rafmacalaba/datause-encoder-data huggingface dataset:
Bancassurance is suited to retail (that is, Household) insurance needs. Typically these first arise when motor fleets begin to grow and third-party liability insurance becomes mandatory. To the extent that purchases of motor vehicles are funded by personal loans, banks have an opportunity to write Credit Life and Motor damage and liability business. In practice they are competing with the car dealers and vehicle-testing stations. The latter two classes will have usually been written by the time the bank becomes aware of the transaction. In addition Motor insurance requires an infrastructure and skill base that is generally not found in banks.

Better opportunities arise in meeting medium-term savings needs. These, together with investment-linked contracts, usually form the product range that generates the early rapid growth of the Bancassurance sector. For this reason a strong correlation tends to be seen between the emergence of Bancassurance as a major channel and the number of banks per unit of population. Additional contexts for the growth of Bancassurance include an effective banking culture (that is, a constructive role of banks in the community) and the role of personal savings in meeting life-cycle needs. Where a good education has to be paid for privately and public pension provision is capped, banks have a distinct opportunity to expand their product range in areas that their front-line staff will be comfortable marketing and servicing. These conditions obtain in Southern Europe and parts of East and North Asia, and it is in these two regions that the model is most developed. Where Bancassurance development has been delayed in these regions, regulatory constraints normally are, or have been, in existence (for example, Korea and the Philippines).

Thus the linking of insurance marketing to bank distribution and client bases appears to be an important mechanism for hastening the development of the insurance sector (for other insurance sector developmental levers see Lester 2010). However, this innovation needs to be handled carefully, because mis-selling and overly aggressive use of financial arbitrage (for example, arising from different capital rules) can lead to strong and potentially negative official reactions. Examples of strong regulatory and supervisory responses have already been seen in the United Kingdom, the Middle East, and North Asia.

# **Bancassurance in Emerging Markets**

## **Selecting Representative Emerging Markets**

Representative emerging markets were selected using the following criteria (table 2.4):

- A credible regulatory and supervisory regime is in place

- Bancassurance data are available

- Population is more than 10 million

- M2/GDP is greater than 50 percent

33
# **6.2.   Results**

In general, the rate of return on education significantly and progressively increases with higher levels of education (Table 5). This is consistent with the finding from Aslam (2009), which shows the return to an additional year of schooling ranges from 7 to 11 percent for men and from 13 to 18 percent for women.<sup>11</sup> The finding is also similar to that in Kingdon and Soderbom (2008), which shows that conditional on occupation, education consistently and substantially raises earnings.<sup>12</sup> However, the analysis also reveals three additional points.

First, there is no significant difference in earnings between youth with primary education and youth without any formal education. This is also true for female adults, but not for male adults. This implies that the labor market might not sufficiently reward individuals with limited education in these disadvantaged groups. This could have the devastating consequence that some families might be reluctant to send their children to school if they perceive that completion of primary education would not increase their future earnings prospects.

Second, it seems that the return on education is compounded with experience (or age). This conclusion comes from the comparison of rate of return on education between youth and adults, for males and females respectively. Coefficients at all education levels are significantly higher for adults than for youth, and such differences increase with the level of education.

Third, there seems to be a scarcity premium for educated females in the labor market. This finding is based on a comparison of coefficients between male adults and female adults with similar education levels. Compared with their less-educated counterparts, women with more education receive much higher returns than men. However, the average earnings of women are still much lower than those of men, even in the categories of higher-educated ones. These 15
ranges from $31.34 to $56.90 per month with a mean of $42.46 (or about $1.40 per day) and median of $41.33. The mean poverty line is $37.98 or $1.25 a day. (The median is $38.51.) On trying various parametric forms, the linear specification was as good as, or better than, others in terms of fit.<sup>26</sup> The estimated regression corresponding to equation (3) is then (with t-ratios in parentheses based on robust standard errors): The robust standard error of the $37.98 per month ($1.25 per day) estimate is $3.03 ($0.10 per day). The rising segment has a slope of about 1/3.<sup>27</sup> (The aforementioned under-prediction at low consumption by the linear regression is corrected for by using the $1.25 line as the lower bound.) To check whether our reference group is consistent, we back out the estimated value of _C_ ˆ * such that _Z_ ˆ * = _f_<sup>ˆ</sup> ( _C_ ˆ * ) , which gives _C_ ˆ<sup>*</sup> = 59.50 (t=3.26). So our choice of the reference group as all countries with _Ci_ < $60 is internally consistent with our estimate of (4).

The $1.25 line is fairly robust to changes in the reference group. The mean poverty line is $37.27 ($1.22 a day) if one focuses instead on the poorest 10 countries and it is $38.33 ($1.26) if one sues the poorest 20. However, these were not consistent reference groups. If instead we focus on the subset of our 15 reference countries for which the poverty line is “current,” in that it was derived from the same survey (rather than updated from a prior line allowing only for inflation), then the mean rises slight to $38.89 (n=11). If one focus on the poorest 10 countries for which the poverty line is current then the mean is $37.22.

With a little rounding off, we can also propose a parsimonious schedule of relative poverty lines, with a lower bound of $1.25 a day, but rising above a critical consumption level with a gradient of 1:3, which is in close accord with our regression model in (4).<sup>28</sup> More precisely, our schedule of relative poverty lines is (in $’s per day): _R Z i_ ≡ max[$.125, $.060 + _Ci_ / 3] = $.060 + max[$.065, _Ci_ / 3] (5)

> 26 The coefficient on a squared term in PCE per capita was not significantly different from zero; t=0.71. Regressing _Z_ on a quadratic function of log PCE performed as well as the linear model in terms of R<sup>2</sup> , and gave a very similar estimate of _Z_<sup>*</sup> ; we opted for the parsimonious linear model.

> 27 The use of the same PPP for converting both the poverty line and PCE could create a spurious correlation (given that a common measurement error term appears in both variables). To check this, we used PCE at 1993 PPP as the instrumental variable for PCE at 2005 PPP (assuming the measurement errors are uncorrelated); this gave a slope of 0.347 (t=8.42) with a slightly smaller sample (n=70); the corresponding poverty line was $37.41 (t=11.73).

> 28

> Statistically, the joint restriction on (4) performs extremely well (F(2.71)=0.014; prob=0.986).

14
that measures to cope with the impact of the crisis can be targeted to areas that need them the most. The objective of this paper is to show how to assess where higher food prices are likely to hurt the most with a case study for Guinea and a focus on rice.

The paper is structured as follows. We first provide findings from a new poverty map for Guinea constructed by the authors with a team from the National Statistical office of Guinea. Next, we provide an assessment of the potential geographic impact of the increase in rice prices. As already mentioned, it turns out that the areas hardest hit by the food crisis are typically not the poorest, which may pose a dilemma for policy makers in terms of whether relief should be targeted to the poorest areas or to areas where the increase in poverty is likely to be largest.

# **2. Poverty Map for Guinea**

# **_2.1. Methodology and data_**

Elbers et al. (2003) have shown how to construct poverty maps by combining census and survey data (see also Mistiaen, 2003 for an application and a useful summary discussion from which this section is inspired). The idea is straightforward. First, a regression of per capita or adult equivalent consumption is estimated using household survey data, limiting the set of explanatory variables to ones common to both the survey and the latest census. Second, the coefficients from that regression are applied to the census data to predict the expenditure level of each household in the census. Third, the predicted household expenditures are used to construct a series of poverty indicators for geographical population subgroups. Although the idea is simple, its implementation requires complex computations due to the need to take into account spatial autocorrelation (expenditure from households within the same cluster or area are often correlated) and heteroskedasticity in the development of the predictive model. Another issue is the need to compute standard errors to assess the degree of precision of 4
Higher rates of protection also apply to individual commodity flows among some regions. For example, the United States and European Union impose higher rates on imports of manufactured intermediates, machinery and equipment from Japan, while Japan imposes higher rates on food imports from most of its trade partners and China imposes higher rate on textile for all its trade partners and on machinery and equipment on ASEAN4. However, these differentiated tariffs by source do not imply that discriminatory tariffs are applied by country. Instead, the differences emerge largely from aggregating the same disaggregated tariff schedule using import weights for each supplier; thus, if one country supplies relatively larger quantities of goods subject to higher tariffs, when this category of goods is combined, the resulting statutory tariff will be higher than for another country supplying low tariff items.

The domestic tax rates presented in the first column of Table 7 indicate that all regions except China subsidize agriculture. The subsidy in the United States is concentrated on grain production, while in EU and Japan it applies to other agricultural products as well.

# 3. **Modeling Regional Economic Integration**

In this paper, APEC-centered regional integration is analyzed through the use of a multi-country computable general equilibrium (CGE) model. Such models are designed to quantify many of the economic forces accompanying regional integration that are considered in international trade theory. The APEC CGE model we have developed is in the tradition of recent multi-country CGE models developed to analyze the impact of the Uruguay Round of GATT negotiations," the impact of the North American Free Trade Agreement,"<sup>2</sup> and more recently the implications and options for Central America and the Caribbean of NAFTA implementation [Hinojosa-Ojeda, Lewis, and Robinson (1994)]. In this section,

> " These models, in turn, have built on multi-country models developed to analyze the impact of the Tokyo Round of GATT - negotiations in particular, the multi-country CGE model developed by Whalley (1985). Our model starts from the WALRAS model developed at the OECD to analyze the impact of the current GATT negotiations on the major OECD countries [OECD (1990)] and the RUNS model described in Goldin, Knudsen, and van der Mensbrugghe (1993).

12 See Hinojosa-Ojeda and Robinson (1992) and Brown (1992) for a review of NAFTA CGE models.

_Lewis, Robinson, and Wang_ 22
Column (5) of Table 4 investigates whether this particular form of conditional independence is a reasonable assumption. It turns out that our findings are robust to excluding all spells other than the first, ruling out dependency among recurring slumps.

Column (6) allows for institutions to change endogenously during the decline phase (meaning we replace _INS_<sup>�</sup> 0 by _INS_<sup>�</sup> _t_ ). We showed earlier that constraints on the executive and a number of other institutional indicators improve in the aftermath of a crisis. So far, we have excluded this endogenous adjustment for two reasons: ( _i_ ) it could bias the estimation in favor of our hypothesis (if institutions both improve as a response and then shorten the remaining decline phase), and ( _ii_ ) elites might find it difficult to credibly promise to bind their hands and not renege later on (Acemoglu and Robinson, 2006). In any case, these concerns are not borne out empirically. The results remain quantitatively and qualitatively similar to our baseline.<sup>20</sup> A serious issue in the cross-country growth literature is the fragility of regression estimates with respect to different GDP series. Johnson et al. (2013) show that new renditions of the Penn World Tables (PWT) incorporating more recent PPP benchmarks can imply vastly different historical growth rates. Worse still, they point out that the findings of several prominent studies, including Ramey and Ramey (1995) and Hausmann et al. (2005) cited in the introduction, are not robust to different versions of the PWT.<sup>21</sup> In fact, this has prompted major changes in how the PWT data are prepared and made available to the public. Starting from version 8 different time series are reported for level and growth rate comparisons.<sup>22</sup> We take these concerns seriously and repeat the restricted structural break search using two very different data sets (in addition to the PWT 9). Specifically, we use the version 7 vintage of the PWT—before the computation of the chained series changed

> 20Another way of tackling this issue is to truncate the data and censor all spells lasting longer than a few, say five, years. Our results are robust to this type of truncation (at various points). We thank an anonymous referee for suggesting this test to us.

> 21 We are indebted to an anonymous referee for pointing this out to us.

> 22The PWT version 7 already offered a new series, _rdgpl_ 2, based on the growth rate of domestic absorption, as a response to the work of Johnson et al. (2013). We obtain very similar results when using this series instead of _rgdpch_ in the robustness checks.

29
_How_ **_Fan Hu Chia, Indtusy_** _Gro'?_ **_9_** Several specific examples can iUustrate what appears to be considerable inconsistency in these data: Data for Beijing industrial enterprises for (million yuan): 1986 1987 Index 1986-100

1. Gross output, 1980 prices (GVIO)

- a. Including village and sub-village units 34858.07 39512.33113.3

- b. Excluding village and sub-village units 32177.14 35723.28111.04

- c. Difference: GVIO for vWillage enterprise 2680.93 3789.05141.3

- 2. Village & sub-village output, current 2480.06 3382.27136.4 prices (CVIO)

3. Ratio for village output: CVIO/GVIO 0.925 0.893 Source: Beijing 1988, pp. 255 (la-b), 364 (2).

These figures imply that village level industrial output is higher in constant than in current prices. But a decline in average prices for industrial output betwean 1980 and 1986 is most improbable. These data also indicate that industrial prices continued to decline during 1986/87, which is definitely incorrect.

Shanghai industrial output figures for 1987 (million yuan) raise further questions:

||A<br>1980<br>prices|B|Ratio<br>B/A|
|---|---|---|---|
|1. Township enterprises|5428|8122|1.496|
|2. Village enterprises|4361|5029|1.153|

Sources: Shanghai 1988, p. 127 (A); TVP 1988, p. 26 (B)
seasons.<sup>19</sup>  is a mean zero disturbance term that reflects unmeasured determinants of _C ijs ijs_ that vary across households. Note also that household consumption is affected by unobserved household heterogeneity (  _ij_ ) and village heterogeneity (  _j_ ).

If  2<sup></sup> 0 , seasonality is not an issue and seasonal income does not track seasonal consumption with possible household’s ability to smooth consumption through selfinsurance and other mechanisms. That is, seasonal consumption depends entirely on overall income and not on seasonal income. Households are thus able to draw resources from alternative sources to compensate for losses in income, if any, during a particular season to maintain the level of consumption. This is a case of perfect consumption smoothing model.

In contrast, when  2<sup></sup> 0 , we have a case of lack of consumption smoothing. Under this scenario, either of the following emerges. (i)  1  2 . This means that seasonal income relative to total income exceeds consumption in a given season. That is, a greater fraction of seasonal crop income relative to total income is saved for managing consumption smoothing (Deaton 1997). (ii)  1  2 . This indicates that households spend more than seasonal income relative to total income. This is possible for the following reasons: Households may draw income from non-crop or non-farm sources not included in total income or resort to distress sale of assets, or advance sale of labor and crop or family and intra-family transfers to augment income in a particular season to avoid starvation.

A major problem with the estimation of equation (3.1) is the joint distribution of income and consumption because of the presence of the errors (  and  ). There is substantial evidence for joint causation between income and consumption (Strauss 1986;

> 19 It is assumed that the sample is uniformly distributed over the entire year, with enough observations from all seasons to carry out the analysis.  For example, the distribution of 7,640 households sampled in 2005 HIES survey include 27% in season one, 22% households in season 2, 27% in season three, and 24% in season 4.

15
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that the persistence is much stronger for the girls born to fathers with low education ( _IGME_ 0 estimates are: 0.65 (daughters) vs. 0.55 (sons)), but, again, the gender difference becomes negligible when the father has college or more education: ( _IGME_ 16 estimates are: 0.33 (sons) vs. 0.35 (daughters)). Please see Figure F4.IU. The evidence on absolute and relative mobility thus favors the idea that gender difference in educational attainment may primarily be a product of low education (and thus low permanent income) of parents. This also brings into focus the incorrect conclusions from the standard linear CEF that the daughters face less relative mobility throughout the parental schooling distribution. A researcher focused exclusively on the linear CEF would completely miss the gender convergence in relative mobility at the right tail of father’s schooling distribution.

# **Sources of Gender Bias**

The extended Becker-Tomes model in section (2) above highlights parental educational investment as a major mechanism through which inequality persists across generations. Thus, it is informative to look first at the estimates of the investment equation to understand the economic mechanisms at play. The estimates of _θ_ 0 and _θ_ 1 for urban India are reported in Table T3.IU, using IHDS 2005 and NSS 1995 data. We report estimates for two age groups of children for each data set to ensure robustness of the conclusions.<sup>22</sup> The evidence suggests strongly that _θ_<sup>ˆ</sup> 1<sup>_d<θ_ˆ</sup> 1<sup>_s_;theinteractionoffather’seducationwith</sup> the daughter dummy is negative across the board, and is statistically significant at the 10 percent or lower level in 7 out of 8 cases.<sup>23</sup> The estimates for the intercept shows a contrasting picture: the daughter dummy is _not_ statistically significant at the 10 percent level in 6 out of 8 cases. The evidence cannot reject the null hypothesis that _θ_<sup>ˆ</sup> 0<sup>_d_=</sup><sup>_θ_ˆ</sup> 0<sup>_s_.</sup>

> 22We also used other age groups for estimating the investment equation. The conclusions reached in this paper are robust to such alternative age samples.

> 23This is consistent with the existing evidence on gender bias against girls in educational expenditure in India (see Kingdon (2007), Azam and Kingdon (2013)). Azam and Kingdon (2013) find significant pro-male bias for the 10 years or older children, using the IHDS 2005 data. They point out that the focus on educational expenditure misses substantial gender bias against girls in terms of schooling continuation decisions. When gender bias manifests as early drop-out of daughters from school, this is captured by lower educational attainment in our data. Datta and Kingdon (2019) use 1995 and 2014 NSS data and find that the role played by gender bias in educational expenditure (the intensive margin) has increased substantially over time in India. _Note, however, that the existing evidence while suggestive does not provide estimates of the effects of father’s education on children’s educational expenditure, which is the focus of the investment equation in intergenerational mobility analysis._

19