rafmacalaba/datause-encoder-data huggingface dataset:
specification with the employed dummy as the dependent variable. We prefer using the LPM over alternative methods, such as probit or logit estimation, for a number of reasons. First, FE specifications are inconsistent in non-linear discrete models. Additionally, the estimation through LPM lends itself to a more straightforward interpretation of the results. The main drawback of using LPM is that it does not guarantee that the probability of the event occurring is bounded between 0 and 1. However this problem does not apply to our case, as there are very few cases in which the estimated probabilities were above 1 or below 0. In any event the results reported below are qualitatively similar to those obtained using probit or logit models (results available from the authors upon request).

So far we have assumed that all the barriers have the same effect on the labor market. Although they all restrict movement, they do so in different ways and intensity. In particular, we find it convenient to split the existing barriers into two types according to the expected effect on the mobility that they may exert: checkpoints – both permanent and partial ( _CP_ ), and road blocks, earth mounds and gates ( _REG_ ), including road gates, barrier gates and agricultural gates. Checkpoints are manned barriers (partial checkpoints are only occasionally manned) and they have been used to restrict the movements over the main roads within and across the Palestinian territory. Because they obstruct important connections between localities, these barriers are expected to have a particularly relevant impact on the movement of goods and labor. The other types of barriers ( _RBO_ ) consist of mainly unmanned permanent obstructions placed on secondary roads and smaller paths and of gates placed on the wall to allow the transit to and from the “seam zone” as well as to and from Israel. Their restriction is expected to be less damaging than that of the checkpoints. Road-blocks and earth mounds can be overcome by alternative route, only slightly more costly than the route they obstruct.<sup>20</sup> In addition most of the gates considered here connect to the seam zone as well as to Israel and allow the relatively unhindered transit to people and vehicles with permit.

In order to differentiate between the effects of these two groups of barriers, we modify regression (2) in the following way:

> 20 There are a few exceptions to this general rule with road blocks obstructing important passages into towns and cities which may severely increase the congestion on the alternative routes.

18
deduce more credibly causal estimates between various drivers of education quality and growth, an elusive yet critical endeavor.

In addition to the size of the database, this is the most current database. Given a series of recent global initiatives which focus on education quality – such as _Education for All_ (UNESCO, 2010) and the recent _World Development Report 2018 (_ World Bank, 2017) _–_ there is significant demand for the most current, credible and globally comparable measures of education quality. This database provides the largest, most comparable and current learning data.

We include numerous methodological improvements in this paper over prior papers linking ISATs with RSATs by Altinok et al (2014), Angrist, Patrinos and Schlotter (2013) and Altinok and Murseli (2007). First, while previous research relied on a single methodology to anchor assessments, we provide several methods for anchoring and indexing, enhancing the reliability and robustness of our dataset. Previous research papers assume that the distribution of scores on ISATs and RSATs for each country are homogenous across subpopulations and different percentiles. By using alternative anchoring methodologies to link assessments, we provide results which account for varying distributions across sub-populations within a country and test.

Another important contribution of our database is the inclusion of the proportion of students within each country who reach three different international benchmarks (minimum, intermediate and advanced benchmarks) in mathematics, science and reading. This provides an estimate of the distribution of performance, which is essential for our understanding of population-level human capital formation. This is especially critical in societies where inequality is high, since the mean score will be a particularly biased estimate relative to the population at large.

We also include measures of variance and provide confidence intervals of our estimates. This enables explicit quantification of the degree of reliability of each estimate. To do this, we restrict the linking of assessments to those which have micro data available. While this might reduce the coverage of countries we can include, it is a worthwhile trade-off, since it substantially enhances our ability to estimate performance with reliable degrees of confidence.

Finally, we disaggregate results by gender, socioeconomic status, rural/urban, language, and immigration status, thus enabling greater precision and equity analysis. This disaggregation ensures estimates for each sub-population are precise and relevant. Moreover, it enables equity analysis and more detailed understanding of a country’s human capital development.

5
Brazil and Colombia also experienced an increase in their private investment shares; an increase that accorded with the small size of their privatization programs. Bolivia’s experience is a rather disappointing one, for despite a strong privatization effort, its private investment share actually dropped from its 1985 level, and then remained about half as large as that of other countries with a similar privatization effort. The decline of Venezuela’s private investment share in the ten years after 1985 cannot be associated with its privatization program, which started only recently; rather, this significant drop was initially due to macroeconomic instability, and subsequently, to an adjustment program. It is interesting to note that some of the countries that did not initiate a privatization effort already had private investment shares in the 1980s of around 80 percent. It is likely that for these countries, namely, Guatemala, Panama, and Paraguay, privatization was not needed as much as in other Latin American countries. Finally, a comparison with the Asian NICs’ reveals that, for the Latin America and Caribbean region on average, private-sector participation in investment is still below that of the NICs. The difference in this respect between the two groups of countries (five percentage points on average in the 1990s) is larger than it seems given that the productivity ofpublic investment is much greater in the Asian NICs than in Latin American countries.

# 2. Indicators on the efficiency of revenue generation

Clearly, the ability of the state to undertake an expenditure program without incurring an unmanageable deficit depends on efficient revenue generation. Latin American countries have relied excessively on trade taxes and seigniorage through money creation to finance their spending programs. High tax rates coupled with a weak tax administration induced many firms to become part of the informal sector. This undermined the ability of the state to generate revenues from income and domestic sales taxes, prompting the state to obtain revenues through both the inflation tax and taxes on imports and exports, which can be more easily monitored. In the late 1980s, many countries reformed taxes to diminish the distortionary effects of taxation and to increase revenue, thus allowing trade liberalization and disinflationary policies and reducing the tax burden on the formal sector.

> 37 Comparison with OECD countries is problematic, given that the available investment figures for this group of countries ~ do not disaggregate corporate investment into its public and private components. Therefore, the “private” investment figures reported for OECD countries in the text and in the appendix include public enterprise investment.

22
# WPS3545

Sustaining Urban Growth through Innovative Capacity: Beijing and Shanghai in Comparison<sup>*</sup>

## **By**

**WANG Jici TONG Xin**

### **Department of Geography**

**Peking University**

#### World Bank Policy Research Working Paper 3545, March 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.
Lederman and Maloney (2007). Al-Marhubi (2000) finds that export diversification encourages economic growth using cross-sectional regressions of growth of countries, using various alternative measures of export diversification. On the other hand, Hesse (2006) estimates an “enhanced” Solow growth model, the results of which show that the concentration of exports is negative for economic growth, after taking into account the effect of investment, population and human capital.

# **3. Anatomy of Peru’s export boom since 2000**

## **Export growth in cross-country comparison**

Growth rates for aggregate exports for a sample of Latin American countries are presented in Table 1. The data clearly show a notable boost in the growth of Peru‟s exports in the last decade, in both nominal and real per-capita terms (see Figure 1). This behavior has been a pattern shared by many economies in the region, although the average growth rate of Peru between 2001 and 2008 stands out as one of the region‟s highest (8.3%, only lower than that of Bolivia).

# **Table 1: Exports of goods and services, Latin American countries 1961-2008** (real growth rate)

||**1961-1970 **|**1971-1980 **|**1981-1990 **|**1991-2000 **|**2001-2008**|
|---|---|---|---|---|---|
|**Argentina**|6.7|5.6|5.5|6.9|6.4|
|**Bolivia**|n.d.|3.0|2.8|4.9|8.5|
|**Brazil**|6.6|10.2|7.7|7.1|8.0|
|**Chile**|3.6|11.3|6.3|9.3|6.1|
|**Colombia**|3.2|6.1|6.5|6.0|5.4|
|**Costa Rica**|10.7|6.1|7.1|10.8|5.7|
|**Ecuador**|1.9|14.3|6.1|6.3|5.9|
|**Mexico**|5.9|11.9|8.0|13.6|4.6|
|**Peru**|3.3|3.3|0.7|8.1|8.3|
|**Uruguay**|4.4|7.6|4.7|5.6|6.6|
|**Venezuela**|n.d.|-6.8|2.9|1.5|-1.7|

_Source: WDI_ 5
|**Table 3 Full specification of the mode**<br>|**l**||||
|---|---|---|---|---|
|Dependent variable: Log GDP per capita|||||
|<br>Item|(1)|(2)|(3)|(4)|
|Intergenerational persistence|||||
|<br>× High Income Countries|4.5824<sup>***</sup>||||
||(1.3357)||||
|× Eastern Europe and Central Asia|1.0773<br>||||
||(1.2991)||||
|× Latin America and the Caribbean|1.2622||||
||(1.8260)||||
|× Other Developing Countries|2.3290||||
||(1.4040)||||
|Intergenerational correlation|||||
|<br>× High Income Countries||3.9513<sup>*</sup>|||
|||(2.3518)|||
|× Eastern Europe and Central Asia||0.6523|||
|||(0.7874)|||
|× Latin America and the Caribbean||5.2302<sup>**</sup><br>|||
|||(2.2642)|||
|× Other Developing Countries||4.8377<sup>**</sup>|||
|||(2.1168)|||
|Uward mobilit a|||||
|p y gp<br>× High Income Countries|||0.0112||
||||(0.0272)||
|× Eastern Europe and Central Asia|||-0.1942<sup>*</sup>||
||||(0.1110)||
|× Latin America and the Caribbean|||0.2970<sup>**</sup><br>||
||||(0.1126)||
|× Other Developing Countries|||0.3477<sup>**</sup>||
||||(0.1484)||
|Upward mobility in higher education|||||
|× High Income Countries||||-0.2144|
|||||(0.5208)|
|× Eastern Europe and Central Asia||||5.6392<sup>***</sup>|
|||||(1.0303)|
|× Latin America and the Caribbean||||5.8244<sup>***</sup>|
|||||(1.4582)|
|× Other Developing Countries||||9.5098<sup>***</sup><br>|
|||||(2.4581)|
|_Year-level controls_|||||
|Log population|-1.4247|-0.6160|1.2397|1.9588|
||(1.7602)|(1.6649)|(1.7795)|(1.5036)|
|Log population (squared)|0.0448|0.0141|-0.0408|-0.0690|
||(0.0541)|(0.0501)|(0.0557)|(0.0460)|
|_Cohort-level controls_|||||
|Average years of education (cohort|-0.0925|-0.1697<sup>***</sup>|-0.1258<sup>**</sup>|-0.1826<sup>***</sup>|
|<br>weighted)|(0.0580)|(0.0554)|(0.0591)|(0.0445)|
|Standard deviation of years of|-0.1785|-0.1777|0.1431|-0.0076|
|education (cohort weighted)|(0.2252)|(0.1815)|(0.1027)|(0.0863)|
|_Cohort-specific initial conditions_|||||
|<br>Log GDP per capita at decade of birth|0.0112|0.0254|0.0014|0.0010|
|<br>(cohort weighted)|(0.0591)|(0.0567)|(0.0542)|(0.0446)|

27
**Figure 1. Spot Prices for EUAs and CERs**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>EUA Phase II<br>CER<br>15<br>10<br>5<br>0<br>1-Jan-08 1-Jan-09 1-Jan-10 1-Jan-11 1-Jan-12<br>EUR / ton CO2<br><!-- End of picture text --> Source: BlueNext (2012) From a peak near Euro 30/t CO2, carbon traded roughly in the Euro 10 – 15 range before falling steadily toward Euro 5 at the beginning of 2012. Aside from the obvious volatility, the key trend visible in carbon market prices is the close correlation to the business cycle, with prices plunging during the global financial crisis in 2008, and dropping steadily in 2011 with the deteriorating fiscal situation in Europe. CERs trade at a discount to EUAs owing to regulated restrictions on swapping the former into the latter; this discount is widening owing to uncertainty about the continuity of offset markets beyond the end of the Kyoto Protocol commitment period (end2012) and regulatory uncertainty around the future extent of swapping of CERs for EUAs.

Turning to the social cost of carbon, Tol (2009) summarizes recent figures based on 232 published estimates. Aside from basic issues concerning future population and economic 5
- ?2 -

- c) whten goverunents must relv on poor people to expand the public resources that are put into projects.10

Participation has proven to be critical to the following productive sectors and sub-sectors:

- a) agriculture, which includes the sub-sectors of forestry, livestock, irrigation, and agricultural extension;

- b) rural water supply;

- c) urban development and in some instances, urban water supply;

- d) environment and resettlement (World Bank 1989); and

- e) credit, particularly group lending schemes, and microenterprise (Grindle et al., 1987).

It may also be important for road maintenance, (Cook cited in World Bank 1989) and labor union activity. In contrast, participation is not essential in sectors that require large and lumpy capital investment such as electrical power or transportation.

Examples from a few of the more participatory sectors demonstrate the impact representation can have on projects. First, in the area of livestock development, about 50 percent of World Bank first generation projects undertaken in the 1970s, failed primarily because they ignored local social and ecological conditions (World Bank's review of African Livestock 10 Community resources can expand programs considerably when they use traditional methods of resource mobilization (commitment of labor and funds), and train locals as paraprofessionals whenever possible to carry out the tasks of outsiders. See Milton Esman, Paraprofessionals in Rural Development: Issues in field-level staffing for agricultural projects, (Washington D.C: World Bank Staff Working Paper #573,cl983).
Figure 4: Extensive margin of foreign competition (N countries) faced by French firms <!-- Start of picture text --> 1999 2000 2001 1999 2000 2001<br>2002 2003 2004 2002 2003 2004<br>.5<br>.4<br>.4<br>.3 .3<br>.2 .2<br>.1 .1<br>0 0<br>.5<br>.4<br>.4<br>.3 .3<br>Herfindhal index China (N° countries) .2 .2<br>.1 Herfindhal index Other Asian (N° countries) .1<br>0 0<br><!-- End of picture text --> Note: authors calculations based on BACI dataset and French customs dataset for 1999-2005.

China. To evaluate the net effect of Chinese unilateral liberalization on French firms’ export patterns, we should take into account explicitly the role of foreign competition in the liberalized market.

# _III. EMPIRICAL STRATEGY_

# _Data description_

We use individual export data on manufacturing goods for France, collected by French Customs.<sup>12</sup> These data contain the value of exports by product, firm and destination over the time period 1999-2005. This database classifies export flows at the firm level within 8 digit product categories. Our analysis is restrict to manufactured products.

We use MFN applied tariffs at the HS6 product level collected from TRAINS.<sup>13</sup> From this database, we consider the tariffs applied over the period 1999 to 2005 by our set of Asian countries, i.e. China, India, Indonesia, Korea, Japan, Pakistan, Philippines, Singapore and Thailand. We exclude Hong Kong and Taiwan from our analysis since they are financial and trade centers, where the wholesale activity is very important. The next section explains how we construct the firm level tariff. We also control for 10
We can recover an estimate of _<u>e</u>_ <u>1</u><sup>ourmaintreatmenteffectestimateof11.4%</sup> _e_ 2<sup>from</sup> (Table 3, Col. 1). Since earnings are a linear function of production due to the piece rate, this is the treatment effect on both earnings and output, which we use as our labor supply measure. To obtain an estimate for _ζ_ , we exploit the fact that: _ζ ≡ ζa_ + _ζe_ , where _ζa_ is the extensive margin elasticity of attendance with respect to the net-of-tax wage, and _ζe_ is the intensive margin elasticity of effort (conditional on attendance).

To estimate the labor supply elasticity, we partner with the factory to randomize workers’ piece rate wages. At the end of the experiment, over the course of 6 days, we randomized the piece rate over 4 possible values: 15% below the usual piece rate, the usual piece rate, 15% above the usual rate, and 30% above the usual rate. We randomize daily at the worker level, implemented by each worker drawing a slip out of a bag that determines her piece rate for that day in an i.i.d. fashion.<sup>36</sup> Due to feasibility constraints in how this exercise could be implemented, workers learn their piece rate after arriving at the factory. Consequently, this variation allows us to estimate the intensive margin elasticity, _ζe_ . This piece rate variation gives an estimate of _ζe_ of 0.17 (Table 6, Col. 1).

**Table 6:** Labor Supply Elasticity

|||**log(Prod**|**uctivity)**||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|log(Piece-rate)|0.166|0.175|0.168|0.159|
||(0.0703)<br>[0.019]|(0.0704)<br>[0.013]|(0.0708)<br>[0.018]|(0.0707)<br>[0.025]|
|Day FE|Yes|No|No|No|
|Linear time trend|No|No|Yes|Yes|
|Quadratic time trend|No|No|No|Yes|
|N: worker-days|1528|1528|1528|1528|
|N: workers|300|300|300|300|

_Notes:_ Unit of observation is worker-day. Workers learned their piece rate for the day after arriving to work. Dependent variable is log output. Col. (1) controls for day fixed effects, Col. (2) has no time controls, Cols. (3)-(4) control for a linear and quadratic time-trend, respectively. Standard errors clustered by worker.

To estimate the attendance elasticity, _ζa_ , we exploit the fact that our intervention— which, by lowering the tax rate, mimics an increase in the effective piece rate per unit of

> 36Note that in expectation, workers’ average piece rate will be higher than the status quo over the 6-day period. To protect workers against earnings losses, we inform them that in the rare possibility that their average piece rate over the 6-day period is lower than the status quo rate, we will apply the status quo rate to their total production over those days. See Appendix A.4 for details. In addition, note that if there is inter-temporal substitution in labor effort across days—where working harder one day leads workers to work less hard the next day due to fatigue—then simply examining the change in effort with the daily change in piece rates would lead us to over-estimate the elasticity. However, we find no evidence for such effects: if anything, we find that working harder one day leads to increased effort the next day.

37
first imposed on exports to the Spain and Italy at the end of 1996, and then to all Member States in mid-1997.

Through the 1980s and 1990s the Lake Victoria fishery became almost entirely export-oriented. Furthermore, the scope of the supply chain progressively extended beyond regional markets to industrialised countries across the globe. In turn, this brought about a significant expansion in total fish and fishery product exports from US$1.4 million in 1985 to a peak of US$51.8 million in 1996. However, despite impressive rates of growth, the fisheries sector continues to account for a relatively small proportion of total exports, averaging around 2.5 percent through the 1990s, particularly in comparison with major exports such as horticultural products<sup>29</sup> , tea and coffee.

Until the later 1980s, exports of Nile perch were negligible. The expansion of the Lake Victoria fishery was largely directed at supplying local markets and trade within regional markets, much of which was ‘undocumented’. International trade in Nile perch became established in the mid1980s and expanded rapidly through to the mid-1990s. Thus, the volume of exports increased from 102 tonnes in 1985 to a peak of 19,300 tonnes in 1994 (Figure 14). Simultaneously, the price of Nile perch increased on international markets, thus the value of exports increased from US$0.1 million in 1985 to US$43.9 million in 1996<sup>30</sup> . By the mid-1990s, Nile perch accounted for up to 90 percent of total fish and fishery product exports from Kenya (Bokea and Ikaiara, 2000).

# **Figure 14. Volume and value of Nile perch exports from Kenya, 1985-1996:**

<!-- Start of picture text --> 25000 50<br>45<br>20000 40<br>35<br>15000 30<br>25<br>10000 20<br>15<br>5000 10<br>5<br>0 0<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996<br>Tonnes<br>US$ million<br><!-- End of picture text --> _Source: CBS_ Through to the mid-1980s, Kenya was unable to satisfy fully the domestic demand for fish and was a net importer (Bokea and Ikaiara, 2000). However, the expansion of fisheries production

> 29For a discussion of horticultural exports from Kenya and the role of food safety and other standards see jaffee (2003). 30Bokea and Ikaiara (2000) suggest that there is significant under-reporting of fish sales and exports to evade tax. Estimates of the degree of under-reporting range from 20 to 29 percent.

35