Impact of a Flexible Work Policy on Employee Output: Difference-in-Differences

Difference-in-Differences for Human Resource Management

Field: Human Resource ManagementLevel: Doctoral dissertation (S3)Method: Difference-in-Differences

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The goal is to estimate the flexible work policy effect by comparing before and after; the data are synthetic and for illustration only. Difference-in-Differences (DiD) estimates the causal impact of a programme or policy by comparing outcome changes in treated and control groups before and after the policy starts. It suits programme evaluation theses when assignment is not random but data for several periods are available.

Impact of a Flexible Work Policy on Employee Output: Difference-in-Differences: result sheet

Project info

Field of studyHuman Resource Management
LevelDoctoral dissertation (S3)
Method typeEconometrics
Estimated build time± 7 working days
Toolsstats::lm, sandwich::vcovCL, lmtest::coeftest, ggplot2
PriceUS$31 - US$203

Research goal

The goal is to estimate the flexible work policy effect by comparing before and after; the data are synthetic and for illustration only.

Variables

Role	Variable	Unit
Main	Output per employee	units
Supporting 1	Flexible work participant	0/1
Supporting 2	Tenure	years

Example result

-36.148DiD estimate
<0.001DiD p-value
2.588Clustered SE
0.204Pre-trend p-value
2.402Coef. Tenure
0.331R2

The DiD estimate shows the programme lowered Output per employee by 36.148 (95% CI -41.227 to -31.069; clustered SE, p < 0.001) relative to the control group. The pre-policy trend test (p = 0.204) supports parallel trends, and the event-study plot shows period-by-period effects relative to period 4.

Computed from illustrative synthetic data, not from a real study.

About Difference-in-Differences

Difference-in-Differences (DiD) estimates the causal impact of a programme or policy by comparing outcome changes in treated and control groups before and after the policy starts. It suits programme evaluation theses when assignment is not random but data for several periods are available.

Steps

1Arrange a unit by period panel with treatment group and post-policy indicators
2Plot mean outcomes per period per group to inspect trends
3Fit the DiD model lm(Y ~ treated * post + covariate)
4Compute unit-clustered standard errors with sandwich::vcovCL and lmtest::coeftest
5Test parallel trends by comparing pre-policy trend slopes
6Fit an event study to see period-by-period effects relative to the last pre-policy period

Assumptions to check

Without the policy, treated and control outcomes would follow parallel trends
No other event affects only one group at the same time
Group composition is stable and there are no spillovers to the control group

Reading and reporting the results

The treated x post interaction is the DiD estimate, the average programme impact on the treated group; report its size, confidence interval and clustered p-value. Support the parallel trends assumption with the pre-trend test and an event-study plot whose pre-policy coefficients are close to zero.

Example R code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

Show code

# Contoh kode R: Difference-in-Differences
suppressPackageStartupMessages({
  library(ggplot2)
  library(patchwork)
  library(sandwich)
  library(lmtest)
})
set.seed(42)
# ---- Gaya grafik ----
pal <- c("#1f6feb", "#e8590c", "#2f9e44", "#ae3ec9", "#f59f00")
warna <- list(bg = "#f4f6fa", panel = "#ffffff", teks = "#1f2933", redup = "#5f6b78", grid = "#e3e8ee", bar = "#1d3557")
tata <- 1
theme_set(theme_minimal(base_size = 11) + theme(legend.position = "bottom"))

# ---- Data sintetis: panel unit x 8 periode, kebijakan mulai periode 5 ----
n_unit <- max(60, round(200 / 3))
n_per <- 8; mulai <- 5
d <- expand.grid(periode = seq_len(n_per), entitas = sprintf("U%03d", seq_len(n_unit)))[, c("entitas", "periode")]
d$entitas <- as.character(d$entitas)
idx <- match(d$entitas, sprintf("U%03d", seq_len(n_unit)))
grp <- rbinom(n_unit, 1, runif(1, 0.4, 0.55))   # 1 = kelompok perlakuan
d$flexible_work_participant <- grp[idx]
d$pasca <- as.integer(d$periode >= mulai)       # 1 = setelah kebijakan berlaku
ui <- rnorm(n_unit)[idx]                         # efek unit
tren <- cumsum(c(0, rnorm(n_per - 1, runif(1, 0.05, 0.15), 0.08)))[d$periode]   # tren waktu bersama
z2 <- 0.3 * ui + rnorm(nrow(d))
d$tenure <- round(30 + 6 * z2, 3)
att <- runif(1, 0.35, 0.8) * sample(c(-1, 1), 1, prob = c(0.35, 0.65))   # efek kebijakan (satuan SD)
rampa <- pmin(1, 0.6 + 0.15 * (d$periode - mulai))                          # efek menguat bertahap
d$output_per_employee <- round(100 + 15 * (0.5 * ui + runif(1, 0.2, 0.5) * d$flexible_work_participant + tren + 0.2 * z2 +
                                      att * d$flexible_work_participant * d$pasca * rampa + rnorm(nrow(d), 0, 0.25)), 3)
write.csv(d, "data_contoh.csv", row.names = FALSE)

# ---- Analisis: DiD dua arah dengan galat baku klaster per unit ----
fit <- lm(output_per_employee ~ flexible_work_participant * pasca + tenure, data = d)
vc <- sandwich::vcovCL(fit, cluster = ~entitas)
ct <- lmtest::coeftest(fit, vcov = vc)
print(ct)
did <- ct["flexible_work_participant:pasca", ]
ci_did <- did[1] + c(-1, 1) * qt(0.975, fit$df.residual) * did[2]
# ---- Uji tren paralel: beda kemiringan tren pra-kebijakan antar kelompok ----
pra <- subset(d, pasca == 0)
fit_pra <- lm(output_per_employee ~ flexible_work_participant * periode + tenure, data = pra)
ct_pra <- lmtest::coeftest(fit_pra, vcov = sandwich::vcovCL(fit_pra, cluster = ~entitas))
print(ct_pra)
p_tren <- ct_pra["flexible_work_participant:periode", 4]
# ---- Event study: efek per periode relatif terhadap periode sebelum kebijakan (periode 4) ----
de <- d
per_lain <- setdiff(seq_len(n_per), mulai - 1)
for (k in per_lain) de[[paste0("ev", k)]] <- de$flexible_work_participant * (de$periode == k)
f_es <- as.formula(paste("output_per_employee ~ factor(periode) + flexible_work_participant + tenure +", paste0("ev", per_lain, collapse = " + ")))
fit_es <- lm(f_es, data = de)
ct_es <- lmtest::coeftest(fit_es, vcov = sandwich::vcovCL(fit_es, cluster = ~entitas))
es <- data.frame(periode = c(per_lain, mulai - 1),
                 b = c(ct_es[paste0("ev", per_lain), 1], 0), se = c(ct_es[paste0("ev", per_lain), 2], 0))
print(es[order(es$periode), ])
r2 <- summary(fit)$r.squared


# ---- Grafik ----
d$kel <- factor(ifelse(d$flexible_work_participant == 1, "Treated", "Control"), levels = c("Treated", "Control"))
# 1) rata-rata Y per periode per kelompok + kontrafaktual kelompok perlakuan
rt <- aggregate(output_per_employee ~ periode + kel, data = d, FUN = mean)
mt <- rt[rt$kel == "Treated", ]; mc <- rt[rt$kel == "Control", ]
celah <- mean(mt$output_per_employee[mt$periode < mulai] - mc$output_per_employee[mc$periode < mulai])
kf <- data.frame(periode = mc$periode[mc$periode >= mulai - 1], y = mc$output_per_employee[mc$periode >= mulai - 1] + celah)
p1 <- ggplot(rt, aes(periode, output_per_employee, colour = kel)) +
  annotate("rect", xmin = mulai - 0.5, xmax = n_per + 0.3, ymin = -Inf, ymax = Inf, fill = pal[5], alpha = 0.08) +
  geom_vline(xintercept = mulai - 0.5, linetype = "dashed", colour = warna$redup) +
  geom_line(data = kf, aes(periode, y), colour = pal[1], linetype = "dotted", linewidth = 0.9, inherit.aes = FALSE) +
  geom_line(linewidth = 1.1) + geom_point(size = 2.2) +
  annotate("text", x = mulai - 0.4, y = Inf, label = "Policy starts", hjust = 0, vjust = 1.5, size = 3,
           colour = warna$teks) +
  scale_colour_manual(values = c(Treated = pal[1], Control = pal[2]), name = NULL) +
  scale_x_continuous(breaks = seq_len(n_per)) +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) +
  labs(title = "Group means by period (dotted = counterfactual)", x = "Period", y = "output_per_employee")
# 2) event study dengan IK 95%
p2 <- ggplot(es, aes(periode, b)) +
  geom_hline(yintercept = 0, colour = warna$redup) +
  geom_vline(xintercept = mulai - 0.5, linetype = "dashed", colour = warna$redup) +
  geom_ribbon(aes(ymin = b - 1.96 * se, ymax = b + 1.96 * se), fill = pal[3], alpha = 0.15) +
  geom_errorbar(aes(ymin = b - 1.96 * se, ymax = b + 1.96 * se), width = 0.15, colour = pal[3], linewidth = 0.8) +
  geom_point(size = 2.6, colour = pal[3]) +
  scale_x_continuous(breaks = seq_len(n_per)) +
  labs(title = sprintf("Event study (ref. period %d, 95%% CI)", mulai - 1), x = "Period", y = "Effect on output_per_employee")
# 3) tabel 2x2 rata-rata sebelum/sesudah
r22 <- aggregate(output_per_employee ~ kel + pasca, data = d, FUN = mean)
r22$waktu <- factor(ifelse(r22$pasca == 1, "After", "Before"), levels = c("Before", "After"))
p3 <- ggplot(r22, aes(waktu, output_per_employee, colour = kel, group = kel)) +
  geom_line(linewidth = 1) +
  geom_point(size = 3) +
  geom_text(aes(label = sprintf("%.1f", output_per_employee)), vjust = -1, size = 3, colour = warna$teks, show.legend = FALSE) +
  scale_colour_manual(values = c(Treated = pal[1], Control = pal[2]), name = NULL) +
  scale_y_continuous(expand = expansion(mult = c(0.12, 0.22))) +
  labs(title = sprintf("2x2 means (DiD = %.2f)", did[1]), x = NULL, y = "Mean output_per_employee")
p_hasil <- if (tata == 1) p1 / (p2 | p3) + plot_layout(heights = c(1.2, 1)) else p1 | (p2 / p3)

print(p_hasil)

The project file you can download

The original project file is available. It is not just the picture above: you get the working R project. It can be downloaded after payment, and we send it by WhatsApp or email.

ZIP: analisis.R + data_contoh.csv + BACA-DULU.txt

What is inside the ZIP

The complete analysis script (analisis.R), commented step by step, ready to run in RStudio.
A sample dataset (data_contoh.csv) so the script runs straight away.
A short guide (BACA-DULU.txt): which packages to install, how to run it and how to swap in your own data.

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

Package 1

Code and Explanation

US$31

excl. 11% VAT

Duration: 7 working days

Output: R files + result figures

Deliverables

Fully commented R code (.R) for Difference-in-Differences
Example data and step-by-step run instructions
Ready-to-use result figures and metrics table
Short explanation of the method, its assumptions and how to read the results

Choose Package 1

Package 2 · most chosen

Your Data Analysis + Interpretation

US$94

excl. 11% VAT

Duration: 13 working days

Output: Code + analysis + interpretation notes

Includes all of Package 1, plus

Adapting the code to your own research dataset (CSV or Excel)
Assumption checks, result validation and data cleaning
Tables and figures formatted for your manuscript
Interpretation notes for the methods and discussion sections, for you to write up in your own words

Choose Package 2

Package 3

Full Guidance

US$203

excl. 11% VAT

Duration: 18-21 working days

Output: Code + analysis + guidance until ready to present

Includes all of Packages 1 & 2, plus

Comparison against one alternative method with the reasoning for the choice
Two 60-minute online coaching sessions so you fully understand the code and results
Revisions following your supervisor feedback (up to 3 rounds)
Practice Q&A to prepare for your seminar and defence
Full documentation (README, R package list (sessionInfo)) so the results can be reproduced

Choose Package 3

Payment: 50% down payment before work starts, balance after the deliverables are handed over
Tax: Prices exclude 11% VAT
Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions

Do I get the real project file?

Yes. The original project file is a ZIP containing the complete analisis.R script, a data_contoh.csv sample dataset and a BACA-DULU.txt guide. It can be downloaded after payment, and we send it by WhatsApp or email. For your own data (Package 2 and 3) the script is adapted to your dataset.

Is this suitable for a Doctoral dissertation (S3)?

Yes. Difference-in-Differences is commonly used at this level in Human Resource Management. We adapt the analysis to your research question, your own data and your supervisor's requirements.

Is the result sheet real data?

No. The sheet is computed from illustrative synthetic data so you can see the kind of output R Studio gives. For your order we run the same method on your own data.

Will I understand the code?

That is the point of the packages. Every script is commented, and Package 3 includes two coaching sessions plus practice questions so you can explain the method and the results at your seminar and defence.

How is the price decided?

Each project has three packages: Package 1 (code and explanation) US$31, Package 2 (your data analysis and interpretation) US$94 and Package 3 (full guidance) US$203, excluding 11% VAT. Larger datasets, extra methods or tight deadlines adjust the quote after a short consultation.

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