rafmacalaba/datause-encoder-data huggingface dataset:
# **Indonesia's Trade in Auto Parts**

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# **COUNTRY STUDIES**

# **_Japan: Maintaining International Competitiveness_**

The Japanese auto parts industry grew extremely rapidly from the 1950s through the 1980s, and while production has levelled off since then, the industry has maintained its leading international position in the face of heightened competition from countries with much cheaper cost structures. Because the industry is mature and highly competitive, government policies presently have only a limited direct impact. Nonetheless, contrary to much of the received wisdom, government policies were crucial in the historical development of the industry, and even today play some role in leading areas such as new propulsion systems and regulations governing safety and emissions. The indirect effects of policy in areas such as labor also remain important.

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account for multiple hypothesis testing we calculate sharpened two-stage q-values as suggested in Benjamini et al. (2006). The results do not show any statistically significant differences in project characteristics at the cutoff. In fact, all sharpened q-values are above 0.7 (i.e. the probability that the observed differences are due to chance is 70% or greater – for the specifications with bandwidth 8 and the local mean optimal bandwidth this probability is 100% for all variables).

Appendix Table 1 replicates Table 1 keeping only the applicants that we are using in our impact analysis, i.e. the applicants who replied to the follow-up survey described in the next subsection. The results are very similar to those in Table 1. We find only a handful of statistically significant differences in applicant characteristics at the cutoff and none of the sharpened q-values are statistically significant at conventional levels (the lowest sharpened q-value is 0.452).

To further describe the projects in our sample, Appendix Table 2 shows the distribution of projects across fields of science and technology. The majority of projects are in either electrical, mechanical, or materials engineering. The distribution of projects looks similar across funded and non-funded projects, but funded projects are more likely to be in material or mechanical engineering and less likely to be in electrical engineering.

# **3.5 Survey Design, Timing, and Attrition**

In collaboration with NCBiR staff, we designed a follow-up survey questionnaire of In-Tech applicants. The survey asked specifically about the project that was the focus of their In-Tech application, and asked whether this project had been started, whether it had been completed, and the different outputs. It also asked about forms of collaboration, research and development, and commercialization activities for other projects. The survey was administered by an independent survey firm, GfK. It took place between June and September 2016. This timing corresponds to over four years since application and approximately 3.5 years since funding was given for those in the first call for proposals; and three years since application and approximately 2.5 years since funding was given in the second call for proposals. As such, all projects under the first round should be completed, while those under the second funding round should have completed the research phase, but some may still be in the implementation phase.

The sample frame for the survey was all projects with scores within the selected bandwidth of 8 points on either side of the funding cutoff. For each project, an attempt was made to interview the 13
- <mark>Social Norms Atlas: Understanding Global Social Norms and Related Concepts. April 2021. The Social Norms Learning Collaborative. Washington, DC: Institute for Reproductive Health, Georgetown University.</mark>

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- <mark>United Nations Children’s Fund, Ending Child Marriage: A profile of progress in Bangladesh, UNICEF, New York, 2020.</mark>

- <mark>Verbeek, M. & Nijman, T. (1992), Can cohort data be treated as genuine panel data? Empirical Economics, 17, 9–23.</mark>

<mark>Verick, S. (2014). Female labor force participation in developing countries. IZA World of Labor.</mark>

- <mark>Weitzman, A. (2014). Women's and men's relative status and intimate partner violence in India. Population and Development Review, 40(1), 55-75.</mark>

- <mark>Weber, A. M., Cislaghi, B., Meausoone, V., Abdalla, S., Mejía-Guevara, I., Loftus, P., ... & Gupta, G. R. (2019). Gender norms and health: insights from global survey data. The Lancet, 393(10189), 24552468.</mark>

- <mark>World Bank. 2012. World Development Report 2012: Gender Equality and Development. World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/4391.</mark>

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The paper is organized as follows. In Section 2, we present Qu and Perron’s methodology which is used to detect breaks. Section 3 applies this methodology to a small-dimensional VAR model using US macro-data. In Section 4, we split the initial sample into different segments using the identified break-points. We then perform impulse-responses and Granger causality tests in each of the segments from the initial VAR model specification. Section 5 contains the concluding remarks and proposes avenues of future research.

# **2. Testing Structural Changes in a Multivariate Model**

The various methods of identifying structural changes in a single regression model have been well-documented in the literature. However, in the multivariate case, only few papers have dealt with structural breaks. Qu and Perron (2007) consider issues related to estimation, inference, and computation when multiple structural changes occur at unknown dates in a system of equations including a vector autoregressive model. The changes here can pertain to the regression coefficients and/or the covariance matrix of the errors. This section presents a brief description of Qu and Perron’s method before proceeding to an application in a VAR model using US macro-data in the next section.

# _2.1 The Model and Estimator_

In the presentation below, we consider a VAR model with three components, _yt_ = ( _y_ 1 _t, y_ 2 _t, y_ 3 _t_ )<sup>_′_</sup> and one lag, as in our application in Section 3. This can be extended to a general VAR model without any major difficulties. The dates and the number of structural changes in the parameters are unknown. We use _m_ to denote the total number of structural changes and _T_ to denote the sample size. The unknown break dates are denoted by the _m_ vector Γ = ( _T_ 1 _, ..., Tm_ ) and we use the convention that _T_ 0 = 1 and _Tm_ +1 = _T_ . Hence there are _m_ + 1 unknown sub-periods, _Tj−_ 1 + 1 _≤ t < Tj , j_ = 1 _, ..., m_ + 1. The model can be written as: where _yt_ = ( _y_ 1 _t, y_ 2 _t, y_ 3 _t_ )<sup>_′_</sup> . In each sub-period _Tj−_ 1 + 1 _≤ t < Tj_ : _πj_ 0 = ( _πj_<sup>(</sup><sup>_i_</sup> 0<sup>))</sup><sup>_i_=1</sup><sup>_,...,_3</sup> is the vector of constant parameters; _πj_ 1 = ( _πj_<sup>(</sup><sup>_kl_</sup> 1<sup>))</sup><sup>_k_=1</sup><sup>_,..,_3;</sup><sup>_l_=1</sup><sup>_,..,_3indicatesthe3X3</sup> matrices of the VAR parameters; and _εt_ is the vector of the residuals with mean _O_ and covariance matrix denoted by Σ _j_ . Here we are interested in the estimation of Λ = _m,_ � _T_<sup>�</sup> 1 _, ..., T_<sup>�</sup> _m,_ Σ<sup>�</sup> _j_ =1 _,...,m_ +1 . We suppose in this paragraph that _m_ is known, � � and will discuss later the estimation of _m_ . For the estimation of model (1) it is convenient to re-write the model as: shocks in the post mid-1980s period (see Stock and Watson 2002 for a survey), not increased responses as in this paper. However, the papers related to that literature do not provide evidence of a change on the effect of house prices shocks on the economy.
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analysis with data from the Household, Income and Labour Dynamics in Australia (HILDA) survey over the past two decades (2001 – 2019).

# **3. Empirical strategy**

To investigate the relationship between refugee mental illness and their labor market outcomes, we estimate the following regression where Yit are labor market outcomes of refugee _i_ who was interviewed in wave _t_ . We focus on two main outcome variables: employment status and weekly labor income (real income in $1,000 Australian dollars). But we also examine other outcomes such as refugees’ LFP, employment type, employment skills levels and sector, life satisfaction and their partners’ and children’s outcomes (including their partners’ employment status and life satisfaction and their children’s mental health and school performance).

The coefficient of interest in Equation (1) is β, which measures the impacts of mental illness (MIit) on labor market outcomes. We also control for a range of (potentially) timevarying refugee characteristics in vector Xit including age groups, marital status, household size, language skills (listening, speaking, reading, and writing), home ownership, and residence areas (i.e., living in major cities versus remote areas).<sup>6</sup> These are important determinants of economic integration of refugees that have been examined in the literature (e.g., Lochmann et al., 2019; LoPalo, 2019).

We include the individual fixed effects (μi) to control for unobserved time-invariant characteristics at the individual level and the survey wave fixed effects (τt) to absorb the effects of unobservable time-varying characteristics that can commonly affect all refugees in each time period. εit is the error term. For continuous outcome variables, we estimate Equation (1) with

> 6 We follow previous studies and use a set of dummies for the age variable to account for its nonlinear relationship with employment (e.g., Staubli and Zweimüller, 2013; Laun, 2017).

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**Table 19.  Funding of Township Capital Investments**

||**Mean**|**Percentage from...**<br>|||
|---|---|---|---|---|
|||**Highergovernment**|**township**|**village**|
|||**2000**<sup>**a**</sup><br>|||
|**Total**|77|26|64|10|
|Roads & bridges|22|23|57|20|
|Irrigation|11|48|31|21|
|Drinkingwater|8|45|49|6|
|Schools|26|16|83|1|
|Clinics|1|11|89|0|
|Other<sup>b</sup>|9|24|75|1|
|||**2004**<br>|||
|**Total**|217|53|37|10|
|Roads & bridges|115|60|30|10|
|Irrigation|31|32|58|10|
|Drinkingwater|17|67|18|15|
|Schools|19|43|53|4|
|Clinics|4|58|42|0|
|Other<sup>b</sup>|31|45|43|12|

a: Total sample is 50 townships in five provinces; only 48 townships have complete information; 1 missing in Shaanxi and 1 missing in Hebei.

b: Others include electricity, office buildings, green for grain, telephones, cable television, broadcasts, etc.

Source: 2005 township survey.

Increasing capital investments have put enormous pressure on village, township and county finances, at a time when reforms are sharply cutting revenues especially at the village and township levels. As shown in Table 19 for the townships surveyed, township contributions have in fact declined substantially as a share of total capital investments, from almost two-thirds (64 percent) in 2000 to just 37 percent in 2004. Nevertheless, this still represented an increase in absolute terms, from about RMB 49 per capita in 2000 to RMB 80 per capita in 2004.

# _Land, asset sales, and debt_

It had been shown earlier in Table 15 that regular fee assessments ( _tiliu_ ) had provided some 40 percent of revenues for villages in 2000, and a roughly equal amount was drawn from contract payments for land and collective enterprises and land sales. With the rural fee 35
The fact that the impact of an improvement in TAXTIME has a stronger impact on reducing tax corruption is in line with intuitive thinking. Time to comply with taxes is a true representative of the complexity of a tax system; a more complex tax law will need more time and effort to understand the provisions to be able to compute the accurate tax liability and prepare and file the tax return. Consequently, complexity can provide incentives to taxpayers to seek to bribe their way into reducing the compliance burdens caused by it. Again, if, as is often the case, complexity arises due to a plethora of tax incentives in the law, it provides opportunities to reduce the tax liability by claiming these incentives, sometimes through corrupt means.

**Table 7 - BRIC countries: Impact of Tax Simplification on Tax Corruption (in percentage terms)**

||10% drop in<br>taxpayments|10% drop in tax<br>time|
|---|---|---|
|Brazil|-3.251|..|
|China|-3.009|-4.424|
|India|-2.662|-4.902|
|Russia|-3.325|-4.698|

Source: Authors' calculation.

Note: The results in the first column are based on Column (4) in Table 5, while the ones in the second column are from Table 5 Column (11).

Similar experiments are run for selected countries to show how the reductions in tax complexity would affect their tax corruption. The BRIC economies are used as examples. The results are presented in Table 7. The experiment results based on TAXPAY are from column (4) of Table 5, while the results for TAXTIME are from column (11). The responses of tax corruption to a 10 percent cut in TAXTIME or TAXPAY are mostly similar across countries, but some differences are observed. In India a 10 percent cut in TAXPAY leads to a 2.66 percent cut in tax corruption, while the same amount of cut can result in a 3.32 percent decline in Russia. The 30
altogether, when household characteristics such as education and location of residence are controlled for. Ranganathan et al. (2017) conduct a similar analysis with the IHDS data, but come to somewhat different conclusions from Azam (2019). Notably, they conclude that income mobility in rural areas is higher for the backward castes than for the forward castes (Ranganathan et al. 2017). The source of this disagreement is not entirely clear, but it could be related to differential treatment of data. For example, Ranganathan et al.’s study does not include split households in the analysis, leaving them with a total of just under 20,000 rural households in their analysis. This contrasts with Azam’s study, in which roughly 23,000 rural households were included in his balanced panel.

Seetahul (2018) examines occupational and earnings mobility in urban areas using the IHDS data. Her analysis shows that women enjoy less occupational mobility than men, but that the data reveal little systematic difference in patterns of earnings mobility across genders. With respect to caste and religious groups, the Hindu upper-caste group is found to be particularly well placed to transition out of casual wage employment into regular jobs, and to transition into increasingly skilled occupations. Scheduled Castes and Scheduled Tribes, in contrast, see little transition into such skilled occupations. Relative income mobility, proxied by rank changes in — earnings, is found to be less pronounced among the upper-caste group possibly as a result of their already generally occupying the upper percentiles of the earnings distribution in the initial period, thus being less able to further improve their ranking.<sup>7</sup> Income mobility has also received attention in more focused microstudies in India. One such study by Himanshu et al. (2018) focuses on income mobility in the village of Palanpur in western Uttar Pradesh over a period of seven decades, extending from the late 1950s to 2015.

> 7 Some smaller panel surveys have been fielded for India, but none of these provides nationally representative data. For earlier studies that analyze these panel surveys, see e.g., Munshi and Rosenzweig (2009), Krishna and Shariff (2011), and Dercon et al. (2013) respectively for analyses of the Rural Economic and Demographic Survey panel between 1982 and 1999, the NCAER panel between 1993/94 and 2004/05, and the International Crops Research Institute for the Semi-Arid Tropics panel between 1975 and 2006.

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# **Appendix F: Balance across Treatment Conditions**

The analyses in this paper rely on randomization, which guarantees that treatment and control areas are similar in expectation. In practice, however, it is possible for them to differ simply by virtue of unlucky draws. To test this, we compare Tuungane treatment and control areas, and – for those areas that partook in the parity lottery – gender parity and control areas. Because we do not have baseline data for the villages, we make use of the data collected in 2010 and 2011 by Humphreys et al. (2019) in 816 randomly selected villages. We limit ourselves to pre-treatment information and variables that do not change due to the treatment. We analyze the following variables. Distance from a set of (nearest) points of importance that are unlikely to change due to the program: mine, post office, and the kingdom headquarters. Distance data (measured in hours of walking) are based on individual responses, mean aggregated to the village level. Note that in a large number of villages, individuals do not know the distance to these locations, and thus we have fewer observations than the 816 visited villages. We also use data on ethnic and religious composition of the village, measured as the probability that two individuals, selected at random from the village, will be of different ethnicities or religious groups. These data come from a survey conducted with village chiefs. In total, 773 of the 816 village chiefs were interviewed. Data were also collected about the characteristics of the previous chief: his year of birth, and whether he was democratically elected. Note that many chiefs responded with “Don’t know”, resulting in considerably fewer than 773 observations for the balance test. We also collected data on the principal economic activities undertaken in the village (as a percentage of the village population). In addition, data were collected on the presence of infrastructure in the village in 2006: wells, schools, clinics, churches and meeting halls. We use data from the chief on the number of IDPs, returned-IDPs, refugees and repatriated refugees that entered the village in 2006. Again, many chiefs responded with “Don’t know”. Finally, at the individual level we analyze gender and age. The data were obtained from the respondent about all the other individuals (both adults and children) in the household.

**Table A5** lists the mean and standard deviation for each variable for the Tuungane and control areas. We also test the difference between both, based on simple OLS regressions. **Table A6** presents the same information comparing villages with and without the gender parity requirement, restricted only to those villages that partook in the gender parity lottery.

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# **Rising Incomes and Inequality of Access to Infrastructure among Latin American Households**

Marianne Fay and Stéphane Straub<sup>1</sup> JEL Codes: H54, O54, D12 Keywords: Infrastructure, Latin America and the Caribbean, Household Consumption

> 1 Marianne Fay: World Bank. Email address: <u>mfay@worldbank.org. Stéphane Straub: Toulouse School of</u> Economics, University of Toulouse Capitole (IDEI and IAST) and World Bank. E-mail address: <u>stephane.straub@tse-fr.eu. We thank Charles Fox and the World Bank LAC Stats team for help with data access,</u> María-José Graña for outstanding research assistance, and Uwe Deichmann, Augusto de la Torre, and Stephane Hallegatte, and participants at the World Bank ‘Unbaked and Wired’ Infrastructure workshop for useful comments and suggestions.
Microeconomic analyses have yielded mixed results. Firms that receive tax incentives appear to exhibit faster growth, better performance and a positive impact on firm productivity. However, in some cases tax incentives and subsidized credit were not correlated with changes in total factor productivity.

This paper contributes to the international literature by examining the impact of fiscal incentives on firm performance in the DR. The analysis is based on a firmlevel data set from 2006 to 2015, which the Dominican authorities provided to the World Bank. It employs both fixed- and random-effects estimation techniques to investigate the relationship between recently introduced fiscal incentives and various outcome indicators selected as proxies for firm performance.

The analysis yields several important policy implications. The results show that the existing exemption regime directly affects firm performance, sector-level competition, and economy-wide productivity. Firms located in special economic zones (SEZ) receive preferential tax treatment, and these firms tend to perform better than their non-SEZ-based peers. However, the disparity in tax liability between SEZ and non-SEZ firms creates distortions that inhibit the efficient allocation of factors, resulting in two parallel production and export structures.

The paper is organized into eight sections. Following the introduction, Section II reviews the international literature on fiscal incentives and firm performance. Section III analyzes the DR’s fiscal incentives related to corporate income tax (CIT), and Section IV identifies the outcome variables used to gauge firm performance. Section V describes the data set, and Section VI details the empirical methodology. Section VII presents the results and Section VIII concludes the analysis.

3