rafmacalaba/datause-encoder-data huggingface dataset:
this region). The difference in the proportion of firms that are fully credit constrained is more pronounced for small firms, indicating the smaller the firm, the more likely it is to be credit constrained. For example, 28.3 percent of small firms in AFR are fully credit constrained, compared to 10.5 percent of large firms.

Firms younger than 5 or 9 years are not more likely to be more credit constrained than older firms. There are no significant differences in the firms age distribution within the credit constrained categories and found no significant differences. This lack of significance maybe in part explained by the age cut off. Firms with up to 5 years of age may not face the same challenges as very young firms (of 1 or 2 years). However, the Enterprise Surveys have a limited number of observations for very young firms making it impractically to have an age cut off less than 5 years.

Figure 5 presents the distribution of credit constrained status by sectors and region. In both the ECA and LAC regions, the distributions of the credit constrained status within the 3 sectors (manufacturing, retail, and other services) are very similar. In EAP, manufacturing firms are more likely to be fully credit constrained than firms in the retail and other services sectors. In AFR, other services sector stands out as being the least credit constrained. In SAR, firms in the retail sector are more likely to be fully credit constrained.

To more formally test the association between firm characteristics and credit constrained status we consider an ordered logit model in which the dependent variable is the ordinal variable: 1=NCC, 2=MCC, 3=PCC, and 4=FCC. Thus, higher values of the dependent variable denote higher levels of credit constraint. Table 3 presents the results of the regression controlling for country and industry fixed effects. There is a negative significant relationship between firm size and credit constraint, i.e. the smaller the firm the higher the probability of being credit 14
controlling for the remaining explanatory variables. In general, the correlation is insignificant, indicating likely no correlation or weak correlation between the IV and the unobservable.

Since the literature emphasizes the importance of having non-weak IV, and the existing testing framework focuses on a single endogenous variable, we conduct our two-stage-leastsquares estimation using all pre-Taiping periods and one single post-Taiping period at a time, so that in total we conduct five 2SLS estimations (i.e., for the years 1880, 1910, 1953, 1982, and 2000), one endogenous variable at a time. When pooling all periods, the results are similar except that we do not have guidance on how to interpret test statistics. The results are in Table 4.

The IV results reinforce our conclusions. The IV satisfies the relevancy condition. All the F statistics for the five post-Taiping years are above 20, suggesting a non-weak IV (Staiger and Stock 1997; Baum et al. 2007). Moreover, the magnitudes become more pronounced. The population growth of the treatment group in 1880 (compared to 1820) is 52 percent lower than in the control group. And the population growth of the treatment group in 2000 (compared to 1820) is 67 percent lower than that in the control group. The population growth gap between the Taiping and control prefectures becomes stable around 1953.

# **V.  Property Rights, Distance to Nanjing, Fiscal Capacity, and Population Evolution**

We now investigate the channels through which the Taiping Rebellion affects population growth: property rights, distance-based stationary banditry, and local state capacity. **Property Rights** We examine whether relative to the Early Taiping areas (i.e., featuring the lack of clearlydefined land ownership), the Late Taiping areas (i.e., featuring clearer land ownership) has less wasteland and faster post-war population recovery. The data on wasteland cover 332 counties on their share of wasteland in the Taiping provinces in 1915. We have data on 151 Early Taiping counties, 95 Late Taiping counties, and 86 control counties (see Panel A, Table 4).

The cross-sectional regression results of the determinants of the percentage of wasteland in 1915 are in panel B of Table 4. The control variables include the distance to the Yangzi River, to the coastline, the dummy for being the prefecture capital seat, the dummy for being the most important county in 1820 (“ _Zuiyao_ ”), and the dummy for being a trade center in 1915.<sup>44</sup> We also control for provincial fixed effects to hold constant province-specific features. The results show that the wasteland ratio in the Early Taiping areas is 3.6 percentage points higher than that in the non-Taiping counties, while the wasteland ratio in the Late Taiping areas is slightly

> 44 We do not control for the distance to the Grand Canal because it did not pass our sample counties here.

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## **13b. By granular regions Median NPL**

<!-- Start of picture text --> 2007 0.65<br>Benchmark Europe 2011 0.95<br>2015 3.07<br>2007 5.40<br>WEST ECA 2011 8.50<br>2015 9.59<br>2007 1.50<br>EAST ECA 2011 5.40<br>2015 11.14<br>0 5 10 15<br><!-- End of picture text -->

## **Median NPL on direct lending**

<!-- Start of picture text --> 2007 0.90<br>Benchmark Europe 2011 2.73<br>2015 5.59<br>2007 4.00<br>WEST ECA 2011 8.15<br>2015 10.70<br>2007 1.50<br>EAST ECA 2011 6.25<br>2015 17.70<br>0 5 10 15<br><!-- End of picture text -->

## **Median NPL on SOE**

<!-- Start of picture text --> 2007 0.07<br>Benchmark Europe 2011 0.02<br>2015 0.16<br>2007 1.70<br>WEST ECA 2011 1.90<br>2015 5.15<br>2007<br>EAST ECA 2011 2.04<br>2015 2.65<br>0 5 10 15<br><!-- End of picture text -->

# 5.4.4 _Capital adequacy_

Median capital adequacy ratios are relatively high among SOFIs, with the highest observed among Development SOFIs that have experienced an upward trend (Figure 14a). The upward trend is mostly driven by Benchmark Europe, whereas there is variability in the other two regions under the granular geographic classification. SOFIs in West ECA experienced an overall drop, but maintained high ratios. East ECA suffered a large drop in CAR after a spike in 2011, with nine institutions having been recapitalized in the region after the crisis (notably, 2014 marks the beginning of a dramatic drop in the oil price and remittances). Compared to the overall banking sector, SOFIs median CAR is higher (except for Benchmark Europe in 2007 and 2011).

Since the GFC in 2007, several SOFIs - Commercial and Development, in EU and non-EU countries - have been recapitalized with government resources (Figure 14b). The percentage is lower in Benchmark Europe, but still stands over 40 percent (just 5 out of 12 institutions). In West 33
has largely been interpreted as stability in realized outcomes such as food production, caloric intake, and dietary diversity.<sup>6</sup> In this paper, we examine a broader conceptualization of stability that treats the absence of anxiety about insecurity, and perceptions of security, as attributes of the stability of food security. This notion of insecurity as being linked to anxiety and perceptions is neither new nor innovative. In the U.S., the distinction between someone who is identified as having “high food security” status and “marginal food security” status is most typically identified by whether the person reported experiencing anxiety over food sufficiency (or expected food sufficiency).<sup>7</sup> The Food Insecurity and Experience Scale (FIES) is now the endorsed SDG indicator 2.1.2 and is used by the Food and Agriculture Organization (FAO) in nearly 150 countries covering 90 percent of the world’s population, and the first question in this instrument is whether the respondent worried whether they would have sufficient food to eat.

What we aim to illustrate in this paper is that stated anxiety about food security is closely linked to perceptions of food insecurity status (suggesting that worrying about food security can itself be viewed as insecurity or as reflecting a lack of certainty or stability in food security status). This supplements the evidence that anxiety is a useful partial indicator of food insecurity. The analysis in this paper also reveals that stated experiences of food insecurity need not be associated with realized deprivations in food access as measured by

> 6 See for example, D’Souza and Jolliffe (2014), Jolliffe, Sharif, Gimenez (2013), Schmidhueber and Tubiello (2007), and Wheeler and von Braun (2013) as examples of examining the time stability of food availability and access as measured by food production, calories and dietary diversity.

> 7 In 2006, The U.S. Department of Agriculture revised their labels for different states of food security (e.g. high, marginal, low and very low food security) based on recommendation of the Committee on National Statistics (National Research Council, 2006) to distinguish the physiological state of hunger from other indicators of food security.

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Table 6: Long-term Effect of FPE on Educational Achievement: pre-FPE (placebo) effect

|VARIABLES|(1)<br>Treatment group:<br>First year students|(2)<br>Treatment group:<br>16-18 year olds|
|---|---|---|
|ATT (<sup>ˆ</sup>_δSDID_)|3.3336|-11.5052|
||(3.6820)|(4.6691)|
||[4.6907]|[8.2950]|
|Observations|3,827|2,409|

_Notes_ : The point estimates of the FPE effect on achievement (OWM) and standard errors in parentheses are produced using `asdid` Stata command by Houngbedji (2016). Bootstrapped standard errors, clustered by high school attended, in brackets with 1000 replications, and significance levels are indicated as follows: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All models are estimated using a linear polynomial function of degree 4 to approximate the propensity score. The covariates used in the estimation of the propensity score are APS, gender, district, and age dummies to approximate the cohort chances that the treatment and control groups were in primary and high school at the same times, though in different grades. This then makes it likely that they have experienced more or less the same teacher and school resource inputs, other than no fee school attendance and free textbooks. We, therefore, can expect the FPE effects to be stronger within this age range. The results are presented in Table 7. Indeed we can see from the table that the FPE effect estimates on academic performance, in columns (1) and (2) are slightly larger than those reported in Table 4 (the main results), although with large bootstrapped standard errors. Nonetheless, these results indicate that the main results are not sensitive to sample restrictions.

# **6.3.4 Discontinuity of the Pre-Entry Science Program (PESP)**

The university began running the Pre-Entry Science program (PESP) in 1996 with the aim of bridging the knowledge gap between high school and university science and mathematics education, and ultimately enhance students’ performance in science programs. During the PESP period, all students admitted into science programs would have to go through PESP, where they would be taught science subjects - biology, chemistry, mathematics, and physics - and English. And all those who would have completed the PESP would now proceed to enroll into what was known as the common first year of science programs.

However, due to financial constraints, PESP was discontinued in 2012. To counter the potential negative effects of this on students’ performance, the Faculty of Science and Technology, which used to run the PESP and the common first year program, employed extra staff and extended the first year program’s academic year by two (2) weeks. These two policy shocks coincided with the entry of the first FPE cohort into the university (i.e. the FPE policy), and will likely bias the 28
# **TABLE OF CONTENTS**

|**1**<br>**INTRODUCTION .................................................................................................... 5**|
|---|
|**2**<br>**THE AUSTRALIAN PENSION SYSTEM ............................................................ 5**|
|2.1<br>PUBLICPENSIONS................................................................................................ 5<br> <br>|
|2.2<br>REPLACEMENTRATES ANDPENSIONELIGIBILITY............................................. 10|
|2.3<br>OCCUPATIONALSCHEMES AND THESUPERANNUATIONGUARANTEE................ 12|
|**3**<br>**THE PRODUCT MENU........................................................................................ 15**|
|3.1<br>ALLOCATED INCOME STREAMS.......................................................................... 16|
|3.2<br>MARKET LINKED INCOME STREAMS................................................................... 17|
|3.3<br>LIFETIME INCOME STREAMS............................................................................... 17|
|3.4<br>LIFE EXPECTANCY INCOME STREAMS................................................................. 18|
|3.5<br>FIXED TERM INCOME STREAMS.......................................................................... 18|
|**4**<br>**TAXATION AND SOCIAL SECURITY RULES............................................... 20**|
|**5**<br>**OVERSIGHT – REGULATION AND SUPERVISION..................................... 23**|
|5.1<br>INSTITUTIONALSTRUCTURES............................................................................. 23|
|5.2<br>APRA................................................................................................................ 23|
|5.3<br>CAPITAL, FUNDING, SOLVENCY......................................................................... 24|
|_5.3.1_<br>_Pension Funds........................................................................................... 24_|
|_5.3.2_<br>_Life Insurance Companies ........................................................................ 29_|
|**6**<br>**TRENDS IN INDUSTRY COMPOSITION......................................................... 36**|
|6.1<br>SUPERANNUATIONFUNDS.................................................................................. 36|
|6.2<br>LIFEINSURANCECOMPANIES............................................................................ 39|
|**7**<br>**TRENDS IN PRODUCT COMPOSITION.......................................................... 41**|
|7.1<br>PRODUCTMIX–A STORY OF ATTITUDES........................................................... 41|
|7.2<br>EQUITYRELEASEPRODUCTS............................................................................. 45|
|**8**<br>**CONCLUDING REMARKS ................................................................................. 46**|
|**REFERENCES: .............................................................................................................. 48**|

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Table **27: NREP -** State-wise Distribution of Employment, 1987-88 (Actual)

|||________|<br>Percent Dis|tribution of|
|---|---|---|---|---|
||Employment<br>|Percent|||
||(million<br>mandays)|women<br>employed|Employment|Ultra poor<br>population|
|Andhra Pradesh|28.8|41.8|9.0|3.9|
|Assam|3.4|Nil|0.7|2.1|
|Bihar|46.8|10.0|12.8|20.0|
|Gujarat|17.2|28.6|3.5|1.7|
|Haryana<br>|2.2|Nil|0.7|0.6|
|Jammu-Kashmir|-|_|_|_|
|Karnataka|1.9|13.4|5.8|4.6|
|Kerala|9.9|26.7|4.0|3.0|
|Madhya Pradesh|50.7|27.1|8.4|8.5|
|Maharashtra|26.2|26.2|6.9|7.5|
|Orissa|22.5|25.7|4.9|5.4|
|Punjab<br>|1.9|Nil|0.8|0.5|
|Rajasthan<br>|24.0|24.2|6.0|3.2|
|Tamil Nadu|32.2|44.1|11.8|8.2|
|Uttar Pradesh|55.3|3.1|21.6|18.3|
|West Bengal|15.5|3.0|3.1|12.7|
|All India|379.6|20.65|100.0|100.0|

Source: Rural Development Statistics, 1988 may destroy some of the merits of this scheme (Subbarao 1987a) such as its self-targeting character.

Only two states - Andhra Pradesh and Tamil Nadu - have been successful in attracting women in large numbers to NREP work sites (see Table 27). The state with a large proportion of the ultra poor - Uttar Pradesh - has a dismal record in the employment of women in NREP projects.

However, the rank correlation between state-wise distribution of mandays of employment generated and the distribution of the ultra poor was
years, tax deductions or direct subsidies for purchasers of household services have been introduced to support their expense for cleaning, cooking, laundry, home maintenance, or care services for children and the elderly or disabled. The overall goals are to induce undeclared work to enter the formal economy and to create jobs in several EU countries. The scope of eligible services, the form of support, the amount of tax deduction or subsidy, and the specific conditions for monetary incentives differ between countries; however, the intention is to move undeclared services to declared services by subsidizing household service expense to reduce the expense gap between the formal and informal economies. Public interventions in different countries to narrow the expense gap for formal versus informal service provision range from direct subsidies to household service providers or users to public revenue expenditure through tax deductions, tax exemptions, or VAT reduced rates. Table 2 provides a summary of representative EU countries’ tax incentives, excerpted from the European Commission report, _Developing Personal and Household Services_ (2013).

Table 2 Tax Incentives for Household Services

|Belgium|Tax reductions are linked to the use of vouchers used to hire household services.<br>Two types of vouchers, the cheque AlE and the Titre‐services, are eligible for tax<br>deduction, the former 30 percent and the latter 30 to 40 percent. The ceiling<br>for voucher tax deductions is €2,400 Euros (the total of both vouchers).|
|---|---|
|Denmark|In 1994, Denmark became the first country to introduce a subsidy of 50 percent<br>of cost to support household services, such as garden work, snow clearance,<br>shopping for daily goods, cooking, cleaning, laundry, and window cleaning.<br>The benefit was reduced in 2004, and benefits were limited to people aged 65<br>or more.<br>In 2011, tax credit was allowed to all private persons; however, it is expected to<br>be abolished as of 2013.|
|Finland|A tax deduction system was introduced in 1997 for household services within<br>the taxpayer’s own house or in an elderly relative’s home.<br>If the tax deduction is larger than the amount of the central government income<br>tax, local government taxes can also be reduced.<br>Since 2009, eligible services include household work, caregiving, and day‐care<br>work at home; repair work; services for a leisure house; and IT services.|

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As a source of final demand, the role of China in the region is still limited. Gross trade statistics show that developing Asian economies vary in their exposure to China, and that the exposure has been growing over time. Yet these indicators do not clarify whether a country is potentially exposed to shocks to Chinese domestic demand or to shocks to demand of other countries that are positioned downstream from China in an international supply chain. Examining the decomposed bilateral exports to China suggests that, among the developing East Asian countries for which we have the required data, the Philippines and Indonesia are relatively more exposed to Chinese demand. This exposure could reflect the fact that these two countries, with exports respectively dominated by services and natural resources, are different from the other East Asian countries which are linked to China through manufacturing value chains that ultimately serve third countries. In general, the exposure of East Asian economies to demand shocks in China is, however, not much higher than that of other economies (Figure 11).

Figure 11. Share of China in gross exports of goods and services, by type of value added, 2011 <!-- Start of picture text --> % of multilateral<br>gross exports<br>Foreign value added in gross exports to China<br>45<br>Domestic value added not absorbed in China<br>40<br>Domestic value added in intermediates absorbed in China<br>35<br>Domestic value added in final goods and services absorbed in China<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>HKG AUS TWN JPN CHL BRA PHL* KOR ZAF IDN* SAU MYS* NZL THA* VNM* RUS ARG SGP IND CRI USA DEU COL BRN ISR CHE KHM* CAN Other<br><!-- End of picture text --> Source: OECD TiVA, Wang et al. (2013) and authors’ calculations.

Note: Countries are sorted by the total domestic value added absorbed in China as a share of multilateral gross exports.

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World Bank (2009) report argued that between 2002 and 2006 while real expenditures had grown strongly, real incomes had remained largely stagnant; moreover, income would have fallen for most Jordanians had transfer incomes not grown. Stagnant household incomes in an otherwise growing economy was likely to have put pressure on households. Also, perhaps the poverty line had been set too low and improvements in poverty statistics did not resonate with public perceptions. A more recent paper by Mansour (2012) revisited the paradox largely through the lens of a rise in inequality coupled with stagnant economic opportunities for the poor and the middle class. The post 2009 period saw economic activity slowdown considerably and unemployment remaining high; at the same time, the top five income deciles recorded much superior growth in consumption than the lower middle class and the poor.

This paper offers a different, and perhaps more direct, explanation for the public perception that poverty is worse than the reported estimates. Public perceptions may be more informed by exposure to poverty spells rather than being informed by average level of wellbeing. This paper illustrates that one approach to measuring poverty that focuses on actual poverty spells (i.e. “cross section” measure) rather than average consumption levels results in an estimate of poverty that is 26 percent greater than the officially reported poverty rate. Another approach to estimating poverty that focuses on whether a household has experienced a single spell of poverty in the last year (i.e. “ever poor” measure), is over two times higher than the status quo poverty measure. Because the status quo measure is based on the average consumption level of households, it reduces the volatility in consumption and thereby conceals the worst outcomes faced by households. Indeed these alternative metrics of poverty appear more in line with prevailing domestic perceptions and anxieties.

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