rafmacalaba/datause-encoder-data huggingface dataset:
eclipsed DEC in FY12. Regions have provided very little inter-VPU cross support, although the networks, especially HDN Anchor, have provided a larger share. DEC is the largest provider of total cross support for policy reports. Over the past five years DEC has consistently provided the largest amount of cross support for policy reports, as indicated in the left panel of Figure 10 below. SDN is the second largest provider, followed the other networks. ECA is the largest regional provider. In the right panel we can see that most of the cross support from DEC is directed toward AFR and LCR.

**Figure 10: Cross Support for Policy Reports**

<!-- Start of picture text --> Inter-VPU Cross Support for Policy Reports DEC Cross Support to Other VPUs<br>1,103 Policy Reports from 2008 through 2012<br>1,977<br>SDN<br>PRM SDN<br>HDN PRM<br>FPD HDN<br>664 SAR FPD<br>MNA 1,062 SAR<br>LCR MNA<br>ECA LCR<br>355 376 385 EAP ECA<br>218 WBIAFR 509 473 381 EAPAFR<br>112 101 82 117 119 136 DEC 239 238 WBI<br>106<br>26 6 1<br>FY2008 to FY2012<br>Source: World Bank Documents & Reports and World Bank SAP database. Source: World Bank SAP database.<br>2,000<br>1,000<br>1,500<br>Staff Weeks 500 1,000<br>500<br>0 Inter-VPU Cross Support Labor Cost, thousands 0<br><!-- End of picture text -->

**DEC also provides considerable cross support for policy reports as a share of total staff years** , but it was eclipsed by PREM in FY2012 (Figure 11).  The shares of cross support for policy reports provided by DEC equals 24 percent of total inter-VPU cross support.  SDN, while a large provider of inter-VPU cross support, does contribute relatively little to policy reports as a share of total staff weeks.

**Figure 11: Cross support for ESW as a share of Total Staff Years**

<!-- Start of picture text --> Inter-VPU Cross Support for Policy Reports<br>Percentage of Staff Years<br>2.84<br>SDN<br>PRM<br>HDN<br>FPD<br>SAR<br>1.62 MNA<br>1.31 LCR<br>ECA<br>EAP<br>0.80<br>0.60 AFR<br>WBI<br>0.33<br>DEC<br>0.04 0.06 0.16 0.11 0.19 0.12<br>FY2008 to FY2012<br>Source: World Bank Documents & Reports and World Bank SAP database.<br>Note: Staff are Grade E through Grade I.<br>3.0<br>2.0<br>1.0<br>Inter-VPU Cross Support Share, percentage<br>0.0<br><!-- End of picture text --> 21
on these years gives us two years of pre-reform data (2008 and 2009) and three years of post-reform data (2010, 2011, and 2012).<sup>19</sup>

**Table 1: Number of firms (sole proprietors) in the database each year**

|**Year**|**# firms**|**# firms with zero or**<br>**missing revenue**|**% zero or missing of**<br>**total**|
|---|---|---|---|
|2005|51,540|16,254|31.54|
|2006|3,378|1,432|42.39|
|2007|104,051|43,831|42.12|
|2008|115,536|32,285|27.94|
|2009|121,670|28,285|23.25|
|2010|122,979|20,244|16.46|
|2011|145,179|30,292|20.87|
|2012|159,384|33,545|21.05|

# _3.2 Revenue distribution of firms_

The majority of sole proprietors in the database fall into the revenue category that is eligible for the micro tax reform (revenues below GEL 30,000). Table 2 shows the number and percentage of firms in this revenue group in 2008 and 2009, as well as the equivalent statistics for firms that were eligible for the small tax reform (revenues of between GEL 30,000 and GEL 100,000) and for larger firms.

**Table 2: Number and percentage of firms (sole proprietors) in 2008 and 2009 by revenue group**

|**Revenuegroup**|**2008**<br>**# of firms**|**% of firms**|**2009**<br>**# of firms**|**% of firms**|
|---|---|---|---|---|
|< GEL 30,000|64,471|77.44|73,799|79.03|
|GEL 30,000 - 100,000|14,793|17.77|15,959|17.09|
|> 100,000|3,987|4.79|3,627|3.88|

Figure 2 shows the distribution of firms according to the revenue declared on their tax return in more detail, excluding the top 1% of outliers in our sample. Figures 3 and 4 zoom in on the distribution around the reform cutoffs (GEL 100,000 and GEL 30,000). Figure 2 illustrates that the number of firms drops sharply after revenue of GEL 100,000, suggesting that firms avoid declaring revenue above GEL 100,000. This was the case even before the recent SME tax reform (data are from 2008 and 2009) and is thus likely driven by the mandatory VAT registration threshold at GEL 100,000. In contrast to the firm distribution around the GEL 100,000 cutoff, the distribution around the GEL 30,000 micro firm reform cutoff looks smooth (Figure 3).

> 19 Note that although the reform implies that micro businesses do not have to pay income tax, they still have to file a tax return post-reform and we thus have data on their declared revenue for both the pre- and post-reform period. Also, even though small businesses may be taxed based on turnover vs. income after the reform, they have to declare revenue on their tax return in either case, so that we have data on their declared revenue pre- and postreform.

8
over the period 2012–2062, while a more “optimistic” state could achieve the same results but in a period ending between 2025 and 2030. Lakner, Negre, and Prydz (2014) acknowledge uncertainty in the speed of change for different percentiles of the income distribution. These authors use scenario analysis to test how meaningful the difference of growth rates between the income growth of the bottom 40 percent (B40) and the mean of the income distribution is for future world poverty attainment. Chapter 4 of World Bank (2014) simulates potential outcomes for income growth and its effects on the poverty headcount under a variety of economic growth contexts and distributional considerations highlighting significant uncertainty in future income poverty attainments. More recently, World Bank (2018) discusses poverty projections up to 2030 and tests its premises—of country future growth rates and the nature of this growth—to stress that there is some degree of uncertainty in poverty outcome attainments.

Our paper approximates growth rates in the mean income and the Gini coefficient by combining household—income and expenditure—survey data from 1980 to 2015 with macroeconomic information from 1970 to 2015 and using country typologies. The country classifications denote different sets of interconnected features—institutions—related to production methods, such as government and political systems, regulation, human capital formation, trade structure, and innovation, among others. In this paper, two embedded institutional characteristics—resource-output orientation and GDP per capita growth information—are exploited to derive future poverty and income distribution outcomes by country and country aggregations. Despite the arbitrary selection of the country typologies, these classifications aim to show that there is heterogeneity in poverty and income distribution changes beyond global aggregated outcomes. Our selected country typologies are two among several others that could be used to understand the commonalities of environments in which firms operate. However, to our knowledge, no other study considers these two specific types of country categorizations to explain future movements in poverty rates, inequality, or shared prosperity measures.

We denote resource-output and non-resource-output oriented economies as ROC and NROC, respectively.<sup>2</sup> While natural resource-related activities present opportunities for economic growth and poverty reduction, they also involve risks such as commodity-export dependency and

> 2 Throughout this paper, we use ROC economies, ROC countries, or simply ROCs interchangeably. The same rule applies for NROC economies.

Page **3** of **45**
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possibility of abuse on anti-competitive grounds is a potential cause for concern and additional inquiry and is an issue to which we will return in more detail in section 3 below.

**Table 2. Developed and Developing Economy Trade Remedy Initiations by Sector, 1Q 2007 - 1Q 2009**

||**Devel**|**oped Econ**|**omies**<br>**1Q**|**Develo**|**ping Eco**|**nomies**<br>**1Q**|
|---|---|---|---|---|---|---|
|**Sectors**|**2007**<br>**Total**|**2008**<br>**Total**|**2009**<br>**Total**|**2007**<br>**Total**|**2008**<br>**Total**|**2009**<br>**Total**|
|Agriculture|2|1|0|0|1|2|
|Chemicals|10|8|0|17|18|8|
|Iron and steel|8|11|1|5|12|2|
|Machinery|1|5|1|8|7|3|
|Materials|1|0|2|4|9|1|
|Misc. manufactures|0|1|1|2|5|0|
|Other metals|0|3|0|2|5|3|
|Plastics and rubber|4|0|0|5|10|1|
|Textiles|1|1|0|6|14|6|
|Vehicles|0|1|0|1|2|1|
|Wood|5|2|2|2|2|1|
|**_Total_**|32|33|7|52|85|28|

Source: Compiled by the author from the _Global Antidumping Database._ These are non-redundant AD, CVD, SG, and CSG at the product level. Developed countries are U.S., EC, Canada, Australia, New Zealand, South Korea, Taiwan, Israel. Developing countries are Argentina, Brazil, India, Turkey, China, South Africa, Pakistan, Colombia, Mexico, Peru, Venezuela, Ukraine.

Developing country firms have also initiated a number of new requests for import protection under trade remedy instruments since 1Q 2008 in the steel, chemicals, and machinery sectors (44 per cent of total developing country initiations). The other two sectors with a high number of new investigations in developing countries are textiles and apparel and plastics and rubber, which combine for another 27 per cent of the total developing economy activity under these instruments during the crisis.

# **2.4 The targeted exporters**

Table 3 illustrates the frequency with which exporters in various countries have been _targeted_ by country-specific trade remedy instruments such as AD, CVD and CSG. Given the economies that are using these trade policies and the sectors that are being targeted for new import restrictions, it is not surprising that the exporters targeted by these actions are primarily located in other developing countries. The frequency with which developing countries as a whole have been targeted in country9
This version: 15 Feb 2024 31 _<mark>The roads are pretty...but when I look at the long lines of vehicles in the morning, I get</mark>_ _<mark>a headache thinking about parents going to work and children going to schools...we</mark>_ _<mark>waste our time in traffic jams.</mark>_ <mark>[Multiple respondents, Bumiputera, Selangor]</mark>

**Table 5** - Individual Responses to the Question _: Compared to five years ago, is your life harder, easier or is there no difference?_

# **Individual Responses by %**

||**All**|**Sela**|**ngor**|||**Terengganu**|**Sabah**|
|---|---|---|---|---|---|---|---|
|||All|Bumi|Chinese|Indian|||
|Harder|55|61|59|66|58|46|51|
|Easier|19|14|09|29|05|24|25|
|No<br>difference|24|25|31|04|36|24|24|
|Total<br>participants|473|227|74|70|83|109|137|

Source: MLSQS 2019 data.

**Table 5** reports individual responses as to whether participants thought that

their lives were harder, easier or the same compared to five years ago. Fifty-five percent said that their lives were harder and only 19 percent said that it was easier. Residents of Selangor were more likely to say that life was harder (61 percent) compared to residents of Terengganu in eastern Malaysia (46 percent) or Sabah (51 percent).

**Dual-income earners and multiple jobs**

31
labor income taxes corresponds to the sum of taxes on individual income, profits and capital gains and social security contributions, both of which (taxes and contributions) are interpreted as being levied mainly on labor income. Hence, τ is set equal to 7.67% C W and τ to 14.79%. Finally, Ellery et al. (2002), using the National Survey by Household Sample, find that, on average, Brazilian households spend 1 3 of their nonsleeping hours working, implying H<sup>ss</sup> = .033 . Putting all pieces of information together leads us to calculate θ = .044 .

The four alternative growth models will exhibit exactly the same benchmark parameterization (with the obvious exception of the parameters in equation (SYS.5)) and associated steady state if the steady state capital stock is normalized to unity, K<sup>ˆss</sup> = 1. Consistent with this normalization, steady state aggregate value added is computed as Y<sup>ˆ</sup> ss = KOR −1 , aggregate consumption as C<sup>ˆ</sup> ss = COR ⋅ Y<sup>ˆ</sup> ss and investment as<sup>ˆ</sup> Iss = (1− COR)⋅Y<sup>ˆ</sup> ss . This normalization is at the cost of introducing a scale parameter S on the right-hand side of equations (SYS.3), (SYS.6) and (SYS.7), which is equivalent to redefining an aggregate value added function whose steady state version is ss ss ss α ss 1−α Y<sup>ˆ</sup> = (1 n) S (K<sup>ˆ</sup> η ) (H ) . Set S = .358 . Further, the steady states of the marginal ss ss product of capital, r , and labor, w , can be obtained from (SYS.6) and (SYS.7) after including the scale parameter S and noting that The remaining parameters are estimated using equation (SYS.5). Set ηH = .0066 , ηI = .0223 and ηY = .0059 , depending on the growth model being considered. Table 2 summarizes the exact values selected for each of the model parameters following the described parameterization strategy as well as the associated steady state, henceforth referred to as baseline or initial steady state. By construction, both parameter values and the initial steady state are assumed to reflect, whenever possible, features of the economy prior to the introduction of a transaction tax (τ<sup>T</sup> = 0), including the pre-existing tax structure.

# **B. Solution Method**

The computation of perfect foresight equilibrium paths of economic variables in our dynamic general equilibrium model is a difficult task because current and future variables are linked and endogenously determined, generating an infinite-dimensional system of nonlinear equations.

To reduce the complexity of the problem, by converting it into a finite-dimensional one, a solution method that exploits the structure of the economy to truncate the domain of the system is used. The solution method employed is somewhat related to Cooley and Hansen’s (1992). This particular solution method seems appropriate given the scope of the paper. The experiments in the next section are aimed at assessing the effect of various 16
The database includes over 70,000 randomly sampled firm-level observations collected in 170 cross-sectional surveys in 104 countries, i.e. many countries include multiple years of data.<sup>2</sup> The database is globally represented, which we summarize by region: Sub-Saharan Africa (AFR); East Asia (EA); South Asia (SA); Eastern Europe and Central Asia (ECA); Latin America and Caribbean (LAC); and the Middle-East and Northern Africa (MENA). The database also includes a few industrialized countries (IND). Figure 1 shows the distribution of countries and observations, by region. A notable difference between the two panels is that surveys in Africa include a relatively small number of firm observations and fewer countries have multiple survey years, while surveys in EA include a relatively large number of firms and include multiple years. A complete list of countries and firm observations is shown in Appendix 1.

Figure 2, Panel A, shows the distribution of firms across income groups, which highlights the uniqueness of our dataset. Unlike similar studies of entrepreneurial finance which focus on firms in the U.S. or other developed countries, firms in our database are distributed across income groups, with a focus on developing countries – which are most likely to face barriers in the business environment. Our database includes 38 low-income and 37 lower-middle income countries, which account for over 73% of observations. The surveys were conducted over the span for 7 years, 1999-2008. Figure 2, Panel B, shows the distribution across firm years. The majority of observations were collected in the past 5 years.

Figure 3 shows the distribution of firms in our sample across sectors, ownership, and output markets. We expect these firm characteristics to affect the usage of external

> 2 We are unable to control for whether an individual firm is included in multiple survey years, although the likelihood of a firm being included more than once is insignificant.

7
the business services sector – the sector with the highest level of output per worker for the period – led to a negative contribution of both static and dynamic reallocation to growth in output per capita.

**Figure 18: Components of labor productivity growth by sector (2015 RM), 1990-2018**

<!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>1990-1997 1997-2007 2007-2018<br>Within-sector changes Static reallocation Dynamic reallocation<br>Contribution to labor productivity growth (%)<br>Agriculture Manufacturing Construction Trade Transport Business Other Agriculture Manufacturing Construction Trade Transport Business Other Agriculture Manufacturing Construction Trade Transport Business Other<br><!-- End of picture text --> While due to the different methodological approaches there are some differences between the findings in Table 14 and Figure 18, as well as those presented in Sections 4.1 and 4.2, at least three main findings hold. _First_ , within-sector labor productivity has been the largest contributor to overall labor productivity growth. _Second_ , Malaysia benefited most from labor reallocation in the period before 1997, after which the contribution of labor reallocation has been relatively low, or even negative. _Third_ , at least after 1997 labor reallocation associated with the manufacturing sector contributed to negative labor productivity growth.

# **5. Discussion and Conclusion**

Since independence in 1957, Malaysia has been transformed from an agriculture-driven economy to one dominated by services. This process of structural transformation has included both periods of industrialization and relative deindustrialization. Against this backdrop, this study analyzed the process of structural transformation in Malaysia by documenting the evolution of output per capita, employment, and output per worker – or labor productivity – between 1987 and 2018 and in three sub-periods, 1987 to 1997, 1997 to 2007, and 2007 to 2018. In addition, the study decomposed output per capita at both the aggregate and the sectoral level using a series of Shapley decompositions and investigated the contributions of static and dynamic reallocation of labor to overall labor productivity growth.

Some of this study’s main findings include: _First_ , Malaysia experienced its most rapid and substantive growth, driven by the manufacturing sector, in the period from 1987 to 1997. Since then, the speed of growth in output per capita has declined, even when periods of crises are excluded. _Second_ , growth after 1997 has been driven by the services sector. _Third_ , Malaysia’s growth in output per capita can largely be attributed to changes in withinsector labor productivity, both on aggregate and at a sectoral level, rather than structural change. _Fourth_ , the reallocation of labor between sectors has had a relatively small impact on growth in labor productivity and output per capita. Aside from the reallocation of labor from agriculture to other sectors, there has only been very limited reallocation of labor toward the more productive sectors of the economy.

24
# **Table S2: Model Performance (10 Fold)**

||**M**|**ain Result**|**s**|**Check**|
|---|---|---|---|---|
|Dataset:|**Stand**|**ard Indica**|**tors**|**Offset**<br>**Indicators**|
|Performance:|O<br>(|ut of Sampl<br>10 fold CV)|e<br>|Out of<br>Time|
|Sample:|All|Has|No|(train early<br>period, test<br>late)<br>All|
||AUC|Bureau<br>Records<br>AUC|Bureau<br>Records<br>AUC|AUC|
|**Baseline Model**|||||
|**Credit Bureau**|||||
|Random Forest|0.516|<br>0.510|-|0.507|
|Logistic, stepwise BIC|0.566|<br>0.565|-|0.550|
|**Our Models**|||||
|**Phone indicators (CDR)**|||||
|Random Forest|0.716|<br>0.713|0.730|0.631|
|Logistic, stepwise BIC|0.765|<br>0.762|0.789|0.595|
|**Phone**<br>**indicators,**<br>**within-week**|||||
|**variation (CDR-W)**|||||
|Random Forest Weekly Ensemble|0.619|<br>0.616|0.632|0.641|
|OLS FE, stepwise BIC|0.637|<br>0.635|0.655|0.593|
|**Combined**|||||
|**Credit**<br>**bureau**<br>**and**<br>**phone**|||||
|**indicators**|||||
|Random Forest|0.717|<br>0.716|-|0.642|
|Logistic, stepwise BIC|0.778|<br>0.775|-|0.616|
|**Credit**<br>**bureau**<br>**and**<br>**phone**|||||
|<br>**indicators, within-week variation**|||||
|Random Forest Weekly Ensemble|0.623|<br>0.621|-|0.639|
|OLS FE, stepwise BIC|0.650|<br>0.648|-|0.586|
|Default Rate (%)|11|<br>12|10||
|N|7,068|6,043|1,025|6,975|

Standard indicators evaluate out of sample performance using 10-fold cross validation, averaged over several fold draws. Offset indicators are derived from only half of the data (the first half for early transitions; the last half for late transitions); the out of time model is estimated on the early half of transitions and tested on the late half. AUC represents the area under the receiver operating characteristic curve, and BIC the Bayesian Information Criterion. For middle two columns, model is trained on all individuals except the omitted fold, and performance is reported for the given subsample within the omitted fold. Source: authors’ analysis from telecom data.

9
– of individuals selecting into the program experienced a job loss in the year prior to the start of their course (commonly known as “Ashenfelter’s dip”). We then see non-completers outpace course completers in their employment rate for the months following the start of their course. There may be a number of reasons for this, the most likely being that job offers received prior to or in the early stages of the course period induce dropout. Nevertheless, the event study provides visual evidence (or not) of the parallel trends assumption in the estimator.

Across the three groups, the employment patterns in the pre-course period are markedly different. Among MDIC-supplied registrants, we find no stark pre-course dip in employment rates (Figure 4). This is likely attributable to the selection of individuals who are registered through the MDIC process who have higher pre-course employment rates than the average trainee and have not been registered for a training course due to recent unemployment. Figure 4 highlights two additionally important points. First, completers are significantly different from non-completers in pre-course employment levels. Second, while employment rates for completers are approximately maintained in the post-course period, those for noncompleters exhibit a flat trend after the course, while completers continue trending upward. The gap in employment rates leads to a nearly ten-percentage-point difference for the year following the end of the course. Among unemployment beneficiaries who also attended firmrequested courses, we find a sharp pre-course reduction in employment rates (Figure 5), reflecting precisely the institutional situation described above. The approximately equal differentials across completers and non-completers before and after the course validate the DD estimates showing that the program had minimal employment effect on these registrants. Table 4 contains a subset of focal coefficients in the _β_ vector capturing the difference in the outcome for the completers relative to non-completers in months before the training begins, in the month of completion, and three, six, nine, and 12 months after the end of the course. Employment effects are statistically distinguishable by the end of the course period (for the overall sample, and MDIC registrants) or within a few months after the completion of 14