rafmacalaba/datause-encoder-data huggingface dataset:
firms with access to a credit line bank with a foreign state bank. On average, foreign banks perform better than state-owned banks, based on our bank health index.

Finally, the legal origins are determined using the data from La Porta et al. (2008). Our sample is almost evenly split between French or German legal country origin. According to La Porta et al. (2008), both French and German legal origins follow civil-law traditions and have their roots in Roman law, but the latter accommodates greater judicial law-making. Countries following common-law or UK legal traditions are barely present in our sample.

**Table 1. Summary statistics**

||**N**|**Mean**|**SD**|**Min**|**Max**|
|---|---|---|---|---|---|
|**New Products/Services Introduced Over Last 3 Yrs –**<br>**_(innovation diffusion or new to thefirm)_**|28239|0.19|0.39|0|1|
|**Products/Services introduced are also new for the**<br>**establishment's main market****_(radical innovation)_**|8002|0.62|0.49|0|1|
|**R&D expenses during the past FY**|28228|0.05|0.21|0|1|
|**Line of credit**|27858|0.28|0.45|0|1|
|**State bank**|26718|0.09|0.29|0|1|
|**Foreign bank**|26053|0.12|0.32|0|1|
|**Foreign state bank**|26718|0|0.01|0|1|
|**Bank health index**|7078|0.56|0.42|-2.33|1.11|
|**Bank account**|28263|0.93|0.26|0|1|
|**Ln(age)**|28113|2.68|0.76|0|5.32|
|**20-99 employees**|28444|0.16|0.37|0|1|
|**>99 employees**|28444|0.03|0.18|0|1|
|**SOE**|28056|0|0.05|0|1|
|**Foreign company?**|28037|0.02|0.15|0|1|
|**Legal origin, France**|27426|0.41|0.49|0|1|
|**Legal origin, Germany**|27426|0.59|0.49|0|1|
|**Legal origin, Scandinavia**|27426|0|0|0|1|
|**Legal origin, UK**|27426|0|0.03|0|1|

Source: authors' computations based on the World Bank Enterprise Survey Data To assess whether banking with a state-owned bank affects the probability of innovating, our baseline framework is: Innovateijt = αLine of creditijt + βLine of creditijt × Stateowned bankkjt + γLine of creditijt × Foreignowned bankkjt + + δLine of creditijt × State owned bankkjt × Foreign owned bankkjt + XijtΘ + τt + λj + εijt 4
Table 9: Pronatec program employment effects, difference-in-differences IV estimates

|Outcome:||**Employed [0/1]**||
|---|---|---|---|
|Sample|All registrants|Unemp. Benefc.|All others|
||(1)|(2)|(3)|
|Post-course * completed|0.005|0.088***|-0.112***|
||(0.016)|(0.021)|(0.013)|
|Mean of outcome|0.561|0.602|0.558|
|St. dev. of outcome|0.496|0.489|0.497|
|_R_<sup>2</sup>|0.45|0.40|0.46|
|_N_|51,082,933|3,505,993|47,576,940|

Notes: Table presents second-stage coefficients capturing effects of course completion induced by receiving a course offer, estimated on the sample of all _Pronatec_ course registrants excluding MDIC-requested courses. Heteroskedasticity-consistent robust standard errors two-way clustered by class and month*year reported in parentheses. All specifications include an unreported constant term and vectors of individual and month*year fixed effects. Significance indicated by: _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01.

36
Korea (estimates are provided for Korea as a whole). We distinguish 174 countries in 1990, before the secession of the Soviet bloc, ex-Yugoslavia, ex-Czechoslovakia, the independence of Eritrea and East-Timor, and the German and Yemen reunifications<sup>4</sup> .

For economic and statistical reasons, working on stocks is more attractive than working on flows. Stock variables are more appropriate to analyze the endogeneity and the dynamics of migration movements (equilibrium values are often expressed in terms of stocks). Regarding statistics, it has long been recognized that migration flow data are less reliable than stock data, due to the impossibility of evaluating emigration and return migration movements.

We count as migrants all working-aged (25 and over) foreign-born individuals living in an OECD country<sup>5</sup> . Skilled migrants are those with at least tertiary educational attainment wherever they completed their schooling. Our methodology proceeds in two steps. We first compute emigration stocks by educational attainment from all countries of the world. Then, we evaluate these numbers in percentage of the total labor force born in the sending country (including the migrants themselves). This definition deserves two main comments.

First, the set of receiving countries is restricted to OECD nations. However, compared to most studies, our database provides many insights about the structure of South-North and NorthNorth migration. The skill level of immigrants in most non-OECD countries is expected to be very low, except in a few countries such as South Africa (1.3 million immigrants in 2000) and the member states of the Gulf Cooperation Council (9.6 million immigrants in Saudi Arabia, United Arab Emirates, Kuwait, Bahrain, Oman and Qatar) where 17.5 percent of adult immigrants are tertiary educated. Two other countries where immigrant skill levels are high are Hong Kong and Singapore, with 4 million immigrants between the two.

Considering that children constitute 25 percent of the immigration stock, we estimate the number of educated workers at 1.9 million in these countries. The number of educated immigrants in the rest of the developing world lies between 1 and 4 million (if the average proportion of educated immigrants among adults lies between 2.5 and 10 percent). This implies that, focusing on OECD countries, we should capture about 90% of the world-wide educated migration. Nevertheless, by abstracting from non-OECD immigration countries, we probably underestimate the brain drain from a dozen developing countries. Incorporating data collected from selected non-OECD countries could refine the data set.

> 4 Note that we report 1990 estimates for a couple of countries which became independent after January 1, 1990 (Namibia, Marshall Islands, Micronesia, Palau).

> 5 Our working-aged concept includes retirees.

5
**Appendix table 1: Household** **<u>and village characteristics by rental status</u>**

||**Rent-in**|**Type of household**<br>**Autarkic**|**Rent-out**|
|---|---|---|---|
|**Household characteristics**||||
|Owned land (mu)|5.60***|6.18|7.65***|
|Owned land per capita (mu)|1.47***|1.67|2.10***|
|Household size|4.02*|3.94|3.87*|
|Members 15-60 years old|3.06*|2.99|2.90*|
|Head’s age|45.82**|46.62|47.11**|
|Head with secondary education. (%)|50.09**|52.88|48.62***|
|Head with high school education. (%)|16.47*|18.74|17.21***|
|**Assets and income**||||
|Value of total assets (Y)|24,039***|27,417|29,467***|
|Per capital net income (Y)|2734.14***|3003.06|3168.60***|
|Share of income from agriculture (%)|64.73***|61.00|53.06***|
|Share of inc. from wage (%)|7.31***|11.85|12.29***|
|Share of inc. from migration (%)|18.78*|17.98|23.65***|
|Share of inc. non-farm self emp.(%)|9.17|9.17|11.01**|
|Agricultural Ability|0.049|-0.016|-0.007|

_Source:_ Own computation from NBS/World Bank land market survey. t-test compares means between sub-group and autarky; *, **, *** imply significance at 10%, 5% and 1% respectively 22
to make the additional employee profitable. Among these 40 percent, a majority (56 percent) say that a lack of capital is an important reason they did not hire anyone under the program. Note that the likelihood an owner reports that capital is a reason for not hiring is independent of either baseline profits or capital stock. Those with low profits and low capital stock are no less likely to hire under the program, though as we noted in Section 4.2 above, they are more likely to hire relatives and workers at very low rates of pay. In April 2010, we asked about the same issue with a different framing, asking how much capital would be required to make a new employee “as productive as (s)he could be.” Only 7 percent of respondents said “zero” to this question, but 43 percent said 15,000 LKR or less, a level which is approximately one month’s profit for the average firm. Only 22 percent said that more than 90,000 LKR – six months’ average profit – would be required. Though suggesting that a lack of capital plays a role in some cases, these data are consistent with the lack of any additional effect when the savings subsidy program is combined with the wage subsidy program.

The survey data also yield some information on several other constraints suggested by theory.

- _Newly-hired employees have lower productivity_ :  This, combined with either formal or informal minimum wages might require that wage payments exceed the marginal contribution of the employee for some period of time. In those circumstances, the subsidy may induce firms to hire when they are otherwise unable to pay an interim wage that reflects the lower marginal productivity of workers during the initial period working in the firm. In April 2010, we asked owners how long they thought it would take a hired worker to become fully productive. The median response was one month, and the mean 4.1 months; 86 percent said the period would be six months or shorter, suggesting that the subsidy was long enough to fully cover the learning period for the majority of the sample. In October 2011, we asked owners how long it took for the most recent worker they had hired (if there was one) to be profitable for the enterprise. Focusing on employees hired after the subsidy period, half the owners said the employee was profitable right away, and another 38 percent said they were profitable within the first month. Only 2 percent said it took six months or more. Hence, the survey data do not suggest that the time taken to integrate an employee is long enough to explain the patterns we see in the data.

25
# **Table 3: Summary Statistics and balance checks**

||I|nsurance (<br>Stnd|T1)|Agricul|tural envel<br>Stnd|ope (T2)|Agricul|tural savin<br>Stnd|gs (T3)<br>Media|Emer|gency savin<br>Stnd|gs (T4)|Equality<br>of means|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|.<br>Dev.|Median|Mean|.<br>Dev.|Median|Mean|.<br>Dev.|n|Mean|.<br>Dev.|Median|p-value|
|**Panel A: Demographics and risk**||||||||||||||
|Male|0.37|0.48|0|0.40|0.49|0|0.37|0.48|0|0.35|0.48|0|0.09|
|Degree of food insecurity|2.58|2.10|3|2.49|2.02|3|2.50|2.03|3|2.55|2.03|3|0.95|
|Delay to buy medicine when ill (days)|1.84|7.51|0|1.41|3.56|0|6.89|71.12|0|1.09|1.87|0|0.12|
|Used savings to cope with most prevalent||||||||||||||
|shock|0.34|0.47|0|0.36|0.48|0|0.45|0.50|0|0.40|0.49|0|0.12|
|Amount saved at home (FCFA)|9,607|30,653|0|7,825|28,156|0|7,487|22,852|0|6,862|19,644|0|0.85|
|Amount in savings account (FCFA)|8,771|54,547|0|8,259|56,269|0|8,677|56,905|0|13,547|106,158|0|0.97|
|Amount contributed to group savings (FCFA)|1,889|9,745|0|1,621|5,863|0|2,749|12,142|0|2,879|19,282|0|0.53|
|Amount of monetary help received over 3<br>months(FCFA)|1,743|7,188|0|2,108|9,023|0|2,323|10,520|0|1,719|7,871|0|0.77|
|**Panel B: Farming**||||||||||||||
|Total area planted (ha)|6.59|5.37|5|6.93|5.29|6|6.81|6.11|5|6.66|6.09|5|0.89|
|Main crop is groundnut|0.30|0.46|0|0.29|0.46|0|0.27|0.44|0|0.28|0.45|0|0.85|
|Main crop is "petit mil"|0.20|0.40|0|0.24|0.43|0|0.29|0.45|0|0.23|0.42|0|0.34|
|Main crop is sorghum|0.09|0.29|0|0.06|0.25|0|0.09|0.29|0|0.10|0.30|0|0.52|
|Main crop is cotton|0.08|0.27|0|0.07|0.26|0|0.04|0.20|0|0.07|0.26|0|0.15|
|Total expenses on inputs (FCFA)|52,700|111,514|17,000|52,321|124,996|15,500|42,322|79,442|12,000|42,706|97,009|13,000|0.68|
|Quantity of fertilizer used (kg/ha)|83.54|137.90|46.06|76.42|160.15|35.71|73.69|131.39|34.52|62.99|118.93|33.33|0.47|
|Normalized output|0.04|0.89|-0.10|0.02|0.76|-0.12|-0.04|0.63|-0.13|-0.02|0.69|-0.11|0.54|

_Note: All treatment sample. P-value for the F test of equality of the means across four treatment groups._ 12
candidate countries – all of which were GATT members in 1994, the year before the creation of the WTO; (b) other EU accession countries; (c) South-Eastern European countries; and (d) the remaining set of countries, all of which belonged to the former Soviet Union. Note that group (b) also includes some ex-USSR members – the Baltic states.

Table 3 displays the GATS commitment indices for these 4 sub-groups of transition economies, using the same methodology as in tables 1 and 2. Group (a) comprises the Czech Republic, Hungary, Poland, and the Slovak Republic. All were GATT-1947 contracting parties and had GATS schedules that took effect in 1994 and were revised subsequently to include additional commitments in the financial services and telecom sectors. Hungary has the highest commitment index (44 percent), followed by the Czech and the Slovak Republics (about 30 percent each) and Poland, with about 24 percent. The relatively low level of the commitment index for the Czech Republic, the Slovak Republic, and Poland is somewhat surprising given that all three countries were preparing to join the EU at the time of submitting the schedules. The high percentage of unbound commitments is also striking. The four other EU accession countries (group b) display higher average levels of GATS commitments. Latvia is the frontrunner, with about 59 percent, followed by Lithuania (47 percent) and Estonia (44 percent). The outlier is Slovenia, which joined the GATT in 1994, with a commitment index that is similar to that of the Central European countries that were GATT-1947 members – around 30 percent. The schedules date from 1995 (Slovenia); 1999 (Estonia and Latvia); and 2001 (Lithuania).

In group (c), the South Eastern European countries, Croatia (49 percent) and Macedonia (42 percent) have higher commitment levels than Bulgaria (26 percent) and Romania (25 percent). Of these countries, Romania was a GATT-1947 contracting party, so that its GATS schedule was submitted in 1994. Schedules for the other countries were submitted in 1997 (Bulgaria), 2000 (Croatia), and 2004 (FYR Macedonia).

The four former republics of the Soviet Union (group d) all committed themselves to a strikingly high degree of market access and nondiscrimination. Moldova is the leader, with 78 percent, followed by Kyrgyzstan (59 percent), Georgia (54 percent), and Armenia (42 percent). The schedules were submitted in 1999 (the Kyrgyz Republic), 2000 (Georgia), 2001 (Moldova), and 2004 (Armenia).

8
**Table D2. Estimated Intent to Treat Program Impacts by Age in Years (at Round 3) and Duration Class Using Nearest Neighbor Propensity Score Matching for Group II (Mixed or No Program Impacts) and Group III (Predominately Negative Program Impacts), Philippines** **<u>ECD Study</u>**

|**Age/ECD Indicator**|**Dura**|**tion of Exposure (m**|**onths)**|
|---|---|---|---|
||**4 to 12**|**13 to 16**|**17+**|
|**_Proportion with diarrhea_**||||
|2|-.117 (.036)*|-.096 (.034)*|-.189 (.043)*|
|3|.010 (..031)|-.001 (.029)|-.014 (.040)|
|4|-.027 (.025)|.076 (.034)+|.042 (.020)+|
|5|-.003 (.025)|.008 (.023)|.003 (.022)|
|6+|-.028 (.024)|.006 (.018)|.007 (.021)|
|**_Proportion with worms_**||||
|2|.0005(.041)|.020 (.034)|-.235 (.037)*|
|3|.017 (.042)|.069 (.040)~|-.148 (.036)*|
|4|.044 (.045)|.066 (.050)|.175 (.040)*|
|5|-.003 (.042)|.128 (.048)*|.085 (.045)~|
|6+|-.066 (.048)|.039 (.046)|-.008 (.041)|
|**_Proportion stunted_**||||
|2|.119 (.032)*|-.100 (.044)+|-.024 (.051)|
|3|.115 (.034)*|.103 (.045)+|.088 (.037)+|
|4|.013 (.027)|-.014 (.027)|-.020 (.031)|
|5|-.047 (.026)~|-.071 (.030)+|-.081 (.034)+|
|6+|.064 (.028)+|-.004 (.038)|-.044 (.029)|
|**_Height for age Z scores_**||||
|2|-.015 (.062)|-.013 (.105)|-.255 (.118)+|
|3|.001 (.064)|-.081 (.062)|-.081 (.057)|
|4|.004 (.044)|.028 (.045)|.089 (.052)~|
|5|.062 (.041)|.006 (.048)|-.007 (.039)|
|6+|-.035 (.029)|.021 (.035)|.001 (.029)|
|**_Proportion anemic_**||||
|2|.189 (.047)*|.307 (.081)*|-.201 (.116)~|
|3|.171 (.047)*|.054 (.045)|.129 (.046)*|
|4|.175 (.050)*|.084 (.044)~|.116 (.046)+|
|5|.078 (.048)|.045 (.056)|.202 (.047)*|
|6+|.144 (.041)*|.039 (.045)|.057 (.044)|
|**_Hemoglobin_**||||
|2|-.494 (.121)*|-.686 (.258)*|-.121 (.240)|
|3|-.499 (.127)*|.024 (.116)|-.430 (.110)*|
|4|-.471 (.112)*|-.233 (.125)~|-.266 (.098)*|
|5|-.190 (.090)+|-.170 (.115)|-.419 (.102)*|
|6+|-.228 (.094)+|.084 (.085)|-.144 (.087)~|

_Note_ : Standard errors in parentheses. The table contains the difference-in-difference defined as (Yp3-Yp1)(Ynp3-Ynp1), where p refers to program, np to non-program areas, and the subscripts 3 and 1 index survey round. ~p<.10 +p<.05 *p<.01.

32
<!-- Start of picture text --> Crop Technology Index<br>Range of all sample paths<br>8.5 Average<br>10% quantile<br>25% quantile<br>8 50% quantile<br>7.5<br>7<br>6.5<br>6<br>5.5<br>5<br>2010 2020 2030 2040 2050 2060 2070 2080 2090<br>Year<br><!-- End of picture text --> Figure 2: Stochastic Crop Productivity Index allocates managed agricultural and forest lands for crop and timber production, the scarce fossil fuels and protected natural forests to solve the following problem: subject to constraints (1)-(19), where Γ is the scrap value function and ∆represents the change of land and fossile fuel, i.e., ∆<sup>_N,A_</sup> _,_ ∆<sup>_N,R_</sup> _,_ ∆<sup>_C,A_</sup> _,_ ∆<sup>_C,H_</sup> _,_ ∆<sup>_C,C_</sup> ,∆<sup>_F,E_</sup> ,∆<sup>_F,n_</sup> .

# **3.4 Method of Solution**

The dynamic programming formulation of the model is the following Bellman equation Bellman (1957): subject to constraints (1)-(19), for _t < T_ . The terminal value function _VT_ = Γ is given by computing the discounted summation of payoffs with fixed control policies over the period [ _T_ + 1 _, T_ + 200], for each possible terminal state vector � _L_<sup>_N_</sup> _T_<sup>_, LA_</sup> _T_<sup>_, LR_</sup> _T_<sup>_, LC_</sup> _T_<sup>_, X_</sup> _T_<sup>_F, θ_</sup> _T_<sup>_c_</sup> �. Here _L_<sup>_C_</sup> represents the _v_ max dimensioanl vector of the managed forest land of all vintage ages.

We solve the stochastic problem by numerical dynamic programming algorithms with value function iteration (Cai and Judd (2010)). Since this is a 20
example, the efficiency of customs is documented in data which record the number of documents as well as the number of days needed for importing or exporting. Fewer documentary requirements, and quicker clearance times, translate into lower administrative costs for exporters and importers. They can also mean lower information costs in terms of understanding the set of steps that must be taken in order to ensure smooth passage through customs and border administrations.

In Figure 4 we present the respective _Doing Business_ data for 20 APEC economies (Brunei was not covered by the survey) as well as the average results for the low-, middle-, and high-income groups. The first interesting observation is that the number of documents and days needed for exports are lower in most economies compared to imports. Only in the case of Australia and the USA are more documents required for exports than for imports. Furthermore, only in Russia, Thailand, and Vietnam does the delay for exports exceed the delay for imports. The two low-income economies among the APEC economies, namely Papua New Guinea and Vietnam, require less documentation for exports and imports than the low-income average. Most middleincome APEC economies require a number of export or import documents that is similar to the world average for this income group. The Philippines and Mexico perform particularly well in this comparison. Among the high-income APEC members Canada achieves the best score, asking for only three export and four import documents.

The difference in APEC economies is particularly pronounced concerning the days needed for imports and exports. Several empirical studies have pointed out the importance of timeliness for the trading performance of economies (e.g. Hummels, 2001, Evans and Harrigan, 2005). In a recent World Bank study, Djankov et al. (2006) find that a one day delay before shipping is estimated to reduce trade by 1%. In nearly all APEC high-income economies, the number of days required for imports and exports is lower than the world average for this income group. In particular, Singapore has been very successful in streamlining the customs procedures. The majority of middle-income APEC economies show a similar above-average performance in the category. Furthermore, it is promising to see that Papua New Guinea and Vietnam have clearance times that are similar to the middle-income average and substantially superior to the average of lowincome economies.

16
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 21: Funding gaps by sector**

|$ millions|||||||
|---|---|---|---|---|---|---|
||ICT|Irrigation|Power|Transport|WSS|Total|
|Spending needs|(119)|(165)|(963)|(341)|(774)|(2,363)|
|Existing spending*|57|Nav|492|103|185|599|
|Efficiency gains|0|0|380|45|46|471|
|Funding gap|**(62)**|**(165)**|**(91)**|**(193)**|**(543)**|**(1,048)**|

* traced to needs

Source: Derived from Foster and Briceño-Garmendia 2009.

Note: Potential overspending across sectors is not included in the calculation of the funding gap, because it would not necessarily be applied toward other infrastructure sectors.

# What else can be done?

The funding gap can be addressed in two ways. First, Côte d’Ivoire may be able to increase the flow of resources to infrastructure both from the public and private sectors. Public spending on infrastructure has been relatively low compared to many neighboring countries. Therefore, there may be scope for reallocation within the overall budget, and the HIPC initiative may be a source of extra funding once the country overcomes the crisis.

Furthermore, since the onset of the crisis, Côte d’Ivoire has not attracted as much private finance for infrastructure as other African peers. Between 2002 and 2007, private investment commitments in Côte d’Ivoire were less than 0.5 percent of GDP per year. By comparison, Benin, Democratic Republic of Congo, Kenya, Nigeria, Senegal, Tanzania, and Uganda each captured between 1.0 and 2.5 percent of GDP, while Guinea-Bissau—the most successful country in this regard—has captured more than 3.0 percent of GDP (figure 24). On the other hand, prior to the crisis, Côte d’Ivoire was a pioneer with private participation in infrastructure and did many of its major transactions during the period 1994 to 2000 (figure 25). Given the country’s strong and successful track record with private participation, there must be significant potential to revive private investment in the future.

41
Table 1a: Summary Statistics (N=2893)

||Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|
|Age|15.611|2.235|12|22|
|Father Alive|0.692|0.462|0|1|
|Mother Alive|0.808|0.394|0|1|
|Female Headed|0.338|0.473|0|1|
|Age Household Head|47.133|13.083|13|110|
|Household Size|6.333|2.300|1|15|
|Muslim|0.187|0.390|0|1|
|Urban Household|0.358|0.480|0|1|
|Read English|0.741|0.438|0|1|
|No Qualification|0.669|0.471|0|1|
|Ever pregnant|0.107|0.309|0|1|
|Never had sex|0.694|0.461|0|1|
|Number of partners|0.449|0.809|0|6|

Table 1b: Equality of Means at Baseline

||Dropouts|(N=889)|School G|irl(N=2003)|
|---|---|---|---|---|
||Control <br>Mean|Treatment<br>Difference|Control<br>Mean|Treatment<br>Difference|
|Age|17.434|-0.301|15.249|-0.199|
|Father Alive|0.646|0.004|0.696|0.019|
|Mother Alive|0.784|-0.038|0.834|-0.042|
|Female Headed|0.420|-0.004|0.351|-0.095***|
|Age Household Head|46.855|-0.502|47.775|-1.323|
|Household Size|6.091|0.011|6.433|-0.105|
|Muslim|0.225|-0.006|0.194|-0.045|
|Urban Household|0.194|-0.010|0.362|0.110|
|Read English|0.469|-0.065|0.829|-0.024|
|No Qualification|0.673|0.012|0.659|0.021|
|Ever pregnant|0.436|-0.020|0.021|0.008|
|Never had sex|0.309|-0.017|0.794|0.008|
|Number of partners|1.135|0.031|0.270|-0.015|

<u>Notes:</u> The entire sample was never married at baseline, so the control and treatment means were both zero. Dropout and school girl refer to schooling status at baseline. The sample was split into dropouts (girls not in school) and school girls at baseline, so the control and treatment means of schooling status were identical at baseline (dropouts were 100% not in school while school girls were 100% in school). These means are weighted to make results representative of all study EAs. Standard errors are clustered at the EA level.

*Denote significance at the 10% level, ** at the 5% level and *** at the 1% level

25