rafmacalaba/datause-encoder-data huggingface dataset:
adoption: the Kleibergen-Paap statistic is 111.8.

# **5.2 Robustness**

In this section we report the results of several robustness tests to our baseline analysis. A potential concern about the baseline results is that the association between trade and robotization could reflect complementary investments in other forms of technological progress and/or changes in the intensity of material inputs or capital usage. To assess this possibility, in Table 2 we add to the baseline specifications presented in Table 1 controls for ICT capital, non-ICT capital and material inputs. We also control for bilateral import tariffs applied by developed countries. The inclusion of these controls does not appreciably alter the estimated magnitude of the association between robot adoption and trade flows.

Another potential concern is that the baseline estimates might be driven by a select few sectors. To assess whether this is the case, in Table 3 we exclude the automotive, rubber and plastics, and metal industries, the three sectors in which automation is most widespread. If anything, excluding these sectors magnifies the estimated elasticity of trade flows with respect to robot use. Moreover, results remain statistically significant.

Our baseline estimation period includes the great depression of 2008/2009. We therefore worry that our results might be influenced by cyclical variations associated with the crisis. To address this concern, Panel A in Table 4 restricts the sample to the pre-crisis (i.e. pre 2008) period, whereas panel B presents estimates for the post-crisis (i.e. post 2009) period. The results suggest that the estimated impact of robotization on trade was qualitatively similar in the pre- and postcrisis periods. In both cases, the estimates of the relationship between robot adoption and trade suggest that robotization catalyzes trade, but reduces net imports from developing countries.

As an additional robustness check, we use variations over longer time periods (i.e., over 5-year intervals) for identification, instead of the yearly variation we exploit in the baseline analysis. The results are presented in Table A3 in the Appendix. Reassuringly, they are qualitatively similar to those of the baseline analysis. In Table A4 in the Appendix, we assess the sensitivity of the results to using an alternative proxy for automation. Specifically, we use the natural log of the IFR stock of robots per million hours worked, instead of the robot stock we constructed using the perpetual inventory method. The results obtained using this alternative measure are very similar to those obtained using our preferred measure. In Table A5, we assess the impact of extreme values by excluding the top 1% of observations of both the automation and each of the dependent 18
# **<mark>Is There an Underside to Economic Growth? A Mixed-Methods Analysis of Malaysia</mark>**

<mark>M Niaz Asadullah</mark><sup>1</sup> <mark>Monica Biradavolu</mark><sup>2</sup> <mark>Vijayendra Rao</mark><sup>3</sup> <mark>Kenneth Simler</mark><sup>4</sup> <mark>1: University of Reading, UK; IZA, Germany</mark> <mark>2: QualAnalytics</mark> <mark>3: Development Research Group, The World Bank</mark> <mark>4: The World Bank</mark>

**<mark>Keywords: Growth; Inequality; Poverty; Qualitative Analysis; Malaysia</mark> JEL:** D63, I32, O53.

**Acknowledgments** : An earlier version of the paper was presented at the World Bank Malaysia country office (January 2020), King's College London (December 2022), the Martin Ravallion Symposium on Poverty and Inequality (December 2023) in Kolkata, India, and ADBI Conference in Manila on inclusive growth (July 2024) in Manila, the Philippines. We thank Shah Faiez, Emanuella Galasso, Peter Lanjouw, Sylvie Lambert, Apurva Sanghi, Rinku Murgai, Michael Woolcock, and Paul Segal for their comments, Richard Record for encouraging us to do a qualitative study of well-being in Malaysia, and Martin Ravallion for his suggestion to investigate preferences for relative versus absolute income. We are particularly grateful to two anonymous referees and the editor Sylvie Lambert for their valuable comments which led to substantial improvements in the paper.  Stella Marie Ambrose, Fathin Amelina Fazlie, Kausalya Nagaraja, Jack Wong, Jo Ann Lee, Prem Anand Raj, Farihah Fahmy, Theresa Chan Siew Yoong, Wan Muhamad Nazhif Wan Yusoff and Maryam Lee did superb work conducting the focus group discussions. The usual disclaimers apply.
and educational endowments across regions, regional differentials between the richest and poorest region are considerably lower than at first sight (i.e. before accounting for differences in endowments). Differences in endowments account for roughly one-half (Poland) to two-thirds (Hungary) of the observed differences in disposable incomes across regions (Table 12). Similarly, differences in demographic and educational endowments help explain a good deal of the observed differences in disposable incomes across urban and rural areas within all EU8 countries.

**Table 12: Unadjusted vs. adjusted regional variation in standard-of-living**

||**Czech**<br>|**Ratio**||
|---|---|---|---|
|**Equivalized Disposable Incomes**|**Republic**|**Hungary**|**Poland**|
|Ratio of richest region / poorest region:||||
|Unadjusted:|1.42|1.33|1.34|
|Controlling for demographic, employment,||||
|and educational differences|1.16|1.11|1.18|

_Source:_ World Bank staff calculations based on data from the 2006 EU-SILC.

# **... but do not help much in explaining differences in living standards across countries**

42. How much of the observed differences in average living standards between EU8 vs. EU15 countries can be explained by differences in human capital and labor market outcomes? We run a pooled regression on data for all EU22 countries to assess the extent to which differences in the independent variables [i.e. demographic composition, employment status, and educational attainment] help explain observed differences in living standards across these countries. However, in contrast to our earlier findings that differences in endowments account for a fair share of observed regional income difference within countries, accounting for such differences exacerbates unexplained differences in living conditions between countries.

43. So far our main focus has been on examining inequality of incomes within countries—in closing, we turn briefly to examining income disparities within the EU-as-a-whole. As noted earlier, much of the income inequality in the European Union is between rather than within countries: average incomes in Lithuania (the poorest member) and Luxembourg (the richest) differ by the same order of magnitude as the income differences between the poorest and richest deciles within these countries. Analysis of income inequality across 22 EU countries in Table 1 yields an overall Gini coefficient of 0.40 for Europe-as-a-whole—considerably higher than the Gini for individual countries, and of similar magnitude to the Gini coefficient of 0.41<sup>18</sup> in the United States (see also Box 2).<sup>19</sup>

> 18 2007 UNDP Human Development Report.

> 19 Inclusion of Germany, Bulgaria, and Romania would further exacerbate observed aggregate EU income inequality.

22
subsequent LSMS have addressed several of these issues (Bosnia and Herzegovina 2001, Panama 2003 and 2008 for example<sup>5</sup> ).

It may be, as is the case in the FINMARK surveys, that a third option for respondent selection arises. In these surveys, a random adult is selected to be interviewed. This is a strategy to ensure that the pool of respondents is a probability sample of all adults in the country: a sample of household heads would be inappropriate for this purpose. Although FINMARK surveys do not presently collect household financial service data, if there were an interest in doing so, logistically it would be easiest to have the randomly selected adult--already being interviewed-- provide financial data for the household. Of concern would be whether this strategy would provide data of similar quality to that garnered from full enumeration of adults or even selecting the head of household to provide the information. It is not clear, a priori, that every individual in the household will be equally well informed about other members’ involvement with the financial sector.

A second key dimension on which surveys vary is the way in which questions are asked. For ease of implementation, lower costs, and low respondent burden, a short set of questions that aggregate individual items into more global questions is preferred. However, research in other areas has shown that such questions may lead to accidental omissions or memory lapses, thus lowering reported incidence or use. Experiments in measuring household consumption have shown that including a greater number of items in the questionnaire leads to higher reported consumption (Joliffe, 2001; Pradhan, 2001; Steele, 1998 and STATIN, 1994). As importantly, in consumption measurement at least,

> 5 See <u>http://worldbank.org/lsms</u> for details of these surveys.

10
**Box 4.1** Tanzania: Commercial Services vs. Unlicensed Spectrum Use

An example from Tanzania highlights the possible inadequacy of unlicensed bands when providing commercial services. In contrast to projects devoted to ubiquitous access, commercial wireless deployments focus on delivering services to organizations, typically those with critical international communications needs. Technical solutions must therefore provide solid, 99.5% availability Internet and data connectivity.

CyberTwiga, one of the first Internet service providers in Africa, was founded in 1995. Commercial services, limited to dial-up email traffic carried over a 9.6 Kb/s SITA link (costing over $4000 per month), began in mid-1996. Frustrated by erratic performace of the local telephone connections used to reach its customers, and buoyed by a successful deployment of a three-node point-to-multipoint (PMP) network for the Tanzania Harbours authority, CyberTwiga negotiated with a local cellular company to place a PMP base station on their central mast. Connecting a handful of corporations to this proprietary 2.4 GHz wireless local access network system in late 1998, CyberTwiga validated the market and its technical capacity to provide wireless services.

As competitors haphazardly deployed unlicensed 2.4 GHz networks, however, two facts emerged: a healthy market for wireless services existed, but a rising radio noise floor in the 2.4 GHz band would undermine network quality. CyberTwiga’s chose to merge with the cellular carrier, in mid-2000, including plans for a nationwide wireless network built on the existing cellular infrastructure (towers and transmission links) and exclusive radio spectrum allocation. _Source:_ Adapted from Limehouse 2006, p.230 implement rules, measures to enforce compliance with rules, and dispute resolution mechanisms. It is not enough to declare bands open for commons use. License-exempt does not mean unregulated, but the debate over which rules and regulations are needed is open and at early stages. In some countries, the regulatory regime will require exempting some uses or equipment from authorization. In others it will require removing unnecessary restrictions, and setting technical standards to promote and ensure successful spectrum sharing.

<u>Rules for access and use. Given that spectrum is a perfectly renewable and non-exhaustible</u> resource, the only characteristic of the spectrum that could lead to overuse and decline of a spectrum commons is the potential for interference and abuse. Each individual user of the spectrum has an incentive to use more and more power or bandwidth, because the user will receive all the benefits, while bearing few of the costs.

Some rules need to be established to reduce the likelihood of interference and prevent overuse and decline. Although users of commons spectrum have no assurance against interference from other such users, a successful spectrum commons will not be unregulated (Ofcom 2005b). Usage will be restricted by the spectrum authority setting technical parameters to reduce the probability of interference and avoid congestion. Users of commons must comply with

**39**
# **3.4. Evolution of services from traditional to modern**

The 3T‘s have changed the very nature of services from traditional to modern. These modern services (such as financial services, information processing services etc.), which are tradable and impersonal, take advantage of ICT, globalization, and scale of economies; and benefit from higher productivity growth. Also, there is ample scope for traditional service (such as tourism, education, musicians, entertainment production media etc. which require face to face contact) to absorb productivity gains, knowledge spillovers, tradability and fragmentation from ICT. In the absence of disaggregated national accounts data on services, we use the IMF Balance of Payments service exports data to classify service exports into modern and traditional service exports. Figure 7 uses available categories in Balance of Payments and shows that modern services are growing faster after the turn of the century. Figure A2 confirms that measurable modern services are growing faster than traditional service exports only after 2000.<sup>6</sup>

**Figure 7. Average annual growth in Modern and Traditional Services, World**

<!-- Start of picture text --> 20<br>18<br>1990-99 2000-08<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Modern Traditional<br><!-- End of picture text --> _Source_ : Balance of Payments, IMF, 2010 and authors‘ calculations.

Notes: Modern Services include communication, insurance, finance, computer & information, royalties and license fees and other business services. Traditional service are Transport, Travel, Construction, Personal, cultural and recreational services.

> 6 Modern Services include communication, insurance, finance, computer & information, royalties and license fees and other business services. Traditional service are Transport, Travel, Construction, Personal, cultural and recreational services.

11
telephone operators practicing termination-based price discrimination are always higher in countries with exclusively autonomous regulators.<sup>34</sup> These pieces of evidence suggest that sound autonomous regulation would likely be one of the important factors facilitating diverse and discriminatory pricing, which could in turn accelerate network development through inducing consumers to reveal their preferences.

|Table 15. Share of Telephone O|perators|Practici|ng Termi|nation-|Based Di|scrimination|
|---|---|---|---|---|---|---|
||Countrie<br>auto<br>|s with exc<br>nomous p<br>regulators|lusively<br>rice|Cou<br>exclusi<br>pri|ntries wit<br>vely auton<br>ce regulat|hout<br>omous<br>ors|
||Peak|Off-peak|Off-off|Peak|Off-peak|Off-off|
|Fixed-line<br>Difference between on-net|||||||
|and fixed-to-mobile costs|1.000|1.000|1.000|1.000|1.000|1.000|
|Prepaid<br>Difference between on-|||||||
|and off-net unit prices|0.857|0.857|0.857|0.786|0.714|0.714|
|Difference between on-net|||||||
|and mobile-to-fixed costs<br>Contract|0.905|0.810|0.714|0.571|0.571|0.571|
|Difference between on-|||||||
|and off-net unit prices|0.800|0.900|0.900|0.769|0.692|0.769|
|Difference between on-net|||||||
|and mobile-to-fixed costs|0.800|0.800|0.750|0.538|0.462|0.538|

Source: Author’s calculations.

> 34 Regulatory authorities are considered as _exclusively autonomous_ when they are called autonomous and have exclusive responsibility for regulating maximum prices in the ITU Regulators Profile database.

38
Figure 5 explores the distribution of returns in our global bond portfolio. We examine the distribution because the standard deviation can understate the degree of risk due to a skewed distribution with fat tails. The return distribution does not look overly skewed. Despite some positive and negative outliers, the plot for the full sample (Panel A) is roughly in line with a normal distribution (which is also true for the monthly data). We nevertheless compute adjusted Sharpe ratios, to account for volatility, skewness, and kurtosis (the differences are small; see, for example, Section 6).

Figure 5: Distribution of bond returns and bond prices, 1815-2016 Panel A: Bond returns - full sample Panel B: Bond returns - (real, arithmetic average total returns) default vs non-default spells <!-- Start of picture text --> 15 Mean: 6.85Median: 6.58 30 Non-default years: Mean: 6.05<br>13 Scewness: 0.26 Median: 4.85<br>Skewness: 1.09<br>25<br>Default years:<br>10 Mean: 2.37<br>20 Median: 1.98<br>Skewness: 0.17<br>8<br>15<br>5 10<br>3 5<br>0<br>0<br>-40 -20 0 20 40 60 -100 0 100<br>Total returns (yearly, real, arithmetic avg.)<br>Total returns (yearly, real, arithmetic avg.)<br>normal distribution Non-default years Default years<br>Panel C: Bond prices – default vs non-default spells<br>20 Non-default years:<br>Mean: 88.47<br>Median: 95.00<br>Skewness: -1.14<br>15 Default years:<br>Mean: 35.09<br>Median: 25.00<br>Skewness: 0.97<br>10<br>5<br>0<br>0 50 100 150<br>Bond prices (end of year)<br>Non-default years Default years<br>Percent Percent<br>Percent<br><!-- End of picture text --> _Notes:_ This upper part of this figure shows the distribution of yearly real returns. Panel A uses aggregate yearly returns based on the global sovereign bond portfolio. Panel B uses bond-level data and distinguishes between years in default and non-default years. Panel C shows the distribution of bond prices as reported in the primary sources (mixing USD and British Pound prices) for default and non-default years and also using bond-level data.

In default years (Panel B), the distribution differs markedly, with a lower average real return and much fatter tails. However, the coefficient for skewness remains close to zero, suggesting that years of investor losses and years of recoveries balance each other out in normal times and in crisis times (see also Section 4 on bond performance following a default). Furthermore, Panel C shows that there is much more skew 25
<!-- Start of picture text --> _ 1000<br>E<br>Aa<br>wy<br>2 @ Expenditure met by HRD<br>2 800 .<br>3 B@ Expenditure met by insurance<br>3 B@ Expenditure met by FCB<br>1:8)<br>2 600<br>uo<br>o<br>a<br>a<br>S 400<br>wi<br>£<br>o<br>‘a<br>200<br>0<br>be be bh hep<br>> =i 5 SS<br>bh Ln amas ta<br>oO o 2<br>Distribution of scale up expenditure (return periods marked)<br><!-- End of picture text --> <!-- Start of picture text --> _ 1000<br>E<br>a2 ®@ Expenditure met by HRD<br>Oo 800 z i<br>5 @ Expenditure met by Budget Reallacation<br>3 mw Expenditure met by FCB<br>1:8)<br>2 600<br>uo<br>o<br>a<br>a<br>° 400<br>£<br>o<br>‘a<br>200<br>0<br>bh be hb hp<br>> =i 5 SS<br>bh Ln amas ta<br>oO o 2<br>Distribution of scale up expenditure (return periods marked)<br><!-- End of picture text -->
We finally turn to city effects. As argued above, we can interpret them as mobility index values. They measure (log) trip speed in cities after conditioning out log trip length and its square, log trip distance to the center and its square, and day and time of day effects. Figure 2 represents a kernel density estimate of the distribution of city fixed effects from column 5 of table 4. The standard deviation is 0.106. The slowest city is 28% slower than the mean while the fastest city is 42% faster. This gap of a factor of two between the slowest and fastest city is extremely large. Using traveler-reported data and a different methodology, Couture _et al._ (2018) find a less than 30% difference in travel speed among the largest 50 us metropolitan areas. The analogous figure for the top 50 in India is 80%. These large differences are unlikely to be due to sampling bias. All cities have at least 70,000 observations, and the largest cities have more than half a million.

Tables 5 and 6 report the 20 slowest and 10 fastest cities, respectively. First, we note that seven of the 10 largest cities by population in 2015 are among the 20 slowest. The three exceptions are Ahmadabad and Surat in Gujarat and Jaipur in Rajasthan. The state of Gujarat stands out in India for its innovative and more efficient urban planning practices (Annez, Bertaud, Bertaud, Bhatt, Bhatt, Patel, and Phata, 2016). The list of the 20 slowest cities also contains 6 cities from the state of Bihar (among 8 in our data). Bihar is the poorest state in India. Most of the other slow cities are from the neighboring states of Jharkhand and Uttar Pradesh, which are also among the five poorest states in India.

The list of the fastest cities is more heterogeneous. Many are small and in more developed parts of India. Others are exceptional in different ways. The fastest, Ranipet, is an independent city based on our delineation procedure. However, it may be viewed more meaningfully for our purposes as a suburb of the city of Vellore, located about 20 kilometers away. Chandigarh hosts a population above a million, but unlike most Indian cities, it is a planned city characterized by a regular grid pattern laid out by the French architect Le Corbusier.<sup>30</sup> Both Srinagar and Jammu, which are in the disputed state of Jammu and Kashmir, receive specific infrastructure funding from the federal government and have a strong police presence. These two features may lead to better mobility.

Table 7 reports a number of variants of our benchmark specification in table 4 column 5. Column 1 uses log effective speed (haversine length divided by time) instead of actual

> 30Figure A.5 in the appendix shows Chandigarh’s road network, which has the most regular grid of all Indian cities in our sample.

20