rafmacalaba/datause-encoder-data huggingface dataset:
# **C Appendix: Big 5 and social networks**

.

Table C1: Comparing individual Big 5 personality between the employees who used social networks and who did not

||Conscien|tiousness|Emotion|al stability|Agreea|bleness|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Social networks|0.138|0.047|0.042|-0.009|-0.009|-0.072|
||(0.130)|(0.097)|(0.061)|(0.048)|(0.063)|(0.078)|
|Schooing in years||0.024<sup>_∗∗_</sup>||0.015||0.006|
|||(0.011)||(0.015)||(0.020)|
|Age at hire||0.002||0.008||-0.014|
|||(0.005)||(0.007)||(0.010)|
|Father’s pri.school||-0.083||-0.029||0.022|
|||(0.098)||(0.065)||(0.060)|
|Mother’s pri.school||0.009||0.024||-0.028|
|||(0.054)||(0.062)||(0.083)|
|Firm-occ fxed efect||X||X||X|
|R-squared|0.038|0.040|0.022|0.043|0.031|0.057|
|N|3386|3386|3346|3346|3340|3340|
||Extra|version|Open|ness|||
||(7)|(8)|(9)|(10)|||
|Social networks|0.086|-0.055|0.017|0.091|||
||(0.063)|(0.109)|(0.077)|(0.064)|||
|Schooing in years||-0.013||-0.014|||
|||(0.020)||(0.013)|||
|Age at hire||-0.002||-0.006|||
|||(0.009)||(0.010)|||
|Father’s pri.school||-0.156<sup>_∗_</sup>||-0.004|||
|||(0.090)||(0.051)|||
|Mother’s pri.school||-0.091||0.097<sup>_∗_</sup>|||
|||(0.101)||(0.050)|||
|Firm-occ fxed efect||X||X|||
|R-squared|0.042|0.060|0.028|0.039|||
|N|3377|3377|3383|3383|||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

52
<!-- Start of picture text --> Eoe | S satialSp Mismatch<br>Index, 2001<br>A | | -o1-0 GM 03-04<br>MO 0-01 Mi >o4<br>GM o.1-02 No Data<br>| Hi 0.2-03 We Major Cities<br>pause 4<br>Chennai f .<br>= 1<br>‘a<br>: 4 ,<br>4 i<br><!-- End of picture text -->

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# **4.2.Sensitivity to spatial deflation**

As mentioned above, the headline result that 80 percent of the extreme poor live in rural areas may be an overestimate. In Africa and much of South Asia, where the majority of the extreme poor live, welfare aggregates are not consistently spatially deflated when estimating international poverty rates. Since the cost of living is typically lower in rural areas, failure to account for regional price differences will overstate the share of poor that are rural.

How much would using nominal welfare aggregates affect the share of the poor that is rural? To get a sense of this, we analyze data from countries in South Asia.<sup>33</sup> For the purposes of this exercise, urban and rural India are considered to be separate countries, because each uses a different PPP exchange rate. As expected, using spatially deflated aggregates lowers poverty in rural areas, and within urban and rural India, reverses the urban-rural gap in the poverty rate (Figure 23). However, using deflated rather than nominal aggregates only moderately decreases the share of the poor in rural areas (Figure 24). In Bangladesh and Nepal, for example, deflating the welfare aggregate using regional poverty lines would cause the share of the poor that are rural to fall 6 to 7 percentage points.

**Figure 23: Sensitivity of urban/rural poverty rate to spatial price adjustment**

<!-- Start of picture text --> Extreme poverty rate (%)<br>60<br>Urban Nominal Urban Spatially deflated<br>51.6<br>50 46.3 Rural Nominal Rural Spatially deflated<br>40<br>29.9<br>30<br>24.8<br>23.4<br>20.8 20.018.9<br>20 17.0 16.3<br>11.7<br>10.29.1 8.9 10.0<br>10 3.43.1 2.32.0 3.95.8 4.7 4.34.8<br>0.20.2 0.00.0 0.00.0 0.30.4<br>0<br>Bangladesh Bhutan India Rural India Urban Sri Lanka Maldives Nepal Pakistan<br><!-- End of picture text -->

> 33 Data on spatial deflation was conveniently available for South Asia. Figures for Bangladesh are based on 2005 PPPs, and spatial deflators for Bangladesh are based on district level poverty lines.

27
virtually the same as non-preferential or “most-favored-nation” (MFN) policies vis-à-vis nonASEAN countries.<sup>1</sup> Alternatively, the agreements could have promoted openness generally vis-à-vis all countries. That too seems doubtful. First, because the ASEAN countries had on average more restrictive policies than any other region of the world except the Gulf states. The average Services Trade Restrictions Index (STRI) for the region was 60 percent higher than the global average. But restrictiveness of applied policies varied widely across countries and income levels. Cambodia and Singapore had the most open policies in the sectors covered. Vietnam and Myanmar were virtually open with few restrictions and the rest (Indonesia, Thailand, Philippines, and Malaysia) had significant restrictions.

The second reason that the agreements did not seem to have promoted openness generally was because the ASEAN countries’ did not undertake significant liberalization in the four year period studied. While there were some instances of market opening, there were also instance of reversal of liberalization. For the six ASEAN Member States for which the same surveys were conducted in 2008, there was little change in the overall policy regime as of 2012 (regional average STRI fell only about 16 percent from its high level). As a consequence, even though actual openness was greater than that promised by current AFAS commitments, it was still not close to the ambitious goals specified in the Blueprint. But it is important also to recognize that the limited instances of reversal of liberalization could be because the AFAS commitments have served the valuable purpose of reducing policy risks.

There are two exceptions to these conclusions: in air transport, where ASEAN countries’ have taken some steps towards regional open skies; and in certain professional services, where mutual recognition agreements have been negotiated. Even in these areas, the regional integration efforts were incomplete: in professional services, domestic regulations have not yet been aligned with the ASEAN MRAs; and in air transport further liberalization is necessary to achieve a truly integrated regional air market. Nevertheless, these initiatives suggest that regionalism could have

> 1 “Most-favored-nation” or MFN means the country which is the recipient of this treatment must receive equal trade advantages as the "most favored nation" by the country granting such treatment.

3
regression with (per capita) GDP growth as sole explanatory variable (column 1). The coefficients on aggregate growth ( θ 1 ) are always significantly negative, with larger magnitudes when labor intensity is controlled for. Most relevant for our purposes, the coefficient on labor-intensity-weighted sectoral growth --or labor-intensive growth, for short-- ( θ 2 ) is also negative and highly statistically significant in our preferred specification (column 2). Interestingly, the regression fit increases considerably (from 15 to 28%) once information on labor intensity is added to that on aggregate growth. Figure 2 shows a partial-regression plot linking the change in poverty and labor-intensive growth; it confirms a negative pattern that is well established by most observations in the sample (we consider the issue of outliers below.) Thus, it appears that in addition to the size of growth, the composition of growth regarding its labor intensity is statistically and economically relevant for explaining poverty reduction.

The coefficient on labor-intensive growth is also negative and statistically significant when we use medians per sector across countries to measure labor intensity (column 3). However, the fit of the regression declines somewhat, revealing that country-specific data on labor intensities contribute useful information for growth composition to explain poverty changes. A similar message is obtained from the alternative specification based on grouping sectors by labor intensity (column 4). The coefficient on growth in _high_ labor-intensity sectors is negative and statistically significant, while that on growth in _low_ labor-intensity sectors is much smaller and not significant. In fact, the null hypothesis that these two coefficients are the same is rejected with a p-value of 0.07. The R-squared in this case falls considerably with respect to the preferred case, confirming that the precise numerical values on country-specific labor intensities provide relevant information that cannot be captured by categorical indicators.

# **Robustness to outliers and extreme observations**

Table 4 presents the results related to the analysis of robustness to ouliers, with our basic regression repeated in column 1 for comparison purposes. The data on labor intensity, _li_<sup>_s_</sup> _i_ , present a few extreme values that are likely to represent either measurement error or rare circumstances; in order to avoid their undue influence, in our basic specification, we truncated the cross-country distribution of labor intensity per 16
presenting policy-makers with robust confirmation of the theory-based assertion that it is the institutional environment in general, and rule credibility, policy credibility, and resource adequacy and predictability in particular, which drive performance. Third, they provide an opportunity to inform in-country reform discussions with current data.

Fourth, the surveys also allow country-specific hypotheses to be tested. Many widely held views on public officials are often repeated but without substantive evidence - more akin to "urban myths" than to empirical observations. The surveys allow such assertions to be tested and supported or refuted.

Finally, the surveys allow some assessment of _which_ aspects of institutional environment are impacting on performance. It suggests those reform actions that seem most promising for higher performance, given the country's existing public sector conditions.

The surveys and analyses were financed under the Bank-Netherlands Partnership Program (BNPP). Surveys of public officials funded by the BNPP have been completed in Albania, Argentina, Bangladesh, Bolivia, East Caribbean States, Guyana and Indonesia. Surveys are in progress or are being initiated in Bulgaria, Cameroon, India, Kenya, Macedonia, and Moldova. This program has provided funds for data analysis of a separate survey of public officials in Armenia, and another data analysis exercise is also planned for an ongoing survey in Benin. The survey instruments were based on a model designed in collaboration with Professor Bert Rockman of the University of Pittsburgh. World Bank staff working on a particular country tailored this basic questionnaire according to country-specific background and issues in public sector reform.

This paper summarizes the premises on which the surveys were designed and analyzed, and the analytical framework. In explaining that analytical framework, examples have been used whenever it was considered their use would help make ideas clear. The examples have been drawn from survey experience during 1999 and 2000, and country data in Albania, Armenia, Bolivia, Guyana, Macedonia, and five countries in the East Caribbean region. The selection of examples was guided by their usefulness in illustrating the steps in the analytical framework, and not to point out any strengths or weaknesses of any country's public sector.

Section 2 lays out the conceptual framework. Section 3 summarizes the survey methodology. Section 4 describes the approach for measuring the institutional environment using the East Caribbean example as an illustration. The approach for measuring aspects of performance is described in section 5, again using the same example of East Caribbean states. The relationship between performance and institutional environment is described in section 6, using the illustration of the Bolivian public sector. The Bolivian example was also used in Section 7 to show how survey results can be used to identify promising prospects for reform.

Section 8 concludes with an overview of how the analytical framework is being used in practice.

2
# Table 21: Characteriutics of pension funds' portfolios

|||United<br>Kingdom|United<br>States<br>an a prop|Canada<br>ortion<br>of|Japan<br> <br>total<br>au|Germany<br>setu"'|lItherlands|Sweden|Denmark|Switzerland|
|---|---|---|---|---|---|---|---|---|---|---|
|Marketable|1970|0.85|0.90|0.77|0.21|0.23|0.28|0.76|0.72|0.31|
|securities<br>b)|1980|0.79|0.86|0.73|0.64|0.34|0.15|0.74|0.66|0.41|
||1990|0.78|0.85|0.86|0.74|0.43|0.44|0.B8|0.74|0.55|
|Real<br>assetsc|1970|0.61|0.45|0.23|0.37|0.17|0.28|0.00|0.00|0.19|
||1980|0.70|0.41|0.20|0.16|0.18|0.19|0.00|0.03|0.27|
||1990|0.72|0.46|0.32|0.29|0.24|0.31|0.02|0.07|0.33|
|Capital-uncertain|1970|0.93|0.90|0.76|0.51|0.36|0.42|0.76|0.72|0.44|
|assets(d|1980|0.94|0.82|0.70|0.7|0.42|0.29|0.74|0.66|0.55|
||1990|0.96|0.82|0.79|0.76|0.49|0.54|0.86|0.74|0.62|
|Long- term|||||||||||
|<br>Fixed-interest-|1970|0.32|0.51|0.65|0.14|0.69|0.61|0.98|0.76|0.58|
|bearing<br>assets"'|1980|0.24|0.43|0.64|0.54|0.76|0.72|1.00|0.70|0.55|
||1990|0.14|0.38|0.51|0.47|0.70|0.66|0.94|0.74|0.43|

- (a) Categories overlap, so they do not add up to unity.

- (b) Equities, bonds and market paper. (c) Equities and property. (d) Equities, property and bonds. (e) Bonds, mortgages (for Canada, the United States, the Netherlands, Denmark, Sweden and Germany), other loans (for Germany, Denmark, Sweden, Switzerland and the Netherlands).

30c
indicate if they knew participant _J_ ’s name, if they knew where her store was located, if they had ever watched her store for her, if they met her on social occasions, and if they were relatives. We then used these data to construct a social network map for each game session.

We also conducted a census of micro-entrepreneurs working in Polvos Azules. The market census serves four purposes. First, it allows us to control for demographic and socioeconomic characteristics in our experimental analysis. Second, we can examine hypotheses about heterogeneous behavior – e.g., do men play differently than women, or do the risk averse play differently than the less risk averse? Third, we use the census data to learn about the matching process for the game in which individuals choose their own partners. Fourth, the census allows us to examine sample frame selection biases to determine the characteristics of individuals that are likely to participate in our experiments. Specifically, we are able to test whether the nature of the games specifically attracts risk-seeking individuals.<sup>4</sup> Demographic summary statistics of our subject pool are provided in Table 3. The data suggest that most of our participants are not the poorest according to local poverty measures: most completed secondary school and own both a refrigerator and a television. However, eleven percent indicated that they cook with kerosene, a characteristic that Peruvians use to identify poor households. Approximately half of our subjects own a microenterprise in Polvos Azules; the rest are microenterprise employees. Only six percent have experience with group lending, but 65 percent have participated in an informal rotating and saving and credit association (ROSCA).

Relative to the broader population of Polvos Azules, participants tend to be older (34 years old on average versus 30); have more work experience (eleven years versus eight); and are more likely to be married (49 percent versus 39), to attend church (28 percent versus 22), to own a microenterprise (51 percent versus 34), to work in an enterprise with a government business license (38 percent versus 27), to have taken a

> 4 The bias caused by nonrandom participation in experiments is rarely mentioned in the experimental literature and almost never studied directly.  Two recent exceptions are Lazear, Malmendier and Weber (2005) and Harrison, Lau and Rutström (2005).

10
for trade flows and specialization were revised and confirmed only partly in oligopolistic settings (Miyagiwa, 1991) and with imperfect information (McAfee and McMillan, 1989).<sup>3</sup> Within general equilibrium models with increasing returns to scale, Brulhart and Trionfetti (2004) find that trade barriers in government expenditure can actually change the patterns of specialization, while Trionfetti (2001) identifies a significant impact of home-biased government procurement on agglomeration following trade liberalization. In all these papers, home bias in the public sector is treated as a parameter. Trionfetti (2000) provides – a first attempt to quantify this home bias. He uses domestic input-output tables which – are part of the ICIO data for seven European countries to compare import penetration ratios across public and private sectors. Rickard and Kono (2014) uses aggregate trade data in a gravity framework and find that countries with larger government procurement import less, thus suggesting public home bias. We extend this empirical strand of the literature by estimating trade barriers in government procurement directly, by using information from input-output tables, both in absolute terms and relative to the private sector. In doing so, we highlight the importance of the composition of government procurement – i.e., its bias towards services.

In this paper, we infer government procurement purchases from inter-country input-output tables in order to estimate the effect of TAs and their provisions on cross-border purchases. Related work has exploited contract-level data mainly for the U.S. and the EU to assess the local bias in public purchases. Using EU data, Herz and Varela-Irimia (2020) find large border effects both across and within European countries adopting a gravity-style estimation approach, and Kutlina-Dimitrova and Lakatos (2014) provide evidence indicating that product market regulation and policies on Foreign Direct investments (FDIs) affect the probability of awarding a procurement contract to a foreign firm.<sup>4</sup> Fronk (2014) estimates the effect of TAs in a gravity model using U.S. federal – procurement data as such, he has one buyer (the U.S.) from multiple suppliers. While measuring precisely procurement purchases (at least by certain public entities and for values above certain thresholds)<sup>5</sup> , the contract-level data cover only a single or a few countries

> 3Cole et al. (2017) extends the model of McAfee and McMillan (1989) to establish an equivalence between price preferences in procurement auctions and import tariffs.

> 4Gourdon and Messent (2019) estimate the effect of TAs on the value of procurement contracts awarded by the EU on non-EU firms using both contract-level and aggregate data. Their set of TAs is thus limited to those signed by the EU.

> 5 The TED (Tenders Electronic Daily) database includes contracts awarded by public authorities (at the national and sub-national level) in countries of the European Economic Area and by EU institutions. Reporting is a requirement if the value of the contract is above a certain threshold (around 5 million euros

6
15 local inequality may lead to increased property violence (Demombynes and Özler, 2005). But there is much less evidence that inequality is indeed so much greater in metropolitan areas than in smaller towns. Although this is a popular notion, it is one for which very limited statistical backing exists – in large part for previously mentioned reasons: household surveys are not representative at the level of smaller towns, and so we know very little about local inequality in them. It may be that the accumulation of anecdotal evidence of large inequalities in developing country metropolises is itself simply another reflection of metropolitan bias: Journalists and photographers, like most economists and policy analysts, prefer to visit Casablanca than Figuig<sup>16</sup> , and Rio de Janeiro than Bertolínia.<sup>17</sup> To shed some additional light on this matter, we now turn to some evidence from Morocco. Table 5 presents FGT(0) and three inequality measures (the Gini coefficient and the two Theil indices) for each of the five largest cities in the country, as well as the aggregate inequality for three city size categories. Overall intra-city inequality does not appear to be positively correlated with city size in this small sample, but this is not the main point. Taking advantage of the fine spatial disaggregation made possible by a poverty map, we calculated inequality measures for various individual neighborhoods within each of these cities. We further classified these neighborhoods into slums and nonslums<sup>18</sup> . We then decomposed the two Theil indices of inequality<sup>19</sup> for each of the five cities, into a component due to inequality _within_ each of the two groups of neighborhoods, and a component _between_ the two. For the argument that within-city inequality is egregiously large in metropolitan areas and large cities to hold (in Morocco), it would be necessary (but not sufficient) for the between-group shares reported in the last two columns of Table 5 to be substantial. In the event, it appears that most inequality in the five largest cities in Morocco is not due to some great divide between slum areas and

> 16 Casablanca is the biggest agglomeration of Morocco (2.9 million inhabitants), Figuig is a small town in L’Oriental (49,000). 17 Bertolinia is a small town in the Brazilian state of Piauí, with fewer than 40,000 inhabitants.

> 18 A district (smallest level of disaggregation after the census track) was considered as a slum if less than 10% of the population had access to water and less than 10% of the population had access to electricity. 19 GE(0), or mean log deviation, is the Theil-L index. GE(1) is the Theil-T index. Both are perfectly decomposable into within- and between-group components, in the sense that the decomposition has no residual. GE(0) weighs within-group inequalities by population shares, while GE(1) weighs them by incomes shares.