rafmacalaba/datause-encoder-data huggingface dataset:
Dadush U and Z Wyne (2012), “Can Spain and Italy export their way out of trouble?”, VoxEU.org, 8 June.

Davis S and J Haltiwanger (1992), "Gross Job Creation, Gross Job Destruction, and Employment Reallocation," _The Quarterly Journal of Economics_ 107(3), 819-63.

Dollar D and NE Wolff (1993), _Competitiveness, Convergence and International Specialization_ , MIT press.

Durand M and C Giorno (1987), “Indicators of International Competitiveness: Conceptual Aspects and Evaluation”, _OECD Economic Studies_ No. 9, 147-182.

ECB (2005), Competitiveness and the export performance of the euro area, _Occasional Paper Series_ 30, European Central Bank.

Fagerberg J (1988),“International Competitiveness”, _The Economic Journal_ 391, 355-374.

Farole J and J Reis (2012), Trade Competitiveness Diagnostic Toolkit. _World Bank_ .

Farole J, J Reis and S Wagle (2010), Analyzing Trade Competitiveness: A Diagnostic Approach. _The World Bank Policy Research Working Paper 5329._ Finicelli A, M Sbracia and A Zaghini (2011), `A disaggregated analysis of the export performance of some industrial and emerging countries', _International Economics and Economic Policy_ 8(1), 93-113.

Gaulier G and V Vicard (2012), “Current account imbalances in the euro area: competitiveness or demand shock?” _Banque de France Quarterly Selection of Articles_ No. 27.

Jayet H (1993), _Analyse Spatiale Quantitative: Une Introduction_ Paris. Economica.

Jiménez N and E Martin (2010), A constant market share analysis of the Euro Area in the period 1994-2007, _Banco de España Economic Bulletin_ , January.

Hanson G and R Robertson (2010), “China and the Manufacturing Exports of Other Developing Countries”, _NBER Chapters,_ in _China's Growing Role in World Trade,_ pages 137-159, _National Bureau of Economic Research, Inc._ Hatsopoulos G, P Krugman and L. Summers (1988) ,“US Competitiveness: Beyond the Trade Deficit” _Science_ 241, pages 200-307.

Hickman NG (1992), “International Productivity and Competitiveness: An Overview” in Hickman eds _International Productivity and Competitiveness_ , Oxford University Press.

Krugman P (1994), “Competitiveness: A Dangerous Obsession” Foreign Affairs, March/April.

Krugman P (1996), “Making Sense of the Competitiveness Debate” _Oxford Review of Economic Policy_ , vol 12, no. 3.

Krugman P and GN Hatsopoulos (1987), “The Problem of US Competitiveness in Manufacturing” _New England Economic Review_ , January/February.

Laursen K (1999), “The impact of technological opportunity on the dynamics of trade performance”, _Structural Change and Economic Dynamics_ 10(3-4), 341-357.

Leamer E and R Stern (1970), _Quantitative International Economics_ , Allyn and Bacon, Boston, Chp. 7.

36
- Claessens, Stijn, and Neeltje Van Horen, N., 2009, ―Being a Foreigner among Domestic Banks: Asset or Liability?‖ Munich Personal RePEc Archive Paper No. 13467, February.

- Clarke, George R. G. 2009. "Beyond Tariffs and Quotas: Why Do African Manufacturers Not Export More?" _Emerging Markets Finance and Trade_ 45(2):44–64.

- Clarke, George R. G. 2011. ―"Are Managers' Perceptions About Constraints Reliable? Evidence From a Natural Experiment in South Africa." _Journal of Globalization and Development_ 2(1):1–28.

- Correa, Paulo, and Mariana Iootty. 2009. "The Impact of the Global Economic Crisis on the Corporate Sector in Europe and Central Asia: Evidence from a Firm-Level Survey." Enterprise Note 9. World Bank, Washington DC.

- Correa, Paulo, and Mariana Iootty. 2010. ―Will the Crisis Affect the Economic Recovery in Eastern European Countries? Evidence from Firm Level Data.‖ World Bank Policy Research working Paper number 5278, World Bank, Washignton DC.

- Corsetti, Giancarlo, Paolo Pesenti, and Nouriel Roubini, 1999. ―What Caused the Asian Currency and Financial Crisis?‖ _Japan and the World Economy,_ 11(3): 305-373.

- Cull, Robert and Maria Soledad Martinez Peria, 2008. ―Crises as Catalysts for Foreign Bank Activity in Emerging Markets.‖ In Justin Robertson, (ed.), _Power and Politics After Financial Crisis: Rethinking Foreign Opportunism in Emerging Markets_ , New York: Palgrave Macmillan, pp 52-81.

- Cull, Robert and Maria Soledad Martinez Peria, forthcoming. ―Foreign Bank Participation in Developing Countries: What Do We Know About the Drivers and Consequences of this Phenomenon?‖ In Thorsten Beck, Stijn Claessens, and Sergio Schmukler (eds.), _Financial Globalization_ .

- Dages, Gerard, Linda Goldberg, and Daniel Kinney. 2000. "Foreign and Domestic Bank Participation in Emerging Markets: Lessons from Mexico and Argentina." _Federal Reserve Bank of New York Economic Policy Review,_ 6(3):17–36.

- de Haas, Ralph, Yevgeniya Korniyenko, Elena Loukoianova, and Alexander Pivovarsky, 2011. ―Foreign Bank During the Crisis: Sinners or Saints?‖ European Bank for Reconstruction and Development, mimeo.

- de Haas, Ralph and Neeltje Van Horen, 2011. "Running for the Exit: International Banks and Crisis Transmission," Working Paper 124, European Bank for Reconstruction and Development, Office of the Chief Economist.

- De Haas, Ralph and Iman van Lelyveld, 2006. ―Foreign Banks and Credit Stability in Central and Eastern Europe. A Panel Data Analysis.‖ _Journal of Banking and Finance_ , 30(7): 1927-1952.

36
<!-- Start of picture text --> 60 ° ° bad e e<br>e * sce. "g ‘ oe<br>2 es & © Fe, ota<br>o e e . ?. ° . om Sy‘ : q 'e<br>22e 4 Pee‘a reody S28o } aginLs tg+ oe, *e<br>2 ay iin i“<br>aio° es be <n<br>3 “ 8 “3 Le °<br>az 20 8 Pe + | ala<br>3 < ae v 68<br>-E 2 e o<br>aU of eo Scatter® ,<br>“3y Levee —— 95%Predicted confidence interval<br>0 —— 45 degree line<br>0 10 20 30 40 50<br>Tax (incl. SSC) to GDP<br><!-- End of picture text -->

<!-- Start of picture text --> 8 ° °<br>a . © Scatterplot<br>S e wet . oan,°ai<br>=> 6 o°*e ° e e Predicted<br>oy2 e &e- .}Ne Goree ide °<br>Q + x °. e e<br>$ Soe.Md ee Ne xy ar<br>© e b> °F 2 .<br>gz 2 . e 4.2e : S82tt te °<br>So- i ~ o ~ “gefe -<br>e<br>0<br>0 10 20 30 40 80<br>Tax (incl. SSC) to GDP<br><!-- End of picture text --> <!-- Start of picture text --> et<br>2 2 °. .<br>8 ar<br>2— ce ee ets<br>3a . wvPyofanMeh.S A<br>3 e eee “8° iad a. cx wg<br>&Tb10 saeed lanesWU A 5 e<br>3 | & “yh ran °.<br>® ~y x oad yee . e<br>2 4 ° Zk» tba °<br>© 5 veo. oh °<br>Fa oe : eas © pe<br>2 oT eaete “eo — Scattér®<br>aa S * 95% confidence interval<br>0 e —— Predicted<br>0 10 20 30 40 50<br>Tax (incl. SSC) to GDP<br><!-- End of picture text -->

<!-- Start of picture text --> 100 e — Scatterplot<br>£a — 95%Predicted confidence interval<br>°aOo CoC4 Fy ese e e. e oo e<br>E> 50 °ofWS oS e eBOreF he 5 ae<br>£ $4 Vita 7 4 “we vos yo *<br>Fy5 fe eesSos 0 aega aYoreo ws°<br>o Ix fe YW &e 8<br>gS oe2 “ere 08 Pte“P*e Bo i0% at<br>fy= *ve4, eo2, Reeeeey ak<br>> } e Boe 38, ee °<br>=<br>fo) ee ote e<br>-50<br>0 10 Tax (incl,20 SSC) to30 GDP 40 50<br><!-- End of picture text -->
17 indicator of the scope for improvement among their local suppliers, which they try to close by providing support.

Findings from productivity spillover studies also indicate that the technology gap can exercise a positive effect. Blalock and Simon (2009) estimate productivity spillovers in Indonesia and find that, compared to less productive firms, more productive local suppliers—firms with a smaller technology gap with MNC affiliates—experience smaller backward spillovers. A similar finding is presented for Romania by Merlevede, Schoors, and Spatareanu (2014). For the same host economy, Jude (2016) finds that a large technology gap positively impacts upon spillovers from horizontal, forward, and backward FDI . For Mexico, Jordaan (2008a) also identifies a significant positive productivity effect of an interaction variable between the technology gap and the level of inter-industry MNC participation.

_Geographic dimensions._ Factors related to geography are increasingly being found to influence FDI productivity spillovers (Jordaan and Monastiriotis 2018). _Agglomerations of economic activity_ are characterized by productivity premiums because they facilitate inter-firm linkages and the occurrence of knowledge spillovers (Duranton 2015). This suggests that MNC affiliates that locate in such agglomerations within host economies will generate larger positive impacts, as they will find it easier to identify local suppliers and create input-output linkages. Also, _the proximity between FDI and domestic firms_ facilitates and intensifies communication and technology dissemination, resulting in larger productivity spillovers.

Several studies present evidence that productivity spillovers vary across regions within host economies. Anwar and Nguyen (2014) estimate productivity spillovers in eight Vietnamese regions and find that positive backward spillovers materialize in only half of these regions. Their findings also indicate that the effects of the technology gap and human capital on productivity spillovers are not constant across the regions. Jordaan (2008b) reports similar findings of regional heterogeneity across states in Mexico of productivity spillovers among local suppliers. Altomonte and Colantone (2008) report for Romania that positive productivity spillovers occur only in the three regions that incorporate the largest shares of the host economy’s manufacturing sector. Similarly, Monastiriotis (2016) finds for EU transition economies that productivity spillovers only materialize in the core regions within these countries.

Another set of studies examines whether productivity spillovers from MNC affiliates are facilitated by geographical proximity between MNC affiliates and local suppliers. Jordaan (2008b) reports for Mexico that positive backward spillovers materialize both within states and between neighboring states. For China, Madaragia and Poncet (2007) find that spillovers are transmitted between neighboring cities. Wang, Ning, and Zhang (2017) also use data for Chinese cities and identify
**Table 4. Hedonic Price Regressions**

||(1)<br>----------|(2)<br>Full Sample|(3)<br>----------|(4)<br>---------|(5)<br>- Round 2 S|(6)<br>ample Only|(7)<br>----------|
|---|---|---|---|---|---|---|---|
||All Clients|Regulars|Casuals|All Clients|All Clients|All Clients|All Clients|
|Vaginal Sex|24.21|42.10|18.44|62.46|60.71|40.72|26.77|
||(25.10)|(36.33)|(24.65)|(44.83)|(44.05)|(45.36)|(55.98)|
|Anal Sex|77.19|64.78|67.81|165.61|159.12|84.09|80.33|
||(20.93)***|(25.40)**|(21.95)***|(50.80)***|(48.20)***|(42.84)*|(39.35)**|
|Oral Sex|23.68|48.38|7.57|74.38|74.49|71.01|97.23|
||(15.06)|(25.97)*|(14.36)|(24.53)***|(24.52)***|(34.38)**|(41.06)**|
|Massage|61.27|67.40|46.78|54.00|54.20|18.04|16.15|
||(14.18)***|(20.80)***|(14.28)***|(24.80)**|(24.68)**|(28.10)|(26.27)|
|Kissing|51.92|56.65|40.66|50.38|50.48|33.40|40.89|
||(12.06)***|(21.73)***|(9.93)***|(26.11)*|(26.06)*|(29.70)|(26.75)|
|Manual Stimulation|48.60|78.27|30.53|86.55|87.01|61.53|55.70|
||(15.79)***|(29.46)***|(14.42)**|(25.29)***|(25.21)***|(32.39)*|(40.99)|
|Company|72.07|49.48|61.65|82.04|81.97|44.43|27.95|
||(12.94)***|(17.94)***|(13.21)***|(23.95)***|(23.93)***|(28.84)|(23.00)|
|Stripping|39.78|31.46|34.80|49.82|48.60|27.65|43.99|
||(11.22)***|(15.97)*|(11.77)***|(19.08)**|(18.62)**|(24.22)|(25.40)*|
|Sex in Thighs|34.87|25.06|46.95|66.43|64.44|112.76|70.58|
||(15.68)**|(22.92)|(18.15)**|(35.51)*|(34.80)*|(51.55)**|(60.58)|
|Other Activities|58.58|95.93|36.70|24.64|25.89|50.12|22.61|
||(33.41)*|(57.99)*|(25.67)|(38.46)|(38.84)|(51.18)|(37.74)|
|Regular Client|-16.64|||-30.14|-30.25|-13.28|7.56|
||(14.69)|||(25.90)|(25.88)|(26.22)|(32.76)|
|Had Unprotected Sex|42.33|85.90|22.32|39.48||27.37|17.68|
||(16.45)**|(21.01)***|(19.60)|(30.74)||(26.19)|(34.21)|
|Had Unprotected Vaginal Sex|||||38.21<br>(27.91)|||
|Had Unprotected Anal Sex|||||91.11<br>(106.62)|||
|Client is Circumcised||||||-27.16|-21.63|
|||||||(20.92)|(24.29)|
|Client is Very Wealthy||||||39.12|42.42|
|||||||(46.14)|(53.17)|
|Client is Handsome||||||-83.36|-109.78|
|||||||(45.40)*|(54.61)**|
|Sex Worker Believes Client is|||||||3.09|
|at High Risk of HIV / AIDS|||||||(29.46)|
|<br>Client Controls|No|No|No|No|No|Yes|Yes|
|Observations|18824|6731|12093|7082|7082|3856|2404|
|Number of women|192|192|192|108|108|108|95|
|R-squared|0.04|0.04|0.04|0.04|0.04|0.04|0.04|

Note: All regressions are fixed effects regressions with controls for the month and the day of the week. Clustered standard errors (at the individual level) in parentheses. All regressions include controls for the round of data collection. The dependent variable is Kenyan shillings. The exchange rate was approximately 70 Kenyan shillings to $1 US during the data collection period. Columns 6 and 7 include controls for other responses to the questions "Is the client wealthy?" and "Is the client handsome?," but the coefficients are omitted for space. Columns 6 and 7 also include controls for client tribe, occupation and cleanliness. Averages prices paid by type of client: regular - 488 shillings; casual - 434 shillings; overall - 453 shillings. * significant at 10%; ** significant at 5%; *** significant at 1%
|Intercept|1.380|2.008||4.235|
|---|---|---|---|---|
||(1.05)|(1.45)||(1.37)|
|Ln (Sales)|0.255|0.217|0.252|0.818|
||(8.25)|(4.01)|(2.37)|(5.28)|
|Ln (Customers)|0.538|0.577|0.238|0.227|
||(8.76)|(6.72)|(1.55)|(1.07)|
|Ln (Service area)|0.079|0.097|0.133|0.092|
||(7.82)|(5.35)|(1.05)|(2.37)|
|Ln (Distribution network)|-0.027|-0.035|-0.002|-0.143|
||(-0.87)|(-0.71)|(-0.03)|(-1.89)|
|Time|-0.059|-0.066|-0.043|0.007|
||(-6.30)|(-6.45)|(-4.15)|(0.25)|
|Time*Private firms dummy|||-0.015<br>(-1.19)||
|Ln (GNP per capita)|-0.160|-0.179|-0.060|0.732|
||(-1.00)|(-1.07)|(-0.96)|(2.18)|
|Private firms dummy|-1.299|-0.460|-0.224|-0.050|
||(-13.54)|(-8.07)|(-2.34)|(-0.28)|
|Intercept (stochastic frontier only)|0.435<br>(31.34)||||
|Stochastic frontier parameter (_γ_ )<br>|0.124<br>(42.12)||||
|Country dummies<sup>†</sup>|Yes|Yes|No|Yes|
|Log likelihood|-141.98|-140.72|244.62|-159.30|
|R-squared||0.93|0.24|0.88|
|Number of firms|110|97|104|72|
|Observations|470|399|350|190|

Notes: t-ratios obtained from heteroskedasticity-consistent standard errors are in parentheses, except for the stochastic frontier specification. In all cases we are estimating a translog form. To save space, second order terms are not shown. In models (1), (2), and (4) the Cobb-Douglas specification is rejected against a translog at the 1% level. In model (3) it is rejected at the 13% level. † In models (1), (2), and (4) the country dummies are significant at the 1% level.

The stochastic frontier estimates for the labor requirement frontier are similar to those of the OLS model except for the intercept, which appears to be biased upwards. This is in accordance with previous expectations because OLS yields consistent estimates of all parameters of the frontier model but the intercept.

The estimated coefficients in the inefficiency model are of particular interest to this study.<sup>9</sup> The coefficient on the private dummy is negative and significant at the 1% level,

> 9 The null hypothesis, _H_ 0 : _γ_ = _λ_ 0 = _λ PR_ = 0 , which specifies that inefficiency effects are absent from the model, is rejected at the 1% level: the likelihood ratio test on the one-sided error is equal to 160.06, which is greater than 10.50, the critical value for three degrees of freedom obtained from Kodde and Palm (1986).

11
Policy Research Working Paper 7237

# **Abstract**

This paper presents a new methodology to measure inequality that optimally combines household survey information and tax records to construct a complete income distribution. Combining the two data sources is necessary because, on the one hand, household surveys do not accurately represent the wealthiest segment of the population, while tax records do; on the other hand, the opposite is true for the lower end of the income distribution: tax records only include incomes above a certain threshold. The key innovation of the proposed methodology—and the main difference from the existing literature—is the choice of an optimal income threshold _b._ The Gini coefficient for the population is then computed combining the conditional income distributions for incomes below _b_ (using household survey data) and above _b_ (using tax records). Central to this methodology is the fact that b is not chosen arbitrarily: it should be determined in such a way as to minimize reliance on household survey data to compute the top of the income distribution. In practice, the optimal _b_ corresponds to the minimum income level that triggers mandatory tax filing. The proposed methodology is applied to the case of Colombia.

This paper is a product of the Poverty 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 tdiazbazan@ worldbank.org or bdiaztania@gmail.com.

_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
Table 9: Industry-level results: entry and exit.

||(1)<br>HH index|(2)<br>Entry|(3)<br>Exit|(4)<br>HH index|(5)<br>Entry|(6)<br>Exit|
|---|---|---|---|---|---|---|
|ETR|0.024|1.674*|4.089*|0.024*|4.838*|12.960*|
||(0.015)|(0.848)|(2.412)|(0.014)|(2.434)|(6.532)|
|Observations|108|108|108|108|108|108|
|Weights|no|no|no|yes|yes|yes|
|Industry FE|yes|yes|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|yes|yes|
|Industry demand shifter|yes|yes|yes|yes|yes|yes|
|First stage F-stat|9.841|9.841|9.841|16.98|16.98|16.98|

The table presents 2SLS estimates of the relationship between industry exposure to robots and business dynamism. Exposure to robots is instrumented with the average exposure in the OECD region. The dependent variables are, respectively, the number of entrants and exiting plants in an industry. Standard errors are clustered at the industry-level. Weights are constructed using 2006 (base year) industry employment. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

Table 10: Industry-level results: aggregate productivity and factor reallocation.

||(1)<br>Aggregate|(2)<br>Average|(3)<br>Covariance|(4)<br>Aggregate|(5)<br>Average|(6)<br>Covariance|
|---|---|---|---|---|---|---|
|ETR|0.033**|0.018|0.015|0.032|-0.021|0.053*|
||(0.015)|(0.011)|(0.010)|(0.027)|(0.026)|(0.031)|
|Observations|108|108|108|108|108|108|
|Weights|no|no|no|yes|yes|yes|
|Industry FE|yes|yes|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|yes|yes|
|Industry demand shifter|yes|yes|yes|yes|yes|yes|
|First stage F-stat|9.841|9.841|9.841|16.98|16.98|16.98|

The table presents 2SLS estimates of the relationship between industry exposure to robots, aggregate productivity and its decomposition according to the formula Π _it_ = Π<sup> ̄</sup> _it_ +<sup>�</sup> _f ∈i_<sup>(</sup><sup>_sft−s_ ̄</sup><sup>_it_)(</sup><sup>_ωft−ω_ ̄</sup><sup>_it_)</sup><sup>_≡_ ̄Π</sup><sup>_it_+</sup><sup>_COV_(</sup><sup>_sft, ωft_).Exposuretorobotsisinstrumentedwiththe</sup> average exposure in the OECD region. Standard errors are clustered at the industry-level. Weights are constructed using 2006 industry employment. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

# **4.3 Local Labor Market Analysis**

In order to examine the local labor market impact of automation we use Indonesian regencies as our key unit of observation. That represents the second level of sub-national administrative divisions (the first being the province). A number of features make it a reasonable proxy for local labor markets in Indonesia. First, the mobility of labor is limited across regencies. In 2010 for example only 5 percent of the workforce worked in a different regency than their residence.<sup>30</sup> Second, regencies hold significant administrative

> 30Indonesia’s island geography and often underdeveloped transportation infrastructures make the hypothesis of limited mobility likely to hold.

29
and covariates at the market (Panel A) and market pair level (Panel B).<sup>19</sup> Overall, the results suggest that there were few statistically significant differences in pre-treatment outcomes between mobile phone and non-mobile phone markets. Most differences in pre-treatment covariates are also not statistically significant from zero, with the exception of a market’s urban status, thereby supporting the earlier results. Although the pre-treatment differences in producer price levels and dispersion are not significantly different from zero for millet and sorghum, there is a statistically significant difference in cowpea producer prices and dispersion. However, a statistically significant difference only exists for one of the two pre-treatment years, suggesting that this was not systematic. In addition, cowpea pre-treatment price dispersion was lower in non-mobile phone markets. Hence, if our findings are biased because of the nonrandom placement of phone towers, this bias is most likely in the direction of underestimating the effect of mobile phone coverage.

The key identification assumption of equation (15) is that of parallel trends across mobile phone and non-mobile phone markets. This assumption might be violated if we do not control for time-varying characteristics, such as road quality, that are simultaneously correlated with mobile phone coverage and price dispersion. We therefore conduct a falsification test by estimating equation (15) using data from _before_ the introduction of mobile phones (Table 4). The rationale behind this test is that if mobile phone and non-mobile phone markets follow different time trends, this difference should have already been apparent before mobile phone coverage was introduced (Aker 2010). We find that the pre-intervention trends for the log of cowpea producer prices (column 3) and cowpea and sorghum producer price dispersion (columns 4 and 6) are not significantly different from zero for markets and market pairs that ever received mobile phone coverage compared with those that did not. However, there seem to be somewhat differing trends for millet and sorghum producer prices and millet producer price dispersion. This finding raises some concerns regarding the parallel trend assumption, especially for millet producer price dispersion. Nevertheless, mobile phone markets had relatively higher producer price dispersion prior to treatment, suggesting that our findings may underestimate the effects on millet producer price 14