Tom Pepinsky

Category: Asia

  • Chinese Overseas Workers in Indonesia

    This week’s issue of Tempo features several stories on Chinese laborers in Indonesia. The cover is as evocative as it gets:

    resize
    “Welcome, Chinese Laborers,” the title reads.

    Under the headline “A Flood of Workers from the Panda Country”* [= Banjir Pekerja Dari Negeri Panda], we find a description of some of these workers. I am certainly not alone in finding the headline to be unnecessarily inflammatory and provocative. A flood? Hardly. But I do think that the details about how labor migration from China to Indonesia works are very interesting. Given that there are strict regulations on foreign labor in Indonesia to prohibit labor market competition, and given that most of the laborers described in that article are low-skilled manual laborers and machinists, how are these workers getting the permits they need?

    Well, one way is to take advantage of bureaucratic “weaknesses” [= kelemahan].

    Seorang kuli asing bisa mengantongi izin karena memanfaatkan “kelemahan” pejabat di bagian pelayanan perizinan.

    Pejabat bagian pelayanan tidak ketat menerapkan syarat: satu pekerja asing harus didampingi satu tenaga lokal. Dalam prosedur, dokumen biodata pekerja lokal harus dilampirkan bersamaan dengan biodata si tenaga asing.

    Seorang calo bercerita, biodata tenaga lokal pendamping hanya formalitas. Ia selalu meminta “klien”-nya menyerahkan biodata karyawan yang disebut sebagai tenaga pendamping. Ia meyakinkan, perusahaan tidak perlu khawatir karena pejabat Kementerian Ketenagakerjaan jarang mengecek keabsahannya. “Kalau ada pengecekan, ya, pura-pura sebagai tenaga pendamping,” katanya.

    A foreign laborer** can get a permit because of the “weakness” of the officials in the permit services division.

    Officials there do not closely follow regulations: a foreign worker must be paired with a local worker. Following the procedures, the biodata of the local worker has to be submitted together with the biodata of the foreign worker.

    But a fixer explained that the biodata of the local counterpart is just a formality. He always instructs his “clients” to submit the biodata of an employee who is deemed the counterpart. He assures them that the company does not need to worry because the officials of the Ministry of Manpower rarely investigate the validity of the data. “If they check, well, they pretend to be the counterpart,” he said.

    So, just fill out dummy forms. Or you can bribe an official.

    Salah satu calo memungut Rp 8,5 juta untuk mengurus izin satu orang tenaga kerja asing. Ia menjamin, dengan tarif itu, perusahaan sponsor memperoleh izin mempekerjakan tenaga asing (IMTA) dan kartu izin tinggal terbatas (kitas).

    One fixer estimated a price of approximatedly US$ 640 to arrange a permit for one foreign worker. He maintained that at this price, the sponsoring company would receive both a work permit (IMTA) and a limited-stay residency permit (KITAS).

    OK, so those are the details. What is the motivation, though? Given that Indonesia is a labor-rich country itself, with a good deal of labor market slack, why would a Chinese firm operating in Indonesia need to import low-skilled laborers from China? Well, according to one Indonesian manager, “etos kerjanya luar biasa” [= they have an extraordinary work ethic]. The other argument we find in the Tempo piece is that the machinery uses manuals that are printed in Mandarin.

    I wonder about that. There has been a lot of press coverage of Chinese workers in Africa, and lately also about Chinese workers in Latin America (see this recent story about Ecuador). I wonder if the recent rise in Chinese labor exports to Indonesia is just following the same pattern, or if it’s something different.

    NOTES

    * Describing countries by with reference to some stereotype is common in Indonesian. So “Panda Country” = China, Negeri Paman Sam [= Uncle Sam Country] is the U.S., Negeri Matahari Terbit [= Country of the Rising Sun] is Japan, Negeri Kanguru [= Kangaroo Country] is Australia, Negeri Beruang Merah [= Country of the Red Bear] is Russia, Negeri Matador is Spain. Some of these terms are more offensive than others.

    ** I love the use of the word kuli here. It shares the same root as—indeed, it is the same word as—coolie. The etymology of coolie is interesting in and of itself.

  • The Chinese Growth Differential

    This months dramatic volatility in the Chinese stock market has raised the question once more about after twenty years of breakneck economic growth, China’s growth trajectory is sustainable. Many consider a correction of some sort to be inevitable (see for example Brad Delong here). One strategy to look for signs that this is happening is to look at various indicators of economic activity within China, as James Hamilton has done. It is also useful, though, to step back a bit to see what we think Chinese growth ought to look like given what we know about economic growth in the rest of the world.

    I’ve done this by creating an empirical model of economic growth for all countries except for China. The model looks like this:

    gyt-t0 = yit0 + Xit + Dt + Dr

    where gyt-t0 is annual (geometric) growth in real GDP per capita over a five year period, yit0 is the initial level of real GDP per capita for that five year period, and Xit are the five-year averages of the other determinants of economic growth. Dt and Dr are period and region fixed effects, respectively. I’ve done this two ways, both including and excluding time and region effects. Naturally, country fixed effects would be nice, but including them effectively precludes me from being able to predict how Chinese growth might differ from growth elsewhere. So to be clear, any distinctive “Chinese” growth effect is captured in the “East Asia” dummy.

    These are what I’d call “appropriately naive Barro regressions.” They follow in the spirit of Barro’s classic Determinants of Economic Growth in modeling economic growth as a function of initial per capita GDP in levels plus measures of human capital, government policy, macroeconomic conditions, and regime type. They differ in that they do not even attempt to untangle issues of causality. No three-stage least squares using lags, colonial history, and regional dummies as instruments, or anything of the sort. Hence these regressions are “naive,” but that is “appropriate.”

    Using data from the World Development Indicators augmented by some additional variables from the QoG dataset, I estimated the two models described above for all countries in the world except for China. I then predicted what Chinese growth would look like using actual data from China. This gives us yearly predictions: in any year, given China’s level of per capita GDP and other variables, what is our best guess about its rate of growth in that year?

    I have plotted the resulting predictions here. The black series is the prediction that includes region and time effects, and the blue series ignores these. The red solid line is China’s actual economic growth.
    growth
    As you can see, since 1991 China has grown far faster than the appropriately naive model predicts. What is also true, though, is that the differences between China’s predicted and actual growth rates are narrowing considerably since 2011. This might be interpreted as a reversion to the “predicted” growth path based on the cross-national experience of the past forty years. What we observe, then, is a “Chinese growth differential.”
    differential
    That differential was lower in 2014 than it had been since the aftermath of Tiananmen.

    There are some other interesting things to note. The standard neoclassical growth model predicts convergence, in which countries growth rates slow as their GDPs rise. As Barro showed, the data do not support this simple story; they instead support a story of conditional convergence in which countries growth rates slow conditional on macroeconomic and political conditions (although see Rodrik recently and Quah not-so-recently for other views). The blue and black lines in the first graph above show a gentle increase in predicted growth rates. One interpretation of this is that, in a conditional convergence world, changes in living standards in China have actually outpacing the increases in GDP as determinants of economic growth. A wealthier China in 2010 should grow faster than China of 1990 due to the rapid increases in health and education.

    What does this mean? Recall that this prediction model cannot account for anything particular to China. So as a result it cannot tell us if any such Chinese particularity is durable or not, or if recent growth has been atypical relative to what China’s growth should be like. The estimated differential is relative to a counterfactual of all other countries’ growth experiences, not relative to some counterfactual version of China.

    But if we hold that caveat aside, as well as the problems of causal identification in naive (but appropriate!) growth regressions, it confirms that slower Chinese growth is to be expected. The interesting part is how this interacts with current events, in particular China’s stock market crisis and its political fallout. None of the above predictions suggests that a stock collapse is inevitable, but such a collapse might indeed hasten the shift toward to a more modest growth path. Check back in six months to see if that is the understatement of the year.

    NOTES

    For R code to produce these graphs, please see the first comment. Here are the full model results for Model 1 and Model 2.

    Dependent variable:
    growth
    (1) (2)
    log(GDPPC.Initial) -0.520*** -0.702***
    (0.093) (0.121)
    Fixed Capital Formation 0.154*** 0.153***
    (0.010) (0.010)
    Gov Final Cons Exp -0.060*** -0.055***
    (0.012) (0.011)
    Trade/GDP 0.008*** 0.011***
    (0.002) (0.002)
    Inflation -0.003*** -0.003***
    (0.0003) (0.0003)
    Life Expectancy 0.036** 0.036*
    (0.017) (0.021)
    Secondary Enrollment Rate 0.005 0.003
    (0.005) (0.006)
    Polity2 Score 0.043*** 0.044***
    (0.015) (0.017)
    Constant -0.054 3.199**
    (0.863) (1.244)
    Observations 867 867
    R2 0.377 0.454
    Adjusted R2 0.372 0.438
    Residual Std. Error 2.447 (df = 858) 2.314 (df = 841)
    F Statistic 65.009*** (df = 8; 858) 27.992*** (df = 25; 841)
    Note: *p<0.1; **p<0.05; ***p<0.01