rafmacalaba/datause-encoder-data huggingface dataset:
The Sri Lankan household survey of 2001/2002 indicates that about 28 percent of remittance recipient households stand within the top income quintile and around 27 percent stand within the lowest two income quintiles. These ratios, however, change considerably in the pre-remittance scenario. When remittances are deducted from total household expenditure, nearly 45 percent of recipient households stand within the lowest two income quintiles. Although information on how Sri Lankan recipient households use remittances is not yet conclusive, the above findings suggest that remittances can have an important impact on recipient households. A broader study by the International Monetary Fund (IMF) on remittances concluded that “remittances can help improve a country’s development prospects, maintain macroeconomic stability, mitigate the impact of adverse shocks, and reduce poverty”<sup>6</sup> . Remittances allow families to maintain or increase expenditure on basic consumption, housing, education, and small-business formation; they can also promote financial development in cash-based developing economies (IMF 2005). Speaking to the role of the financial sector in facilitating remittances, the IMF study also concluded that long-run output growth resulting from additional investments in physical and human capital financed by remittances might be especially likely “where a well-developed financial system and institutions allow remittances to be effectively intermediated and efficiently used” (IMF 2005).

The Sri Lankan government<sup>7</sup> like other developing country governments has recognized the importance of remittances and introduced incentives to facilitate foreign employment—including pre-migration facilities and services, identification of employment opportunities, training, and pre-departure loan schemes (Annex 1). It is also debating the capacity of formal financial institutions to facilitate migration policies and the challenge of enhancing the quality and outreach of the remittance infrastructure, especially in the face of an active informal remittance sector. To the extent remittances are transferred through formal financial institutions, the government’s balance of payments benefits from the increased foreign-exchange reserves. In addition, the anonymity that is possible through informal remittance systems renders remittance systems highly vulnerable to abuse and illegal activities. Like other governments in remittance recipient countries, the Sri Lankan authorities are interested in having as much of the remittance flows go through formal conduits.

_This paper reviews the key issues affecting the formal financial infrastructure for remittances in Sri Lanka._ Section 2 details how the combined infrastructure of banking institutions, postal system, and microfinance institutions provides a dense network for remittances flows. It also shows, however, that the formal infrastructure’s potential is not being maximized. The country remains largely cash dominated, and rural areas, where the greatest share of remittances flow, enjoy less access to formal remittance products (Figure 3).

3
- Brown, J. (n.d.). Quitters never win: The (adverse) incentive effects of competing with superstars. Kellogg School of Management, Northwestern University.

- Castell-Climent, A. (2008). On the distribution of education and democracy, _Journal of Development Economics_ **87** (2): 179–190.

- CIPPEC (2004). _Los estados provinciales frente a las brechas socioeducativas. Una sociolog ́ıa pol ́ıtica de las desigualdades educativas en las provincias argentinas_ , Area de Pol ́ıtica Educativa, Buenos Aires.

- Coleman, J. S. e. (1966). _Equality of educational opportunity_ , Government Printing Office, Washington, DC: U.S.

- Duflo, E., Dupas, P. & Kremer, M. (forthcoming). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in kenya, _American Economic Review_ . (also NBER Working Paper No. 14475).

- Duflo, E., Kremer, M. & Robinson, J. (forthcoming). Nudging farmers to use fertilizer: Evidence from kenya, _American Economic Review_ . Unpublished Manuscript, Massachusetts Institute of Technology.

- Duflo, E. & Saez, E. (2003). The role of information and social interactions in retirement plan decisions: evidence from a randomized experiment, _Quarterly Journal of Economic_ **118** (3): 815842.

- Etchart, M., Gasparini, L., Bohorquez, P., Curia, J., Ferroni, B. & Hontakly, P. (2004). Las po ́ıticas compensatorias mejoran la equidad? plan social educativo, educaci ́on primaria.

- Evans, W. N., Oates, W. E. & Schwab, R. M. (1992). Measuring peer group effects: A study of teenage behavior, _Journal of Political Economy_ **100** (5): 966–991.

- Fiszbein, A. (1999). Institutions, service delivery and social exclusion: A case study of the education service in Buenos Aires. LCSHD Paper Series No. 47, Human Development Department, World Bank.

- Galiani, S., Gertler, P. & Schargrodsky, E. (2008). School decentralization: helping the good get better, but leaving the poor behind, _Journal of Public Economics_ **92** (10-11): 2106–2120.

30
To underline the explanatory power of the results from the smaller GBA sample, the decomposition analysis is also carried out using the ARG16 sample from 1992-2002 as a robustness check. For the analysis, the sample of urban centers is not continuously expanded to 28 cities as survey coverage increases over time. This is because changes in the survey’s coverage can have substantial effects on the residual variance induced by geographical differences, which we cannot observe. This may be the case even if there are no important changes in the (observable) means. Regional variation in the ARG16 sample is accounted for by adjusting all incomes to the level of GBA, using a one-time comparison of price levels in 2001. This method effectively incorporates the assumption that relative regional price differences have not changed over time. However, due to the convertibility regime from 1991 to 2001 and the according price stability this assumption may be justified for most years of the ARG16 sample, yet arguable for later years.

Data inspection reveals a strong spike in all wage dispersion figures centered around 1989, the worst year of hyperinflation in Argentina (see Figures A1 and A2, appendix). Prices soared up to nearly 4000 percent annually, which led to the introduction of the Argentine currency board in April 1991. Measurement error is likely to be higher in times of high inflation, if people have to recall their earnings in an environment of constantly changing prices and wages. Second, during hyperinflation, prices and wages change monthly, weekly or even daily. Since surveys cannot be carried out at the same point in time for the whole sample, sequenced interviewing will introduce an upward bias to the wage variance in times of high price volatility and wage contract turnover.

Thus, the figures for the 1980s must be analyzed with caution. Using a base year with a bloated wage variance might lead to wrong conclusions of variance changes over time. What matters for the data quality from periods of high inflation is not only the yearly inflation but also the inflation figures from the month of interviewing. We use 1980 and 1986 as base years, as they were years of moderate inflation during both the whole year and in the survey month of October.

We apply the reweighing methodology to analyze changes in the residual variance over time against a base year by re-weighting the observations of the more recent year. The educational and demographic distribution of the Argentine labor force has changed noticeably since the 1980s. In particular, the overall improvement in educational attainment may have increased wage dispersion over time.

14
Table 2: Financial development of countries included in 4 digit ISIC sample Sample corresponds to those countries included in the UNIDO 2000 database (4 digit ISIC) that passed the following requirements: (1) data started no later than 1985, (2) had at least 5 observations of real value added growth rate in at least two industries, (3) at least one of the main measures of financial development used in the analysis was available around 1981. The measures of financial development are: Column(1) shows the quality of accounting standards in 1990 built by CIFAR and extracted from Laporta et al.(1996). Countries with a larger index have better quality of accounting standards. Column (2) presents the level of private credit by domestic banks and other financial institutions as a fraction of GDP in 1980, obtained from Beck et al (1999). Column (3) presents the level of stock market capitalization as fraction of GDP (also extracted from Beck et al (1999)). Finally, column (4) reports the number of sectors at 4 digits ISIC used in the estimation.

|COUNTRY|Pvt.<br>Credit<br>(1)|Acc.<br>Std.<br>(2)|Mkt.<br>Capit.<br>(3)|No<br>sect.<br>(4)|COUNTRY|Pvt.<br>Credit<br>(1)|Acc.<br>Std.<br>(2)|Mkt.<br>Capit.<br>(3)|No<br>Sect<br>(4)|
|---|---|---|---|---|---|---|---|---|---|
|Australia|0.90|0.75|0.32|60|Jordan|0.46|.|0.45|45|
|Austria|0.80|0.54|0.03|48|Korea|0.54|0.62|0.06|70|
|Bangladesh|0.08|.|0.00|56|Malaysia|0.54|0.76|0.54|64|
|Belgium|0.28|0.61|0.10|9|Malta|0.30|.|.|34|
|Cameroon|0.27|.|.|10|Mauritius|0.20|.|.|44|
|Canada|0.77|0.74|0.38|60|Mexico|0.17|0.60|0.05|52|
|Chile|0.42|0.52|0.26|65|Netherlands|1.10|0.64|0.19|17|
|Colombia|0.29|0.50|0.04|69|New Zealand|0.33|0.70|.|3|
|Costa Rica|0.22|.|0.04|56|Norway|0.77|0.74|0.05|64|
|Cote d’Ivoire|0.41|.|0.04|13|Panama|0.54|.|.|36|
|Cyprus|0.57|.|.|45|Peru|0.12|0.38|0.04|69|
|Ecuador|0.18|.|.|64|Philippines|0.41|0.65|0.07|63|
|Egypt|0.21|0.24|0.01|67|Portugal|0.80|0.36|0.01|69|
|Fiji|0.21|.|.|25|Singapore|0.83|0.78|2.14|54|
|Finland|0.45|0.77|0.07|67|Spain|0.76|0.64|0.09|70|
|France|1.00|0.69|0.08|15|Sri Lanka|0.16|.|.|62|
|Germany|0.86|0.62|0.10|54|Sweden|0.86|0.83|0.13|64|
|Ghana|0.02|.|.|51|Trinidad|0.34|.|0.15|34|
|Greece|0.49|0.55|0.07|42|Turkey|.|0.51|0.01|67|
|Honduras|0.28|.|.|55|U.K.|0.29|0.78|0.38|70|
|Hong Kong|.|0.69|1.29|54|U.S.A.|1.06|0.71|0.46|70|
|Iceland|0.25|.|.|28|Uruguay|0.34|0.31|0.01|57|
|India|0.26|0.57|0.03|70|Venezuela|0.54|0.40|0.04|67|
|Indonesia|0.09|.|0.00|69|Zimbabwe|0.28|.|0.05|25|
|Japan|1.30|0.65|0.34|70||||||
|Correls.||||||||||
|Pvt. Cred.|1.00|||||||||
|Acc. Std.|0.44|1.00||||||||
|Mkt. Capit.|0.29|0.42|1.00|||||||

~~<u>21</u>~~
# **3. Cost Per Ton Abatement of All Cases**

Table C2 includes the cost per ton abatement under all scenarios listed in section III C.

# **Table C2. Cost Per Ton Abatement of Different ETS in Different Scenarios**

|||**CAT**|**USPS**|**UEPS**|**DEPS**|
|---|---|---|---|---|---|
|**NO**||||||
||Phase1|5.4|9.5|12.7|19.7|
||Phase2<br>|8.8<br>|12.7<br>|16.3<br>|21.0<br>|
||Phase3|14.1|18.2|25.5|32.1|
|**R2**||||||
||Phase1|12.1|13.5|13.7|15.3|
||Phase2|14.2|15.8|15.9|17.3|
||Phase3|15.1|17.8|18.5|22.5|
|**R*2**||||||
||Phase1|5.4|9.4|10.0|13.7|
||Phase2|8.9|12.4|12.9|15.6|
||Phase3|14.4|17.3|17.8|20.4|
|**RN**||||||
||Phase1|12.1|13.5|13.7|15.3|
||Phase2|14.1|15.9|16.0|17.5|
||Phase3|14.8|18.4|19.1|24.2|
|**R*N**||||||
||Phase1|5.4|9.4|10.0|13.7|
||Phase2|8.8|12.7|13.1|16.0|
||Phase3|14.1|17.9|18.5|21.7|
|**S2**||||||
||Phase1|12.3|15.1|17.2|20.0|
||Phase2|14.3|17.9|20.3|22.4|
||Phase3|15.1|18.2|22.0|25.5|
|**SN**||||||
||Phase1|12.3|15.1|17.2|20.0|
||Phase2|14.4|18.1|20.7|22.9|
||Phase3|14.9|19.0|23.9|29.0|
|**S*2**||||||
||Phase1|5.4|9.4|11.4|16.7|
||Phase2|8.9|12.4|14.5|18.2|
||Phase3|14.4|17.4|21.2|24.8|
|**S*N**||||||
||Phase1|5.4|9.4|11.4|16.7|
||Phase2|8.8|12.7|14.9|18.9|
||Phase3|14.1|18.1|23.1|25.0|
|**N2**||||||
||Phase1|5.4|9.4|12.6|19.6|
||Phase2|8.9|12.4|15.8|20.1|
||Phase3|14.4|17.4|23.2|27.5|

61
# **Table 1: Unit Root Tests**

|Variable|Order of<br>ADF test|Test Statistic<br>(without trend)|Test Statistic<br>(with trend)|Critical<br>Value<br>_(95%)_|Level of<br>Integration|
|---|---|---|---|---|---|
|Y|ADF(10)|-3.3966||-3.0039|1(2)|
|FDC|ADF(0)|-2.5555|-|-2.9750|I(1)|
||=_____|____|-3.6780|-3.5867|I(1)|
|FD|ADF(l)|-3.8811|-|-2.9750|I(l)|
||||-3.9113|-3.5867|1(1)|
|P|ADF(0)|-1.8012|-|-2.9750||
||||-5.4375|-3.5867|I(1)|
|RD|ADF(l)|-3.0410|-|-2.9706|I(0)|
||||-3.5379|-3.5796|I(0)|
|I|ADF(0)|-5.0305||-2.9750|I(1)|
||||-5.0699|-3.5867|I(l)|
|T|ADF(0)|-5.5522|_|-2.9750|I(1)|
||||-5.4432|-3.5867|I(1)|
|M|ADF(I)|-3.8506||-2.9750<br>|I(1)<br>|
||._________|_________________|-3.8588|-3.5867|<br>I(1)|
|LRE|ADF(O)|-3.9534|-|-2.9750|I(1)|
||||-4.5067|-3.5867|I(1)|
|LRT|ADF(0)|-3.5900|-|-2.9750|I(l)|
|X_________|___________|________________|-3.6263|-3.5867|<br>I(1)|
|SC|ADF(2)|-3.1424|-|-2.9750<br>|I(l)<br>|
||________**_**_<br>__ _<br>_|_<br>_ _-3.1441||-3.586|7|

Results obtained using Microfit 4.0 by Pesaran and Pesaran (1997).

# **(ii) Non-Stationarity, the ECM Model and Parsimonious Specification**

Unit root tests carried out above have indicated that, for all variables (with an exception of the real deposit rate variable), non-stationarity exists with implications that a classical regression analysis would produce spurious and inconsistent estimates. To overcome the problem of non-stationarity, we develop a specification of the Error Correction Model (ECM), as was done by Demetriades and Luinter (1996) and Khan and Hasan (l998)3. Alternatively, we could have used a vector autoregression (VAR) For a survey of ECMs, see Alogoskoufis and Smith (1991).

9
large and significant effect.<sup>8</sup> Individuals with poor physical health are between 10 percent (India) and 18 percent (Mexico) more likely to be in the worst 10% of mental health.

Less of a pattern is found with respect to household size and the presence of old and young dependents in the household. Individuals in larger households have marginally better mental health in BiH, with no significant relationship elsewhere. Elderly dependents in the household are associated with significantly better mental health in BiH and Tonga, but no significant relationship elsewhere, while the significant positive and negative associations with young dependents in the OLS results become less pronounced in the probit regressions.

While we find similar demographic patterns to the existing literature, the association between mental health and per-capita household expenditure is tenuous at best, in contrast to the view that poverty is strongly associated with mental health disorders (Patel and Kleinman, 2003). In the OLS results, only BiH and Tonga show a significant negative relationship between per-capita consumption and individual mental health. The effects are small in magnitude, and reduce considerably once we consider the probit results: A doubling of household PCE in BiH results in a 0.4 percentage point greater likelihood of an individual being in the worst 10% of mental health scores. There is a small significant negative gradient in Indonesia in the probit results for those in the worst 5% of mental health scores. The OLS results for Mexico, India, and Tonga actually show a slight positive gradient although this is not significant at conventional levels.<sup>9</sup> The association between schooling and mental health is also small in magnitude. All five countries have a negative coefficient on years of schooling in the probit regressions. However, this is only significant at the 5 percent level in Mexico, where one additional year of schooling is associated with a 0.4 percentage point drop in the likelihood of being in the worst 10% of mental health scores, and in BiH where the equivalent association is a 0.08 percentage point drop. The OLS results show similarly small effects. The largest effect is in India, where those with high school education have a 0.15 standard deviation better mental health score than those with less than primary.

> 8 No individuals reported themselves in poor physical health in the Tongan survey, and so we do not include this variable in the Tongan analysis.

> 9 The lack of association between mental health and poverty is not a result of the household average mental health status capturing a poverty effect. We obtain similar results when the household average mental health is omitted from these regressions.

- 8 -
as for instance the creation of credit bureaus or registries that help to collect, analyze, and disseminate information about potential borrowers to lenders. The evidence suggests, for instance, that more effective credit information sharing is associated with greater availability and lower cost of bank loans to firms (World Bank (2014, Chapter 3)). Such a policy may actually be more effective in middle-income countries, given a weak institutional environment and the practical difficulties that may arise in selecting research projects whose outcomes are inherently uncertain.

Our analysis also points out that improved access to finance for innovative firms has side benefits–it may improve incentives to acquire skills, and therefore indirectly help to promote innovative research. At the same time, however, it is important to realize that in practice a strategy of promoting the financial sector across the board may not yield only benefits. The reason is that it could lead to a misallocation of talent, as discussed elsewhere in the literature (see, for instance, Ang (2011) and Agénor and Canuto (2012)). Indeed, if the expansion of the financial sector, by offering higher earnings and rewards, draws too many talented individuals, it could hamper in the long run the development of the innovation sector. Thus, rather than promoting finance in general, what is needed is a well-targeted policy aimed at making it easier for innovative firms to secure adequate funding for their activity.

# 7 Concluding Remarks

The purpose of this paper was to study interactions between access (or lack thereof) to finance, product innovation, and labor supply in a growing economy. To do so we developed an overlapping generations model of horizontal innovation in the tradition of Romer (1990), with an endogenous distribution of skills, credit market imperfections, and engaging in research activity involves borrowing from financial intermediaries. In the basic model, financial intermediaries provide uncollateralized loans to researchers in the design sector to finance wages, and monitor borrowers at a cost that depends solely on the amount lent and the level of effort required from researchers. The model also assumes that there is a one-to-one relationship between the level of effort and the probability of success of a research project. The model was subsequently extended 29
welfare, however their analysis remains beyond the scope of this paper.<sup>3</sup> The paper is organized as follows. The following section reviews what is known about the impact of the crisis in the Philippines. In the course of this review, we also make some methodological comments on related literature for other countries in the region. Sections 3 and 4 respectively describe the data and our methodology. Our results are presented in Section 5. The final section sums up with some concluding observations.

# **2. What do we know about the distributional impact of the crisis?**

While it is generally believed that the Philippines escaped the worst of the regional financial crisis<sup>4</sup> , relatively little is known about the distributional impact of the crisis (which for the Philippines turned out to be a combination of financial and weather-related shocks). One strand of work for other countries in the region has involved comparisons of distributional parameters, including measures of absolute poverty, based on household survey data before and after (or during) the crisis.<sup>5</sup> For the Philippines, the latest available household survey is the 1998 Annual Poverty Indicators Survey (APIS) conducted by the National Statistics Office (NSO).<sup>6</sup> Using these data in conjunction with data from the 1997 Family Income and Expenditure Survey (FIES), Reyes, de Guzman, Manasan and Orbeta (1999) reported that per capita income declined

> 3 Some of the non-income effects may of course be mediated through changes in household incomes or consumption. An assessment of the income or consumption impact thus has some relevance for the potential magnitude of non-income effects too.

> 4 See for instance, World Bank (1999).

> 5 See, for instance, estimates in World Bank (2000). Some of this literature is also reviewed in Booth (1999). For recent estimates for Indonesia, see Suryahadi, Sudarno, Suharso, and Pritchett (1999).

6 A second round of the APIS for 1999 was also recently fielded by the NSO, though data from this survey are not yet available.

3
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