rafmacalaba/datause-encoder-data huggingface dataset:
For comparability with Dimaranan, Ianchovichina and Martin (2007), we considered accelerated growth over a fifteen year baseline which resulted in output levels 34.6 percent higher in each region than under the baseline scenario. Consistent with Kaldor’s (1957) stylized facts of economic growth, we also increased the stock of human and physical capital in line with the overall output increase in these two growing economies.

Recent empirical evidence (see, in particular Hummels and Klenow , 2005) suggests that economic growth of the type considered increases both the quality and the variety of the goods exported by the growing economy. Building on Hummels and Klenow (2005), the quality of goods exported is represented using a variable, λ, which determines the number of effective units of a good obtained from each actual, physical unit of that good. It has a counterpart in the popular iceberg specification for international transport costs, but is much more general in allowing the additional units of effective output to arise from a wider range of sources than changes in transport costs. Where purchasers have utility or demand functions that value gains in variety, the effective prices of goods decline as the variety of goods supplied increases.

The macroeconomic closure of the simulation model assumes a constant level of employment, perfect mobility of skilled and unskilled labor between sectors but none between regions. Because we look at long-run trends, we have doubled the elasticity of substitution between imported goods from different sources and between composite imported and domestic goods from the values used in the given GTAP database. In all simulations the trade balances as shares of GDP were fixed for China and India to avoid potentially important changes in welfare resulting from changes in financial inflows from abroad when growth rates in these countries change substantially.

# **Results**

## **Impact of global growth 2005-2020**

According to the widely-used projections for the growth in GDP, labor force, capital and population presented in Table 1, the most rapidly growing regions are expected to be the countries of South East Asia—China, Malaysia, India, Vietnam and Indonesia—all growing 12
“critical mass” more broadly to include not just numbers but also “diversity” of the people living in the same neighborhood.

Survey results in Wong et al (2004b) lend some support to such a view. When asked about the difficulty of finding or attracting the right type of individuals to undertake innovation work, 100% of the firms surveyed in the creative content industries said this was a real issue, compared with 89% and 79% respectively for high-tech manufacturing and KIB industries. On what is needed to develop a greater pool of creative talents in the economy, a large number of respondents point to measures that would lead to a society that is less regulated and more tolerant of diversity (56% of respondents).

The change of the premiership from Goh Chok Tong to Lee Hsien Loong on 12 August 2004 appeared to mark a new significant development in the government’s approach to openness. Both in his swearing-in ceremony speech and his National Day Rally speech, Lee had asked Singaporeans to be bold and to move away from conventional thinking, promising that his government would make it easier for this to happen and offering the vision of a more “open and inclusive” society.<sup>36</sup> How such a new vision is translated into reality would have a major impact on Singapore’s attempt to transform the city into an innovation hub.

# **_VI. Efficiency Enhancement in Singapore’s Services Sector_**

Singapore’s economic growth has been driven hitherto by the manufacturing sector, especially the electronic clusters. As part of its efforts to diversify the economic growth base, the ERC recommended that the role of the services sector be substantially enhanced. In particular, it suggested that the city be developed into a regional hub for a number of service industries. Compared with the manufacturing sector, possibilities for commoditization in the service sector are smaller. A hub status, once built up, is not easy to break. Hence Singapore’s ability to maintain its leadership position for many years in financial services, transport and logistics and healthcare industries. While regional competition may make it difficult for Singapore to command the same premium or to achieve the same robust growth rate in these industries as it did in the past, it would be some time before other cities in the region can generate sufficient agglomeration to completely replace Singapore as a hub. The revamp of the port business by the Port of Singapore Authority (PSA) shows that even in industries where

> 36   See swearing-in speech by Lee on 12 August 2004 and national Day Rally speech by Lee on 22 August 2004.

25
countries, fiscal austerity involving major infrastructure spending cuts not only entailed a substantial growth cost, but also may have been largely ineffective at strengthening public finances – it amounted to attempting to ‘walk up the down escalator’ (Easterly, Irwin and Servén 2008).

This begs the related question of whether infrastructure can pay for itself – i.e., whether public infrastructure projects can be ‘self-liquidating’ (Mintz and Smart 2007). Conceptually, the answer depends on three factors (Servén 2008). The first is the magnitude of the growth impact of infrastructure spending, given by the marginal productivity of infrastructure assets and the cost of acquiring them. Other things equal, the growth contribution is likely to be higher when the government has strong project selection capabilities, when the initial endowment of infrastructure capital is low, and when budgetary institutions ensure that the acquisition of infrastructure assets is free from waste and corruption. The second factor is the government’s ability to capture at least part of the marginal product of infrastructure, directly via user fees or indirectly through taxes––which, in the latter case, depends on the strength of the tax system and its administration. If these are weak – as happens in many Latin American countries -- and fiscal revenues only capture a small fraction of the extra income, then even projects with high growth impact may weaken public finances. The third factor is the marginal cost of borrowing faced by the government, related to its level of debt and to investors’ perceptions about the government’s commitment to fiscal stability.

These ingredients involve a host of country-specific, and even project-specific features, so it is not surprising that empirical assessments of the self-financing potential of infrastructure reach different conclusions for different countries. For example, Perotti (2004) investigates whether public investment can “pay for itself” using data from five rich countries––the United States, Germany, the United Kingdom, Canada, and Australia. Perotti’s conclusion, based on the estimation of structural VARs, is in the negative – which is not very surprising given that in these countries the public capital stock is probably among the highest in the world.

For Latin America, the only published empirical evaluation of this question is that of Ferreira and Araujo (2007) using Brazilian data in a framework very similar to that of Perotti. Overall, their conclusion is that public infrastructure investment is generally self28
(eq. 3)

- (eq. 4)

The tax rate on imported fuel also increases linearly at a constant rate starting from 2012. Tax rates used in different scenarios are given in Table 4.

**_Business as Usual (BAU) GDP Growth Scenario_**

**14.** The (BAU) GDP growth scenario refers to a purely economic baseline and is based on past economic performance for 2007–10 and on IMF projections of GDP for 2011–2015, with associated projections up to 2030 derived from projections for population and TFP. The model then calculates the required investments to achieve the projected growth, along with the demands for different types of fuel. Domestic prices for fuel as well as other goods are determined so that demand and supply are equated. Some emission reduction (and therefore decline in PM intensity of GDP) happens under BAU due to autonomous technological change built into the model.<sup>15</sup> This is partly driven by the macro-economic structural shift away from the agriculture sector towards knowledge-based industries, greater and easier access to global knowledge, technology and capital, and the growth impetus provided by the commercial and services sectors. In addition the shift also reflects the recent policy initiatives to reduce the sulfur content of diesel in the transport sector, the use of compressed natural gas for public transport, emissions limiting performance standards for passenger vehicles, and stricter enforcement of existing environmental laws.<sup>16</sup>

> 15 Autonomous energy efficiency (kg CO2 emitted per unit of GDP in 2000$) improved by 1% per year between 1980-2008 (WDI) and our BAU reproduces the same trend.

> 16 New substitution elasticity between capital and energy was introduced into the standard GTAP model to capture this effect.  This is based on the notion that technical progress is entirely embodied in the design and operating characteristics of new capital plant and equipment. For example, the energy saving effects of embodied technical progress depends critically on the rate at which new investment goods diffuse into the economy. By introducing substitution between capital and energy in the model, we mitigate CO2 emissions by 20% (India would have emitted emit 3246 mtons in 2030 but with the substitution only emits 2631mtons under BAU).

9
8

# **References**

- Arabsheibani, G.R. and Rees, H. (1998), “On the weak versus strong version of the screening hypothesis: a re-examination of the _P_ -test for the U.K.”, _Economics of Education Review_ , 17(2): 189-192.

- Arbache, J.S. and Corseuil, C.H. (2001), “Liberalização Comercial e Estruturas de Emprego e Salário”, IPEA Working Paper No. 801, Rio de Janeiro.

Arrow, K. (1973), “Higher education as a filter”, _Journal of Public Economics_ , 2: 193-216

- Bauer, T.K. and Haisken-DeNew, J.P. (2001), “Employer learning and the returns to schooling”, _Labor Economics_ , 8(2): 161-180.

- Buchinsky, M. (1994) “Changes in the U.S. wage structure 1963-1987: application of quintile regression”, _Econometrica_ , 62(2): 405-458.

- Buchinsky, M. (1998), “Recent advances in quintile regression models: a practical guide for empirical research”, _Journal of Human Resources_ , 33(1): 88-126.

- Carneiro, F.G. (1998), “Productivity effects in Brazilian wage determination”, _World Development_ , 26(1): 139-153.

- Carneiro, F.G. and Henley A. (1998), “Wage determination in Brazil: the growth of union bargaining power and informal employment”, _Journal of Development Studies_ , 34(4): 117-138.

- Dickerson, A., Green, F. and Arbache, J.S. (2001), “Returns to education and trade liberalization: a pseudo-panel approach”, _mimeo_ , University of Kent at Canterbury.

- Green, F., Arbache, J.S. and Dickerson, A. (2001), “A picture of wage inequality and the allocation of labor through a period of trade liberalization: the case of Brazil.”, _World Development_ , 29(11), 1923-1940.

- Hartog. J.,  Pereira, P.T. and Vieira, J.A.C. (2001), “Changing returns to education in Portugal during the 1980s and early 1990s: OLS and quintile regression estimators”, _Applied Economics_ , 33(8): 1021-1037.

Koenker, R. and Bassett, G. (1978), “Regression quintiles”, _Econometrica_ , 50:43-61.

- Lam, D. and Levinson, D. (1992), “Declining inequality in schooling in Brazil and its effects on the inequality of earnings”, _Journal of Development Economics_ , 37: 199-225.

- Lee, K-H (1980), “Screening, ability and the productivity of education in Malaysia”, _Economics Letters_ , 5: 189-193.

- Machado, J.A.F. and Mata, J. (2001), “Earnings functions in Portugal 1982-1994: evidence from quintile regressions”, _Empirical Economics_ , 26(1): 115-134.

- Mollick, A.V. (2002), “Rising Relative Wages, Technology and Trade in Mexican Manufacturing Sector, 1987-1995”, _mimeo_ , ITESM, Monterrey, Mexico.
# Table 1: Financial Development and Trade Survival.

The dependent variable is the hazard rate of trade relationships for product k of ...rm i operating in country c to destination country j: All regressions are estimated using the Cox Proportional Hazard Model and account for various strati...cation variables and ...xed e¤ects. (See details for each column). The main variables of interest are sanitary risk of product k (sanitary_riskk ) and its interaction with ...nancial development in country c (FDc*sanitary_riskk). The control variables include ...nancial development of country c (FDc), initial export value (initial_exportickj), number of products exported by ...rm i to the world market (NProductsci), number of destination service by ...rm i with product k (NDestinationscik), gravity variables (Contiguitycj,Com_languagecj,Colonycj, Distancecj). Robust standard errors clustered at (exporting country) � HS4 sector level are in parentheses. *, **, and *** denote statistical signi...cance at the 10%, 5%, and 1% levels, respectively.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|FDc x sanitary_riskk|-0.141**|-0.109*|-0.263**|-0.458*|
||(0.069)|(0.066)|(0.107)|(0.252)|
|sanitary_riskk|0.018***|0.015**|||
||(0.007)|(0.006)|||
|FDc|-3.557***<br>(0.726)||||
|initial_exportcikj|-0.075***|-0.094***|-0.088***|-0.096***|
||(0.007)|(0.006)|(0.007)|(0.010)|
|NProductsci|-0.003***|-0.003|-0.004**|-0.009**|
||(0.001)|(0.002)|(0.002)|(0.004)|
|NDestinationscik|-0.024***|-0.060***|-0.057***|-0.065***|
||(0.004)|(0.008)|(0.009)|(0.014)|
|Contiguitycj|-0.087||-0.150|-0.190|
||(0.090)||(0.105)|(0.251)|
|Com_languagecj|-0.249***||-0.126***|-0.123|
||(0.040)||(0.040)|(0.101)|
|Colonycj|0.123||-0.086|0.122|
||(0.089)||(0.084)|(0.197)|
|Distancecj|0.095**||0.090*|0.319**|
||(0.044)||(0.049)|(0.147)|
|...rm fe|no|yes|yes|yes|
|destination fe|yes|no|yes|no|
|destination x exporter fe|no|yes|no|no|
|year fe|yes|yes|yes|yes|
|HS4 fe|yes|no|no|no|
|HS2 x exporting country fe|no|yes|yes|yes|
|HS8 strata|no|no|yes|no|
|HS8 x destination strata|no<br>17|no|no|yes|
|Observations|14870<br>|14870|14870|14870|
WPS3945 doing some work and among children age 15 and up, more than a third report some work activity. Interestingly, there appears to be little difference in reported work activity by gender. Using an eight hour work day and 30 days of work per month, the median days worked by children who report some labor market activity is 1.3 months over a one year period. The average number of days worked is substantially higher at 2 months since there is a strong positive correlation between age and labor market participation. Again, there are no discernible gender differences in days worked. While boys work 12 more days, on average, per year, this difference arises entirely from children 16 and older where boys work for 24 more days per year than girls (p-value<.00). In all other age groups the difference is small and insigni fi cant. If we use the broader de fi nition of work, the labor activity of children age 7-17 rises to 29%. The median number of days worked are close to 2 months and the average number of days worked rises to about 3 months.

The principal work activity of children is unpaid work on the family farm. 63% of working children report working on their own family farms. However, more than a third (35%) also report working as agricultural wage labor and 13% report working on a family enterprise. In contrast, only 8% report working as non-farm wage labor. There are strong gender differences in the type of work undertaken by male and female children. If we focus on unpaid family labor, boys are much more likely to work on the family farm (68% of boys and 58% of girls: p-value <.000), but girls are more likely to work on the household’s non-farm enterprise (16% of girls and 10% of boys: p-value <.00). If we focus on daily wage labor instead, girls are much more likely to work as agricultural wage workers (51% of girls as compared to only 20% of boys: p-value<.0000), while boys are more likely to work as non-agricultural wage workers (14% of boys but only 2% of girls: p-value <.0000). These differences are consistent with other work that has highlighted mobility constraints for girls, particularly after adolescence.<sup>12</sup> Non-agricultural wage work typically requires travel outside the village, and is often undertaken individually. In contrast, agricultural wage work is typically undertaken jointly with other family members, particularly other adult females. Wage labor is largely undertaken by children 14 and older in our sample, but this is particularly evident for non-farm wage work, which is almost exclusively done by boys 14 and up.

This pattern is re fl ected in Figures 7-10 (see Appendix). Girls work more than boys until age 13 if we use de fi nition I (age 14 if we use de fi nition II). Girls also bear a substantially larger burden of work in poorer households. Their participation in labor market

> 12 See the Pakistan Country Gender Assessment, The World Bank (2005)

8
Goux, D., and E. Maurin. 2010. “Public School Availability for Two Year-olds and Mothers' Labour Supply.” _Labour Economics_ 17 (6): 951–62.

Havnes, T., and M. Mogstad. 2011. “Money for Nothing? Universal Child Care and Maternal Employment.” _Journal of Public Economics_ 95 (11–12): 1455–65.

Jaumotte, F. 2005. “Female Labour Force Participation: Past Trends and Main Determinants in OECD Countries.” OECD, Paris.

Kruger, D., and M. Berthelon. 2011. “Risky Behavior among Youth: Incapacitation Effects of School on Adolescent Motherhood and Crime in Chile.” _Journal of Public Economics_ 95 (1–2): 41–53.

Lefebvre, P., and P. Merrigan. 2003. “Assessing Family Policy in Canada: A New Deal for Families and Children.” Institute for Research on Public Policy. Montreal, Canada. ———. 2008. “Childcare Policy and the Labor Supply of Mothers with Young Children: A Natural Experiment from Canada.” _Journal of Labor Economics_ 23: 519–48.

Lundin, D., E. Mork, and B. Ockert. 2007. “Do Reduced Child Care Prices Make Parents Work More?” Working Paper 2007-2, Institute for Labour Market Policy Evaluation. Uppsala, Sweden.

Medrano, P. 2009. “Public Day Care and Female Labor Force Participation: Evidence from Chile.” Working Paper No. 306, University of Chile, Department of Economics. Santiago, Chile.

Pires, T., and S. Urzúa. 2010. “Longer School Days, Better Outcomes?” Unpublished Manuscript, Northwestern University, Evanston, IL.

27
civil war. With the use of P-UACD data, I increase the number of conflicts accounted for by 28%. For the period 1946-2001, 249 onsets are recorded using a two year hiatus from reported fighting to define a new onset. These onsets account for 3.6% of the dataset. The recurrence variable is coded ‘1’ for every subsequent war after the cessation of the first civil war within an independent state. A variable for peace years counted the years since the cessation of a conflict, initial or recurrent. There are 97 episodes of recurrent conflict in these data, accounting for 1.5% of the total data. Approximately 40% of all conflict onsets are recurrent wars. Considerable regional differences in the number of recurrent war episodes are apparent (see Table 3.1), but for the eight larger regions specified<sup>2</sup> , still almost 40% of all civil wars across regions are recurrent.

Walter’s (2004) study of recurrence using Correlates of War data found no consistent pattern in the timing of renewed war and a median duration of peace at 14 years. The P-UACD recurrence data display two distinct patterns: 1) these data show that the median duration of peace is between four to five years. As Collier et al (2004) note, most conflicts recur within a one to five year period after civil war has ended; 2) the most war prone period is between two and three years after initial cessation.

**Table 1: Conflict Onsets and Recurrence by Region**<sup>**3**</sup>

|_Region_||||
|---|---|---|---|
||_Onsets (#)_|_Subsequent Conflict (#)_|_% of Subsequent Conflicts_|
|Europe|20|8|40%|
|Middle East|27|8|30%|
|East Asia|6|2|33%|
|South/Central Asia|37|14|38%|
|South East Asia|32|16|43%|
|South/Central America|39|14|41%|
|Africa|86|34|40%|
|Oceania|2|1|50%|

> 2 Regions specified include Europe, Middle East, East Asia, Central and South Asia, South East Asia, South and Central America, North America, Africa, and Oceania.

> 3 North American region not included as it had no conflicts.

11
program - ICDS - for India).

Impact data are in general weak and available niainlv for (possibly unrepresentative) research studies. Hence data on cost-effectiveness are particularly deficient. This is also a priority area, since otherwise there is a possibility of misallocating health resources towards other types of interventions for which cost-effectiveness data are more readiiy available and more persuasive. Information on financing are also very limited. Data on all aspects tend to be (unsurprisingly) least good for Africa.
On this crucial question of partial effects of disaggregated indicators on outcomes of interest, empirical evidence has not kept pace with the proliferation of very detailed indicators of institutional quality and the regulatory environment. While identifying these partial effects is central to realizing the promise of disaggregated indicators to identify specific reform priorities, in this paper we argue that it is also extremely difficult – if not impossible – to do so convincingly. The problem is simply one of degrees of freedom: the more disaggregated indicators become, the more partial effects of individual indicators on outcomes of interest there are to be estimated, and the less precisely each individual partial effect can be estimated. For example, the overall Ease of Doing Business ranking of the Doing Business project consists of 41 measures of the business regulatory environment, for 181 countries. This means that there are on average just 4.5 data points with which to estimate the partial effects of each individual indicator on some outcome of interest in a cross‐sectional regression. In the case of Global Integrity, the degrees‐of‐freedom problem is even more stark, as there are over 300 variables in this dataset which spans just 92 countries, so that it simply is infeasible to estimate the partial effects of each of them in a single encompassing cross‐country regression including all indicators.

One possible solution to this degrees of freedom problem is to apply data reduction techniques of some sort. That is, one might simply re‐aggregate the highly disaggregated variables by averaging them together in some way. For example, one could simply average together the 41 indicators of the business regulatory environment in the Doing Business dataset to obtain the overall “Ease of Doing Business” ranking, and then estimate a regression of the outcome of interest on this overall ranking. However, this amounts to imposing the restriction that the partial effects of each of the 41 individual variables are the same and equal to 1/41 of the impact of the aggregate indicator. And if this really were true, then it would not matter at all which dimension of the regulatory environment is improved, since each individual indicator is assumed to have the same effect on outcomes. This seems quite implausible, and moreover contrary to the entire spirit of developing such disaggregated and ‘actionable’ indicators. A slightly more sophisticated and commonly‐used alternative would be to extract the first principal component of the 41 indicators and use it as an explanatory variable. But this would be no improvement. The first principal component is simply a weighted average of all of the individual indicators, with weights proportional to their intercorrelations. Thus, the few variables that happen to be highly correlated with each other would receive more weight in the aggregate. But there 4