Dashboard update: Jobs data and new uncertainty

Dashboard PDF file:

Macro and Markets Dashboard: United States (May 7, 2016 — PDF)
Dashboard Update Summary:

Jobs data for April showed payrolls continue to grow, but at a slower rate. Wage data was strong, however, the labor force participation rate gave up much of its recent improvement. Uncertainty surrounding markets and economic policy seems to have increased in the recent week, and fewer economists now predict a Fed rate hike in June. U.S. equity markets were down for the second consecutive week, while corporate bond yields rose and treasury yields fell. Recent data showed improvement in the trade balance from the weaker dollar, however, the recent depreciation trend has also become less certain.

Jobs Report showed slower jobs growth but wage improvement

The U.S. added 160,000 jobs in April, compared with 208,000 in March and 233,000 in February (both previous months were also revised downward). By sector, much of the growth came from the services side, on an annualized basis. Construction jobs, which make up less than five percent of nonfarm payrolls, were up 4.1 percent, while mining and logging jobs continued their decline and are now down more than fifteen percent over the past year (this is the smallest industry sector shown in the figure below, and represents only 0.4 percent of nonfarm payrolls). Weekly data on new jobless claims, as of April 30, showed still very low, but slightly increased, levels.


The latest jobs report shows continued improvement in both nominal and real wages in practically all sectors. Nominal wages increased most rapidly over the past year in financial services, information services, and leisure and hospitality. On average, wages from the goods sector are higher, largely as a result of low-wage service-sector jobs in leisure and hospitality.


Equity and Bond market conditions deteriorated

Equity markets were down for the second straight week. The S&P 500 was down 0.4 percent, the Nasdaq composite index was down 0.8 percent, and the Dow Jones industrial average was 0.2 percent lower. Volatility was higher during the week, and the VIX closed Friday at 14.7. The Shiller index of price to earnings ratios was up to 26.02 percent in April from 25.54 in March. Corporate bond yields ticked up during the week. The Merrill Lynch index of junk bond yields was up to 7.56 percent. Ten year treasury yields fell to 1.79 percent.

Economic policy uncertainty improved in April but may revert

Economic policy uncertainty, as measured by Baker, Bloom, and Davis, fell sharply in April, as there was little speculation of Fed action at the April meeting. However, I expect this index to bounce back; uncertainty will increase as the Fed June meeting and Brexit grow closer.


Oil was down on the week, while April food prices increased

Oil prices closed lower on the week. The U.S. measure of crude oil prices, West Texas Intermediate crude front-month contracts, fell 2.7 percent during the week, to $44.66 a barrel. World food prices from the Food and Agriculture Organization (which I half-jokingly also use as a proxy of political instability) ticked up slightly in April, but remain low.

A weaker dollar improved the trade balance in March

The Fed’s trade-weighted dollar broad index against major currencies fell last Friday (April 29–past week data is released on Mondays) to its lowest level since May 2015. The year-to-date rapid depreciation of the dollar has cut import quantities, as further evidenced in the March data on trade. The trade deficit, which remains roughly 2.2 percent of GDP, improved to -40.4B in March. However, more recent foreign exchange data shows uncertainty about recent depreciation trends. The dollar was stronger against nearly all major trading partners during the past week, notably 1.2 percent against the British pound, 2.76 percent against the Canadian dollar, 3.16 percent against the Australian dollar, 4.5 percent against the Turkish lira, 3,86 percent against the Mexican peso, and 4.3 percent against the South African rand.


Dollar-indices: hitting recent lows (and how to plot with Python)

While the dollar has depreciated against most major currencies over the past month (notably 4.4% against the Canadian dollar, 3.3% against the Yen, 4.2% against the Real, and 8.6% against the Rand), the Fed H.10 release provides measures of exactly how much the value of the greenback has fallen relative to a weighted set of its trading partners. The latest data show that the U.S. dollar has fallen on April 12 to its lowest level since June 2015 compared to other major currencies, and its lowest level since October 2015 compared to a broad index of currencies.

This post covers the trade-weighted dollar and is split into two segments: a description of the index and its recent behavior, and a short python script showing how you can use the pandas and matplotlib libraries to retrieve the time series and plot it.

Two trade-weighted dollar indices, described

The Fed’s two most common trade-weighted indices of the foreign exchange value of the U.S. dollar are the major-currencies index and the broad-index. Both measure the value of the dollar relative to other countries’ currencies and intend to be aligned, through their weighting system, with the currencies closely related to U.S. trade patterns. The broad index, however, covers more currencies, and especially more emerging market currencies.

Discrepancies between even the broad index and the way the dollar is actually traded are inevitable. For example, the broad index weight in 2016 for Canada is 12.664 percent (broad index weights are updated yearly), whereas year-t0-date, 15.2 percent of total U.S. trade has been with Canada. From a share-of-total-U.S.-trade perspective, the broad index is somewhat overweight the Yuan, Euro, and Yen, and underweight the Canadian dollar and Mexican peso.

The major currencies index contains fewer currencies than the broad index, and has fallen to a 22-month low. (plot from dashboard)

From 2005 through 2015, the major currencies index remained largely within a range of 70-85. After a steep climb through 2015, in January, the measure peaked at 95.8, to the frustration of U.S. exporters whose customers essentially pay more for the same goods (and as a result buy fewer). On April 12, the major currencies index hit its lowest level since June 2015.

Meanwhile, the broad index has moved in the same direction, hitting a five month low on April 12. The plot of the broad index is below, including how to obtain it. You can substitute DTWEXB with DTWEXM if you are interested in the major currencies index.

Python: Retrieve and plot the trade-weighted dollar

Next we show how Python can be used to gather and plot data on the Fed’s broad index of the foreign exchange value of the dollar. The script gathers data from Fred and plots each business day’s index value since the start of 2014.

Gathering data

First, we import pandas, numpy, and matplotlib and give them conventional short names. We will also use datetime and date.

In [1]:
# Import libraries
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import datetime
from datetime import date

Next, we use the pandas.io.data package to request the data from Fred. I’ve found the code for our series of interest, DTWEXB, by searching, but you can also find it on the Fred site by source (Board of Governors of the Federal Reserve System), or by release (H.10). We paste the series code into the datareader and provide start and end dates. Pandas retrieves the data into a dataframe.

In [2]:
import pandas_datareader.data as webdata
tstart = datetime.datetime(2014, 1, 1)
#retrieve trade-weighted dollar data from fred
dtwexb = webdata.DataReader("DTWEXB", "fred", tstart);
#display five most recent observations
2016-04-11 119.4527
2016-04-12 119.2860
2016-04-13 119.6278
2016-04-14 119.6472
2016-04-15 119.6701

When was the last time the measure was as low as its April 12 value?

In [3]:
print dtwexb[119.29>=dtwexb].dropna().tail(2)
2015-10-21  119.1786
2016-04-12  119.2860

Line plot of data

Lastly, we can use matplotlib to plot the data.

In [4]:
#Create figure and plot dtwexb
fig = plt.figure(figsize=[7,5])
ax1 = plt.subplot(111)
line = dtwexb.DTWEXB.plot(color='blue',linewidth=2)

#Add a title
ax1.set_title('Trade-Weighted U.S. Dollar, Broad Index (1997=100)', fontsize=15)

#Add y label and no x-label since it is dates

#Axis options
ax1.tick_params(axis='x', which='major', labelsize=8)

#Annotate with text
fig.text(0.15, 0.85,'Last: ' + str(dtwexb.DTWEXB[-1]) \
         + ' (as of: ' + str(dtwexb.index[-1].strftime('%Y-%m-%d'))\
         + ')');
url = 'https://research.stlouisfed.org/fred2/series/TWEXB'
fig.text(0.05, 0.025, 'Source: ' + url)
fig.text(0.65, 0.16, 'briandew.wordpress.com')

#Save as png
plt.savefig('dtwexb.png', dpi=1000)
The broad index hit a five-month low on April 12.