Assessment of Idiosyncratic Income Shocks and Food Insecurity during the Covid-19 Pandemic in Mozambique: An Endogenous Ordered Probit Model

Variables	Mean	Std. dev.	Min	Max
Total income (Mozambican metrical)	22 277.30	34 274.52	0	465 998.3
Provinces
Cabo Delgado	0.0898	0.2859	0	1
Gaza	0.1082	0.3106	0	1
Inhambane	0.0994	0.2992	0	1
Manica	0.0989	0.2985	0	1
Maputo	0.1245	0.3302	0	1
Nampula	0.1089	0.3116	0	1
Niassa	0.0928	0.2902	0	1
Sofala	0.0941	0.2920	0	1
Tete	0.0848	0.2786	0	1
Zambezia	0.0986	0.2982	0	1
Education dummy
Formal education	0.7937	0.4047	0	1
Gender of households’ heads
Household gender	0.7597	0.4273	0	1
Age group (years)
< 35	0.6367	0.4810	0	1
35 < 49	0.2443	0.4297	0	1
49 < 70	0.1089	0.3116	0	1
≥ 70	0.0101	0.0998	0	1
Housing characteristics
Improved toilet	0.6481	0.4776	0	1
Improved water	0.9059	0.2920	0	1
Improved electricity	0.8797	0.3253	0	1

[i] Source: computed from the DIEM (Waves 2 and 3) data.

Table 2.

Respondents’ production activities, social assistance, and coping methods during the COVID-19 pandemic

Variables	Mean	Std Dev.	Min	Max
Agricultural Activities
None	0.3384	0.4732	0	1
Both Crop and Livestock	0.1809	0.3850	0	1
Crop	0.3595	0.4799	0	1
Livestock	0.1213	0.3265	0	1
Social assistances received
Food	0.0272	0.1626	0	1
Cash vouchers	0.0045	0.0671	0	1
Seeds	0.0035	0.0592	0	1
Extension services	0.0030	0.0549	0	1
Livestock feed	0.0010	0.0317	0	1
Other needs	0.0206	0.1422	0	1
Households’ coping method with food problems
Begged food	0.1006	0.3009	0	1

[i] Source: computed from the DIEM (waves 2 and 3) data.

Distribution of respondents across the number of shocks and food insecurity status
Source: authors’ computations from DIEM (waves 2 and 3) data.

Food insecurity status across the Mozambican provinces
Source: authors’ computations from DIEM (waves 2 and 3) data.

Distribution of households’ food security status across their main livelihood sources
Source: authors’ computations from DIEM (waves 2 and 3) data.

Table 3.

Determinants of Income, COVID-19 Problem and Food Insecurity

Variables	Income shocks		Income		Food insecurity
Variables	coefficient	t stat	coefficient	t stat	coefficient	t stat
Provinces (Cabo Delgado is reference)
Gaza	−0.1598	−1.65^*	−10310.63	−4.19^***	−0.2990	−3.36^***
Inhambane	−0.2108	−2.19^**	−4939.85	−2.00^**	−0.2875	−3.32^***
Manica	−0.2435	−2.53^**	−2608.75	−1.06	−0.1586	−1.85^*
Maputo	−0.2741	−2.95^***	−6766.97	−2.85^***	−0.1083	−1.26
Nampule	−0.0321	−0.34	−4212.97	−1.76^*	−0.1039	−1.25
Niassa	−0.2360	−2.40^**	6053.72	2.42^**	−0.1294	−1.08
Sofala	−0.1204	−1.24	−7178.09	−2.89^***	−0.1006	−1.09
Tete	−0.1565	−1.57	2887.34	1.13	−0.2317	−2.07^**
Zambezia	−0.0042	−0.04	−7592.77	−3.08^***	−0.3379	−4.07^***
Agric activities (none is the reference)
Crop and livestock	0.1872	3.09^***	8225.95	5.24^***	−0.1247	−1.28
Crop	0.2380	4.74^***	−21.27	−0.02	−0.1695	−3.47^***
Livestock	0.2501	3.64^***	2912.03	1.64^*	−0.1592	−2.15^**
Social assistances
Food	0.4789	3.46^***	676.65	0.21	−0.0483	−0.41
Cash vouchers	−0.0263	−0.08	962.81	0.12	−0.0108	−0.04
Seeds	0.1072	0.31	3776.64	0.42	−0.0972	−0.33
Extension services	−0.1871	−0.48	−4506.71	−0.46	0.2879	0.87
Livestock feed	−0.4909	−0.64	13598.14	0.81	−0.1460	−0.26
Others	0.1496	1.01	7577.65	2.04^**	0.0046	0.03
Third Wave	0.0081	0.19	−445.93	−0.41	−0.2001	−4.10^***
Gender	−0.0698	−1.43	5290.09	4.20^***	0.0303	0.51
Formal education	−0.0816	−1.52	3580.52	2.61^***	0.0309	0.56
Begged for assistance	0.2503	3.58^***	−9458.11	−5.35^***	0.4944	2.55^**
Improved water	0.0512	0.72	3352.10	1.82^*	0.0135	0.20
Improved toilet	−0.1065	−2.39^**	5897.56	5.05^***	−0.1251	−1.37
Shock exposure					0.9750	7.28^***
Total income					0.0001	−4.93^***
Covid problem index	0.1722	12.35^***	1374.13	4.21^***
Improved light			5559.19	3.33^***
Age group (reference is < 35)
35 < 49	0.1003	2.22^**
49 < 70	−0.0199	−0.31
≥ 70	−0.0324	−0.17
Constant	0.1286	1.09	6574.52	2.03^**
cut1					−1.1423
cut2					−0.8053
cut3					−0.3742
var(e.tot_income)					1090000000
corr(e.shock,e.efis)					−0.4877	−5.99^***
corr(e.tot_income,e.efis)					0.5906	3.5^***
corr(e.tot_income,e.shock)					−0.0739	−3.59^***

Significant at:

*** 1%,

** 5%,

* 10%.

Source: computed from the DIEM (waves 2 and 3) data.

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.17306/J.JARD.2024.00009R1 | Journal eISSN: 1899-5772 | Journal ISSN: 1899-5241

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Language: English

Page range: 437 - 450

Accepted on: Dec 2, 2024

Published on: Dec 31, 2024

Published by: Poznań University of Life Science

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

idiosyncratic shocks,

income,

food insecurity,

fragility,

social assistance programmes

Related subjects:

Business and economics,

Political economics,

Political economics, other,

Business management,

Business management, other,

Life sciences,

Life sciences, other,

Social sciences,

Political science,

Political science, other

© 2024 Thonaeng Charity Molelekoa, Abayomi Samuel Oyekale, published by Poznań University of Life Science
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 74 (2024): Issue 4 (December 2024)

Assessment of Idiosyncratic Income Shocks and Food Insecurity during the Covid-19 Pandemic in Mozambique: An Endogenous Ordered Probit Model

Figures & Tables

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Fig. 1.

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