Another cool thing from Google and R.

I've found a new cool stuff. Well I don't think it's really a new thing as Hans Rosling already started it here. Yet I used Indonesian Province-Level Data (e.g. Regional GDP, Net Enrollment rate in senior high school etc). The Chart below is aimed to look at the relationship between Regional GDP percapita (i.e. Province GDP) and Net Enrollment Rate in Senior School Level, for a given the population size.

I also set up the data by using R and a Google Project.

Before you play it: to make the chart interesting (and meaningful), you should change Color box in the upper right from Date to Unique Colors.  Then, you also need to change the X-Axis from Year to Income (i.e. Regional GDP percapita). Now we can observe the relationship between school and income percapita from 2004 to 2010.

Once you set all of these, now you're ready to play. Click play button in the bottom-left. Do you see any strong relationship?...if you think so, let me know :) [data are taken from BPS, 2004-2010]

CSIS made the 2008 political survey data for public

Voting behaviour survey data is available

On May 2008 the Centre for Strategic and International Studies conducted a voting behaviour survey with 3000 respondents and covering 13 provinces. The data is now available upon request for those who are interested. It is our hope that these individual level data may be useful to increase our understanding on the development of Indonesians voting behavior.

Please contact Sunny Tanuwidjaja to request the data.

His email is: sunny_tanuwidjaja@csis.or.id.

.....And We're Back!

It's official. We're back! As you may have noticed we've been quite inactive for a long period of time (blame us....we're procrastinator and good at it). However, while this blog fell into prolonged hibernation, we were kept busy elsewhere. Yudo was (and is still) occupied by his research projects, while also having a blog of his own. Meanwhile, I have just finished my studies, in my spare time also moonlights as a guest DJ at the cafe next door, and am now in the early stages of my reserch projects.

Then, some unknown and unseen force(s?) drove us back to revisit this blog. Yudo got the ball rolling by posting the result of his recent research on labor issues. One thing led to another, we thought why not revitalize the whole look for the blog? So we did a full cosmetic change, cleaning our blog from spammers, remove unnecessary stuffs, etc. Hope you'll like the end result.

Finally, as a bonus we present you with Thin Lizzy's "The Boys Are Back in Town". We think this song is appropriate for the occasion, enjoy :)

More on labor issue

This posting leads me to re-read again what I wrote and I stumbled with this fact. First, unit wage cost became less sensitive after the crisis period to employment. In the pre-crisis period, One percent increase in nominal wage cost in the TCF industry was associated by 0.25 percent employment reduction. Yet in the post-crisis period, the one-percent change in wage cost was associated by 0.09 percent employment change.

The impact of nominal wage cost on employment

Statistik Industri

My argument is :“..After the crisis, firms are likely not to dismiss workers immediately in responding to rising wage cost, particularly older workers with permanent job status, because of, largely, high severance payments. However, at the same time, stringent dismissal regulations leads firms to restrain hiring new permanent workers, though wage cost declines..”

Second: while at the same time, speed of adjustment in labor demand due to a shock is getting longer in the post-crisis period.

The Speed of Adjustment in Labor Demand (year)

“…Comparing between the pre and post crisis, we can say that the post-crisis labor market environment in the industry is much tighter that the pre-crisis environment. Two explanations can be offered here. First rising uncertainty in demand for output leads firms to keep the existing level of employment, despite a growing demand for output (e.g. footwear industry immediately after the crisis). Second, slow adjustment can be attributed to surging adjustment cost (i.e. firing cost). Therefore pointing high dismissal cost as an explanation behind this is quite justified…”

This situation applies only to permanent job in textile industry- part of formal employment. So given this, I cannot say that the impact of wage cost on formal employment will be the same as in the case of permanent workers. Yet this gives indication that even though firms' compliance rate to other labour regulations (non minimum wage) may be low, these have backwash effect for permanent job creation and, in turn, secure job.

Does trade-off between MW and employment levels apply to All?

The debate is going interesting. Though I used textile industry (not the whole economy) as my basic argument, the industry is still important as an indicator of formal employment. Prior to the crisis, employment in the textile industry was accounted for around one-third of all jobs in manufacturing.

Ape in his comment argued that “the wage-formal employment trade off is apparent” but paradoxically this is not the case of textile industry-one of labor-intensive industry and supposedly is much more sensitive to minimum wages (MW). Based on Sakernas data, such trade-off is absent indeed. From the figure below, we can clearly visualize that the percentage of formal employment followed closely MW.

MW is left axis & % of formal employment right axis (sakernas)

However, if we juxtapose real value added of textile industry and % of formal employment in the textile industry, it becomes obvious that output determines employment levels. Putting technical change together, it seems that the change in factor intensity affects employment levels.

Real value added (trillion Rp)

If the formal sector employment starts to grow amid stringent EPL, it does not necessarilly mean that EPL is not an obstacle (or has less impact). Solid domestic output demand may compensate the impact of EPL on employment. But if we relaxe this, perhaps we may see that formal employment grows faster. But it is better for me to say that i don't know exactly which one is the main obstacle (MW or EPL) on employment. But based on my study in textile industry, the employers are concerned much more on the latter (EPL) rather than the former (MW).

Conclusion:
The impact of MW on employment levels seems to vary within formal jobs. I don't say that MW does not bring an adverse impact on employment levels. Yet growing output demand recently is likely to compensate the impact of MW. MW and EPL are determinants affecting input cost of firms and in turn labor demand. But output demand is another driver affecting labor demand as well. Another issue is about data reliability-well I don’t know exactly how to get around this issue.

Update
this is one more

How technical change affects the employment levels

This posting is triggered by Ape’s posting. Increasing minimum wages (MW) is considered as the main factor of slowing formal employment growth. But is it true? I’m a bit skeptical to this argument. I do not undermine the impact of MW on formal sector employment, but just question its magnitude. Based on my research on textile industry (labor-intensive industry), I found how technical change also contributes to decelerating employment growth in the formal sector. Here is the excerpt

“…fiber industry and spinning industry are characterized as relatively capital-intensive industry. On the other hand, the apparel industry and footwear industry are classified as labor-intensive industry. This category is reflected by the figure X. The capital intensity of textiles industry (left-axis) is far above the capital intensity of apparel and footwear industry (right-axis). However, several trends are worth pointing. First the 1998 economic crisis has brought a significant change in the factor intensity. In three sub sectors, the capital intensity increased dramatically in the crisis period. The main driver was because the crisis led to a surging capital price and massive-scale of job dismissal (i.e. compositional change).

Figure x. Capital Intensity in Textile, Apparel and Footwear industrycalculated from statistical industry
green: textile; red:apparel and black: footwear

..Nevertheless the capital intensity of textile industry returned back to the pre-crisis level with the economic recovery. This trend, surprisingly, did not occurred in apparel and footwear industry. The capital intensity in apparel and footwear industry continued to grow. It is obvious that apparel and footwear industry would not change dramatically into capital-intensive industry. Yet the increase brings a notable consequence to job creation. Moreover, the increase in the capital intensity of apparel and footwear industry has decelerated employment creation in these sectors. In sum, a rising capital intensity occurring in 1995 has been followed by slowing employment growth in the apparel and footwear industry (discussed in the Chapter on Labour). This suggests that there has been a strong substitute from labor to capital in the TCF industry…”

In other words, slowing employment growth in textile industry already took place before the crisis and, surely, before the rapid increase in MW. I’m thinking that perhaps other labour regulations affecting much employment growth instead of MW.

Is trade diversion bad? (2)

Now we relax our first assumption. Suppose consumption on hat is closely (but not perfectly) substituted by clothing. Notice the difference in the utility curve. In the graph 2 now we have a curly utility curve because of our assumption: both goods are closely substituted.

The story is the same as before-NJ signs FTA with WJ and utility level of North Jakarta under FTA, U_NJ^FTA, is lower than utility level under free trade, U_NJ^FT. But look at carefully, U_NJ^FTA curve crosses blue dash line T** (situation where NJ imposes tariff to both WJ and Tangerang). What does it mean? It clearly shows that bundle consumption in B gives the same utility level as bundle consumption in C. In other word, utility level under FTA is as good as utility level under tariff-to-all system.

From these two graphs, i conclude that: it is true FTA may create trade diversion. But is trade diversion really bad in terms of welfare? My answer is not necessarily. Trade diversion is not necessarily bad for the welfare of North Jakarta if West Jakarta efficiency in producing hat is quite close to Tangerang efficiency.

Some of you may argue, why I don’t draw blue dash line crossing point A which is the same as the graph 1. Well, I can do that but as in this case the individual preference is different from the one on the graph 1, the blue dash line crossing point A means nothing (why?).

What happen next if we, once again, take both assumptions off (there are substitution goods and more than one factor production). The result strongly proposes that trade diversion is not necessarily bad for welfare.

If so, does it mean that FTA is the best deal? The answer is surely no. This simple example only justifies the welfare of home country due to FTA where its partner’s efficiency in FTA is pretty close to other countries out of FTA. More importantly, in this case I assume that the cost of doing and cost of FTA itself are close to zero-which is in many cases unrealistic.