rafmacalaba/datause-encoder-data huggingface dataset:
analysis based on the coefficient estimates reported in Tables 3 and 4. Meta-analysis is a tool that can be fruitfully used to summarise, and explain variations in results of a number of similar empirical studies concerned with one research topic.<sup>15</sup> To be more precise, we estimate an equation of the form (2) coefficientj = β Xj + ε j where the dependent variable is the coefficient reported in Tables 3 or 4 and X is a vector of potential explanatory variables, including both variables relating to the estimation method and country characteristics. We allow the error term to be correlated within but not across countries. A detailed list of variables included in our analysis is provided in Appendix II.

The results for a meta-regression analysis on the results from Tables 3 and 4 are provided in Table 5. Columns (1) to (3) provide results based on the coefficients in Table 3. In the first column we use as explanatory variables some characteristics of the estimation method, i.e., whether the estimation is fixed effects or not, the number of observations used, and dummy variables for the size class for which the estimation was carried out.<sup>16</sup> Also, we include a full set of country dummy variables with Slovenia being the baseline country. The results show that the estimation technique matters – unsurprisingly, fixed effects estimates are, on average, lower than those obtained from OLS. The number of observations and size class does not seem to matter, however. As for the country dummies, we find that all countries except Sweden have a positive and statistically significant coefficient, i.e., the export premium is, all other things equal, higher in these countries than in Slovenia. However, the point estimates vary substantially, from 1 (UK) to 21 (Belgium).

[Table 5 near here] We then try to explain the strong differences across countries that were indicated by the dummies. In a first step (column 2) we replace the dummies by two characteristics of the

> 15 Görg and Strobl (2001) is a recent example of a meta-analysis in the international economics literature.

11
Table 1: Firm Descriptives

||All Sectors|
|---|---|
|Number of frms|1027|
|Carpentry (%)|49.3|
|Metal fabrication (%)|37|
|Grain milling (%)|13.7|
|_Panel A: Firm characteristics_||
|Number of employees|4.9|
|Monthly revenues (USD)|1481|
|Monthly profts (USD)|243.6|
|Firm age (years)|10.1|
|_Panel B: Owner characteristics_||
|Owner is male (%)|96.1|
|Owner age (years)|40.3|
|Owner years of education|10|
|Owner hours usually worked per day for the frm|9.2|
|_Panel C: Employee characteristics_||
|Employee is male (%)|98|
|Employee age (years)|28.5|
|Employee years of education|9.3|
|Employee tenure (years)|3.5|
|Employee hours usually worked per day for the frm|9.9|
|Employee monthly wage (USD)|71|

Notes: Descriptive statistics across firms in our firm survey. Firm, owner and employee characteristics are reported in Panels A, B and C, respectively. Statistics for the average firm are shown. Monetary amounts, originally in UGX, are converted to USD using the exchange rate 1 USD = 3,800 UGX.

41
# **Annex 3 List of outcome variables**

|**_Governance_**|**Afrobarometer Question**|**Coding**|
|---|---|---|
|**Trust**|How much do you trust each of the following, or haven’t you<br>heard enough about them to say: Local council? (Not at all,<br>just a little, somewhat, a lot, don’t know/haven’t heard<br>enough, and missing)|Equals to 1 if<br>“somewhat” and “a lot”<br>and zero otherwise|
|**Accountability**|How much of the time do think the following try their best to<br>listen to what people like you have to say: Local Government<br>Councilors? (Never, only sometimes, often, always, don’t<br>know, and missing)|Equals to 1 if “often”<br>and “always” and zero<br>otherwise|
|**Corruption**|How many of the following people do you think are involved<br>in corruption, or haven’t you heard enough about them to say:<br>Elected Local Government Councilors? (None, some of them,<br>most of them, all of them, don’t know/haven’t heard enough,<br>and missing)|<br> <br>Equals to 1 if “most of<br>them” and “all of them”<br>and zero otherwise|
|**_Local Capacity_**|||
|**Maintaining roads**<br>**Maintaining markets**<br>**Keeping community clean**<br>**Maintaining health standards**<br>**Collecting taxes**<br>**Spending decisions**|<br>What about local government? How well or badly would you<br>say your local government is handling the following matters,<br>or haven’t you heard enough about them to say: Maintaining<br>our roads? Maintaining local markets? Keeping our<br>community clean? Maintaining health standards in<br>restaurants? Collecting local taxes? Deciding how to spend<br>local revenues? (Very badly, fairly badly, very well, fairly<br>well, don’t know/haven’t heard enough, and missing)|Equals to 1 if “very<br>well” and “fairly well”<br>and zero otherwise|
|**_Quality of Services_**|||
|**School**<br>**Health**|Have you encountered any of these problems with your local<br>public schools during the past 12 months? Services are too<br>expensive / Unable to pay, lack of textbook or other supplies,<br>poor teaching, absent teachers, overcrowded classes, poor<br>conditions of facilities, demand for illegal payments? (never,<br>once or twice, a few times, often, no experience, don’t know,<br>missing data)<br>Have you encountered any of these problems with your local<br>public clinic or hospital during the past 12 months? Services<br>are too expensive / Unable to pay, lack of medicines or other<br>supplies, lack of attention or respect from staff, absent doctors<br>long waiting time, dirty facilities, demand for illegal<br>payments. (never, once or twice, a few times, often, no<br>experience, don’t know, missing data)|,<br>Equals to 1 if “once or<br>twice”, “a few times”<br>and “often” and zero<br>otherwise<br>Equals to 1 if “once or<br>twice”, “a few times”<br>and “often” and zero<br>otherwise|

33
related to the transportation of goods to the relevant sea port; administrative costs related to customs clearance, technical controls, and inspections; and ports and terminal handling charges. The indicator thus provides a useful cross-section of information in relation to a country's approach to trade facilitation, in the broad sense in which that term is used by Wilson et al. (2005). The data are collected from local freight forwarders, shipping lines, customs brokers, and port officials, based on a standard set of assumptions, including: the traded cargo travels in a 20ft full container load; the cargo is valued at $20,000; and the goods do not require any special phytosanitary, environmental, or safety standards beyond what is required internationally.

The average import cost across those ASEAN Members for which data are available is around $900 per container. This figure is slightly below the overall regional average for East Asia and the Pacific, of $1037 per container, and is only a little higher than the OECD average ($883). However, the average masks large variation in import costs across countries: costs in Singapore run at $333 per container—the lowest in the world—while in Laos they are over five times higher ($1690).

In all but two countries, Laos and the Philippines, the cost of importing is lower than the Upper Middle Income group average.<sup>7</sup> Within ASEAN, we can identify three groups of countries. The first—Singapore and Malaysia—are very strong performers in terms of import costs, at under $500 per container. The second group—Cambodia, Indonesia, Thailand, and Vietnam—still perform well, broadly in the region of the OECD average. The third group—Laos and the Philippines—do markedly less well.

> 7 Among ASEAN countries, there are two high-income countries (Brunei Darussalam, and Singapore), one uppermiddle income country (Malaysia), three lower-middle income countries (Indonesia, the Philippines, and Thailand), and four low income countries (Cambodia, Laos, Myanmar, and Vietnam).

- 6 -
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS15S8t In Search of Price Rigidities<br>(Recent Sector Evidence from<br>Argentina|Jacques Morisset|December 1995|N. Cuellar<br>37892|
|WPS1559 Have Transport Costs Contributed<br>to the Relative Decline of Sub-<br>Saharan African Exports? Some<br>Preliminary Empirical Evidence|Azita Amjadi<br>Alexander J. Yeats|December 1995|S. Lipscomb<br>33718|
|NPS31560 Trade and Fluctuations|Aart Kraay<br>Jaume Ventura|December 1995|R. Martin<br>39065|
|WPS51561 Income nequality and Aggregate<br>Saving The Cross-Country Evidence|Klaus Schmidt-Hebbel<br>Luis Serven|January 1996|E. Khine<br>37471|
|AVP31562Catching Up with Eastern Europe?<br>The European Union's Mediterranean<br>Free Trade Initiative|Bernard Hoekman<br>Simeon Djankov|January 1996|F. Hatab<br>35835|
|WPS1563 Equity and Growth in Developing<br>Countries: Old and New Pespectives<br>on the Policy Issues|Michael Bruno<br>Martin Ravallion<br>Lyn Squire|January 1996|P. Sader<br>33902|
|WPS1564 Frorn Plan to Market: Patterns of<br>Transition|Martha de Melo<br>Cevdet Denizer<br>Alan Gelb|January 1996|C. Rollison<br>84768|
|WPS1565 Housing Finance in Transition<br>Economies: The Early Years in<br>Eastern Europe and the Former<br>Soviet Union|Bertrand M. Renaud|January 1996|R. Garner<br>37670|
|i'VPS'.566 Liquidity: Banks, and Markets:<br>Effects of Financial Development<br>on Banks and the Maturity of<br>Finan6ial Claims|Douglas W. Diamond|January 1996|D. Evans<br>38526|
|/VPS1567 Population Growth, Factor<br>Accumulation, and Productivity|Lant Pritchett|January 1996|S. Fallon<br>38009|
|WPS1568 Determinants of Diarrheal<br>Disease in Jakarta|Anna Alberini<br>Gunnar S. Eskeland<br>Alan Krupnick<br>Gordon McGranahan|January 1996|C. Bernardo<br>37699|
|WPS1569 Improving Water Resource<br>Management in Bangladesh|Rashid Faruqee<br>Yusuf A. Choudhry|January 1996|C. Anbiah<br>81275|
explained in Table 2. The BFI is reproduced in Annex Table 1, together with the variable combinations that yield the scores for the five factors in Table 2. The table also explains the meaning of each of five factors – extraversion, agreeableness, conscientiousness, neuroticism and openness by adjectives that describe low and high scores on a particular factor.

||**Table 2: Big Five Factors**<br>||
|---|---|---|
||**Low Scorers**|**High Scorers**|
|Extraversion|Loner; Quiet; Passive; Reserved|Joiner; Talkative; Active;<br>Affectionate|
|Agreeableness|Suspicious; Critical; Ruthless;<br>Irritable|Trusting; Lenient; Soft-<br>hearted; Good-natured|
|Conscientiousness|Negligent; Lazy; Disorganized;<br>Late|Conscientious; Hard-working;<br>Well-organized;Punctual|
|Neuroticism|Calm; Even-tempered;<br>Comfortable; Unemotional|Worried; Temperamental;<br>Self-conscious;Emotional|
|Openness|Down-to-earth; Uncreative;<br>Conventional; Uncurious|Imaginative; Creative;<br>Original; Curious|

Source: Coon and Mitterer, 2010 Personality traits as measured by BFI have shown to be of widespread applicability and BFI has been used to gain insight in various applied fields including occupational stress (Bakker et al., 2002), job performance (Hurtz and Donovan, 2000), entrepreneurship (Zhao et al., 2010; Caliendo et al., 2011), learning styles, and academic achievement (Noftle and Robins, 2007). BFI has been applied in studies of leadership and meta-analysis of the findings from over a hundred studies has been conducted (Judge, et al., 2002; Bono and Judge, 2004). This paper may be the first published application of BFI to School Based Management (SBM), though at least one previous study has explored the relationship between participative management and personality traits following the five factor model (Benoliel and Somech, 2010).

# **3. Political Skills Inventory (PSI)**

Pfeffer (1981) is credited with being the first to use the term ‘political skill’ and to popularize the idea through a series of books that all organizations are inherently political in nature. In spite of the negative value supposedly attached to “playing politics” in avowedly apolitical organizations like profit-making corporations, Pfeffer persuasively argues from the perspective of more than two decades of research, that politics is an important determinant of personal and organizational success. ‘Politics’ here is taken to mean the securing and deployment of information and resources to meet specific goals. Essentially, to be political is to be influential over the behavior of other individuals, with or without realization by the other individuals that they are being influenced. There is clearly a negative value judgment attached to the deceitful manipulation of others for hidden selfish goals, but the exercise of influence more generally is a crucial element of leadership. Ferris, et al., 2005, define political skills as “the ability to effectively understand others at work, and to use such knowledge to influence others to act in ways that enhance one’s personal and/or organizational objectives”. The same authors declare 8
performance and _direction_ indicates that before June 2007, PFAs with poor relative performance tended to move more strongly towards peers. After June 2007 there is no evidence that relative performance is correlated with the direction of trades.

To summarize, the loosening in the MRG in June 2007 is associated with three important changes in the data for pension funds: **(F1)** less trading in the direction of peers; **(F2)** increase in cross-section dispersion of returns between funds; and **(F3)** a disappearance of the negative correlation between relative performance and trading in the direction of peers.

# **3.3. Individual Stocks and Trading Strategies**

In this section I further investigate herding behavior using data on individual stock trades. For each stock, the fund’s distance to the peer benchmark is measured as _d_<sup>_i_</sup> _st_<sup>=</sup><sup>_π_</sup> _st_<sup>_i−w_</sup> _st_<sup>_i_,wherethefundcanbe</sup> overexposed ( _d_<sup>_i_</sup> _st_<sup>_<_0),underexposed(</sup><sup>_di_</sup> _st_<sup>_>_0)orhavethesameweight(</sup><sup>_di_</sup> _st_<sup>= 0)asitspeers.Iestimate</sup> the following model of a fund’s changes in individual stock weights: where ∆ _wst_<sup>_i_isadjustedforstockreturnsasintheprevioussection,</sup><sup>_xi_</sup> _st_<sup>arefundandstockspecific</sup> characteristics and _MRGt_ is a time dummy equal to one for dates before July 2007 and zero thereafter, representing the policy change. The objective here is twofold, first to determine what fund based characteristics determine PFA trading on individual stocks, and second to measure whether there was any change in the impact of these characteristics after the MRG formula was modified.

More specifically, I set _x_<sup>_i_</sup> _st_<sup>=(</sup><sup>_di_</sup> _st_<sup>_, rel_</sup> _t_<sup>_i, di_</sup> _st_<sup>_× rel_</sup> _t_<sup>_i, sizei_</sup> _t_<sup>_, Controlsst, Marketi_</sup> _st_<sup>).Here</sup><sup>_sizei_</sup> _t_<sup>istheshare</sup> of assets under management of fund i relative to the industry. The vector of _Controlsst_ contains stock specific variables. I introduce lagged returns at one, three, six and twelve months to account for momentum trading, defined as purchasing (selling) assets with positive (negative) past returns.<sup>9</sup> This popular investment strategy has been widely documented for institutional investors.<sup>10</sup> Chan et al. (1996) suggest that momentum trading may be caused by a delayed reaction of investors to the information in past returns and past earnings. I also control for firm size and liquidity, as institutional investors may share an aversion to stocks with certain characteristics, as documented by Wermers (1999), who found evidence that US mutual funds tend to herd in small stocks.

> 9Selling past losers can also be explained by window dressing. For US pension funds see Lakonishok et al. (1991)

> 10See Grinblatt et al. (1995), Grinblatt and Keloharju (2000) among many others. Raddatz and Schmukler (2013) also document the presence of momentum trading for Chilean PFAs

10
|**Table 3: Occupation Status**<br>|**in Palanpu**<br>**1957**|**r 1957-58 to**|**2008-09**<br>**1983**||**1993**||**2008**||
|---|---|---|---|---|---|---|---|---|
||**Prim**|**Sec**|**Prim**|**Sec**|**Prim**|**Sec**|**Prim**|**Sec**|
|Cultivation and Livestock|141 (81)|12|141(50)|32|187 (55)|13|184 (48)|122|
|Self Employment|||||||||
|(Non Farm)|6 (3)|2|17 (6)|6|16 (5)|7|45 (12)|26|
|Skilled Self Employed|6|2|5|3|9|5|13|3|
|Unskilled Self Employed|||12|3|7|2|32|23|
|Wage Employment|||||||||
|(Regular/Semi Regular)|5 (3)|6|72 (26)|2|46 (14)|3|43 (11)|8|
|Regular (Skilled)|1||7|1|7||13||
|Regular (Unskilled)|4|4|48||21|1|17||
|Semi Regular (Skilled)|||1||1||6|3|
|Semi Regular (Unskilled)||2|16|1|17|2|7|5|
|Wage Employment|||||||||
|(Casual)|22 (13)|24|23 (9)|36|34 (10)|34|36 (9)|74|
|Agriculture Labor|22|7|10|21|16|17|2|30|
|Non farm Casual Labour|0|17|13|15|18|17|34|44|
|Study|0 (0)||9 (3)||28 (8)||46 (12)||
|Other|0 (0)||5 (2)|2|4 (1)||9 (2)|1|
|None|1(1)|131|17(6)|206|25 (7)|280|24(6)|156|
|Total|175(100)|175|284(100)|284|340(100)|340|387|387|

Source: Mukhopadhyay, 2011.

15
# **4 Probability of Early Termination** **_4.1 Econometric Model_**

The probability of early termination of a PPP at a specific point during its contract period is predicted by estimating a flexible parametric proportional hazards model (Royston and Parmar 2002; Royston and Lambert 2011).<sup>6</sup> The model is an extension of the parametric proportional hazards model with a Weibull baseline hazard function. The generalization allows for a nonmonotonic baseline hazard function using restricted cubic splines. Accordingly, coefficients of the following equation are estimated to maximize the likelihood of the observed distribution of failure times: 2 i 0 + p p p i ,i i i i i ,m m , (1) mm mm ii,p mmp mmp ln HH(tt|XX ) = γγ �γγ zz (ln tt) + XX ββ + XX ββ + XX ββ where i ) is the log cumulative hazard at time mm=1 for project _i_ conditional on i = p i ,i i i ,m � ; p is the vector of project-specific time-invariant covariates; ln HH(tt|XX tt XX i ii,p,i i is the vector of country-specific time-varying institutional covariates; and mmp ii,p i ,m is the �XX , XX , XX XX vector of country-specific time-varying macroeconomic shocks. The terms under the summation mmp XX XX operator represent the set of restricted cubic spline terms in log time scale, . The time scale is the percentage of the contract period elapsed. mm zz (ln tt) The probability of early termination, , between time 0 and , given that the project survives until 0 can be recovered using the log cumulative hazard from equation (1) and the relationship ii PPE tt tt between the survival function, and the cumulative hazard function (see appendix A for tt detailed discussion). The estimated probability of early termination can be written as follows: SS(tt), � 0) . (2) ii ii **_4.2 Data_** PPE = 1 −SŜ (tt|tt

The data come from four sources: the World Bank’s Private Participation in Infrastructure Project Database (PPI),<sup>7</sup> the Polity IV Project,<sup>8</sup> _World Development Indicators_ (WDI),<sup>9</sup> and Laeven and Valencia (2020). The PPI database includes data on project characteristics agreed to at the time of the signing of the PPP contract or at the time of financial closure. These characteristics include the type of project, the sector, the contract period, the government level granting the contract, the sponsors, types of government support, the amount of investment commitments, and

> 6 The model was estimated in Stata using the stpm2 routine, written by Lambert and Royston (2009).

> 7 See https://ppi.worldbank.org/en/ppidata.

> 8 See http://www.systemicpeace.org/inscrdata.html.

> 9 See http://datatopics.worldbank.org/world-development-indicators/.

9
# _Health impacts_

Building on a longstanding epidemiological case‐study literature,<sup>4</sup> recent empirical work using panel methods find that heat waves can trigger large‐scale mortality responses even in rich countries with high levels of electrification. In the US, for example, an additional day with mean temperature above 32°C (90°F) leads to an increase in annual age‐adjusted mortality of about 0.11 percent (Deschenes and Greenstone, 2014).<sup>5</sup> Not surprisingly, the impact of temperature stress on health seems to vary across the age distribution, with infants and the elderly experiencing generally higher risk (Graff Zivin and Schrader, 2015).

# _Consumption Impacts_

Moderate climates can be a consumption amenity which affect individual’s utility directly as well as the marginal utility associated with forms of consumption that are affected by climatic conditions (e.g. dining outdoors, going to the park). A longstanding hedonic pricing literature documents substantial willingness to pay (WTP) for milder climates (Hoch and Drake, 1987; Maddison, 2003). There is a well‐established pattern of residential sorting by WTP for climate amenity which suggests that mild climates (for instance, having fewer extreme heat and cold days) are a normal consumption good (Sinha and Cropper, 2013). In effect, individuals are willing to accept lower wages for any given job‐locational amenity bundle in areas that have more temperate climates.

Valuations of climate amenities have placed projected welfare losses due to climate change in the range of 2‐5% of total income (Albouy et al, 2011; Sinha and Cropper, 2013). For the most part, the hedonic valuation of climate as a consumption amenity has focused on developed economy contexts, due primarily to data availability.

# _Production Impacts_

Emerging evidence across a range of experimental and quasi‐experimental studies suggests that heat events have an adverse causal impact on economic production, at least in the short run. Such impacts are relatively novel to the climate and development economics literature, but seem to be of non‐trivial welfare significance.<sup>6</sup>

> 4 Health impacts from extreme heat stress are well‐documented in the epidemiological case study literature (Kovats and Hajat, 2008). Hotter regions are also associated with grater heat‐related mortality and morbidly burdens, and even lower rates of physical exercise (Center for Disease Control and Prevention, 2010).

> 5 A day with mean temperature below 20° F is associated with an increase in annual mortality of roughly 0.07 to 0.08 percent. While mortality impacts arise from both hot and cold days, there seems to be greater non‐linearity in response for heat than for cold.

> 6 For the most part, current social cost of carbon estimates do not incorporate these channels in damage estimates (Tol, 2009).

4