rafmacalaba/datause-encoder-data huggingface dataset:
standard policy instruments – fines and enforcement of quotas – may do little to reverse the population decline when the carrying capacity and hence productivity of the ecosystem is diminished.<sup>19</sup> This might suggest the need to avoid damage in the first instance if the economic gains outweigh the foregone benefits. To explore these trade-offs in greater detail the following section examines wider economic impacts in a general equilibrium context. It does this by embedding the bioeconomic results into a CGE model.

Table 1

||Baseline|20% Reduction<br>in carrying<br>capacity|20% increase<br>agricultural<br>profits|Combined 20%<br>increase in ag. profits<br>and 20% reduction in<br>carryingcapacity|Combined +<br>Doubling of fines|
|---|---|---|---|---|---|
|Wildebeest(#)|1,120,000|855,000|810,000|600,000|740,000|
|Harvest(#)|300,000|210,000|200,000|140,000|180,000|
|Tourists(#)|289,000|260,000|240,000|200,000|210,000|
|Land to wildlife<br>(km<sup>2</sup>)|17,300|17,600|16,900|17,200|18,100|

# **5. General Equilibrium Simulations**

This section outlines the results of a CGE simulation<sup>20</sup> . At the core of this CGE is a social accounting matrix (SAM), whose architecture reflects the main components of the Tanzanian economy. The information for the SAM is drawn from the GTAP data base which is augmented with other data to extend the natural resource component of the model. A detailed description of the data and model is in the Appendix. A CGE approach seems warranted in this context given the size of tourism and wildlife sector and the importance of the Serengeti to the national economy of Tanzania. Tourism is the second largest source of foreign exchange, estimated at over US$1.28 billion, and directly contributes to more than 13 percent of GDP, with a considerably higher indirect contribution (World Travel and Tourism Council, 2013). The overwhelming majority of benefits derive from tourist visits to the Serengeti – one of the primary attractions. Additionally the government earns significant revenue from fees and licenses for tourism and trophy hunting estimated at close to $100 million a

> 19 This has implications for the way infrastructure impacts are managed.  It is not unusual to seek payments for damage to the environment with the revenues being used to improve enforcement of regulations. This result suggests this strategy could be ineffective.

> 20 Cataldo Ferrarese, of the University of Rome “Tor Vergata”, collaborated and provided expert research assistance for the construction of the CGE model.

20
# **5 Conclusions**

Along the same lines as World Bank (2015), this paper has established that under assumptions of distribution-neutral growth, the World Bank’s poverty goal of less that 3% of the world’s population living on less than a $1.25/day will be difficult to reach. This paper has shown that growth patterns which ‘boost’ growth of the bottom 40% of populations, while maintaining growth in the mean, make the goal much more viable. We therefore conclude that boosting shared prosperity can contribute significantly to reaching the goal of ending global poverty by 2030.

The proposed shared prosperity premium simulated in this paper does not impose a large 'cost' on the rest of the distribution. Because of the large income share of the top 60%, the reduction in the annualized growth rate of the top 60% necessary to ensure that the bottom 40% grows pp above the mean is relatively small. For example, in the case of China, a growth incidence such that the bottom 40% mm grows 2pp above the mean (8.1% vs 6.1%), implies that the top 60% grows at an annualized rate of 5.7%, just 0.4pp below the growth in the mean, or what would have been the case without a shared prosperity premium ( ). mm= 0 The impact on poverty of boosting shared prosperity is different across countries, and depends on the initial level of poverty, the shape of the distribution and the growth rate. When the poverty headcount is close to, but below 40%, the effect of the shared prosperity premium is greatest, due to the high density of the distribution at this point. At high levels of initial poverty (above 40%), boosting the growth of the bottom 40% in the manner done in this paper and assuming that mean growth is unchanged, will lead to a decrease in the pace of poverty reduction in the short term in comparison with a distributionneutral growth scenario. This highlights a certain tradeoff between the two goals of extreme poverty reduction and focus on the bottom 40%. Nevertheless, in such cases the effect may still be positive on the poverty gap, thus helping the poorest of the poor.

Inequality falls rapidly across all countries if we assume a positive shared prosperity premium. While the model used in this paper uses an artificially imposed growth incidence curve, the resulting difference between the growth of the bottom 40% and the mean is not unprecedented. Our discussion indicates that similar shared prosperity premiums can imply very different performances in terms of how growth is accrued throughout the income distribution. Similarly, the distributional changes implied by these premiums should be assessed relative to the country-specific initial level of inequality as this affects their feasibility. This is something that may well merit attention in assessing a country’s performance in terms of shared prosperity.

21
# **5. New Zealand: The New Zealand Superannuation Fund**

## **5.1 Objectives**

The New Zealand Superannuation Fund (NZSF) and the Guardians of the NZSF were established in October 2001 by the New Zealand Superannuation Act 2001. The aim of the fund is to partially fund the universal public pension that is paid by the government to all old-age persons residing in New Zealand. The accumulation of the fund and its use during the payout phase will offset the steep increase in the cost of the universal pension, particularly after 2020. The fund will thus smooth out the financial burden on the budget from the impact of demographic aging on the universal pension scheme.

The government decided to set aside substantial capital contributions for the fund over the ensuing twenty years. As a result, the fund is expected to grow to around 100 billion NZD by 2023, after which date funds may be withdrawn to meet part of the increased cost of universal pensions. With public debt amounting to only 14 percent of GDP, New Zealand, like Ireland and Norway, did not face a difficult policy dilemma in deciding to create the fund. If anything, creating the fund and investing in global assets is likely to earn a higher rate of return than the interest cost of the public debt.

## **5.2 Funding Sources**

The NZSF is funded by annual government contributions. These vary from year to year and depend on a formula that calculates annually the required contribution for meeting the financial objective of the fund. This is not the maintenance of a particular funding ratio but the smoothing out of the annual cost of universal pensions. Instead of doubling the annual cost would rise by half if the fund policies are successful (McCulloch and Frances 2004). The contribution rate reached 1.5 percent of GDP in 2006. The first transfer of contributions was made in October 2003. This included funds that had been set aside in fiscal 2002 and 2003 in anticipation of the creation of the fund.

Table <u>5.1: Total Assets of the NZSF, 2004-2006</u>

|end June|2004|2005|2006|
|---|---|---|---|
|Annual Transfers (bn NZD)|3.8|2.1|2.3|
|Percent of GDP|2.5|1.3|1.5|
|Total Assets (bn NZD)|4.0|6.6|10.1|
|Percent ofGDP|2.7|4.2|6.3|

Source: NZSF and IFS Total assets of the NZSF rose from 4 billion NZD (or 2.7% of GDP) in June 2004 to 10 billion NZD, corresponding to 6.3% of GDP in June 2006 (Table 5.1). The growth of assets also reflected the high level of net investment returns. As the fund was created after the recovery of global equity markets from the bursting of the high tech bubble in 55
Table 2: Balance Table - Baseline Characteristics

||(1)<br>Control<br>mean|(2)<br>Control<br>s.d.|(3)<br>Treatment v.<br>Control<br>dif.|Diference in<br>(4)<br>Sanctions v.<br>Control<br>dif.|Means (t-test)<br>(5)<br>Procedures v.<br>Control<br>dif.|(6)<br>Moral duty v.<br>Control<br>dif.|
|---|---|---|---|---|---|---|
|Individual Business|0.40|(0.49)|-0.01**|-0.02**|-0.01|-0.01|
|Self-employed service providers|0.06|(0.24)|0.01**|0.01|0.01**|0.01|
|Corporations, IB or self-employed|0.79|(0.41)|-0.01|-0.01*|0.00|-0.01|
|Reported revenue (Sales) (2019) (L1,000s)|615.33|(3142.60)|-4.83|32.59|-46.69|-0.22|
|Declared revenue (Sales) (2019) (L1,000s)|1391.46|(4749.50)|-80.87|-264.16|13.36|6.86|
|Declared income tax in 2018|0.86|(0.35)|-0.01|-0.01*|-0.01|0.00|
|Reported revenue (Income) (2018) (L1,000s)|533.62|(1830.09)|-21.08|26.93|-68.67|-21.25|
|Declared revenue (Income) (2018) (L1,000s)|1303.26|(2672.35)|-3.70|11.18|-9.87|-12.30|
|Declared revenue 2018 | declaring (L1,000s)|1512.46|(2823.38)|5.18|30.78|0.89|-16.18|
|Not liable for taxes|0.47|(0.50)|0.01|0.01|0.00|0.01|
|Liable for income taxes|0.50|(0.50)|-0.01|-0.01|-0.00|-0.01|
|Liable for asset taxes|0.03|(0.16)|0.00|-0.00|0.00|0.00|
|Taxable base 2018 | declaring (L1,000s)|116.40|(270.68)|1.82|0.32|3.56|1.58|
|Tax liability 2018 | declaring (L1,000s)|15.52|(73.46)|0.36|0.14|0.25|0.70|
|Efective tax rate|0.12|(0.11)|0.00|0.00|0.00|0.00|
|Declared income tax in 2017|0.80|(0.40)|-0.00|-0.01|-0.00|0.00|
|Reported revenue (Income) (2017) (L1,000s)|466.91|(1709.31)|-32.10|3.91|-64.74|-35.28|
|Declared revenue (Income) (2017) (L1,000s)|1230.89|(2513.12)|-5.64|-30.70|9.53|4.07|
|Declared revenue 2017 | declaring (L1,000s)|1531.06|(2719.64)|-3.05|-22.44|14.46|-1.11|
|Taxable base 2017 | declaring (L1,000s)|122.99|(272.04)|2.98|2.32|2.49|4.14|
|Tax liability 2017 | declaring (L1,000s)|17.11|(76.76)|1.06|1.46|1.07|0.65|
|Revenue reported by other taxpayers|0.88|(0.33)|0.01**|0.01*|0.01*|0.00|
|Revenue reported by POS operators|0.22|(0.42)|-0.01|-0.00|-0.00|-0.01*|
|Revenue reported by government|0.03|(0.17)|-0.00|-0.00|-0.00|-0.00|
|Revenue reported by customs|0.01|(0.09)|0.00|0.00|0.00|0.00|
|Declared losses for fve years|0.08|(0.27)|-0.00|0.00|-0.00|0.00|
|Atypical fnancial transactions|0.07|(0.26)|0.00|0.00|-0.00|0.00|
|Atypical declared revenue|0.48|(0.50)|-0.01**|-0.02**|-0.01|-0.01|
|Observations|15399|15399|31396|20705|20731|20758|

_Note_ : This table compares average characteristics between control and treatment arms. Column (1) presents average taxpayer characteristics and previous filing behavior in FY2018 and FY2017, while Column (2) standard errors. Column (3) presents the difference in averages between the pooled treatment group and control, while columns (4) through (5) presents the differences between taxpayers in control group and each of treatment arms (Sanctions, Procedures and Tax morale arms, respectively) and indicate whether we reject the null of equal averages. (* p<0.1, ** p<0.05, *** p <0.01) 25
and others (2008) show that women with 16+ years of education spend 9.7 hours more every week on childcare relative to high-school dropouts. In contrast to the broad agreement on time spent in childcare, Pakistani women allocate a lot more of their time to housework. _Non-employed_ women in the US spent just over 31⁄2 hours a day on housework in 1995 and employed women 21⁄2 hours a day (Bianchi and Robinson 1998-99)—less than a quarter of the time spent by women in our rural Pakistani sample. The difference in time allocated to childcare is then perhaps not as large as we may have imagined given the dominance of housework in the Pakistani context.<sup>6</sup>

# **Children**

The survey also covered every child between the ages of 5-15 in the sampled households for a total of 4,331 children. The mean age for a child in our sample is 10 years and 47 percent of the sample is female. Overall child enrollment is 66 percent with girls 10 percentage points less likely to be enrolled than boys; given our list frame and the private school explosion in rural Pakistan (see Andrabi and others, 2008), 30 percent of the children in our sample are enrolled in private schools.

Child time-use was reported by parents for a typical school-day in the previous week. The main categories are: rest, play (unstructured, unsupervised, leisure time), time in school, time spent on educational activities (school preparation, homework, formal tutorial sessions), paid work, housework and the residual category, “other”. Apart from “Rest,” “Play” is the largest component of the out of school day for the children averaging 183 minutes, followed by educational activities at around two hours per day. Less time is spent by children on housework and paid work, at 65 and 16 minutes respectively. Given the concern in the literature on issues of child labor, both inside the house and in paid-work, the comparison of these categories for enrolled and out-of-school children is of interest. Table 2, Panel B shows that children who are out-ofschool do spend more time on housework—out-of-school children spend 197 minutes on housework compared to 24 minutes for children enrolled in school. The extent of paid-work is fairly low with enrolled children reporting virtually no paid-work and out-of-school children reporting 62 minutes a day. Of particular

> 6 The Americans’ Use of Time survey classifies childcare as a primary or secondary activity, where the former is child spent exclusively with children while the latter includes time spent on multiple tasks, one of which is with children (such as cooking while supervising homework). The usual caveats of comparability across surveys with different questions thus apply in force.

10
7

**Table** is **Cross-Country correlations** on Long-run Indicators of **Financial** Activity

in Developing countrias (1965-89)

||r6589|LSRCB6589|<br>CPY6589|CGY6589|CBAFA6589|FIY6589|M3Y65U|
|---|---|---|---|---|---|---|---|
|r6589|1.00|-0.3's|0.48|-0.24|-0.43|0.31|0.28|
|BSRC36589||1.00|-0.09|0.14|0.31|0.06|-0.17|
|CPY6589|||1.00|0.06|-0.33|0.60|0.72|
|CGY6589||||1.00|0.23|0.20|0.35|
|CBAFA6589|||||1.00|-0.47|-0.47|
|FIY6589||||||1.00|0.75|
|X3Y6589<br>wasus|s~~~~~<br>**.=s:=====**|**================**|**=====c========**|**===============**|**=**||1.00|

# Notes:

Number of countries: 52 (see Appendix 3 for the list of countries).

Definition of variables: (Appendix 1 for data sources) All valueo are annual average figures for the period 1965-89 r: Real interest rate. BSRC3s The ratio of commf-cial bank reserves with the Central Bank to total assets of the financial system. CPY: The ratio of credit to the private sector to GDP. CGY: The ratio of credit to the government sector to GDP. CDAFA: The ratio of Central Bank assets to total assets of the financial system. FIlY: Financial intermediation as a ratio of GDP.

**N3Y** The ratio of total assets of the financial system to GDP.
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data reports the extent of underreporting in sales to the tax office applying the indirect approach across cities, industries and firm sizes, with underreporting calculated as . Estimated underreporting is much smaller than revealed by the direct approach (table 1) and averages about 15%. If survey sales reflect true sales and firms tend to underreport sales to tax office, the indicator should be between 0 and 100, but we observe negative values for 96 firms.<sup>25</sup> This suggests the presence of serious measurement error and/or underreporting in the survey sales.

|**Av**|**erage % of Sales**|**T**<br>**Underre**<br>|**ABLE 2**<br> **ported by**<br>|**the Indirec**<br>|**t Approac**<br>|**h**<br>|
|---|---|---|---|---|---|---|
|||Obs.|Mean|Std. Dev.|Min|Max|
|**_City_**|_Ulaanbaatar_|139|14.9|41.5|-125.9|91.3|
||_Darkhan_|50|18.2|55.3|-121.2|91.3|
||_Edernet_|46|13.5|54.2|-154.3|90.1|
||_Hovd_|20|16.4|49.8|-132.8|86.7|
|**_Industry_**|_Manufacture_|131|18.0|46.2|-121.2|91.3|
||_Construction_|66|12.4|47.1|-154.3|91.3|
||_Service_|45|19.9|36.5|-98.0|90.8|
||_Tourism_|13|-2.1*|16.7|-22.4|64.5|
|**_Size_**|_Small_|69|25.7**|42.3|-121.2|90.6|
||_Medium_|156|13.3|46.8|-154.3|91.3|
||_Large_|29|4.4|28.3|-80.9|71.5|
|**Total**||255|14.8|45.3|-154.3|91.3|

Note: the top/bottom 5% of the observations are not reported; all figures are weighted by sampling weights; *, ** denote significance at 10% and 5% respectively; _Source: WB PICS Mongolia (2004) & Tax Office Data 2003_ The MIMIC approach has three main advantages over the direct and indirect approaches. First, the MIMIC approach uses more information by incorporating firm-level incentives to comply with taxes and a production function model linking true sales with observable firmlevel production characteristics. Second, the MIMIC model allows for measurement errors in sales reported in the survey and to the tax office. And third, the MIMIC model allows for underreporting not only to the tax office but also in the survey.

> 25 37 out of the 97 negative indicators are below minus 10%.

23
“Does the job involve driving?” (“Operating vehicles”), and; 2) “Does the job involve repairing items/instruments?” (“Control/Feel objects”; “Manual dexterity”).

**Table 1. Tasks to skill mapping using STEP skill measurement surveys**

|**Skill**<br>**Bracket**|**STEP Task**|**Questio**<br>**n**|**Corresponding**<br>**O*Net Task **|**Coding**|
|---|---|---|---|---|
||Type of document<br>read|m5a_q0<br>5||Summation of<br>"Yes"(0-5)|
|Non-<br>routine|Length of longest<br>document typically<br>read|m5a_q0<br>4*m5a_<br>q06|Analyzing<br>data/information|Categorical (0-5)|
|<br>Analytical|Math tasks|m5a_q1<br>8||Summation of<br>"Yes"(0-5)|
||Thinking for at least<br>30 minutes to do<br>tasks.|m5b_q1<br>0|Thinking creatively|Categorical (1-5)|
|Non-<br>routine|Supervising<br>coworkers|m5b_q1<br>3|Guiding subordinates|Dummy|
|<br>Interperson<br>al|Contact with clients|m5b_q0<br>5*m5b_<br>q06|Establishing personal<br>relationships|Categorical(0-10)|
||How often your work<br>involves learning new<br>things|m5b_q1<br>7|Importance of<br>repeating the same<br>task(inverse)|Categorical(0-5)|
|Routine<br>Cognitive|Autonomy|m5b_q1<br>4|Structured vs<br>unstructured work|Categorical (1-10)|
||Repetitiveness|m5b_q1<br>6|Importance of<br>repeating the same<br>task|Categorical (1-4)|
|Routine|Operate|m5b_q0<br>9|Controlling Machines<br>and processes|Dummy|
|Manual|Physical demanding|m5b_q0<br>3|**-**|Categorical (1-10)|
|Non-<br>|Driving|m5b_q0<br>7|Operating vehicles|Dummy|
|Routine<br>Manual|Repair|m5b_q0<br>8|Control/Feel objects;<br>Manual dexterity|Dummy|

**Note** : the question codes are for Wave2 of the STEP Questionnaire (they do not coincide with the codes of Wave1).

To construct the indexes using STEP, each variable is standardized over the entire population of the pooled STEP surveys for all countries, where all countries are equally weighted. We then sum 8
**Table 4.** **<u>Oaxaca-Decomposition (2005-2016)</u>**

|VARIABLES|(1)<br>2005|(2)<br>2010|(3)<br>2013|(4)<br>2016|
|---|---|---|---|---|
|male|6.902***|7.134***|7.564***|7.353***|
||(0.006)|(0.003)|(0.003)|(0.003)|
|female|6.333***|7.061***|7.528***|7.258***|
||(0.020)|(0.011)|(0.005)|(0.006)|
|difference|0.569***|0.073***|0.035***|0.096***|
||(0.021)|(0.011)|(0.006)|(0.006)|
|explained|0.024***|0.020***|-0.020***|0.004|
||(0.009)|(0.004)|(0.003)|(0.003)|
|unexplained|0.545***|0.053***|0.055***|0.091***|
||(0.019)|(0.011)|(0.006)|(0.006)|
|Observations|22,567|40,687|24,913|71,149|

Source: Calculations based on Bangladesh labor force surveys. Note: Standard errors are in parentheses. Statistically significant coefficients at the 10,5, and 1 percent level are indicated by one, two, and three asterisks respectively.

We present results from our Mincerian wage equations in Table 5 and Table 6. We observe that in 2005, on average, women in Bangladesh earned 60 percent less, on average, than males. As expected, substantial increases in demand for Bangladesh exports in the female-intensive garments sector significantly reduced this wage differential throughout the following years: the female-male wage gap decreased to -12 percent in 2010 and -11 percent in 2013. Despite a reverse in this trend in the next years, when exports fell, the female-male wage gap still remained substantially below 2005 levels. Similarly, high-skilled wage premiums relative to low-skilled workers decreased during the same time, from 70 percent in 2005 to 52 percent in 2016 (see Table 6).

**Table 5. Mincerian Wage Premium Estimates, by each year**

|||Logarithmo<br>|f realwages<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Only2005|Only2010|Only2013|Only2016|
|Female|-0.56***|-0.09***|-0.06***|-0.15***|
||(0.01)|(0.01)|(0.01)|(0.00)|
|Textile and Apparel|0.57|0.15|0.16***|0.09***|
||(0.42)|(0.15)|(0.00)|(0.00)|
|Age|0.04***|0.02***|0.03***|0.03***|
||(0.00)|(0.00)|(0.00)|(0.00)|
|High Skilled|0.70***|0.48***|0.25***|0.51***|
||(0.01)|(0.01)|(0.01)|(0.00)|

28
Policy ReseaRch WoRking PaPeR 4837

# **Abstract**

This paper examines the implications of bank activity and short-term funding strategies for bank risk and returns using an international sample of 1,334 banks in 101 countries leading up to the 2007 financial crisis. Expansion into non-interest income generating activities such as trading increases the rate of return on assets, and it may offer some risk diversification benefits at very low levels. Non-deposit, wholesale funding, by contrast, lowers the rate of return on assets, although it can offer some risk reduction at commonly observed low levels of non-deposit funding. A sizeable proportion of banks, however, attract most of their short-term funding in the form of non-deposits at a cost of enhanced bank fragility. Overall, banking strategies that rely prominently on generating non-interest income or attracting non-deposit funding are very risky, which is consistent with the demise of the U.S. investment banking sector.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to bank riskiness. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted atAdemirguckunt@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team