rafmacalaba/datause-encoder-data huggingface dataset:
potential selection bias, having familiarity with the concept of innovation and the gender of the respondent are not significant predictor of providing a wrong answer in the short questionnaire. The main results suggest that a lack of quality in the interview, represented by the number of enumerators supervised by the same supervisor, increases the likelihood of inaccuracy; whether if the firm pays informal payments decrease the likelihood of inaccuracy. Furthermore, size is inversely correlated with inaccuracy with larger firms being more likely to correctly report innovation in the short questionnaire. Other unexplained factors appear to play an important role in explaining such inaccuracy.

# **6. Conclusions**

This paper shows the results of a survey experiment aiming to identify the best survey instrument for measuring innovation in developing countries. The results from the experiment are clear: a few short questions in a more general firm‐level survey do not provide an accurate picture of firm‐level innovation activity. This result confirms the findings of studies conducted for more advanced countries such as Norway or Belgium that suggest that shorter surveys tend to overestimate firm level innovation rates. More context to the questions is likely to be needed; although the innovation rates found in the longer questionnaire, which attempts to provide more context, also requires substantial cleaning and are unreasonably high given the level of development of the countries in the study.

The paper also tries to understand empirically what factors could explain the inaccurate responses to the short questionnaire and the fact that a significant number of firms provide different responses to the same question across questionnaires; sometimes even when the same respondent answered the questionnaire. Results show that lack of knowledge does not seem to play a key role in explaining inaccuracies, as neither the use of explanatory cards nor the experience of the respondent seem to matter. It is also unclear how personal incentives affect the accuracy of response rates. We find that a lower quality interview is more likely to produce inaccurate responses, while whether the firm reports the payment of informal payments and the size of the firm are likely to reduce inaccuracies. Also, in the case of explaining differences in reported innovation between surveys, the time elapsed between interviews and interviewing the same person are strong predictors of differences, likely the result of the recall effect.

Given the prominent role that innovation plays in raising productivity and contributing to economic growth, and the need for a better understanding of firm level innovation dynamics, these findings call for a renewed effort to better measure firm‐level innovation outcomes. This will require the revision of existing tools in the context of the revision of the Oslo manual, but perhaps also to test new approaches 24
L�,� � β� � Σ����� β��� ED�,�,��� � θ� � e�, � where L�,� is the light level in cell i in month t and ED�,�,� represents the vulnerability curve value in the same cell and at the same time. Lags are allowed from month t to t‐12. β� is the intercept, θ� are the cell fixed effects and e�,� is the error term.

# **III. Earthquakes**

To construct damage indices for earthquakes, this paper uses the same methodology as in Skoufias, Strobl and Tveit (2017), which in turn is based on modeling from contour maps generated by seismological ground stations (International and Regional Development 2001) (Agency 2006) (De Groeve, et al. 2008).

In short, these contour maps are ShakeMaps from USGS, which are automatically generated maps providing several key parameters following an earthquake, such as peak ground acceleration (PGA), peak ground velocity (PGV) and modified Mercalli intensity (MMI), are used as a base for localized impact. More specifically, the ShakeMaps use data from seismic stations that is interpolated using an algorithm which is similar to kriging. To model the intensity in a given coordinate, the model also takes into account ground conditions and the depth of earthquake. The ShakeMaps are interpolated grids with point coordinates spaced approximately 1.5 kilometers apart (0.0167 degrees).

With regards to the output data, PGA is a measure of the maximum horizontal ground acceleration as a percentage of gravity, PGV is the maximum horizontal ground speed in centimeters per second and MMI is the perceived intensity of the earthquake, a subjective measure. It is assumed that damage starts at an MMI level of V and a PGA of 3.9 percent of gravity ( _g_ ). These damage levels are found for California in (Wald, et al. 1999), but the relationship has been found for other areas in the United States in Atkinson and Kaka (2006) and Atkinson and Kaka (2007) and for places such as Costa Rica (Linkimer 2007) and Japan, Southern Europe and the Western United States (Murphy and OBrien 1977). It is worth noting that the numerical relationship differs from region to region.

The different measures are largely interchangeable, and in the International and Regional Development 2001 report, they use PGA to measure damage, pointing to the fact that PGA, unlike MMI is an objective measure, implying that MMI is not easy to obtain reliably across the globe. Also, for large scale modeling, where it is unfeasible to model local conditions precisely, PGA serves as a good proxy for intensity of earthquakes.

For the actual construction of the damage index, two types of data will be used; the intensity data ‐ expressed as PGA ‐ and building inventory data. The building type data stems from the USGS building inventory for earthquake assessment, which provides estimates of the proportions (based on total number of buildings) of building types observed by country; see 14
10 <!-- Start of picture text --> '~alnQ,w, ___In___<br>(5) dlnR -5t_ _~- 2 -XSM R jM-y- ___ mp _a Olnp, zanp 1+a aInL 3i--1 SL-SM _ 8 QM QS_S _ + S<br>dlac snp 1+s Rc alpl + Wmlnp3 lp h alnL a@^L J<br><!-- End of picture text --> where SL Ea I(alnR)/(alnL)J is the share of labor in (privite) income, SM B **[plQM/RJ,** SZ EB p3Z/RJ are the shares of fin.' importable production and of imports of intermediates in total income R, respectively. It can be shown that the elasticity of employment with respect to the tariff is The first three terms of the left-hand of expression _(5)_ account for the conventional efficiency losses of increasing the tariff or production subsidy on importables. If the domestic production of import substitutes increases and imports of intermediate inputs decline (i.e., which is likely to occur when domestic production of intermediates expands), in response to a tariff increase, efficiency losses worsen. In fact, given a single tariff the sum of the first 3 right-hand terms inside the bracket is necessarily positive indicating that social income d'ereases when the tariff goes up. The last 3 right-hand terms account for the employment effect of raising a tariff. If employment falls after the tariff increase, i.e., if [(OlnL)/(8lnr)J _<_ 0, there is a further social loss by moving the economy further inside the production possibility frontier. This is captured by the 4'h right-hand term in (5). The last 2 right-hand terms account for the indirect efficiency effects caused by employment changes. If employment falls, QM is likely to further increase and Z to fall if these goods are capital intensive, i.e., if [(8lnQm)/(8lnL)J _<_ 0 and [(alnZ)/(alnL)J > 0. In this case, the social loss of the tariff is increased further.

The effect of a wage distortion on social revenue in the presence of a tariff distortion can be estimated by logarithmic total differentiation of (1)
A more structured version of this contrarian view involves looking at the relationship between the change in female LFPR and the change in average wages at the district level between 200405 and 2011-12. Based on the income-effect interpretation, one would expect larger declines in female LFPR in districts experiencing a faster increase in average wages. The available evidence confirms that such relationship exists, but its explanatory power is very limited (figure 3). Even taking the estimated relationship at face value, the doubling of average wages in real terms – which is roughly what was observed in practice – would be associated with a reduction in LFPR by about 3 percentage points. The estimate is similar in rural and urban areas (3.4 and 2.5 percentage points respectively). Yet, it is only in rural areas that female LFPR declined substantially, and the observed decline was about three times larger than what this crude districtlevel relationship would suggest.

Figure 3: Higher wages are associated with only a modest decline in female participation rates at the district level <!-- Start of picture text --> All India Rural Urban<br>-200 0 200 400 600 -200 0 200 400 600 -200 0 200 400 600<br>real wage growth (%) real wage growth (%) real wage growth (%)<br>50 50 50<br>25 25<br>25<br>0 0<br>0<br>LFPR Change LFPR Change<br>-25 -25<br>-25<br>-50 -50<br>-50<br><!-- End of picture text --> _Notes:_ District-level changes in average labor force participation rates and real wages during the 2004-05 to 2011-12 period.

This admittedly crude evidence suggests that something else, in addition to supply-side effects, is happening in rural areas. In fact, households with similar living standards are characterized by different female LFPR depending on their place of residence. Monthly per capita expenditures (MCPE) provide a defensible proxy for household income. If all households are classified by 5
Irregularities in the documentation can mean that the goods initially sent to the yellow channel are subsequently allocated to the red channel.<sup>21</sup> An important feature of risk management for our identification strategy is that the selection of goods for inspection occurs for two reasons. Some shipments are selected for inspection because their risk score is high enough to merit a “targeted” inspection. Other shipments are selected randomly for inspection, in order to monitor compliance for consignments that are deemed to be low-risk.<sup>22</sup> If our data identified the shipments that were selected randomly, these shipments would provide a perfect instrument for the IV strategy we employ. Unfortunately our data do not identify the randomly-selected shipments. Instead, our identification strategy mimics the systematic components of the risk model with a comprehensive vector of fixed effects, thereby isolating pseudo-random variation in inspection probabilities in the first-stage regression.<sup>23</sup> Another contextual matter that is important for understanding the Albanian setting we study is the relationship between the level of traders’ compliance with import regulations and the ability of improved risk management capabilities to facilitate reductions in inspection rates. Responsible reductions in inspection rates are only possible in environments in which a presumption of compliant behavior is justified. We present evidence in Section 3 that suggests that levels of compliance in Albania during the reform period were quite high. While this evidence alone is not sufficient to conclude that compliance was indeed high, we argue that Albania’s low tariffs during the reform episode meant that incentives for tariff evasion, at least, were quite low.

# **3. Data and Descriptive Evidence**

> 21 We observe these irregularities in our customs transaction dataset. In Section 3 we show that they are rare, accounting for roughly 1 percent of the declarations that assigned to the yellow channel.  In such cases we consider the transaction to be allocated to the highest level of scrutiny, the red channel.

> 22 Random selection is both an enforcement tool and a strategy that facilitates the updating of selection weights for use with future shipments with the same characteristics.

> 23 In Appendix B we check to see that, in addition to removing systematic variation at the level of countries, products and firms, our methods purge the two most plausible sources of additional non-random variation: endogeneity of the inspection to value of the consignment, and serial correlation observed as an inspection allocation that depends on the previous inspection allocation for a similar consignment.

10
<!-- Start of picture text --> Figure 1<br>Oil Prices (WTI), U.S. CPI-deflated<br><!-- End of picture text --> <!-- Start of picture text --> US$/barrel<br>First Gulf War: 2008 financial crisis:<br> 150<br>Price drops 48% in 71 days Price drops 77% in 113 days<br> 120<br> 90<br> 60 Sample average price: $52.28/barrel<br> 30<br>OPEC abandons price targeting: OPEC abandons price targeting:<br>Price drops 66% in 82 days Price drops 51% in 83 days<br> ‐<br>Jan‐70 Sep‐73 May‐77 Jan‐81 Sep‐84 May‐88 Jan‐92 Sep‐95 May‐99 Jan‐03 Sep‐06 May‐10 Jan‐14<br>Source : World Bank<br>Note : Last observation is February 2015. Oil prices, which refer to WTI have been deflated by the U.S. CPI (2014 terms)<br><!-- End of picture text -->

# **Figure 2 Oil Price Volatility, 1985-2015**

<!-- Start of picture text --> Std. Dev. of [log p(t)/p(t‐1)]*100<br> 7<br>OPEC abandons 2008 financial crisis<br> 6 price targeting<br>First Gulf War<br> 5 OPEC abandons<br>price targeting<br> 4<br> 3<br> 2<br> 1<br> ‐<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>Source : World Bank<br>Notes : Volatility is the standard deviation of the oil price (WTI) changes, presented as a 60‐day trailing window.<br><!-- End of picture text --> <!-- Start of picture text --> 10 —<br><!-- End of picture text --> _— 10 —_
# **Figure 1**

**A. Synthetic index of infrastructure quantity** (medians by country group)

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>SSA (35) Low income (9) SA (5) EAP7 (7) IND (21)<br>1991-1995 2001-05<br><!-- End of picture text -->

**B. Synthetic index of infrastructure quality** (medians by country group)

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>SSA (33) Low income (7) SA (5) EAP7 (7) IND (21)<br>1991-1995 2001-05<br><!-- End of picture text --> 42
for the non-socialist party municipalities. The difference is statistically significant at the 0.15 level for public pre-primary enrollment, and at the 0.02 level for public pre-primary quality and education spending. Thus, both channels appear to be operative, and their relative importance is remarkably similar to the PB analysis despite the fact that this is a much larger and more varied set of municipalities. Given an exogenous revenue increase, richer municipalities are less likely to expand pre-primary enrollment for two reasons: because they have a collective choice of less public pre-primary education (about 65% of the effect), and because the poor have relatively less political power in such settings (about 35% of the effect). In the case of public education spending, the collective choice channel explains about 60% of the effect of median income on how revenue is spent, and the political power channel explains about 40%. In the case of public pre-primary teacher quality, the collective choice channel explains about 84% of the effect, and the political power channel explains about 16%. Once again, both channels matter.

# **7.2 Addressing the issue of heterogenous expenditure constraints**

While not an issue of identification, one may be concerned that the results are driven in part by how constrained municipalities are to spend FUNDEF/B receipts on education (as opposed to another good, or a tax rate reduction). If median income or inequality is correlated with this constrainedness, the results may be partly mechanical. In this section, I explore this possibility using two different methods, and conclude that the estimated effects are robust.

Recall that a municipality must spend at least 25% of revenue on education, and must spend all net FUNDEF/B receipts on education. This implies that FUNDEF/B receipts are more fungible for municipalities spending above-minimally on education before the reform; they can reduce _voluntary_ education spending in response to a windfall. Doing so effectively allows them to use some of the windfall on _any_ spending priority (including tax rebates). But this means that the same education investment is effectively more expensive for a less-constrained municipality, as there are more potential uses of the money. That is, there is a price effect. While highly-constrained municipalities include high and low income, equal and unequal municipalities, a concern is that the intensity of the price effect is correlated with median income or inequality.

31
In this paper, we present empirical results that support this argument. In doing so, we follow a growing literature demonstrating the role of migration in facilitating trade (Gould 1994, Head and Ries 1998, Rauch and Trindade 2002, Rauch and Casella 2003, Combes et al. 2005, Iranzo and Peri 2009, Parsons and Vezina 2017, Felbermayr and Toubal 2012, Felbermayr, Jung and Toubal 2010), FDI (Kugler and Rapoport 2007, Leblang 2010, Javorcik et al. 2011, Aubry et al. 2016), and the diffusion of knowledge (Kerr 2008, Agrawal et al. 2011, Bahar and Rapoport 2017).<sup>2</sup> The paper most closely related to ours is Leblang (2010) who proposes a comparative analysis of the effect of migration on FDI and portfolio asset flows. Regarding the latter, Leblang (2010) focuses on the main effect, which we believe is hard to properly identify, while we focus on differential effects by degree of information imperfection as discussed below.

We test the hypothesis that migration affects international financial flows. We proceed as follows. First, we introduce migration into an otherwise standard gravity model of financial flows, following Martin and Rey (2004) and Aviat and Coeurdacier (2007). As a general proposition, we expect the effect of migration on financial flows to be larger where informational imperfections are more pervasive. This is supposedly the case for country pairs that differ in terms of cultural proximity, or for investment in developing countries compared to developed countries. The effect of migration should also depend on migrants’ skills. In particular, highly educated migrants are more likely to be part of international business and financial networks, and, hence, likely more able to transfer information and reduce bilateral transaction costs. Hence, we expect their influence on financial flows to be larger.

Our empirical strategy relies on the estimation of differential effects along a number of dimensions: a skill dimension, a cultural dimension, an asset-type dimension, and an income status dimension. We start by showing that migration has a significant positive impact on international bank loans. When we distinguish between skilled and unskilled migrants, we find consistently a larger coefficient for skilled migration. For example, when we introduce skilled and unskilled migrants jointly into a gravity model of international bank loans, we find a significant positive elasticity of skilled migrants of about 0.2 while no significant effect is found for unskilled migrants. We take this result as initial supportive evidence of our conjectures.

> 2. See Docquier and Rapoport (2012) and Rapoport (2016) for recent surveys of the literature on migration networks, globalization, and development.

3
- Goldberg, Dror, 2009, “The Massachusetts Paper Money of 1690,” _Journal of Economic History_ , Vol. 69, No. 4.

- Goldberg, Linda, 2010, “Is the International Role of the Dollar Changing?,” _Current Issues in Economics and Finance_ , Federal Reserve Bank of New York, Vol. 16, No. 1.

- Gourinchas, Pierre Olivier, Hélène Rey and Nicolás Govillot, 2010, “Exorbitant Privilege and Exorbitant Duty,” Mimeo. May 22, 2010. University of California, Berkeley.

- Gourinchas, Pierre O. and H. Rey, 2005,”From World Banker to World Venture Capitalist:  US External Adjustment and the Exorbitant Privilege,” NBER Working Paper 11563, August 2005.

- Habib, Maurizio M., 2010, “Excess Returns on Net Foreign Assets:  The Exorbitant Privilege from a Global Perspective,” European Central Bank Working Paper Series No. 1158, February 2010.

- Hausmann, Ricardo and F. Sturzenegger, 2006, “Global Imbalances or Bad Accounting?  The Missing Dark Matter in the Wealth of Nations,” Harvard Center for International Development Working Paper No. 124, January 2006.

- Helleiner. Eric. 2010. “The New Politics of Global Reserve Reform.” _Journal of Globalization and Development_ 1 (2): Article 12.

Henning, C. Randall, 2009, “The Future of the Chiang Mai Initiative: An Asian Monetary Fund?,” Petersen Institute for International Economics, Policy Brief Number PB09-5.

- Hsieh, Chang-Tai. and Chirstina Romer, “Was Federal Reserve Constrained by the Gold Standard During the Great Depression? Evidence from the 1932 Open Market Purchase,” _The Journal of Economic History_ , (2006), pp. 140-176.

- Huang, Yipling et al, 2011, “China Beyond the Miracle: The Coming Financial Revolution,” Barclays Capital Emerging Markets Research, October, 2011.

International Monetary Fund. 2011a, “Strengthening the International Monetary System: Taking Stock and Looking Ahead,” Policy paper of March 23, 2011.

- ------------,2011b, “Enhancing International Monetary Stability—A Role for the SDR?”  Policy paper of January 7, 2011.

- ------------, 2011c, “Assessing Reserve Adequacy,” Policy paper of February 14, 2011.

- ------------, 2011d, “Analytics of Crises and the Role of Global Financial Safety Nets,” Policy paper of May 31, 2011.

- -----------, 2011e, “The Multilateral Aspects of Policies Affecting Capital Flows,” Policy paper of October 13, 2011.

- ------------, 2010, “Reserve Accumulation and International Monetary Stability.”  Policy paper of April 13, 2010.

56
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1425 On the Intersectoral Migration of<br>Agricultural Labor|Donald Larson<br>Yalr Mundlak|February 1995|J. Jacobson<br>33710|
|WPS1426 Russian Unemployment: Its<br>Magnitude, Characteristics, and<br>Regional Dimensions|Simon Commander<br>Ruslan Yemtsov|February 1995|V. Reid<br>35195|
|WPS1427 Corporate Govemance and Equity<br>Prioes: Evidence from the Czech<br>and Slovak Republics|Stiln Claessens|February 1995|F. Hatab<br>35835|
|WPS1428 Short-Term Supply Response to a<br>Devaluation: A Model's Implications<br>for Primary Commodity-Exporting<br>Developing Countries|Bnrno Boccara<br>Fabien Nsenglyumva|February 1995|M. Plelffenberger<br>34963|
|WPS1429 The WorldTrade Organizaton's<br>Agreement on Govemment<br>Procurement Expanding Disciplines,<br>Declining Membership?|Bernard M. Hoekman<br>Petros C. Mavroidis|March 1995|F. Hatab<br>38535|
|WPS1430 Intergovemrnmental<br>Fiscal Relations<br>and Poverty Alleviaton In Viet Nam|Richard M. Bird<br>Jennie I. Litvack<br>M. Govinda Rao|March 1995|G. Coward<br>80494|
|WPS1431 The Industrial Pollution Projection<br>System|Hemamala Hetlige<br>Paul Martin<br>Manjula Singh<br>David Wheeler|March 1995|A. Williams<br>37176|
|WPS1432 Using Financial Futures in Trading<br>and Risk Management|Ignacio Mas<br>Jes(is Saa-Requejo|March 1995|K. Binkdey<br>81143|
|WPS1433 EnterpriseRestructuringin Eastem<br>Europe: How Much? How Fast?<br>Where? Preliminary Evidence from<br>Trade Data|Bemard Hoekman<br>Gerhard Pohl|March 1995|F. Hatab<br>35B35|
|WPS1 434 Government Credit Policy and<br>Industrial Performance (Japanese<br>Machine Tool Producers, 1963-91)|Charles W. Calomiris<br>Charles P. HimmelbeTg|March 1995|P. Infante<br>37642|
|WPS1435 The Political E;onomy of Formal<br>Sector Pay and Employment in<br>Developing Countries|Arup Banerji<br>J. Edgardo Carnpos<br>Richard H. Sabot|March 1995|D. Evans<br>37496|
|WPS1 436 An Empirical Model of Sunk Costs<br>and the DecisiontoExport|Mark J. Roberts<br>JamesR. Tybout|March 1995|J. Ngaine<br>37959|