Seminar Ekonomi Internasional



The heterogeneous impact of tariff and NTMs on Total Factor Productivity of Indonesian firms

Depok, 30 Oktober 2023

I Made Krisna Gupta

Tentang Penulis

Fokus penelitian

  • larangan ekspor nikel dan industrialisasi EV.

  • Omnibus Law dan Sovereign Wealth Fund.

  • tarif dan NTM terhadap impor, kesejahteraan, dan produktivitas.

  • Kompetisi dan keseimbangan harga di industri makanan.

  • Kebijakan perdagangan, industrialisasi, dan dampak lingkungan.

Hari ini

  • Tentang paper terbaru saya.

  • research process from problem setting, objectives, novel value, hypothesis, methodology, testing, results and conclusions.

  • Some tips

  • Q & A about this paper, writing in general, or econ in general is all fine!

Tentang penelitian ini

  • Penilitian ini bermaksud mengevaluasi dampak dari berbagai jenis hambatan perdagangan terhadap produktivitas industri.
  • Ditemukan bahwa hambatan impor mengurangi produktivitas industri secara heterogen.
    • Dampak lartas impor berlawanan dari tujuannya.
  • Diterima di Bulletin of Indonesian Economic Studies, versi working paper di ACDE working paper repository.
  • Kode replikasi ada di github tapi datanya ga ada

Pendahuluan

  • Indonesia sedang mengejar pertumbuhan ekonomi yang terfokus pada pertumbuhan manufaktur.

  • Di era Global Value Chain (GVC), mengimpor nilai tambah melalui backward linkage adalah strategi yang baik (World Bank, 2020):

    • Akses terhadap bahan baku yang murah dan berkualitas (Amiti dan Konings, 2007; Bas dan Straus-Kahn, 2014; Castellani dan Fassio, 2019; Ing, Yu dan Zhang, 2019)
    • Mempercepat inovasi dan meningkatkan produktivitas (Fernandez dan Farole, 2018; Pane dan Patunru, 2019)
    • Meningkatkan kemampuan ekspor ke pasar global yang lebih cuan (An dan Maskus, 2009; Cadot et al., 2013; Fugazza, Olarreaga dan Ugarte, 2017)

Pendahuluan

Tren proteksionisme
  • Didorong paham merkantilis dan kekhawatiran akan defisit neraca pembayaran.
  • Tarif secara umum meningkat, juga dengan hambatan non tarif (Munadi, 2019)
  • Proteksionisme berantai:
    • jagung \(\Rightarrow\) ayam
    • kain \(\Rightarrow\) baju

Pendahuluan

  • Penilitian ini bermaksud mengevaluasi dampak dari berbagai jenis hambatan perdagangan terhadap produktivitas industri.

  • Ditemukan bahwa tarif terhadap barang yang diimpor oleh perusahaan mengurangi produktivitas perusahaan tersebut.

  • Diantara kebijakan non-tarif, trade licensing dan kuota memiliki pengaruh yang buruk terhadap produktivitas.

  • Dampak yang heterogen: Perusahaan kecil terdampak lebih serius daripada yang besar.

Approach

  • Indonesia wants to grow its manufacturing capacity using various intervention (a.k.a. the infant industry argument).

    • I am interested in trade barriers e.g., tariff & NTMs

  • Basically, how policy \(\rightarrow\) industrial growth.

  • Policy definition is straightforward: tariff and non-tariff (this one’s a bit harder)

  • how to measure industrial performance? In this paper, TFP is used.

Data

  • 1.512 perusahaan dari 2008-2012
  • Survey Industri (SI)
    • output, jumlah pekerja, asing/domestik
  • Data bea cukai:
    • impor dengan firm identifier
  • Tariff didapat dari berbagai Peraturan Menteri Keuangan dan situs rekan FTA
  • NTMs dari UNCTAD TRAINS,
    • 7 kategori umum didominasi SPS dan TBT.

Permasalahan data SI

Vial (2006) dan Marquez-Ramos (2020):

  • Datanya banyak yang missing
  • Missing data tidak random
  • Response rate semakin rendah

zero reports sangat berpengaruh.

Metode: regresi TFP

  • Diasumsikan bahwa perusahaan memiliki fungsi produksi cobb-douglass:

\[Y_{it}=AL_{it}^{\beta_l}K_{it}^{\beta_k}M_{it}^{\beta_m}N_{it}^{\beta_n} \ \ \ \ \ (1)\] - transformasi log:

\[y_{it}= \beta_0 + \beta_l l_{it}+ \beta_k k_{it}+ \beta_m m_{it} + \beta_n n_{it} +\mu_{it} \ \ \ \ \ (2)\]

Problem endogenitas

  • Ada informasi asimetris di mana kapital dan material bersifat endogen.

    • \(\mu_{it}\) tidak acak, violate OLS assumption

  • investasi dan material digunakan sebagai instrumen untuk eror yang tidak random.

    • missing data menambah pelik permasalahan ini

  • Eror yang tidak random mengikuti first-order Markov process (Levinsohn dan Petrin, 2003)

  • Di Stata, command levpet dapat digunakan. Sekarang ada prodest.

Regresi TFP

  • Pertama, estimasi parameter dengan algoritma Levinsohn-Petrin1:

\[y_{it}= \beta_0 + \beta_l l_{it} + \beta_n n_{it} + \phi (m_{it},k_{it})+\mu^*_{it} \ \ \ \ \ (3)\]

  • kemudian prediksi TFP dengan estimated parameters dari step sebelumnya:

\[TFP_{it}=y_{it}-\hat{\beta}_l l_{it}+\hat{\beta}_n n_{it}+\hat{\beta}_m m_{it}+\hat{\beta}_k k_{it} \ \ \ \ \ (4)\]

Trade policy variable

  • Tarif diambil secara semi-manual:
    • ada 7 PMK yang mengover 2008-2012 meliputi 10 ribu tariff lines
    • Dengan data asal negara, kita dapat mengasosiasikan setiap transaksi dengan tarif yang cocok.
    • semua tarif diagregasi per perusahaan.
  • NTM juga merupakan agregasi per perusahaan.

\[C_{\theta i t}=\frac{\sum TP_{\theta scit}V_{\theta scit}}{\sum V_{\theta scit}} \ \ \ \ \ (5)\]

Regresi final

  • Dengan TFP dan variabel kebijakan perdagangan dengan index firm-year, digunakan TWFE untuk melihat dampak tarif dan non-tarif ke TFP:

\[tfp_{it}=\gamma_0+\sum_{\theta} \gamma_{\theta}c_{\theta i t}+\sum_{\theta} \delta_{\theta}c_{\theta i t}*l_{it}+FO_{it} \\ +\alpha_i+ISIC_i+ \eta_{it} \ \ \ \ \ (6)\]

  • parameter \(\delta\) menunjukan variasi heterogenitas dampak kebijakan perdagangan ke TFP.

Hasil Regresi

dampak langsung
Variables TFP
tariff -0.371***
SPS -0.381
TBT 0.074
Pre-shipment inspection 0.16
licensing -0.896***
price control -10,221
competition -2.204*
export-related -0.291
dummy FDI 0.061
dampak heterogen terhadap jumlah pegawai
Variables TFP
tariff.l 0.068***
SPS.l 0.062
TBT.l 0.013
Pre-shipment inspection.l -0.043
licensing.l 0.147***
price control.l 1,666
competition.l 0.413**
export-related.l 0.075
foreign ownership 0.024*

Tarif impor, perijinan impor, dan keistimewaan BUMN:

  • berdampak negatif pada produktivitas.
  • Semakin besar firms, dampak semakin kecil.

Dampak ke tenaga kerja

Variables OLS TWFE
tariff -0.260*** -1.368***
SPS -0.176 -1.650***
TBT 0.064 0.452*
Pre-shipment 0.066 1.997***
licensing -0.818*** -4.455***
Price-control 14,832*** 6,015
competition -0.999 -2.788***
Export-related -0.246 -0.617*
foreign dummy 0.028 0.091*
Variables OLS TWFE
tariff*l 0.043*** 0.251***
SPS*l 0.021 0.288***
TBT*l -0.008 -0.095**
Pre-shipment*l -0.008 -0.345***
licensing*l 0.140*** 0.809***
Price-control*l -2,312*** -802
competition*l 0.207 0.360**
Export-related*l 0.042 0.132**
% foreign -0.009 -0.007

  • Hasil dari tarif impor dan PI cukup robust.

  • Secara umum, tarif dan PI berkontribusi mengurangi pertumbuhan penyerapan tenaga kerja.

    • perusahaan kecil lebih volatile.

Diskusi kebijakan

  • Penelitian ini memperkaya bukti bahwa restriksi perdagangan justru mengurangi produktivitas manufaktur.

  • GVC memperbesar dampak restriksi perdagangan.

    • Kebijakan perdagangan semakin penting untuk transisi ekonomi menuju manufacture-based.

  • Perubahan kebijakan perdagangan berdampak besar ke perusahaan kecil.

    • Potensi meningkatnya konsentrasi di industri domestik.

Diskusi kebijakan

  • Membatasi impor demi surplus neraca perdagangan malah berakibat sebaliknya:
    • Perusahaan yang kesulitan akses bahan baku akan kehilangan pasar ekspor.
    • Perusahaan domestik akan mengandalkan pasar domestik \(\Rightarrow\) perlu mark-up extra \(\Rightarrow\) minta proteksi hilir.
  • tools moneter lebih efektif untuk manage neraca pembayaran (Irwin, 2022).
    • Pemerintah harus senantiasa koordinasi dengan BI.

Other novelties

  • Shows difference between WITS tariff (widely used by researchers) vs scrapped tariff.

  • Adds to the compilation of problems with SI data.

  • Shows heterogeneous effects & fit in the heterogeneous & superstar firms literature.

  • Shows the importance of imported input, fits the new industrial policy literature.

Caveats

  • TFP mungkin merefleksikan market power.

    • Mungkin dampaknya tidak begitu besar di penelitian ini.

  • Eksportir dan importir sangat berbeda dengan perusahaan lain.

  • Data yang tidak sempurna.

    • banyak masalah di SI.
    • data bea cukai sangat terbatas.

Research process

  • Gali literatur terkini

    • kuliah = head start
    • gunakan keyword yang tepat
    • Kuliah tamu saya di UI tentang literature review

  • Pahami teori dan mekanisme, derivasi jika perlu

  • Simpulkan literatur, temukan gap, kenali kontribusi kita, lalu buat hipotesis.

  • Siapkan mekanisme identifikasi bukti dan kebijakan.

Mengolah data

  • Waktu paling banyak habis di sini.

  • Memahami apakah data yg diperlukan ada semua

    • ga ada? Ganti approach.

  • Data Indonesia umumnya tidak machine friendly

    • tapi ada aja udah syukur.

  • Siapkan dokumentasi yang jelas.

Your supervisor

  • sering-sering tanya ke pembimbing anda.

  • jangan pasif. pembimbing anda pasti sibuk. Anda yang harus tau mau tanya apa.

  • Kalau bimbingan jangan kelamaan. mendingan bimbingan sering tapi progres minim daripada dateng-dateng udah bab 5. Dijamin salah semua.

  • gak perlu terlalu takut dengan seminar dan sidang. yang penting menguasai.

References

  • Márquez-Ramos, L. (2020). A Survey of Papers Using Indonesian Firm-Level Data: Research Questions and Insights for Novel Policy-Relevant Research in Economics. Bulletin of Indonesian Economic Studies, 1-49. https://doi.org/10.1080/00074918.2020.1862410

  • Vial, V. (2006). New estimates of total factor productivity growth in indonesian manufacturing. Bulletin of Indonesian Economic Studies, 42(3), 357-369. https://doi.org/10.1080/00074910601053227

  • Amiti, Mary, and Jozef Konings. 2007. “Trade Liberalization, Intermediate Inputs, and Productivity: Evidence from Indonesia.” The American Economic Review 97 (5): 1611-1638. https://doi.org/10.1257/000282807783219733

  • An, Galina, and Keith E. Maskus. 2009. “The Impacts of Alignment with Global Product Standards on Exports of Firms in Developing Countries.” World Economy 32 (4): 552-574. https://doi.org/10.1111/j.1467-9701.2008.01150.x

  • Bas, Maria, and Vanessa Strauss-Kahn. 2014. “Does importing more inputs raise exports? Firm level evidence from France.” Review of World Economics 150 (2): 35.

  • Cadot, Olivier, Alan Asprilla, Julien Gourdon, Christian Knebel, and Ralf Peters. 2015. Deep Regional Integration and Non-Tariff Measures: A Methodology for Data Analysis. United Nations (New York and Geneva: United Nations)

  • Castellani, Davide, and Claudio Fassio. 2019. “From new imported inputs to new exported products. Firm-level evidence from Sweden.” Research Policy 48 (1): 322-338. https://doi.org/10.1016/j.respol.2018.08.021

  • Fugazza, Marco, Marcello Olarreaga, and Christian Ugarte. 2017. “On the heterogeneous effects of non-tariff measures: Panel evidence from Peruvian firms.” UNCTAD Blue Series Papers 77. https://ideas.repec.org/p/unc/blupap/77.html

  • Ing, Lili Yan, Miaojie Yu, and Rui Zhang. 2019. “the evoltion of export quality: China and Indonesia.” In World Trade Evolution: Growth, Productivity and Employment, edited by Lili Yan Ing and Miaojie Yu. Abingdon, New York: Routledge.

  • Levinsohn, James, and Amil Petrin. 2003. “Estimating Production Functions Using Inputs to Control for Unobservables.” The Review of economic studies 70 (2): 317-341. https://doi.org/10.1111/1467-937x.00246

  • Munadi, Ernawati. 2019. Indonesian non-tariff measures: updates and insights. Economic Research Institute for ASEAN and East Asia (Jakarta: Economic Research Institute for ASEAN and East Asia).

  • Pierola, Martha Denisse, Ana Margarida Fernandes, and Thomas Farole. 2018. “The role of imports for exporter performance in Peru.” The World Economy 41 (2): 550-572. https://doi.org/10.1111/twec.12524

  • Pane, Deasy, and Arianto Patunru. 2019. “Does export performance improve firm performance? Evidence from Indonesia.” Working Papers in Trade and Development 05

  • World Bank. 2020. World Development Report 2020 : Trading for Development in the Age of Global Value Chains. Washington, DC: World Bank.

  • Irwin, D. A. (2022). The Trade Reform Wave of 1985-1995. National Bureau of Economic Research Working Paper Series, No. 29973. https://doi.org/10.3386/w29973

Beberapa summary statistics

Firms’ Characteristics

Table 1. Firms’ characteristics, 2008-2012

Characteristics All_SI Non_customs Customs_only
foreign ownership (%) 8.15 5.96 34.77
foreign ownership (%) (26.17) (22.60) (45.06)
fraction of output exported (%) 0.23 0.21 0.4
fraction of output exported (37.52) (0.37) (0.42)
fraction of input imported (%) 0.08 0.07 0.31
fraction of input imported (%) (0.24) (0.21) (0.38)
no. of labour employed 191.07 162.75 535.44
no. of labour employed (711.73) (602.46) (1,457.65)
capital stock (Million IDR) 198 194 250
capital stock (Million IDR) (44,800.00) (46,500) (10,400)
total intermediate input (Million IDR) 50.8 41 170
total intermediate input (Million IDR) (617.00) (515) (1,330)
total output (Million IDR) 90.3 73.3 296
total output (Million IDR) (958.00) (861) (1,740)
total value added (Million IDR) 38.5 31.6 123
total value added (Million IDR) (455.00) (414) (789)
value added per labour (IDR) 137,987.10 126,074 282,857
value added per labour (IDR) (2,515,300.00) (2,600,177) (1,012,159)
No. of observation 117,598 108,662 8,915

Non-Tariff Measures

Table 2. Mean (St.Dev) of each NTM for all HS-6-digits

NTM Codes N2008 N2009 N2010 N2011 N2012 Examples
Sanitary & Phytosanitary (SPS) A 1.715 2.337 2.222 2.255 2.774 Authorization requirements
Sanitary & Phytosanitary (SPS) A (2.644) (4.018) (3.950) (4.054) (5.128) Quarantine requirements
Technical Barrier to Trade (TBT) B 0.481 0.455 0.641 0.682 0.663 Testing requirements
Technical Barrier to Trade (TBT) B (0.962) (0.978) (1.334) (1.361) (1.352) labeling requirements
Pre-shipment inspections and other formalities C 0.562 0.466 0.443 0.462 0.776 pre-shipment inspection
Pre-shipment inspections and other formalities C (1.202) (1.081) (1.059) (1.046) (1.075) only trough specific ports
Non-automatic licensing, quotas, QC, etc E 0.623 0.56 0.605 0.618 0.594 licensing
Non-automatic licensing, quotas, QC, etc E (0.809) (0.818) (0.873) (0.861) (0.853) quota
Price-control measures, extra taxes, charges F 0 0 0.015 0.014 0.016 customs service fee
Price-control measures, extra taxes, charges F (0.000) (0.000) (0.168) (0.165) (0.168) consumption tax
Measures affecting competition H 0.019 0.052 0.05 0.048 0.046 Only SOEs
Measures affecting competition H (0.139) (0.238) (0.233) (0.229) (0.224) -
Export-related measures P 0.901 0.704 0.708 0.683 1.172 export permit
Export-related measures P (1.172) (1.132) (1.109) (1.098) (1.465) export quota
observations - 1,675 2,204 2,318 2,400 2,510 -

Simple average of tariffs from 2008-2012

Table 3. Tariff from MoF regulations (left) compared to WITS (right)

Kind T2008 T2009 T2010 T2011 T2012
MFN 7.049 7.612 6.928 6.975 6.96
MFN (12.213) (12.536) (8.037) (7.231) (7.145)
ASEAN 2.478 2.49 0.15 0.15 0.15
ASEAN (11.094) (11.206) (4.559) (4.559) (4.559)
China 7.049 3.819 2.193 2.208 1.941
China (12.213) (12.673) (7.941) (7.941) (7.927)
South Korea 7.049 2.624 1.912 1.912 1.542
South Korea (12.213) (12.265) (7.131) (7.131) (7.102)
India 7.049 7.612 6.394 5.874 5.341
India (12.213) (12.536) (7.809) (7.517) (7.322)
Japan 6.11 4.639 3.274 2.618 2.23
Japan (11.967) (12.356) (7.353) (7.114) (6.487)
ANZ 7.049 6.446 2.948 2.278 1.545
ANZ (12.213) (11.922) (6.765) (6.318) (6.065)
Kind T2008 T2009 T2010 T2011 T2012
MFN 7.762 7.595 7.564 7.051 7.053
MFN (12.631) (12.456) (12.412) (7.015) (7.016)
ASEAN - 1.84 1.843 0.152 0.152
ASEAN - (11.079) (11.067) (4.285) (4.287)
China - 3.665 2.743 1.85 1.579
China - (12.342) (12.392) (6.853) (6.823)
South Korea - 2.564 2.56 1.698 1.326
South Korea - (12.087) (12.084) (6.395) (6.349)
India - - - 5.409 4.991
India - - - (6.726) (6.620)
Japan - - - - -
Japan - - - - -
ANZ - - - - -
ANZ - - - - -

Coverage ratio

  • Table 5 shows simple mean (a) and coverage ratios (b)
  • Import licenses and quotas are more important than SPS and TBT
  • Coverage ratios vs simple mean:
    • no visual difference on tariff
    • firms import more goods with NTMs

Table 5a. Simple average

Variable Mean St.Dev. Min Max
Tariff 3.503 4.971 0 150
SPS (A) 0.108 0.718 0 29
TBT (B) 0.140 0.663 0 13
Pre-shipment inspection (C) 0.028 0.214 0 5
Licensing, quota, etc (E) 0.321 0.550 0 6
Price control etc (F) 0.000 0.008 0 2
Competition measures (H) 0.007 0.083 0 2
Export-related (P) 0.063 0.376 0 7

Table 5b. Coverage Ratio

Variable Mean St.Dev. Min Max
Tariff Coverage Ratio (T) 3.420 5.646 0 150
Coverage ratio A 0.246 0.931 0 19
Coverage ratio B 0.202 0.478 0 9
Coverage ratio C 0.059 0.237 0 4
Coverage ratio E 0.337 0.468 0 6
Coverage ratio F 0.000 0.001 0 0
Coverage ratio H 0.014 0.083 0 1
Coverage ratio P 0.110 0.353 0 7