rafmacalaba/datause-encoder-data huggingface dataset:
districts manufacturing base, aggregating together the organized and unorganized sectors. A higher value of this index indicates a more concentrated local industrial environment in terms of the manufacturing industries represented. In general, there seems to be no connection between local industrial diversity and greater average counts of inputs used in Indian manufacturing. This even holds with our measure considering input usage in a binary form, compared to an intensity of use formulation. The one exception is a small weak correlation in Column 1 for more concentrated local environments based upon establishment distributions having reduced average input counts for the organized sector. This overall weakness for the city may reflect the longer spatial horizons for material inputs between customer and supplier firms.<sup>7</sup> Table 6a considers multivariate analyses of these local traits and continues to analyze the organized and unorganized sectors separately. We calculate the average raw input use among plants in each district _d_ and industry _j_ , weighting plants by their sample weights in the survey. We regress this average input usage for each district on a vector _Zd_ of district traits and a vector γ _j_ of industry fixed effects using the specification: District-level traits in the vector _Zd_ are taken from the 2001 Census and follow Panel A of Table 5. Non-logarithm variables are transformed to have unit standard deviation for interpretation. The industry effects directly control for broad differences across industries in their average input counts. We cluster standard errors at the district level to reflect the repeated mapping of district traits to the district-industry observations.

Given the small cells that some district and industry combinations form, we weight observations by an interaction of log district size and log industry size. This interaction approach places greater emphasis on the observations expected to be substantial in size due to the associated districts and industries being large. The advantage of this approach compared to directly weighting by observed district-industry size is that it places less emphasis on very abnormal spatial concentrations of activity, where reverse causality of particular industry agglomerations can overly shape local traits (e.g., Glaeser and Kerr 2009). The weights

> 7 For example, Rosenthal and Strange (2001, 2004) and Ellison, Glaeser, and Kerr (2010).

12
# **1. Introduction**

Fraud is an ancient crime of deception that aims to obtain money or other benefits from people. In Dante’s description of inferno in the Divine Comedy, fraud is considered as one of the worse sins--people who committed fraud would endure punishment at the _Bolgia_ eight of _Malebolge_ (eighth circle of hell).

Fraud victimization has profound economic and social implications. With the widespread leakage of private information, especially via the internet, and the everevolving fraud schemes, people frequently encounter fraud schemes, yet find it increasingly difficult to avoid victimization. Millions of people suffer from fraud victimization with enormous economic losses every year. As much as 11 percent of Americans (about 25.6 million) in 2011 and 4 percent of Australians in 2012 were victims of fraud schemes (Anderson, 2013; Jorna and Hutchings, 2013). A Federal Trade Commission (FTC) survey reveals that the individual/household level economic losses due to fraud victimization ranged from $40 to $40,000 in 2005 (Reisig and Holtfreter, 2007). In 2011 alone, 2.7 percent of Americans (i.e., 6.4 million people) paid at least $1.9 billion in total to fraudsters in various forms (Anderson, 2013), and the direct monetary costs incurred by victimization could reach $50 billion (Brenner et al., 2020).

The issue is of greater importance in developing countries. In China, for instance, fraud incidents have grown at an annual rate of 20 to 30 percent in the past decade.<sup>2</sup> According to a report by the Government of China, nearly half of internet users encountered fraud schemes in 2018, and 28 percent of them suffered economic losses.<sup>3</sup> Based on judicial data, fraud is one of the most frequent crimes, accounting for 32 percent of cybercrime that involves 46,000 fraudsters.<sup>4</sup> The previous literature, mostly in criminology, has examined the behavioral patterns of fraudsters (Levi, 2008). Behind the commonly encountered fraud phone calls and emails are well-trained fraudsters in criminal organizations with sophisticated technologies.<sup>5</sup>

> 2 <u>https://www.tisi.org/4714</u>

> 3 - <u>http://www.gov.cn/xinwen/2019 09/17/content_5430594.htm</u>

> 4 <u>https://www.secrss.com/articles/15226</u>

> 5 Levi (2008) classifies the common fraud schemes into 6 categories and 34 items according to the characteristics of fraud victims (such as financial-services-related, non-financial-services-related, individuallevel, national-level, and so on). Based on the intermediation and formation of schemes, the Federal Bureau of Investigation (FBI) has categorized common fraud into 24 different items, including common schemes such as credit card fraud, investment fraud, Nigerian letter fraud (also called 419), Ponzi schemes, and

2
# **Figure 6-3 All transfers are progressive, except for fuel subsidies and pension income**

Progressivity of transfers, measured by the Kakwani Index at Market Income <!-- Start of picture text --> a. Progressivity of transfers<br>Fuel subs. (direct effect) -23.0<br>Pension income -14.0<br>Fuel subs. (total) -8.3<br>Fuel subs. (indirect effect) -4.7<br>Elec. subs. (direct effect) 33.4<br>Secondary educ. 39.0<br>Basic educ. 45.2<br>Primary healthc. 47.9<br>Hospital healthc. 47.9<br>Near-cash transfers 73.0<br>Cash Transfer Program (CTP) 100.3<br>-50 0 50 100<br>Kakwani Index w.r.t. Market Income<br><!-- End of picture text --> Source: Authors' estimates based on PECS 2016/2017; LFS 2017. Notes: [1] The Kakwani Index is a summary statistic of progressivity [>0 = progressive]. [2] It is calculated for transfers by subtracting the concentration coefficient from the Gini coefficient.

**All taxes are progressive relative to Market income. The PIT is the most progressive, followed by the total VAT, the fuel excises, and the customs tax (with a score of 14.1, 12.6, 11.9 and 3.7 on the Kakwani Index respectively).** The PIT is only slightly more progressive than the total VAT (direct and indirect effects combined). This is because of the flat design of the PIT in the West Bank and Gaza with relatively few income brackets.<sup>74</sup>

**The progressivity of the VAT is primarily driven by its direct effects, with a Kakwani index of 19.5, higher than both the PIT and the indirect effects of the VAT (3.2 on the Kakwani index).** The direct effects are more targeted than the indirect effects due to exemptions and zero-ratings on certain strategic goods. However, the progressivity of the VAT is also partly due to our methodological approach which accounts for differential informal consumption across deciles. The poor have a higher share of informal consumption, thus making the VAT more progressive (Bachas et al. 2020).

**The direct effects of the fuel excise are the most progressive component of the fiscal system.** Although it is not designed for progressivity, the consumption patterns of wealthier households render it particularly progressive.

> 74 The PIT paid according to the West Bank and Gaza tax schedule has a Kakwani Index of only 13.1 compared to the PIT paid according to Israeli tax schedule which has a Kakwani Index of 57.6.

46
9 However, the outcome of falling consumption in most northern economies has been a sharp rise in unemployment almost everywhere, with aggregate employment in the OECD falling by 2.2m between the 2<sup>nd</sup> quarters of 2008 and 2009 (Holland, et al, 2009), and unemployment growing to exceed 10 percent of the labor force in the US in late 2009. It has also led to a sharp fall in exports in major surplus economies (Table 4). In June 2009 Germany’s exports had declined by 34 percent and Japan’s by 24 percent compared to the same period in the previous year. China, too, saw a fall in employment after global trade fell significantly in the first year after the financial melt-down (13 percent fall in exports between June 08/09).

This decrease in output in the north, and increase in unemployment - both arising out of falling personal consumption - have been met by a massive “saltwater Keynesian” injection of funds through bank-bailouts and quantitative easing in most of the deficit economies, fuelling a “freshwater” response warning of the dangers of inflation. Although not historically unprecedented, government debt as a share of GDP has risen sharply in almost all economies as actual (and projected) fiscal deficits have grown (Table 5). Without this growth in government expenditure, there is little doubt that the already almost unprecedentedly large fall in output and rise in unemployment would have been substantially greater. As a result, there has been some revival in economic activity, with both the US and the EU (but not the UK) moving out of recession (in the sense that output stopped falling) in the final quarter of 2009 and a revival of China’s exports. Virtually no observer doubts the reflationary consequences of government deficit-financing – the debate is on the sustainability and long-term consequence of this deficit spending program and the extent of the economic revival.

**Table** **<u>5: General Government Fiscal Balance (Percent of GDP)</u>**

|**Country**|**Germany**|**Japan**|**Spain**|**United**<br>**Kingdom**|**United**<br>**States**|
|---|---|---|---|---|---|
|1980|-3|-5|-2|-3|-3|
|1990|-2|2|-4|-2|-4|
|2000|1|-8|-1|1|2|
|2005|-3|-5|1|-3|-3|
|2008|0|-6|-4|-5|-6|
|2009|-4|-10|-12|-12|-12|
|2010|-5|-10|-12|-13|-10|
|2011|-4|-8||-11|-8|
|2012|-2|-8||-9|-6|
|2013|-1|-8||-8|-7|
|2014|0|-8||-7|-7|

Source: IMF World Economic Outlook Database, accessed November 2009. Shaded areas are estimates.

We are thus faced with two clear trends in major northern economies. First, personal consumption has fallen back and is unlikely to rise in the near-to-mid-term as households rebuild their savings and cut personal debt. Second, continued government dis-saving has limited the fall in aggregate consumption and output, but it is unsustainable in the medium and long term, both for fiscal reasons and because of sustained trade deficits. So, the issue is in what other ways can the structural deficits in key northern economies be resolved if the past growth trajectory is to be sustained, that is if any of the V, U or W scenarios are to be achieved. One possibility is for
- 28 -

**Table 3**

> Effects of an **increase in public spending of 3S of** (potential) GDP. JCSapacity constrained-growth reglime

|base year<br>solution<br>(1)|solution with a**3Z**<br>increase in the government<br>spending share<br>(2)|difference<br>(2)-<br>(1)<br>percentage<br>(3)|
|---|---|---|
|rate of growth<br>of GDP,_Z_<br>6.43|5.36|-<br>1.07|
|real exchange<br>110.27<br>rate (index)|104.88|-<br>5.39|
|real wages<br>100.00<br>(index)|104.36|4.36|
|rate of growth<br>of GDPZunder a<br>5.90<br>dominant fiscal<br>constraint|5.26|-<br>0.64|
|______________|||
|a balanzed increase|||
|increase in public<br>6.43<br>spending (matched by<br>an increase in fiscal|6.43|0.0|
|revenues of 3 points of GDP)|||

A main result of Table 3 is that an (unbalanced) increase in the public spending share of 3? slows the rate of growth of GDP by 1Z . This effect is due to the reduction in public savings (taxes and/or other type of public spending remain unchanged in this simulation). The fall in public savings, in turn, leads to a reduction of domestic savings that forces (given foreign savings) to cut a-,regate (and public) investment and decelerate growth. As displayed in Figure 4 the increase in government spending shifts
<!-- Start of picture text --> 10%><br>8% > \<br>6% > Of<br>2% + os \~ hh<br>-2%4% +4 ‘ \Y ‘4<br>ox|<br>sx|<br>-10%+BRREEBREBRBERHRESEEERESHEEEEESEERSRRRERSRSSSRSESSSSSSSSSSSSSRRRRRRERRRR RRERRRSSSSSSESBRSARSRRSSSSSRRSRSSSSSSRSES<br>——GDP Growth == PhysicalCapital === Labor —3*—TFP Cobb Douglas «**** Human Capital<br><!-- End of picture text -->
The reserve requirements and minimum maturities changed frequently over the 1990s; in fact, the Colombian restrictions were far more complex than the Chilean ones and the level of the tax equivalent of the unremunerated deposits was significantly higher. This suggests that the Colombian authorities pursued a more active management policy, as far as the capital account is concerned, when compared to Chile and/or it could reflect the greater relative instability of the Colombian economy and the political strength of the export sector. In addition, the complexity of the capital controls arguably increased uncertainty and speculation, as financial market players attempted to anticipate changes in the regulations.

# **3. A Survey of the Econometric Literature on the Controls on Inflows**

Nadal de Simone & Sorsa (1999) provided an extensive review of the early econometric work on the effects of the unremunerated reserve requirements on capital flows in Chile. The studies considered by those authors presented some evidence that the URR was capable of altering the composition of capital flows towards long-term flows, but there was only mixed evidence that the URR has affected the level of total flows. Nonetheless, the econometric methodology used in most cases suffered from several shortcomings. Misspecification problems abounded (evidence of heterocedasticity, serial correlation, non-normality of the residuals, etc.) and a variety of authors failed to test for the non-stationarity of variables included in the regressions and for co-integration.

Edwards (1999) estimated a series of unrestricted VAR models and concluded that the taxes on capital flows had been unsuccessful in reducing the real exchange rate appreciation. De Gregorio et al. (2000) also considered VAR systems and found that the URR had large and significant negative effects on short-term inflows; nevertheless, it also had a positive effect on long-term inflows (and overall no effect on total flows). Their impulse response functions indicated that the URR only had transitory effects on the real exchange rate. The latter result is confirmed by Gallego et al. (2002), who estimated a two-step error-correction model for the real exchange rate, combining determinants of the equilibrium in non-traded goods markets and interest rate differentials.

5
conflict-affected northern Uganda. This effect, still visible four years later, was stronger for young women (who started from lower incomes) than for young men (Blattman, Fiala, and Martinez 2013). It may be that this powerful effect of a large cash windfall on young women’s earnings would extend to adult women running subsistence enterprises, meaning that access to capital alone would grow these adult women’s earnings provided that it was a large enough amount and women were given time to show results.

_Timing of Effects._ Panel household survey data for Bangladesh covering a twenty-year period support the notion that time influences the outcomes we observe. These data show a beneficial effect, greater for females than for males, of 20-year cumulative microcredit borrowing on household per capita income and the reduction of extreme poverty (Khandker and Samad 2014). The authors speculate that past credit may affect current credit, leading to more risk taking over time. This could be particularly relevant for women, who may be more risk averse than men. Similarly, a two-month grace period versus immediate repayment requirements for poor urban women borrowers in Kolkata, India significantly raised long-run (three-year) business profits by encouraging risk taking (Field et al. 2013).

# _Capital plus Training_

_Poor and Very Poor Women._ Bandiera et al. (2013a) show that a large capital transfer (most often a cow or other livestock with an asset value of approximately $140), intensive assetspecific training, and regular follow-up visits by an asset specialist and BRAC program officer during a two-year period had a significant, transformative impact on the occupational choices of very poor women in Bangladesh. These women changed occupational choices from casual day 8
Kolstad, Erik W., and Kjell Arne Johansson. 2010. “Uncertainties Associated with Quantifying Climate Change Impacts on Human Health: A Case Study for Diarrhea.” _Environmental Health Perspectives_ 119 (3): 299–305.

- Kriegler, Elmar, Brian C. O’Neill, Stephane Hallegatte, Tom Kram, Robert J. Lempert, Richard H. Moss, and Thomas Wilbanks. n.d. “The Need for and Use of Socio‐Economic Scenarios for Climate Change Analysis: A New Approach Based on Shared Socio‐Economic Pathways.” _Global Environmental Change_ . doi:10.1016/j.gloenvcha.2012.05.005.

- McMillan, Margaret S., and Dani Rodrik. 2011. “Globalization, Structural Change and Productivity Growth.” w17143. National Bureau of Economic Research.

- http://www.nber.org/papers/w17143.

- Noy, Ilan, and Pooja Patel. 2014. “Floods and Spillovers: Households after the 2011 Great Flood in Thailand.” Working Paper Series 3609. Victoria University of Wellington, School of Economics and Finance. https://ideas.repec.org/p/vuw/vuwecf/3609.html.

- Olivieri, Sergio, Sergiy Radyakin, Stanislav Kolenikov, Michael Lokshin, Ambar Narayan, and Carolina Sanchez‐Paramo. 2014. _Simulating Distributional Impacts of Macro‐Dynamics: Theory and Practical Applications_ . World Bank Publications.

- O’Neill, Brian C., Elmar Kriegler, Keywan Riahi, Kristie L. Ebi, Stephane Hallegatte, Timothy R. Carter, Ritu Mathur, and Detlef P. van Vuuren. 2013. “A New Scenario Framework for Climate Change Research: The Concept of Shared Socioeconomic Pathways.” _Climatic Change_ 122 (3): 387–400. doi:10.1007/s10584‐013‐0905‐2.

- Patankar, Archana. 2015. “The Exposure, Vulnerability and Adaptive Capacity of Households to Floods in Mumbai,” World Bank Policy Research Working Paper No. 7481, .

- Patankar, Archana, and Anand Patwardhan. 2014. “Estimating the Uninsured Losses due to Extreme Weather Events and Implications for Informal Sector Vulnerability: A Case Study of Mumbai, India,” Forthcoming in Nat Hazards, .

- Rozenberg, Julie, and Stephane Hallegatte. 2015. “The Impacts of Climate Change on Poverty in 2030, and the Potential from Rapid, Inclusive and Climate‐Smart Development,” Forthcoming as a World Bank Policy Research Working Paper, .

- World Bank. 2013. “Risk and Opportunity ‐ Managing Risk for Development.” World Development Report. The World Bank.

20
-  16  -

of Bulgaria’s EU-oriented export growth during the initial stages of transition; its overall lackluster performances through the 1990s and turn around in the early 2000s. Bulgaria increased its presence in EU markets in 1993-96 roughly to the same extent as CEEC-8, albeit significantly less than Romania. Indeed, until 1995 Bulgaria’s performance in these markets did not diverge from that of CEEC-8 or Romania.<sup>10</sup> However, in contrast to them, its performance was highly volatile indicating the persistent status of a marginal supplier (Figure 3).

**Table 9:** _Share of Bulgaria, CEEC-8 and SEE-5 in EU-15 external imports in 1993, 1996-97, 2000 and 2003 (in percent)_

|||||||Index,<br>1996|Index,<br>1999|Index,<br>2003|Index,<br>2003|Change|
|---|---|---|---|---|---|---|---|---|---|---|
||1993|1996|1997|2000|2003|1993=100|1997=100|2000=100|1993=100|in 2003|
|Bulgaria<br>|0.22|0.29|0.31|0.29|0.37|132|90|128|168|5.7|
|CEEC-8|5.29|7.12|7.40|8.34|11.1|135|115|133|210|8.6|
|Romania|0.37|0.61|0.66|0.74|1.10|165|111|149|297|7.8|
|SEE-5|0.51|0.49|0.53|0.42|0.54|96|85|129|106|8.0|

Source: Based on EU as reporter from UN COMTRADE Statistics.

**Figure 3:** _Annual change in shares in EU external imports in 1993-2003 (1999=100)_

<!-- Start of picture text --> 40<br>CEEC-8<br>30<br>20 Romania<br>10<br>SEE-5<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>-10 Bulgaria<br>-20<br><!-- End of picture text --> Source: As in Table 9.

While both CEEC-8 and Romania expanded their presence in EU markets in the 1990s, Bulgaria’s share in EU external imports stagnated. Consider that in 1999 its share was exactly the same as that in 1994. CEEC-8 and Romania’s shares were respectively 38 percent and 43 percent higher in 1999 than in 1994. Hence, the 1996-97 crisis, preceded by several years of policies destroying Bulgaria’s industrial base, exacted a heavy toll on Bulgaria’s export performance in EU markets.

The improvement in export performance during the current expansionary phase so far has not been sufficient to compensate for the losses in Bulgaria’s competitive position experienced in the 1996-99 period and triggered by half decade of gross economic mismanagement. In 1996 Bulgaria’s share in EU imports contracted 12 percent and it fell again in 1999 by 10 percent. Since 1999 Bulgaria has expanded its presence in EU markets roughly at the same pace as on average the SEE-5 economies and slightly below the growth in the share of the CEEC-8 in EU imports. Yet, its share in EU external imports was in 2003 only 12 percent above its peak level in 1995 while that of CEEC-8 was 57 percent and Romania’s 80 percent in the same period.

> 10 During the 1989-95 phase Bulgaria recorded an average annual growth rate of its EU destined exports of 21 percent and its share in EU external imports expanded on average at 14 percent. This compares very favorably to corresponding averages for Czech Republic, Hungary and Poland. Only the Czech Republic registered a faster growth of 23 percent and 16 percent. The differences emerged in 1996-99 (Annex Table 3).
(table l). Moreover, despite its recent relative decline, short-term borrowing still accounts for about a

# quarter of total debt-creating flows.

Table 1. CEE Countries: Size and Composition of Net Private Capital Inflows (US$ millions)

||1990|1991|1992|1993|1994|1995|1996|1997|
|---|---|---|---|---|---|---|---|---|
|_Debt-creatingflows_|194|1,779|_-5_|6,075|5,614|11,288|8,431|11,529|
|Medium- and long-tenn debt|974|2,541|1,144|5,703|3,443|7,872|6,356|8,928|
|o/w Commercial banks|154|-901|-1,894|206|-340|4,638|4,577|4,096|
|Bonds<sup>2</sup>|273|1,563|1,002|4,066|2,102|2,478|801|1,782|
|Short-term debt|-781|-762|-1,149|371|2,171|3,416|2,074|2,601|
|_Non-debtcreatingflcws_|450|2,246|3,056|5,637|5,567|13,681|12,026|12,464|
|Foreign direct investment|300|2,246|2,991|5,186|4,707|11,735|9,154|9,125|
|Equity investnent|150|0|_65_|_451_|861|1,946|2,873|3,339|

# 1. Estimate

2. 1997 figure includes only international bond issuance.

_Source:_ World Bank, _Global Development Finance_ 1998; Bondware Database, _Euromoney;_ International Monetary Fund, _International Finance Statistics_ (IFS) July 1998; and Bank for International Settlements (BIS).

Fifth, a large part of the total _stock_ of debt (around 38 percent in 1997) is owed to commercial banks (albeit proportionately less than in East Asia-55 percent-and Latin America-42 percent; see table 2). More important, the overall maturity of debt owed to commercial banks has been progressively shortening; 40 percent of commercial bank debt now has a maturity of one year or less, compared with about 30 percent in 1994. Judged by other regions' standards, that proportion can be expected to continue to increase.

Finally, compared with East Asia and Latin America, a much greater proportion of the lending by international banks is being borrowed by domestic banks in CEE countries (an average of 50 percent during the period 1994-97, compared with 40 percent in East Asia and 25 percent in Latin America). In the CEE region there has also been a shift in the lending by international commercial banks toward the private nonbank sector, while lending to the public sector has been steadily declining.

_5_