what determines firms‘ decision to formalize? · 2009. 5. 15. · – sectoral licenses: trade...

What Determines Firms‘ Decision to Formalize?

Empirical Evidence from Rural Indonesia

Neil McCulloch Günther G. Schulze Janina Voss

IDS, Univ. of Sussex/ UK Univ. of Freiburg/ Germany

Fourth IZA/ World Bank Conference on Employment and DevelopmentBonn, May 5, 2009

Motivation

• Informal sector is large– most firms, much employment, particularly of the poor

• We don´t know much about the pattern of formalization (who is formal and who isn‘t)

• We don‘t know much about the determinantsof formalization (costs and benefits and how they vary by firm and owner characteristics)

05.05.2009 Formalization in Rural Indonesia 2

Structure of the Talk

1. Introduction

2. Our Data: The Rural Investment Climate Survey in Indonesia

3. The Empirical Approach

4. Costs and Benefits of Formalization

5. Who Goes Formal?

6. Concluding Remarks


The Rural Investment Climate Survey (RICS)

• Survey in the field early 2006, data refer to 2005

• Household, enterprise, community questionaires

• 2461 micro and small firms

micro (1‐4 empl., 2198 firms), small (5‐19 empl.)

• Six districts throughout rural Indonesia, 149 villages

• Enterprises: 1757 household, 618 standalone, 146 listed

• Sector: 54 % trading, 35 % services, 11% manufacturing

• Manager/Owner: 63% male, 37 % female


Business Licensing in Indonesia

• Complicated, unclear process, mainly at district level

• Required licenses:– Tax identification number (NPWP)– Physical licenses: Construction/building license (IMB)– Sectoral licenses: trade license (SIUP), industrial registration (TDI)

– Business registration (TDP)

• Only 2 % of firms are fully licensed, 23% have at least one license.


Cost‐Benefit Approach

• Firms decide to get licensed if benefits > costs• Avg. Costs: 550,000 IDR, 11 days per lic.• Benefits: ∆ Taxes, bribes, sales, access to credit, government contracts ?

• Do benefits depend on firm characteristics ?• Stylized facts: Licensed firms are larger and older, they pay more taxes, bribes, have better access to credit and sell more to the govnmt.

↔ Endogeneity problem !


Econometric Approach

• Endogeneity probleme.g., do firms pay higher taxes due to the license or do high tax

payers decide to get a license to reduce the tax burden?

• IV approach, instrument: community averages for licensing

• Interaction with firm characteristics to capture firm heterogeneity


8

(1) OLS

(2) 2SLS

(3) 2SLS

licensed 1.045 -1.423 -3.760 (0.381)*** (0.581)** (1.116)*** L*qsales_2 1.147 (0.944) L*qsales_3 2.293 (1.184)* L*qsales_4 2.684 (0.995)*** L*qsales_5 2.997 (1.445)** education 0.342 0.388 0.411 (0.060)*** (0.096)*** (0.089)*** age 0.013 0.021 0.022 (0.011) (0.012)* (0.011)** female -0.404 -0.359 -0.389 (0.228)* (0.213)* (0.193)** Ind_ethn -0.189 -0.374 -0.470 (0.270) (0.251) (0.257)* chinese 0.826 0.705 0.327 (0.636) (0.880) (0.910) islam -0.469 -0.587 -0.676 (0.317) (0.345)* (0.334)** employee 0.105 0.099 0.082 (0.036)*** (0.045)** (0.044)* lnsales 0.251 0.347 (0.083)*** (0.059)*** lnfasset 0.087 0.089 0.092 (0.037)** (0.031)*** (0.028)*** Firm age -0.021 -0.010 -0.009 (0.015) (0.015) (0.014) villtax 0.804 0.819 (0.062)*** (0.061)*** Constant -3.056 -3.950 -0.572 (1.196)** (1.467)*** (1.357)

Observations 1901 1782 1782 R-squared 0.33 0.42 0.43 Robust standard errors in parentheses Regression contains district dummies * significant at 10%; ** significant at 5%; *** significant at 1%

Add. Controls for Sectors, Rural/Urban, Owner residing in village, district

dummies

1. Taxes

05.05.2009

1. Taxes

• Licensed firms pay less taxes !

• This effect is smaller for larger firms.

• It is stronger for rural firms (not shown).

• Evidence for discrimination (religion, ethnicity).

Controls:

• Large firms pay more.

• Village effects


(1) (2) (3) (4) (5) (6)

No instruments License instrumented

amount Did pay amount Did pay amount Did pay

licensed ‐0.190 0.109 ‐1.968 ‐1.457 8.225 ‐2.150

(0.307) (0.155) (0.958)** (0.464)*** (2.697)*** (1.445)

L*lnsales ‐0.902 0.053

(0.234)*** (0.123)

Resides in vill ‐0.551 ‐0.088 ‐1.165 ‐0.664 ‐1.417 ‐0.662

(0.308)* (0.160) (0.447)*** (0.208)*** (0.415)*** (0.211)***

indethn ‐0.042 0.011 ‐0.182 0.015 ‐0.303 0.023

(0.338) (0.139) (0.308) (0.170) (0.282) (0.171)

chinese 0.755 0.471 1.244 0.512 1.399 0.635

(0.621) (0.411) (0.705)* (0.529) (0.565)** (0.500)

lnsales 0.070 0.174 0.170 0.130 0.394 0.111

(0.089) (0.043)*** (0.095)* (0.043)*** (0.118)*** (0.060)*

lnfasset 0.045 ‐0.017 0.079 0.011 0.080 0.010

(0.034) (0.017) (0.033)** (0.017) (0.031)*** (0.017)

villcorrpt2 0.271 0.721 0.337 0.724

(0.227) (0.070)*** (0.212) (0.070)***

employee 0.065 0.073 0.071

(0.028)** (0.033)** (0.036)**

Constant 3.579 ‐1.474 2.056 ‐0.747 0.502 ‐1.818

(1.390)** (0.722)** (1.492) (0.783) (1.463) (0.801)**

Obs. 1901 1901 1782 1782 1782 1782

Robust standard errors in parentheses Additional controls for sector, rural/urban, age of firm, female, islam, education, age, district dummies

License

Size effect

Neighborhood effect

discrimination

Village effect

2. Informal payments

05.05.2009 10

2. „Informal Payments“

• Licensing reduces probability of paying bribes !

• It reduces amount of bribes as well!

• Esp. Large firms profit from reduction in corruption payments

Controls:

• Owner residing in the village pays less bribes

• Larger firms pay more and are more likely to

• Chinese pay more, village effects


Formalization in Rural Indonesia

(1) (2) (3) OLS 2SLS

Licensed 0.424 -0.379 -1.226 (0.233)* (0.291) (0.349)*** L*qemployee_2 0.701 (0.594) L*qemployee_3 0.516 (0.502) L*qemployee_4 1.699 (0.465)*** edu 0.084 0.129 0.123 (0.030)*** (0.028)*** (0.027)*** age -0.009 -0.005 -0.005 (0.003)** (0.005) (0.005) indethn -0.294 -0.089 -0.108 (0.164)* (0.152) (0.161) qemployee_2 0.120 0.221 0.111 (0.178) (0.148) (0.128) qemployee_3 0.391 0.575 0.502 (0.176)** (0.158)*** (0.194)** qemployee_4 1.364 1.503 1.004 (0.204)*** (0.207)*** (0.287)*** qlnfasset_2 -0.179 -0.191 -0.156 (0.176) (0.156) (0.155) qlnfasset_3 -0.267 -0.189 -0.149 (0.150)* (0.137) (0.135) qlnfasset_4 0.064 -0.056 -0.013 (0.148) (0.153) (0.160) qlnfasset_5 0.097 0.113 0.137 (0.139) (0.130) (0.130) entagedum_2 0.445 0.436 0.438 (0.107)*** (0.107)*** (0.107)*** entagedum_3 0.434 0.475 0.482 (0.141)*** (0.172)*** (0.166)*** villsales 0.836 0.863 (0.095)*** (0.091)*** Constant 10.630 1.421 1.278 (0.441)*** (1.180) (1.111) Observations 1901 1782 1782 R-squared 0.39 0.45 0.46 Robust standard errors in parentheses Regression contains district dummies * significant at 10%; ** significant at 5%; *** significant at 1%

3. Total Revenue

Education matters !

Firm size

Firm ageAdditional controls: sectors, rural/urban, female, Chinese, Islam, residing in village, district dummies

05.05.2009 12

3. Business Expansion/ Revenue

• Again: Endogeneity• Overall: No effect of formalization on revenue• „Large“ firms will gain from formalization, small ones will not.

Controls:• Factor input (labor matters)• Education• Firm age


4. Further results

1. Access to credit largely unaffected by licenses

2. Access to government contracts: – large firms will profit from licensing,

– no overall effect


Determinants of Formality

Previous results show which characteristics are associated with lower costs‐increased benefits.

Reduced form Probit of Formality

– Sales, employment, assets,

– Sector, rural/urban

– Female, ethnicity, religion, Chinese


Who goes formal? Results

• More likely:– Large firms do (highest quintile in employmt, assets, sales)

– Chinese (by a third)

– Better educated owners

• Less likely– Majority ethnicity (by 10%)

– Owners who live in the village (by 15%)

– Rural firms (by 15‐20%)

• Cost differences (time, money) do not matter


Concluding Remarks

• The main reason firms go formal is to reduce rent‐extraction!

• Firms that are easier targets (large, Chinese, minority ethnicity, out of village) have a bigger incentive to get a license


Formalization in Rural Indonesia

Firms’ Decision to Formalize

Marginal effects from probit regression on licensed

(1) (2)

Probit: Formalized? qlnsales_2 0.023 -0.026

(0.052) (0.045)

qlnsales_3 -0.010 -0.015

(0.051) (0.047)

qlnsales_4 0.117 0.103

(0.067)* (0.063)

qlnsales_5 0.221 0.161

(0.079)*** (0.074)**

qemployee_2 0.048 0.050

(0.044) (0.043)

qemployee_4 0.069 0.055

(0.054) (0.051)

qemployee_5 0.175 0.087

(0.072)** (0.063)

qlnfasset_2 0.106 0.121

(0.068) (0.069)*

qlnfasset_3 -0.005 0.002

(0.058) (0.061)

qlnfasset_4 0.149 0.149

(0.070)** (0.068)**

qlnfasset_5 0.206 0.183

(0.069)*** (0.067)***

female -0.022 -0.016

(0.036) (0.036)

indethn -0.102 -0.091

(0.041)** (0.042)**

islam -0.083 -0.059

(0.084) (0.077)

Chinese 0.353 0.379

(0.158)** (0.156)**

rural -0.195 -0.163

(0.031)*** (0.032)***

meancost 0.000 0.000

(0.000) (0.000)

meantime 0.002 0.002

(0.002) (0.002)

edu 0.045

(0.010)***

Robust standard errors in parenthesesRegression contains district dummies

* significant at 10%; ** significant at 5%; *** significant at 1%Additional controls for sector and district dummies

edu 0.045 0.045 (0.010)*** (0.010)*** Residing in vill. -0.155 -0.155 (0.064)** (0.064)** District dummies yes yes Observations 1851 1682 1682

05.05.2009 18

ADDITIONAL MATERIAL


Literature

• Levenson and Maloney (1998)older and larger firms invest in formality as they have proven to be successful

• Jäckle and Lee (2003) older and larger firms, Peru

• Fajnzylnber et al. (2006) quasiexperimental regres. Discont., Brazil, formalized firms have higher revenue, employm., investment

• McKenzie and Sakho (2006) Bolivia, IV approachformalization increases profits for small firms (2‐5 wrks), micor and large firms lose


RICS

• Geographical coverage– Labuhan Batu, North Sumatra – a plantation area

– Kutai, East Kalimantan – an area rich in mineral resources

– Barru, South Sulawesi – a forest fringe area

– Malang, East Java – a rich agricultural area

– Badung, Bali – a semi‐urban agglomeration area

– Sumbawa, NTB – a dryland area


Mean Size and Enterprise Age

formal informal

mean number of employees 3.93 2.23

mean of log total sales 11.03 9.92

mean enterprise age 10.77 8.54


IV Approach: first stage

• Sargan test on overidentification passed

• Strong identification, high F‐statistic

• Community characteristics no strong instruments

• Village w/ high share of licensed firms may perform better, thus have higher benefits. ⇒

• Additional control for village averages for the respective benefit analyzed


05.05.2009 Formalization in Rural Indonesia

(1) (2) (4) (5) (6)

Vill avg licensed 0.562 0.547 0.538 0.582 0.531 (0.086)*** (0.086)*** (0.081)*** (0.084)*** (0.094)*** education 0.039 0.039 0.039 0.039 0.037 (0.007)*** (0.007)*** (0.007)*** (0.007)*** (0.008)*** age 0.003 0.003 0.003 0.003 0.003 (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** female -0.017 -0.017 -0.017 -0.015 -0.016 (0.024) (0.024) (0.023) (0.024) (0.024) Resides in vill. -0.246 -0.244 -0.247 -0.242 -0.253 (0.076)*** (0.077)*** (0.074)*** (0.074)*** (0.074)*** indethn -0.085 -0.084 -0.081 -0.085 -0.081 (0.058) (0.056) (0.054) (0.055) (0.053) Chinese 0.056 0.048 0.046 0.047 0.049 (0.105) (0.105) (0.100) (0.100) (0.101) Islam -0.043 -0.047 -0.044 -0.049 -0.054 (0.046) (0.047) (0.046) (0.047) (0.049) Empl above median 0.085 0.086 0.108 0.087 0.081 (0.046)* (0.046)* (0.041)*** (0.046)* (0.044)* Sales above median 0.044 0.041 0.039 0.044 (0.032) (0.033) (0.033) (0.033) Fixed assets a.med. 0.069 0.070 0.069 0.070 0.068 (0.018)*** (0.018)*** (0.016)*** (0.017)*** (0.018)*** Firm age above med 0.014 0.013 0.017 0.014 0.015 (0.029) (0.029) (0.028) (0.028) (0.028) rural -0.015 -0.015 -0.011 -0.005 0.001 (0.029) (0.028) (0.027) (0.026) (0.027) manufac -0.032 -0.033 -0.035 -0.029 -0.028 (0.028) (0.028) (0.028) (0.029) (0.028) service -0.028 -0.029 -0.037 -0.025 -0.028 (0.019) (0.019) (0.019)* (0.018) (0.018) bank 0.032 (0.024) villtax -0.002 (0.007) villcorrpt 0.007 (0.012) villsales 0.018 (0.018) villgovs -0.008 (0.004)** villcred 0.104 (0.056)* Observations 1676 1676 1715 1676 1636

Robust standard errors in parentheses Regression contains district dummies * significant at 10%; ** significant at 5%; *** significant at 1%

First Stage IV Estimates

Marginal effects from Probit regression on licensed

24

05.05.2009 Formalization in Rural Indonesia

(1) (2) (3) (4) (5) (6)

No instruments License instrumented

amount Did pay amount Did pay amount Did pay

licensed ‐0.190 0.109 ‐1.968 ‐1.457 8.225 ‐2.150

(0.307) (0.155) (0.958)** (0.464)*** (2.697)*** (1.445)

L*lnsales ‐0.902 0.053

(0.234)*** (0.123)

edu 0.014 ‐0.097 0.146 0.008 0.150 0.013

(0.083) (0.042)** (0.091) (0.055) (0.087)* (0.055)

age 0.009 ‐0.014 0.013 ‐0.010 0.007 ‐0.010

(0.012) (0.006)** (0.013) (0.007) (0.013) (0.007)

female ‐0.275 ‐0.234 ‐0.396 ‐0.221 ‐0.383 ‐0.229

(0.307) (0.147) (0.307) (0.150) (0.277) (0.150)

Resides in vill ‐0.551 ‐0.088 ‐1.165 ‐0.664 ‐1.417 ‐0.662

(0.308)* (0.160) (0.447)*** (0.208)*** (0.415)*** (0.211)***

indethn ‐0.042 0.011 ‐0.182 0.015 ‐0.303 0.023

(0.338) (0.139) (0.308) (0.170) (0.282) (0.171)

chinese 0.755 0.471 1.244 0.512 1.399 0.635

(0.621) (0.411) (0.705)* (0.529) (0.565)** (0.500)

islam 0.291 0.250 0.069 ‐0.235 ‐0.123 ‐0.196

(0.397) (0.286) (0.402) (0.319) (0.372) (0.313)

lnsales 0.070 0.174 0.170 0.130 0.394 0.111

(0.089) (0.043)*** (0.095)* (0.043)*** (0.118)*** (0.060)*

lnfasset 0.045 ‐0.017 0.079 0.011 0.080 0.010

(0.034) (0.017) (0.033)** (0.017) (0.031)*** (0.017)

villcorrpt2 0.271 0.721 0.337 0.724

(0.227) (0.070)*** (0.212) (0.070)***

employee 0.065 0.073 0.071

2. Informal payments

25

Who goes formal?

1. Licenses affect firms differently


The costs and benefits of formality

Interacting characteristic cost or benefit

sales taxes (+)

‘other levies’: outcome stage (‐)

sales to government (+)

fixed assets ‘other levies’: outcome stage (‐)


employees credit (‐)

revenue (+)

female taxes (+)

‘other levies’: selection stage (+)

rural taxes (‐)

‘other levies’: selection stage (‐)

outcome stage (+)

manufacturing sector credit (‐)


Ind_ethnicity ‘other levies’: selection stage (‐)

Chinese sales to government (‐)

Islam revenue (‐)

other levies: outcome stage (+)

what determines firms‘ decision to formalize? · 2009. 5. 15. · – sectoral licenses: trade...

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