rafmacalaba/datause-encoder-data huggingface dataset:
- [73] STEIN, J.C. (2002) “Information Production and Capital Allocation: Decentralized Versus Hierarchical Firms,” _Journal of Finance_ 57 (5): 1891-1921.

- [74] STIGLITZ, J.E. (2017) “The Revolution of Information Economics: The Past and the Future,” National Bureau of Economic Research Working Paper No. 23780.

- [75] TEREDA, A. (2014) ”Design and Implementation of Balanced Scorecard in the Development Bank of Ethiopia,” Mimeo.

- [76] UCHIDA, H., G.F.UDELL AND N.YAMORI (2012) “Loan officers and relationship lending to SMEs,” _Journal of Financial Intermediation_ 21 (1): 97-122.

- [77] WORLD BANK (2012) “Better Results from Public Sector Institutions,” Washington, DC: World Bank

- [78] WORLD BANK (2016a) “World Development Report 2016: Digital Dividends,” Washington, DC: World Bank

- [79] WORLD BANK (2016b) “Ethiopia Public Expenditure Review,” Washington, DC: World Bank

# **Data Appendix**

## **Measuring the Errors of Officialdom**

This section describes in detail the creation of the error variables, their relative scale, and a descriptive assessment of their basic determinants. For each individual surveyed, we obtained their subjective assessments (‘claims’) of demographic characteristics of their constituency, of the sector-specific variables relating to their constituency, and of the variables relating to the public service rules.

Where individuals stated that they did not know the answer at all, a value of 0 was entered to signal that the respondent did not know the answer to a particular question at all. Of the 891 individuals who make up our common-claims sample used in the core analysis, 275 (31%) of individuals respond ‘Don’t Know’ to at least one of the questions asked. 181 individuals (20%) respond ‘Don’t Know’ to all of the questions in the information module. There is, therefore, selection into the sample of respondents who provide non-zero estimates. Regressing individual characteristics on a binary variable that takes the value 1 if a respondent answers ‘Don’t Know’ to all questions, we find evidence that district officials are less likely to do so. We make two comments regarding the selection into the error sample: firstly, it seems a reasonable assumption that the probability of responding ‘Don’t Know’ is positively associated with the most extreme (latent) errors, and hence our estimates of the difference across tiers can be interpreted as a lower bound. Secondly, the error data provide consistent estimates of the accuracy of information that is present within a public office in Ethiopia – for example, if one entered a public office at random in Ethiopia and asked about a particular service delivery indicator, the information that one would receive would be reflected within the error data we use for our regressions. To ensure that these 0 claims do not affect the 35
28 Tables 7 - 9 contain a great wealth of information about a large number of simulated economic changes, always by bringing combinations of 1996 coefficients to the populations of 1976, 1981 and 1985. In order to address the two puzzles posed in the Introduction - namely the increase in extreme urban poverty between 1976 and 1996 despite (sluggish) growth and (mildly) reducing inequality; and the coexistence of a deteriorating labour market with stable 'headline' poverty - we now focus on a comparison of 1976 and 1996. To do so, we plot differences in the (logarithms) of incomes between the simulated distribution and that observed for 1976, for a number of the simulations in Table 7 **16** Figure 6 below plots the combined price effects (aX and **0),** separately for wage-earners and the self-employed. As can be seen, these effects were negative (i.e. would have implied lower income in 1976) for all percentiles. The losses were greater for wage earners than for the selfemployed and, for the latter, were regressive. These losses are exactly what one would have expected from the downward shifts of the partial earnings-education and earnings-experience profiles, shown in Figures 4 and 5.

# **[See Figure 6 in Appendix 41**

In Figure 7, we adopt a different tack to the price effects, by plotting the income differences for each individual price effect simulation (for both sectors combined), and then aggregating them. As we would expect from figure 6, the returns to education and experience are both immiserizing. The change in partial returns to education alone is mildly equalizing (as can be seen from table 7). The change in the partial returns to experience are unequalizing, as well as being immiserizing. The change in the intercept, calculated at the mean values of the independent variables, was also negative throughout. This proxies for a 'pure growth' effect, capturing effects on earnings from processes not captured by education, experience, gender, or the unobserved characteristics of individual workers. It is intended to capture the effects of capital accumulation, managerial and technical innovation, macroeconomic policy conditions, and other factors likely to determine economic growth, not included explicitly in the Mincerian equation. Its negative

> **16** In computing these differences, we compare the percentiles of the two different distributions described above. A different, but equally interesting exercise, is to compare the percentiles of the simulated distribution _ranked as in the observed 1976 distribution,_ with that 1976 distribution. These exercises were also performed, but are not reported
Policy Research Working Paper 6447

# **Abstract**

Abstract: Recent events, including the Arab Revolutions and protest movement of unemployed youths in OECD countries, have contributed to the popular sentiment that access to good jobs is an important driver of social cohesion. While economic dimensions of labor market outcomes are relatively well documented, evidence on the link between social cohesion and employment conditions is still surprisingly scarce. This paper, a background report for the WDR 2013 on Jobs, presents descriptive evidence that illustrates possible linkages between labor market outcomes and social cohesion. The findings suggest that, once one passes the threshold from low to lower middle income countries, formal employment emerges as a determinant of a range of outcomes relating to social cohesion, such as membership in social associations or levels of political activism. There are also indications of an increasing association between work and life satisfaction across higher and lower middle income countries. The paper concludes with a discussion of the study’s implications for emerging economies whose labor market and social institutions are still in transition.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2013: Jobs_ —is a product of the Development Economics Vice Presidency. The views expressed in this paper are those of the authors and do not reflect the views of the World Bank or its affiliated organizations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at f.b.wietzke@lse.ac.uk.

_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
14 Javanese courts (wooden furniture was not an object in everyday household use in traditional Indonesia). These woodcarving skills have persisted to the present day, and for the past century or more have been incorporated<sup>into the</sup> production of heavily carved wooden furniture for the domestic market. Carned mahogany and teak furniture from Jepara began to find its way onto world markets in 1986. As of mid-1992 exports were estimated<sup>4</sup> to amount to 10-15% of total sales.

--Jepara's furniture industry is organized into three t-iers workers, production groups cum subcontractors, and primary contractors. Estimates of the number of workers with carpentry and carving skills range from 22,000 to 80,000. As of 1989, there were 1,900 registered furniture companies, of which 100 reportedly were primary contractors.5 While no data were available on the total value of domestic or export sales, extrapolating from the firm surveys conducted for this study, $25-$40 million appears to be a reasonable range for J-epara's 1991-2 furniture exports.

Field interviews were conducted in mid-1992 with 24 of the 40 or so enterprises that had participated at least to some extent in export markets. All but 5 of the 24 firms reported that exports accounted for 50% or more of their sales (Table 3). Export level was bimodally distributed: estimated' exports per firm were less than $250,000 annually for 14 interviewees, and for

# 4 By the local chairman of the furniture producers association.

S The estimate of 1,900 formal firms is from the Ministry of Industry. The figure of l00 primary contractors was provided by the local chairman of the furniture industry association. 6 In general, firms were unwilling to provide explicit estimates of sales or export nalues. However, they were willing to provide quantitative estimates (number of 20 foot or 40 foot containers exported per month), and some of the more co-operative participants confirmed that the average value of furniture in a 40 foot container was $20-25,000.
# **5.1 Main Results**

Table 2 presents results on the effect of early-life exposure to coffee mills on aggregate (all subjects) test scores as well as STEM and non-STEM courses. For each outcome, we estimate three variant specifications. Our preferred results are in columns 3, 6, and 9. Also, we present two sets of results: (i) panel A where our dependent variable is the standardized grade points; and (ii) panel B, where the dependent variable is the raw (unstandardized) grade points.

Starting with panel A, we find that exposure to coffee mills in early life is associated with a 0.09 standard deviation (sd) increase in overall test scores in the primary school exit exams (column 3). Disaggregating the overall effects into performance in STEM and non-STEM courses, we find relatively large effects on non-STEM relative to STEM courses. Early life exposure to coffee mills increases test scores in STEM and non-STEM subjects by 0.06 and 0.1 sd respectively.

In panel B, we find similar results. Exposure to students exposed to coffee mills in early life experienced a 0.68 increase in grade points relative to cohorts without exposure to the coffee mills (column 3). Relative to the mean, this represents a 4.4% increase in aggregate test scores.<sup>10</sup> The corresponding increase in test scores in STEM and non-STEM courses are 3.7% and 4.7% respectively.

To further understand the impact of exposure to coffee mills on students’ performance, Table 3 presents disaggregated analysis by looking at the effects on the five respective subjects (Math, Science, Social, English, & Kinyarwanda). Exposure to coffee mills increases Math, and Science test scores by about 0.08 sd. Meanwhile, the effect on students’ test scores in Social Studies, English, and Kinyarwanda is 0.08, 0.09, and 0.06 sd respectively. Overall, the above results suggest that exposure to coffee mills at the time of birth improves the cognitive development of children leading to increased academic performance.

## **5.1.1 Event Study**

To further understand the effects of early-life exposure to coffee mills, we present results from an event study analysis using the Borusyak et al. (2024) "imputation" estimator, as shown in Figure 6, which corrects for biases arising from heterogeneous treatment effects that are akin to staggered treatments.<sup>11</sup> The event study analysis allows us to: (i) examine the validity of our parallel trends assumption, and (ii) explore how the effects of the coffee mills vary with time.

Starting with panel (a), the results show relatively similar trends in test scores of students

> 100.6791/15.4015=0.044

> 11It is important to note that, unlike the conventional event study estimators, the reference group for pre-trends test in the Borusyak et al. (2024) "imputation" estimator is all _k_ periods prior to the event date and all never-treated observations.

11
**Table 2. Availability of Firm Global Integration Data by Country and Year**

|**Country**<br>**Code**|**Year**|**No. Obs.**|**Frac. FDI**<br>**Obs.**|**Avg. FDI**<br>**(%)**|**Frac. For.**<br>**Input Use.**<br>**Obs.**|**Avg. For.**<br>**Input**<br>**(%)**|**Frac. Exp.**<br>**Int. Obs.**|**Avg. Exp.**<br>**Int.**<br>**(%)**|
|---|---|---|---|---|---|---|---|---|
|AFG|2008|535|0.99|2.23|0.22|62.10|0.99|4.30|
|AGO|2006|425|1.00|10.84|0.50|40.25|1.00|0.64|
|AGO|2010|360|0.97|15.40|0.39|28.01|0.97|1.73|
|ALB|2007|304|1.00|13.19|0.35|72.10|1.00|13.93|
|ARG|2006|1063|1.00|11.57|0.61|26.26|1.00|11.51|
|ARG|2010|1054|1.00|11.68|0.74|28.21|1.00|11.25|
|ARM|2009|374|0.99|5.99|0.30|55.88|1.00|7.73|
|AZE|2009|380|1.00|7.96|0.32|21.25|1.00|2.98|
|BDI|2006|270|1.00|16.84|0.38|42.72|1.00|0.87|
|BEN|2009|150|0.99|13.02|0.48|48.14|0.99|7.53|
|BFA|2006|139|1.00|7.55|0.37|28.04|1.00|5.27|
|BFA|2009|394|0.99|10.24|0.23|50.43|0.99|4.62|
|BGD|2007|1504|1.00|1.70|0.77|34.97|1.00|29.08|
|BGR|2007|1015|1.00|10.19|0.53|45.28|1.00|21.34|
|BGR|2009|288|1.00|10.07|0.33|39.61|1.00|12.30|
|BIH|2009|361|1.00|6.08|0.32|49.31|1.00|14.61|
|BLR|2008|273|0.93|7.27|0.27|36.58|1.00|8.77|
|BOL|2006|613|1.00|10.56|0.59|52.13|1.00|8.85|
|BOL|2010|362|0.99|9.36|0.32|46.05|1.00|8.60|
|BRA|2009|1802|0.67|4.72|0.73|13.02|1.00|3.86|
|BTN|2009|250|1.00|5.68|0.00||1.00|13.07|
|BWA|2006|342|1.00|38.58|0.33|56.46|1.00|4.18|
|BWA|2010|268|0.99|41.51|0.32|61.49|1.00|3.75|
|CHL|2006|1017|1.00|5.72|0.62|39.31|1.00|7.47|

21
The set of national poverty lines used in this paper also have both greater temporal and spatial coverage. They cover approximately 25 percent more countries than those used by Ravallion and Chen and three-fold more than Atkinson and Bourguignon. Similarly, the temporal coverage of the Jolliffe-Prydz poverty lines is much greater. Previous analysis has been based on data that cover only one point in time for each country represented. But as already noted in this paper, over time, countries adjust their evaluation of basic needs. For our analysis, we draw on an average of more than six observations of national lines for each of the 107 countries we consider. We refer to this sample of 699 national poverty lines as our full sample.

In order to support our claim that the analysis in this paper continues to be relevant to more recent assessments of basic needs in different countries, we also examine a subsample of recent poverty lines. We select the poverty line from each of 104 countries that is closest in time to 2011 (and later than year 2000),<sup>17</sup> and refer to this as our circa-2011 sample. We focus on 2011 primarily because this is the year for which the most recent PPP conversion factors exist. In order to examine national poverty lines expressed in local currency units across countries, we use PPP conversion factors to convert all local currency units into a common currency – as with others in this literature, we use US dollars. In addition to being more recent than the RCS sample, the 2011 subsample also allows us to check whether the findings from the full sample are sensitive to the quality of inflation data.<sup>18</sup> The poverty lines in the 2011 subsample require on line, but rather have sub-national lines (e.g. rural and urban lines). For countries with subnational lines, RCS for example, estimate a national poverty line by using a weighted average, but in most cases, they do not use population weights and therefore their estimated national line will generally not correspond with poverty headcounts.

> 17 Three countries have no poverty line after 2000, and we drop these countries from the circa-2011 sample.

> 18 For discussion of the importance of inflation data in aligning national poverty lines to a common comparison year, see Jolliffe and Prydz (2016) and Ferreira et al. (2016). For a general discussion on data quality in low-income countries, see Jerven (2013).

11
TABLE 11: ordered logit, probit and 2SLS ordered probit regressions on multiple outcome and binary borrower

||Ologit (1)|Ologit FE<br>(2)|Oprobit<br>(1)|Oprobit FE<br>(2)|Opro|bit 2SLS (1)|
|---|---|---|---|---|---|---|
||Outcome|Outcome|Outcome|Outcome|Outcome|Borrower|
|Borrower|3.904***<br>(0.154)|4.121***<br>(0.164)|2.143***<br>(0.075)|2.727***<br>(0.081)|2.919***<br>(0.081)||
|Size<br>GDP p.c.<br>Volatility<br>Growth|-0.094<br>(0.076)<br>-0.00006<br>(0.00006)<br>-0.0003<br>(0.0003)<br>0.751***<br>(0.278)|-0.155<br>(0.095)<br>-0.00002<br>(0.0003)<br>-0.0001<br>(0.0004)<br>0.771<br>(1.078)|-0.051<br>(0.038)<br>-0.00003<br>(0.0002)<br>-0.00001<br>(0.0001)<br>0.417***<br>(0.155)|-0.072<br>(0.049)<br>-0.0001<br>(0.0001)<br>-0.00003<br>(0.0002)<br>0.464<br>(0.562)|-0.002<br>(0.027)<br>-0.0001<br>(0.0002)<br>-0.0003<br>(0.0003)<br>1.499<br>(0.936)|0.005<br>(0.010)<br>-0.00002<br>(0.0003)<br>0.00004<br>(0.00004)<br>0.009<br>(0.105)|
|Infrastructure|1.452<br>(2.907)|3.231<br>(8.501)|1.077<br>(1.501)|1.138<br>(4.545)|-3.782<br>(7.132)|0.558<br>(0.796)|
|Bureaucracy|<br>-0.085<br>(0.135)|<br>-0.362**<br>(0.170)|<br>-0.034<br>(0.071)|<br>-0.229**<br>(0.095)|<br>-0.152*<br>(0.086)|<br>0.0004<br>(0.035)|
|Corruption|0.138*<br>(0.072)|-0.197<br>(0.255)|0.070<br>(0.040)|-0.102<br>(0.135)|-0.248<br>(0.226)|0.0008<br>(0.028)|
|Accountability<br>Stability<br>Ethnic|<br>0.021<br>(0.038)<br>-0.016<br>(0.061)<br>0.051<br>(0.082)|<br>0.235**<br>(0.092)<br>-0.067<br>(0.100)<br>0.240<br>(0.228)|<br>0.004<br>(0.038)<br>-0.003<br>(0.032)<br>0.026<br>(0.043)|<br>0.152***<br>(0.051)<br>-0.036<br>(0.052)<br>0.094<br>(0.120)|<br>0.152***<br>(0.048)<br>-0.110*<br>(0.065)<br>0.095<br>(0.225)|<br>-0.035<br>(0.046)<br>-0.001<br>(0.019)<br>0.009<br>(0.026)|
|Ext. conflict<br>Instruments<br>Bank<br>Bank1<br>Bank2|0.095<br>(0.037)|0.125<br>(0.065)|0.047**<br>(0.021)|-0.063<br>(0.063)|-0.117<br>(0.077)|0.012<br>(0.032)<br>2.18*** (0.09)<br>16.8*** (3.0)<br>73.4*** (12.6)|
|Borr1<br>Borr2||||||<br>-21.8*** (3.84)<br>-102.8*** (16.4)|
|Country FE||Yes||Yes|Yes||
|Year FE||Yes||Yes|Yes|Yes|

# Other Statistics

|Observ.|1710|1710|1710|1710|1710|
|---|---|---|---|---|---|
|L.Likelihood<br>|-1875.52|-1780.69|-1884.79|-1786.43|-2304.54<br>|
|Pseudo R<sup>2</sup><br>|0.216|0.255|0.212|0.253|0.577<sup>#</sup>|
|Prob(χ<sup>2</sup>)|0.000|0.000|0.000|0.000|0.000|

Note:<sup><u>–</u></sup> some observations removed because of collinearity between the country and year effects.<sup><u>+</u></sup> In this estimation the 2SLS ordered probit estimaton contains only year fixed effects, because the introduction of country dummies prevents matrix convergence to take place.<sup>#</sup> this R<sup>2</sup> is not a pseudo, like in ordered logit and probit, but unadjusted R<sup>2</sup> .

28
||R-squared|0.504|0.504|0.504|0.503|0.504|0.503|0.504|
|---|---|---|---|---|---|---|---|---|
||(1) GVC|0.584|0.194|0.419|0.598|0.348|1.014**|1.655**|
|||(0.453)|(0.714)|(0.683)|(0.487)|(0.479)|(0.374)|(0.777)|
||(2) Obstacle|1.066***|-0.666*|0.765**|0.288|0.438|0.0985|-0.484|
|GVC4||(0.321)|(0.359)|(0.355)|(0.442)|(0.327)|(0.448)|(0.498)|
||(1)*(2)|0.903|2.308|0.994|0.486|1.839|-1.199|-4.934|
|||(2.439)|(2.558)|(1.885)|(1.695)|(2.251)|(1.545)|(2.999)|
||Observations|4,857|4,857|4,857|4,857|4,857|4,857|4,857|
||R-squared|0.503|0.503|0.503|0.503|0.503|0.503|0.503|
||||(d) Fem|ale Production|Workers||||
||(1) GVC|0.241|0.172|0.369|0.266|0.307*|0.160|0.294***|
|||(0.186)|(0.170)|(0.231)|(0.207)|(0.152)|(0.150)|(0.0929)|
||(2) Obstacle|-0.218|-0.784|-0.698|-0.176|-1.047**|-0.663**|-0.114|
|GVC1||(0.509)|(0.467)|(0.496)|(0.535)|(0.506)|(0.298)|(0.337)|
||(1)*(2)|0.694|0.965|0.114|0.405|0.441|0.888**|0.725|
|||(0.592)|(0.638)|(0.434)|(0.468)|(0.521)|(0.417)|(0.495)|
||Observations|6,861|6,861|6,861|6,861|6,861|6,861|6,861|
||R-squared|0.472|0.472|0.472|0.472|0.472|0.473|0.472|
||(1) GVC|0.698**|0.238|0.652|0.688*|0.840**|0.386|0.881***|
|||(0.295)|(0.368)|(0.480)|(0.402)|(0.384)|(0.317)|(0.315)|
||(2) Obstacle|0.00541|-0.719|-0.672|-0.0633|-1.026*|-0.537*|0.144|
|GVC4||(0.511)|(0.460)|(0.541)|(0.543)|(0.516)|(0.290)|(0.394)|
||(1)*(2)|-0.222|1.560|-0.0109|-0.112|-0.806|0.956|-1.156|
|||(1.247)|(1.344)|(1.088)|(0.986)|(1.174)|(1.155)|(1.146)|
||Observations|6,861|6,861|6,861|6,861|6,861|6,861|6,861|
||R-squared|0.465|0.466|0.465|0.465|0.466|0.466|0.465|

Robust standard errors clustered by country and year in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Notes: i) Each regression controls for firms’ age, city of operation and share of government ownership ii) All the regressions include country, year, sector and size fixed effects.

iii) Country-year-sector averages are used to reduce the risk of endogeneity between the business environment and firm-level.

iv) Each column for each GVC definition represents a separate regression.

v) All the variables are in log.

vi) The intercept is included.

22
**Figure 10: Location of commercial wheat farms in 2003/4 with smallholder kebeles and their yields in 0-25, 25-50, 50-100, and 100-150 km distance bands**

<!-- Start of picture text --> Legend<br>Kebele boundary 2004<br>Yield (q/ha) by quintile<br>1 - 5<br>6 - 8<br>9 - 11<br>12 - 16<br>17 - 23<br>Region boundary<br>Buffer around Wheat farm (04)<br>Distance (km)<br>25<br>50<br>100<br>150<br><!-- End of picture text --> 35
**Table 5: Dependent variable: Inflow FDI (GMM)**

||1|II|III|IV|V|VI|
|---|---|---|---|---|---|---|
|CFDI|-0.268<br>(0.274)|-0.245<br>(0.153)|-0.213<br>(0.186)|-0.159<br>(0.294)|-0.211<br>(0.188)|-0.138<br>(0.297)|
|FDIt-1|0.647***|0.306***|0.446***|0.403***|0.441***|0.426***|
||(0.038)|(0.036)|(0.153)|(0.128)|(0.054)|(0.130)|
|GPCGR|-0.066***|0.012|0.012|-0.026|0.011|-0.025|
||(0.025)|(0.016)|(0.018)|(0.032)|(0.018)|(0.034)|
|GDP||0.541***|0.623***|0.883***|0.555***|0.799***|
|||(0.092)|(0.104)|(0.215)|(0.115)|(0.230)|
|WFDI|0.497**|0.270*|0.060|-0.036|0.121|-0.018|
||(0.248)|(0.140)|(0.156)|(0.232)|(0.170)|(0.253)|
|WAGE|0.206|-0.050|0.137|-0.101|0.154|-0.100|
||(0.201)|(0.124)|(0.151)|(0.172)|(0.166)|(0.196)|
|ILLIT||-0.034|0.003|-0.781**|-0.021|-0.767**|
|||(0.028)|(0.028)|(0.339)|(0.145)|(0.346)|
|OPEN|-0.005|0.001|0.008**|0.012***|0.008**|0.012**|
|TARIFF|(0.004)|(0.003)|(0.003)|(0.005)<br>-0.003|(0.004)|(0.005)<br>0.001|
|||||(0.043)||(0.045)|
|INCTAX||||-0.023||-0.020|
|||||(0.017)||(0.018)|
|PSR|||0.012|0.032|0.005|0.024|
||||(0.018)|(0.021)|(0.019)|(0.023)|
|TELE|||||-0.189|-0.025|
||||||(0.310)|(0.442)|
|CRISIS||0.024|-0.005|-0.101**|-0.015|-0.104**|
|||(0.016)|(0.018)|(0.044)|(0.021)|(0.046)|
|ACCESS||0.310*|0.330*|0.329|0.311|0.307|
|||(0.184)|(0.202)|(0.222)|(0.204)|(0.224)|
|Obs.|157|132|101|62|99|61|
|Sargan|0.040|0.483|0.480|0.473|0.480|0.473|
|test|||||||
|SOC test|0.472|0.387|0.660|0.097|0.541|0.095|

Notes: (1). Standard errors are reported in parentheses. ***, ** and * indicate coefficient significant at 1, 5 and 10%, respectively.

(2). Sargan test (p-value): null hypothesis is no misspecification with instrument sets. (3). SOC test (p-value): null hypothesis of no second-order correlation in differenced errors.

37