rafmacalaba/datause-encoder-data huggingface dataset:
useful comparisons across all three countries. In addition, their specific locations are useful for assessing the potential impacts of cross-border integration<sup>4</sup> . For example, borders aside, producers in the Jhapa district in Nepal may find it easier to reach markets in Siliguri or even Kolkata in India than they would Kathmandu.

The analysis is composed of two parts. First we carry out desk-based research to profile the case study districts in terms of demography, education and employment outcomes, poverty, economic structures, and infrastructure. Much of this analysis relied on detailed micro-data, including household and labor force surveys, regional economic accounts and industrial census. The main sources for this analysis are summarized in Table 2. Additional data came from national government reports, the state government in West Bengal, the Tea Board of India, FAO and others.

**Table 2: Summary of main secondary data sources: India and Nepal**

|**Country**|**Data sources**|**Purpose**|
|---|---|---|
|_India_|Census|Population|
||IndiaStat|Output, value added, wages, employment|
||NSS|Employment, wages, sectoral employment|
||Meso Dataset|Population, road and rail networks|
||ASI (panel data)|Real wages, output, employment, value-added by sector|
||Labour Bureau of India|Prices|
|_Nepal_|Census|Population|
||Living Standards Survey|Education, production, consumption, poverty|
||Census of Manufacturing<br>Establishments; National<br>Planning Commission<br>estimates for 2014 Human<br>Development Report|Output, employment, and value-added by sector|
|_Bangladesh_|Census|Population|
||LivingStandards Survey|Education, poverty, production,consumption, prices|

We use these data also to assess the extent to which deeper integration could lead to economic gains for the various districts. We use two sets of measures to that end. The first set consists of indices of complementarity in employment as well as in production-consumption between bordering districts. These measures provide some indication of the potential trade that may occur should barriers to trade be lowered. The second type of measures is the price differential in some of the main consumption items across the borders. These differentials are rough proxies for the ‘thickness’ of the borders.

The second part of the analysis focuses specifically on the value chain for tea within and across these regions. This focus is due to several reasons:

- The primary economic activity in all the regions is agriculture – within the agricultural sector, tea is common across all these regions and is one of the most important (but not always the main) cash crops in all of them.

- It is among the most widely traded crops in the region, locally, nationally, and internationally and in fact it is the only major agricultural product that is essentially only traded as its local consumption is negligible relative to production.

> 4 There are also practical reasons for this approach – the logistics for undertaking the field research is made easier by concentrating on nearby locations across the three countries.

5
catered to. The competitive funding system promotes innovation, efficiency and excellence. The competition that is created in the Dutch market leads to efficiency gains as schools are able to cater to different markets and parents exercise their choice to find the provider for their children that best fits their needs. The system is also highly equitable. Although there are competition effects whereby private school students achieve slightly higher than public school students, nevertheless the system overall boasts high average attainment levels. That is, achievement is high for all groups, with the average score of Dutch students significantly above the OECD average in all three subjects.

The question that remains is why we obtain such a large impact of private schools when the raw differential is so small? In other words, the test score outcomes of public and private schools are almost equal. Moreover, choice and competition has existed for almost 100 years. Yet when we control for selection we obtain a positive impact of attending private schools. The answer may lie in the fact that the competition that is created in the market leads to efficiency gains as schools are able to cater to different markets and parents exercise their choice to find the provider for their children that best fits their needs. In fact, the students of private schools come from slightly less well-off families. This is evident in Table 2 which shows that the mothers of private school students are slightly less well educated than the mothers of students in public schools (see also Figure 4). Therefore, one possibility is that the true private school effect operates via the value it adds for students from relatively less well-off backgrounds.

The institutional features associated with school choice are important as well. School choice leads to competition which can manifest itself in schools firing ineffective teachers for instance; or through schools using achievement data to evaluate teachers. School autonomy can also lead to schools taking a more direct role in establishing teachers’ salary increases among 21
cost of a passport is found to range from $0 to $333<sup>3</sup> , and from 0 percent to 125 percent of annual per capita national income. Passport costs as a share of national income are higher in poorer countries, and in countries with worse measures of governance, in particular, in countries with poor bureaucracy. High passport costs are found to be associated with lower levels of migration, suggesting scope for increasing migration through domestic policy reform.

Evidence on the time costs of obtaining a passport shows differences across countries as to whether or not a passport can be obtained by post rather than in person, and in the time taken for processing a passport. High fees for expedited service are indications of the demand for more rapid processing in many countries. In addition to passport costs, this study documents legal restrictions on exit, such as requirements that citizens obtain exit visas or that women receive the permission of their husband or father to travel. Countries with restrictions on travel are shown to have lower rates of migration than countries of similar population and income levels without such restrictions.

Overall the evidence does suggest that there are real barriers placed in the way of emigration in a number of countries. Countries that wish to benefit from remittances, diaspora, technology transfer, return migration from trained nations, and other developmental impacts of migration can start by tearing down the paper walls they put around their own citizens.

> 3 Unless indicated otherwise, all prices given in this paper are in terms of October 2005 United States Dollars.

- 3 -
27 Table 1. Elasticity of Poverty, Estimated Using International Migration Data

||Dependent<br>Poverty H<br>($1.08/pe|Variable =<br>eadcount<br>rson/day)|Dependent<br>Pover|Variable =<br>ty Gap|Dependent<br>Squared P|Variable =<br>overty Gap|
|---|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|(6)|
|Per capita GDP (constant 1995<br>dollars)|-1.178<br>(-8.84)**||-1.349<br>(-8.82)**||-1.417<br>(-7.51)**||
|Per capita survey mean income||-2.336||-2.623||-2.660|
|(expenditure)||(-16.85)**||(-15.24)**||(-11.49)**|
|Gini coefficient|3.396<br>(6.88)**|4.025<br>(12.08)**|4.170<br>(7.89)**|4.798<br>(11.60)**|4.600<br>(6.88)**|5.002<br>(9.29)**|
|Migrants as share of country|-0.156|-0.188|-0.120|-0.153|-0.029|-0.048|
|<br>Population<sup>1</sup>|(-2.49)**|(-4.48)**|(-1.68)*|(-2.93)**|(-0.27)|(-0.69)|
|Constant|13.549<br>(10.94)**|16.273<br>(19.75)**|14.089<br>(9.96)**|17.397<br>(16.55)**|14.021<br>(8.03)**|16.827<br>(12.03)**|
|N|109|106|109|106|106|100|
|Adj R<sup>2</sup>|0.494|0.767|0.481|0.722|0.399|0.598|
|F-Statistic|36.11|116.09|34.41|92.0|22.91|50.12|

Notes: Estimates obtained using ordinary least squares. All variables expressed in logs. T-ratios shown in parenthesis. Number of observations reduced for certain equations because of missing values. See Annex Table A1 for countries and survey dates.

1 Migrants measured as number of immigrants from country recorded as living in the United States or OECD (Europe).

* Significant at the 0.10 level.

** Significant at the 0.05 level.
# **4. Robustness Check and Placebo Tests**

Two straightforward robustness checks of RD estimates consists of (1) running the RD regressions using December 1996 as threshold and (2) running the RD regressions with November 1996 as threshold, but excluding firms created in October 1996. The latter strategy is referred to as the ‘donut-RD approach’. First stage estimates are provided with several bandwidth sizes. Throughout this paper, I use spline linear specification because the results are similar with quadratic polynomials and quadratic splines.

**Table 4 – First Stage Regression – Effect of SIMPLES on Formalization Rates**

||3-month|4-month|5-month|6-month|7-month|8-month|
|---|---|---|---|---|---|---|
||||_Cutoff = No_|_vember 1996_|||
|D|0.13***|0.099**|**0.099****|**0.11*****|**0.10*****|**0.12*****|
||(2.63)|(2.3)|(2.57)|(3.03)|(3.27)|(3.92)|
|N|1399|1664|2012|2315|2860|3236|
||||_Cutoff = De_|_cember 1996_|||
|D|**-0.17****|-0.042|0.026|0.042|0.051|0.058*|
||(-2.06)|(-0.84)|(0.61)|(1.11)|(1.5)|(1.92)|
|N|1404|1831|2178|2562|2971|3453|
|||_Cutoff = Nove_|_mber 1996(exclu_|_desfirms create_|_d in Oct 1996)_||
|D|0.059|0.13|0.11|0.079|0.076|0.095*|
||(0.33)|(1.19)|(1.31)|(1.16)|(1.45)|(1.88)|
|N|744|1009|1357|1660|2205|2581|
|||_Cutoff = Nove_|_mber 1996(dumm_|_y forfirms creat_|_ed in Oct 1996)_||
|D|0.055|0.0069|0.035|0.065*|0.072**|0.096***|
||(0.90)|(0.13)|(0.80)|(1.67)|(2.07)|(2.98)|
|N|1312|1577|1925|2228|2773|3149|

Note: ***, **, * statistically significant at 1, 5, and 10 percent. These are spline linear regressions in which y is regressed on a constant, a dummy that is 1 for firms created after the threshold and 0 otherwise (D), the assignment variable defined in months (Z) and an interaction term between the ‘after’ dummy and the assignment variable (DZ) to allow for different trends in each side of the threshold. Robust t-statistics in parentheses. The average of eligible firms aged 12 to 20 months (control firms) that are formal is 22.6 per cent. Excluding firms 12 months old, the average is 23.5 per cent.

Table 4 has some interesting results. The first block of results uses November 1996 as cutoff. The coefficients are large and statistically significant at the 1 percent level in most cases. The effect size and the statistical significance are very similar, regardless of the window size.

The second block of results shows the first stage with the threshold defined in December 1996 instead. The coefficient is negative, large and statistically significant for a three-month bandwidth. It turns positive and statistically significant only for a relatively larger window. This result is further evidence of 14
**References:**

- Adams, Jennifer M. (1991 a). "Female Wage Labor in Zimbabwe", _World Development,_ Vol. 19, No. 2/3, 163-78.

- Adams, Jennifer M. (1991b). "The Rural Labor Market in Zimbabwe", _Development and Change,_ Vol. 22 No.2, 297-320.

- Addison, Tony (1996) "Zimbabwe: The impact of economic reform on poverty in income distribution", Draft prepared for the World Bank.

- Barro, Robert J. (1991). "Economic Growth in a Cross Section of Countries", _Quarterly Journal of Economics,_ Vol. 106, No. 2, 407-43.

- Becker, Gary S. (1971). _"The Economics of Discrimination ",_ Chicago: University of Chicago University Press.

- Biggs, Tyler; Manju Shar and Pradeep Srivastava (1 995a). "Technological Capabilities and Learning in African Enterprises", _African Technical Department Paper, 288,_ World Bank.

- Biggs, Tyler; Manju Shar and Pradeep Srivastava (1995b). "Training, Technology and Firm efficiency in Sub-Saharan _Africa", RPED discussion paper, African Technical Department Paper,_ World Bank.

- Booth, Alison L. and Monojit Chatterji (1997). "Training and Unions", _CEPR Discussion Paper_ No. 1573.

- Diwan and Walton (1997). "How International Exchange, Technology, and Institutions Affect Workers", _World Bank Economic Review, Vol. 11_ No. 1 Jan.

- Fallon, P. R. and R. E. Lucas (1991). "The Impact of Changes in Job Security in India and Zimbabwe", _World Bank Economic Review,_ Vol. 5, No. 3, 395-413.

- Fallon, P. R. and R. E. Lucas (1993). "Job Security Regulations and the Dynamic Demand for Industrial Labor in India and Zimbabwe", _Journal of Development Economics,_ Vol 40, No. 2, 241-76.

- Grilliches, Zwi and Vidar Ringstad (1971). _"Economies of Scale in the Form of the Production Function_ ", Amsterdam: North-Holland Publishing Company.

- Groshen, Erica L. (1991). "The Structure of the Female/Male Wage Differential: Is it Who You Are, What You Do, or Where You Work?", _The Journal of Human Resources,_ 226, 3, 457-472.

37
the environment, even at the cost of slower economic growth or fewer jobs, compared with 66 percent of U.S. citizens, 77 percent of Canadians, and 75 percent of Germans (Pew Global Attitudes Project 2007).

Over 20,000 tons of solid waste is produced every day in Peru, much of which is dumped in waterways or left in informal dumps, making solid waste management an area of increasing concern in the country (Chauvin 2009). Various programs have been implemented across Peru to address the environmental issues posed by solid waste disposal. In 2002, PRISMA started a program called the “Improvement and Expansion of the Scope of Micro and Small Enterprise Solid Waste Management in the Districts of Piura, Castilla and Catacaos.” This program provides technical assistance and training to help informal recyclers develop and launch small formal recycling businesses that engage in door-to-door recycling collection. The project has facilitated the creation of three recycling microenterprises and continues to expand with the dual goals of creating formal jobs for the informal trash collectors and increasing recycling.

To enroll new families in the recycling program, PRISMA conducts five-weeklong marketing campaigns in areas where the three microenterprises do not currently work. In the first week, marketers visit households in the new area, present the program to them, and invite them to join. From the second to the fourth week, PRISMA’s marketers accompany the recycler on his route across the new area to introduce him to the participating families. On the last visit, households that have given recyclables at least one time are given a sticker that is fixed near their door indicating that they participate in the program. During this period, households are provided with disposable recycling bags free of charge.

6
including Chile, the Czech Republic and Malaysia. In addition, there are new locations that are beginning to compete in the industry, such as South Africa, Morocco and Egypt.

**Table 2. Top 20 Offshore Services Providers**

|**#**|**Company**|**Total**<br>**Sales, 2008**<br>**(US$ Mil)**|**Total**<br>**Employees**|**Total Services**<br>**Sales, 2008**<br>**(US$ Mil)**|**Main Services Activities**|
|---|---|---|---|---|---|
|1|**IBM –**US|103,630|398,455|58,892|Consulting, IT services, application and<br>outsourcing services|
|2|**Accenture**– US|23,171|177,000|23,171|Consulting,IT and outsourcingservices|
|3|**Electronic Data Systems**<br>**Corporation**(EDS, now<br>HP Enterprise Services) -<br>**US**|22,100|139,500|22,100|IT, applications and BPO services|
|4|**Computer Sciences**<br>**Corporation (CSC) -**US|16,740|92,000|16,740|ITO (software management) BPO in CRM,<br>supply chain management and KPO in legal<br>matters|
|5|**Capgemini**- France|12,740|89,453|12,740|Consulting, IT and outsourcingservices|
|6|**Automatic Data**<br>**Processing (ADP) -**US|8,867|45,000|8,867|BPO (human resource, payroll, tax and benefits<br>outsourcing)|
|7|**Affiliated Computer**<br>**Services **–US|6,523|76,000|6,523|ITO and BPO in CRM and HRM. Also e-<br>Government|
|8|**Logica (Formerly**<br>**LogicaCMG) **–UK|6,577|39,525|6,320|Business consulting, IT and BPO services|
|9|**Tata Consultancy**<br>**Services **– India|6,048|111,407|5,824|Consulting, IT, engineering and BPO (includes<br>KPO) services|
|10|**Infosys Technologies**<br>- India|4,717|105,453|4,533|IT, engineering, consulting and BPO services<br>(knowledge andlegalservices)|
|11|**Wipro Technologies**<br>- India|<br>5,645|98,521|4,234|Consulting, IT and BPO services|
|12|**CGI Group**- Canada|3,673|25,500|3,673|Consulting, IT, BPO and systems integration<br>services|
|13|**Hewitt Associates**- US|3,228|23,000|3,228|Human resource consultingand outsourcing|
|14|**Cognizant Technology**<br>**Solutions **-US|2,816|68,000|2,816|Consulting, IT and BPO services|
|15|**Convergys Corporation**–<br>US|2,786|75,000|2,786|BPO (Customer Care - Call Centers)|
|16|**Perot Systems**–US|2,779|23,100|2,779|Consulting, IT and BPO services|
|17|**Teleperformance Group**<br>– France|2,605|102,186|2,605|BPO (Customer Care - Call Centers)|
|18|**SITEL**– US|1,700|66,000|1,700|BPO(Customer Care - Call Centers)|
|19|**Ceridian Corporation -**<br>US|1,695|8,776|1,695|Payroll services & Human Resources<br>management solutions|
|20|**Genpact Ltd. -**India|1,041|36,200|1,041|IT and BPO services|
||**TOTAL**|240,210|1,815,519|192,267|--|

Source: CGGC, Duke University based on OneSource, companies’ websites and companies’ annual reports.
**<u>Table 3: Land fragmentation and incidence of crop shock: household level</u>**

||**Fragm**|**entation**|**Crop**|**diversity**|
|---|---|---|---|---|
||**Simpson**|**No. of parcels**|**Shannon**|**No. of crops**|
|**Fragmentation-related measures**|||||
|Simpson land fragmentation index|-0.045**||||
||(-2.376)||||
|Log of number of parcels||-0.026***<br>|||
|||(-3.237)|||
|Shannon crop diversity measure|||-0.021**||
||||(-2.098)||
|Log of number crops||||-0.016|
|||||(-1.535)|
|Coefficient of variation of parcel altitude|0.167|0.242|0.118|0.122|
||(0.891)|(1.269)|(0.634)|(0.651)|
|Average distance parcel- homestead (min)|-0.000|0.000|-0.000|-0.000|
||(-0.150)|(0.182)|(-0.474)|(-0.395)|
|**Parcel characteristics**|||||
|Log of total cropped area in hectares|-0.005|-0.002|-0.003|-0.003|
||(-1.550)|(-0.690)|(-0.975)|(-0.762)|
|Soil type diversity index|0.018|0.019|0.011|0.010|
||(0.882)|(0.953)|(0.554)|(0.529)|
|Parcel topography diversity index|0.007|0.007|-0.001|-0.002|
||(0.336)|(0.329)|(-0.050)|(-0.082)|
|**Household characteristics**|||||
|Age of household head|0.004**|0.004**|0.004**|0.004**|
||(2.432)|(2.386)|(2.390)|(2.396)|
|Age of household head squared|-0.000**|-0.000**|-0.000**|-0.000**|
||(-2.173)|(-2.122)|(-2.060)|(-2.082)|
|Female headed household|0.012|0.012|0.014|0.014|
||(1.304)|(1.306)|(1.467)|(1.462)|
|Head completed only primary school|0.007|0.008|0.006|0.006|
||(0.822)|(0.908)|(0.732)|(0.729)|
|Head completed secondary education|0.006|0.007|0.004|0.004|
||(0.393)|(0.469)|(0.266)|(0.264)|
|Members aged 14 and less|-0.003|-0.003|-0.003|-0.003|
||(-1.118)|(-0.991)|(-1.098)|(-1.104)|
|Adult members 15 -35|-0.007**|-0.007**|-0.008**|-0.008**|
||(-2.317)|(-2.173)|(-2.457)|(-2.443)|
|Adult members 35 - 60|-0.019**|-0.018**|-0.019**|-0.019**|
||(-2.545)|(-2.445)|(-2.511)|(-2.522)|
|Members aged 60 and above|<br>-0.019|<br>-0.019|<br>-0.020*|<br>-0.019*|
||(-1.598)|(-1.601)|(-1.675)|(-1.647)|
|Constant|<br>0.119***|<br>0.132***|<br>0.126***|<br>0.126***|
||(2.719)|(3.001)|(2.853)|(2.778)|
|Number of observations|6,713|6,724|6,724|6,724|
|R-squared|0.007|0.007|0.006|0.006|

_Note:_ Absolute value of t-statics in parenthesis: *** significant at 1%; ** significant at 5%;* significant at 10%. Time varying village specific fixed effects are included, but not reported.

17
_3_ _A distinctive characteristic of the Indonesian pension system is the absence of a mandatory public pension plan, which normally covers all workers in the formal sector and is financed by a payroll tax on a pay-as-you-go basis._ These public pension plans are often considered desirable because if, properly designed and implemented, they offer a mechanism for redistributing income across groups and across generations, and thereby serve as a social safety net. In practice, however, these goals are often elusive. Public pension plans have generally been fraught with evasion, political manipulation and poor asset management, which often involve serious fiscal implications. Public sector plans based on individual capitalization accounts, which are funded and not redistributive, have proved to be more sustainable, as with the case of Malaysia and Singapore.

In addition to the programs above, most companies in the formal sector provide termination, including retirement, benefits, mostly in the form of lump-sum payments. Some companies also provide pensions (annuities) and health care benefits. These plans, however, are largely unfunded, do not always provide vesting and may not have worker representation in setting pension policy. And since these plans are not formally approved by the authority, they are also not subject to pension regulations or entitled to the tax benefits available to approved pensions.

# The Size of Pension Assets

At the end of 1994, pension assets in Indonesia are estimated at Rph. 21.1 trillion (US$ 9.6 billion), with the following breakdown:

||Rph. Trillion /b|US$ Billion|
|---|---|---|
|Employer-sponsored plans /a|9.6|4.4|
|Astek (Jamsostek)|2.5|1.1|
|Taspen (Civil Service)|8.3|3.8|
|Askes and Asabri|0 70||
|Total|211|2&|

Notes: a/ Includes public enterprises', private companies' and financial institutions' plans.

b/ Based on "investment" data which include all assets, except self-occupied premises.

Sources: Ministry of Finance These assets represented about 5.6 percent of concurrent GDP. By international standards, this is a relatively low level of pension asset accumulation, compared to other countries in the region and in the same income group (see the chart below). Clearly, pension assets tend to rise as a share of GDP as the country's income rises, as shown in the chart. Indonesia's may thus be expected to rise along with the rapid economic growth.