ES2015_RMarkdown.Rmd

---
title: "Malaysia 2015 Enterprise Survey"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
	echo = FALSE,
	message = FALSE,
	warning = FALSE
)
library(tidyverse)
library(plotly)
library(oidnChaRts)
library(highcharter)
library(readstata13)
library(ggplot2)
setwd("C:/Users/**/WB Enterprise Surveys")
ES2015MY <- read.dta13("Malaysia-2015-full data LN.dta")
```

## Introduction

This post is the first in a series of my analysis of the 2015 Enterprise Surveys conducted by the World Bank. I obtained the data set directly from Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank, for research purposes.

The Enterprise Surveys use standard survey instruments to collect firm-level data on the business environment from business owners and top managers. The surveys cover a broad range of topics including access to finance, corruption, infrastructure, crime, competition, labor, obstacles to growth, and performance measures. Below is a distribution of firms surveyed by region and business type. You can hover your mouse over each bar to get more information:

```{r}
ES2015MY %>%
  group_by(region, b1) %>%
  summarise(total = sum(id)) %>%
  ungroup() %>%
  hchart("bar",
         hcaes(y = total,
               x = region,
               group = b1)) %>%
  hc_plotOptions(series = list(stacking = "stack"))
```

The above illustrates that the bulk of the firms surveyed are from the Central region, which includes the Klang Valley and the capital Kuala Lumpur. The North, inclusive of Penang follows in second place. Partnerships are the most common business type among the firms surveyed. Sole Proprietorships are also common. Below is a breakdown of shares of business types surveyed by region:
```{r}
ES2015MY %>%
  group_by(region, b1) %>%
  summarise(total = sum(id)) %>%
  ungroup() %>%
  hchart("bar",
         hcaes(y = total,
               x = region,
               group = b1)) %>%
  hc_plotOptions(series = list(stacking = "percent")) %>%
  hc_tooltip(shared = TRUE)
```


## Performance Drivers
The panel below shows the relationship between exports and the share of sales from a new product or service. It appears that Manufacturing firms whose sales from a new product are over 50% are likely to export. Retail services, which is a domestic-oriented sector shows limited propensity to export and create new products and services. Note that each dot represents a city/town/village where the establishment is based.
```{r}
p <- ggplot(data = ES2015MY, aes(y = newprod_sales, x = exports)) +
  geom_point(aes(text = paste("Location:", location)), size = 0.3) +
  geom_smooth(aes(colour = industry, fill = industry)) + facet_wrap(~ industry) + theme(legend.position="none")
 
(gg <- ggplotly(p))

```
Another driver of export propensity appears to be government or government-linked support as shown below:
```{r}
library(ggplot2)
bp <- ggplot(data=ES2015MY, aes(x=k9, y=exports, fill=k9, outline=FALSE)) + geom_boxplot() + theme(legend.position="none")
(gg <- ggplotly(bp + coord_flip()))
```

## Biggest Obstacle to Enterprise Operations
When asked what the main obstacles to business was here is what firms across the 5 regions had to say:
```{r}
ES2015MY %>%
  group_by(region, obstacle) %>%
  summarise(total = sum(id)) %>%
  ungroup() %>%
  hchart("bar",
         hcaes(y = total,
               x = region,
               group = obstacle)) %>%
  hc_plotOptions(series = list(stacking = "percent")) %>%
  hc_tooltip(shared = TRUE)
```

Most state that competition from firms in the informal sector is the main challenge. In the East Coast of Peninsular Malaysia, it was Business Licensing and Permits. In Sabah/Sarawak, firms stated Customs and Trade Regulations, in addition to Practices of Informal Firms and Tax Rates.

Data Source: Enterprise Surveys (http://www.enterprisesurveys.org), The World Bank