rafmacalaba/datause-encoder-data huggingface dataset:
Policy and Performance in Customs: Evaluating the Trade Facilitation Agreement<sup>_∗_</sup> Russell Hillberry<sup>_†_</sup> Xiaohui Zhang The World Bank Murdoch University

**Key words:** Trade facilitation, Trade costs, World Trade Organization, Discrete-time transition model, Multiple imputation

**JEL codes:** F13, F14, F15

**Sector Board:** Economic Policy (EPOL)

> _∗_ The authors would like to thank Evdokia Mo ̈ıs ́e and Silvia Sorescu of the OECD for providing us with the Trade Facilitation Indicator data, and Aaditya Mattoo and William Gain for inspiring the project. Caroline Freund, Selina Jackson, Marcus Johns, Aaditya Mattoo, Evdokia Mo ̈ıs ́e, Gunjan Sharma, Silvia Sorescu and an anonymous referee provided helpful feedback on an earlier draft. The research was partially funded by the governments of Norway, Sweden, and the United Kingdom through the Multi-donor Trust Fund for Trade and Development.

> _†_ Corresponding author. Development Research Group, World Bank, 1818 H Street NW, Washington DC, 20433, United States; E-mail: rhillberry@worldbank.org.
future policy measures in program countries, possible widespread forbearance in the banking system, and a weak economic outlook in Europe.

**Curtailing lending will probably be a core component of this multi-year deleveraging process which is estimated to be well in excess of $2 trillion.**<sup>**2**</sup> Moreover, to the extent that banks will offload loans to non-banks, risks might be transferred and sow the seeds for future instability.

**Taken together, European bank deleveraging warrants close attention.** The key challenge is to manage the process in order to further strengthen the European banking sector while avoiding a disorderly or disproportionate lending retrenchment, particularly in emerging and developing economies.

# **I. Introduction**

**Before the subprime mortgage crisis erupted in 2007, European banks had been expanding rapidly on a global scale while hidden systemic risks built up.** In the run up to the global financial crisis, European banks significantly increased their lending activities both domestically and outside home markets<sup>3</sup> driven by a pro-cyclical spiral of cheap abundant funding, increasing profitability, and economic growth. In the process, European banks became excessively leveraged and reliant on sources of wholesale short-term funding<sup>4</sup> making them more susceptible to shocks which could force them to adjust their operations abruptly and shrink their balance sheets (Exhibits I.1 and I.2). Moreover, as banks expanded, the build-up of risks and their potential spillover effects to other parts of the world largely escaped supervisors.

**When the crisis erupted, a process of bank deleveraging was put into motion.** This pre-crisis process of financial expansion and integration has been dramatically put in reverse since the US subprime crisis broke out and imbalances and risk underpricing became apparent. Faced with a new reality, policy makers responded by supporting financial markets and initiated an overhaul of supervisory mechanisms and the international regulatory framework. At the same time, banks reacted by boosting capital, slashing trading assets, reducing excessive lending, focusing on core deposits as a funding source, and realigning their business models.

**While bank deleveraging is necessary, excessive deleveraging harbors the risk of negatively affecting global credit conditions especially for longer-term finance**<sup>**5**</sup> **.** The deleveraging process of shrinking, strengthening, and cleaning up balance sheets is desirable and one of the

> 2 For details, see ECB (June 2012) and IMF (October 2012) and Section III.

> 3 European banks not only provided cross-border capital flows, but became increasingly involved in domestic financial markets via lending activities of their local affiliates.

> 4 For a discussion on European bank funding models, also see Le Lesle (2012).

> 5 For conceptual purposes of this paper, “longer-term” is taken to be maturities of at least five years or known to being relatively stable over time. However, in the empirical analysis, the definition will depend on the nature of the available data.

3
# Figure 6. Poverty and Labor Force Status in 2004: Working-Age Individuals

<!-- Start of picture text --> 45<br>40 Out of labor force Unemployed Employed<br>35 Formal Informal<br>sector<br>30<br>25<br>20<br>15<br>10 Average poverty headcount<br>5<br>0<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100<br>Percent of the Poor<br>Source: LSMS and staff estimates.<br>Poverty Headcount<br><!-- End of picture text --> Note: Includes the working-age population (15-64) only. The X axis represents the distribution of the poor by labor force status while the Y axis represents the poverty headcount by labor force status. For example, those who are unemployed have the highest poverty headcount but they account for a smaller fraction of the poor, compared to those out of the labor force.

Figure 7. Poverty and Labor Force Status in 2004: Working-Age Women <!-- Start of picture text --> 45<br>40 Out of labor force Unemployed Employed<br>35 Formal Informal<br>sector sector<br>30<br>25<br>20<br>15<br>10 Average poverty headcount<br>5<br>0<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100<br>Percent of the Poor<br>Poverty Headcount<br><!-- End of picture text --> <!-- Start of picture text --> Employed<br>Formal Informal<br>sector sector<br><!-- End of picture text --> Source: LSMS and staff estimates.

Note: Includes the working-age population (15-64) only. The X axis represents the distribution of the poor by labor force status while the Y axis represents the poverty headcount by labor force status. For example, those who are unemployed have the highest poverty headcount but they account for a smaller fraction of the poor, compared to those out of the labor force.

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<u>μy μy μy</u> = C −5 <u>m m m</u> PPMM2.5 PPVVKK,tt PPVVKK,tt<sup>3</sup> PPVVKK,tt<sup>3</sup> PPVVKK,tt<sup>3</sup> where CCC , SSll ∗5.35XX and are coal, oil and gas consumed in Pakistan in year + GGSSss ∗2.96XX<sup>−5</sup> + PPiill ∗3.53XX<sup>−5</sup> , measured in kkSSCCpp kkSSCCpp kkSSCCpp ktoe. Notice that the total PM 2.5 pollution concentration in the country can be higher than tracked in the PPVVKK,tt PPVVKK,tt PPVVKK,tt SSll GGSSss PPiill SS model due to additional emissions from natural sources, industrial processes other than combustion and pollution from foreign sources. Further, pollution concentration varies considerably within the country. The country-wide aggregation adopted from CPAT is a simplification, but a necessary one for a country model.

# 3.8 Damage module

Potential GDP is the supply potential of the economy and anchors the real side of the model. It determines how much output can be produced when all of the resource in the economy are fully employed (given existing distortions, technology and preferences). In PAKMod, the standard CobbDouglas specification for potential GDP ∗) is modified along five dimensions to accommodate the climate focus of the model: tt (YY

1. Energy is included as a factor of production (see Hassler, Krussel and Olovsson, 2012);

and the production function is modified to account for damages from climate change, including: 2. Reductions in Aggregate TFP due to lower agricultural productivity 3. Reduction in labor productivity and supply due to higher temperatures 4. The impact of pollution on the labor force and 5. The impact of flooding on capital stock.

Before accounting for damages the standard MFMod production function is modified to include energy as a factor of production. In the revised form potential GDP is given as below, as a function of total factor K ∗ productivity (TFP or ) ), capital stock ) , energy � and structural employment ) . tt tt EE,tt tt (AA (KK �YY (CC ∗ K αα ββ γγ Where is the income share of capital, YY is labor’s share in total income and tt = AAttKKtt CCtt YYEE,tt is the share of energy, where the values of each of these parameters is drawn from the input-output table for Pakistan and set ∗ αα ββ γγ to 0.2, 0.7, and 0.1, respectively. represents total factor productivity, is productive capital and the equilibrium employment level and is determined as: tt tt tt AA KK CC ∗ ∗ P 1564 ∗ is the equilibrium unemployment rate, derived from a NAWRU (non-accelerating wage rate of tt tt tt tt CC = (1 −DDCCRR )PPRR PP unemployment) specification, is the participation rate while P 1564 is the working age population, tt DDCCRR which (like the total population) is exogenous and taken from the UN Population forecasts. The structural tt tt PPRR PP 41
all levels of development. The matching results offer robust evidence that food insecurity determines both vertical and horizontal dimensions of trust.

However, while matching methods ensure findings are free of observable bias, it is still possible for unobserved factors to introduce hidden bias. Hidden bias may be caused by unobservable variables that cannot be controlled for in the matching process that simultaneously affect trust and the probability of being food insecure. To explore possible unobserved mechanisms, we examine whether measures of poor health and subject well-being mediate the results discussed above (Table C2, Appendix C). _Health problems_ is a binary variable that equals one if the respondent has “ _any health problems that prevent you from doing any of the things people your age normally can do_ .” We also use GWP’s _Life Evaluation Index_ , which is a categorical measure of respondents’ subjective well-being. The life evaluation index has two items where respondents were asked to place their perceived life status on two ladders that ranged from 0 to 10 corresponding to the worst and best possible life. The first ladder refers to the present and the second ladder refers to the future (i.e., about five years from the present time). The responses were categorized as _Thriving_ if they scored 7 or higher on current life score and 8 or higher on future life score; _Suffering_ if they scored 4 or lower on current and future life scores; and _Struggling_ otherwise (Gallup, 2016).

Overall, Table C2 shows that while experiencing a health problem and poor subjective wellbeing are significant negative factors determining both dimensions of trust, the effects of food insecurity are substantively unchanged from previous results. The coefficient for health problems is statistically insignificant in the total sample but is associated with an increase in trust in national institutions for low-income countries, and a decrease in trust in national institutions in high-income countries. Experiencing a health problem is also negatively associated with trust in friends in family for each income sample. Struggling and suffering, compared to thriving, are strongly negatively associated with both dimensions of trust, across all income classes. While poor health and subjective well-being are clearly important determinants of trust, their inclusion in the model 16
The main conclusion of the paper is that young people across all three countries follow a similar trend over their early life cycle:<sup>3</sup> they leave school to spend a short time in the informal sector, move on to a formal position for longer spells, and finally become selfemployed. Thus, the informal sector appears to play the role of informal job training, as suggested by Hemmer and Mannel (1989), where youth have the flexibility to continue in the labor market or return to school, while a formal job implies entering labor force for good and self-employment is a sector to enter once skills and connections have been accumulated through several years of working.

The next Section presents a literature review of the hypotheses to be explored in the paper. In Section 3, we describe the methodology and the data. Section 4 presents analysis and results. Section 5 concludes by drawing together the findings from specific transitions to sketch the school-to-work transitions process over the early life cycle.

# **2. Literature Review**

Across the world, young people’s (completed) employment tenure is of a shorter duration than that of adults. The OECD literature suggests that there is frequent job turnover among younger workers who engage in a search process of “shopping around” temporary jobs until they find a career path to follow (Quintini, 2006; 2007).<sup>4</sup> This phenomenon is not exclusive of developed economies. Balán, Browning and Jelin (1973) observe that young Mexican workers engage in a search process where they try out various possible life choices. In Chile, Fajnzylber and Reyes (2005) find greater job instability for young people than for adults, partly because a high percentage of their employment contracts were short-term. Chacaltana (2005) uses a Peruvian panel from 1998 to 2001 to show that 60 percent of young workers changed their work status while only 40 percent of adults did.

> 3 Strictly speaking, the available data do not allow us to follow the same people for the entire time that it is necessary to build a complete life cycle pattern. However, separating transitional matrices into different age ranges permits us to estimate age-specific transition probabilities of moving between employment states to approximate such pattern.

> 4 As Topel and Ward (1992) say, “job changing is a critical component of workers' movement toward the stable employment relations of mature careers”.

3
MINIMUM WAGES IN CAMBODIA 28 Table A1. Impact on participation rates with 18 months window

||LFP|Wage|Garment<br>wage|Self-emp|Agric<br>self-emp|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||_All worke_|_rs_<br>|||
|1 quarter before|0.005|-0.004|0.010*|0.001|0.017|
||(0.005)|(0.011)|(0.006)|(0.010)|(0.011)|
|0-1 quarters after|-0.002|-0.024***|-0.008**|0.027***|0.024***|
||(0.004)|(0.008)|(0.004)|(0.008)|(0.008)|
|2+ quarters after|-0.001|-0.003|0.000|-0.005|0.003|
||(0.003)|(0.007)|(0.003)|(0.006)|(0.007)|
|Average|0.821|0.380|0.075|0.504|0.314|
|||_Female wor_<br>|_kers_<br>|||
|1 quarter before|0.005|0.003|0.021**|-0.002|0.018|
||(0.008)|(0.012)|(0.009)|(0.011)|(0.012)|
|0-1 quarters after|-0.003|-0.015*|-0.011*|0.018**|0.020**|
||(0.006)|(0.009)|(0.006)|(0.008)|(0.009)|
|2+ quarters after|-0.003|0.001|0.004|-0.007|0.003|
||(0.005)|(0.008)|(0.005)|(0.007)|(0.007)|
|Average|0.772|0.326|0.121|0.552|0.329|

_Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : The table is identical to Table 1 except that it restricts the observations to those surveyed within 6 quarters of the minimum wage hike. Standard errors, clustered by survey year _×_ psu shown in parentheses. ***: p _<_ 0.01; **: p _<_ 0.05; *: p _<_ 0.1.
23 To ensure the absence of association between the variables analyzed, the 'Pearson chisquare probability' is used with 95 per cent accuracy.

TABLE 5 SAMPLE DESIGN

|BY OUTPUT|TEXTILE<br>32114,32115,32116,<br>**32121**|GARMENT<br>32210|FOOTWEAR<br>**32412,32411**|
|---|---|---|---|
|LARGE<br>85%|% of Workers 19.67%.<br>Sample: 19.67% x<br>0.85x300 respondents<br>= 50 respondents<br>Required 10 firms|% of Workers 51.42%.<br>Sample: 51.42% x<br>0.85x300 respondents<br>= 130 respondents<br>Required 26 firms|% of Workers 28.91%.<br>Sample: 28.91% x<br>0.85x300 respondents<br>= 70 respondents<br>Required 14 firms|
|**MEDIM**<br>10%|% of Workers 19.67%.<br>Sample: 19.67% x<br>0.1x300 respondents<br>= 5 respondents<br>Required 1 firm|% of Workers 51.42%.<br>Sample: 51.42% x<br>0. 1x300 respondents<br>= 15 respondents<br>Required 3 firms|% of Workers 28.91%.<br>Sample: 28.91% x<br>0. 1x300 respondents<br>= 10 respondents<br>Required 2 firms|
|SMALL<br>5%|% of Workers 19.67%.<br>Sample: 19.67% x<br>0.05x300 respondents<br>=5 respondents<br>Required 1 firm|% of Workers 51.42%.<br>Sample: 51.42% x<br>0.05x300 respondents<br>=10 respondents<br>Required 2 firms|% of Workers 28.91%.<br>Sample: 28.91% x<br>0.05x300 respondents<br>=5 respondents<br>Required 1 firm|
|TOTAL|12|31|17|

- _Source: BPS, Industrial Statistics 1993. For the first time workers in industrial sector distinguished according to gender._

Note:

**1.** Survey population: Women Workers in West Java and DKI Jakarta on Textile, Garment and Footwear Industries

2. Research area: Jakarta, Bogor, Tangerang, Bekasi, and Bandung 3. Total Respondents: 300 4. Maximum 5 Respondents are taken from each firm.
Table A.11: Overall performance (percent correct) with covariate interactions

||All c|ases|||By case|||
|---|---|---|---|---|---|---|---|
||(1)<br>Across<br>cases|(2)<br>Across<br>responses|(3)<br>Simple 1|(4)<br>Simple 2|(5)<br>Complex<br>1|(6)<br>Complex<br>2|(7)<br>Complex<br>3|
|_A. Interaction with male_||||||||
|Reward|4.21***|2.58***|3.84|10.09***|1.84**|0.88|4.41***|
||(1.13)|(0.53)|(3.75)|(3.45)|(0.83)|(0.57)|(0.96)|
|Penalty|3.18***|2.50***|1.14|7.50*|2.15**|0.90|4.20***|
||(1.19)|(0.51)|(4.02)|(3.83)|(0.93)|(0.60)|(0.93)|
|Male|-0.41|0.24|-0.42|-2.61|0.30|-2.19*|2.85|
||(2.06)|(0.89)|(6.27)|(7.03)|(1.76)|(1.15)|(1.76)|
|Reward _×_ Male|0.41|-2.32*|6.54|3.55|-2.28|1.23|-6.97***|
||(2.64)|(1.24)|(8.00)|(8.46)|(2.41)|(1.44)|(2.25)|
|Penalty _×_ Male|0.77|-2.94**|6.69|7.42|-3.95|0.80|-7.09***|
||(2.96)|(1.27)|(9.09)|(10.06)|(2.60)|(1.69)|(2.28)|
|Constant (Information)|53.32***|56.39***|68.71***|27.52***|62.24***|52.56***|55.55***|
||(0.77)|(0.35)|(2.53)|(2.49)|(0.60)|(0.39)|(0.62)|
|Facility fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|P-value Penalty v Reward|0.339|0.872|0.463|0.462|0.710|0.967|0.816|
|N respondents|1,363|1,363|1,363|1,363|1,363|1,363|1,363|
|_B. Interaction with job grade_||||||||
|Reward|4.74***|2.33***|6.46**|11.18***|1.63**|1.73***|2.71***|
||(1.03)|(0.47)|(3.20)|(3.36)|(0.80)|(0.54)|(0.90)|
|Penalty|3.71***|2.19***|3.27|9.26***|1.55*|1.48**|2.97***|
||(1.05)|(0.46)|(3.53)|(3.50)|(0.88)|(0.58)|(0.82)|
|Doctor or nurse|4.91*|6.42***|-4.89|9.59|7.52**|4.84***|7.47***|
||(2.70)|(1.44)|(8.76)|(9.77)|(3.07)|(1.64)|(2.45)|
|Reward _×_ Doctor or nurse|-5.55|-4.84***|-8.94|-4.52|-5.54*|-6.04***|-2.68|
||(3.68)|(1.70)|(13.10)|(12.00)|(3.23)|(1.90)|(3.03)|
|Penalty _×_ Doctor or nurse|-5.54|-5.54***|-7.90|-3.16|-4.75|-4.84**|-7.03**|
||(4.61)|(2.00)|(14.98)|(14.43)|(3.96)|(2.06)|(3.44)|
|Constant (Information)|52.88***|56.01***|69.04***|26.09***|61.79***|51.64***|55.81***|
||(0.64)|(0.28)|(2.02)|(2.08)|(0.50)|(0.33)|(0.52)|
|Facility fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|P-value Penalty v Reward|0.272|0.768|0.318|0.556|0.917|0.662|0.750|
|N respondents|1,363|1,363|1,363|1,363|1,363|1,363|1,363|

* p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. OLS models with facility fixed effects and robust standard errors.

57
28 Table 4

|Priva<br>(De|te saving: <br>pendent va<br>|Alternative e<br>riable: Gross <br>|stimators, <br> private sav<br>|full sample<br>ing/GPDI)|||
|---|---|---|---|---|---|---|
||||||e<br>||
|Estimator<br>Regression<br>Instruments|OLS-CS<br>Levels<br>-|OLS-Static<br>Levels<br>-|OLS-Pool<br>Levels<br>-|Within<br>Devia.<br>from mean|GMN-Diff<br>Differences<br>Levels|GMN-Syst<br>|<br>Levs - Diffs<br>Diffs - Levs|
|Lagged private saving rate|-|-|0.832<br>(28.42)|0.570<br>(14.699)|0.302<br>(3.855)|0.587<br>(9.254)|
|Real per-capita GPDI*|0.046<br>|0.048<br>|0.010<br>|0.068<br>|0.069<br>|0.049<br>|
||(3.925)|(4.108)|(2.868)|(4.914)|(2.877)|(2.458)|
|Real growth rate ofper-capita GPDIP|0.480<br>|0.300<br>|0.406<br>|0,340<br>|0.271<br>|0.450<br>|
||(0.971)|(6.008)|(8.62)|(8.062}|(3.741)|(5.828)|
|Real interest rate? °|0.154|-0.122|0.032|0.049|-0.404|-0.253|
|;|(0.982)|(-1.964)|(1.347)|(1.242)|(-5,588)|(-5.011)|
|M2/GNP|0.044<br>|0.044<br>|0.0142<br>|-0.035<br>|-0,171<br>|-0.020<br>|
||(1.168)|(1.845)|(1.301)|(-2.227)|(-4.139)|(-0.562)|
|Terms oftrade*<br>|-0.089<br>|0.003<br>|0.006<br>|0.036<br>|0.109<br>|0.078<br>|
|.|(-3.142)|(0.468)|(0.848)|(3.661)|(5.676)|(5.096)|
|Urbanization ratio|-0.127<br>|-0.115<br>|-0.023<br>|0.004<br>|-0.070<br>|-0.382<br>|
||(-2.5)|(-3.229)|(-1.939)|(0.045)|(-0.508)|(-3.538)|
|Olddependencyratio|-0.437|-0.623|-0.086|-0.249|-0.168|-0.655|
||(-2.353)|(-3.477)|(-2.16)|(-2.252)|(-0.638)|(-3.069)|
|Young dependency ratio|-0.107|-0.143|-0.012|0.104|-0.155|-0.299|
||(-1.685)|(-2.102)|(-0.815)|(2.437}|(-1.846)|(-4.017)|
|Governmentsaving/GPDI|-0.201|-0.065|-0.057|~0.154|-0.203|-0.285|
||(-0.976)|{-0.89)|(-2.367)|(-4.605)|(-2.618)|(-5.097)|
|Private credit flow/GPD!|0.904<br>|-0.003<br>|-0.429<br>|-0.114<br>|-0.192<br>|-0.318<br>|
||(2.029)|(-0,058)|(-4.312)|(-3.772)|(-2.188)|(-3.989)|
|Inflationrate*°|-0.031|-0.187|0.052|0.123|-0,.134|0.143|
||(-0.212)|(-2.918)|(1.64)|(2.503)|(-1.514)|(2.034)|
|Wald test ofjoint significance (p-value)|0.000|0.000|0.000|0.000|9.000|0.000|
|Sargantest(p-value)|-|-|-|-|0.291|0.400|
|<br>Test for 1st-order serial correlation (p-value)|-|0.000|0.095|0.000|0.033|0.001|
|Testfor2nd-orderserialcorreiation(p-|-|0.000|0.578|0.922|0.081|0.421|
|<br>vatue)|||||||
|Test for 3d-order serial correlation (p-value)<br>|-<br>|0.000<br>|0.081<br>|0.146<br>|0.873<br>|0.221<br>|
|‘Numberofobservations (Numberof—==—=~=<br>countries)|~«2(72)”~*«“|‘é‘4B(GQY”~=©|©«1079469)|1010(69)|872669)|~——2872(69)|

Notes: T-statistics (in brackets) computed with heteroskedasticity-consistent standard errors.

= Expressed in logs (log of [1+x] for the real interest rate and the inflation rate). © Measured by the first difference of the log.

© Both the real interest rate and the inflation rate are bounded between —50 percent and 50 percent.
# **_4.3  Poverty correlates_**

This section considers a few province- and district-level correlates of local headcount rates, in an attempt to look beyond its spatial distribution to more generalizable patterns. The results refer to figures 1-5 in the appendix.

The data displays a clear positive relationship between poverty rate and Gini index. A more equal distribution is associated with a lower poverty rate (Figure A.1 appendix). However, what is also clear from this Figure is that at any particular poverty rate there is heterogeneity across provinces and districts in terms of inequality outcomes.

Although Vietnam remains a rural country, the urbanization process has been accelerating in recent years. About 30 percent of people now reside in urban areas (General Statistics Office, 2011). Overall, urban areas tend to have lower poverty, and as a result it is a general tendency that poverty is lower as the urban population share increases (Ravallion et al., 2007). In Vietnam, poverty is negatively correlated with the urban population share at the provincial and district level (Figure A.2 Appendix).

Also, poverty is substantially higher in areas with large concentrations of ethnic minorities (Figure A.3 Appendix). This finding relates to the earlier one that the poor are now concentrated in Northern mountain and central highlands where there are large population shares of such ethnic minorities. It also follows that poverty rates are higher in areas with low population and low population density (Figures A.4 and A.5 Appendix).

# **5. The 2009 inequality and wealth maps**

We now turn, briefly, to the spatial distribution of inequality across provinces and districts. Table A.9 in the Appendix presents the Gini index and the ratio of the 90<sup>th</sup> to 10<sup>th</sup> expenditure percentile (a measure of “absolute” inequality) for all provinces. In addition, we also estimate the percentage of people belonging to the richest 20% of the population.

15