rafmacalaba/datause-encoder-data huggingface dataset:
of 11 years starting from year-end 2005, capturing the peak valuations in 2007, the ensuing recession, the ongoing recovery, the kick-start of growth, and an already apparent slowdown.

The capital markets in Southeast Asia are classified as frontier, which means that the availability of data is very limited. This paper uses end-of-quarter data for the calendar year, with the initial quarter starting on December 31, 2005 and the data set ending on December 31, 2016. In this sense, the study uses data with a starting point _ex post_ to the establishment of the frontier capital markets and initial transitional years, which were marked by frequent transaction failures. More so, the study captures approximate data for the full economic cycle, thus lowering the seasonality bias in the results. The data is sourced from Bloomberg, Eurostat, the European Commission Directorate-General for harmonized data, and the central banks and national statistics agencies in the countries under study.

In this empirical research, the model variables are chosen by the common times series variables included in the studies of capital markets performance, the popularity and frequency in research, the state of the underlying Southeastern European economies, and the pervasiveness of the indicators. The other factors of choice are the simplicity in accounting and the availability of the data. Given that prices in capital markets are influenced by company-specific, industryspecific, and external sociopolitical impacts that are not controlled for in this paper, the focus of this research is to measure, _ceteris paribus_ , the implied relationship with the selected macroeconomic indicators. This research addresses the limitation of the omitted variables error by broadening the sample data set in conducting a cross-country panel study and utilizing the advancements in the PMG methodology, which have improved the reliability of results. The dependent variables that were chosen are the end-of-quarter values of the individual stock exchange indexes. The independent variables, on the other hand, are the end-of-quarter values of a set of chosen macroeconomic indicators.

Among the chosen independent variables, the economic rationale establishes that countries with higher GDP per capita tend to simultaneously have more developed capital markets, which in turn have larger capitalizations and more liquidity. As a result, these more developed markets tend to show less of a correlation with stock price movements (Morck, Yeung, & Yu, 2000).

In further theoretical and empirical rationale, stock prices commonly respond negatively to the actual unexpected inflation rate. This rate directly and negatively impacts purchasing power, 13 | P a g e
Table 7.3: Determinants of Producer Income in Dispersed Rural Areas of Argentina, 2003

|Dependent variable: Log Household Farm Income|Marginal impact (%)|P>|t||
|---|---|---|
|_Gender_|||
|Male|-7.13|0.00|
|_Education_|||
|Primary education complete|10.96|0.00|
|Secondary complete|124.34|0.00|
|University studies|185.48|0.00|
|_Land_|||
|2-10 hectares|100.77|0.00|
|11-35 hectares|283.44|0.00|
|36-100 hectares|312.06|0.00|
|101-250 hectares|877.67|0.00|
|More than 250 hectars|1880.63|0.00|
|% rented hectares/ total hectares|0.10|0.00|
|% owned hectares/ total hectares|0.00|0.01|
|% shared hectares/ total hectares|-1.09|0.00|
|% occupied hectares/ total hectares|0.40|0.00|
|_Infrastructure and production inputs_|||
|Access to a paved road|29.30|0.00|
|Access to electricity|43.48|0.00|
|Use fertilizer|25.61|0.00|
|Access to irrigation|28.27|0.00|
|Constant|63170.23|0.00|
|AdjustedR2: 0.35|||

Notes: Excluded variables: No education or primary incomplete and 0-1 hectares. The marginal impact/percentage return is calculated as (exp (coefficient estimate) – 1) * 100. Number of observations: 124. _Source:_ Own calculation based on RHS 2003.

The size of the producer household’s land holdings is also important for the income generated on the farm. The farm size variables are all statistically significant and positive. The regression analysis presented in Table 7.3 reveals that at average values of other variables, income increases with farm size, in a nonlinear fashion. In rural Argentina, an average producer experiences an increase in income of 101, 283, 312, 878 and 1881 percent for holding 2-10, 11-35, 36-100, 101-250, and 250 or more hectares respectively, compared to farmers with only one hectare of land. Hence larger farms earn dramatically higher incomes than do their counterparts with smaller farms. However, whether the farmland is rented or owned has very little measurable effect on incomes.

28
Pension institutions reacted to the financial crisis and the new stress testing by reducing their equity portfolios (in large part caused by the very fall of equity prices), selling shortduration bonds and buying long-duration ones, especially foreign bonds, and engaging in extensive hedging operations, mostly through the use of long-term interest rate swaps in the more liquid Euro market<sup>5</sup> . Although such policies ran the risk of "locking-in the losses", it was generally accepted that pension institutions could not afford to suffer additional losses and endanger their financial solvency.

Although detailed data were not available to estimate the price effect and separate it from the asset allocation effect, published data support the argument that ATP has engaged in a significant switch away from domestic and foreign equities and domestic bonds and in favor of foreign bonds and derivatives. If there was no quantity effect but only a price effect, the share of equities should have recovered between 2002 and 2004. In fact, the share of equities fell from 40 percent in 1999 to 22 percent in 2002 and continued to fall to less than 18 percent in 2004.

**Table 4:  Change in Asset Allocation by ATP**

||Domestic<br>Bonds|Foreign<br>Bonds|Fixed<br>Income<br>Derivatives|Domestic<br>Equities|Foreign<br>Equities|Investment<br>Trusts|Other<br>Assets|
|---|---|---|---|---|---|---|---|
|1998|55.0|2.4|0.0|25.2|10.7|1.2|5.5|
|1999|47.7|3.7|0.0|24.8|15.3|3.3|5.3|
|2000|39.8|9.7|0.0|21.1|16.4|6.9|6.1|
|2001|29.1|16.5|0.0|16.8|19.0|12.5|6.1|
|2002|32.8|27.6|5.3|11.6|9.2|6.0|7.5|
|2003|29.6|35.1|4.2|11.2|8.6|4.2|7.2|
|2004|26.1|32.2|10.2|9.6|7.9|7.1|7.0|

Source: DFSA and ATP

> 5 The response of pension institutions to the financial turmoil and the growing use of derivatives are analyzed in some detail in Ladekarl et al (2006).

31
<mark>WPS3568</mark>

**Singapore as an Innovative City in East Asia: An Explorative Study of the Perspectives of Innovative Industries**<sup>*****</sup>

_Poh Kam WONG Yuen Ping HO Annette SINGH Entrepreneurship Centre National University of Singapore_

# World Bank Policy Research Working Paper 3568, April 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> * This paper was prepared for the East Asia Prospect Study being conducted in DECRG at the World Bank.  The authors thank Shahid Yusuf, Anjum Altaf, and Kaoru Nabeshima for their suggestions and guidance.  The authors also are grateful for the helpful comments provided by the participants of the workshop organized for this study held in Bangkok, February 22-23, 2004.
especially affected by natural disasters. In particular, Christiaensen and Demery (2007) estimate that 1 percentage point of (aggregate) GDP growth originating in agriculture is on average about twice as effective in reducing 1$-day poverty than 1 percentage point of GDP growth originating outside agriculture, with the difference in poverty reducing performance further increasing for less developed countries. As a result, the poor are likely to suffer disproportionately from droughts and storms, with their effects often felt many years thereafter, especially in case of severe droughts as in the 1984-85 Ethiopian famine (Dercon, 2004). On the other hand, nationwide, the poor may also be benefitting disproportionately when _moderate_ floods are reported. To the extent that earthquakes and storms result in labor intensive reconstruction efforts, the poorer segments of the population could benefit as well. While informative, these preliminary insights regarding the distributional effects of natural disasters must be tested further against the poverty data, an important agenda for future research.

# **6. Concluding Remarks**

Over the past couple of decades there has been a steady increase in the occurrence of natural disasters. This has instigated an interest in a better understanding of their effects on economic growth to inform policymakers of the benefits from disaster risk reduction and mitigation. The literature has so far remained inconclusive regarding the effects of natural disasters on growth. While several studies point to negative effects, others also report no effects or even positive effects of natural disasters on growth. Guided by insights from the traditional Solow-Swan growth model on the channels through which natural disasters may affect economic growth as well as the extensive literature on intersectoral linkages, this study went beyond the averages and explored the effects of natural disasters separately by disaster and economic sector in both developed and developing countries.

Three major insights emerged. First, disasters do affect economic growth – but not always negatively, and the effects differ substantially across disaster and economic sector, confirming the gains from a richer disaggregated analysis that looks beyond the averages. In particular, droughts are found to have a negative impact that is mainly 27
Table A.11: Determinants of refugee settlement at the community level

|Outcome:|Share of|refugees|
|---|---|---|
||in 1923|in 1928|
||(1)|(2)|
|Log population density in 1920|0.730|0.212|
||(0.717)|(0.370)|
|Dummy: city in 1920|10.476***|3.718*|
||(1.426)|(2.193)|
|Dummy: island|24.736***|4.644***|
||(1.212)|(1.098)|
|Distance to railway network in 1920 (km)|-0.012|-0.047|
||(0.022)|(0.033)|
|Distance to shoreline (km)|-0.082***|-0.005|
||(0.020)|(0.049)|
|Distance to border (km)|-0.009|-0.080|
||(0.017)|(0.062)|
|Altitude (m)|-0.003|-0.003|
||(0.002)|(0.003)|
|Mean annual precipitation|0.003|-0.013|
||(0.007)|(0.012)|
|Mean annual temperature|-0.316*|0.211|
||(0.168)|(0.369)|
|Crop suitability score|1.251**|0.996**|
||(0.583)|(0.443)|
|N|4,388|4,388|
|R2|0.54|0.60|
|Province FE|Y|Y|
|Average of outcome|4.19|7.05|

**Notes.** This table examines the determinants of the share of refugees in 1923 and 1928 and the change in the share of refugees between the two years across municipalities. The unit of analysis is the municipality as defined by the 1920 population census of the Kingdom of Greece. All specifications controls for province fixed effects and a set of municipal-level covariates, which comprises: log population density in 1920; dummy variables indicating whether the municipality was a city in 1920 and whether it is an island; distances to railway network in 1920, shoreline, and national borders; altitude, mean annual precipitation and temperature, and crop suitability of the municipality. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

41
0.088 under the minmax t-stat method. The size of the difference in balance achieved with different methods shrinks as the sample size increases – asymptotically all methods will be balanced.

The key question is then the extent to which achieving greater balance on baseline variables translates into better balance on future values of the outcome of interest in the absence of any treatment. The follow-up period is six months for the Sri Lankan microenterprise data and Mexican labor income data, one year for the Pakistan test-score and child height data, and three years for the Indonesian schooling and expenditure data. Figures 1 to 6 show the distribution of difference in means between treatment and control at follow-up for each method, while Table 4 summarizes how the different methods perform in obtaining balance in follow-up outcomes.

Panel A of Table 4 shows that on average, all randomization methods give balance on the follow-up variable, even with a sample size as small as 30. This is the key virtue of randomization. Figures 1 to 6 and Panel B shows there are generally fewer differences across methods in terms of avoiding extreme imbalances than with the baseline data. This is particularly true of the Sri Lanka profit data and the Indonesian schooling data, for which baseline variables explained relatively little of future outcomes. With a sample size of 30, stratification and matching are reducing extreme differences between treatment and control, but with samples of 100 or 300, there is very little difference between the various methods in terms of how well they balance the future outcome.

Baseline variables have more predictive power for the realizations at follow-up for the other outcomes we consider. The Mexican labor income and Indonesian expenditure data lie in an intermediate range of baseline predictive power, with the baseline outcomes plus six other variables explaining about 28 percent of the variation in follow-up outcomes. Figures 2a to 2c and 4a to 4c show that, in contrast to the Sri Lanka and IFLS schooling data, even with samples of 100 or 300 we find matching and stratification continue to perform better than a single random draw in reducing extreme imbalances. Table 4 shows that with a sample size of 300, the 95<sup>th</sup> percentile of the difference in means between treatment and control groups is 0.23 s.d. under a pure random draw for both expenditure and labor income. This difference falls to 0.20 s.d. for

- 18 -
Policy Research Working Paper 8585

# **Abstract**

According to the official statistics published by the National Institute of Statistics of Rwanda, the country registered a decline in poverty from 46 percent in 2010/11 to 39 percent in 2013/14. This declining poverty trend was broadly debated and repeatedly questioned in national and international forums, which provided the primary motivation for this study. Using data from the third and fourth rounds of the Integrated Household Living Conditions Surveys, this paper revisits the national poverty numbers and corroborates the poverty rates published by the National Institute of Statistics of Rwanda. Underlying the paper’s conclusions is a detailed theoretical and analytical framework for making poverty comparisons over time. Furthermore, the paper shows that after adjusting for spatial and temporal price differences, the poverty rate based on the international poverty line of $1.90 per day per capita shows that there was a reduction in poverty between 2010/11 and 2013/14.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at ffatima@worldbank.org and nyoshida@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
Labor market clearing requires that _li,t_ = _l_ for all _t_ , and thus _wtl_ = _bt_ . Using the FOC with respect to _li,t_ : Substituting into the FOC with respect to _bi,t_ +1: Using the FOC with respect to _Bi,t_ +1, note that: It follows that _λ_<sup>3</sup> _t_<sup>_>_0andthus</sup><sup>_bt_=</sup><sup>_Bt_=</sup><sup>_mt_=</sup><sup>_θemF_(</sup><sup>_kt, l_)forall</sup><sup>_t_.</sup> Since _γ_<sup>_em_</sup> = 0, the FOC of the household with respect to _ki,t_ +1 can be rewritten as the standard Euler equation: Market clearing with respect to _bt_ and _bt_ +1 implies the aggregate budget constraint. Furthermore, since there is no supply of collateral backed claims, ̃ _rt_ = _−_ 1 (an infinite price for collateral backed claims in terms of current goods) clears the market.

44
Table A.3: Baseline Parameters (continued)

|Parameter|Description|Units|Value|
|---|---|---|---|
|Preferences|and Welfare|||
|_αf_|AIDADS Marginal Budget Share at Subsistence<br>Income for Services from Processed Food||0.2|
|_αe_|AIDADS Marginal Budget Share at Subsistence<br>Income for Energy Services||0.13|
|_αw_|AIDADS Marginal Budget Share at Subsistence||0.136|
||Income for Services from Processed Timber|||
|_αr_|AIDADS Marginal Budget Share at Subsistence<br>Income for Ecosystem Services||0.012|
|_αo_|AIDADS Marginal Budget Share at Subsistence||0.522|
||Income for Other Goods and Services|||
|_βf_|AIDADS Marginal Budget Share at High||0.034|
||Income for Services from Processed Food|||
|_βe_|AIDADS Marginal Budget Share at High||0.065|
||Income for Energy Services|||
|_βw_|AIDADS Marginal Budget Share at High||0.092|
||Income for Services from Processed Timber|||
|_βr_|AIDADS Marginal Budget Share at High||0.042|
||Income for Ecosystem Services|||
|_βo_|AIDADS Marginal Budget Share at High<br>Income for Other Goods and Services||0.767|
|_γ_<sup>_g_</sup>|AIDADS Subsistence Parameter for Processed<br>Grain Products||0.1|
|_γ_<sup>_e_</sup>|AIDADS Subsistence Parameter for Energy<br>Services||0.01|
|_γ_<sup>_w_</sup>|AIDADS Subsistence Parameter for Processed<br>Timber Products||0.1|
|_γ_<sup>_r_</sup>|AIDADS Subsistence Parameter for Ecosystem<br>Services||0.1|
|_γ_<sup>_o_</sup>|AIDADS Subsistence Parameter For Other<br>Goods and Services||0.1|
|_γ_|Risk Aversion Parameter||2|
|_δ_|Social Discount Rate||0.95|

8
**Table 7: Comparisons of poverty rates (%) between the HIES to HIES and the HIES to LFS imputations**

||Backward i<br>HIES to HIES|mputation<br>HIES to LFS|Direct<br>estimation<br>of 2006|Forward i<br>HIES to HIES|mputation<br>HIES to LFS|Direct<br>estimation<br>of 2009|
|---|---|---|---|---|---|---|
|Urban|7.2|4.1|7.4|6.6|4.4|6.1|
||(1.6)|(1.5)|(1.3)|(1.5)|(1.4)|(1.1)|
|Rural|9.5|8.8|15.4|12.7|12.3|8.9|
||(0.9)|(0.9)|(0.8)|(0.9)|(0.8)|(0.6)|
|Estate|11|10.8|33.6|30|31.5|10.4|
||(3.4)|(3.5)|(0.7)|(2.7)|(3.3)|(1.7)|

Source: Authors' estimations using the corresponding rounds of HIES and LFS Notes: For the backward imputation, consumption models are estimated using HIES 2009/10 data; for the forward imputation, consumption models are estimated from HIES 2006/07 data. For both imputations, we did not include district level wage data. Also, for comparisons, the HIES to HIES imputations use exactly the same models as the HIES to LFS imputations. The figures in parentheses are cluster-robust standard errors for the direct estimations and the standard errors that include the modeling and sampling errors for imputation The LFS and the HIES generally use a similar sample design, but there are several differences that can partly explain these differences in predicted poverty rates. Both surveys are stratified on urban, rural, and estate sectors within each district, and each selected around a total of 2500 census blocks and ten household per block, based on the 2001 population census. However, the surveys use different sampling strategies. In the HIES, the number of PSUs in each sector are selected using a Neymann allocation to minimize the variance of estimated mean per capita consumption, while the LFS uses a similar approach that minimizes the variance of the estimated unemployment rate. As a result, the HIES includes nearly two and a half times more primary sampling units than the LFS in the urban and estate sectors.

|**Table 8: **|**Number of h**<br>HIES: July-<br>Primary<br>Sampling<br>Units|**ousehold respon**<br>December 2009<br>Households|**dents by secto**<br>LFS: July-D<br>Primary<br>Sampling<br>Units|**rand survey**<br>ecember 2009<br>Households|
|---|---|---|---|---|
|National|1001|8,927|907|8,464|
|Urban|261|2,240|108|966|
|Rural|637|5,768|747|7,005|
|Estate|103|919|52|493|

Source: Authors’ calculation based on 2009/10 HIES and 2009 LFS data This difference in sampling allocation strategy does not lead to biased estimates in either survey, because both samples are weighted to be representative of the 2001 census. However, the unique sampling strategy of the LFS hampers our ability to test the accuracy of SSI. This is because the households 14
As a result of the booster interventions, we succeeded in getting an additional 114 individuals to attend the course, giving a total of 426 attendees out of 1,751 treatment group individuals (24.3 percent). Offering a monetary incentive of $36 increased the take-up rate from about 18 percent to 27 percent of those assigned to treatment, while the $72 incentive increased takeup further to 33 percent. While the difference between the two monetary amounts is not statistically significant, they both suggest that individuals are rationally responding to higher benefits of training by being more likely to attend. The treatment impact is exactly the same when US$36 is offered immediately at the completion of training, or one month after training. This suggests that high discounts or present-bias are not the main barriers to participation in training.

In contrast to the monetary incentives, we find that transportation assistance and the testimonials did not significantly increase attendance. One reason for the lack of impact of transportation assistance were security concerns in Mexico City, with people reluctant to take a taxi cab that came to their home. The lack of impact of the testimonials could reflect people updating their beliefs about the efficacy of the training very little after the receipt of this treatment, or that lack of information about the benefits and quality of the training was not the main reason for non-participation.

Finally note that even when participants were offered US$72 to attend, it is still the case that the majority of individuals who had initially expressed interest in attending financial literacy training do not attend.<sup>14</sup>

# **5.4 Which Individuals Attend Training?**

If we break down the take-up rate by individuals who are clients of a financial institution and those who are not, we find that the attendance rate was 28.1 percent among clients vs. 18.1 percent among non-clients. Since we do not expect to be able to detect treatment effects with a take-up rate of 18.1 percent and since our randomization was stratified by being a financial institution client, we decided to collect follow-up data only on the 2,178 individuals who are clients of a financial institution. From this point on, all tables and analysis in this paper cover only these individuals.

Using data from the screener survey, we analyze which individual background characteristics are correlated with training attendance in the treatment group. Table 3 displays this analysis, using two different measures of take-up (i) a dummy variable for whether the individual

> 14 This dissociation of intentions and follow through has been documented in studies of retirement seminars (Madrian and Shea, 2001).

18