Parental Investments During Labor Shocks: Evidence from Vietnam’s Marine Disaster

Eric W. Chan; Luong Nguyen

doi:10.1515/bejeap-2024-0046

Article

Parental Investments During Labor Shocks: Evidence from Vietnam’s Marine Disaster

Eric W. Chan and Luong Nguyen

Published/Copyright: February 20, 2025

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From the journal The B.E. Journal of Economic Analysis & Policy Volume 25 Issue 2

Abstract

We examine the impacts of a marine environmental disaster on parents’ educational investments in the context of Vietnam. Previous research finds that the disaster caused a labor shock in the fishing industry, a primary source of income for workers in the area studied. Using administrative data and a difference-in-differences framework, we find that parents in affected provinces decreased educational investments by 29 % during the four years after the disaster. In households with male and female children of compulsory schooling age, we estimate that affected families spent approximately 5–6 % less on educational expenditures for girls than boys in affected provinces after the disaster.

Keywords: human capital; parental investments; education; disasters; labor shocks

JEL Classification: I20; I25; J16

Corresponding author: Eric W. Chan, Assistant Professor, Babson College, Wellesley, MA 02457, USA, E-mail: echan1@babson.edu

We have no external funding or grant to report. We especially thank Khanh Linh Ha for gathering us together in inconceivable ways, along with WebEx and Slack for keeping us in communication across time zones around the world. This project started from a mentorship opportunity and continued as the mentee started her journey towards the world of academic research.

Appendix A: Details of Treatment Status

Details About Treatment and Control Years

We obtained access to VHLSS data for five available years: 2008, 2012, 2016, 2018, and 2020. The environmental disaster initially occurred in April 2016, which is one the years for which we have data. For this year, the data contained two variables, the month and year, for which the data were collected. Approximately 24 % of the data were collected in March, 76 % in June, September, and December. We included the data collected in March as part of the pre-treatment years and excluded the June through December data as part of the post-treatment time frame. The reason is the complicated relationship between the time-frame representation of the survey questions and the research question at hand. Specically there were two primary reasons for this:

First, the question posed to participants, translated into English, was: “What is the cost for ‘…[specific category of educational expense]…’ to go to school during the last 12 months for subjects in regulation curriculum ?” For the months of June 2016 through December 2016, it would be impossible to disentangle pre-treatment expenses and post-treatment expenses, which would convolute estimates.
The second reason was that, psychologically, families that went through the trauma of the environmental disaster might be under- or over-estimating their expenses, or have expenses change greatly due to the disaster, making it difficult to provide accurate estimates. For example, a family might have substantially decreased labor income and could no longer afford transportation to school for their children. As a result, it would be difficult for families to provide accurate estimates for inconsistent transportation expenses over the past year.

We add some tests of robustness here as a result of the decision to include and exclude 2016 data. First, Table A1 includes all 2016 as part of the control group. Second Table A2 excludes 2016 entirely. Regardless of specification, we find that the estimates are highly comparable in magnitude and direction relative to the main estimates in Table 2.

Table A1:

Robustness of main results: inclusion of all 2016 data as control.

Variable	Control mean	Effect	Std error	P-value	N
Panel A. Full sample; pecuniary variables
Tuition (in-district)	120.2	−69.8**	30.85	0.02	41,745
Tuition (out-of-district)	4.69	−0.72	2.99	0.81	41,890
Contribution	104.25	77.82***	21.81	0.00	40,917
Class fund	77.2	4.67	9.52	0.64	40,984
Uniform	133.85	−34.88***	7.22	0.00	41,251
Textbook	131.21	10.95	7.65	0.15	41,126
Stationery	144.5	−20.87***	6.34	0.00	41,131
Extra class	316.58	−295.18***	68.5	0.00	41,450
Other	123.56	−45.21*	26.5	0.09	41,143
Total	1,156.05	−373.22***	92.4	0.00	42,358
Panel B. Full sample; likelihood spending on each item
Tuition (any in-district)	0.16	0.01	0.02	0.63	43,945
Tuition (any out-of-district)	0.01	0.00	0.00	0.49	43,945
Contribution (any)	0.53	−0.03	0.02	0.13	43,945
Class fund (any)	0.75	−0.08**	0.02	0.00	43,945
Uniform (any)	0.67	−0.1***	0.02	0.00	43,945
Textbook (any)	0.77	−0.03	0.02	0.67	43,945
Stationery (any)	0.91	−0.02*	0.01	0.06	43,945
Extra class (any)	0.31	−0.14***	0.02	0.00	43,945
Other (any)	0.39	0.01	0.02	0.56	43,945
Total (any)	0.96	0.01	0.01	0.32	43,945

Notes: This table shows the results of DiD specifications as described in the paper, except with all the 2016 data included as part of the pre-treatment group. Panel A shows estimates for outcome variables of interest for monetary outcomes, whereas Panel B does the same for whether any amount (above 0 VND) was spent on each item. Note that the analysis is limited to only children of compulsory schooling age in Vietnam. Also note that the units for pecuniary expenditures are in thousands VND. ***p < 0.01, **p < 0.05, *p < 0.10.

Table A2:

Robustness of main results: exclusion of all 2016 data.

Variable	Control mean	Effect	Std error	P-value	N
Panel A. Full sample; pecuniary variables
Tuition (in-district)	120.69	−70.42**	27.24	0.01	33,530
Tuition (out-of-district)	4.71	−0.73	2.98	0.41	33,691
Contribution	104.52	71.05***	25.75	0.00	32,717
Class fund	77.27	4.61	9.51	0.32	32,787
Uniform	134.62	−29.17***	7.16	0.00	33,051
Textbook	132.41	10.98*	7.63	0.07	32,925
Stationery	144.85	−21.92***	7.34	0.00	32,922
Extra class	319.39	−292.57***	68.41	0.00	33,254
Other	124.07	−41.33*	29.43	0.08	32,925
Total	1,157.40	−375.30***	87.89	0.00	34,159
Panel B. Full sample; likelihood spending on each item
Tuition (any in-district)	0.16	0.01	0.02	0.30	33,747
Tuition (any out-of-district)	0.01	0.00	0.00	0.59	33,747
Contribution (any)	0.53	−0.03*	0.02	0.07	33,747
5 class fund (any)	0.75	−0.08***	0.02	0.00	33,747
Uniform (any)	0.67	−0.10***	0.02	0.00	33,747
Textbook (any)	0.78	−0.03	0.02	0.11	33,747
Stationery (any)	0.92	−0.02**	0.01	0.02	33,747
Extra class (any)	0.31	−0.14***	0.02	0.00	33,747
Other (any)	0.39	0.01	0.02	0.50	33,747
Total (any)	0.96	0.01	0.01	0.16	33,747

Notes: This table shows the results of DiD specifications as described in the paper, except with all the 2016 data excluded completely. Panel A shows estimates for outcome variables of interest for monetary outcomes, whereas Panel B does the same for whether any amount (above 0 VND) was spent on each item. Note that the analysis is limited to only children of compulsory schooling age in Vietnam. ***p < 0.01, **p < 0.05, *p < 0.10.

Additionally, as a falsification test, we include a robustness testing in the form of placebo tests. In Table A3, we include two sets of estimates (1) first pretending the event happened in 2014, using 2008 and 2012 as pre-treatment years, and March 2016 as outcome data, then (2) pretending the event happened in 2010, using 2008 data as pre-treatment and 2012 data as outcomes. Note that none of the outcomes are significant at any level.

Table A3:

Placebo tests: using fictional event dates.

Variable	Control mean	Effect	Std error	P-value	N
Panel A. DiD estimates using 2014 as fictional event date: March 2016 data as outcomes
Tuition (in-district)	108.87	19.04	49.67	0.70	18,777
Tuition (out-of-district)	3.88	−0.35	3.78	0.92	18,867
Contribution	92.39	51.28	50.60	0.32	18,322
Class fund	75.68	2.38	29.99	0.94	18,361
Uniform	134.54	−2.73	14.75	0.86	18,509
Textbook	126.41	0.65	25.98	0.98	18,438
Stationery	137.23	−2.50	13.12	0.86	18,436
Extra class	295.93	21.26	22.60	0.37	18,622
Other	108.57	15.89	60.11	0.80	18,438
Total	1,083.50	155.82	123.66	0.22	19,129
Panel B. DiD estimates using 2010 as fictional event date; 2012 data as outcomes.
Tuition (in-district)	107.38	−7.71	30.05	0.80	14,418
Tuition (out-of-district)	3.83	−0.18	5.90	0.98	14,487
Contribution	89.57	12.74	41.83	0.76	14,068
Class fund	74.49	0.98	44.27	0.98	14,098
Uniform	132.76	−1.75	18.05	0.46	14,212
Textbook	124.14	0.15	29.67	0.99	14,158
Stationery	137.19	−2.23	17.19	0.90	14,156
Extra class	291.48	17.50	24.80	0.47	14,299
Other	105.33	15.47	75.93	0.84	14,158
Total	1,078.11	81.31	230.17	0.73	14,688

Notes: This table shows the results of DiD specifications using placebo time frames. Note that the analysis is limited to only children of compulsory schooling age in Vietnam. ***p < 0.01, **p < 0.05, *p < 0.10.

Finally, to make estimates more comparable to Hoang et al. (2020) and as an additional test for robustness, Table A4 uses a set of coastline provinces along south Vietnam from Phu Yen to Ca Mau as the control group provinces. As Hoang et al. (2020) argues, this is arguably a more appropriate control group to use as they are fishing-based coastline provinces that were likely not affected by the disaster, which reduces the likelihood of control group contamination. We find that these estimates are slightly stronger than our primary estimates, which uses a control group containing all unaffected provinces.

Table A4:

Effects on parental investments re-estimated using a different control group.

Variable	Control mean	Effect	Std error	P-value	N
Panel A. Full sample; pecuniary variables
Tuition (in-district)	109.73	−73.1**	29.55	0.01	11,240
Tuition (out-of-district)	3.89	−0.98	2.77	0.72	11,266
Contribution	101.25	21.55	22.93	0.35	10,917
Class fund	77.26	2.56	10.22	0.80	10,926
Uniform	105.68	−41.79***	6.26	0.00	11,107
Textbook	122.91	8.44	6.9	0.22	11,059
Stationery	134.22	−25.71***	7.42	0.00	11,065
Extra class	295.87	−256.11***	61.39	0.00	11,143
Other	120.01	−46.49*	27.23	0.09	11,074
Total	1,070.82	−409.33***	88.71	0.00	11,370
Panel B. Full sample; likelihood spending on each item
Tuition (any in-district)	0.13	−0.01	0.02	0.68	11,742
Tuition (any out-of-district)	0.01	0.00	0.00	0.84	11,742
Contribution (any)	0.57	−0.02	0.02	0.33	11,742
Class fund (any)	0.73	−0.08***	0.02	0.00	11,742
Uniform (any)	0.63	−0.07***	0.02	0.00	11,742
Textbook (any)	0.71	−0.02	0.02	0.31	11,742
Stationery (any)	0.85	−0.02*	0.01	0.06	11,742
Extra class (any)	0.26	−0.17***	0.02	0.00	11,742
Other (any)	0.35	0.01	0.02	0.61	11,742
Total (any)	0.92	−0.02**	0.01	0.04	11,742

Notes: This table shows the results of DiD specifications using estimating equation (1) as described in the paper by using a different control group than the one proposed in the paper. Specifically, we follow Hoang et al. (2020) by using a set of coastline provinces along south Vietnam from Phu Yen to Ca Mau as the control group provinces. This is in order to elicit comparable estimates to Hoang et al. (2020) and also to test the robustness of estimates. Panel A shows estimates for outcome variables of interest for monetary outcomes, whereas Panel B does the same for whether any amount (above 0 VND) was spent on each item. Note that the analysis is limited to only children of compulsory schooling age in Vietnam. Also note that the units for pecuniary expenditures are in thousands VND. ***p < 0.01, **p < 0.05, *p < 0.10.

Details About Treatment Status of Vietnamese Provinces

Following Hoang et al. (2020), there were four provinces that were considered to be affected by the environmental disaster: Tinh Ha Tinh, Tinh Quang Binh, Tinh Quang Tri, and Tinh Thua Thien Hue. Table A5 provides the details of the treatment status of Vietnamese provinces (Table A5).

Table A5:

Treatment status of Vietnamese provinces.

Province	Treatment status
Tinh Ha Giang	Control
Tinh Cao Bang	Control
Tinh Bac Kan	Control
Tinh Tuyen Quang	Control
Tinh Lao Cai	Control
Tinh Dien Bien	Control
Tinh Lai Chau	Control
Tinh Son La	Control
Tinh Yen Bai	Control
Tinh Hao Binh	Control
Tinh Thai Nguyen	Control
Tinh Lang Son	Control
Tinh Quang Ninh	Control
Tinh Bac Giang	Control
Tinh Phu Tho	Control
Tinh Vinh Phuc	Control
Tinh Bac Ninh	Control
Tinh Hai Duong	Control
Thanh pho Hai Phong	Control
Tinh Hung Yen	Control
Tinh Thai Binh	Control
Tinh Ha Nam	Control
Tinh Nam Dinh	Control
Tinh Ninh Binh	Control
Tinh Thanh Hoa	Control
Tinh Nghe An	Control
Tinh Ha Tinh	Treatment
Tinh Quang Binh	Treatment
Tinh Quang Tri	Treatment
Tinh Thua Thien Hue	Treatment
Thanh pho Da Nang	Control
Tinh Quang Nam	Control
Tinh Quang Ngai	Control
Tinh Binh Dinh	Control
Tinh Phu Yen	Control
Tinh Khanh Hoa	Control
Tinh Ninh Thuan	Control
Tinh Binh Thuan	Control
Tinh Kon Tum	Control
Tinh Gia Lai	Control
Tinh Dak Lak	Control
Tinh Dak Nong	Control
Tinh Lam Dong	Control
Tinh Binh Phuoc	Control
Tinh Tay Ninh	Control
Tinh Binh Duong	Control
Tinh Dong Nai	Control
Tinh Ba Ria – Vung Tau	Control
Thanh pho Ho Chi Minh	Control
Tinh Long An	Control
Tinh Tien Giang	Control
Tinh Ben Tre	Control
Tinh Tra Vinh	Control
Tinh Vinh Long	Control
Tinh Dong Thap	Control
Tinh An Giang	Control
Tinh Kien Giang	Control
Thanh pho Can Tho	Control
Tinh Hau Giang	Control
Tinh Soc Trang	Control
Tinh Bac Lieu	Control
Tinh Ca Mau	Control

Notes: This table shows the treatment status for every province in Vietnam.

Appendix B: Testing for Parallel Trends

We provide additional information for the parallel trends assumption. See Table A6 for the results of testing for parallel trends.

Table A6:

Testing for parallel trends.

Interaction	Coefficient	St. error	t-value	p-value
Treatment × 2008	−95.55	63.78	−1.50	0.134
Treatment × 2012	−52.99	182.04	−0.29	0.771
Treatment × 2016	−217.85	191.23	−1.14	0.255
Treatment × 2018	−200.95**	89.31	−2.25	0.026
Treatment × 2020	−461.15***	238.01	−3.16	0.016
Constant	633.74***	16.54	38.32	0.000
N	38,791
Household FE	Included
Time FE	Included
Controls	Included

Notes: This table is the result of testing for parallel trends. We regress the total education amount spent on a set of interaction variables (treatment × year), unit and time fixed effects, and a set of controls that include age of the child, school level, and parental marital status. ***p < 0.01, **p < 0.05, *p < 0.10.

Appendix C: Impact on Income

The key mechanism for the effects we estimate on educational expenditures is household income. Other papers, such as Hoang et al. (2020) and Ty et al. (2022), have provided evidence that household incomes decreased as a result of the disaster. Hoang et al. (2020) uses the Labor Force Survey to estimate that short-term household incomes decreased between 30 and 45 percent shortly after the disaster, with especially pronounced declines for fishing industry workers. Ty et al. (2022) provided qualitative evidence that many households were still struggling economically two years after the disaster. While the focus of this paper is not to estimate labor outcome changes, here we run similar difference-in-differences regressions (Equation (1)) to estimate that the income effects of the disaster still persisted up to four years after the disaster in the regions affected by the disaster (See Table A7). We estimate that incomes were still 10 percent lower than the control group four years after the disaster. Note that the data used here are cross-sectional data from the VHLSS. Estimates are unlikely to be as robust as estimates from Hoang et al. (2020), as part of their estimates were completed using panel data. Regardless, these estimates provide supporting context into why education expenditures could still be affected four years after the disaster.

Table A7:

Effects on monthly income.

	(1)	(2)	(3)	(4)
	Income (thousands VND)	Log(Income)	Log(Income) 2018	Log(Income) 2020
Treat × post	−1,146.98**	−0.111**	−0.123**	−0.104**
St. error	522.65	0.054	0.056	0.051
Observations	40,065	40,065	29,075	28,098
R-Squared	0.227	0.226	0.234	0.221

Notes: This table shows the impact of the fishing disaster on income across all outcome years (2018 and 2020) in columns 1 and 2, for 2018 only in column 3, and for 2020 only in column 4. The first column shows the raw income and the second column shows the log-transformed income for the combined outcome years. Columns 3 and 4 shows the log-transformed income outcomes for 2018 and 2020, respectively. Note that columns 2 through 4 are transformed to show the percentage changes in income. Also note that the units for income are in thousands VND. ***p < 0.01, **p < 0.05, *p < 0.10.

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Received: 2024-02-05

Accepted: 2025-02-01

Published Online: 2025-02-20

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Articles in the same Issue

https://doi.org/10.1515/bejeap-2024-0046

Keywords for this article

human capital; parental investments; education; disasters; labor shocks