rafmacalaba/datause-encoder-data huggingface dataset:
|**Variable**|**Description**|**Source**|
|---|---|---|
|_e_|Gross secondary school enrollment, average 1980‐2000|WDI|
|_e0_|Initial gross secondary school enrollment, 1980|WDI|
|_y0_|Initial level of GDP per capita, 1980|PWT|
|_gini_|Gini index for income|WDI|
|_life0_|Life expectancy, 1980|WDI|
|_vol_|Volatility of GDP per capita growth|WDI|
|_polity_|Polity 2 indicator|PIV|
|_pavroad_|Percentage of paved roads|WDI|
|_cred_|Credit to private sector (% GDP)|WDI|
|_war_|Dummy variable for occurrence of war|‐‐|
|_t_<sup>_d_</sup>|Disaster risk, based on total disasters per thousand squared km|EMDAT|
|_c_<br>_d_|Disaster risk, based on climatic disasters per thousand squared km|EMDAT|
|_g_<br>_d_|Disaster risk, based on geologic disasters per thousand squared km|EMDAT|

WDI: World Development Indicators (World Bank, 2006); PWT: Penn World Tables (Summer et al. 2006); PIV: Polity IV Database(Marshall and Jaggers, 1995); EMDAT: OFDA/CRED International Disaster Database (EMDAT, 2004).

As potential explanatory variables, we consider thus proxies of initial income and initial school enrollment ( _y_ 0 and _e_ 0 ) in order to account, on the one hand, for wealth‐induced human capital accumulation effects and for the observed persistence of human capital accumulation variables within countries, as well as for potential convergence in human capital across countries. In order to capture differences in income distribution across states we include also the country‐wide Gini index, and the potential effect of macroeconomic instability is analyzed using the standard deviation of annual GDP growth rates as a proxy. We also control for differences in health by including life expectancy at birth in the initial period. Credit constraints are included into the model by using a proxy of financial depth: domestic credit provided to the
Table 7: Interaction estimations for industry capital and land intensity

|||Plants|DV: C|hange in urba|nization share<br>Employment|for district-in|dustry|Output||
|---|---|---|---|---|---|---|---|---|---|
||1994-2005|1994-2000<br>|2000-2005|1994-2005|1994-2000<br>|2000-2005|1994-2005|1994-2000<br>|2000-2005|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||A|. Estimations|using industr|y land intensit|y|||
|Initial urban share|-0.824+++|-0.840+++|-0.777+++|-0.811+++|-0.811+++|-0.739+++|-0.730+++|-0.814+++|-0.761+++|
||(0.033)|(0.031)|(0.032)|(0.034)|(0.030)|(0.035)|(0.031)|(0.029)|(0.031)|
|Education level x|-0.021|-0.026+|-0.015|-0.026|-0.020|-0.018|-0.028|-0.007|-0.019|
|Industry land intensity|(0.019)|(0.015)|(0.019)|(0.018)|(0.014)|(0.019)|(0.023)|(0.019)|(0.023)|
|Infrastructure level index x|-0.036++|-0.042+++|-0.030+|-0.022|-0.039+++|-0.016|-0.022|-0.035++|-0.012|
|Industry land intensity|(0.016)|(0.016)|(0.016)|(0.015)|(0.014)|(0.015)|(0.018)|(0.017)|(0.019)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.428|0.490|0.399|0.445|0.486|0.406|0.418|0.451|0.433|
|<br>|||B.|Estimations|using industry|capital intens|ity|||
|Initial urban share|-0.824+++|-0.839+++|-0.776+++|-0.810+++|-0.809+++|-0.739+++|-0.731+++|-0.817+++|-0.761+++|
||(0.034)|(0.031)|(0.032)|(0.034)|(0.030)|(0.035)|(0.031)|(0.029)|(0.031)|
|Education level x|-0.026|-0.025|-0.018|-0.022|-0.029+|-0.012|-0.016|0.000|-0.008|
|Industry capital intensity|(0.016)|(0.016)|(0.016)|(0.017)|(0.015)|(0.017)|(0.020)|(0.017)|(0.020)|
|Infrastructure level index x|-0.025+|-0.026+|-0.025+|-0.015|-0.024+|-0.014|-0.007|-0.027|-0.004|
|Industry capital intensity|(0.015)|(0.015)|(0.015)|(0.015)|(0.013)|(0.015)|(0.018)|(0.019)|(0.018)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.428|0.489|0.399|0.444|0.486|0.406|0.417|0.451|0.432|

Notes: Estimations consider changes in district-industry employment shares in urban areas for 1994-2005. Positive values indicate an increase across the period in the urban employment share of the district-industry. Estimations report standard errors clustered by district, include state and industry fixed effects, include 1700 observations, and weight observations by the interaction of log district size and log industry size. + significant at 10% level; ++ significant at 5% level; +++ significant at 1% level.
Karabarbounis L, Neiman B. 2014. The global decline of the labor share. _The Quarterly Journal of Economics_ **129** : 61–103.

URL http://qje.oxfordjournals.org/content/129/1/61.abstract Krusell P, Ohanian LE, R ́ıos-Rull JV, Violante GL. 2000. Capital-Skill Complementarity and Inequality: A Macroeconomic Analysis. _Econometrica_ **68** : 1029–1054.

URL http://ideas.repec.org/a/ecm/emetrp/v68y2000i5p1029-1054.html

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Voskoboynikov IB. 2012. New measures of output, labour and capital in industries of the Russian economy. GGDC Research Memorandum GD-123, Groningen Growth and Development Centre, University of Groningen. URL http://ideas.repec.org/p/dgr/rugggd/gd-123.html Vries Kd, Vries Gd, Timmer M. 2014. Patterns of Structural Change in Developing Countries. GGDC Research Memorandum GD-149, Groningen Growth and Development Centre, University of Groningen.

URL http://ideas.repec.org/p/dgr/rugggd/gd-149.html Vu K. 2005. Measuring the impact of ict investments on economic growth. Manuscript, Harvard University.

# Appendix A. Capital Stocks Based on WITSA & ITU

As described in Section 2, we construct nominal ICT investment series based on data from the the World Information Technology and Services Alliance (WITSA) and the International Telecommunication Union (ITU) databases. We then treat NICT investment as the residual between total capital investment in the PWT and our measure of ICT investment.

WITSA produced reports (Digital Planet 1998, 2000, 2002, 2004, 2006, 2008, 2010) that provides data on Information Communication Technology investment from 1993 to 2011. The number of countries varies across the reports (55-75 countries). WITSA uses data provided by International Data Corporation (for reports 1998, 2000, 2004) & Global Insights, Inc. (for reports 2004, 2006, 2008, 2010). For more details please see http://www.witsa.org.

ITU publishes the World Telecommunication/ICT Indicators database covering data on 150 telecommunication/ICT statistics from 1975 to 2013 for over 200 countries. ITU collects it data from annual questionnaire that are sent to official country contacts. For more details please see http://www.itu.int/en/ITU-D/Statistics/Pages/publications/wtid.aspx 33
# **Can E-Filing Reduce Tax Compliance Costs in Developing Countries?**

# **1. Introduction**

Tax compliance costs (TCC), defined briefly as the costs of complying with tax regulations, are usually highly regressive in firm size and therefore are a notorious burden, especially for small and medium size businesses (SMEs).<sup>1</sup> By reducing these costs, firms can allocate more resources towards productive activities.<sup>2</sup> In this respect, electronic filing (e-filing) is a promising candidate that can reduce TCC for taxpayers, by making the reporting process easier and less prone to errors and reducing visits to the tax office.<sup>3</sup> This paper takes the first steps to investigate whether, and under what circumstances, the adoption of electronic filing decreases tax compliance costs (TCC) for small and medium size enterprises (SME) in developing countries.

Superficial consideration may suggest that e-filing should reduce TCC since it simplifies the workload of tax compliance (TC) with respect to (mainly) filling out and submitting tax forms. However, the results of this paper show that there may be offsetting costs associated with e-filing such as additional capital that may need to be invested to adopt e-filing, the time required to learn the system and practical implementation of the policy in the country.<sup>4</sup> In particular, a learning curve associated with e-filing experience is evident: as firms gain more experience with e- filing, they enjoy further reductions in TCC in the long-run. However, the curve is concave, which simply implies that the learning effect diminishes over time.<sup>5</sup> Our interpretation of this result is that e-filing can potentially decrease TCC in the long-

> 1 Sanford (1994 and 1995), James (2003), Evans (2003) and Coolidge (2012) provide detailed literature reviews and empirical findings.

> 2 Government of New Zealand (2007), Gatti and Honoratti (2008), and OECD (2008) discuss benefits that could be generated from reducing tax compliance cost for small and medium size firms.

> 3 Che Azmi, et. al., (2010); E-filing may also reduce tax-administrating costs for the public sector (see, e.g., IFC 2012) although, these are not discussed here.

> 4 E.g. whether the policy was enforced or optional; whether the policy replaces the old paper filing or requires double filing.

> 5 Nevertheless, this effect was only captured in the South African case due to limitations in data from other countries.

2
- Crepon, B., F. Devoto, E. Duflo, and W. Parient ́e (2015). Estimating the impact of microcredit on those who take it up: Evidence from a randomized experiment in morocco. _American Economic Journal: Applied Economics 7_ (1), 123–50.

- De Lima, P., D. Revoltella, J. L. Rodriguez Meza, and H. Schweiger (2016). _What’s holding back the private sector in MENA? Lessons from the enterprise survey_ . World Bank.

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- Enders, Z., F. H ̈unnekes, and G. J. M ̈uller (2019a). Firm expectations and economic activity. _CESifo Working Paper 7623_ .

- Enders, Z., F. H ̈unnekes, and G. J. M ̈uller (2019b). Monetary policy announcements and expectations: Evidence from German firms. _Journal of Monetary Economics 108_ , 45–63.

- Fisman, R. and J. Svensson (2007). Are corruption and taxation really harmful to growth? firm level evidence. _Journal of development economics 83_ (1), 63–75.

- Friesen, J. and K. Wacker (2019). Who is afraid of informal competition? the role of finance for firms in developing and emerging economies. _The European Journal of Development Research 108_ (31), 1126–1146.

- Gennaioli, N., Y. Ma, and A. Shleifer (2016). Expectations and investment. _NBER Macroeconomics Annual 30_ (1), 379–431.

- Gonzalez, A., J. E. Lopez-Cordova, and E. E. Valladares (2007). The incidence of graft on developing-country firms. _World Bank Policy Research Working Paper 4394_ .

- Gonzalez, A. S. and F. Lamanna (2007). Who fears competition from informal firms ? evidence from latin america. _World Bank Policy Research Working Paper 4316_ .

- Gronau, R. (1974). Wage comparisons–a selectivity bias. _Journal of political Economy 82_ (6), 1119–1143.

- Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. _Annals of economic and social measurement 5_ (4), 475–492.

20
# **Table 4: Probability regressions for new activity specializations at the intensive and extensive margins**

||Dependent<br>|variable: wheth<br>|er country_c_has a<br>_t+5_(xj,j<br>|comparative a<br>,t+5= 1)<br>|dvantage in act<br>|ivity_j_at time<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|ASj,j,t<br>|0.068***|0.171***|0.049***|0.083***|0.255***|0.058**|
||(0.023)|(0.006)|(0.019)|(0.029)|(0.006)|(0.023)|
|ωj,j,t|0.050***|0.033***|0.050***|0.045***|0.014***|0.052***|
||(0.005)|(0.003)|(0.004)|(0.005)|(0.003)|(0.004)|
|Year FE|X|X|X|X|X|X|
|Country-Industry FE|X|X|X||||
|Country-Occupation FE||||X|X|X|
|Sample|Total|Advanced|Developing|Total|Advanced|Developing|
|N|137,723|56,919|80,804|137,723|56,919|84,804|
|Adjusted R<sup>2</sup>|0.145|0.150|0.148|0.124|0.165|0.105|

_Notes_ : see Table 3.

_Routine-intense activities_ Advances in information technology have changed the way in which certain tasks are performed. In particular, Autor et al. (2003) argue that computers and robots tend to displace labor in the performance of routine and non-cognitive tasks. This is nowadays typically referred to as routine-biased technological change. Lewandowski et al. (2020) and Caunedo et al. (2022) find that all countries experienced a shift away from routine to non-routine jobs, but its pace was slower in developing countries than in developed countries. Reijnders and de Vries (2018) show that relocation of routine occupations from advanced countries accounted for a major part of this difference, moderating the effect of the technology bias for developing countries. At the same time, innovation generates new tasks and activities and allows for the development of new specializations (Acemoglu and Restreppo 2019).

To investigate possible differences in the development of activity specializations, we constructed for each of our 455 activities a measure of routine task intensity. We closely follow the approach by Acemoglu and Autor (2011) and make a distinction between two types of routine tasks: manual and cognitive tasks based on O*NET measures.<sup>12</sup> Routine manual tasks are more prevalent in

> 12 In this approach, O*NET work activities and work context importance scales are combined and standardized. However, whereas Acemoglu and Autor (2011) construct task measures by broad occupation classes, we match to detailed occupation-industry data from the U.S. Bureau of Labor Statistics and then collapse using labor supply weights to create task measures by activity. See Appendix F for details.

20
The model was applied to a regular latitude/longitude grid covering the whole continent. Each of the 0.5<sup>o</sup> cells (which have an area ranging from about 2500 km<sup>2</sup> to 3100 km<sup>2</sup> ) depending on latitude) is regarded by the model as a small catchment. The model parameters, which define the size of the store and the rate of water removal from it, are derived in part from physical characteristics and in part by calibration. The inputs are monthly rainfall and climatic data required to estimate potential evapotranspiration. Water enters the soil moisture store through precipitation and is removed either by evapotranspiration, surface runoff or subsurface runoff. The water balance component of the model comprises three parameters related to i) surface runoff, ii) sub-surface runoff and iii) maximum catchment water-holding capacity. The monthly soil moisture balance is written as: where:

- Peff = effective precipitation (length/time)

- Rs = surface runoff (length/time)

- z = relative storage (length) (0 ≤ z ≤ 1)

- P = precipitation (length/time)

- Rss = subsurface runoff (length/time)

- P = precipitation (length/time)

- Ev = evapotranspiration (length/time)

- Pet = potential evapotranspiration (length/time) CSmax  = maximum catchment storage (length)

8
The results are shown in Table 1. As can be seen, there are 177 surveys which gives a total of 1,770 data points (for all ten deciles).<sup>13</sup> Each regression is run over 177 points belonging to a given decile. The panel is unbalanced as some countries have many more observations than others. However, since we adjust for unobserved fixed country effects this should not affect the estimated values of the coefficients. The R-square runs between 0.5 and 0.6 for the bottom six deciles and the top decile. For the four uppermiddle deciles, R<sup>2</sup> are lower, ranging between 0.2 and 0.38.

> 13 Some of income distribution (decile) information is lost because of lack of independent variables for those particular countries and years.

15
# **Figure 1**

Fraction who Remit by Destination Country Survey <!-- Start of picture text --> Australia LSIA<br>Belgium IRSHS<br>France DREES<br>Italy NIDI<br>Norway LBI<br>Spain ENI<br>Spain NIDI<br>USA NIS<br>0 .2 .4 .6 .8<br>Fraction who remit<br><!-- End of picture text -->

**Figure 2**

<!-- Start of picture text --> Total Remittances by Destination Country Survey<br>Australia LSIA<br>Belgium IRSHS<br>Italy NIDI<br>Spain ENI<br>Spain NIDI<br>USA NIS<br>0 1,000 2,000 3,000<br>Average total remittances ($ p.a)<br><!-- End of picture text --> The large differences across surveys in both the fraction of migrants who remit and the average total remittances across surveys suggest caution in interpreting any raw statistics from the pooled dataset of all surveys, given that each survey focused on 8
**Table 7a Total and incremental welfare effects under different scenarios A1W-C5W** <u>($ million)</u>

|**A. Alternative FTA**|A1W:|TPP|A2W: US|-EU27|A3W: Indi|a-EU27|A4W: Indi|a-ASEAN|A5W: TPP<br>India-EU27 In|US-EU27<br>dia-ASEAN|
|---|---|---|---|---|---|---|---|---|---|---|
||US|India|US|India|US|India|US|India|US|India|
|1. Allocative efficiency effect|918|-108|1184|-165|-74|4410|-30|5651|2096|11130|
|1.1 Net TC vs. TD|334||541|||3677||3466|912|6715|
|Trade Creation (TC)|709||913|||8640||8293|1465|15173|
|Trade Diversion (TD)|-375||-371|||-4963||-4828|-554|-8458|
|1.2 Other allocative efficiency effect|584||642|||733||2186|1185|4415|
|2.Terms of Trade(TOT)Effects|5434|-211|5169|-222|-91|-2284|8|-3133|10458|-4968|
|**Total EV Change (A)**|**6352**|**-319**|**6353**|**-387**|**-165**|**2126**|**-22**|**2518**|**12555**|**6162**|
||B1W:|A1W|B2W: A|2W|B3W:|A3W|B4W:|A4W|B5W: A|W5|
|**B. Alternative FTA (A) + India-US**|+India|-US|+ India|-US|+ India|-US|+ Indi|a-US|+ India|-US|
||US|India|US|India|US|India|US|India|US|India|
|1. Allocative efficiency effect|1480|1701|1713|1656|327|6945|400|7980|2465|13836|
|1.1 Net TC vs. TD|541|830|720|807|54|3677|72|4929|1005|8685|
|Trade Creation (TC)|973|4136|1197|4126|311|11590|337|11496|1763|16301|
|Trade Diversion (TD)|-433|-3306|-477|-3319|-257|-7913|-264|-6567|-758|-7616|
|1.2 Other allocative efficiency effect|940|871|993|849|274|3268|328|3051|1460|5151|
|2. Terms of Trade(TOT)Effects|8549|-649|8242|-646|2445|-2250|2812|-3250|12731|-4861|
|**Total EV Change (B)**|**10030**|**1052**|**9954**|**1009**|**2772**|**4696**|**3212**|**4730**|**15196**|**8975**|
|**C. Alternative FTA (A) + India-US (B) +**|C1W:|B1W|C2W: B|2W|C3W:|B3W|C4W:|B4W|C5W: B|5W|
|**India MFN Liberalization**|+ India|MFN|+ India|MFN|+ India|MFN|+ India|MFN|+ India|MFN|
||US|India|US|India|US|India|US|India|US|India|
|1. Allocative efficiency effect|819|26116|1085|26065|-165|27054|-167|26729|2078|27338|
|1.1 Net TC vs. TD|184|16285|391|16264|-191|17143|-207|16697|844|17291|
|Trade Creation (TC)|1124|16285|1302|16264|420|17143|457|16697|1824|17291|
|Trade Diversion (TD)|-940||-911||-611||-664||-980||
|1.2 Other allocative efficiency effect|635|9831|694|9801|26|9911|40|10032|1235|10047|
|2. Terms of Trade(TOT)Effects|6103|-12949|5822|-12946|351|-10480|511|-11782|10807|-10109|
|**Total EV Change (C)**|**6922**|**13167**|**6907**|**13119**|**186**|**16574**|**344**|**14947**|**12885**|**17229**|
|**D. Incremental EV Change (B) –(A)**|**3678**|**1371**|**3601**|**1396**|**2937**|**2569**|**3234**|**2212**|**2642**|**2813**|
|**E. Incremental EV Change (C) –(B)**|**-3108**|**12115**|**-3047**|**12110**|**-2586**|**11878**|**-2868**|**10217**|**-2311**|**8254**|

_Source_ : Authors’ simulation results.

Notes: The simulations are based on a hypothetical scenario, namely, reciprocal removal of 100 percent and 50 percent of AVE protections for goods and services respectively.

31
# **5. Conclusions**

This paper aims to examine the drivers of surges in net and gross capital inflows for a sample of 67 countries using quarterly data from 1975 to 2010. We conduct Probit and complementary loglog panel regression analysis. Our findings show that domestic and external factors (pull and push factors, respectively) drive surges in both net and gross capital inflows. While both types of drivers significantly contribute to a higher incidence of surges in industrial countries, domestic factors represent the major driving force of surges in developing countries.

We also provide a characterization of surges in gross inflows for the period 1970-2011. The duration of surges in gross inflows is almost even across countries while we find a recent increase in the amplitude of these surges. This finding implies that surges in gross inflows have become more volatile —especially after 2000— and it is correlated to the fact that financial cycles have also amplified. In fact, gross surges are highly likely to accompany lending booms —with the degree of coincidence varying with the definition of lending boom (approximately, 16 percent for MT-lending booms and almost 40 percent for GVL-booms). In addition, the degree of synchronization between inflow surges and GVL-lending booms have recently increased in industrial countries and decreased in developing countries. Finally, over the past three decades, surges end up in a sudden stop in half of gross inflow episodes for developing countries and in one third for industrial countries.

Our empirical analysis highlights the following results:

- Pull and push drivers help explain the likelihood of episodes of surge in both net and gross inflows regardless of any criteria to define these surges, any sample of countries and whether we use overall or private inflows.

- Pull factors —including, domestic growth, current accounts, exchange rate flexibility, natural resource abundance, and debt-led gross inflows— contribute significantly to explain surges of inflows.

- Current account developments tend to play a larger role explaining surges in net inflows. Our estimates suggest that a greater current account deficit would help trigger a surge in net inflows —as flows of foreign capital come to the domestic economy to finance the external imbalance.

- Financial booms —as captured in this paper by an excessive expansion of credit and overvalued currencies— tend to attract massive capital inflows. The rapid increase in the leverage of the domestic financial economy tends to explain a greater likelihood of surges in gross inflows while the evidence is not as robust for surges in net inflows.

- Push factors —including global policy uncertainty, S&P 500 returns and volatility— play an important role in driving surges of inflows —and, especially, for industrial countries.

- The greater leverage of US brokers and dealers —which measures the expanded balance sheets of US financial institutions— helps explain both surges in net and gross inflows.

27