rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7916

# **Abstract**

The information and communications technology sector has undergone a dynamic process of globalization and fragmentation in the past few decades, leading to the creation of global value chains. Global value chains are populated by a constellation of specialized actors collectively responsible for bringing goods and services to market. Most prominently, these key actors include lead firms (brands), contract manufacturers, platform leaders, and increasingly, information and communications technology services and information and communications technology—enabled services providers. Like other emerging markets, Vietnam is coming to play an important role in this global industry. The recent influx of foreign investors, driven by the country’s low wages and easy access to regional supply chains, as well as the emergence of various local information and communications technology services and information and communications technology-enabled services firms opens opportunities, yet raises important questions for policy makers about how best to leverage global engagement for local capacity building. This paper situates Vietnam in the global information and communications technology industry, and identifies several constraints to future growth, including the limited availability and quality of trained information and communications technology professionals, ineffective supplier development initiatives, and weak entrepreneurial ecosystem, especially in management skills. The paper concludes with a set of policy recommendations and forward-looking statements aimed at helping Vietnam move into higher-value activities in the coming years. The analysis is based on relevant statistics published by the United Nations, Organisation for Economic Co-operation and Development, World Trade Organization, Government of Vietnam, and Vietnamese industry associations, as well as interviews and site visits conducted by the authors during January 19–30, 2015.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sturgeon@mit.edu@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
or submitted a third-party informative declaration (D151) about a client or supplier in 2013.

Table 2 covers experiment 1 in columns 1-4 and experiment 2 in columns 5-8. Column 1 (5) displays the average for the control group, columns 2 and 3 (5 and 7) show the dierence between the control group and treatment groups 1 and 2, respectively, and column 4 (8) reports p-values from a test of the hypothesis that the two treatment groups are jointly equal to the control group. All p-values exceed the 10% signicance cuto and for 40 out of 42 coecients, we fail to reject at the 10% level of signicance the hypothesis that the treatment group mean equals the control group mean. We thus conclude that the control and treatment groups in both experiments are well balanced.

In experiment 1, only 15% of rms are corporations, 61% use a legal representative and 35% and 18% respectively have medium or high amounts of third-party information. The experiment 2 sample features a 26% share of corporations, and a 66% share of rms using a legal representative. The fact that self-employed below the exemption threshold do not have to pay income tax, and may be unaware that they are nonetheless required to le a declaration, could explain the high share of self-employed compared to corporations in both experiments. Only 6% of rms in experiment 1 and 5% of rms in experiment 2 have a second email address on le. In both experiments, about 50% of rms are located in the capital city. As for past compliance, only 2% of rms in experiment 1 made advance payments for the income tax for 2014, 33% led income tax in 2013, and 5% made a payment in 2013. Past compliance rates are even lower among rms in experiment 2, as would be expected given that these rms are not covered by any third-party information. The rms who were contacted early mostly pertain to experiment 1, representing 12% of the sample, but are perfectly balanced across control and treatment groups.

Table 3 examines the balance of outcomes of interestincome tax compliance and other compliance outcomesmeasured on the day prior to the start of the rst email experiment (March 3, 2015). The fact that all declarations and payment receipts carry a time-stamp and indicate the corresponding scal period allows us to precisely capture compliance at dierent points in time. The structure of this table is identical to table 2. We nd that about 5% of rms in experiment 1 and 1% of rms in experiment 2 led their income tax declaration after the extraction of the non-ler list and before the start of the experiment. The share of rms reporting a positive net liability and making a payment is below 2% for experiment 1, and close to zero for experiment 2. Despite not ling their own tax declaration, 16% of rms in experiment 1 and 1% in experiment 15
- ———. 2014. _Improving Learning in Primary Schools of Developing Countries: A Meta-Analysis of Randomized Experiments_ .

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- Pradhan, Menno, Daniel Suryadarma, Amanda Beatty, Maisy Wong, Armida Alisjahbana, Arya Gaduh, and Rima Prama Artha. 2012. _Improving Educational Quality through Enhancing Community Participation: Results from a Randomized Field Experiment in Indonesia_ .

- Pritchett, Lant, and Justin Sandefur. 2013. _Context Matters for Size : Why External Validity Claims and Development Practice Don’t Mix_ . 336. Working Paper.

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- Vivalt, Eva. 2014. “How Much Can We Generalize from Impact Evaluation Results ?” New York University.

- World Bank. 2012. _Evaluation Study of Malawi’s Open Distance Learning Teacher Training Program: Year 1 Baseline Report_ .

- ———. 2013. _International Development Association Project Appraisal Document on a Proposed Grant in the Amount of SDR 10 Million (US$15 Million Equivalent) to the Republic of Chad for an Education Sector Reform Project Phase II_ . Washington DC.

- ———. 2014. “Data for Community, Contract, and Civil Service Teachers Extracted from World Bank Public Expenditure Reviews”. Washington DC.

16
# **<u>Table 2. The Effect of Payment Systems Reform on Credit - Post-Reform Trend Breaks</u>**

# **The dependent variable in all three panels is the Credit/GDP ratio**

||(1)|(2)|(3)|
|---|---|---|---|
|**Post-Reform Trend Break**|**0.055****|**0.040****|**0.042****|
||**(0.019)**|**(0.015)**|**(0.016)**|
|Inflation|0.006*|0.005*|0.005*|
||(0.003)|(0.002)|(0.002)|
|Inflation*(High Inflation dummy)|-0.005**|-0.004*|-0.004*|
||(0.002)|(0.002)|(0.002)|
|High Inflation dummy|0.038**|0.026|0.026|
||(0.016)|(0.015)|(0.015)|
|Expenditure over GDP ratio|-1.137*|-1.294**|-1.294**|
||(0.612)|(0.544)|(0.496)|
|Log real GDP per capita|0.867*|0.768*|0.779*|
||(0.409)|(0.379)|(0.359)|
|Banking reform index|-0.687||-0.712*|
||(0.391)||(0.387)|
|Banking crises|-0.132||0.035|
||(0.138)||(0.069)|
|Public Banks' Assets Share|0.006*|0.006*|0.005*|
||(0.003)|(0.003)|(0.003)|
|Foreign Banks' Assets Share|0.0004|-0.001|-0.001|
||(0.003)|(0.002)|(0.002)|
|Foreign Exchange reserves/GDP||0.01***|0.01***|
|||(0.001)|(0.002)|
|Country specific trends|x|x|x|
|Year fixed effects|x|x|x|
|Country fixed effects|x|x|x|
|Seasonal Dummies|x|x|x|
|Within R-squared|0.86|0.88|0.89|
|Nbr. obser.|404|400|400|

Note: Robust clustered (at the country level) standard errors in parentheses. Significant at (*) 10 per cent level; (**) 5 per cent level; (***) 1 per cent level.
**Table 4.I: Returns to Education Estimates for India**

||**URB**|**AN (U)**|**RUR**|**AL (R)**|**ALL**<br>**(R**|**INDIA**<br>**+U)**||
|---|---|---|---|---|---|---|---|
||**Men**|**Women**|**Men**|**Women**|**Men**|**Women**|**Year**|
|||**SEC**|**ONDAR**|**Y**||||
|Duraisamy (2002)|14.7|32.4|17.9|34.1|||1993/94|
|Kingdon (1998)|4.9|13.4|||||1995|
|Kanjilal et al. (2017)|74.1|91.9|||||2011/12|
|Bargain et al. (2009)|||||24|64|1987/88|
|Bargain et al. (2009)|||||28|64|1993/95|
|Bargain et al. (2009)|||||25|41|2002/04|
|||**HIGHER**|**SECON**|**DARY**||||
|Duraisamy (2002)|10.1|12.9|8.4|11|||1993/94|
|Kanjilal et al. (2017)|108|123|101.4|124.3|||2011/12|
|Kingdon (1998)|17.6|20.8||||||
|Bargain et al. (2009)|||||64|124|1987/88|
|Bargain et al. (2009)|||||60|136|1993/95|
|Bargain et al. (2009)|||||55|109|2002/04|
|||**C**|**OLLEGE**|||||
|Duraisamy (2002)|13.2|9.3|11.6|10.1|||1993/94|
|Kanjilal et al. (2017)|150.9|153.6|141|146.9|||2011/12|
|Kingdon (1998)|18|8.9||||||
|Bargain et al. (2009)|||||111|174|1987/88|
|Bargain et al. (2009)|||||108|175|1993/95|
|Bargain et al. (2009)|||||121|170|2002/04|

Note: The complete references for the studies cited are in the online appendix.
emigrants choose to return home, so their household may (wrongly) be considered as not being affected by emigration.

In this paper, we have shown how these selection problems invalidate the approaches used in most of the existing literature and we have addressed these selectivity problems by using survey data designed specifically to take advantage of a randomized lottery that determines which applicants to the over-subscribed Samoan Quota (SQ) may immigrate to New Zealand. We find that migration reduced poverty among former household members.

In addition to forming experimental estimates of the average impact of migration on left behind household members, we estimate models which allow for duration dependent heterogeneity in these impacts. While our sample size is small for precise estimation of such effects, we do find suggestive evidence that allowing for this type of heterogeneity may be important in practice. Our point estimates suggest that income among sending households decays as SQ migrants spend increasing time in New Zealand, with agricultural income and remittances declining with duration. While these results are only suggestive, they do point to a need for other studies with larger samples to model seriously the multiple sources of selection and to not assume a homogenous impact of migration with time spent abroad.

19
||**Ap**|<br>**pendix Tab**|<br>**le 1. List of Ec**|<br>**onomies**|<br>**and Data Avai**|**Foreig**<br><br>**lability (c**|**n Direct**<br><br>**ont.)**||
|---|---|---|---|---|---|---|---|---|
|||**Bank Loans**|**and Deposits**|**Portfolio**|**Investment**|**Inve**|**stment**|**Int. Reserves**|
||**Economy**|**Source**|**Destination**|**Source**|**Destination**|**Source**|**Destination**|**Source**|
||Andorra|●|●||●||●||
||Anguilla|●|●||●||●|●|
||Aruba|●|●|●|●|●|●|●|
||Bahamas, The|●|●|●|●||●|●|
||Belize|●|●||●||●|●|
||Bermuda|●|●|●|●||●||
||British Virgin Islands|●|●||●||●||
|**s**|Cayman Islands|●|●|●|●||●||
|**ter**|Cook Islands||||●||●||
|**en**|Cyprus|●|●|●|●|●|●|●|
|**al C**|Gibraltar|●|●|●|●||●||
|**nci**|Guernsey|●|●|●|●||●||
|**na**|Isle of Man|●|●|●|●||●||
|**e Fi**|Jersey|●|●|●|●||●||
|**or**|Liechtenstein|●|●||●||●||
|**ffsh**|Macao SAR, China|●|●|●|●|●|●|●|
|**O**|Monaco||||●||●||
||Montserrat||||●||●|●|
||Netherlands Antilles|●|●|●|●||●|●|
||Palau|●|●|●|●|●|●||
||Panama|●|●|●|●|●|●|●|
||Samoa|●|●||●|●|●|●|
||Seychelles|●|●||●|●|●|●|
||Turks and Caicos|●|●||●||●||
||Vanuatu|●|●|●|●||●|●|
||Number of North Countries|22|22|21|22|21|22|22|
||Number of South Countries|178|178|57|192|95|192|148|
||Number of Offshore Centers|21|21|14|24|7|25|12|

Note: The coverage of bank loans and deposits has been extended by the use of mirror data obtained from the 47 Bank for International Settlements (BIS) reporting countries reporting assets and liabilities vis-à-vis all countries and jurisdictions (appendix, section A.1). Sources and destinations not marked with dots are not identified as separate countries by the BIS, but are included in the data reported by their governing country. For foreign direct investment (FDI), coverage was enhanced through a similar procedure, using liabilities reported by the destination country when asset data are missing (appendix, section A.3).

This table indicates, for each country in the sample, whether we have data on that country as a source and destination. A country is considered a source (destination) of international investments if, for at least one year, the sum of all international investment positions for which the country is a source (destination) of funds is non-missing. For international reserves, we have country-to-region data, so we only indicate whether a country is a source. The list of offshore financial centers (OFCs) is based on the list of countries included in the International Monetary Fund's Staff Assessments on Offshore Financial Centers (OFCs) (available at: https://www.imf.org/external/np/ofca/ofca.aspx).
# **Supplementary Information**

# **SI-1 Supplementary text**

## **SI-1.1 Country level correlations**

To provide context for the relationship between wealth and pollution globally we draw on data from the World Bank to estimate the country-level correlation across the entire world The general intuition that pollution is higher in lower income countries is borne out by the country-level data. Two results stand out: first, as has been well documented, countrylevel average pollution is lower on average in higher income countries (Figure SI-1a). Only two countries with a per capita income above $50,000 have average pollution levels above 20 _μg/m_<sup>3</sup> (Macau and Qatar). Countries with low levels of per capita income (less than $15,000) span the full range of pollution levels but the majority have pollution concentrations above 25 _μg/m_<sup>3</sup> . Simple cross-country regression confirms this correlation, every $10,000 increase in per capita income, is associated with a decline in pollution of 1.8 _μg/m_<sup>3</sup> ( _t-stat_ : 4.3).

Second, consistent with the negative relationship between income and pollution the most polluted countries in the world are concentrated in Central and West Africa, the Middle East, and South Asia (Figure SI-1b-c). With the exception of the Middle East, these are among the lowest income regions in the world. Of the four countries that are notable outliers with pollution levels higher than would be expected given their income levels (labeled in Figure SI-1a), three are highly urbanized city states (The United Arab Emirates, Macau, and Monaco). The fourth, Qatar, has a significant portion of air pollution coming from natural pollution sources like dust storms.

## **SI-1.2 Heterogeniety by per capita income and pollution source**

The spatial pattern of ambient particulate pollution, and hence its correlation with wealth, may also vary depending on the primary sources of particulate pollution and on country-level income. For example, in places where transportation is the primary source of emissions, high pollution areas may be less spatially concentrated than in places where pollution is primarily generated by industrial activity which is more likely to be concentrated in a handful of cities. Because the nature of economic activity changes at different levels of development<sup>1</sup> these spatial patterns may also vary by country-level per capita income.

We do not find evidence that suggests either of these hypotheses can explain our country level results. We observe similar patterns of heterogeneity across countries when we group by average per capita GDP or by the primary source of particulate pollution in the country.

20
**Table 3.3a: Regression results**

||Dependent variable:<br>Weighted CV|
|---|---|
|Independent variable|Regression 2|
|Intercept|-1.86|
||_0.048**_|
|Unitary dummy|0.28|
||_0.016**_|
|log(Population)|0.1|
||_0.022"*_|
|# of observations<br>|2<br>|
|R-square|0.34|
|p-values in italics||
|**significant at 5% lev|el (two-tailed test)|

We also computed the Spearman rank correlations of the weighted inequality measures with the unitary dummy. We computed the rank correlations in two different ways. First, we took just assigned an average rank of 5.5 to all the 9 federal countries and 15.5 to all the 12 unitary countries. In the second case, we ranked the countries within each group (federal or unitary) according to the level of decentralization. The level of decentralization was measured as the proportion of subnational government expenditures to that of total expenditures. Government expenditure data was taken from the IMF's Government Finance Statistics.

**Table 3.3b: Spearman rank correlation**

||Weighted<br>CV|Weighted Gini<br>index|Theil index|
|---|---|---|---|
|Federal/Unitary|0.458|<br>0.423|<br>0.458|
|FevdralUnitary 8||||
|<br>Decentralization|<br>0.382|<br>0.37|1<br>0.411|

Table 3.3b presents the results of our calculations. The first row presents the rank correlations of the 22 countries in our sample with ranking according to only whether a country is federal or unitary. The second row shows the results when the ranking taking into account the level of decentralization in each country. In this case, our sample contains only 15 countries<sup>6</sup> as 16
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# b) Two-stage Bidding Process

There are many variants of two-stage bidding processes. However, the main feature of a two-stage bidding process is to include a formal pre-qualification stage. The prequalification stage is followed by the bid submission stage. As discussed earlier, the main objective of organizing a competitive bidding process is to select the project developer which is the most capable to deliver promised or anticipated outcomes in terms of services, prices, or coverage. In that context, the objective of the pre-qualification stage is to establish a list of technically and financially qualified bidders which can deliver the project development objectives. As opposed to restricted bidding discussed earlier in the case of one-stage bidding processes, the pre-qualification stage is a formal round of selection that aims to identify the most competent bidders. Only bidders meeting the requirements or criteria stated in tender documents will be invited to submit a bid.

However, determining the _right number of bidders_ is not simple. For example, a twostage bidding process that does not attract more than two bidders will not ensure enough competition in bidding. Similarly, a two-stage bidding process for which more than five bidders are qualified may not induce competition in bidding because the probability to win the bid decreases with the number of pre-qualified bidders. As a result, determining the right number of bidders is a strategic decision that should take into account an assessment of the market conditions, the probability that some of the pre-qualified bidders may drop out of the bidding, and the sector specific conditions. There is no analytical rule or methodology to decide on the right number of bidders.

A study (ENGREF 2001) found that 3.5 bidders expressed their interest in tenders organized for the renewal of water concessions in France in 2000, of which 2.1 were qualified to submit bids. But, in 33% of the cases, there was only one bid submitted. Another World Bank study (Foster, V. 2005) made comparable or similar findings in reviewing water concessions in Latin American countries<sup>9</sup> . Chart#1 shows that the

> 9 Foster, V (2005, p. 13): “Not only has the number of bidders for Latin American water contracts been comparatively small, but they have always tended to come from the same handful of predominantly French companies.”

14
Financial Regulation and Development Table 6: Taxes and Antebellum USA Banks: Balance Sheet Shares

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Variables|Deposits<br>% Asset|Loans<br>% Asset|Safe Assets<br>% Asset|ROA<br>% Point|
|||Policy Phase Out|||
|Policy|0.0474***|0.0730***|0.0176***|0.00418|
||(0.00683)|(0.00970)|(0.00599)|(0.00350)|
|Bank FE|Yes|Yes|Yes|Yes|
|State FE|Yes|Yes|Yes|Yes|
|Month FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|||Policy and Big Banks|||
|Policy _×_ Big|0.0288***|_−_0.0237|0.0192**|_−_0.00265|
||(0.00965)|(0.0155)|(0.00926)|(0.00376)|
|Bank FE|Yes|Yes|Yes|Yes|
|State FE|Yes|Yes|Yes|Yes|
|Month FE|Yes|Yes|Yes|Yes|
|State _×_ Year FE|Yes|Yes|Yes|Yes|
|Obs.|17,958|17,958|17,958|17,958|
|Number of Banks|1,280|1,280|1,280|1,280|
|Mean Dep. Var.|0.156|0.794|0.120|0.033|
|SD Dep. Var.|0.116|0.174|0.111|0.038|

_Note:_ This table reports OLS estimates. The unit of observation is bank level, and bank, month, state, year, and state _×_ year fixed effects are included. Standard errors are clustered at bank level. Deposits is a variable reporting the overall deposits at bank level; it is continuous and measured in USD. Safe assets is the amount of liquid assets held by banks in cash, bank-to-bank notes, real estate assets, and other private assets; it is continuous and measured in USD. Loans and Discounts embodies all lending operations at bank level; it is continuous and measured in USD. Profits and Losses is the difference between the asset-generated income and liability-induced costs after taxes; it is continuous and measured in USD. The means and standard deviations of these variables are reported in the final two rows. The following states are included in the sample because they are treated in some year by the policy: CT, GA, MA, MD, ME, NC, NH, NJ, NY, PA, RI, and VT. However, because of data limitations we observe a bank balance sheet before and after the policy only for the following six states: MA and ME in 1812; NH and RI in 1822; NY in 1839; CT in 1850. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

40