rafmacalaba/datause-encoder-data huggingface dataset:
# Non-linear e¤ects of tax changes on output: The role of the initial level of taxation<sup>�</sup>

Samara Gunter Daniel Riera-Crichton Colby College World Bank Carlos Vegh Guillermo Vuletin World Bank World Bank JEL Classi...cation: E32, E62. Keywords: tax multiplier, tax policy, tax rate, value-added tax, non-linear, narrative.

�This pap er was previously circulated under the title “Non-linear distortion-based e¤ects of tax changes on output: A worldwide narrative approach.” We would like to thank sem inar participants at the International M onetary Fund, Inter-Am erican D evelopm ent Bank, World Bank, Federal Reserve Board, Europ ean Stability M echanism , Central Bank of Argentina, Central Bank of Chile, Central Bank of Spain, UN Econom ic Com m ission for Latin Am erica and the Caribb ean, G eorge Washington University, Johns Hopkins University, W illiam s College, D avidson College, Universidad Nacional de La Plata, Sao Paulo School of Econom ics-G etulio Vargas Foundation, Escola Sup erior d’Adm inistració i D irecció d’Em preses (ESAD E), Institute of Education and Research (INSPER), International M acro Workshop-RID G E, Latin Am erican and Caribb ean Econom ic Association (LACEA), Annual Sym p osium of the Spanish Econom ic Association, and XLIV M eeting of the Network of Central Banks and Finance M inistries-ID B for m any helpful com m ents and suggesttions. We would also like to thank Alb erto Alesina, Silvia Albrizio, Leop oldo Avellan, Frank Bohn, Fernando Broner, Eduardo Cavallo, Javier G arcia-Cicco, Aitor Erce, D avide Furceri, Vitor G aspar, Alejandro Izquierdo, Herm an Kam il, G raciela Kam insky, Aart K raay, G erardo Licandro, Alessandro Notarpietro, Peter M ontiel, Eduardo M oron, Ilan Noy, Pablo O ttonello, Peter Pedroni, Javier Perez, Rob erto Ram os, D avid Robinson, D iego Saravia, O lena Staveley-O ’Carroll, Jay Shambaugh, Ham ilton Taveras, Teresa Ter-M inassian, and M artin Urib e for helpful discussions, the IM F Archives sta¤ m emb ers for their hospitality during our rep eated visits, and José Andrée Cam arena Fonseca, D iego Friedheim , Pablo Hernando-Kam insky, Luis M orano, Q i Sun, and Siyang Xu for excellent research assistance.
the characteristics of small geographical units. In addition, nighttime lights have a tendency to extend into neighboring regions, a phenomenon called blooming effect (Small et al., 2005; Abrahams et al., 2018), which complicates the identification of the actual source of lights. Lastly, nighttime light data is saturated at a certain threshold of light intensity which is often exceeded in very bright urban cores, thus making the analysis of nighttime light data at small geographies difficult. The degree to which these shortcomings of the data are relevant for studies in developing counties is however largely unknown, as economic data at small geographies is hard to find in the relevant context.

This paper presents an alternative to nightlights and use Landsat imagery to predict economic activity. It is in the tradition of other papers that have explored the potential of other remotely sensed data products that are not based on nightlights. A variety of studies use high-resolution imagery and convolutional neural network models to extract object features that are then used to predict economic outcomes such as poverty (Jean et al., 2016; Engstrom et al., 2016; Babenko et al., 2017; Perez et al., 2017). The drawback to these approaches is that they tend to use computationally intensive algorithms and rely on expensive proprietary satellite imagery. While these studies have made remarkable advances in the use of remote sensing in economics, applied researchers interested in measuring economic variables remotely might lack the skills or resources to use such methods or data.

This study therefore follows a different route and rely on medium-resolution imagery and simple regression models to predict economic outcomes from sky. It looks at imagery from the Landsat program, a public domain satellite product that has been used to survey Earth over the last thirty years, and investigate its prediction power for economic activity. In contrast to nightlights, Landsat satellites measure light reflectance during daytime at different wavelengths of the spectral band. Having a much finer spatial (30m) and temporal resolution (16 days) than nightlights, Landsat imagery have the potential to detect very different correlates of economic activity and income on the ground. Yet unlike high-resolution imagery 3
decline in corporate other investment debt is offset by an increase in corporate portfolio debt outflows. EM public sector sees a drop in both portfolio and other investment outward investment around the crisis, but portfolio debt recovers robustly in the following years. However, public sector outflows, and total EM debt outflows, are clearly dominated by reserves, as seen in panel (d), with a large buildup and collapse mirroring the private sector inflow and outflows pattern.

# **4 Empirical Analysis**

## **4.1 Comovement of Capital Inflows and Outflows**

So far, we have have analyzed the patterns in the raw data. These dynamic patterns in the raw data can be due to inflows and outflows by sector responding to domestic and external shocks differentially. In this section, we will analyze how these responses work in detail.

Table 1 presents correlations of inflows and outflows across sectors. These are partial correlations of debt flows/country GDP, conditional on country fixed effects, lagged log of VIX, and lagged GDP growth.<sup>34</sup> The sample is our asset flow sample detailed in Appendix A.4, consisting of 31 countries (15 advanced and 16 emerging) over 2004q1-2014q4. The public sector consists of general government and central bank sectors. Debt is the sum of portfolio debt and other investment debt, and also reserves in the case of public sector outflows.

The strength of the inflow-outflow correlation within the bank sector is striking. In fact, the only positive correlation that is more than 50 percent for inflows and outflows is the correlation between banking inflows and outflows. Conditioning on countries’ own GDP growth and the VIX, both of which can drive capital flow behavior as we show below, is 34The patterns hold in unconditional aggregate correlations.

24
- Michaelowa, Axel, Marcus Stronzik, Frauke Eckermann and Alistair Hunt. 2003. Transaction costs of the Kyoto Mechanisms. Climate Policy 3(3), 261-278.

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Nelson Kristen C., Ben H. J. de Jong. 2003. Making global initiatives local realities: carbon mitigation projects

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- Peters-Stanley, Katherine Hamilton, Thomas Marcello and Milo Sjardin. 2011. Back to the Future: State of the Voluntary Carbon Markets 2011. New York and Washington: Ecosystem Marketplace and Bloomberg New Energy Finance.

- Pfaff, A. S. P., S. Kerr, R. F. Hughes, S. Liu, G. A. Sanchez-Azofeifa, D. Schimel, J. Tosi, and V. Watson. 2000. The Kyoto Protocol and payments for tropical forest: an interdisciplinary method for estimating carbon-offset supply and increasing the feasibility of a carbon market under the CDM. Ecological Economics 35(2), 203-21.

- Platjouw, Froukje Maria. 2009. Reducing greenhouse gas emissions at home or abroad? The implications of Kyoto's supplementarity requirement for the present and future climate change regime. Review of European Community and International Environmental Law 18(3), 244-256.

- Post, W.M., Emanuel, W.R., Zinke, P.J., Stangenberger, A.G., 1982. Soil carbon pools and world life zones. Nature 298, 156-159.

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- Republic of Turkey. 2011. Ministry Of Environment and Forestry, Climate Change Department Announement, available on the Internet at:

<u>http://www.iklim.cob.gov.tr/iklim/AnaSayfa/Kyoto.aspx?sflang=en</u> (downloaded July 6, 2011.)

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37
We asked each individual to divide their work history into years as a wage worker, a self employed worker, an overseas worker, and an unpaid family worker or apprentice. Perhaps the most surprising finding is that more than half of the own account workers have spent their entire working life as a self employed worker. Only a third have ever been a wage worker, and only 5% have worked overseas. SME owners are significantly more likely to report having been a wage worker at some point during their life (45 percent).

We measure two attitudes suggested by two different strains of the entrepreneurship literature: attitudes toward risk and psychological measures. We measure risk in two ways. First, we conducted a lottery exercise with payoffs representing between a few hours and a few days wages. For the own account workers, we asked for behavior on three separate lotteries. We then rolled a ten-sided die and played one of the lotteries for real money. The payoffs were 40 rupees for the certain outcome and 100 rupees for the risky outcome. These represent about 3 hours and one day’s reported mean earnings for the own account workers. Before actually playing we asked respondents which outcome they would choose as the probability of winning varied from 10 to 100 percent. We use these responses to calculate a coefficient of relative risk aversion.

For both the SME owners and wage workers, we first conducted the lottery exercise with the same payoffs. We then repeated the exercise with both the certain and risky payoffs increased by a factor of five. Since SME owners have much higher incomes than either own account or wage workers, the higher payoffs we used to come closer to matching the payoffs ass a proportion of income. In practice, only a few respondents changed their behavior with the higher payoffs. The CRRAs shown on Table 5 therefore use the 40/100 payoffs for all three groups. A higher CRRA indicates more aversion to risk. The lottery exercise produces results which run counter to expectations. SME owners are the more risk averse than either own account or wage workers.

We obtain results more in line with expectations by asking more general questions about the willingness to take risks. Respondents were asked how willing they were, on a scale of one to ten, to take risks in life, and how willing they were to take financial risks. SME owners are the most willing to take risks (6.9 for life risks and 5.6 for financial
far, and asked whether there were particular topics people would like to see covered, again encouraging people to check out the blog.

# _5.2. Follow-up Survey_

A follow-up survey was then sent at the start of June 2011 to both treatment and control groups. This was therefore 2 months after the blog was launched. There were two main reasons for this time frame. First, we were concerned that some of the sample would graduate or change email addresses over the summer, making it hard to track them over a longer period. Second, given the quite rapid increase in readership experienced over the first two months of the blog and links to Development Impact from other blogs such as Marginal Revolution, IPA, and Chris Blattman, we were concerned that the control group might rapidly become readers of the blog, thereby reducing the encouragement effect.

The follow-up survey was answered by 445 of the 619 initial respondents (72 percent), which is high for an online survey. The response rate was slightly higher in the control group than in the treatment group (74.9 percent vs. 68.8 percent, p=0.092). A comparison of those who responded quickly to those who responded after multiple attempts shows no significant differences in age, gender, location, or interest in becoming an academic researcher. However, those who required more time and effort to get them to reply were less likely to be frequent blog readers at baseline. Appendix Table 1 however shows that the treatment and control groups are still balanced on baseline characteristics among the follow-up sample, so that any selective attrition according to observable characteristics is not significantly related to treatment status.

Neither the treatment nor the control group was told that they were in an experiment, and both had been surveyed previously on similar topics. It therefore seems unlikely that any results obtained are the result of Hawthorne effects.

20
|||Promot|Type of Pro<br>ing contestabl<br>|duct Market Re<br>emarkets|gulation (PM|R) studied in a<br>Liberaliz|paper<br>ing firm opera|tion<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
||Pro-<br><br>competiti <br>vePMR<br>s<br>in general|Lowering<br>entry barriers<br>and<br>delicensing|Liberalizing<br>markets to<br>tradeand<br>foreign<br>Participation|Distortionary<br>government = <br>policiesand = <br>practices<br>|Antitrust law<br>and its<br><br>enforcement<br>|Distortionary<br><br>|PMR<br>|Regulations<br>affecting firm<br>ownership<br>structures|Thespecial<br>roleof<br>upstream<br>sector<br>regulation|Mixof<br>twoor<br>more<br>PMAs|Number<br>ofpapers<br>fooking<br>ateach<br>outcome|
|Business<br>Resilience/<br>Sustainability!<br>gInnovation|1|&|]|0|0|2|2|3|8|20|
|<br>5 Competition<br>3<br>(national)<br>oa|10|12|2|0|3|1|oO|6|8|a2|
|3 Competition<br>= and FDI (cross-<br>S<br>border)<br>-|2|0|q|Qo|0|2|0|1|6|18|
|o<br>3<br>informality<br>=|1|4|0|0|0|1|Oo|0|1|6|
|oa<br>-_<br>©<br>Interaction<br>2PMRandLMR<br>|al|5|3|1|0|2|1|1|i)|1|9|
|<br> <br>Investments|2|2|2|Lt]|0|4|2|3|6|19|
|Reallocation<br>between<br>sectors! firms<br>Implications<br>forworkers<br>(added in<br>bottom half if<br>yes)|3<br>i5|6<br>10|a<br>8|2<br>1|0<br>3|t<br>3|1<br>2|2<br>2|2<br>5|2g<br>34|
|Consumption<br>|5<br>||<br>|3<br>|U1]<br>|F<br>|1<br>|oO<br>|0<br>|2<br>|13<br><br>|
|Employment|-_|i:|8|1|0|2|1|1|5|a|
|Employment<br><br>|||||||||||
|w<br>(particular<br>£ groups)<br><br>|1|5|4|1|0|0|0|ii}|it|}<br>10|
|3<br>Labor<br>= earings<br>|rd|4|4|1|2|a|1|1|a|18|
|=]<br>cH<br>=<br>Labor<br>_<br>earnings<br>&<br>(particular<br>i groups)<br>|it)|5|2|1|2|0|0|Li)|0|10|
|i]<br>2 Labor<br>eC earnings<br>Inequality|2|2|1|0|1|0|0|0|0|4|
|Labor<br>reallocation|3|6|2|1|0|4|oO|1|2|16|
|Productivity<br>and Training|6|1|2|Lt]|0|2|3|2|3|13|
|Social<br>cohesion|it}|i]|2|0|0|0|0|a|Lt|}<br>z|
|Number of<br>papers<br>studyingeach||||||||||Total<br>numberof<br>papers=|
|<br>type of PMR|16|15|10|2|3|10|4|6|13|<br>,79|
|Totalnumberof (u<br>‘outcomes.|nique) pape|rs = 79, Each s|tudy appears on|ly once per cal|urnn, Most stu|dies appear in|more than one|row as the|y look at|multiple|
46 adopted for each household group.<sup>40</sup> What Table 8 shows is that under a universalistic scheme, the transfer program to eradicate total poverty will<sup>be beyond the means of the</sup> government.<sup>4</sup> ' In the case of a perfect targeting program, the cost<sup>of a poverty-reducing</sup> program would represent 3 percent of GDP.

40 The size of the transfers are obtained by shifting the function<sup>such that the z-score equals</sup> -2.5; i.e. the head count ratio equals zero for each group.<sup>Note that the size of the transfer</sup> program is not equal to T. because of the log-normality<sup>assumption whereas Equation 41</sup> assumes that it is uniformly distributed.

**4'** Using _T._ to calculate the cost of a universalistic<sup>targeting program will give slightly less</sup> than 50 percent of GDP. As a comparison, Boetang et al. (1991)<sup>calculate the cost of a perfect</sup> targeting scheme as well as a universalistic program for<sup>Ghana. For the country as a whole, the</sup> perfect targeting program amounts to 7 percent of<sup>GDP, while the universalistic program</sup> amounts to 60 percent of GDP. World Bank (1990c)<sup>shows that for Malawi, a perfect targeting</sup> program will cost 4 percent of GDP.
53

|S~~~~<br>**.g .......**<br>_;:::A_<br>_X_<br>1<br>ESA<br>.1<br>MINISTRIES WHICH SPEND<br>|_V4.:.S_<br>|gnr<br>|::<br>. <br>7<br>9o<br>W<br>Lek<br>|<br> able_:.4_,-Z<br>pRnti<br>of 7Albai<br>si<br>A<br> <br>|. ....<br>'V--,<br>.>. '<br>.<br>.<br>a<br>at<br>|vX<br>X'<br>n-,,<br>i-a<br>|.....<br>|<br>Perc<br>|**_-n;_**<br>ent<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|ATSUBNATIONAL LEVEL|Distdcts|Municipalities|Communes|National|TOTAL|Districts|Municipalities|Communes|National|TOTAL|
|Ministry of Agriculture &Food|133,700|-976,3|00||1,110,00a|12|-||8|100|
|Ministry of Construction|112,232|2,925,620||216,470|3,254,322|3|_90_||7|100|
|Ministry of Transport|677,445|188,300||1,525,255|2,391,000|28|8|-|64|100|
|Ministxy of Education|169,132|<br>882,541|1,444,552|1,259,645|<br>3,755,870|_5_|23|38|34|100|
|Ministry of Health|2,058,000||-|1,338,000|3,396,000|61|-|-|39|100|
|OthersnI/|118,101|383,004|13,398|233,621|748,124|1C|_51_|2|31||
|TOTAL|_3,26,610_|I<br>_4,379,46j_|1,457,950|_5,549,291_|14,655,31|61I_<br>22|30|10|3|1o|

# SBrne: Minity of Finance and Economy, Government of Albania

1/ Ministry of Culture, Youth mad Sport; Ministry of Trade and Foreign Economic Relations.
# **Figure 1. Fiscal Deterioration in Tamil Nadu**

<!-- Start of picture text --> 8.0%<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>-1.0%<br>Fiscal Deficit Revenue Deficit Primary Deficit Consolidated Fiscal Deficit<br>Percent of GSDP<br>1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/2000 2000/01 2001/02 2002/03 2003/04<br><!-- End of picture text --> Source: Government of Tamil Nadu’s Budget documents.

Note: The deficits in 2000/01 and 2001/02 underestimate the actual levels because they do not account for non-pension arrears equaling 1.7 percent of GSDP in 2000/01 and 2 percent of GSDP in 2001/02. The deficit (revenue and fiscal) in 2002/03 and 2003/04 includes partial clearance of Rs.30.62 billion in payment arrears accumulated from the late 1990s.

The fiscal stress of Indian states echoed the fiscal deterioration or crises of subnational governments in several other emerging economies. Examples include the three subnational debt crises in Brazil in the 1980s and the 1990s, the subnational defaults associated with the financial crisis in Mexico in 1994-1995, and the defaults by close to two-thirds of Russian subnational governments in 1998-1999. Subnational governments in countries such as Hungary, Colombia, and South Africa also experienced varying degrees of fiscal stress in the 1990s.

Since subnational fiscal sustainability analysis is an important, yet largely ignored, topic, the paper makes three extensions to the literature. First, it applies the standard framework for fiscal sustainability analysis presented in Burnside (2005) at the subnational level. The exposition emphasizes the differences between fiscal sustainability analysis at the national and subnational levels. Second, by conducting sensitivity analysis and discussing key fiscal risks and constraints to fiscal adjustment, the paper also attempts to take into account the effects of uncertainty – one aspect of fiscal sustainability analysis that has been largely ignored in the traditional literature. Finally, we do not limit the analysis to calculations of 3
their prior wages. It is the program’s weak coverage and low benefit levels the scheme’s impact on poverty is modest (Ravallion, 2009b).

This pattern of restricted coverage identified in the literature on DB may well be a sensible response by the authorities to the incentive concerns about the program’s design. If indeed the program was creating a poverty trap, then rationing access would be the only way of dealing with the problem without more fundamental reforms in the program’s design. Absent such reforms, one would be loath to expand coverage to all who claim eligibility.

While we agree that one should be concerned about expanding coverage if the program is generating a poverty trap, our results do not suggest that this is an important concern in practice. Expanded coverage would probably not entail significant efficiency costs due to the program’s incentive effects. The greater concern would appear to be whether the scheme is adjusting flexibly enough to household income shocks to provide adequate protection. That would call for design changes to assure a higher BWR than appears to be the case at present. A combination of such design changes and expanded coverage would be needed to assure greater poverty impact.

# **6. Conclusions**

Past discussions of redistributive policies, including antipoverty programs, have been somewhat polarized between those who emphasize incentive effects and those who ignore them. Most empirical studies of tax and benefit incidence have used either statistical methods based on survey data or simulations (assuming that the policy works as it is designed) to study incidence relative to incomes net of taxes paid or benefits received. As is well recognized in principle, these methods ignore behavioral responses. The statistical method also ignores income measurement errors, although that problem is less well recognized. Nonetheless, these practices largely continue uncritically in applied work on benefit incidence—work that has also had a great deal of policy impact.

We have offered a new applicable approach to estimating average benefit incidence that can be implemented with essentially the same data as prevailing methods, but without ignoring incentive effects and measurement errors. The point of departure from past work is that we focus directly on what is arguably the key policy parameter, namely the benefit withdrawal rate (or marginal tax rate). Our key assumption is that incentive effects and classical measurement errors only impact certain income components but that these still have predictive power for isolating 19