rafmacalaba/datause-encoder-data huggingface dataset:
the transregional trade arrangement, ASEM (Asia-Europe Meeting), under its Investment Promotion Action Plan (IPAP).<sup>3 9</sup> The next largest investor is the U.S. with a share of 14 to 23 percent in 1998-2000. The intra-ASEAN share dropped precipitously after 1997, which is understandable given the deterioration in the major ASEAN economies during the crisis.

While all source countries/regions had a net withdrawal of investment from Indonesia in 2000,40 the largest was that of Japan (i.e., US$1.7 billion, or 38 percent of Indonesia's $4.5 billion of lost investment). But even without the effect of its disinvestment in Indonesia, the share of ASEAN's FDI that comes from Japan dropped significantly over this time period - from 19 percent in 1997 to only 10-11 percent in 1999-2000.

Table 4.8 gives an idea of the major sources of FDI for individual countries in East Asia, but it covers different time periods, types of flows and is drawn from different sources for each country. It is thus not comparable across countries. Table 4.8

|**Dist**|**ributionofFDIin**Selected**DevelopingCountri**|**es**|
|---|---|---|
|**Countryand DataYear**|**TopThreeSectors**<br>**(%oftotal)**<br>.___________<br>_________**__** **_________**_____<br>(%|**TopThreeOriginating**<br>**Countries**<br>___<br>**of total)**|
|China<br>(1998-2000accumulated<br>flows)|Manufacturing(46%)<br>Realestatemanagement(16%)<br>Utilities(6%)|HongKong(4**1%/)**<br>UnitedStates(10%)<br>VirginIslands(9%)|
|Hong Kong<br>(2000year-end stock)|Investmentholding/realestate (60%)<br>Wholesale/retail (11%)<br>Banking(9%)|VirginIslands(32%)<br>Mainland China(31%)<br>Bermuda(10%)|
|Indonesia<br>(cumulative1967-mid2000)|Chemicalsandpharmacy(30%)<br>Paper(11%)<br>Electronics,tradingandother services (10%)|Japan(16%)<br>UnitedKingdom(9%)<br>Singapore(8%)|
|Malaysia<br>(flows2000-01)|Electrical andelectronics(51%)<br>Paper, printing,publishing(9%)<br>Non-metallicmineral products(8%)|UnitedStates(28%)<br>Japan(16%)<br>Netherlands(11%)|
|Philippines<br>(flows2000)|Manufacturing(46%)<br>Energy-related(32%)<br>Serviceexport(13%)|UnitedStates(36%)<br>Japan(27%)<br>HongKong(11%)|
|Singapore<br>(2000inflows)|Electronicproductsandcomponents(48%)<br>Chemicalsandchemicalproducts(30%)<br>Transportequipment (5%)|United States(40%)<br>Japan(16%)<br>France(4%)|
|Taiwan<br>(totalapprovedflows1952-<br>2000)|Electronicsandelectrical(24%)<br>Bankingand insurance (15%)<br>Services (11%)|UnitedStates(24%)<br>Japan(21%)<br>HongKong(8%)|
|Thailand|Trade(25%)|Japan(27%)|
|(totalnetinflows1995-99)|Machineryandtransport(11%)<br>Electrical appliances(10%)|UnitedStates(17%)<br>Singapore(13%)|
|Vietnam<br>(flows2000)|Oilandgas (59%)<br>Lightindustry(18%)<br>_Heavy industry(9%)<br>.|UnitedKingdom(30%)<br>India(25%)<br>Taiwan_(15%)_|

NOTE: Concentrations are not comparable across countries as they are defined differently by national govemments. Source: Compiled from OECD, Foreign Direct Investment for Development, 2002, 56.

> For most of the countries in this table inward FDI comes from only a few sources i.e., around 60 to 70 percent from only three source countries. The lack of diversification

20
estimation (SAE) – and show that it can provide more disaggregated poverty estimates even when using only remotely-sensed satellite data.

This is an important finding, since traditional SAE methods require census data that is often not available in many developing countries or is available only sporadically across time. “Big data” from satellites, on the other hand, are widely available for most countries and is often available in near real-time. Recent advances in the availability of satellite and mobile phone data, as well as increases in computing power, have sparked great interest in combining these data with survey data to generate more precise estimates of socio-economic characteristics (World Bank, 2021; Burke, 2021). Traditionally, small area estimation has been carried out by utilizing survey data to estimate a model and simulating that model in household-level census data (e.g., Elbers, Lanjouw, and Lanjouw, 2003). In the last five years, however, a growing body of innovative research has used geospatial or other big data to predict poverty and generate purely synthetic predictions of poverty or welfare, often generated by convolutional neural networks (Jean et al., 2017; Yeh et al., 2020; Engstrom et al., 2022; McBride et al. 2021). These estimates are typically validated against survey data at the village level, using sample data withheld from the prediction model. The results demonstrate the ability of methods utilizing novel sources of data to extrapolate poverty estimates to countries with no sample data with a reasonable degree of accuracy.

The existing literature is, however, less informative about the performance of methods that seek to combine survey and geospatial data to generate more granular estimates within a country, for two reasons. First, much of the existing literature stops short of combining synthetic predictions with prior estimates based on survey data, which is important to generate more accurate and precise estimates for well-defined administrative levels (Masaki et al., 2022). Second, it is challenging when utilizing neural networks or other machine learning models to properly estimate uncertainty, which is crucial when evaluating the reliability of small area estimates. This paper aims to address both of these issues.

A few studies examine the extent to which combining survey data with geospatial data improves small area estimates of proxies related to household welfare, with generally encouraging results (Steele et al., 2017; Masaki et al., 2022). No studies to date, however, have examined the gain in both accuracy and efficiency from combining survey and remote sensing data for predicting monetary poverty for small areas. Furthermore, we know of no study that has compared predictions 3
32 introducing export varieties into the GDP function. In exploiting the translog GDP function we are following Harrigan (1997), who hypothesized that export prices would differ across countries due to total factor productivity in exports. We have used the industry CES price indexes that differ across countries due to export variety, and enter as “price effects” into the GDP function and sectoral share equations. Estimating the share equations simultaneously with the GDP equation (transformed to become relative country productivity) allows us to identify and estimate the elasticity of substitution σ n between export varieties in each sector, and then infer the contribution of export variety to country productivity.

The resulting elasticity estimates range from a low of -0.02 in the electronics industry, to a high of –2 in the agriculture industry and the petroleum & plastics industry. Because these are the elasticity of substitution between _outputs_ (measuring the curvature of the concave production possibilities frontier), we have less intuition about the magnitude of the expected estimates than for inputs. But the ranking we have obtained seems reasonable, since there is the least substitution between export varieties in electronics, and the greatest substitution between varieties within agriculture and petroleum & plastics. In electronics, the estimate of -0.02 indicates that a 10% expansion of product varieties has the same effect as a 10/1.02 = 9.8% increase in prices, in terms of drawing resources into that sector. For agriculture and petroleum, however, a 10% increase in product variety has the same effect as a 10/3 = 3.3% rise in prices, since these products are more highly substitutable in terms of production.

We have treated export variety as an endogenous variable, and as instruments use those suggested by the work of Eaton and Kortum (2002) and Melitz (2003): tariffs, transport costs and distance. By using an over-identifying restriction test on the nonlinear system estimation, we have also been able to test the important exclusion restriction suggested by these models, that
(SSA) and the omitted category of Europe and Central Asia (ECA). Classification of countries into these regions is taken from the World Development Indicators of the World Bank.

Fourth, there is some evidence that civil conflict is more frequent in natural resource rich countries. As Rodrik (1999a) suggests, such conflicts can seriously undermine the capacity to reform. We follow this literature in controlling for a dummy variable that equals 1 if a country faced one or more incident of civil war during the reform period 2003-2008 (Civil War (current)) and 0 otherwise. To capture any effects of previous wars, we use a similar dummy variable covering the five year period before the reforms (Civil War (lagged)). The data source for these variables is the Armed Conflict Database.<sup>3</sup> Fifth, micro-economic reforms are unlikely to be of much use if the broader institutional environment does not provide adequate security and protection of private investment. We control for this factor using the Rule of Law measure taken from World Bank’s Governance indicators (2003 values). The measure broadly captures respect for private property, the incidence of crime and the enforceability of private and government contracts.

Sixth, successful implementation of reforms requires building a social consensus on the desirability of such reforms. Achieving such a consensus may be particularly difficult in countries that are more fragmented along ethnic or religious lines. We control for this factor using three separate measures of the degree of fragmentation or

> 3 We also experimented by distinguishing between minor and major civil wars as defined in the Armed Conflict Database and also with the total number of civil wars. These variations make little difference to the main results.

9
thoza workers directly affected by changes in the minimum wage might be overshadowed by a number of indirect effects.<sup>2</sup> A variation in the minimum wage can induce changes in the wages of workers earning more than the minimum wage<sup>3</sup> , as well as the earnings of those not covered by the legislation. It could also lead to changes in the aggregate level and skill composition of employment by those firms or sectors that are covered and abide by the legislation. As a result, changes in open unemployment and reallocation of labor between the covered and uncovered sectors could result. Furthermore, the size of the labor force itself might be affected in those circumstances. As a result, analyzing the distributive impact of minimum wages implies quantifying all these effects, a task that requires the use of complex general equilibrium models.<sup>4</sup> 2See for example U.S. Minimum Wage Study Commission (1981] (volume 1).

3As long as there is the possibility of substituting workers with different levels of skills, an increase in the minimum wage could induce an increase in the demand for relatively skilled workers, inducing an increase in their wages. Also, the existence of some type of wage-comparison mechanism linking tae wages of different categories of workers could result in spill-over effects (Grossman [1983]).

4A few of such models have been built for the case of the United States. See for example Cox-Oaxaca (1981, 1984], Johnson-Browning (1983] and Wolf-Nadiri (1981]. A study for Philippines can be found in Rodgers et al [19783.

4
Table 1 Characteristics of Rate Cycles: January 1970-May 2024 (Mean values across economies)

||Durat|ion|Amplit|ude|Veloc|ity|Rate|s "In Syn|c" with Pha|se|
|---|---|---|---|---|---|---|---|---|---|---|
||(in mon|ths)|(total chan|ge, pp)|(pp/fir<br>mont|st 6<br>hs)|# Chan|ges|Pace|(pp)|
||Tightening|Easing|Tightening|Easing|Tightening|Easing|Tightening|Easing|Tightening|<br>Easing|
|Australia|71|81|9.0|-6.6|1.3|-2.0|30|15|0.6|-0.6|
|Austria|43|69|4.5|-4.3|1.8|-1.2|12|16|0.5|-0.3|
|Belgium|79|49|8.6|-7.0|1.2|-3.0|17|18|0.9|-0.5|
|Canada|41|55|4.6|-7.6|1.4|-2.5|11|25|0.4|-0.6|
|Denmark|31|114|4.7|-6.6|1.7|-3.1|8|19|0.9|-0.6|
|Euro area|27|138|2.9|-3.9|1.3|-2.3|9|12|0.3|-0.3|
|Finland|51|90|9.2|-8.2|1.4|-1.2|16|23|0.9|-0.5|
|France|51|88|9.0|-8.5|1.4|-0.7|15|30|0.9|-0.3|
|Germany|32|49|4.4|-4.7|1.3|-1.3|11|13|0.5|-0.5|
|Greece|60|26|12.9|-14.3|6.2|-6.5|28|19|3.4|-1.6|
|Ireland|54|61|32.7|-29.2|0.8|-25.1|28|30|1.5|-1.3|
|Israel|25|87|4.9|-6.3|1.7|-1.4|10|20|0.6|-0.4|
|Italy|36|47|6.6|-6.4|2.2|-1.5|4|10|1.7|-0.9|
|Japan|24|156|3.3|-4.1|1.4|-1.2|4|8|0.7|-0.4|
|Korea, Rep.|34|77|2.5|-2.7|0.6|-0.9|9|10|0.3|-0.3|
|New<br>Zealand|71|128|8.3|-10.9|1.6|8.4|31|30|0.8|-1.1|
|Netherlands|34|42|4.5|-5.6|1.1|-1.4|7|13|0.7|-0.6|
|Norway|29|100|3.5|-6.5|0.6|-1.3|10|17|0.4|-0.6|
|Portugal|84|58|12.7|-11.3|4.7|-3.2|4|11|2.4|-1.1|
|Spain|98|50|15.3|-9.7|1.4|-2.9|34|21|2.4|-0.8|
|Sweden|42|63|3.4|-4.6|1.1|-1.7|11|13|1.0|-0.4|
|Switzerland|39|106|3.3|-4.2|1.3|-1.7|6|8|0.6|-0.5|
|United<br>Kingdom|42|102|6.2|-6.5|2.0|-1.8|10|14|1.0|-0.6|
|United<br>States|34|56|6.8|-5.7|1.1|-2.6|16|12|0.5|-0.6|
|Full Sample|||||||||||
|Mean|47|79|7.7|-7.7|1.7|-2.6|14|17|1.0|-0.6|
|Median|41|73|5.6|-6.5|1.4|-1.7|11|15|0.7|-0.6|
|St. Dev.|20|33|6.4|5.3|1.2|5.4|9|7|0.8|0.3|
|Min|24|26|2.5|-2.7|0.6|8.4|4|8|0.3|-0.3|
|Max|98|156|32.7|-29.2|6.2|-25.1|34|30|3.4|-1.6|

Sources: Authors’ calculations based on the rate cycles described in Section II.

17
tt<sup>= A</sup> tt<sup>(11)</sup> where FFPPE A E represents the annual investment for the biodiesel extraction plant (NRs), and<sup>E</sup> + LLE the land rent in the year _t_ (NRs). Annual investment for establishing the biodiesel extraction plant tt<sup>is</sup> LLE (A E ) represents the annual flow of total investment. The total investment in the 1<sup>st</sup> year is allocated for the productive years of the biodiesel extraction plant. The biodiesel production starts from the 2<sup>nd</sup> year and ends on 20<sup>th</sup> year. The annual investment for the biodiesel extraction plant (A E ) is considered as the annuity of total investment for the productive periods of the biodiesel extraction plant. A E (12) −1 = TTAAE . rr. (1 + rr)<sup>nn</sup> where is the total investment for the establishment of the biodiesel extraction plant (NRs), (1 + rr)<sup>nn</sup> _r_ represents a discount rate, and _n_ represents the economic life of the biodiesel extraction plant (here TTAAE 20 years). Total investment for the establishment of the biodiesel extraction plant includes costs incurred in the first year for purchasing and establishing of machineries and equipment, construction of required buildings and infrastructure, and the first year land rent.

Total variable cost of biodiesel production is calculated by summing up the costs of jatropha seeds and the operation and management costs in biodiesel extraction. tt<sup>= P</sup> tt<sup>+ PPOO</sup> tt<sup>(13)</sup> where VVPPE P represents the cost of jatropha seeds required for the biodiesel extraction plant in the year _t_ (NRs), and tt represents the operation and management cost in biodiesel extraction in the year _t_ (NRs). tt PPOO P ∗P P (14) tt tt tt where = TTPP is the total quantity of jatropha seeds required for the biodiesel extraction plant in the year _t_ (kg), and tt P P is the per unit cost of jatropha seed production in the year _t_ (NRs/kg). TTPP tt = P P ∗T E tt tt tt<sup>(15)</sup> where PPOO P OOPPE P is the per unit operating cost of biodiesel extraction in the year _t_ (NRs/liter), and T E tt OOPPE biodiesel plant (tt<sup>is the total quantity of biodiesel produced in the year</sup> P P ) is assumed as NRs 12/liter (CJP website).<sup>_t_(liter). Per unit operating cost of the</sup> OOPPE 10
SOS-Arsenic.net 2004. Agrochemicals: Imported Pollutants in Bangladesh. Available at: <u>http://www.sos-arsenic.net/index.html.</u>

- SUNS 1998.  Pesticide Overuse Takes Serious Turn in Bangladesh. Monday, Jan. 24, (Dhaka, Jan. 23 IPS/Tabibul Islam).

- Tardiff, R. G. (ed.) 1992. _Methods to Assess Adverse Effects of Pesticides on Non-Target Organisms_ . New York: John Wiley and Sons.

- Tomlin, C. D. S. (ed.) 2003. _The e-Pesticide Manual: A World Compendium, 13th Edition_ , Alton, Hampshire: British Crop Protection Council.

- Warburton, H., F. G. Palis and P. L. Pingali 1995.  Farmer Perceptions, Knowledge, and Pesticide Use Practices. In: _Impact of Pesticides on Farmer Health and the Rice Environment_ . (P. L. Pingali and P. A. Roger, eds.). Kluwer Academic Publishers, Boston. pp. 59-95.

- Wilde, J. 2000.  Identification of Multiple Equation Probit Models with Endogenous Dummy Regressors. _Economics Letters_ **69** : 309-312.

- World Health Organization (WHO) 1990. _Public Health Impact of Pesticides Used in Agriculture, 1990_ .  World Health Organization: New York, USA.

- World Resources Institute, UNEP, UNDP, the World Bank 1998. Environmental Change and Human Health. _World Resources 1998-99_ .

- Xuyen, K., N. C. Hoi and P. Q. Trung 1998. Occupational Environment and Skin Diseases in Pesticide Exposed Subjects in some Tea Farms in Vietnam. Presentation given at the Third National Scientific Conference on Occupational Health, December 4- 5, 1998. Hanoi.

- Zahm, S. H., M. H. Ward and A. Blair 1997. _Pesticides and Cancer_ . In: _Occupational Medicine: State of the Art Reviews_ . Vol. 12: Pesticides (M. Keifer, ed.). Philadelphia: Hanley and Belfus, Inc., 269-289.

18
**Figure 18: Firm Turnover, 2007**

<!-- Start of picture text --> 20% 18.3%<br>16.0%<br>15.3%<br>15% 13.0%<br>8.8%<br>10%<br>5.7%<br>5%<br>0%<br>Croatia Greece Slovenia Romania Bulgaria Turkey<br><!-- End of picture text --> Source: World Bank, Entrepreneurship Survey 2008 Below follows a review of the entry and exit procedures, the reforms that the Croatian government has undertaken to date to accelerate both entry and exit and the suggestions on the way forward,

# **4.2 State control of economic activity**

**The privatization process in Croatia has been long, controversial in terms of its impacts, and still incomplete.** The first phase of enterprise privatization, which was launched in 1991, was characterized by sales to insiders (managers and employees) and distribution to political supporters.  It did not provide the hoped-for boost to investment and growth, largely owing to the method of privatization used (companies were not transferred to those who could operate them most efficiently), but also because of Government’s unwillingness to impose financial discipline on privatized enterprises.  Instead of establishing hard budget constraints and pushing the chronic loss-makers into liquidation, the Government has kept bailing them out.  As a result, the list of state-owned enterprises slated for privatization—or re-privatization—remains long today, over 750 companies.

**After taking off again in 2005-2007 period, enterprises with high state ownership remained mainly in shipbuilding and tourism sectors.** Many of them are loss-making, heavily indebted, and cannot survive without direct subsidies (including for working capital). Completion of enterprise privatization or liquidation has become urgent to create space for the private sector and to release the significant assets locked in these enterprises to more productive use.

**Croatia performs remains quite far from OECD and EU levels with respect to both the extent of public ownership and state involvement in business operations** . The two measures of state control in the PMR system are public ownership and state involvement in business operations (through price controls and coercive, as opposed to incentive-based, regulation). On both accounts, the extent of control performed by the state in Croatia is heavier than in comparators.

18
Policy ReseaRch WoRking PaPeR 4836

# **Abstract**

This paper presents the evaluation of the program Computers for Education. The program aims to integrate computers, donated by the private sector, into the teaching of language in public schools. The authors conduct a two-year randomized evaluation of the program using a sample of 97 schools and 5,201 children. Overall, the program seems to have had little effect on students’ test scores and other outcomes. These results are consistent across grade levels, subjects, and gender. The main reason for these results seems to be the failure to incorporate the computers into the educational process. Although the program increased the number of computers in the treatment schools and provided training to the teachers on how to use the computers in their classrooms, surveys of both teachers and students suggest that teachers did not incorporate the computers into their curriculum.

This paper—a product of the Education Team, Human Development Network—is part of a larger effort the network to estimate the impact of Information and Communication Technologies on education. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fbarrera@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
with recent information about the distribution of energy subsidies between the richest and poorest quintiles, estimated using detailed Household Income and Expenditure Survey in their country. This information was selected as a very simple way to illustrate the regressivity of energy subsidies, as it was expected that even respondents with very low levels of education would be able to understand it. The environment treatment provided respondents with information about the amount of money (in local currency units) that energy subsidies cost their country due to their contribution to air pollution and climate change (based on IMF [2023]). This number was complemented by a generic figure across countries highlighting some of the likely consequences of climate change, specifically extreme temperatures, floods, droughts (according to IPCC [2023]), and air pollution (which is identified as a key environmental externality in the IMF [2023] estimates of the implicit costs of subsidies). Examples of each of these information treatments for Indonesia are shown in Figure 1 below.

# **[Insert Figure 1]**

# **3.1.4 Additional design features**

The survey included two additional layers of randomization to test whether there were any differences between the amount of information provided to respondents about subsidies in their country and the level of energy price rises that would occur from the removal of energy subsidies. The first additional layer of randomization occurred in the second section of the survey, where respondents were informed that energy subsidies do exist in their country. Half of the respondents were randomly allocated to be informed about the share of energy prices that the government effectively pays on their behalf (the average subsidization rate that is presented in IEA [2023]) and the other half were informed only about the existence of subsidies (i.e., they were not told the amount of subsidization). The second additional layer of randomization occurred in the third section of the survey, where respondents were asked about their levels of support for a reduction in energy subsidies that would result in a specific 20