rafmacalaba/datause-encoder-data huggingface dataset:
26

# **TABLE 1. Basic Benchmark Regressions**

Panel A

||_Bank Private_<br>_Credit_|_Net Interest_<br>_Margin_|_Bank Claims_<br>_On Dom._<br>_Fin. Sector_|_Bank Credit_<br>_To_<br>_Government_|_Bank_<br>_Domestic_<br>_Deposits_|_Bank Non-_<br>_Deposit_<br>_Funding_|_Insurance_<br>_Premiums_<br>_(Life)_|_Insurance_<br>_Premiums_<br>_(Non-Life)_|
|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|
|Log Initial GDPPC|0.372***|−0.261***|0.822***|0.285***|0.288***|0.380***|0.619***|0.267***|
|Log GDPPC minus Log<br>Initial GDPPC|0.840***|0.120|−0.286|1.634***|1.535***|−0.271|0.745**|−0.155|
|Interaction|7.95<sup>e−05</sup>|−0.0837***|0.223***|−0.183***|−0.0964***|0.146***|0.133***|0.0686***|
|Log Population|0.0721***|−0.0660***|0.243***|0.0940***|0.0367***|0.0717***|0.0424**|−0.0496***|
|Log Population<br>density|0.0193***|−0.0293***|0.339***|0.200***|0.0870***|0.0452***|0.0999***|−0.0403***|
|Fuel dummy|−0.272***|0.00729|−0.256***|−0.262***|−0.163***|−0.0551|−0.687***|−0.202***|
|Offshore dummy|0.331***|0.105**|−0.634***|0.166***|0.333***|0.428***|−0.130|0.107**|
|Transition dummy|−0.0350|0.187***|−0.102|−0.0864|−0.170***|0.220***|−0.779***|−0.0863*|
|Constant|0.285***|3.709***|−8.413***|−1.285***|0.815***|−0.441***|−6.126***|−1.708***|
|Observations|4,075|1,785|1,643|4,003|4,097|3,983|2,138|2,308|
|Pseudo R<sup>2</sup>|0.388|0.294|0.247|0.141|0.401|0.285|0.384|0.357|
||||P|anel B|||||
||_Pension_<br>_Fund Assets_|_Mutual Fund_<br>_Assets_|_Stock Market_<br>_Turnover_|_Stock Market_<br>_Capitalizatio_<br>_n_|_Domestic_<br>_Private Debt_<br>_Securities_|_Domestic_<br>_Public Debt_<br>_Securities_|_Foreign_<br>_Private Debt_<br>_Securities_|_Foreign_<br>_Public Debt_<br>_Securities_|
||1|2|3|4|5|6|7|8|
|Log initial GDPPC|0.317***|0.734***|0.672***|0.415***|1.010***|0.159***|1.030***|−0.134***|
|Log GDPPC minus|||||||||
|Log initial GDPPC|−3.501**|−1.423***|1.853***|0.253|2.332***|0.0639|−0.426|−2.144***|
|Interaction|0.566***|0.472***|−0.0452|0.0900|−0.0578|−0.0207|0.239***|0.167**|
|Log population|−0.0994|0.135***|0.462***|0.118***|0.112***|0.0973***|0.122***|−0.243***|
|Log population<br>density|−0.152***|0.00934|0.0661***|0.0756***|−0.131***|0.0571***|−0.0115|−0.253***|
|Fuel dummy|0.360**|−0.224**|−0.0575|0.0716|−0.785***|−0.357***|0.0507|−0.00290|
|Offshore dummy|−0.278|0.960***|−0.592***|0.391***|−0.0158|−0.345***|0.150|−0.0280|
|<br>Transition dummy|−1.834***|−1.421***|0.722***|−0.669***|−0.504**|−0.118|−0.499***|−0.474***|
|Constant|0.247|−5.554***|−4.359***|−0.975***|−6.488***|1.579***|−7.983***|4.601***|
|Observations|568|613|1,682|1,818|889|978|985|1,198|
|Pseudo R<sup>2</sup>|0.169|0.383|0.375|0.274|0.353|0.0808|0.382|0.138|

_Note:_ This table displays the median regression results of equation (1) using a panel of country-year data for the 1980–2010 period. GDPPC stands for gross domestic product per capita. _Source:_ De la Torre, A., E. Feyen, and A. Ize (2013).
Lewis and Lockheed

- <u>number of years or level of schooling completed</u> in Bolivia, Ecuador, Guatemala, Laos PDR, Mexico, Peru (Jimenez 2004; Garcia Aracil and Winkler 2004; Hallman and Peracca 2007; King and van de Walle 2007; Ramirez 2006; Trivelli 2006); and

- <u>achievement as measured by tests</u> in Ecuador, India, Laos PDR, Peru, South Africa (Garcia Aracil and Winkler 2004; Wu and others 2007; Postlethwaite personal communication 2006; Cueto and Secada 2004; Lewis and Lockheed 2006).

Figure 3 shows the distribution of school enrollment by gender, location and caste for India based on the 2001 census. The highest caste girls and boys are about even in years of schooling, followed by rural males from the majority population. The lowest school attainment is among rural girls from scheduled castes/scheduled tribes followed by rural girls from the majority group. Rural boys from both groups are more likely to be enrolled across all age groups when compared to girls. It is only the urban girls from the majority population that outpaces any boy.

**Figure 3:  India: School Enrolment by Gender, Location and Caste, 2001**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40% Majority Urban Male Majority Urban Female<br>Majority Rural Male Majority Rural Female<br>30% SC/ST Rural Male SC/ST Rural Female<br>20%<br>7 8 9 10 11 12 13 14 15-19 Age<br><!-- End of picture text --> _Source: Census of India 2001_ (http://www.censusindia.net/results/C Series) In some cases, historical inequities found in adult populations are not found for current schoolage populations, as in Ghana and Malawi (Lewis and Lockheed 2006), suggesting that change does occur across ethnic and geographic groups over time.

9
<!-- Start of picture text --> 10<br>@ Service<br>9 @ Industry<br>8 @ Agriculture<br>7<br>°<br>5<br>4<br>3<br>2<br>1<br>0<br>2s 8 £8 888 £8 8 #<br>s s § 8 §8§ § § § 8 § 8 8<br><!-- End of picture text --> <!-- Start of picture text --> 100,000 1000<br>90,000 900<br>80,000 ~ 800<br>70,000 ~ 700 2<br>= a<br>a& 60,000 600 2<br>§8<br>b 50,000 500 w=)<br>3 rs<br>© 40,000 400 &<br>a a<br>30,000 300 §<br>cs]<br>20,000 — Coffee production 200<br>—Tea production<br>10,000 —Coffee price 100<br>—Tea price<br>° °<br>2002. 2004 2006 «©2008 ~=—2010-~—S 2012<br><!-- End of picture text --> <!-- Start of picture text --> 90<br>@ Electricity<br>80 @ Transport<br>= Telecom<br>@ Access to finance<br>70 @ Informal practices<br>60<br>50 —<br>40<br>30 —<br>20<br>10<br>0 4<br>Burundi (06) Kenya(07) Rwanda(06) Rwanda(11) Tanzania (06) Uganda (06)<br><!-- End of picture text -->
# **Figure 6.   Structure of Production for Increasing Returns to Scale Multinational Business Service Firms: Representative Business Service in a Representative Regional Market (RM) m**

**Sales to Own Regional Market**

**Gross output σ = 15 = etaf CES Variable inputs Specific factor (fixed supply) σ = All other variable inputs Imported primary inputs Leontief σ = 0 Value-added and Business Services Intermediate Goods Other Services CES σ = 1.25 Leontief Leontief σ = 0 σ = 0 Business Services .... Value-added Cobb-Douglas σ = 1 Cobb-Douglas σ = 1 IntermediateComposite .... IntermediateComposite Services 1Other .... Services 5Other IRTS good i**<sup>**<u>a</u>**</sup> **/ CRTS good j**<sup>**<u>b</u>**</sup> **<u>/</u> Business Business .... Sector Skilled CES CES Service 1 Service 9 Capital Unskilled σ =3 σ =3 Specific Labor Labor CES Resources σ =3 CES σ =3 CES CES σ = 1.5 Services Intermediates Cross Services Intermediates Cross σ = 1.5 Intermediates Intermediates from Imported from RMm from other Russia Services Border from RMm from other Russia Services Border Cross Border Domestic Cross Border in RMmm σ=3 other Russia σ=3 Intermediate σ=3 Intermediatesfrom Russiafrom Russia IntermediateImportedImported σ =1.5 sigmadm**

**CES CES σ = 1.5 σ = 1.5 Intermediates Intermediates from Imported Domestic Cross Border Domestic Cross Border in RMmm σ=3 other Russia σ=3 Intermediate σ=3 Intermediatesfrom Russiafrom Russia IntermediateImportedImported Presence Services Presence Services σ =1.5 sigmadm σ=3 Services Services σ =3=esub σ =3=esub Firms in RMm Firms from Foreign-based other Russia Firms Own Other Foreign-based Multinational Russian Multinational Russian Regional Market Russian Imports Firms Services Services in RMm Services Services in RMm CES σ = 2 * sigmadm CES σ =3**<sup>**<u>e</u>**</sup> **/ CES σ =3**<sup>**<u>e</u>**</sup> **/ CES σ =3 CES σ =3=esub Imports from Imports from Imports from Regional Regional Regional Multinational Russian Multinational Russian Market 1 Market m Market q Service Service Service Service** 69 **Providers Providers Providers Providers in RMm in RMm**

**Notes:**

**<u>a/   i = l ,..., g  where g is the number of IRTS goods b/   j = l ,..., l  where l is the number of CRTS goods</u>**
||Table A1.8 World Economic Forum (cont)|
|---|---|
||Africa Competitiveness Report|
||<br>Number Question|
||Institutions<br>|
|32|1 (Now) Extent of market competition|
|33|2 Effectiveness of anti-trust policies<br>|
|34|3 (Now) Legal system effectveness at enforcing commercial contracts|
|35|4 (Now) Private business has recourse to independent and impartial courts for challenging the legality<br>of govemment actons|
|36|6 (Now Private business can readily file and try lawsuits against other business, foreign or domestic,<br>) at independent and impartial courts|
|37|6Citizens'willingness to accept legal means to adjudicate disputes rather than depending on|
||6 physical force or illegal means|
|38|7 Change in Insttutons,|
|39|9 Costs of uncertain rules, laws, or government policies|
|40|10 Government honors commitments of previous regimes|
|41|1 1Strength and experbse of the civil service to avoid drastc interruptons in government services|
||in tmes of politcal instabilitv|
|42|13 Police effectveness in safeguarding Personal securitv|
|43|14 Costs of orpanized crime for business|
|44|15 Costs of petty crime and theft for business|
||<br>Openness|
|45|1 (Now) Negative impact of tariffs on costs and availability of equipment and materials|
|46|2 (Now) Negative imPact of hidden barriers to trade|
|47|14 Openness of public sector contracts to foreign investors|
|48|15 Policies for dividend remittances as obstacies to development|
||Macroeconomics|
|49|1 Dominance of state owned or state controlled enterprise|
|50|2 Regulatory burden|
|51|5 Competence of public sector personnel,|
|52|4 State interference in Private business|
|53|6 Regulatory discretionality (vague regulabons)|
|54|8 The tax svstem hinders business competitiveness|
|55|9 Extent of tax evasion|
|56|10 Wasteful govemment expenditure,|
||<br>Finance|
|57|1 Domestic banks are protected from foreign competition -|
||<br>Govemance|
|58|1 (Now) Irregular, additional payments connected with import and export permits, business licensees,|
||<br>exchanoe controls, tax assessmnents,<br>police Protection or loan applicatons<br>General questons|
|59|a<br>Regulations for starting a business as an obstacle to business development|
|60|b<br>Price controls as an obstacle to business development|
|61|c<br>Regulabons on foreian trade as an obstacle to business development|
|62|f<br>Foreign currency regulations as an obstacle to business develoPment|
|63|d<br>Transfer costs associated with exportng capital as an obstacle to business development|
|64|m<br>General uncertainty on costs of regulabons as an obstade to business development|
|65|<br>n<br>Crime and theft as an obstacle to business development|
|66|o<br>Corrupton as an obstacle to business development|
|67|p<br>Tribal conflict as an obstacle for business development|
|68|<br> <br>q<br>Govemment coups or politcal instabilitv as an obstacle to development|

46
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 19 Productivity

- _Exchange_index_ = 1 and _Perimeter_index_ = 1; suggesting a perfectly circular border of the urban extent.

- _Fullness_index_ � 1; suggesting a dense/compact city.

- _Circuity_avg_ = 1; suggesting a planned/regular/geometric urban structure.

- _Intersection_density_ →∞ and _Street_density_ →∞; suggesting high levels of connectivity.

- Sprawl = 0; suggesting a homogeneously distributed population across the urban extent.

Table 4 shows the correlation coefficients between population density and urban form variables. Most of the urban form variables are weakly correlated with population density, suggesting that the urban form variables provide complementary information about the urban form, not captured by population density alone. The highest correlation is found for Sprawl, followed by intersection density. The direction of the correlation between population density and sprawl is similar as that found in earlier work for US metropolitan areas (Fallah, Partridge, & Olfert, 2011) with the correlation being higher in absolute terms for Latin American and Caribbean cities (-0.65 for LAC compared to -0.42 for US metro areas).
youngest and oldest age cohorts. For physical harassment on public/private buses, it is the rural population, particularly the rural non-poor, and urban females who tend to underreport compared to rural poor and urban males.

The paper proceeds as follows: In the next section, we review literature that motivates the study. In section III, a simple model is developed and specification is set out. In Sections IV and V, we provide the results and discuss their implications. In Section VI we conclude.

# **II. Background and Literature Review**

The question of whether domestic violence is underreported in most countries has been discussed in academic circles as well as in the media. As mentioned earlier, the extent of underreporting of domestic violence has not been analyzed using techniques such as list randomization. However, there are related strands of research that help motivate our analysis.

The National Family Health Survey of India (2005-06) finds that the percentage of ever-married women who have experienced physical violence at the hands of their husbands in Kerala is 15.3 percent, compared to 35.1 percent for India as a whole (Ministry of Health and Family Welfare, 2005-06). Moreover, studies indicate that domestic violence is an endemic problem in many Indian states and has existed for thousands of years (Kavitha, 2012; Martin et al., 2002; 1999). A large number of studies focus on dowry-related violence, and during the 1980s there were frequent reports of dowry deaths called “bride burning” (Chowdhry, 1997). In recent years, despite economic development, dowry payments in India are on the rise (Anderson, 2007; 2003). Dissatisfaction with the amount of dowry has resulted in increasing violence against brides, particularly in rural India (Sekhri and Storeygard, 2014; Bloch and Rao, 2002). A study in the Salem district of Tamil Nadu indicates that larger dowries reduce marital violence by increasing the economic resources of the marital household, enhancing the social status of the groom and his family, and serving as an asset over which the woman enjoys relatively more control. However, while women with generous dowries may benefit, a continued upward spiral in dowry expectations will 5
A deeper look at some specific areas of the grid provides useful additional insights. Consider for example contractual recommendations (Figure 3). If one looks at how steeply the frequency of recommendations changes with the level of economic development, it is not surprising that strengthening the judiciary has the steepest slope. Clearly, without a functioning judiciary not much else can be achieved. It is rather interesting, however, that the slope for insolvency is flat or inverted. This probably highlights the fact that the insolvency legislation is difficult to calibrate and may need to be revisited again and again as financial systems develop and legal practices evolve.

<!-- Start of picture text --> Figure 3. Contractual Recommendations<br>(as % of total)<br>7<br>6<br>5<br>HIC<br>4<br>MIC<br>3<br>LIC<br>2<br>1<br>0<br>Judiciary Creditor Rights Insolvency<br><!-- End of picture text --> In turn, a peek inside the judiciary recommendations (Figure 4) suggests that improving judiciary procedures and building up capacity are the most basic ingredients for strengthening the judiciary. Interestingly, enforcement has an inverted slope, suggesting that the judiciary authorities need to put their act together before cranking down on enforcement. Also, as already noted above, there is a notable difference between resolutions through specialized courts and out-of-court arrangements. While the former are most highly recommended for the lowest income countries, the latter are more recommended in somewhat more advanced legal environments, perhaps because they presume a minimum level of acceptance of basic rules of the game.

A look at the informational recommendations also provides some interesting insights (Figure 5). The basic message here is a natural progression, from accounting to auditing, auditing to disclosure, and disclosure to rating. Within the recommendations on registries (Figure 6), real pledges and companies are the most basic. Debtor registries come next. Security registries are last.

19
Krosch, A., Figner, B., & Weber, E. U. (2012). Choice processes and their consequences in morally conflicting military decisions. Under review, _Judgment and Decision Making_ .

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Letson, D., Laciana, C. E., Bert, F., Weber, E. U., Katz, R. W., Gonzalez, X. I., & Podesta, G. (2009). Value of perfect ENSO phase predictions for agriculture: Evaluating the impact of land tenure and decision objectives. _Climatic Change, 97_ , 145-170.

Levin, I. P. & Gaeth, G. J. (1988). Framing of attribute information before and after consuming the product. _Journal of Consumer Research, 15_ , 374-378.

Lichtenstein, S. & Slovic, P., (Eds.), _The Construction of Preference_ . New York NY: Cambridge University Press.

Löfstedt, R., Fischhoff, B., & Fischhoff, I. (2002). Precautionary principles: General definitions and specific applications to genetically modified organisms (GMOs). _Journal of Policy Analysis and Management, 21,_ 381-407.

Loomes, G., R. Sugden (1982). Regret theory: An alternative theory of rational choice under uncertainty. _The Economic Journal_ , 92 **,** 805-824.

Loewenstein, G. and Elster, J. (Eds.) (1992). _Choice Over Time_ . New York: Russell Sage.

Loewenstein, G.F., & Haisley, E. (2008). The economist as therapist: Methodological ramifications of “light” paternalism. In A. Caplin & A. Schotter (Eds.), _The Foundations of Positive and Normative Economics._ Oxford, UK: Oxford University Press.

Loewenstein, G.F., Weber, E.U., Hsee, C.K., & Welch, E. (2001). Risk as feelings. _Psychological Bulletin, 127_ , 267-286.

Mandel, N. & Johnson, E. J. (2002). When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices. _Journal of Consumer Research_ , _29_ , 235–245.

March, J.C. & Heath C. (1994). _Primer on Decision Making: How Decisions Happen_ . New York: The Free Press.

March, J.C. (1993). _Primer on Decision Making: How Decisions Happen._ New York: The Free Press.

Marx, S., & Weber, E. U. (2007). Decision making under climate uncertainty: The power of understanding judgment and decision processes. In T. Dietz & D.C. Bidwell (Eds.), 42
# **Figure 4: Treatment effect heatmaps**

<!-- Start of picture text --> Panel A Panel B<br>Treatment effect (%) Treatment effect (%)<br>16.244 16.855<br>4 4<br>14.554 14.422<br>12.864 11.988<br>11.173 9.5549<br>9.4833 7.1214<br>3 3<br>7.7932 4.6879<br>6.1031 2.2545<br>4.4129 -.17904<br>2.7228 -2.6125<br>2 2<br>1.0327 -5.046<br>-.65748 -7.4795<br>-2.3476 -9.913<br>-4.0377 -12.347<br>1 1<br>-5.7279 -14.78<br>-7.418 -17.213<br>1 2 3 4 1 2 3 4<br>Asset index In situ adaptive capacity<br>Panel C Panel D<br>Treatment effect (%) Treatment effect (%)<br>12.737 14.593<br>4 4<br>10.686 12.355<br>8.6349 10.118<br>6.5838 7.88<br>4.5326 5.6425<br>3 3<br>2.4815 3.4049<br>.43031 1.1674<br>-1.6208 -1.0701<br>-3.672 -3.3077<br>2 2<br>-5.7231 -5.5452<br>-7.7743 -7.7827<br>-9.8254 -10.02<br>-11.877 -12.258<br>1 1<br>-13.928 -14.495<br>-15.979 -16.733<br>1 2 3 4 1 2 3 4<br>Asset index GS-SPEI (reversed)<br>In situ adaptive capacity<br>Lagged GS-SPEI (reversed)<br>Lagged GS-SPEI (reversed) Lagged GS-SPEI (reversed)<br><!-- End of picture text --> _Note_ : the values within each cell represent the mean predicted treatment effects for observations belonging to that cell. Each cell is defined by the corresponding combination of conditioning variable quartiles.

27
participation become statistically significant but remain insignificant for forward and overall GVC participation at the global level. At the global level, initial human capital endowment seems to be the strongest determinant for both backward and forward manufacturing GVCs participation compared to other determinants. This suggests that at the global level, countries with strong initial human capital endowment do better in integrating their production processes into manufacturing GVCs than countries with low initial human capital endowment. For SSA, the HCI estimates are insignificant in the forward participation, but strongly positive in the backward integration. This is a very interesting result as it indicates that SSA countries with relatively high initial capital endowments are more likely to benefit from the opportunities provided by GVCs in terms of incorporating foreign value-added products in their manufacturing exports (the upstream participation).

**Table 4: Fixed effects estimation results – resource endowments and fragility indicators**

|VARIABLES|(16)<br>DVX LCX<br>Global|(17)<br>DVX LCX<br>SSA|(18)<br>FVA LCX<br>Global|(19)<br>FVA LCX<br>SSA|(20)<br>GVC Participation LCX<br>Global|(21)<br>GVC Participation LCX<br>SSA|
|---|---|---|---|---|---|---|
|log_distance|-0.791***<br>(0.162)|3.013***<br>(0.658)|-0.590***<br>(0.184)|3.963***<br>(0.659)|-0.0999***<br>(0.0215)|0.201*<br>(0.103)|
|log_GDP|0.876***|0.998***|0.723***|0.816***|-0.0461***|-0.0665***|
||(0.0365)|(0.110)|(0.0482)|(0.109)|(0.00690)|(0.0156)|
|LACEX_sk|1.781|18.93***|3.507*|25.18***|0.330|6.848***|
||(1.507)|(6.746)|(1.853)|(8.377)|(0.358)|(2.447)|
|LACEX_usk|-0.532|-4.013***|-0.537|-4.563**|-0.0811|-1.260**|
||(0.456)|(1.537)|(0.497)|(1.816)|(0.109)|(0.519)|
|log_population|0.0849**|0.127|0.0776|0.265**|0.0202***|0.0461**|
||(0.0405)|(0.122)|(0.0518)|(0.113)|(0.00715)|(0.0196)|
|HCI|4.706***|2.703|7.658***|7.997***|0.504***|0.271|
||(0.491)|(1.663)|(0.613)|(1.797)|(0.0832)|(0.210)|
|FCS|0.0239|0.529***|-0.0192|0.449**|0.0210|-0.0138|
||(0.0426)|(0.177)|(0.0456)|(0.180)|(0.0154)|(0.0202)|
|non_oil_rich|0.523***|0.324*|0.574***|0.334*|0.0735***|0.0516**|
||(0.162)|(0.167)|(0.190)|(0.180)|(0.0204)|(0.0253)|
|oil_rich|0.300|0.432|-0.535*|-0.387|0.100**|0.0939*|
||(0.235)|(0.338)|(0.315)|(0.375)|(0.0406)|(0.0485)|
|Constant|-9.325***|-46.40***|-9.608***|-56.04***|2.005***|-0.691|
||(1.555)|(6.103)|(1.824)|(6.244)|(0.217)|(0.914)|
|Observations|2,626|508|2,626|508|2,626|508|
|Number of panelid|947|205|947|205|947|205|
|Sector FE|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES<br>Robu|YES<br>st standard erro|YES<br>rs in parentheses|YES<br>|YES|

<mark>Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1</mark> 26
Table 3: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Age

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.309|-0.149|0.011|-0.066|0.268|0|
||(0.076)|(0.084)|(0.047)|(0.052)|(0.069)|(0.07)|
|Log(Rev. per worker)|-0.154|-0.151|0.033|-0.047|0.184|0.037|
||(0.046)|(0.049)|(0.028)|(0.029)|(0.04)|(0.039)|
|Invest (binary)|-0.004|-0.002|-0.004|-0.001|-0.003|-0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.197|0.02|-0.049|-0.082|0.069|-0.165|
||(0.124)|(0.14)|(0.07)|(0.08)|(0.097)|(0.099)|
|Log(Avg. wage)|-0.109|-0.048|-0.008|-0.008|0.074|0.025|
||(0.037)|(0.039)|(0.022)|(0.025)|(0.033)|(0.034)|
|Log(Capacity Util)|0.009|0.023|-0.008|0.016|-0.022|0.01|
||(0.018)|(0.019)|(0.011)|(0.012)|(0.016)|(0.016)|
|Log(Energy Intensity)|0.034|0.104|-0.027|0.116|-0.076|0.125|
||(0.053)|(0.057)|(0.037)|(0.038)|(0.056)|(0.055)|
|Innovation|-0.033|-0.001|-0.014|-0.004|0.001|-0.007|
||(0.013)|(0.014)|(0.008)|(0.008)|(0.012)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and age. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of firm age and columns labeled “high” evaluate the estimated model at the 90th percentile of firm age within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

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