|r covariate balance||
|Chi-square|5.734646|||
|P-value<br>_Source_: Authors’ calculations|0.955279<br>from CIMS (2020)|||

50
||Short-term ECT-R1 - Able t<br>|o financiallysupport child's retur<br>|n to school<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.144492|0.197026|1.007653|0.97411|
|Age|0.232264|-0.0301|0.755094|0.618669|
|Household dependency|||||
|ratio|0.395131|0.014127|1.270649|0.72094|
|Household head|0.312932|-0.09522|0.804875|1.036004|
|Completed Secondary<br>School|-0.3179|-0.07419|0.717916|0.937632|
|Main income source:|||||
|Business|0.446019|-0.02242|0.69752|1.009794|
|Main income source:|||||
|Wage/Salary|-0.37333|-0.02929|0.551646|0.965149|
|Main income source:|||||
|Other|-0.08171|-0.01847|0.577404|0.891167|
|Asset score|-0.20761|-0.09643|0.645923|0.629428|
|Bombali|-0.12124|0.047648|0.788359|1.088685|
|Kenema|-0.05569|-0.05387|0.871798|0.863822|
|Port Loko|0.142204|-0.04285|1.492264|0.873241|
|Western Area Urban|-0.01466|0.111592|1.010608|1.004693|
||Overidentification test fo|r covariate balance|||
|Chi-square|8.771452||||
|P-value|0.845407||||
|_Source_: Authors’ calculations|from CIMS (2020)||||

51
||Medium-term ECT-R1 -<br>|Worked last week<br>|||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.325107|0.007003|0.948769|1.000568|
|Age|-0.07059|-0.04472|0.642092|0.696045|
|Household dependency|||||
|ratio|0.23065|0.184583|0.97709|0.880408|
|Household head|-0.05253|-0.02222|1.036986|1.011144|
|Completed Secondary<br>School|-0.22285|0.035711|0.837987|1.021944|
|Main income source:|||||
|Business|0.510121|0.076759|0.695971|0.97134|
|Main income source:|||||
|Wage/Salary|-0.5132|-0.03366|0.440884|0.966429|
|Main income source:|||||
|Other|-0.13828|-0.12328|0.393588|0.427012|
|Asset score|-0.45839|-0.15991|0.477536|0.713749|
|Bombali|-0.09423|5.76E-05|0.790404|1.000385|
|Kenema|-0.228|-0.15207|0.487973|0.630465|
|Port Loko|0.254439|0.028066|2.042123|1.093537|
|Western Area Urban|0.320215|0.085345|1.014534|1.015124|
||Overidentification test fo|r covariate balance|||
|Chi-square|5.267087||||
|P-value<br>_Source_: Authors’ calculations|0.981737<br>from CIMS (2020)||||

52
||Medium-term E<br>|CT-R1 - Hours worked last week<br>|<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.325107|0.007003|0.948769|1.000568|
|Age|-0.07059|-0.04472|0.642092|0.696045|
|Household dependency|||||
|ratio|0.23065|0.184583|0.97709|0.880408|
|Household head|-0.05253|-0.02222|1.036986|1.011144|
|Completed Secondary<br>School|-0.22285|0.035711|0.837987|1.021944|
|Main income source:|||||
|Business|0.510121|0.076759|0.695971|0.97134|
|Main income source:|||||
|Wage/Salary|-0.5132|-0.03366|0.440884|0.966429|
|Main income source:|||||
|Other|-0.13828|-0.12328|0.393588|0.427012|
|Asset score|-0.45839|-0.15991|0.477536|0.713749|
|Bombali|-0.09423|5.76E-05|0.790404|1.000385|
|Kenema|-0.228|-0.15207|0.487973|0.630465|
|Port Loko|0.254439|0.028066|2.042123|1.093537|
|Western Area Urban|0.320215|0.085345|1.014534|1.015124|
||Overidentification test fo|r covariate balance|||
|Chi-square|5.267087||||
|P-value<br>_Source_: Authors’ calculations|0.981737<br>from CIMS (2020)||||

53
|Medium-term ECT-R1 - Main s<br>|ource of income increased or st<br>|ayed the same<br>||
|---|---|---|---|
|Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female<br>0.347752|-0.00649|0.947546|0.999685|
|Age<br>-0.05166|-0.02583|0.647642|0.716448|
|Household dependency||||
|ratio<br>0.265708|0.225829|0.970369|0.824953|
|Household head<br>-0.05489|-0.02432|1.03927|1.012513|
|Completed Secondary<br>School<br>-0.26668|0.023589|0.795508|1.015206|
|Main income source:||||
|Business<br>0.538904|0.083592|0.650224|0.963876|
|Main income source:||||
|Wage/Salary<br>-0.56407|0.000718|0.395336|1.000913|
|Main income source:||||
|Other<br>-0.14072|-0.12622|0.39268|0.422959|
|Asset score<br>-0.45786|-0.11116|0.479889|0.749331|
|Bombali<br>-0.08674|0.004311|0.808028|1.010608|
|Kenema<br>-0.21222|-0.16091|0.512958|0.60416|
|Port Loko<br>0.270219|0.034098|2.130724|1.114496|
|Western Area Urban<br>0.302118|0.098466|1.006715|1.012144|
|Overidentification test fo|r covariate balance|||
|Chi-square<br>6.364688||||
|P-value<br>0.956446<br>_Source_: Authors’ calculations from CIMS (2020)||||

54
||Medium-term ECT-R1 - M<br>|ain source of income decreased o<br>|r stopped<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.347752|-0.00649|0.947546|0.999685|
|Age|-0.05166|-0.02583|0.647642|0.716448|
|Household dependency|||||
|ratio|0.265708|0.225829|0.970369|0.824953|
|Household head|-0.05489|-0.02432|1.03927|1.012513|
|Completed Secondary<br>School|-0.26668|0.023589|0.795508|1.015206|
|Main income source:|||||
|Business|0.538904|0.083592|0.650224|0.963876|
|Main income source:|||||
|Wage/Salary|-0.56407|0.000718|0.395336|1.000913|
|Main income source:|||||
|Other|-0.14072|-0.12622|0.39268|0.422959|
|Asset score|-0.45786|-0.11116|0.479889|0.749331|
|Bombali|-0.08674|0.004311|0.808028|1.010608|
|Kenema|-0.21222|-0.16091|0.512958|0.60416|
|Port Loko|0.270219|0.034098|2.130724|1.114496|
|Western Area Urban|0.302118|0.098466|1.006715|1.012144|
||Overidentification test fo|r covariate balance|||
|Chi-square|6.364688||||
|P-value<br>_Source_: Authors’ calculations|0.956446<br>from CIMS (2020)||||

55
|Medium-term ECT-R1 - Other<br>|source of income increased or st<br>|ayed the same<br>||
|---|---|---|---|
|Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female<br>0.273857|0.027878|0.980656|1.001761|
|Age<br>0.025975|-0.00871|0.681612|0.670358|
|Household dependency||||
|ratio<br>0.225126|0.157866|1.028576|0.995633|
|Household head<br>0.096459|0.038705|0.959858|0.980652|
|Completed Secondary||||
|School<br>-0.28538|0.033886|0.790476|1.020189|
|Main income source:||||
|Business<br>0.553208|0.05126|0.705147|0.986437|
|Main income source:||||
|Wage/Salary<br>-0.58263|-0.03943|0.424025|0.965869|
|Asset score<br>-0.46745|-0.16371|0.497625|0.780001|
|Bombali<br>-0.07445|0.064321|0.846515|1.146496|
|Kenema<br>-0.29993|-0.17744|0.386978|0.596691|
|Port Loko<br>0.241668|0.029713|1.891039|1.091494|
|Western Area Urban<br>0.342601|0.062745|1.059769|1.020777|
|Overidentification test fo|r covariate balance|||
|Chi-square<br>3.017594||||
|P-value<br>0.99787<br>_Source_: Authors’ calculations from CIMS (2020)||||

56
|Medium-term ECT-R1 - Ot<br>|her source of income decreased<br>|or stopped<br>||
|---|---|---|---|
|Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female<br>0.273857|0.027878|0.980656|1.001761|
|Age<br>0.025975|-0.00871|0.681612|0.670358|
|Household dependency||||
|ratio<br>0.225126|0.157866|1.028576|0.995633|
|Household head<br>0.096459|0.038705|0.959858|0.980652|
|Completed Secondary||||
|School<br>-0.28538|0.033886|0.790476|1.020189|
|Main income source:||||
|Business<br>0.553208|0.05126|0.705147|0.986437|
|Main income source:||||
|Wage/Salary<br>-0.58263|-0.03943|0.424025|0.965869|
|Asset score<br>-0.46745|-0.16371|0.497625|0.780001|
|Bombali<br>-0.07445|0.064321|0.846515|1.146496|
|Kenema<br>-0.29993|-0.17744|0.386978|0.596691|
|Port Loko<br>0.241668|0.029713|1.891039|1.091494|
|Western Area Urban<br>0.342601|0.062745|1.059769|1.020777|
|Overidentification test fo|r covariate balance|||
|Chi-square<br>3.017594||||
|P-value<br>0.99787||||
|_Source_: Authors’ calculations from CIMS (2020)||||

57
||Medium-term EC<br>|T-R1 - Inabilityto buystaple food<br>|s<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.270454|-0.1971|0.941497|0.97275|
|Age|0.006313|0.126964|0.783657|0.906111|
|Household dependency|||||
|ratio|0.487109|0.303494|0.751922|0.408769|
|Household head|0.212845|0.060917|0.874476|0.966316|
|Completed Secondary|||||
|School|-0.26632|0.150894|0.762734|1.100262|
|Main income source:|||||
|Business|0.662185|0.034682|0.576618|0.988823|
|Main income source:|||||
|Wage/Salary|-0.4893|0.130671|0.457093|1.119184|
|Main income source:|||||
|Other|-0.09739|-0.15081|0.611851|0.404139|
|Asset score|-0.21558|-0.09474|0.633631|0.676225|
|Bombali|-0.08121|0.171797|0.818628|1.423689|
|Kenema|-0.36662|-0.11399|0.244446|0.7231|
|Port Loko|-0.00249|0.122602|1.001481|1.526815|
|Western Area Urban|0.580684|-0.12337|0.788946|0.982667|
||Overidentification test fo|r covariate balance|||
|Chi-square|16.11727||||
|P-value<br>_Source_: Authors’ calculations|0.306265<br>from CIMS (2020)||||

58
||Medium-term ECT-<br>|R1 - Child identified as malnouris<br>|hed<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.198341|0.130961|1.010053|0.983851|
|Age|0.293792|0.491933|0.68682|0.900374|
|Household dependency|||||
|ratio|0.554716|0.297804|2.493232|1.36491|
|Household head|0.109697|-0.02343|0.992767|1.00874|
|Completed Secondary|||||
|School|-0.2917|-0.07643|0.821139|0.953858|
|Main income source:|||||
|Business|0.851924|0.143018|0.454255|0.951622|
|Main income source:|||||
|Wage/Salary|-0.72242|0.006718|0.340264|1.006764|
|Asset score|-0.65286|-0.04445|0.332041|0.578509|
|Bombali|0.205946|0.266292|1.509115|1.538423|
|Port Loko|0.316544|0.049909|3.101695|1.20894|
|Western Area Urban|0.0645011|-0.21293|1.04224|0.927701|
||Overidentification test fo|r covariate balance|||
|Chi-square|1.826647||||
|P-value|0.999629||||
|_Source_: Authors’ calculations|from CIMS (2020)||||

59
||Medium-term ECT-R2 -<br>|Worked last week<br>|||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.365174|-0.01629|0.824682|1.003837|
|Age|0.111013|0.090337|0.870673|1.052575|
|Household dependency|||||
|ratio|0.194286|-0.1805|3.234074|1.336616|
|Household head|0.001935|-0.03197|1.022384|1.01169|
|Completed Secondary|||||
|School|-0.16927|0.055904|0.826611|1.056511|
|Main income source:|||||
|Business|0.254888|-0.17184|0.825316|1.073115|
|Main income source:|||||
|Wage/Salary|-0.23946|0.068772|0.663537|1.096681|
|Asset score|-0.31364|0.007725|0.67199|0.911279|
|Bombali|-0.13839|0.267097|0.740313|1.52662|
|Kenema|-0.1917|0.026184|0.613047|1.058022|
|Port Loko|0.128681|-0.03446|1.52749|0.883245|
|Western Area Urban|0.254694|-0.16893|0.959249|0.977487|
||Overidentification test fo|r covariate balance|||
|Chi-square|8.025189||||
|P-value|0.841955||||
|_Source_: Authors’ calculations|from CIMS (2020)||||

60
||Medium-term E<br>|CT-R2 - Hours worked last week<br>|<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.365174|-0.01629|0.824682|1.003837|
|Age|0.111013|0.090337|0.870673|1.052575|
|Household dependency|||||
|ratio|0.194286|-0.1805|3.234074|1.336616|
|Household head|0.001935|-0.03197|1.022384|1.01169|
|Completed Secondary|||||
|School|-0.16927|0.055904|0.826611|1.056511|
|Main income source:|||||
|Business|0.254888|-0.17184|0.825316|1.073115|
|Main income source:|||||
|Wage/Salary|-0.23946|0.068772|0.663537|1.096681|
|Asset score|-0.31364|0.007725|0.67199|0.911279|
|Bombali|-0.13839|0.267097|0.740313|1.52662|
|Kenema|-0.1917|0.026184|0.613047|1.058022|
|Port Loko|0.128681|-0.03446|1.52749|0.883245|
|Western Area Urban|0.254694|-0.16893|0.959249|0.977487|
||Overidentification test fo|r covariate balance|||
|Chi-square|8.025189||||
|P-value<br>_Source_: Authors’ calculations|0.841955<br>from CIMS (2020)||||

61
|Medium-term ECT-R2 - Main s<br>|ource of income increased or sta<br>|yed the same<br>||
|---|---|---|---|
|Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female<br>0.411515|0.039543|0.800282|0.9889|
|Age<br>0.110411|0.200938|0.866374|1.035344|
|Household dependency||||
|ratio<br>0.150199|-0.10502|2.640978|1.276105|
|Household head<br>0.002645|0.060737|1.023317|0.970422|
|Completed Secondary||||
|School<br>-0.20963|-0.0349|0.777|0.961766|
|Main income source:||||
|Business<br>0.310255|-0.12346|0.749441|1.070476|
|Main income source:||||
|Wage/Salary<br>-0.2391|0.114025|0.676103|1.150042|
|Asset score<br>-0.30093|0.095959|0.68808|0.970241|
|Bombali<br>-0.21232|0.176645|0.597337|1.361031|
|Kenema<br>-0.16997|0.016914|0.653593|1.037888|
|Port Loko<br>0.145959|-0.05208|1.602414|0.825917|
|Western Area Urban<br>0.25594|-0.10566|0.953631|0.995148|
|Overidentification test fo|r covariate balance|||
|Chi-square<br>5.687832||||
|P-value<br>0.95678<br>_Source_: Authors’ calculations from CIMS (2020)||||

62
||Medium-term ECT-R2 - M<br>|ain source of income decreased o<br>|r stopped<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.411515|0.039543|0.800282|0.9889|
|Age|0.110411|0.200938|0.866374|1.035344|
|Household dependency|||||
|ratio|0.150199|-0.10502|2.640978|1.276105|
|Household head|0.002645|0.060737|1.023317|0.970422|
|Completed Secondary|||||
|School|-0.20963|-0.0349|0.777|0.961766|
|Main income source:|||||
|Business|0.310255|-0.12346|0.749441|1.070476|
|Main income source:|||||
|Wage/Salary|-0.2391|0.114025|0.676103|1.150042|
|Asset score|-0.30093|0.095959|0.68808|0.970241|
|Bombali|-0.21232|0.176645|0.597337|1.361031|
|Kenema|-0.16997|0.016914|0.653593|1.037888|
|Port Loko|0.145959|-0.05208|1.602414|0.825917|
|Western Area Urban|0.25594|-0.10566|0.953631|0.995148|
||Overidentification test fo|r covariate balance|||
|Chi-square|5.687832||||
|P-value|0.95678||||
|_Source_: Authors’ calculations|from CIMS (2020)||||

63
|Medium-term ECT-R2 - Other<br>|source of income increased or st<br>|ayed the same<br>||
|---|---|---|---|
|Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female<br>0.513653|0.014403|0.73864|0.997515|
|Age<br>0.067122|0.161388|0.917287|0.859361|
|Household dependency||||
|ratio<br>-0.314|-0.24329|0.664501|0.637824|
|Household head<br>-0.02505|0.173392|1.045448|0.909966|
|Completed Secondary||||
|School<br>-0.22348|-0.16097|0.78504|0.825713|
|Main income source:||||
|Business<br>0.319282|-0.04108|0.836893|1.014228|
|Main income source:||||
|Wage/Salary<br>-0.21222|0.101187|0.74316|1.12261|
|Asset score<br>-0.10245|0.04081|0.815513|0.92257|
|Bombali<br>-0.24542|0.128724|0.560097|1.251391|
|Kenema<br>-0.09843|-0.0285|0.834379|0.94338|
|Port Loko<br>0.144247|-0.1404|1.566493|0.580967|
|Western Area Urban<br>0.393254|0.137241|0.944385|0.994477|
|Overidentification test fo|r covariate balance|||
|Chi-square<br>2.548511||||
|P-value<br>0.999134<br>_Source_: Authors’ calculations from CIMS (2020)||||

64
|Medium-term ECT-R2 - Ot<br>|her source of income decreased<br>|or stopped<br>||
|---|---|---|---|
|Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female<br>0.513653|0.014403|0.73864|0.997515|
|Age<br>0.067122|0.161388|0.917287|0.859361|
|Household dependency||||
|ratio<br>-0.314|-0.24329|0.664501|0.637824|
|Household head<br>-0.02505|0.173392|1.045448|0.909966|
|Completed Secondary||||
|School<br>-0.22348|-0.16097|0.78504|0.825713|
|Main income source:||||
|Business<br>0.319282|-0.04108|0.836893|1.014228|
|Main income source:||||
|Wage/Salary<br>-0.21222|0.101187|0.74316|1.12261|
|Asset score<br>-0.10245|0.04081|0.815513|0.92257|
|Bombali<br>-0.24542|0.128724|0.560097|1.251391|
|Kenema<br>-0.09843|-0.0285|0.834379|0.94338|
|Port Loko<br>0.144247|-0.1404|1.566493|0.580967|
|Western Area Urban<br>0.393254|0.137241|0.944385|0.994477|
|Overidentification test fo|r covariate balance|||
|Chi-square<br>2.548511||||
|P-value<br>0.999134||||
|_Source_: Authors’ calculations from CIMS (2020)||||

65
||Medium-term EC<br>|T-R2 - Inability to buy staple food<br>|s<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.199451|0.03436|0.941202|0.990411|
|Age|0.018971|0.047896|0.651603|0.580698|
|Household dependency|||||
|ratio|0.266639|0.100594|1.109815|0.81727|
|Household head|-0.24005|0.123391|1.033034|0.955766|
|Completed Secondary|||||
|School|-0.10596|0.176796|0.886679|1.191172|
|Main income source:|||||
|Business|0.363563|0.069144|0.787511|0.968387|
|Main income source:|||||
|Wage/Salary|-0.25489|-0.16381|0.692152|0.789024|
|Asset score|-0.31039|-0.08586|0.696983|0.944909|
|Bombali|-0.23111|0.145755|0.564162|1.298586|
|Kenema|-0.11412|0.005173|0.810597|1.009557|
|Port Loko|0.065539|-0.01225|1.288094|0.954945|
|Western Area Urban|0.297173|-0.00477|0.931013|1.000342|
||Overidentification test fo|r covariate balance|||
|Chi-square|6.084817||||
|P-value<br>_Source_: Authors’ calculations|0.943011<br>from CIMS (2020)||||

66
||Medium-term ECT-<br>|R2 - Child identifies as malnouris<br>|hed<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.437432|0.331687|0.857061|0.863702|
|Age|0.070508|0.24601|0.568777|0.672155|
|Household dependency|||||
|ratio|0.187255|0.089587|1.630749|1.202679|
|Household head|-0.27311|-0.2004|1.068523|1.02474|
|Completed Secondary|||||
|School|-0.37063|-0.15487|0.656425|0.851183|
|Main income source:|||||
|Business|0.640397|0.301112|0.684208|0.871224|
|Main income source:|||||
|Wage/Salary|-0.63745|-0.285|0.357599|0.699253|
|Asset score|-0.32767|-0.02131|0.57054|0.809347|
|Bombali|-0.41243|-0.12747|0.299632|0.751353|
|Kenema|-0.1991|-0.07075|0.630943|0.853942|
|Port Loko|0.351471|0.024098|3.814846|1.110667|
|Western Area Urban|0.345241|0.177521|0.921881|0.958517|
||Overidentification test fo|r covariate balance|||
|Chi-square|2.725001||||
|P-value|0.998758||||
|_Source_: Authors’ calculations|from CIMS (2020)||||

67
||Medium-term ECT-R2 -<br>|Childgiven Vitamin A suppleme<br>|ntation<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.42627|0.247279|0.841892|0.910028|
|Age|-0.00046|0.195852|0.470924|0.489853|
|Household dependency|||||
|ratio|0.386636|0.080619|2.099085|0.943181|
|Household head|-0.09371|-0.09919|1.053348|1.029306|
|Completed Secondary|||||
|School|-0.07242|-0.20546|0.952315|0.781106|
|Main income source:|||||
|Business|0.599141|0.221124|0.620482|0.883014|
|Main income source:|||||
|Wage/Salary|-0.44544|-0.13376|0.481014|0.839602|
|Asset score|-0.20454|-0.03295|1.16488|1.298996|
|Bombali|-0.52886|-0.20075|0.174967|0.636225|
|Kenema|-0.17371|-0.09016|0.700147|0.829298|
|Port Loko|0.322578|-0.02005|2.959776|0.922085|
|Western Area Urban|0.368979|0.072075|0.962162|1.003674|
||Overidentification test fo|r covariate balance|||
|Chi-square|5.623444||||
|P-value|0.958791||||
|_Source_: Authors’ calculations|from CIMS (2020)||||

68
||Pooled ECT-R1<br>|and ECT-R2 - Worked last week<br> <br>||
|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted<br>Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.317476|0.055515<br>0.933596|0.996481|
|Age|0.03099|-0.01329<br>0.771258|0.790843|
|Household dependency||||
|ratio|0.195185|0.002332<br>0.958411|0.823199|
|Household head|0.03448|-0.02096<br>0.992484|1.007795|
|Completed Secondary<br>School|-0.1685|-0.02919<br>0.86412|0.977169|
|Main income source:<br>Business|0.512727|-0.03453<br>0.751549|1.004984|
|Main income source:||||
|Wage/Salary|-0.39952|0.065452<br>0.521977|1.072577|
|Main income source:||||
|Other|-0.10614|-0.1117<br>0.512448|0.469018|
|Asset score|-0.37695|-0.07308<br>0.525677|0.775805|
|Bombali|-0.17486|0.161954<br>0.677803|1.310566|
|Kenema|-0.30823|-0.02235<br>0.427671|0.953302|
|Port Loko|0.030698|-0.04316<br>1.078924|0.901675|
|Western Area Urban|0.329879|-0.00861<br>1.033772|0.997171|
||Overidentification test fo|r covariate balance||
|Chi-square|7.146145|||
|P-value<br>_Source_: Authors’ calculations|0.928931<br>from CIMS (2020)|||

69
||Pooled ECT-R1 an<br>|d ECT-R2 - Hours worked last we<br>|ek<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.317476|0.055515|0.933596|0.996481|
|Age|0.03099|-0.01329|0.771258|0.790843|
|Household dependency|||||
|ratio|0.195185|0.002332|0.958411|0.823199|
|Household head|0.03448|-0.02096|0.992484|1.007795|
|Completed Secondary|||||
|School|-0.1685|-0.02919|0.86412|0.977169|
|Main income source:<br>Business|0.512727|-0.03453|0.751549|1.004984|
|Main income source:|||||
|Wage/Salary|-0.39952|0.065452|0.521977|1.072577|
|Main income source:|||||
|Other|-0.10614|-0.1117|0.512448|0.469018|
|Asset score|-0.37695|-0.07308|0.525677|0.775805|
|Bombali|-0.17486|0.161954|0.677803|1.310566|
|Kenema|-0.30823|-0.02235|0.427671|0.953302|
|Port Loko|0.030698|-0.04316|1.078924|0.901675|
|Western Area Urban|0.329879|-0.00861|1.033772|0.997171|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.146145||||
|P-value<br>_Source_: Authors’ calculations|0.928931<br>from CIMS (2020)||||

70
|Poole|d ECT-R1 and ECT-R2 - Mai<br>|n source of income increased or<br>|stayed the same<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.32657|0.065189|0.924584|0.99414|
|Age|0.052608|0.021551|0.775216|0.800541|
|Household dependency|||||
|ratio|0.217035|0.01807|0.944594|0.778084|
|Household head|0.046006|0.012142|0.987015|0.99501|
|Completed Secondary|||||
|School|-0.20087|-0.05122|0.835909|0.959453|
|Main income source:|||||
|Business|0.543058|-0.02729|0.712085|1.005178|
|Main income source:|||||
|Wage/Salary|-0.39996|0.099109|0.52847|1.10455|
|Main income source:|||||
|Other|-0.10576|-0.11615|0.517616|0.454917|
|Asset score|-0.36986|-0.03777|0.53682|0.819484|
|Bombali|-0.22022|0.144175|0.592974|1.283515|
|Kenema|-0.29812|-0.02153|0.441624|0.954747|
|Port Loko|0.046599|-0.03521|1.117964|0.919109|
|Western Area Urban|0.34925|0.013336|1.021568|1.003815|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.132129||||
|P-value<br>_Source_: Authors’ calculations|0.929498<br>from CIMS (2020)||||

71
|P|ooled ECT-R1 and ECT-R2 -<br>|Main source of income decrease<br>|d or stopped<br>||
|---|---|---|---|---|
||Standardized|Standardized differences-|Variance|Variance Ratio-|
||differences- Raw|Weighted|Ratio- Raw|Weighted|
|Female|0.32657|0.065189|0.924584|0.99414|
|Age|0.052608|0.021551|0.775216|0.800541|
|Household dependency|||||
|ratio|0.217035|0.01807|0.944594|0.778084|
|Household head|0.046006|0.012142|0.987015|0.99501|
|Completed Secondary<br>School|-0.20087|-0.05122|0.835909|0.959453|
|Main income source:|||||
|Business|0.543058|-0.02729|0.712085|1.005178|
|Main income source:|||||
|Wage/Salary|-0.39996|0.099109|0.52847|1.10455|
|Main income source:|||||
|Other|-0.10576|-0.11615|0.517616|0.454917|
|Asset score|-0.36986|-0.03777|0.53682|0.819484|
|Bombali|-0.22022|0.144175|0.592974|1.283515|
|Kenema|-0.29812|-0.02153|0.441624|0.954747|
|Port Loko|0.046599|-0.03521|1.117964|0.919109|
|Western Area Urban|0.34925|0.013336|1.021568|1.003815|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.132129||||
|P-value<br>_Source_: Authors’ calculation|0.929498<br>s from CIMS (2020)||||

72
|Poole|d ECT-R1 and ECT-R2 - Oth<br>|er source of income increased or<br>|stayed the same<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.363793|0.065311|0.916764|0.993696|
|Age|-0.00601|0.019239|0.735458|0.770118|
|Household dependency|||||
|ratio|0.207948|-0.06378|1.135738|0.877051|
|Household head|0.069648|0.011191|0.981626|0.996367|
|Completed Secondary|||||
|School|-0.21384|-0.06615|0.832673|0.948934|
|Main income source:|||||
|Business|0.593785|-0.0395|0.75769|1.001865|
|Main income source:|||||
|Wage/Salary|-0.45342|0.12658|0.496658|1.122887|
|Main income source:|||||
|Other|-0.09614|-0.10607|0.553317|0.48674|
|Asset score|-0.41852|-0.05135|0.502444|0.755212|
|Bombali|-0.22384|0.223223|0.61846|1.386332|
|Kenema|-0.30075|-0.04809|0.449715|0.89958|
|Port Loko|-0.02262|-0.06539|0.960168|0.864506|
|Western Area Urban|0.413181|-0.02067|1.059287|0.991409|
||Overidentification test fo|r covariate balance|||
|Chi-square|7.904167||||
|P-value<br>_Source_: Authors’ calculations|0.894259<br>from CIMS (2020)||||

73
|Pooled ECT-R1 and ECT-R2 -<br>|Other source of income decrease<br>|d or stopped<br>||
|---|---|---|---|
|Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female<br>0.363793|0.065311|0.916764|0.993696|
|Age<br>-0.00601|0.019239|0.735458|0.770118|
|Household dependency||||
|ratio<br>0.207948|-0.06378|1.135738|0.877051|
|Household head<br>0.069648|0.011191|0.981626|0.996367|
|Completed Secondary<br>School<br>-0.21384|-0.06615|0.832673|0.948934|
|Main income source:||||
|Business<br>0.593785|-0.0395|0.75769|1.001865|
|Main income source:||||
|Wage/Salary<br>-0.45342|0.12658|0.496658|1.122887|
|Main income source:||||
|Other<br>-0.09614|-0.10607|0.553317|0.48674|
|Asset score<br>-0.41852|-0.05135|0.502444|0.755212|
|Bombali<br>-0.22384|0.223223|0.61846|1.386332|
|Kenema<br>-0.30075|-0.04809|0.449715|0.89958|
|Port Loko<br>-0.02262|-0.06539|0.960168|0.864506|
|Western Area Urban<br>0.413181|-0.02067|1.059287|0.991409|
|Overidentification test fo|r covariate balance|||
|Chi-square<br>7.904167||||
|P-value<br>0.894259<br>_Source_: Authors’ calculations from CIMS (2020)||||

74
||Pooled ECT-R1 and<br>|ECT-R2 - Inability to buy staple fo<br>|ods<br>||
|---|---|---|---|---|
||Standardized<br>differences- Raw|Standardized differences-<br>Weighted|Variance<br>Ratio- Raw|Variance Ratio-<br>Weighted|
|Female|0.273111|0.184681|0.940942|0.956988|
|Age|0.112318|0.087058|0.677163|0.693254|
|Household dependency|||||
|ratio|0.477583|0.097305|1.558982|0.843397|
|Household head|0.014814|-0.09048|1.003843|1.025577|
|Completed Secondary|||||
|School|-0.205|-0.10764|0.828928|0.907788|
|Main income source:|||||
|Business|0.578807|0.039674|0.687818|0.991156|
|Main income source:|||||
|Wage/Salary|-0.49227|-0.06195|0.4638|0.936657|
|Asset score|-0.52741|-0.1024|0.439546|0.683311|
|Bombali|-0.01795|0.070634|0.973481|1.141096|
|Kenema|-0.17326|-0.08697|0.657662|0.816611|
|Port Loko|-0.05307|0.094355|0.880773|1.234011|
|Western Area Urban|0.254263|0.020447|1.00754|1.003648|
||Overidentification test fo|r covariate balance|||
|Chi-square|5.716287||||
|P-value<br>_Source_: Authors’ calculations|0.955872<br>from CIMS (2020)||||

75