rafmacalaba/datause-encoder-data huggingface dataset:
situation as of end-1999, which is towards the end of our data period. As in other studies, we rely on the relative stability of the regulation and supervision frameworks. Data on inter industry competition and the country's general development refer to the beginning of the estimation period.

For the structure of the banking system, we use, from the date base established by Barth, Caprio, and Levine (2001), three variables: the 5-bank concentration ratio, a measure of banking system concentration; the Logarithm of the number of banks per million inhabitants in a particular country, as proxy for the density of banks; and the Foreign bank ownership variable, that is, the share in assets/numbers of banks which are foreign-controlled.<sup>9</sup> All of these measures have been used by others when investigating the impact of banking structure on performance, stability, and efficiency.

For the contestability of the respective markets, we use, again from the Barth, Caprio, and Levine (2001) data base, the Activity restrictions variable, indicating the limits imposed on commercial banks to engage in securities markets, insurance and real estate activities with higher scores indicating more restrictions; and the Entry fit test variable, an indicator of the severity of the entry regime with higher scores indicating less severe restrictions. The Activity variable refers to the legal rules in place while the Entry fit variable refers to the actual practices of the supervisory agencies in the country.'I We use two indicators to describe the competition coming from inter industry. To investigate the impact of the degree of competition banks face from capital markets, we use the size of the country's stock market capitalization to GDP. As a proxy for competition from non-

> 9 We also used from the Barth, Caprio, and Levine (2001) data base the degree of state-owned banks, but did not find this variable to have significant effects and therefore did not add it.

> '° We also used from the Barth, Caprio, and Levine (2001) data base the degree of entry applications denied, but again did not find this variable to have significant effects and therefore did not include it.

16
Graph 1 shows the dropout rate per grade. The dropout rates in concession schools are lower and exhibit smooth behavior across grades. In contrast, public schools mimic the behavior of the dropout rates found in others datasets using national statistics (for example, Barrera and Dominguez 2005), in which there is a significant increase in the dropout rate in the transition between grades five and six (last year of primary and first year of secondary).

**Graph 1. Drop-out rates, concessions versus non-concessions**

<!-- Start of picture text --> 0,08<br>0,07<br>0,06<br>0,05<br>0,04<br>0,03<br>0,02<br>0,01<br>Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 Grade 8 Grade 9 Grade 10 Grade 11<br>Non-concession Concession<br><!-- End of picture text --> Interestingly, dropout rates for concession schools increase significantly in grade 8. This may indicate a change in the composition of students in secondary education in the concession schools. Since attending a concession school reduces the dropout rates for grade 6, more students, including those who would have dropped out in the regular public system, are reaching higher grades. The population of students in secondary education in the concession schools includes those in the lower part of the income distribution, who have a higher probability of dropping out of school.

17
capacity of 7,763 MW were registered under the CDM. An additional 441 projects with a combined capacity of 16,883 MW are in the process of registration (see Table 5). While these projects are distributed across the globe, about 90% of the total projects with about 85% of the total capacity are concentrated in China and India. China alone accounts for almost 60% of total installed capacity. Mexico, South Korea and Brazil account for the bulk of the remaining projects.

**Table 5.** CDM Wind Projects

||Total<br>|projects<br>|||
|---|---|---|---|---|
|Country|(alreadyregister|ed andinprocess)|Registere|d projects|
||Projects (No.)|Capacity (MW)|Projects (No.)|Capacity (MW)|
|China|303|16,452|91|4,583|
|India|258|4,741|62|1,484|
|Mexico|12|1,272|6|958|
|Brazil|11|687|4|166|
|South Korea|10|314|4|156|
|Cyprus|4|207|2|44|
|Dominican Republic|3|173|1|65|
|Egypt|3|285|1|120|
|Philippines|2|73|1|33|
|Morocco|2|70|2|70|
|Costa Rica|2|69|1|20|
|Nicaragua|2|60|0|0|
|Panama|1|81|0|0|
|Mongolia|1|50|0|0|
|Jamaica|1|21|1|21|
|Colombia|1|20|1|20|
|Israel|1|12|1|12|
|Argentina|1|11|1|11|
|Chile|1|19|0|0|
|Vietnam|1|30|0|0|
|Ecuador|1|2|1|2|
|Total|621|24,646|180|7,763|

_Source_ : URC (2009a).

Wind power projects account for approximately 14% of the total CDM projects already registered or in the pipeline. In terms of GHG mitigation, these projects share 9% of annual potential (see Table 6). In addition to CDM projects, 18 wind energy projects were being implemented in economies in transition by early 2009 under Kyoto’s joint implementation mechanism (URC, 2009b).

25
Table 6: Role of Enforcement Threshold _b_

||0_∗bbench_|0_._5_∗bbench_|Benchmark<br>_bbench_ = 1_._91|1_._5_∗bbench_|2_∗bbench_|
|---|---|---|---|---|---|
|Mean size|6.97|4.62|4.77|4.90|5.03|
|Informal mean size||1.35|2.03|2.50|2.89|
|Frac. Of informal plants (%)|0|72.94|77.32|78.65|79.36|
|<br>Aggregate output|100.84|99.49|100|100.60|100.77|
|<br>Employment share of 50+ (%)|48.51|45.86|42.08|38.98|36.08|
|<br>Growth rate, 0-4 to 35-39 (%)|92.15|68.97|93.12|113.15|127.57|
|<br>Entrepreneurial Quality|130.20|97.45|100|102.05|103.72|
|<br>Tax revenue|138.97|113.24|100|91.40|84.46|

Note: This table presents the effects of different enforcement levels, _b_ ’s, on the benchmark economy when holding other parameters constant. The aggregate output, the entrepreneurial quality and the tax revenue at the benchmark case are normalized to 100 for ease of exposition.

of informal plants increases, and as a result, the mean size, entrepreneurial quality, and overall growth rate decline.

Table 7: Role of Probability of Meeting Tax Official _φ_<sup> ̄</sup>

||0_∗_<sup> ̄</sup>_φbench_|0_._5_∗_<sup> ̄</sup>_φbench_|Benchmark<br> ̄_φbench_ = 0_._30|1_._5_∗_<sup> ̄</sup>_φbench_|2_∗_<sup> ̄</sup>_φbench_|
|---|---|---|---|---|---|
|Mean size|4.18|4.45|4.77|5.16|5.54|
|Informal mean size|1.94|1.98|2.03|2.08|2.13|
|Frac. Of informal plants (%)|95.41|87.11|77.32|65.65|51.02|
|Aggregate output|99.64|99.87|100|100.42|100.66|
|<br>Employment share of 50+ (%)|39.86|40.98|42.08|43.52|44.81|
|<br>Growth rate, 0-4 to 35-39 (%)|89.68|91.59|93.12|94.12|94.27|
|Entrepreneurial Quality|91.61|95.54|100|105.69|110.89|
|<br>Tax revenue|84.83|92.35|100|108.38|116.81|

Note: This table presents the effects of different probabilities of being assigned to a tax official, _φ_ ’s, on the benchmark economy when holding other parameters constant. The aggregate output, the entrepreneurial quality and the tax revenue at the benchmark case are normalized to 100 for ease of exposition.

# **6. Revenue-Neutral Changes in Enforcement**

Table 8 shows the results of a policy experiment where we reduce the enforcement threshold _b_ while we adjust the tax rate relative to the benchmark to keep the tax revenue constant. When there is full enforcement, mean size increases 63.5% (from 4.77 to 7.8). Average entrepreneurial ability increases by 68.1%, and the average life cycle growth rate increases by 25.2%. As a result, aggregate output increases by 16.4%.

Figure 6 presents the effects of revenue-neutral full enforcement on average cumula24
cyclone under climate change an additional 3,461 km of roads is projected to be partially damaged and 2,205 km fully damaged from a 10 year return period cyclone. Repair costs of Taka 1 million and Taka 2 million respectively in case of partial and full damage from the post damage loss assessment exercise of Sidr in 2007 have been applied in the computation. The post assessment of Sidr (2007) has further suggested that damages to bridges, culverts etc. were 1.13 times the damages to roads. The combination of all these damages indicates **an additional damage of $239.5 million to roads, bridges, culverts etc. in a changing climate by 2050**<sup>40</sup> .

# **The estimated potential additional loss in a changing climate is $52.7 million** .<sup>41</sup>

# _<u>Potential Damage in power infrastructure</u>_ <u>:</u>

In 2007, Sidr induced a damage of $8.2 million in the power sector of coastal Bangladesh. The projection of population indicates 1.53 times growth of coastal inhabitants in the coastal area between 2007 and 2050. The consumption of power in Brazil, Lebanon, Malaysia, Uruguay and South Africa ‐countries with the present per capita income similar to the projected per capita income of Bangladesh indicates per capita consumption of power is likely to increase 20 times in Bangladesh (and power infrastructure 5 times<sup>42</sup> ) by 2050. Damages recorded during the Sidr (2007) coupled with the projection of the change in power infrastructure in the inundation areas (with and without climate change) indicates **an additional damage of $60.2 million.**<sup>**43**</sup>

# **The estimated potential additional loss is $150.0 million** .

# _<u>Potential damage in coastal protective infrastructure</u>_ <u>:</u>

In the sixties, 123 polders and supporting infrastructure were constructed to protect low lying coastal areas against tidal flood and salinity intrusion in Bangladesh. In 2007, Sidr

> 40 Estimate of potential damages to roads, bridges, culverts etc. is $173.6 million by 2050 even without climate change, and the corresponding estimate with climate change is $413.1 million.

> 41 Loss estimates have been computed using 22% ratio between loss and damage in post Sidr assessment.

> 42 Experts of the Asian Development Bank working on Bangladesh power sector suggested this adjustment factor.

> 43 Estimates of potential total damage without and with climate change are $239.1 million and $449.3 million respectively.
Miller 2015) and report p-values from wild cluster bootstrap consistently for coefficients of interest ( _β_ 6 and _γ_ 8) and take this into account consistently in interpreting results. From a substantive perspective, the validity of our identification strategy hinges on two assumptions. First, we need to ascertain there are no pre-existing time varying unobservables between treatment and control. Second, there is need to control for other observables, including interventions that may have been implemented differentially across treatment and control areas to avoid mistakenly attributing changes in outcome variables to property rights reform.

# **3. Descriptive statistics and the nature of the reforms**

While households inside and outside Chengdu differed from each other in terms of income, time use, and agricultural yields between, there are no statistically significant differences in pre-reform trends except agricultural production, which declined more rapidly within as compared to outside Chengdu. Reform was implemented swiftly, with strong participation, few disputes, and at reasonable cost. Coverage was almost complete, allowing issuance of long-term or permanent contracts for most of the land.

# **3.1 Descriptive statistics**

Table 1 reports descriptive statistics on household characteristics, welfare, agricultural production, and time use for the pre- and post-reform periods inside and outside Chengdu (col. 1-4), estimated pre-reform trends (cols. 5 and 6), and a test of the significance of differences in such trends between households inside and outside the Chengdu border. We note that households in the treatment and control are comparable with respect to basic characteristics: they comprise 3-4adults, had a head born in the late 1950s, education between junior high and high school, some 8% of female heads. At the same time, three sets of differences emerge. First, households in Chengdu had higher levels of assets, income, and consumption (Y16,063 and Y40,025, Y4,928 and Y8,549, and Y3,150 and Y6,078 in pre- and post-reform periods, respectively) than those outside (Y11,564 and Y29,752, Y3,770 and Y8,069, and Y2,668 and Y4,997). They also allocated labor differently across sectors, presumably due to proximity to urban income earning opportunities: with a time commitment of 43% and 50% in 2005/06, farming was the most important activity for households inside and outside Chengdu, followed by migration (18% and 23%, respectively, and local off-farm employment (14% and 10%). These shares changed significantly over time -to 30% and 40% for agriculture, 16% and 25% for migration, and 21% and 11% in local off-farm employment for treatment and control group, respectively. But the last column indicates pre-reform trends are not significantly different between the two, supporting our identification strategy.

With respect to agricultural production, households in Chengdu prefecture cultivated smaller areas (3.45 vs. 4.79 mu) in 2005/06 but spent more on inputs (456 vs. 368 Y/mu) and obtained higher monetary output per mu (1,880 vs. 1,254 Y/mu) and net revenues (1,406 vs. 957 Y/mu) than those outside. While pre-reform 9
The main variables used in the paper are the following. Real GDP per-capita corresponds to the PPP adjusted measure and was obtained from the Penn World Tables (version 6.1). Commodity price fluctuations are captured with the Deaton-Miller commodity-based terms-of-trade index (henceforth DM index; see Deaton and Miller (1996)). The updated data for this index was obtained from Dehn (2001). The broad terms-of-trade index corresponds to the ratio of export to import prices computed using the current and constant price values of exports and imports from the national accounts component of the Penn World Tables (version 6.1). Among these two measures of terms-of-trade, in the analysis we will focus on the DM index for two reasons. First, as correctly pointed out by Aghion et al. (2005) broad terms-of-trade indexes capture fluctuations in the exchange rate that are less arguably exogenous to the business cycles than fluctuations in commodity prices. Second, from an empirical perspective, by using the DM index we give a better chance to exogenous shocks to actually play a role because, as it will be shown later, this index has a larger explanatory power for output fluctuations than the broader terms-of-trade index. Real per capita aid flows include the flows of official development assistance (ODA) and official aid in constant 1995 US dollars, and was obtained from the WDI. Aid as a fraction of Gross National Income was also obtained from the WDI.

The variables capturing the incidence of natural disasters are non-standard and deserve further discussion. Data for natural disasters was obtained from the Emergency Disasters Database (EMDAT) maintained by the Center for Research on the Epidemiology of Disasters (CRED). This is a comprehensive database that includes data on the occurrence and effects of over 12,800 massdisasters in the world since 1900, compiled from a diversity of sources. As a general principle, to enter into the database an event has to meet any of the following conditions: there are 10 or more people reported killed; there are 100 or more people reported affected; a state of emergency is declared; or there is a call for international assistance. Similarly to Skidmore and Toya (2002), we classify disasters into three categories to increase the parsimony of the analysis. Geological disasters include earthquakes, land slides, volcano eruptions, and tidal waves. An important characteristic of this type of events is their unpredictability and relatively fast onset. The second category is Climatic disasters. This category includes floods, droughts, extreme temperatures, and wind storms (e.g. hurricanes). Compared to the previous category, some of these disasters can be forecasted well in advance (so precautions can be undertaken) and have a relatively long onset. The final category is 7
7 foreign investment is determined not only by the cost of labor, but also by a combination of factors including the investment climate of the country, favorable exchange rates, stable macroeconomic conditions, sound infrastructure, a sophisticated banking system and a healthy relationship between workers and management.

A thorough and comprehensive understanding of women workers in Indonesia is limited by the paucity and limitations of research in this area. This study undertaken by CSIS, in cooperation with the Department of Labor and the World Bank, attempts to rectify the situation by undertaking a study with a relatively large sample, and including a broad perspective of the situation. The survey was done between August 1995 and March 1996 and the purpose of the survey was to identify the problems faced by women workers and to indicate where attention should be focused to rectify identified problems. It is important to get an accurate picture in order to recommend policy changes, ensure effective implementation of policy and to introduce broader institutional changes.

This study was undertaken based on a survey of women workers who were asked to respond to questions revolving around the following general themes:

1. Are women workers receiving the legal minimum wage?

2. Are firms complying with Government of Indonesia labor legislation, standards and health and safety provisions for the protection of women workers ?

3. Are women workers aware of the legal Indonesian standards of working conditions, wages and benefits ?

4. Does the presence of the official labor union (SPSI) in a workplace make a difference in the implementation of the labor standards, working conditions, health and safety procedures, wages and benefits, to which women workers are entitled?

5. What are the links between women's labor market mobility in the Jakarta-West Java region, and poverty alleviation in rural and/or urban areas?

The remainder of the paper is divided into five sections. The next two sections analyze the issues faced by women workers, the growth of labor intensive industries and the labor legislation and institutional situation for women. In the fourth section a description of the survey used in this study is provided. The findings and recommendations are then discussed in the final section.
**<u>Table 12: Probit regression modelling health seeking behaviour (marginal effects reported)- cont.</u>**

||**TOT**|**Children (0-15)**|**Adults (16-64)**<br>**Kosovo**|**Elderly (65+)**|
|---|---|---|---|---|
|Quantiles 2 of pc consumption|-0005|-0016|-0004|<br>0046|
||.<br>(0.49)|.<br>(1.04)|.<br>(0.26)|.<br>(1.00)|
|Quantiles 3 of pc consumption|-0.001|0|-0.017|0.101|
||(0.11)|(0.02)|(1.14)|(2.16)**|
|Quantiles 4 of pc consumption|-0.011|-0.016|-0.022|0.07|
||(0.98)|(0.94)|(1.44)|(1.43)|
|Quantiles 5 of pc consumption|0.012<br>|-0.007<br>|0.007<br>|0.078<br>|
|Chronic illness|(1.00)|(0.36)|(0.43)|(1.41)|
|Acute illness|||||
|Age|0.005|0.003|0.014|-0.096|
||(8.94)***|(0.59)|(6.53)***|(2.76)***|
|Age squared|0|0|0|0.001|
||(4.14)***|(0.82)|(4.46)***|(2.62)***|
|Sex (female)|-0.012|0.01|-0.016|-0.037|
||(1.68)*<br>|(0.87)<br>|(1.71)*<br>|(1.17)<br>|
|N. of infants in the hh (0-5)|0.005|-0.01|0.003|0.011|
||(1.56)<br>|(2.07)**<br>|(0.69)<br>|(0.7)<br>|
|N. of children the hh (6-18)|-0.004<br>(2.28)**|-0.003<br>(0.8)|-0.004<br>(1.49)|-0.022<br>(2.23)**|
|N. of adults in the hh (15-64)|-0.006<br>(355)***|0.008<br>(279)***|-0.013<br>(525)***|0.008<br>(093)|
|N of elderly hh members (65+)|.<br>-0.007|.<br>0.008|.<br>-0.022|.<br>0.148|
||(1.18)|(0.96)|(2.79)***|(5.13)***|
|Education level (A):|||||
|Primary edu.level|0.02|-0.036|0.108|0.975|
||(0.54)|(0.85)|(1.76)*|(1.39)|
|Secondary edu.level|0.021<br>|-0.042<br>|0.118<br>*|0.981<br>|
||(0.57)|(1.02)|(1.80)|(1.43)|
|Vocational edu.level|0.085|-0.025|0.187|0.802|
||(2.03)**|(0.51)|(2.61)***|(1.5)|
|University and higher edu.level|0.027|-0.063|0.12|0.806|
||(0.66)|(1.37)|(1.72)*|(1.48)|
|Health insurance|||||
|Ethnicity (B):|||||
|Roma|-0.01|-0.017|0.009|-0.069|
||(0.35)|(0.44)|(0.22)|(0.4)|
|Greek|||||
|Macedonian|||||
|Vllahe|||||
|Croatian|0.259|0.55|0.201|0.317|
||(3.31)***|(2.47)**|(2.19)**|(1.41)|
|Yugoslav|0.073||||
||(022)||||
|Serb|.<br>-0.006|-0.011|-0.005|-0.033|
||(0.47)|(0.4)|(0.3)|(0.73)|
|Moslem|-0.024|-0.053|-0.028|0.022|
||(098)|(111)|(085)|(029)|
|Turk|.<br>-0.06|.<br>-0.023|.<br>-0.09|.<br>-0.108|
||(1.65)*<br>|(0.44)|(1.79)*<br>|(0.74)|
|Other|0.267||0.265||
||(1.56)||(1.51)||
|No answer|||||
|Other urban|||||
|Rural area|0.001<br>(018)|0.01<br>(086)|0.001<br>(007)|-0.025<br>(079)|
|Observations|.<br>15697|.<br>5235|.<br>9425|.<br>1034|

Absolute value of z statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 25
Southeast Asia. Finally, we explore the extent to which Southeast Asian countries have been involved in the production sharing network to support China’s role as the hub for final assembly.

This study adds new findings to the already rich literature on trade in East Asia. We document both competing and complementary forces as economies in East Asia integrate. We also explore the extent to which changes in structure and value of exports in machinery from Southeast Asian countries can be credited to China. In many aspects our paper complements other studies on intraregional trade (such as Kawai, 2005), production fragmentation and trade (Ando and Kimura, 2003, 2007, and Kimura et al, 2005), and technology upgrading in exports from East Asia (Gaulier et al, 2005). In addition, this paper sheds light on China’s and Southeast Asia’s progress on the quality ladder of machinery products.

# **II. The Pattern of Trade in Machinery in China and Southeast Asia**

The data show clearly that China has taken off to become one of the world’s most important machinery exporters. Figure 1 indicates that in 1975 China was an underdog in the world market of machinery products, even compared to Malaysia and Singapore. In 1989, Chinese exports of machinery overtook Malaysia and less than 7 years later superseded Singapore. In 2003, total machinery exports from China were already higher than machinery exports from Indonesia, Malaysia, Philippines, Singapore, and Thailand combined.

Office data and telecommunications products have played a crucial role in China’s success in terms of machinery exports. China alone has market shares of almost 16 and 8 percent respectively in the global export of those commodities. However, other countries in Southeast Asia are also becoming important players in global trade in machinery. For example, exports of office/data processing machines, telecoms equipment and electrical machinery from Malaysia and Philippines are increasingly gaining importance.

> <sup>4</sup>