rafmacalaba/datause-encoder-data huggingface dataset:
15 reduction and an increase in foreign capital inflow to the private sector on the main endogenous variables of the model. The effects of debt-reduction are probably an underestimate of the full impact, because the improvement in country risk considerations would help attract additional foreign financing 20 and lead to a return of capital flight .

# 2. AN EMPIRICAL TEST : ARGENTINA (1962-86)

The model developed in the last section could be used in principle to examine the direct and total effects of a variety of policies on private investment and other endogenous variables of the model. By estimating the model for Argentina and by stimulating it, the response of key variables to changes in foreign debt levels will be derived for this country.

# 2.1 Data and estimation results

The data have been obtained from the International Monetary Fund's International Financial Statistics, the World Bank's reports on Argentina (1985 and 1990), Fundaccion Mediterranea (1984), the Central Bank of Argentina and Dornbusch and de Pablo (1988). All series are described in Appendix 1.

The general framework presented in section 1 was slightly altered to reflect the lack of data of certain variables and some important characteristics of the Argentinean economy. First at all, we were not able to use a consistency approach to measure the economic agents budget constraints over the estimation period. Second, because data on public bonds in hands of the private sector was not available over a long period of time, we assumed that the private sector agents do not hold public bonds in their portfolios. This simplification does not appear excessive in the case of Argentina since, practically, all public bonds issued by the public sector were directly held by commercial banks in recent years. The modified budget constraints of the financial sector and the private sector are presented in Appendix 2. Third, we assume that the interest rate on public bonds is determined by the interest on bank deposits since deposit and public bonds markets are viewed as competitive in a country like Argentina2 l. In other words, these two assets are assumed to be perfect substitutes.

Also, we modified the estimated inflation equation to account for the influence of both international price arbitrage and domestic factors. The estimation procedure has been to substitute money demand equation (equation (15)) into the inflation function (equation (25)) and, then, to introduce external factors into the resulting equation. The presence of both international inflation rate and monetary disequilibrium effects arises because the Argentinean economy was neither completely closed nor opened over 20The model assumes only a reversal of capital flight.

21see Santomero and Langohr (1986) for further explanations on this simplification.
# **Two Case Studies on Electronic Distribution of Government Securities**

The U.S. TreasuryDirect System The Philippine Expanded Small Investors Program

## Thomas G. Glaessner and Zeynep Kantur<sup>*</sup>

**Abstract:** The case study on the US TreasuryDirect examines the evolution of the electronic distribution systems for marketable and nonmarketable government securities, the main objectives and the basic legal infrastructure and the preconditions enabling the system. The U.S. experience highlights that the enabling environment and infrastructure (e.g. in terms of information databases such as Pay.Gov) make a large difference in terms of both the security and convenience that customers can expect in the use of the system. The system also achieved important cost savings for the Bureau of the Public Debt.  The case study on the _Small Investors Program_ of the Philippines looks at a program that the Philippine government has been experimenting with to sell its securities directly to retail investors over the Internet. The recently revised version of the program—called the Expanded Small Investors Program aims to increase access to government securities and distribute them more widely, develop better savings products, and enhance competition in the primary markets for these securities. This study analyzes whether the program’s main goals can be achieved while mitigating the risks. The analysis suggests that there are good reasons to believe that the new program will succeed. Still, regular and responsive assessments and adjustments will be required as the program moves forward.

World Bank Policy Research Working Paper 3372, August 2004 _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._

> * _Thomas Glaessner is Lead Financial Economist and Business Group Leader of the Capital Market Group and Zeynep Kantur is a Financial Analyst, the Capital Group within the Financial Sector Operations and Policy Department of the World Bank. The authors wish to give special thanks to Gamliel Pascual (SMETRIX), Jeppe Ladekarl, Tom Kellermann, Valerie McNevin, and officials from the U.S. and Philippine Treasuries._
# **Figure 2 Cross-section of Net Foreign Asset Postitions, 2004**

<!-- Start of picture text --> 200%<br>150%<br>100%<br>50%<br>China<br>India<br>0%<br>6.00 7.00 8.00 9.00 10.00 11.00<br>-50%<br>-100%<br>-150%<br>GDP per capita, PPP<br>NFA to GDP<br><!-- End of picture text --> Net foreign assets expressed as a ratio to GDP. Source: Lane (2006), drawing on the dataset constructed by Lane and Milesi-Ferretti (2006).
- 20 -

# IV. DATA AND VARIABLE SPECIFICATIOF'

30. The data for commodity prices, economic announcements, and expected values of economic announcements **are** discussed in this section. The sample period begins on January 2, 1985, and ends on May 31, 1989. Because of the important role of expectations, the expectations data are discussed in detail.

# Comodity Prices

31. Table 1 gives a suary of the important characteristics of the commodities considered. Figures 4 to 23 show the monthly movements of the comodity prices used in this study. To investigate the responses of comodity prices to new information, daily percentage changes in closing price quotations on "nearby" futures contracts were used. A nearby is a continuous price series for a contract. Since a futures contract stops trading on its expiration date, nearbys were created by "splicing" individual successive futures contracts together. For example, if a commodity (e.g. cotton) had contracts that matured in the months numbered 3 (March), 5 (May), 7 (July), 10 (October), 12 (December), the futures prices of the contract maturing in month 3 were used until calender month 3, then prices of contracts maturing in month 5 were used until calendar month 5, etc. 1/ 1/ All cmrmodity futures price data are from Data Resoures, The McGraw-Hill Financial and Economic Information Company, Lexington, Massachusetts.
**<mark>Do Public Works Programs Have Sustained Impacts? A Review of Experimental Studies from LMICs</mark> ***

Aanchal Bagga, Tufts University** Marcus Holmlund, DIME, The World Bank Nausheen Khan, DIME, The World Bank Subha Mani, Fordham University and IZA Eric Mvukiyehe, Duke University Patrick Premand, DIME, The World Bank Keywords: Public works programs, Experimental evidence, Low- and middle-income countries, Sustainability, Social protection, Safety nets, Employment.

JEL codes: H41, C93, O12, J22, I38 <mark>*We thank the authors of the various studies reviewed in this paper, as well as Samih Ferrah, Christelle Tchoupe, Jules Gazeaud, Oliver Sterck, Raphael Cottin, Issa Kiemtore, Brooke Hill, and Horacio Vera Cossio for their excellent research assistance during the implementation of the public works studies across countries. We specifically thank Girum Abebe, Ugo Gentilini, S</mark> amuel Kembou Nzale, <mark>Elizaveta Perova, Hrishikesh Tirumala Madabushi, Michael Weber, and an anonymous reviewer from DIME for detailed comments and suggestions on an earlier draft.</mark> Funding from the World Bank Jobs, UFGE and i2i Trust Funds is gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, the Executive Directors of the World Bank, or the governments they represent. <mark>**Aanchal Bagga (e</mark> mail: <u>Aanchal.Bagga@tufts.edu</u> <u><mark>),</mark></u> <mark>Marcus Holmlund (e</mark> mail: <u><mark>mholmlund@worldbank.org),</mark></u> <mark>Nausheen Khan (e</mark> mail: <u><mark>nkhan8@worldbank.org),</mark></u> <mark>Subha Mani, Corresponding author (e</mark> mail: <u><mark>smani@fordham.edu), Eric Mvukiyehe (e</mark></u> mail: <u><mark>eric.mvukiyehe@duke.edu),</mark></u> <mark>and Patrick Premand (email: ppremand@worldbank.org).</mark>
23 contract prices (which are used in the next illustration) are available publicly. Smoothed prices significantly reduce price volatility, although world oil prices and domestic prices move in the opposite direction half the time―that is, domestic prices (black solid line in the figure) are rising when world prices (gray solid line) are falling, and vice versa. The plot also shows over- and under-recoveries in each month (dotted line).

Figure 7: Comparison of Monthly Average Spot Prices and Nine-Month Average Prices for U.S. Heating Oil <!-- Start of picture text --> $1.20<br>$1.00 Spot price<br>9-month average price<br>$0.80<br>Over/under-recovery<br>$0.60<br>$0.40<br>$0.20<br>$0.00<br>-$0.20<br>-$0.40<br>Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13<br>$/liter<br><!-- End of picture text --> _Source_ : EIA 2013b and author’s calculations.

Table 7 summarizes the results of an analysis for different averaging periods—ranging from taking the average price from the previous month (third column) to averaging prices from the previous nine months and the next three months (last column)—using the historical spot and futures contract prices of U.S. heating oil since January 2003. The analysis is based on returns, which are differences in successive data entries. When logarithms of prices are taken, as in the table, returns approximate fractional changes in prices from month to month, so that multiplying returns by 100 approximates percentage changes; a return of 0.05, for example, represents a price change of about 5 percent. The hypothetical smoothing schemes maintain the same price for a month at a time and make monthly adjustments. Cumulative losses between January 2003 and March 2013 for smoothing prices for 10,000 bpd are also shown.
Policy Research Working Paper 5436

# **Abstract**

This paper examines the evolving, cross-country use of antidumping, safeguard, and countervailing duty policies—temporary trade barriers (TTBs)—over the period 1990-2009. The author constructs two new measures of imported products subject to the combined use of these TTBs before applying these measures to new data drawn from the World Bank’s Temporary Trade Barriers Database. The research establishes a number of facts regarding trends in historical use to benchmark against policy activity during the global economic crisis of 2008–2009. The 2008–2009 economic shock mostly accentuates patterns and trends already visible in the pre-crisis data: e.g., while the major users of such policies overall combined to increase the product lines subject to TTBs by 25 percent during the crisis, this was driven almost entirely by developing economies which increased their product coverage by 40 percent. On the export side, a previously unidentified feature of the data is that a much larger share of China’s exports to other developing economies is subject to foreign-imposed antidumping than its exports to developed economies. The evidence confirms this feature is shared by a number of other major developing economy exporters, deepening concern that these discriminatory trade barriers are increasingly a “South-South” phenomenon.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to understand the changing nature of market access in the world trading system. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at cbown@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
1. Given the importance of analyzing PM2.5 we report results for a shorter window of 7 weeks for it.

Missing observations in each included week do not exceed 20 percent. Each estimate is calculated from a separate regression (equation (1)), where the dependent variable is the natural logarithm of hourly pollutant. Controls are third order polynomial in time; hour of the day, weekday and interaction between the two; current and up to 4-hour lags of temperature, humidity, wind speed, and rainfall and quartics of current and 1-hour lags of the same weather variables; and dummy variables for public holidays and festivities such as Diwali. Std. errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level and *** at 1 percent level.

**Table 5. Effect of Delhi Metro on Air Quality: Five Week Symmetric Window**

||NO2|CO|PM2.5|
|---|---|---|---|
||(Percenta|ge change in level|of pollutant)|
|ITO||||
|Yellow Line first extension<br>(Jul 03, 2005)|-21.4|-77.8***|—|
|Std. error|(21.3)|(6.7)|—|
|Number of observations|748|772|—|
|Blue Line introduction (Dec<br>31, 2005)|-55.2**|-56.0*|-52.8***|
|Std. error|(7.7)|(22.8)|(5.3)|
|Number of observations|848|848|840|
|Blue Line Second extension<br>(Nov 11, 2006)|-6.4|-10.1|-15.8|

45
In actuality, income growth for rural households has been stronger than predicted by the simulations even in the long run. Thus, the last column of Table 5 shows that income has increased by 4 percent during this period compared to the predicted 1.8 percent. Income growth has been particularly strong for households in the bottom 30 percent of total per capita expenditures and it has exceeded 9 percent for this group. Interestingly, income of farmers of all sizes has increased by 10.5 percent instead of the decline of 4.2 percent. The urban households are the only category that experienced a worse growth performance than what was predicted by the simulation: 5.7 percent decline instead of 4.1 percent growth.<sup>6</sup> The discrepancy between the results of the ex-ante simulation and ex-post income growth could be due to several factors. The assumed demand and supply elasticities could actually be different. But even more importantly, the simulation has omitted the labor market and multiplier effects of a change in rice prices and productivity. Therefore, the simulated welfare impact is an overestimate of the losses on net sellers, particularly if they experience significant gains from an improvement in productivity, and it is an empirical question whether or not these gains outweigh the losses. The positive multiplier effects resulting from growth of the non-farm rural sector, particularly the service sector for the newly available irrigation equipment, further magnify welfare gains and mitigate losses of net buyers and net sellers. Hence, the direction of the true welfare impact could change if the omitted effects are sufficiently large.

# **5. Ex-post analysis:  dynamic welfare impacts of productivity and price changes**

A comprehensive evaluation of the impact of changes in rice prices and productivity on household welfare would include three components: the direct first-round effect of the price and productivity changes on net buyers and net sellers in rural and urban areas, the direct second-round effects brought about by labor market repercussions, and the multiplier effects of agricultural growth on rural and urban communities. Owing to data constraints, we are unable to estimate the magnitude of these effects in a structural model.

> 6 The poor performance of income growth in the urban areas may be a result of migration from rural to urban areas. According to census results, the urban population in Bangladesh grew nearly four times as  fast as the rural population in the 1990s, possibly contributing to the observed low growth rate of the incomes of the urban poor (World Bank 2002a). However, the focus of this paper is on rural population, and we do not examine this tendency in urban income growth any further.

17
# I. Introduction

The introduction of new seed technology in agriculture has enabled India to attain self-sufficiency in basic food grains. Yet, as evidence suggests, poverty remains obstinately high in many parts of rural India (Ahluwalia, 1978; Bardhan, 1985; Vaidyanathan, 1988).1 The continued rural poverty amidst surplus food production is perhaps a result of an inadequate growth in rural employment and income (Mellor, 1988; Mellor and Johnston, 1984). Poverty alleviation thus critically depends on how fast the government can generate productive employment and income for the rural unemployed. One proximate cause of rural poverty is a slow growth in agricultural wage income. Thus, one way to alleviate rural poverty-National Sample Survey (NSS) of India reports that in 1983 about 40 percent of the rural labor force were wage workers--is to increase real rural wtage.

- 1/ There are two ways rural poverty has been measured in the literature. One approach quantifies the incidence of poverty (i.e., in terms of the head count ratio, the Sen's index and other measures) based on the mean per capita consumption levels in rural areas and the distribution of the rural population around the mean at different points of time. The other approach looks at the trend of real wage of agricultural workers who are the majority of the rural poors.
**Table 7:  Firm-level outcomes of city upgrading using PSM for incumbents**

|**_Incumbent Firms_**|(1)|(2)|(3)|
|---|---|---|---|
|_Firm-level Outcome_||||
|Log main operating revenue|0.126|0.109|0.109|
|Log main operating cost|0.127|0.108|0.108|
|Log main operating profit|0.0673|0.0275|0.0275|
|Log # of employees|-0.0195|-0.0859|-0.0859|
|Log labor vocational|-0.0780|-0.0531|-0.0531|
|Log wage per employee|-0.0528|-0.0409|-0.0395|
|Log paid-in capital|-0.116|-0.117|-0.117|
|Logexport output value|-0.102|-0.118|-0.126|
|Controls|Block FE|Block FE|Block FE|
|||Industry FE|Industry**×**Year|
|||Year FE|<br>FE|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. When we report the firm-level results after PScore matching, we control for block fixed effects where samples are stratified and compared against each other in each block.

3. Analysis is restricted to firms opened before 1997.

4. Vocational labor data is only available in 2004.

To ensure that the above aggregate results are not sensitive to different matching methods, we report below the aggregate firm-level outcomes using three different matching methods commonly used in the propensity score literature. We used the same propensity scores generated above to conduct the matching. We can see that compared with Table 7, similar conclusions can be reached. Upgrading counties into county-level cities alone has very limited effects on aggregate firm level outcomes in terms of firm profits, export, and employment.

**Table 8: Firm-level outcomes using different propensity score matching methods**

|**_Incumbent Firms_**|Kernel|Local Linear<br>Regression|5-Nearest Neighbor|
|---|---|---|---|
|_Firm-level Outcome_||||
|Log main operating revenue|0.116***|0.340|0.457***|
|Log main operating cost|0.125***|0.378|0.552***|
|Log main operating profit|0.052|0.162|0.077|
|Log # of employees|-0.027|-0.096|-0.308***|
|Log labor vocational|-0.108|-0.114|-0.206|
|Log wage per employee|-0.064***|0.085|0.206***|
|Log paid-in capital|-0.158***|-0.214|-0.452***|
|Logexport output value|-0.062|0.206|0.359|

1. Significance levels of 10%, 5%, 1% are represented by *, **, ***, respectively.

2. Average treatment on treated effects are reported, i.e. upgraded cities and their counterfactual counties are compared to obtain the upgrading effects.

12
22 than $1.00 per person per day. However, the more sensitive poverty measures – the poverty gap and squared poverty gap – suggest that international remittances will have a slightly larger impact on poverty reduction. The point estimates for the poverty gap and squared poverty gap suggest that a 10 percent increase in the share of remittances will lead to about a 2.0 percent decline in the depth and/or severity of poverty in the developing world. While international migrants do not come from the ranks of the poor, the income that migrants remit to their origin communities appears to both increase average income and to reduce both the incidence and severity of poverty.

The final finding is more of a plea than a conclusion. From the standpoint of future work on this topic, more attention needs to be paid to collecting and publishing better data on international migration and remittances. With respect to migration, it would be useful if developing countries would start publishing records on the number and destination of their international migrants. In many developing countries, these data are already being collected, but they are not being published. With respect to international remittances, the International Monetary Fund should make greater efforts to count the amount of remittance monies that are transmitted through private, unofficial channels. Poor people, especially poor people from countries located near the major labor-receiving regions of the world, are more likely to remit through informal, unofficial channels. For this reason, a full and complete accounting of the impact of the remittances on poverty in the developing world needs more accurate data on the large and currently unknown level of unofficial remittance transfers.