The effects of COVID-19 on employment, labor markets, and gender equality in Central America

Allan Webster; Sangeeta Khorana; Francesco Pastore

doi:10.2478/izajodm-2022-0001

Table 1

Change in employment by country, firm size, and gender: full-time permanent employees compared to December 2019

	COVID follow-up: Round 1		COVID follow-up: Round 2
	All (%)	Female (%)	All (%)	Female (%)
El Salvador	–8.9	–9.1	–11.6	–7.0
Small	–11.9	–15.5	–48.4	–46.3
Medium	–3.3	–12.6	–10.9	–14.7
Large	–9.8	–7.8	–7.6	–3.1
Guatemala	–15.7	–25.6	–9.0	–16.7
Small	–22.7	–36.9	6.6	11.1
Medium	–30.9	–55.4	–31.0	–64.0
Large	–10.7	–16.0	–4.2	–8.6
Honduras	–21.9	–30.2	–15.2	–17.2
Small	–28.7	–25.8	–17.0	–7.5
Medium	–24.1	–34.4	–18.5	–19.4
Large	–18.5	–28.3	–12.1	–16.6
Nicaragua	–8.8	–9.2	–11.9	–23.0
Small	–10.6	–39.9	–9.3	6.8
Medium	–15.9	–16.2	–17.0	–41.9
Large	–6.2	–6.2	–10.8	–21.1
Full sample	–11.7	–14.3	–11.5	–12.8

1 Notes: *COVID follow-up Survey 1 (June–July 2020); follow-up Survey 2 (November 2020–January 2021).

2 COVID, coronavirus disease.

Table 2

Change in employment by sector and gender: full-time permanent employees compared to December 2019

	COVID follow-up: Round 1		COVID follow-up: Round 2
	All (%)	Female (%)	All (%)	Female (%)
Food	–12.1	–11.0	–9.8	–22.8
Tobacco	–5.0	–10.2	7.6	–4.8
Textiles	–12.8	–15.3	6.6	6.0
Garments	–20.4	–25.2	–10.3	–26.2
Leather	–15.7	–16.7	–4.0	–15.1
Wood	–14.0	–12.0	–10.8	–30.0
Paper	–1.9	–51.1	–2.8	–22.4
Publishing, printing	–16.3	–24.4	–24.8	–16.6
Chemicals	3.0	6.2	2.1	4.7
Plastics and rubber	2.8	2.4	8.5	6.1
Nonmetallic mineral products	–8.6	–28.6	–46.4	–93.5
Fabricated metal products	–39.3	–60.3	–20.0	–95.7
Machinery and equipment	–12.3	0.0	–21.0	–16.7
Electronics	0.0	0.0	23.7	19.1
Transport machinery	–18.0	0.0	–15.4	–8.0
Furniture	–20.6	–24.0	–18.5	–14.3
Construction	–18.6	–17.0	–24.8	10.7
Servicing of motor vehicles	–15.9	–24.0	–23.4	–5.7
Wholesale	–0.5	–14.5	–22.0	–13.0
Retail	–15.6	–17.0	–21.8	–52.2
Hotels, restaurants etc.	–41.0	–94.2	–26.0	–30.2
Transport services	–9.4	–16.8	–7.7	–25.0
IT services	–15.4	–17.8	–26.2	–20.5
All sectors	–11.7	–14.3	–11.5	–12.8

3 Notes:

4* COVID follow-up Survey Round 1 (June–July 2020); follow-up Survey Round 2 (November 2020–January 2021).

48 COVID, coronavirus disease; IT, information technology.

Table 3

Total weeks of temporary firm closures*

	Round 1 COVID-19 survey		Round 2 COVID-19 survey
	Average	Labor weeks	Average	Labor weeks
	Weeks closed	Lost**	Weeks closed	Lost**
A. By country and firm size
El Salvador	6.32	213598	2.89	102291
Small	7.23	20048	2.63	36601
Medium	5.22	24614	2.74	49116
Large	5.33	168936	3.57	16574
Guatemala	4.18	47756	2.92	55205
Small	5.03	4639	2.73	32310
Medium	3.78	13751	3.13	17205
Large	3.41	29366	3.04	5690
Honduras	7.81	46655
Small	9.31	7582
Medium	6.91	17548
Large	4.63	21525
Nicaragua	1.68	14350
Small	2.74	1723
Medium	1.05	2491
Large	0.83	10136
B. By sector*
Food	1.99	8091	3.64	26547
Tobacco	2.83	20696	3.69	25415
Textiles	8.33	34020	2.70	29913
Garments	8.13	102594	5.71	136
Leather	6.00	1315	3.80	671
Wood	4.13	290	0.00	0
Paper	3.80	665	2.50	3990
Publishing, printing	5.61	3603	1.47	2073
Chemicals	2.10	2895	3.60	652
Plastics and rubber	3.44	4770	2.65	2303
Non-metallic mineral products	5.44	6872	0.00	0
Fabricated metal products	7.47	6606	2.17	5705
Machinery and equipment	6.00	4893	1.14	1544
Electronics	6.00	18570	0.00	0
Transport machinery	7.20	1200	3.20	32
Furniture	9.60	11977	2.25	20465
Construction	6.66	8858	4.80	3449
Servicing of motor vehicles	4.34	10179	2.22	4879
Wholesale	3.55	20696	2.60	4750
Retail	5.41	40703	2.69	12239
Hotels, restaurants etc.	6.22	7942	3.06	1621
Transport services	2.85	3576	2.62	1672
IT services	7.60	1348	9.33	10940
Full sample	5.08	322359	2.89	158996

5 Notes:

6* El Salvador and Guatemala only at the second COVID survey (November 2020–January 2021).

7** Total weeks of full-time permanent employees.

49 COVID, coronavirus disease; IT, information technology.

Table 4

Workers furloughed as a % of full-time permanent employees*

	Round 1 COVID-19 survey		Round 2 COVID-19 survey
	All (%)	Female (%)	All (%)	Female (%)
A. By country and firm size
El Salvador	20.2	18.2	10.3	11.7
Small	26.9	23.4	6.9	7.4
Medium	11.3	9.1	16.1	15.8
Large	14.7	17.8	9.6	13.7
Guatemala	29.9	21.1	5.5	5.4
Small	27.2	25.9	6.9	8.9
Medium	22.4	17.8	2.6	2.2
Large	42.4	18.1	7.0	4.3
Honduras	24.4	19.9
Small	25.9	20.4
Medium	24.0	21.8
Large	19.9	13.0
Nicaragua	10.9	10.3
Small	14.9	16.0
Medium	9.3	8.1
Large	4.9	3.2
B. By sector*
Food	12.2	10.1	4.6	5.1
Tobacco	9.5	9.5	47.6	56.5
Textiles	50.5	12.1	3.6	3.1
Garments	36.8	41.6	0.9	14.9
Leather	19.0	20.0	0.5	0.0
Wood	16.8	8.3	1.2	2.9
Paper	20.0	25.0	21.5	33.9
Publishing, printing	22.2	17.7	5.5	8.5
Chemicals	0.9	0.0	0.3	0.3
Plastics and rubber	13.6	16.2	5.6	2.9
Non-metallic mineral products	33.1	29.5	0.0	0.0
Fabricated metal products	29.6	14.7	9.2	7.6
Machinery and equipment	15.5	15.4	42.9	51.2
Electronics	0.0	0.0	0.0	0.0
Transport machinery	27.3	20.0	0.3	0.5
Furniture	35.9	24.2	7.0	5.9
Construction	24.0	20.3	21.3	20.7
Servicing of motor vehicles	16.1	16.7	16.1	11.4
Wholesale	11.9	11.1	2.2	0.4
Retail	16.4	14.3	5.8	7.0
Hotels, restaurants etc.	37.6	37.4	30.2	52.3
Transport services	13.4	10.4	6.2	8.0
IT services	18.3	22.1	1.6	0.0
Full sample	20.9	17.3	8.4	8.8

50 Notes:

8* El Salvador and Guatemala only at the second COVID survey (November 2020–January 2021).

51 COVID, coronavirus disease; IT, information technology.

Table 5

Proportion of responding firms who cut wages or hours for at least some workers

	Wages (%)	Hours (%)
A. By country and firm size
El Salvador	25.4	32.5
Small	28.3	30.0
Medium	19.5	32.0
Large	26.7	38.1
Guatemala	34.0	24.0
Small	53.3	26.7
Medium	25.0	15.0
Large	26.7	33.3
Honduras	31.6	40.4
Small	30.9	41.8
Medium	26.1	34.8
Large	53.8	53.8
Nicaragua	22.9	29.6
Small	22.4	31.0
Medium	22.2	32.2
Large	25.8	19.4
Full sample	26.3	32.4
B. By sector
Food	21.0	32.0
Tobacco	20.0	20.0
Textiles	23.5	11.8
Garments	31.3	29.7
Leather	10.0	40.0
Wood	33.3	16.7
Paper	33.3	16.7
Publishing, printing	27.3	36.4
Chemicals	27.3	22.7
Plastics and rubber	10.0	20.0
Non-metallic mineral products	40.0	33.3
Fabricated metal products	19.0	19.0
Machinery and equipment	21.1	26.3
Electronics	0.0	0.0
Transport machinery	50.0	50.0
Furniture	31.4	45.1
Construction	35.3	32.4
Servicing of motor vehicles	25.5	27.7
Wholesale	26.4	35.8
Retail	26.0	38.3
Hotels, restaurants etc.	26.8	29.3
Transport services	28.2	33.3
IT services	20.0	0.0
Full sample	26.3	32.4

9 Note: IT, information technology.

Table 6

Expected survival times of firms

	Average number of weeks that firms could survive with
	No sales*	No sales + finance**
A. By country and firm size
El Salvador	7.69	12.29
Small	5.85	12.48
Medium	10.05	12.64
Large	8.51	11.46
Guatemala	9.42	11.85
Small	9.63	11.05
Medium	9.63	13.67
Large	8.89	10.87
Honduras	8.20	10.28
Small	9.20	10.45
Medium	7.24	10.27
Large	7.18	9.50
Nicaragua	8.53	12.69
Small	6.91	7.92
Medium	8.06	14.95
Large	12.59	14.41
Full sample	8.30	12.00
B. By sector
Food	6.41	14.38
Tobacco	23.60	15.60
Textiles	7.13	11.44
Garments	7.85	12.08
Leather	4.25	6.71
Wood	5.43	7.33
Paper	8.80	11.20
Publishing, printing	6.23	9.04
Chemicals	6.25	9.00
Plastics and rubber	12.73	13.64
Nonmetallic mineral products	7.06	10.33
Fabricated metal products	7.88	18.82
Machinery and equipment	5.11	7.22
Electronics	11.50	15.00
Transport machinery	4.40	10.80
Furniture	5.23	10.00
Construction	13.12	25.79
Servicing of motor vehicles	6.65	11.87
Wholesale	9.40	12.95
Retail	7.38	9.27
Hotels, restaurants etc.	7.39	11.68
Transport services	7.35	10.00
IT services	5.86	8.86
Full sample	7.65	12.00

10 Notes:

11* Keeping the cost structure as it is now, how many weeks would this establishment be able to remain open if its sales stopped as of today?

12** How many additional weeks could this establishment continue paying all costs and payments (such as payroll, suppliers, taxes, or loan repayment) relying on the external sources of finance that this establishment has access to?

52 IT, information technology.

Table 7

Changes in firm sales

Average percentage change in firm sales compared to same month in 2019*
	Unweighted (%)	Sales weighted (%)
A. By country and firm size
El Salvador	–23.3	–9.8
Small	–31.4	–12.6
Medium	–18.4	–10.0
Large	–12.9	–9.6
Guatemala	–24.0	–10.3
Small	–34.8	–30.5
Medium	–22.6	19.4
Large	–10.3	–17.7
Honduras	–33.7	–26.3
Small	–39.7	–23.6
Medium	–29.6	–23.1
Large	–23.3	–31.2
Nicaragua	–22.9	3.4
Small	–37.8	–23.6
Medium	–15.5	–4.6
Large	–16.0	5.1
Full sample	–24.8	–4.4
B. By sector
Food	–19.5	–5.3
Tobacco	4.5	8.0
Textiles	–14.8	–11.5
Garments	–45.9	–27.2
Leather	–31.0	–1.5
Wood	–42.0	–29.9
Paper	–16.4	5.3
Publishing, printing	–33.2	–33.2
Chemicals	–7.5	8.6
Plastics and rubber	–4.8	0.7
Nonmetallic mineral products	–28.2	–9.0
Fabricated metal products	–12.1	–13.8
Machinery and equipment	–36.3	8.3
Electronics	10.0	18.0
Transport machinery	–18.8	–25.0
Furniture	–26.5	–15.3
Construction	–39.2	–56.8
Servicing of motor vehicles	–24.6	–28.5
Wholesale	–15.5	–6.8
Retail	–22.9	–1.1
Hotels, restaurants etc.	– 49.5	– 45.1
Transport services	–14.8	–3.5
IT services	–32.5	–32.1
Full sample	–24.1	–4.4

13 Notes:

14* From the second COVID follow-up survey conducted from. November 2020 to January 2021.

15 COVID, coronavirus disease; IT, information technology.

Table 8

Recipients of government support (at time of the second COVID survey)

	Support
	Received (%)	Expected (%)	Neither (%)
Full sample	19.2	4.0	76.8
Wholly domestic owned	20.4	4.5	75.1
Foreign owned (part or full)	12.7	1.6	85.7
Female top manager	20.0	3.4	76.6
Male top manager	19.0	4.1	76.9
A. By country and firm size
El Salvador	32.7	7.2	60.2
Small	32.5	9.8	57.7
Medium	51.0	6.9	42.2
Large	10.7	2.4	86.9
Guatemala	12.4	2.6	85.0
Small	13.3	0.0	86.7
Medium	12.2	2.0	85.7
Large	11.4	6.8	81.8
Honduras	9.5	0.9	89.7
Small	0.0	0.0	100.0
Medium	17.0	2.1	80.9
Large	23.1	0.0	76.9
Nicaragua	5.0	1.1	93.9
Small	6.8	0.0	93.2
Medium	2.2	2.2	95.5
Large	9.4	0.0	90.6
B. By sector
Food	21.6	6.9	71.6
Tobacco	0.0	0.0	100.0
Textiles	23.1	3.8	73.1
Garments	24.0	8.0	68.0
Leather	12.5	25.0	62.5
Wood	0.0	12.5	87.5
Paper	0.0	0.0	100.0
Publishing, printing	32.3	3.2	64.5
Chemicals	20.0	4.0	76.0
Plastics and rubber	16.7	0.0	83.3
Nonmetallic mineral products	12.5	12.5	75.0
Fabricated metal products	11.8	0.0	88.2
Machinery and equipment	33.3	0.0	66.7
Electronics	0.0	0.0	100.0
Transport machinery	20.0	20.0	60.0
Furniture	24.3	5.4	70.3
Construction	14.7	2.9	82.4
Servicing of motor vehicles	9.8	4.9	85.4
Wholesale	14.5	1.8	83.6
Retail	20.5	1.9	77.6
Hotels, restaurants etc.	25.0	2.8	72.2
Transport services	17.1	2.4	80.5
IT services	14.3	0.0	85.7

16 Note: COVID, coronavirus disease; IT, information technology.

Table 9

Reasons for not receiving government support (percentage of firms)

	El Salvador (%)	Guatemala (%)	Honduras (%)	Nicaragua (%)
Not aware	17.6	12.3	30.8	24.3
Too cumbersome or costly to apply	10.0	13.8	7.7	3.0
Not eligible	29.5	12.3	5.8	2.4
Do not expect to get it because of lack of right connections	2.4	1.5	6.7	10.7
Applied but not received it	7.6	7.7	5.8	3.0
No need of support	24.8	38.5	15.4	15.4
Other reason	4.8	13.1	26.0	39.6
Nonresponse	3.3	0.8	1.9	1.8

Table 10

What would be the most-needed government measures to support this business over the COVID-19 crisis?

Round 2 survey
	% of respondents
El Salvador
Cash transfer	11.9
Deferral of credit payments, utility bills, rent, or mortgage; suspension of interest payments; or rollover of debt	11.6
Access to new credit	32.2
Tax reductions or tax deferrals	24.3
Wage subsidies	10.6
Support (technical assistance or subsidies) for adoption of digital technologies	4.7
Other measures	4.7
Guatemala
Cash transfer	5.9
Deferral of credit payments, utility bills, rent, or mortgage; suspension of interest payments; or rollover of debt	10.2
Access to new credit	18.2
Tax reductions or tax deferrals	44.9
Wage subsidies	7.0
Support (technical assistance or subsidies) for adoption of digital technologies	4.3
Other measures	9.6
Honduras
Cash transfer	8.3
Deferral of credit payments, utility bills, rent, or mortgage; suspension of interest payments; or rollover of debt	14.0
Access to new credit	10.2
Tax reductions or tax deferrals	55.4
Wage subsidies	4.5
Support (technical assistance or subsidies) for adoption of digital technologies	3.8
Other measures	3.8
Nicaragua
Cash transfer	5.3
Deferral of credit payments, utility bills, rent, or mortgage; suspension of interest payments; or rollover of debt	15.0
Access to new credit	15.0
Tax reductions or tax deferrals	56.1
Wage subsidies	3.2
Support (technical assistance or subsidies) for adoption of digital technologies	0.5
Other measures	4.8

17 Note: COVID, coronavirus disease.

Table 11

Changes in liquidity since the outbreak of COVID-19 (percentage of firms)*

	Liquidity
	Increased (%)	Same (%)	Decreased (%)
Full sample	10.9	29.8	59.3
Wholly domestic owned	10.4	28.2	61.4
Foreign owned (part or full)	13.6	38.4	48.0
Female top manager	11.0	26.7	62.3
Male top manager	10.9	30.5	58.6
A. By country and firm size
El Salvador	12.9	26.4	60.6
Small	9.8	20.7	69.5
Medium	11.9	27.7	60.4
Large	20.5	36.1	43.4
Guatemala	11.1	31.4	57.5
Small	3.3	28.3	68.3
Medium	16.3	34.7	49.0
Large	15.9	31.8	52.3
Honduras	8.7	25.2	66.1
Small	3.6	19.6	76.8
Medium	15.2	26.1	58.7
Large	7.7	46.2	46.2
Nicaragua	8.3	37.8	53.9
Small	5.1	30.5	64.4
Medium	11.2	33.7	55.1
Large	6.3	62.5	31.3
B. By sector
Food	11.8	30.4	57.8
Tobacco	0.0	100.0	0.0
Textiles	19.2	34.6	46.2
Garments	4.0	20.0	76.0
Leather	0.0	25.0	75.0
Wood	0.0	12.5	87.5
Paper	20.0	0.0	80.0
Publishing, printing	0.0	10.0	90.0
Chemicals	28.0	40.0	32.0
Plastics and rubber	0.0	58.3	41.7
Nonmetallic mineral products	12.5	18.8	68.8
Fabricated metal products	17.6	11.8	70.6
Machinery and equipment	0.0	22.2	77.8
Electronics	0.0	100.0	0.0
Transport machinery	0.0	0.0	100.0
Furniture	8.1	27.0	64.9
Construction	14.7	23.5	61.8
Servicing of motor vehicles	12.2	31.7	56.1
Wholesale	12.8	35.8	51.4
Retail	11.3	33.1	55.6
Hotels, restaurants etc.	10.8	18.9	70.3
Transport services	14.6	34.1	51.2
IT services	0.0	57.1	42.9

18 Notes:

19* At time of the second COVID survey.

53 COVID, coronavirus disease; IT, information technology.

Table 12

Proportion of firms applying for a loan

Since the outbreak of COVID-19	%
Full sample	31.9
Wholly domestic owned	33.3
Foreign owned (part or full)	24.0
Female top manager	32.2
Male top manager	31.8
A. By country and firm size
El Salvador	39.0
Small	39.3
Medium	38.4
Large	39.0
Guatemala	26.3
Small	15.9
Medium	30.6
Large	36.4
Honduras	38.3
Small	28.6
Medium	52.2
Large	30.8
Nicaragua	19.0
Small	10.2
Medium	21.6
Large	28.1
B. By sector
Food	28.2
Tobacco	0.0
Textiles	23.1
Garments	34.7
Leather	12.5
Wood	12.5
Paper	60.0
Publishing, printing	32.3
Chemicals	48.0
Plastics and rubber	33.3
Nonmetallic mineral products	6.3
Fabricated metal products	44.4
Machinery and equipment	44.4
Electronics	0.0
Transport machinery	60.0
Furniture	31.4
Construction	26.5
Servicing of motor vehicles	35.0
Wholesale	37.6
Retail	32.1
Hotels, restaurants etc.	32.4
Transport services	24.4
IT services	28.6

20 Note: COVID, coronavirus disease; IT, information technology.

Table 13

Perceived main obstacles to doing business (average scores)

	El Salvador	Guatemala	Honduras	Nicaragua	All countries
Electricity supply	1.2	1.1	2.0	1.5	1.4
Telecommunications	1.0	1.1	1.6	1.1	1.1
Transport	1.2	1.6	1.6	1.0	1.3
Customs	1.1	1.5	1.8	1.3	1.3
Access to land	0.9	1.2	1.0	0.6	0.9
Crime, theft, and disorder	2.1	2.1	1.8	0.8	1.8
Access to finance	1.3	1.3	1.5	1.0	1.3
Tax rates	1.8	1.7	2.6	1.3	1.8
Tax administration	1.4	1.5	2.2	1.2	1.6
Business licensing	1.2	1.4	2.0	1.2	1.4
Political instability	2.1	2.6	2.2	1.3	2.1
Corruption	2.1	2.9	2.5	1.6	2.3
Courts	1.6	2.0	2.0	1.2	1.7
Labor regulations	1.1	1.6	1.6	1.1	1.3
Taken from the original enterprise survey
No obstacle = 0
Minor obstacle = 1
Moderate obstacle = 2
Major obstacle = 3
Very severe obstacle = 4

Table 14

OLS regression results

Dependent variable	Expected weeks of survival			% change in sales since 2019			% change in FT permanent			% change in the share of females
	with no sales						employees since Dec 2019			in FT permanent employment
Independent variables	Coefficient	Robust	t	Coefficient	Robust	t	Coefficient	Robust	t	Coefficient	Robust	t
		standard error			standard error.			standard error			standard error.
% change in sales since 2019	0.019*	0.010	1.81				0.003***	0.001	4.99	0.002	0.002	1.37
Expected weeks of survival with no sales				0.200**	0.093	2.15	0.002*	0.001	1.6
% change in FT permanent employment since Dec. 2019	1.342	0.954	1.41	14.120***	3.181	4.44				–0.197**	0.103	–1.91
% change in debt in Jan–Sept 2020	–2.966***	1.101	–2.69							0.039	0.045	0.86
% of foreign ownership				0.085**	0.040	2.12				0.005	0.007	0.71
Started or increased delivery (0,1)	–1.868*	1.061	–1.76				7.795	2.726	2.86	–0.102	0.145	–0.70
Started or increased online sales (0,1)										0.183	0.182	1.00
Female top manager (0,1)	–1.604*	0.942	–1.70							0.073	0.088	0.83
Online sales as a % of total sales	0.040	0.033	1.23	–0.180***	0.063	–2.83				0.003	0.003	1.03
No government support (0,1)	2.101*	0.767	2.74
Firm applied for loan (0,1)							0.051	0.041	1.23
Exports as a % of total sales							0.001	0.000	1.56	–0.003	0.005	–0.69
Number of weeks closed				–1.468***	0.208	–7.04
Proportion of employees furloughed	–2.242*	1.183	–1.89	–15.707***	4.289	–3.66	0.452**	0.230	1.97
Labor intensity–labor as a share of total cost	2.991**	1.443	2.07							–0.046	0.164	–0.28
Containment and health index										–0.041	0.041	–1.00
Government work-place closures-stringency	–9.862**	4.672	–2.11	15.527**	7.444	2.09	0.185**	0.094	1.96	–0.289*	0.171	–1.69
Stay-at-home restrictions-stringency				–15.781***	4.608	–3.42				0.588	0.444	1.32
Closures of public transport-stringency				–46.252***	10.951	–4.22				0.392	0.362	1.08
Total COVID-19 cases per million	0.004*	0.002	1.84				–0.00004**	0.000	–2.12
Total COVID-19 deaths per million	–0.067	0.046	–1.46	0.148***	0.045	3.27				0.008	0.007	1.06
Transport machinery sector (0,1)	–5.551***	0.826	–6.72
Transport sector (0,1)	1.938	1.266	1.53	19.436***	5.542	3.51
IT services sector (0,1)	–6.490	2.237	–2.90
Garment sector (0,1)							–0.149**	0.058	–2.58
Chemical sector (0,1)				15.396**	7.252	2.12
Rubber and plastics sector (0,1)				22.680***	7.508	3.02
Nonmetalllc mineral products sector (0,1)										1.115*	0.662	1.68
Constant	2.434	1.821	1.34	–32.045***	3.469	–9.24	–0.029	0.043	–0.67	0.163	0.430	0.38
Number of observations	386			618			626			285
F	2.81	(15, 370)		10.03	(16, 601)		11.64	(8, 617)		1.76	(17, 267)
Probability > F	0.0004			0			0			0.0338
R²	0.1022			0.2107			0.1312			0.1006
Adjusted R²	0.0658			0.1897			0.1199			0.0433
RMSE	9.468			31.104			0.42401			0.91075

21 Notes:

22* Significant at 90%,

23** at 95%, and

24*** 99% confidence levels.

25 COVID, coronavirus disease; FT, full time; IT, information technology; OLS, ordinary least squares; RMSE, root mean square error.

Table 15

SURE estimation for changes in sales and employment

	Observations	RMSE	R²	χ²	P-value
% change in sales since 2019	615	31.2941	0.1782	133.61	0.000
% change in FT permanent employment	615	0.4233	0.1305	96.64	0.000
(since December 2019)

	Coefficient	Standard error	z	P > z
(a) % change in sales since 2019
Expected weeks of survival with no sales	0.233*	0.125	1.86	0.063
Started or increased deliveries (0,1)	7.685***	2.725	2.82	0.005
% of foreign ownership	0.088**	0.041	2.15	0.032
Share of online sales in total	– 0.172***	0.067	–2.56	0.010
Number of weeks of temporary closure	–1.622***	0.226	–7.17	0.000
% of workers furloughed	–9.560**	4.301	–2.22	0.026
Stringency of government workplace closures	17.557*	9.872	1.78	0.075
Stringency of stay-at-home restrictions	–17.193***	5.955	–2.89	0.004
Extent of public transport closures	–50.386***	13.491	–3.73	0.000
Total COVID-19 deaths per million	0.155***	0.049	3.18	0.001
Chemical sector (0,1)	16.573**	7.559	2.19	0.028
Plastics and rubber sector (0,1)	23.315**	10.605	2.2	0.028
Transport sector (0,1)	18.890**	8.885	2.13	0.033
Constant	–34.324***	3.769	–9.11	0.000
(b) % change in FT permanent employment
Expected weeks of survival with no sales	0.002	0.002	1.1	0.271
% change in sales since 2019	0.003***	0.001	5.64	0.000
Firm applied for loan (0,1)	0.049	0.037	1.31	0.190
Exports as a % of total sales	0.001	0.001	1.05	0.296
% of workers furloughed	0.458***	0.057	7.99	0.000
Stringency of government workplace closures	0.183	0.123	1.48	0.138
Total COVID-19 cases per million	–0.00004*	0.000	–1.79	0.073
Garments sector (0,1)	–0.153**	0.069	–2.21	0.027
Constant	–0.021	0.048	–0.43	0.667

26 Notes:

27* significant at 90%,

28** at 95%, and

29*** at 99% confidence levels.

30 COVID,coronavirus disease; FT, full time; RMSE, root mean square error; SURE, seemingly unrelated regression equations.

Table 16

SURE estimation for changes in sales and the share of females in employment

	Observations	RMSE	R²	χ²	P-value
% change in sales since 2019	302	29.747	0.210	80.650	0.000
% change in female share in employment	302	0.872	0.087	28.190	0.003
(since December 2019, FT permanent employees)

	Coefficient	Standard error	z	P > z
(a) % change in sales since 2019
Expected weeks of survival with no sales	0.218	0.167	1.31	0.19
Started or increased deliveries (0,1)	10.5765***	3.823	2.77	0.01
% of foreign ownership	0.067	0.064	1.04	0.30
Share of online sales in the total	–0.194**	0.097	–2.00	0.05
Number of weeks of temporary closure	–2.221***	0.414	–5.37	0.00
% of workers furloughed	–11.818*	6.353	–1.86	0.06
Stringency of government workplace closures	20.128	15.445	1.30	0.19
Stringency of stay-at-home restrictions	–21.220**	8.913	–2.38	0.02
Extent of public transport closures	–61.621***	19.831	–3.11	0.00
Total COVID-19 deaths per million	0.1999***	0.070	2.84	0.00
Chemical sector (0,1)	27.697***	10.860	2.55	0.01
Plastics and rubber sector (0,1)	39.221***	15.521	2.53	0.01
Transport sector (0,1)	27.660**	11.596	2.39	0.02
Constant	–36.796***	5.570	–6.61	0.00
(b) % change in the share of females in FT permanent employment
% change in full-time permanent employees	–0.200	0.128	–1.57	0.12
% change in sales since 2019	0.002	0.002	1.00	0.32
% change in debt (January–September 2020)	0.058	0.084	0.69	0.49
Online sales as a % of total	0.216**	0.104	2.08	0.04
% of foreign ownership	0.005**	0.002	2.43	0.02
Exports as a % of total sales	–0.003	0.002	–1.33	0.19
Overall containment index	–0.035	0.029	–1.18	0.24
Stringency of stay-at-home restrictions	0.363	0.271	1.34	0.18
Stringency of restrictions on internal movements	–0.297*	0.160	–1.86	0.06
Total COVID-19 deaths per million	0.007	0.004	1.59	0.11
Nonmetallic mineral products sector (0,1)	0.793**	0.369	2.15	0.03
Constant	0.083	0.314	0.26	0.79

31 Notes:

32* significant at 90%,

33** at 95%, and

34*** 99% confidence levels.

35 COVID, coronavirus disease; FT, full time; OLS, ordinary least squares; RMSE, root mean square error; SURE, seemingly unrelated regression equations.

Table 17

IPWRA analysis for online sales and remote working

IPWRA Analysis - decreased liquidity and increased debt
Outcome		Absolute effects
		Liquidity decreased	Debt increased	Both
Expected survival with no sales (weeks)	ATT	–5.850**	–3.583	–6.556**
standard error	(2.179)	(2.699)	(2.991)

	Relative effects
	Liquidity loss versus debt increase	Only liquidity loss versus both	Only raised debt versus both
ATT	2.121	– 0.3232	–2.509**
standard error	(2.428)	(0.929)	(1.205)

Outcome		Absolute effects
		Liquidity decreased	Debt increased	Both
Change in sales from December 2019 (%)	ATT	–30.289***	–1.102	–27.681***
standard error	(3.818)	(4.703)	(4.362)

	Relative effects
	Liquidity loss versus debt increase	Only liquidity loss versus both	Only raised debt versus both
ATT	28.612***	3.416	–26.933***
standard error	(3.782)	(2.935)	(3.639)

36 Notes: Robust standard errors are in parentheses.

37* Significant at

38** 95%, and

39*** 99% confidence level.

40 ATT, Average Treatment Effect for the Treated Group; IPWRA, inverse-probability-weighted regression adjustment.

Table 18

IPWRA analysis for liquidity and debt

IPWRA analysis - started or increased (a) online sales and (b) remote working
Outcome		Absolute effects
		Online sales	Remote working	Both
Expected survival with no sales (weeks)	ATT	0.577	2.721**	1.431
standard error	(2.028)	(1.283)	(1.003)

	Relative effects
	Online sales versus remote	Only online sales versus both	Only remote working versus both
ATT	2.736	1.288	–1.496
standard error	(2.119)	(1.821)	(1.277)

Outcome		Absolute effects Guate
		Online sales	Remote working	Both
Change in sales from December 2019 (%)	ATT	–0.504	5.736	8.303**
standard error	(3.779)	(3.779)	(3.848019)

	Relative effects
	Online sales versus remote	Only online sales versus both	Only remote working versus both
ATT	5.334	8.856**	5.475
standard error	(4.255)	(3.767)	(4.280)

41 Notes: Robust standard errors are in parentheses.

42 Significant at **95%, and ***99% confidence level.

43 ATT, Average Treatment Effect for the Treated Group; IPWRA, inverse-probability-weighted regression adjustment.

Table 19

World governance indicators (2019)

Control of corruption		Government effectiveness
(a) Estimate		(a) Estimate
El Salvador	–0.553	El Salvador	– 0.465
Guatemala	–0.898	Guatemala	–0.677
Honduras	–0.808	Honduras	–0.612
Nicaragua	–1.122	Nicaragua	– 0.771
(b) Percentile rank		(b) Percentile rank
El Salvador	32.692	El Salvador	35.577
Guatemala	18.750	Guatemala	26.442
Honduras	23.077	Honduras	30.288
Nicaragua	12.500	Nicaragua	21.635
Political stability and absence of violence		Regulatory quality
(a) Estimate		(a) Estimate
El Salvador	–0.127	El Salvador	0.022
Guatemala	–0.546	Guatemala	–0.225
Honduras	–0.531	Honduras	–0.493
Nicaragua	–1.035	Nicaragua	– 0.689
(b) Percentile rank		(b) Percentile rank
El Salvador	42.857	El Salvador	56.250
Guatemala	25.238	Guatemala	44.231
Honduras	27.143	Honduras	34.135
Nicaragua	13.333	Nicaragua	25.000
Rule of law		Voice and accountability
(a) Estimate		(a) Estimate
El Salvador	–0.762	El Salvador	0.138
Guatemala	––1.052	Guatemala	–0.313
Honduras	–1.009	Honduras	–0.552
Nicaragua	–1.176	Nicaragua	–1.077
(b) Percentile rank		(b) Percentile rank
El Salvador	23.558	El Salvador	51.724
Guatemala	13.942	Guatemala	35.468
Honduras	15.385	Honduras	31.034
Nicaragua	9.615	Nicaragua	19.212

44 Note:

45* Estimate – normal distribution, minimum value of –2.5 and maximum of +2.5. Significant at

46** 95%, and

47*** 99% confidence level.

Table A1

Change in full-time permanent workers from December 2019 by skill category and education*

A. By skill category
Country/firm size	Nonproduction	Production workers
Workers (%)	Skilled (%)	Semiskilled (%)	Unskilled (%)
El Salvador	–2.8	–4.4	6.3	0.6
Small	–24.3	–22.7	–20.8	–30.7
Medium	–14.1	–11.9	–13.2	–18.2
Large	–1.3	–2.1	8.6	2.7
Guatemala	–8.5	3.8	–14.2	6.6
Small	–21.9	–28.5	–25.1	–19.7
Medium	–36.4	–38.1	–38.7	–34.4
Large	–2.4	9.9	–7.4	15.1
Honduras	–18.8	–18.7	–15.6	–15.5
Small	–8.1	–7.5	–5.1	–12.0
Medium	–22.3	–21.8	–31.7	–21.1
Large	–17.6	–18.0	–10.3	–11.6
Nicaragua	–19.4	–14.4	–26.3	–23.2
Small	8.3	–5.4	35.1	–8.6
Medium	–2.5	–0.6	2.5	–4.5
Large	–22.1	–18.5	–30.5	–25.8
Full sample	–6.9	–4.8	–5.0	–3.0

B. By education
	All (%)	Education level
		University (%)	Secondary (%)
El Salvador	–23.2	–23.4	–25.3
Small	–54.8	–74.2	–53.4
Medium	–29.7	–25.2	–33.1
Large	–17.1	–14.4	–19.2
Guatemala	–28.1	–27.1	–33.5
Small	–45.8	–19.2	–59.9
Medium	–43.7	–36.3	–48.8
Large	–22.3	–24.9	–26.9
Honduras	–39.1	–36.3	–39.7
Small	–34.7	–38.3	–36.9
Medium	–21.5	–23.1	–20.0
Large	–55.5	–47.5	–57.0
Nicaragua	–18.1	–25.8	–23.8
Small	–39.7	–59.9	–46.7
Medium	–26.3	–21.8	–27.1
Large	–14.0	–22.8	–20.9
Full sample	–25.0	–26.3	–28.2

54 Notes:

54* As at the time of the second COVID-19 survey (November 2020–January 2021): calculated assuming same proportions of each category as in the enterprise survey.

56 COVID, coronavirus disease.

Table A2

Average change in debt between January and September 2020

Full sample (%)	6.3
Wholly domestic owned (%)	10.4
Foreign owned (part or full) (%)	0.0
Female top manager (%)	9.8
Male top manager (%)	6.2
A. By country and firm size (%)
El Salvador	3.2
Small	–11.6
Medium	0.4
Large	5.6%
Guatemala	–2.5
Small	–17.3
Medium	–4.4
Large	–1.0
Honduras	–0.5
Small	–7.7
Medium	8.0
Large	–9.6
Nicaragua	21.1
Small	–8.2
Medium	–13.7
Large	25.7
B. By sector (%)
Food	–3.5
Tobacco	–18.2
Textiles	–0.3
Garments	47.0
Leather	–1.9
Wood	–0.6
Paper	–0.7
Publishing, printing	–19.5
Chemicals	25.4
Plastics and rubber	–0.4
Nonmetallic mineral products	–6.6
Fabricated metal products	4.0
Machinery and equipment	6.1
Electronics	0.0
Transport machinery	12.4
Furniture	6.7
Construction	–17.3
Servicing of motor vehicles	–4.7
Wholesale	21.2
Retail	–7.4
Hotels, restaurants etc.	8.9
Transport services	–10.0
IT services	–35.5

57 Note: IT, information technology.

Table A3

Main reason for not applying for a loan and the outcomes of loan applications (% of firms by country)

	El Salvador (%)	Guatemala (%)	Honduras (%)	Nicaragua (%)
A. Reasons for not applying
No need for a loan - establishment had sufficient capital	56.7	71.3	56.3	49.7
Application procedures were complex	8.6	4.3	9.9	5.5
Interest rates were not favorable	5.7	4.3	9.9	12.4
Collateral requirements were too high	1.0	0.9	2.8	7.6
Size of loan and maturity were insufficient	0.5	0.9	0.0	0.0
Did not think it would be approved	7.1	6.1	7.0	8.3
Other measures	19.0	12.2	14.1	16.6
Nonresponse	1.4	0.0	0.0	0.0
B. Outcome of loan application
Application still in process	44.7	12.2	11.4	5.9
Application was approved in full	40.2	70.7	68.2	55.9
Application was approved in part	9.1	9.8	6.8	23.5
Application was rejected	4.5	7.3	9.1	11.8
Application was withdrawn	1.5	0.0	4.5	2.9