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60
13

**Table** 7: **Share of developing countries in industrial country exports and imports of environmentally sensitive goods,1970-90**

|Regiona/Countries||In expozU|**_In_import.**||
|---|---|---|---|---|
||1970|1990|1970|1990|
|of-<br>**Iadustrial**|**22**|**20**|(percent)<br>**IS**|**17**|
|**Autris**|25|14|12|10|
|**Finland**|13|9|6|9|
|Norway|7|6|11|10|
|**Sweden**|10|9|11|7|
|**_Goennny_**|19|15|13|11|
|<br>**Japan**|45|61|34|40|
|US|34|39|22|33|

**Source:** Derived ***rom** United nations COMTRADE database.

# 4. **Environmental expenditures and competitiveness**

The dade pattems in environmentally sensitive **goods** suggest that the factors explaining the trade flows are diverse. The level and changes in environmental control expenditure incurred is only one factor among many. For policy analysis it is also important to underline that costs of environmental policies also depends on the type of measures chosen. Good policies can achieve abatement targets at lower cost than bad policies. Available data indicate, for example, that there are substantial differences in results in abatement of main pollutants among industrial countries for relatively similar levels of costs incurred. In comparing the expenditure data, one also has to keep in mind that differences in environmental standards and expenditures can reflect differences in environmental endowments. A country may have a comparative advantage in environmentally sensitive goods due to the fact that its natural conditions permit a greater ability to absorb pollution.

This section will explore the nature and extent of environmental expenditures in general and by industry. The impact of environmental expenditures on competitiveness can depend on the level of environmental standards across countries. High shares of investment in total expenditures can be an indicator of the internalization of environmental costs, or the
# 1. Introduction

This study analyzes poverty dynamics and economic mobility in Malaysia over the period 20042022. We make use of a synthetic panel based on nationally representative cross-sectional microdata to investigate the movements of Malaysian households across the income distribution. We take three perspectives: first, we study mobility at the lower end of the distribution by focusing on poverty dynamics. Second, we take a broader view of economic mobility by dividing the distribution into three groups and analyzing upward and downward mobility along those dimensions. Finally, we assess relative mobility by considering transitions across quintiles over the entire income spectrum.

analysis by including survey data on 2019 and 2022. Second, it takes an expanded view by explicitly analyzing various scenarios for setting the poverty line and for incorporating the concepts of vulnerability and economic security. Third, it gives the first estimates of relative mobility based on these microdata. Lastly, we introduce a methodological refinement such that we are able to present bootstrap point estimates for all transitions, whereas the previous study estimated upper and lower bounds on mobility only.

We conclude that the picture of economic mobility in Malaysia over the period 2004-2022 is generally positive, with large reductions in chronic poverty and a growing group that we classify as economically secure. However, these gains are not uniform and depend on how we define income groups, and on which population subgroup we consider. Chances of remaining poor and of falling into poverty are highest in rural East Malaysia. Moreover, attaining economic security through sustained upward mobility is particularly difficult in rural East Malaysia and for East Malaysia Bumiputeras, two groups that overlap to an important extent. Downward mobility seems to have increased for these groups. Lastly, relative mobility results show considerable persistence in the position of sizeable groups at either the bottom or the top of the distribution. Over our study period, we do not observe episodes of greater relative mobility – the ordering of Malaysian society as expressed through its income distribution did not exhibit major changes.

The paper proceeds by discussing methodology and data in the next section. Thereafter, we provide results in section 3, which consists of three subsections. We draw some conclusions in the final section.
Figure 17: Average predicted probability of implementing change.

<!-- Start of picture text --> Increased use Innovation<br>of digital Investment in into health Other products<br>platforms digital solutions products innovation<br>0.50<br>0 .4 1<br>0.40<br>0 .3 6<br>0.30 0.29<br>0 .2 6<br>0 .2 2 0 .2 3 0 .2 3<br>0.20 0.18 0 .1 7<br>0 .1 3<br>0.10 0.09 0 .0 9 0 .1 0<br>0.00<br>Micro Small Medium Large Micro Small Medium Large Micro Small Medium Large Micro Small Medium Large<br><!-- End of picture text --> Note: Average predicted probability for size from a Probit analysis that controls for country, size, subsector, and weeks before and after the peak of the mobility shock.

Figure 18: Correlation between average predicted probability of implementing each solution.

<!-- Start of picture text --> .4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br>0 .2 .4 .6 .8 1 0 .05 .1 .15 .2 .25 0 .1 .2 .3 .4<br>Predicted probability of increasing Predicted probability of introducing Predicted probability of introducing<br>use of digital technology non-health products innovations health products innovations<br>investing in digital solutions investing in digital solutions investing in digital solutions<br>Average of the predicted probability of Average of the predicted probability of Average of the predicted probability of<br><!-- End of picture text --> Note: Average predicted probabilities from Probits that control for country, size, sector, and weeks before and after the peak of the mobility shock. The analysis first conditions on the variable on the x-axis and computes the average over businesses in that category in the y-axis.

29
cat bonds are more transparent and simpler to use and hence have been the preferred type in lesser developed countries.

_Advantages of using a cat bond._ There are several advantages of using a cat bond to provide protection against a catastrophic disaster. They are: (1) _Multi-year coverage and price stability._ Insurance and reinsurance contracts are typically issued for one year and are subject to price increases particularly after a large-scale disaster.<sup>11</sup> Cat bonds offer an important element of stability for their users by guaranteeing a predefined price over several years. As of 2008, more than 170 cat bonds had been issued since 1996, and their average maturity has been three years with a few bonds being as long as five or ten years. Longer bonds reduce upfront costs by allowing fees to be amortized over a longer period of time (Michel-Kerjan and Morlaye, 2008).

(2) _Guaranteed expedited payment._ Another key advantage of a parametric cat bond is that the money can flow to the government in just a few weeks. By design, the capital of the bond is commonly invested in risk-free assets, such as U.S. Treasury money market funds, so there is limited credit risk.<sup>12</sup> (3) _An alternative to a government reserve._ A typical financial policy tool for governments is to build up a reserve of money over time to be used in the case of a catastrophe. However, a catastrophe could occur in the very first years so that the fund simply does not have enough money to pay for the losses. If the country does not suffer major losses for a long period, attention fades and the reserves may be transferred to other programs particularly when budgets are tight.<sup>13</sup> It is difficult to have a long-term perspective on these issues for reasons discussed

> 11 The Guy Carpenter Rate-on-Line index shows a 30 percent annual volatility over the past ten years. Premiums also differ markedly among perils that increase the concentration of risk to the reinsurers and perils which provide diversification. And it is not unusual to see reinsurance prices in a region increase by 20 to 50 percent after a major disaster. Catastrophe reinsurance prices in Florida increased by nearly 100 percent the year after Hurricane Katrina (Kunreuther and Michel-Kerjan, 2009, chapter 7).

12 Note that some reinsurers now provide collateralized reinsurance treaties as well, but those are more expensive than traditional reinsurance treaties.

13 This was suggested in the United States for the Hurricane Relief Fund in Hawaii in 2009. Another example relates to the U.S. Pension Benefit Guaranty Corporation. In the 1990s there were interest groups lobbying the PBGC to reduce premiums because they were “too high,” as evidenced by the fact that the PBGC was running a surplus.

23
# **2. Alternative hypotheses**

There is more than one way to interpret the finding that poorer countries tend to devote lower shares of their national income to SP (as seen in Figure 1). The “luxury good” interpretation assumes that public spending choices are reasonably responsive to the preferences of citizens. Then Figure 1 is seen to stem from a preferred policy hierarchy that simply reflects the preferences of citizens, most of whom (it is inferred) consider SP to be a luxury good. The take-away for policy is that the relatively low level of SP spending in poor countries should not be of much concern. If citizens of poor countries would rather see public spending going elsewhere, why should we care?

However, there are a number of alternative explanations for the rising SPEC, with different implications for development policy. These can be thought of as differences in the costs incurred by governments in supplying SP, as reflected in omitted country characteristics in a static cross-country comparison, such as Figure 1. This can be addressed in part by introducing country fixed effects into a SPEC estimated on a longitudinal data set. However, there are also concerns about omitted time-varying characteristics correlated with GDP. We can motivate thinking about those omitted characteristics in the form of a series of hypotheses, as follows. We note if the existing literature already includes what appears to be an adequate basis for rejecting the hypothesis and proceeding no further. For the rest, we proceed to further empirical testing. Our hypotheses can be grouped under four headings: relative prices, governance, distribution, technology and demographics.

The first hypothesis starts from the long-standing observation that Engel curves can shift with relative prices. While SP does not have its own “relative price” in any obvious sense, there are explicit prices for substitutes for SP within the gamut of public spending on human development. For 2017, the price data (described later) indicate that the price of health services relative to food and non-alcoholic beverages has a correlation coefficient of 0.61 with (log) GDP per capita; for education services the correlation coefficient is 0.62 (The Addendum provides the scatter plots, Figure A1). Given that health and educational services are more costly in richer countries we expect to see substitution in favor of SP within public budgets. This leads to:

**_Prices: The relative prices hypothesis._** _Higher relative prices of competing human development services in richer countries lead their governments to switch to SP transfers._ Our next set of hypotheses relates to governance and political economy, starting with:

6
- 19  Yürekli A, Önder Z, Elibol M, _et al._ The economics of tobacco and tobacco taxation in Turkey. _Paris: International Union Against Tuberculosis and Lung Disease_ 2010; **5** .

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18
**Figure 14. Continuous Treatment Effects of Exports and Related Exports on the Difference between the Change in Average Log Wage of Stayers and the Change in Average Log Wage of all workers**

<!-- Start of picture text --> Coefficients on Direct Exports and Related Exports<br>Dep Variable: Diff Change in Log Wage (Stayers Minus All)<br>(Shock: October -November 2008, Wage Regressions)<br>0.04 0.2<br>0.15<br>0.03<br>0.1<br>0.02 0.05<br>0.01 0<br>-0.05<br>0 -0.1<br>-0.01 -0.15<br>-0.2<br>-0.02<br>-0.25<br>-0.03 -0.3<br>coef: direct export shock coef: related export shock<br>2007:2 2007:3 2007:4 2008:1 2008:2 2008:3 2008:4 2009:1 2009:2 2009:3 2009:4<br><!-- End of picture text --> 46
There appear to be many holes in the information provided. In electricity for example, most information is on quality (outages and cost thereof) but basic information on cost and availability of service would be needed: average cost of a kilowatt-hour (KwH) of electricity from the public grid / cost of generators. Similarly, in water, information is sought on the sources of provision, but it should be complemented with the respective average unit costs.

In transport, data on the possibility to access different types of services (roads, railroads, etc.), together with an assessment of their unit cost and quality, and the ownership of different types of vehicles, would make it possible to assess the significance of the transport mix chosen by firms. In the case of telecommunications, as mentioned above, mobile telephony is completely absent from existing surveys. Here again, data on access, unit cost and quality of service would be necessary. One could also wonder why questions geared at the use of internet are restricted to the sub-sample of service firms.<sup>104</sup> Finally, in all cases, a few key dimensions need to be added. First, information on the institutional nature of service providers and regulatory arrangements would be crucial from a policy perspective. Moreover, in a context where the geographical dimension is increasingly recognized to be important,<sup>105</sup> data need to be spatially referenced. Obviously, the practical task of gathering this type of data (including in particular several hours spent with directors and managers of firms, who often have imperfect knowledge about the things they are asked to report) implies a trade-off between being exhaustive and the quality of the data collected. However, the fact that such exercises are bound to aim at second best results should not impede that we try to address the most obvious shortcomings of current surveys.

> 104 Similarly, questions on innovation are restricted to manufacturing firms.

> 105 See Straub, 2008. Also, Gibson and McKenzie, 2007.

32
14 Table **1.9.** Moscow households who privatized their dweLlings by tpe of category of housing (% of total in each group).

|Type of housing|1994|1996|
|---|---|---|
|Communal apartments|5.3|5.5|
|Separate apartments, average|40|43|
|of which:|||
|Old low-rise stock|36|46|
|Khrushchev-era buildings|29|32|
|Stalin-era buildings|65|66|
|Panel buildings (1970s design)|36|41|
|Brick buildings (1970s design)|45|46|
|Modem panel buildings|38|38|
|Modem brick buildings|63|65|
|Average for**city**|37|41|

Source: Ihe Moscow Longitudinal Household Survey in Dec.1994 and Jan-1996.

Table 1.10. Moscow households who privatized their dwellings by location of housing

||(% of total in e|ach group).|
|---|---|---|
|Location|1994|1996|
|City center|49|59|
|Near the center|49|52|
|Far from the center but not outskirts|36|38|
|Outskirts|32|36|
|Outside the Moscow ring road|36|32|
|Averagefor|_ity<br>37|41|

Source: The Moscow Longitudl Household Survey in Dec.1994 and Jan-1996.

Table 1.11. Percentage of Moscow households who privatized their dwellings by

||demogra|phic categories (% of total in each group).|
|---|---|---|
|De<br>og pic category|1994|1996|
|...................|||
|single|70.3|73.4|
|husband and wife|62.1|60.6|
|Single:..........................|||
|childless|38.1|38.7|
|childless + parents|38.8|45.8|
|with children|28.1|22.1|
|with children + parnts|30.8|33.0|
|childless|38.8|33.3|
|childless + parents|37.3|41.5|
|with children|26.3|29.6|
|with children + parents<br>|27.2<br>|35.0<br>|
|_*: A '''ts 'X::~~~~~~~~~..._<br>.<br>* ::: w:^fiw<br>::Y ............ . .................. ...........|_._**_......._**_._**_......._**_....._<br>_. . . . . _.._. . ._..|_._|
|Source: The Moscow Longit<br>Hous|ehold Survey in Dec|-1994 and Jan-1996.|

Some conclusions from an analysis of the data in Tables 1.9-1.11 follow.

- the concentration of privatized housing in the center of Moscow continued in the period under examination. This was largely determined by the development of the housing market, which in turn was driven by the consolidation of communal apartments (located almost exclusively in the center of the city) accompanied by their transition from municipal to private ownership (Table 1.10);