rafmacalaba/datause-encoder-data huggingface dataset:
either to pay for or to justify having bank accounts declined as income levels increased. While 75% among the lowest 20% of income distribution pointed to the cost of banking, or to their lack of resources, this is only an issue for 50% of the wealthiest 20%. The higher income groups point, instead, to not needing an account (24%) and to high management costs (13%). While banking services are considered to be costly by persons at all income levels, lower income groups in particular believe that their incomes are too low to justify using banking services. This confirms the finding of the supply side cost analysis (see Chapter 2) that the costs of banking services in Colombia are too high for low income people to afford, in particular those who earn less than one minimum wage per month.

**Figure 3. 16  Main reason for not having a bank account**

<!-- Start of picture text --> 12%<br>Q5 13% 24%<br>51%<br>7%<br>Q4 11% 17%<br>65%<br>6%<br>Q3 9% 15%<br>71%<br>6%<br>Q2 7% 14%<br>73%<br>8%<br>Q1 6% 10%<br>76%<br>0% 10% 20% 30% 40% 50% 60% 70% 80%<br>%<br>Lack of Resources Does not need an account High Costs Other<br>Population quintiles by income level<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_

# **3.7 ARE THE UNBANKED BANKABLE?**

31. Given their different socio-economic characteristics, the question has been raised whether the unbanked have “bankable characteristics”. Home ownership rates by the unbanked above 60%, with minimal differences to the level of the banked, illustrate that the unbanked have asset building capacity. Access to formal financial services would therefore provide the unbanked with the opportunity to build financial assets through savings and increase their asset base through access to credit services.

32. The DANE survey shows a high correlation between educational levels and likelihood of being banked or unbanked. At the same time, it is noteworthy that approximately 15% and 25% of the group with less than elementary and elementary school only respectively do have bank accounts in formal sector institutions. This suggests that while 60
# **3. Data**

Table 1 provides an overview of the data sources used. For robustness, we exclude from the sample 4-digit sectors with fewer than 5 firms.

**Table 1 Overview of data sources**

|**Indonesia**|The sample includes information from the Manufacturing Survey of Large and<br>Medium-Sized Firms (Statistik Industri). It provides plant-level data of all<br>firms with at least 20 employees from 2000 to 2015.<br>The sample focuses on a subset of Indonesian enterprises. The 2016 economic<br>census indicates that 98.3% of enterprises are micro and small business with<br>fewer than 20 employees. However, the remaining 1.7% of firms interviewed<br>in Statistik Industri, employ a non-negligible amount (23.7%) of workers.|
|---|---|
|**Malaysia**|The sample is based on the 2000, 2005, 2010 and 2015 census of<br>manufacturing firms with at least 10 workers. A representative sample of firms<br>is constructed for each wave by the Malaysian Statistical Office. The final<br>sample including only non-negative values for firms with at least 10 workers<br>includes 37,206 observations across the four waves of census|
|**Vietnam**|The sample is from the enterprise surveys conducted by the Vietnamese<br>General Statistical Office (GSO) from 2009 to 2014. During this period, there<br>are 259,721 manufacturing firms. The survey includes manufacturing firms<br>with at least 1 worker, but for consistency the final sample includes only firms<br>with at least 10 workers.|

Table 2 reports basic summary statistics for the main variables used in the analysis. TFPR levels are comparable across countries, irrespective of the estimation method used (HK or BKR). Conversely, there is more dispersion in TFPQ -proxy for true productivity- both within and across countries. Accounting for measurement error substantially lowers productivity estimates, except in Indonesia. Foreign firms are a tiny fraction in Malaysia, while they account for 7% and 18% of the Indonesia and Vietnam samples, respectively. SOEs account for as much as 12% of the Indonesia sample but only 2% of the Vietnam’s, possibly reflecting differences in the construction of the two data sets.

**Table 2 Descriptive statistics**

||N|Mean|SD|Min|Max|
|---|---|---|---|---|---|
|**_Indonesia_**||||||
|ln(TFPQ)|223,635|7.69|1.55|4.24|12.18|
|ln(TFPR) (HK)|194,850|0.56|0.81|-2.07|3.49|
|ln(TFPR) (BKR)|194,850|0.47|0.75|-2.47|3.75|
|ln(Age)|223,635|2.63|0.79|0.00|4.72|
|ln(Employment)|223,635|4.11|1.12|3.00|10.46|
|Foreign owned?|223,635|0.07|0.25|0|1|
|SOE?|223,635|0.12|0.33|0|1|
|**_Malaysia_**||||||
|ln(TFPQ)|35,730|4.87|1.06|2.53|7.89|
|ln(TFPR) (HK)|22,061|0.86|0.55|-0.85|3.04|

10
<!-- Start of picture text --> B I|ntergenerational persistence ECA B Intergenerational persistence HIC<br>| PIP= .3787 PIP= 1<br>0.8 qa ee rr| 1<br>| |<br>5 06 | 06+ |<br>x}Cc —--—-+-—---\---------- | 2=<br>2 | 3<br>= 04 5 04 5 8<br>2a<br>Qg|<br>02 0.2 |<br>|<br>0.0 0 00 -------—He 0<br>0.0 1.0 2.0 3.0 4.0 0.0 2.0 4.0 6.0<br>B \Intergenerational persistence LAC B Intergenerational persistence OTH<br>PIP= .3088 PIP= 1<br>0.8 alainneia SRD. anninnnnnn 1 0.8 prot ttt ROT Torr 1<br>| |<br>& 0.6 (A 0.6 |<br>3 | 2<br>-3<br>=2 04 5 0.4 5 8a<br>© |<br>OQ 0.2 0.2 |<br>|<br>0.0 0 0074 —-—-—-------------H —+0<br>-1.0 0.0 1.0 2.0 3.0 0.0 1.0 2.0 3.0 4.0<br>Range of estimated coefficient Range of estimated coefficient<br>B \Intergenerational correlation ECA B \|ntergenerational correlation HIC<br>PIP = .3413 PIP =1<br>15 Saianinnta nnn 1 0.8 porn 1<br>| |<br>Ss= I 0.6 |<br>255c1.0 —---5----4s-- 5 0.4 || 58532=<br>5 0.5 |<br>fa) 0.2 |<br>|<br>0.0 0 oop+----—A —-------—-—-—-—~-—-+0<br>-0.5 0.0 0.5 1.0 1.5 2.0 0.0 2.0 4.0 6.0<br>B Intergenerational correlation LAC B Intergenerational correlation OTH<br>PIP =1 PIP =1<br>1__———~~~~~~~>—------41 os tt—-—-—-~--~-~~--~-~-~-------41<br>06 4 | |<br>| |<br>5 | 064 | x<br>= 0.45 | | =<br>= | 5 044 | 58<br>O°<br>cB 02+] | a<br>a 0.2<br>||<br>||<br>00oH-----— a——-—--—---—--—~>--+0 woHy-----— o<------- —-- +0<br>0.0 2.0 4.0 6.0 8.0 0.0 2.0 4.0 6.0 8.0<br>Range of estimated coefficient Range of estimated coefficient<br><!-- End of picture text -->
**_Citizens also showed limited knowledge about government policies with respect to health insurance, about health insurance reimbursement practice, and about the prices of medical interventions_** .  The majority of respondents, for instance, reported that they lack information about the prices of medical treatments (only 5% of respondents reported that they knew the price before treatment, while 79% reported that they found out the price only shortly before or during payment).

**_As to water, a major problem for citizens is access to information about the quality – including safety – of tap water_** .  Only 18% of respondents said that they heard about water quality inspection reports.  At 26%, Chengdu had the largest share of respondents who said that they heard about water quality inspection (see Figure 21).  Case studies have revealed that inspection results, as in the case of tap water quality, have been generally kept

**Figure 21 Heard about tap water quality inspection, 2006** (% of population)

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Chengdu Shanghai Xi'an Shenzhen Dalian<br><!-- End of picture text --> confidential and unavailable through the public channels. Citizens also appeared unaware about the cost of tap water, including the fact that the current fees still fall short of full cost recovery. The survey revealed that citizens wish for both low fees and better service, which is obviously not realistic. With respect to reliability of water supply, Shanghai seemed to be doing the best job among the five cities in informing citizens about any water outages in advance.

**_Hence, the survey indicated that citizens had only a limited access to relevant information regarding local public services and that the institutional mechanisms to safeguard their interest are yet to be fully developed_** (as illustrated above in the case of the inadequate resolution of citizens’ complaints across sectors).  This poses a significant challenge for governance in public service delivery discussed in the next chapter.

29
smallholder families on average purchase about 50 percent of their food, with higher proportions in households with less than two hectares (GOK and UNICEF, 1984). Since women have primary responsibility for these food purchases, their ability to earn (and control) income is an important corollary to their ability to contribute to household food security. Rural women's income is most often derived from household-based activities related to food and beverages, trade, weaving and traditional medicines. The sale of surplus agricultural produce from their individual plots usually constitutes their main source of income over which they have control. Other sources of income include petty trade (sale of charcoal, beer and vegetables), their labor on other farms or plantations, and remittances (World Bank, 1988b; Kabira and Njau, 1985; Riugu, 1985). Activities undertaken by women within women's groups are also seen as a means to supplement individual incomes. Men tend to control the income (and therefore the expenditures) from the sale of cash crops even in situations where women contribute substantial amounts of labor.

Table 3 Labor Allocation and Income Sources by Sector

||Males|Females|
|---|---|---|
|Export agriculture|||
|estates|76|24|
|smallholder|42|58|
|Food agriculture|37|63|
|Public sector|81|19|
|Import substitution|88|12|
|Non-tradable capital goods|96|4|
|Private sector wage employment|<br>79|21|

Source: cited in Collier, 1989 The livelihood of the smallholder family as a whole is derived from a combination of resources from the holding itself, from wage employment and from informal rural enterprises. There are in fact very few true subsistence farmers -- only one in five farmers retains 90 percent of what is produced on the farm. The vast majority of crop farmers are integ.ated inLo the monetized economy and the value of monetized agriculture exceeds that of subsistence production (Rogers, 1985). Slightly over half of smallholders market more than a third of their output (GOK and UNICEF, 1984).

8
TA6E _1._ fftaI. Ta =ta mm id thai. ota r Caul ie 14. 1d ad 14.11i 1m-_ (1006 Equity Finsce)

||||||etf.c<br> <br>|tiw.<br>Ta. R<br>|atWSTO<br>Z|3|t-tO)||Thai Ioa"|(t,Z4)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||ongladesh|(to.7)|Mt d'lvoir.|(tern?)|NIiss<br>|(t-7)|onen||||||
||<br>|<br>|||lil<br>l|Ihdn<br>r|dldi|1<br>r|lWildis|o-ihmr|bj<br>gM|_Iichui.ubry_|
||Buildings<br>Iii|hinery<br>13ialdinga||lchia.ry|us<br>|<br>y|g|<br>|||<br>||
|Durhum<br>ihid"|||||||||146|**04**|043|045|
|t * 0|15.75|**6.**|-1.1s|-909.11|_-1.96_<br>|-44.1s<br>|2.4.01<br>|**_81.4_**<br>|.<br>24|**.**<br>215|.<br>O?5|.<br>103|
|t_*_3|10.95|_52.45_|1.U6|610.45<br>|-2.75|-7.61<br>|_81.31_<br>|29.0<br>|.<br>|.<br>**373**|.|.|
|t*5|44.66|43.31|1.66|J255.0|-3.45|-119.91|21.71|**29.46**<br>|**3.4**<br>|**.**<br>|||
|tam|||||||2975|31.75|**6.46**|**_6.91_**|||
|d of tilway|so.1s|96.|6.as|61.5|U.S4|51.15|S9.1|66.26|631.16|75.75|41.13<br>|51.75<br>|
|Post Mblidoy|44.6|46.75|45.2S|34.1|36.911|6.45|5.0|**S4.**|**63.46**|14.4|34.46|12.96|
|||<br>||<br>|U_ha ,<br>l0i|ta<br>afCg<br>t-?|ta<br>IJR/<br>|<br>go-n|00=2)||Th4<br>Ti<br>las|d (ta)|
||basladeeb|<br>(to-i)|Cat.dlIvore|<br>(to-?)|syss|(o)||<br>.|<br> <br>||<br>|<br>|
||uilding.|hachisery|b ildiags|1401me-|bildiag.|1ul.e|idlini.|"mbcaey|_wildiage_<br>I_ldi;g|_NacEy_|b|Ibeimey|
|During<br>HD IIW||||||<br>|||_1846_|8146|2126|3125|
|t<br>0|**.4**|18.2s|10.15|10.46|1343<br>|20**.**4O<br>-8 75<br>|16.46<br>||_._<br>|.<br>8146|.<br>2126|.<br>513|
|t.* *|***.4**|13.9|10.15|29.03|12.96<br>|19.66<br>|**16.11**<br>|8.43<br>|l.45<br>|.<br>**2**|.|.|
|**t*** 5fi|10.46|20.6|10.15|27.75|12.66|13.75|_16.75_|**28.41**<br>|**13.46**<br>"|**.66**<br>|||
|**.6**<br>**'16s.i4**||||||||27.2|13.s|20.1i<br>|||
|lEd of 1H4 I dew|10.26|_57.71_|186|43.11|21.46|31.66|26.46|41.15|2S.41|49.15|62.75<br>|4S.45<br>|
|fot Ohl iday|10.46|W0S|14.51i|16.4|17.66|_3.91_|92.75|_81.81_|6.75|4.26|21.46|39.26|
|Mimsi Latrest<br>|1t.o<br>14.|43|**_9._**|**_91_**|12.2<br>|6<br>||i3.<br>|11<br>||1.6<br>2|6i<br>6|
|laltiim<br>Rat.|11.|43|4.|75|4.1|5||7|.81||.||
|_l/_<br>tMta-d**a.** <br>volue<br>of agir<br>_Ei_lfoctiv<br>lox|t(I'-s)/(W.F'<br>l tproduct<br>_s_<br>_rate_<br>_I*_highl|_o_<br>fb capit<br>y aetlve.|t<br>l<br>al.|**r**<br>|**a**<br>sadI<br>s|etof-<br>ra|teof ro,em|oa,i-l.|Theaserest|b of<sup>eapitl</sup>|is<br>qosl to F|,<br>the|
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23
retirement decisions that are more similar to those of residents in developed countries. Residents of China’s rural areas, by contrast, share more in common with residents of other developing countries, and make labor supply decisions in the absence of both pension availability and the constraint imposed by a mandatory age of retirement from the formal sector.

This paper brings together information from several data sources to highlight differences in labor supply of older workers across urban and rural China, Indonesia and Korea, and to review patterns and trends in the context of institutional differences across these three countries and between urban and rural areas. For perspective on retirement patterns in East Asian economies, we then place the retirement decision in China, Indonesia and Korea in the context of employment patterns of older workers in the United States (US) and the United Kingdom (UK). In common with findings from the retirement literature focusing on developed economies, the descriptive evidence presented in this paper is suggestive of the role that ability to collect a pension (or social security benefit) plays in the retirement decision.<sup>4</sup> Mandatory retirement provisions in each of these East Asian economies, however, condition decisions of when and how to exit from productive activity. While significant numbers of retirees return to work in self-employed activities or informal work after reaching mandatory retirement age, the types of work that “retirees” are able to find may be unattractive for some older workers. Differences in the mandatory retirement age for men and women in China likely contributes to differences across genders in participation in work later in life, with important consequences for relative pension wealth and relative financial security of older men and women.

After reviewing descriptive trends, we lay out an empirical model to examine correlates of labor supply with own and spouse eligibility to receive a pension, own and spouse health status and proxies for household wealth. We next review data sources and correlates of employment separately for China, Indonesia and Korea. The paper presents comparative descriptive evidence

> modeled on the rural pension program, was first introduced in July 2011 with the aim of providing financial protection in old age to non-working urban residents and informal sector workers.

> 4Blau (1994) suggests that social security eligibility contributes to relatively high exit from the labor force at age 65; Krueger and Pischke (1992) exploit design features of the US social security system to demonstrate the effects of benefits on labor force participation. Gruber and Wise (1999, 2004) present evidence from OECD economies of the effects of social security and public pension systems on labor supply decisions of older workers.

3
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21
# **B2. Estimation results**

# **B2.1 Unit root tests**

The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) based unit root tests for the variables under consideration are applied to identify their integration orders. Like the Cambodian case, for both Malaysia and Singapore, the unit root test results show that _z_ , _gygap_ , and _infl_ are I(0) (i.e. integration order of 0), and _ffr_ , _tbeugdp_ , _tbusagdp_ , _bmgdp,_ and _domint_ are I(1) (i.e. integration order of 1). These results can be provided upon request.

# **B2.2 VAR-based impulse response results**

Since _ffr_ , _tbeugdp_ , _tbusagdp_ , _bmgdp,_ and _domint_ are I(1), their first differences are included in the VAR estimation. For I(0) variables, _z_ , _gygap,_ and _infl_ , their levels are included. The selected lag length for this VAR estimation is one for both Malaysia and Singapore, based on Akaike information criterion (AIC) and Schwarz information criterion (SIC).<sup>20</sup> VAR-based impulse responses for the variables under consideration are obtained by applying the Cholesky method. Variables in the VAR system are ordered from the exogenous one to the most endogenous one, following the Cambodian case. Specifically, the order is from D( _ffr_ ), to z, to D( _tbeugdp_ ), to D( _tbusagdp_ ), to D( _bmgdp_ ), to D( _domint_ ), to _gygap_ , and to _infl_ , where “D” indicates the first difference. Two exogenous variables are also considered. One is _goilp,_ capturing the fluctuation of crude oil price, and another is a dummy variable, capturing impacts of the Asian financial crisis and global financial crisis. Impulse response results for the variables under consideration to a one standard deviation (S.D.) shock of the federal fund rate are shown in Figure B1 for Malaysia and Figure B2 for Singapore.

As shown in Figure B1 and Figure B2, except for the trade balance with the EU, impulse response results for both Malaysia and Singapore are roughly as expected from a theoretical viewpoint and are consistent with the case of Cambodia. For trade balance with the EU, a one S.D. shock of the US federal fund rate temporarily improves it for both Malaysia and Singapore, in contrast to the result for Cambodia. This finding provides more evidence on the disadvantage of high dollarization in terms of Cambodia’s external competitiveness.

> 20 Both AIC and SIC indicate optimal lag length of one for Singapore. For Malaysia, while SIC always chooses optimal lag length of one, AIC indicates different optimal lag length, depending on maximum lags set for the estimation. Optimal lag length of one is applied for Malaysia. This also helps save the degree of freedom for the estimation, given our relatively small sample size for Malaysia.

25
in the data. Figure 8 shows that the model does a fair job in tracking this nontargeted moment of the firm-size distribution.

Figure 8: Fraction of Firms with 250 Workers or More <!-- Start of picture text --> 0.3<br>Kenya<br>0.2<br>Chile<br>0.1 Peru France<br>Salvador<br>Finland Belgium<br>Colombia<br>Hungary<br>Ethiopia India Romania<br>Ghana Bulgaria<br>Spain<br>Italy<br>Portugal<br>0.0 Latvia<br>0.0 0.1 0.2 0.3<br>MshareTop<br>Mshare250<br><!-- End of picture text --> Note: The figure shows the share of firm with 250 workers or more, both in the model (horizontal axis) and in the data (vertical axis). As throughout the paper, the data refer to firms with 10 workers or more.

# **5.5.2 Mark-Ups**

An alternative interpretation of the model’s inference of entry barriers is that they are picking up cross-country differences in mark-ups. Given that the underlying theory assumes constant and identical mark-ups across countries, it is plausible that this source of model misspecification may be biasing the inference of the entry barrier. This hypothesis gains traction in the context of a growing interest in global trends in market power (De Loecker and Eeckhout, 2018). We argue, however, that within the theoretical framework that underpins the recent estimation strategies, mark-ups manifest in the data as part of the idiosyncratic distortions. For instance, under the Cobb-Douglas production and assuming mark-ups are the sole distortion, De Loecker and Warzynski, 2012 show that the markup is closely related to the ratio of total expenditure on variable inputs over sales. This ratio, however, is exactly picking up the 41