rafmacalaba/datause-encoder-data huggingface dataset:
agricultural extension service. Promotion was administered by sub-Assistant Agricultural Officers, whom we refer to as extension agents. Each extension agent works in a single sub-district ( _Union_ ), spanning on average 9 villages. To limit the potential for our demonstration interventions to displace extension agents’ attention away from farmers in control and non-experimental villages, our experimental sample consists of 2 randomly selected villages in 55 Unions of the Barisal district, yielding a study sample of 110 villages across the district.<sup>4</sup> One of the primary activities for extension agents under IAPP was to form farmer field groups of approximately 25 farmers in each of the project village. These farmers were self-designated during a village meeting with the extension agent; the only requirement was for these farmers to manifest a high level of interest in learning about new agricultural technologies and attend regular group meetings. The farmer field groups discussed a range of agricultural practices and technologies, including the adoption and properties of the new flood-saline-resilient seeds demonstrated in our experiment. They met monthly, on average, during the dry season.

# **2.2 Dry season agriculture in Barisal**

Agriculture comprises two main growing seasons in Barisal: the monsoon season ( _kharif_ season) spans roughly from April to October, while the dry season ( _rabi_ season) runs roughly from November to March. Rice is the traditional staple crop in Bangladesh, and is well-suited to cultivation in the kharif season. While productive, rice cultivation in the rabi season ( _boro_ rice) requires heavy irrigation which is primarily provided by groundwater.<sup>5</sup> Heavy reliance on groundwater irrigation poses economic and environmental challenges: at the time of this study, heavy energy subsidies were in place to ensure the profitability of boro rice cultivation, and associated groundwater extraction was drawing down the water table.

We present basic descriptive statistics on dry season production of pulses and boro rice in Barisal in Appendix Table A5; our household survey data used to construct this table is described in Section 3.3. Consistent with the context above, the production of pulses, relative to boro rice, is associated with much less intensive irrigation but are also lower profits for farmers; these lower profits may be in part a consequence

> 4These villages are mapped in Appendix Figure A1.

> 5Most irrigation in Barisal originates from pump-operated shallow tube wells (67% in our study sample, compared to 5% using surface water).

8
Supplementary materials S5. Full regression table of pooled outcomes of ECT-R1 and ECT-R2

||||Main source of|Main source of|Other source of|Other source of||
|---|---|---|---|---|---|---|---|
||Worked last<br>week|Hours worked<br>last week|income<br>increased or<br>stayed same|income<br>decreased or<br>stopped|income<br>increased or<br>stayed same|income<br>decreased or<br>stopped|Inability to buy<br>staple foods|
|ATE of ECT-R1|0.121**|10.448***|0.173***|-0.173***|0.156**|-0.294***|-0.031|
||(0.056)|(3.270)|(0.062)|(0.062)|(0.061)|(0.070)|(0.034)|
|POM (Control)|0.509***|19.517***|0.347***|0.653***|0.284***|0.614***|0.120***|
||(0.018)|(0.930)|(0.017)|(0.017)|(0.020)|(0.022)|(0.012)|
|Female|0.579**|0.579**|0.605**|0.605**|0.667***|0.667***|0.208|
||(0.238)|(0.238)|(0.241)|(0.241)|(0.259)|(0.259)|(0.274)|
|Age|-0.000|-0.000|0.001|0.001|-0.008|-0.008|0.015*|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.010)|(0.010)|(0.009)|
|Household dependency ratio|0.126|0.126|0.145|0.145|0.194|0.194|0.420***|
||(0.106)|(0.106)|(0.107)|(0.107)|(0.133)|(0.133)|(0.118)|
|Head of household|0.388|0.388|0.424*|0.424*|0.580**|0.580**|-0.007|
||(0.253)|(0.253)|(0.254)|(0.254)|(0.281)|(0.281)|(0.270)|
|Completed secondary school|-0.066|-0.066|-0.143|-0.143|-0.168|-0.168|-0.018|
||(0.240)|(0.240)|(0.245)|(0.245)|(0.265)|(0.265)|(0.276)|
|Main income source: Business|0.539|0.539|0.735*|0.735*|0.626*|0.626*|0.940**|
||(0.356)|(0.356)|(0.386)|(0.386)|(0.376)|(0.376)|(0.378)|
|Main income source: Wage/Salary|-0.458|-0.458|-0.247|-0.247|-0.551|-0.551|-0.263|
||(0.442)|(0.442)|(0.465)|(0.465)|(0.476)|(0.476)|(0.486)|
|Main income source: Other|-0.439|-0.439|-0.247|-0.247|-0.446|-0.446||
||(0.772)|(0.772)|(0.785)|(0.785)|(0.801)|(0.801)||
|Asset score|-2.464***|-2.464***|-2.423***|-2.423***|-2.589***|-2.589***|-3.473***|
||(0.665)|(0.665)|(0.675)|(0.675)|(0.699)|(0.699)|(0.800)|
|Bombali|-0.329|-0.329|-0.442|-0.442|-0.530|-0.530|0.345|
||(0.416)|(0.416)|(0.443)|(0.443)|(0.481)|(0.481)|(0.460)|
|Kenema|-0.915*|-0.915*|-0.824*|-0.824*|-0.867*|-0.867*|-0.104|
||(0.468)|(0.468)|(0.472)|(0.472)|(0.502)|(0.502)|(0.509)|
|Port Loko|0.209|0.209|0.335|0.335|0.053|0.053|0.328|
||(0.385)|(0.385)|(0.386)|(0.386)|(0.430)|(0.430)|(0.504)|
|Western Area Urban|0.518*|0.518*|0.548*|0.548*|0.634*|0.634*|0.622|
||(0.298)|(0.298)|(0.305)|(0.305)|(0.338)|(0.338)|(0.381)|
|Constant|-2.155***|-2.155***|-2.474***|-2.474***|-1.684***|-1.684***|-3.122***|
||(0.533)|(0.533)|(0.533)|(0.533)|(0.604)|(0.604)|(0.623)|
|Observations<br>_Source_: Authors’ calculations from CIMS (2020)<br>_Note_: Standard errors in parentheses: *** p<0.01|908<br>, ** p<0.05, * p<0.1.|908<br>Inverse probabilit|893<br>y weighted regres|893<br>sions. For each es|627<br>timation, overide|627<br>ntification tests s|810<br>hows that the IPW|

_<mark>Note</mark>_ <mark>: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. Inverse probability weighted regressions. For each estimation, overidentification tests shows that the IPW model balances the covariates.</mark> 43
<!-- Start of picture text --> Figure 1: Import Competition<br>-and Labor Productivity<br>Labor productivity<br>140<br>130 _- _.. -<br>120 _ -.<br>1 10 *-- -- - - - -<br>100 _.-<br>9 0 . -I<br>12.5 is 17.5 20 22.5<br>Import tariff rate<br><!-- End of picture text --> Equation: Labor productivity - aO + al _A&tr_ Notes: 1. Figure 5.1 is based on equation 1. 2. Equation 1: Five adjustars (Colombia, Izdoousia, Mxico, Pakistan and Tureky) Labor productivity - 155.3 - 2.6ATZ (6.7) (-L.7) R-uquared 0.18, DW: 0.44 3. Equation 2: Intensive adjusters (Tndonasia, Mexico and Turkey) Labor productivity - 138.3 - 2.3AAR (17.4) (-5.6) R-squared 0.48, DW: 0.78 3. Sample ranga: 1978-1986. a-u-l data 4. Labor productivity is given by real outpur. **per** worker In the Manufacturing sector (1980-100) and _A3Z_ is import duties collectad as a percentage of value of CIF imports. 5. Data used are the average for the group of adjusters. Sources: World Tables, World Bank; IFS and CEM.
the 5 digit ISIC level for 1992. In Figure 4, the shift of the curve to the right indicates that the skill content of China’s exports has increased over the sample period. For example, in 1992, 20 percent of the least skill-intensive industries produced 55 percent of China’s export share. By 2005, the export share that these industries produced fell to 32 percent.<sup>3</sup> However, given the high share of processing trade in China, an increase in the skill content of China’s exports could be due to China importing intermediate inputs with higher skill content that it then assembles for exporting. We assess this possibility by plotting the cumulative of export shares against the skill intensity with non-processing manufacturing exports only. That is, we exclude any exports that have been classi fi ed as processing trade. From Figure 5, we see that there is hardly any shift in the curve indicating no change in the skill content of China’s non-processing exports.

Processing exports make up a large share of China’s manufacturing exports and by excluding processing exports we are excluding around 54 percent of China’s manufacturing exports (see Table 1). Although imported inputs account for a large share of the value of processing exports, there still remains a signi fi cant amount of value added in China in processing exports and there could be a shift in the skill content within that portion. To examine this possibility, we compare the change in the skill content of imported manufacturing inputs for processing trade to the skill content of imported inputs for non-processing trade in Figures 6 and 7. Using US industry skill data to rank the skill intensity of imports, we fi nd a much larger increase in the skill content of processed imports than of non-processing imports. Of course this rise in the skill-content of processing imports does not rule out the possibility that the Chinese value-added has become more skill-intensive too.

> 3 This approach only gives an indication of shifts between industries, thus we cannot say if there has been any skill upgrading within an industry.

6
**Figure 6: Impulse responses due to a 1 percentage point increase in EM7 and China’s growth**

<!-- Start of picture text --> Percentage point<br>1.2<br>EM7 China<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>On Impact 1 Year 2 Years 3 Years<br><!-- End of picture text --> _Note:_ The graphs show the cumulated impulse responses of growth in other EFM, at different horizons, due to a 1 percentage point increase on impact in EM7 and China’s growth. Solid bars represent medians, and error bars represent 16-84 percent confidence bands.

20
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Table 3: Robustness: Regional log Formal Earnings Premia - 1995, 2000, 2005, 2010

|Change in log formal earnings premium:<br>1991-1995|1991-2000|1991-2005|1991-2010|
|---|---|---|---|
|(1)|(2)|(3)|(4)|
|Panel A: Post-liberalization tariff changes||||
|Regional tariff change<br>0.089|0.319|0.970***|1.847***|
|(0.189)|(0.224)|(0.299)|(0.304)|
|Post-liberalization tariff change<br>n/a|2.305|10.451**|1.708|
||(2.921)|(4.284)|(1.042)|
|Panel B: Imports and exports (ADH)||||
|Regional tariff change<br>0.083|0.363|1.183***|1.542***|
|(0.193)|(0.253)|(0.319)|(0.386)|
|Imports (ADH)<br>0.003|-0.459**|-0.181|-0.127|
|(0.124)|(0.231)|(0.493)|(0.186)|
|Exports (ADH)<br>-0.359|1.986|-0.128|-0.051|
|(0.950)|(1.219)|(0.375)|(0.067)|
|Panel C: Real exchange rate||||
|Regional tariff change<br>0.133|0.358|1.244***|1.674***|
|(0.190)|(0.227)|(0.313)|(0.293)|
|Real exchange rate change - trade weighted<br>-0.226|-0.001|-0.092|0.832**|
|(0.304)|(0.362)|(0.590)|(0.415)|
|Panel D: Privatization||||
|Regional tariff change<br>0.089|0.328|1.187***|1.585***|
|(0.189)|(0.218)|(0.243)|(0.303)|
|Change in state-owned share of empl.<br>n/a|0.1785|0.4509**|0.3504|
||(0.181)|(0.223)|(0.234)|
|Panel E: Individual Fixed Effects||||
|Regional tariff change<br>0.175|0.265|1.011***|1.201***|
|(0.174)|(0.199)|(0.211)|(0.254)|
|Panel F: 1980-1991 Census Pre-Trends||||
|Regional tariff change<br>0.315|0.500*|1.364***|1.832***|
|(0.207)|(0.260)|(0.281)|(0.361)|
|Panel G: Manufacturing Sector Earnings||||
|Regional tariff change<br>0.626**|0.630**|1.700***|2.160***|
|(0.262)|(0.316)|(0.379)|(0.462)|
|Panel H: Nontradable Sector Earnings||||
|Regional tariff change<br>0.078|0.284|1.221***|1.637***|
|(0.238)|(0.259)|(0.268)|(0.299)|
|Panel I: Sample restriction-above median urban share-238 obs||||
|Regional tariff change<br>0.072|0.272|1.297***|1.939***|
|(0.205)|(0.232)|(0.277)|(0.343)|
|Panel J: Sample restriction-above median formal share-238 obs||||
|<br>Regional tariff change<br>0.182|0.412*|1.417***|1.907***|
|<br>(0.205)|(0.223)|(0.244)|(0.311)|
|Panel K: Alternate tariff measure-effective rate of protection (ERP)||||
|Regional ERP change<br>0.009|0.165|0.669***|0.886***|
|(0.106)|(0.133)|(0.151)|(0.178)|
|Panel L: Alternate RTC measure-employment shares including form|al and informal|labor||
|Alternate regional tariff change<br>-0.000|0.483***|1.367***|1.833***|
|(0.150)|(0.167)|(0.167)|(0.215)|
|Formal earnings pre-trend (86-90)<br>✓|✓|✓|✓|
|State fixed effects (26)<br>✓|✓|✓|✓|

Positive coefficient estimates for the regional tariff change imply larger declines in formal earnings in regions facing larger tariff declines. 475 microregion observations, unless otherwise noted. Outcomes calculated using RAIS data. Standard errors (in parentheses) adjusted for 112 mesoregion clusters. Weighted by the inverse of the squared standard error of the estimated change in log formal earnings premium. Pre-trends computed for 1986-1990 period, unless otherwise noted. *** Significant at the 1 percent, ** 5 percent, * 10 percent level.

65
||**10‐yearpanel, **<br>|**within variation**<br>||
|---|---|---|---|
||PIP/PM(PSD)|PIP/PM(PSD)|PIP/PM(PSD)|
|0<br>_e_|0.301 / 0.058 (0.136)|0.299 / 0.057 (0.136)|0.277 / 0.051 (0.130)|
|0<br>_y_|_0.697 / 9.55 (9.934)_|_0.704 / 9.707 (9.974)_|_0.697 / 9.499 (9.886)_|
|0<br>_life_|0.092 / 1.175 (15.53)|0.092 / 1.147 (15.48)|0.090 / 1.039 (15.24)|
|_vol_|0.236 / 0.221 (0.636)|0.234 / 0.218 (0.633)|0.238 / 0.223 (0.638)|
|_polity_|0.082 / ‐0.00 (0.117)|0.082 / ‐0.00 (0.117)|0.081 / 0.000 (0.117)|
|_pavroad_|0.085 / ‐0.00 (0.014)|0.085 / ‐0.00 (0.014)|0.084 / ‐0.00 (0.014)|
|_cred_|_0.930 / 0.156 (0.095)_|_0.931 / 0.157 (0.095)_|_0.940 / 0.161 (0.095)_|
|_t_<sup>_d_</sup>|0.095 / 5.126 (54.70)|‐|‐|
|_c_<br>_d_|‐|0.126 / 12.21 (69.85)|‐|
|_g_<br>_d_|‐|‐|0.293 / ‐234. (560.4)|
|_g_‐prior|BIC|BIC|BIC|
|Prior model size|4|4|4|
|Obs.|153|153|153|
|Number of models|256|256|256|

PIP stands for ``Posterior inclusion probability", PM is ``Posterior Mean" and PSD is ``Posterior Standard Deviation". Figures in italics correspond to entries with PIP > 0.5.

The results in Table 3 reveal that the robust negative effect of natural disaster risk on human capital accumulation found in the cross‐country regression setting does not appear if we concentrate exclusively on within‐country variation in school enrollment rates. Although the sign of the parameter attached to geological disasters remains negative, it is estimated with low precision and has an inclusion probability below 0.5. It is interesting to see that the inclusion probability of the disaster variables, and particularly for the geological disaster variable, increases when the horizon under consideration moves towards long‐run comparisons. These results give us an interesting insight to the determinants of human capital accumulation in the short and medium run. The posterior inclusion probabilities of the variables for the five‐year panel show that, apart from the natural persistence of human capital accumulation variables, income volatility plays the most important role as a determinant of secondary school enrollment rate changes over time. For the case of the ten‐year panel, income developments and access to credit appear as the most robust variables. In particular the BMA estimate of _cred_ is very precisely estimated and implies that credit constraints play a privileged role in determining medium‐run human capital accumulation dynamics. These results are in line and complement those found by Flug et al. (1998).

# **3.4  Parameter heterogeneity and interaction effects**

An obvious question to ask in the framework of the analysis carried out in this paper is whether the effect of natural disaster risk on human capital accumulation depends on other country characteristics. The effects of natural disaster risk on several macroeconomic variables have been shown to be modulated by institutional and economic factors. Noy (2009) shows that the costs of disasters in terms of GDP depend on the strength of institutions in place in a given country, as well as on the level of income per capita. In a similar fashion, Crespo Cuaresma et al.
18 only for those countries that, according to the country-specific notes in the Balance of Payments Statistics Yearbook, explicitly advice to do so<sup>23</sup> .

While recognizing that the balance of payments data on remittances very likely underestimate the true amount, we believe that measurement error does not significantly affect our findings for at least two reasons. First, since classical measurement error causes an attenuation bias, that is a reduction in the significance of the coefficients of the variables imprecisely measured, we conclude that our results on remittances are possibly even stronger than what found here. Secondly, we do not see any a-priori reason for the underestimation of remittances to bias the cross-country variance which our results are drawn from; along these lines, the just shown exercise with a different measure of migrants’ remittances can be seen as a partial and indirect proof of it.

A different issue concerns the validity of the results over the entire distribution of observations: unpleasantly, as it can be inferred by combining the coefficients of the linear, the quadratic and the interaction terms, the intuitive negative (positive) effect of reserves (external debt) on the probability of current account reversals may become, less intuitively, positive (negative) for high values of reserves (external debt) over GDP. We believe, though, this not to be a too serious issue for two reasons. On one hand, a linear model can not be asked to capture strong nonlinearities occurring in the tails; in particular, the apparent inversion could instead be reflecting that the probability effect of decreasing reserves and increasing external debt asymptotically converges to 1. On the other hand, these turning points occur at the very extremes of the distribution of the variables<sup>24</sup> .

As a further check, we have excluded the country-year cells falling below the 1<sup>st</sup> or above the 99<sup>th</sup> percentile of the distributions in terms of international reserves and external debt: the

> 23 Countries for which compensation of employees are to be excluded from total remittances are: Argentina, Azerbaijan, Barbados, Belize, Benin, Brazil, Cambodia, Cape Verde, China, Cote d’Ivoire, Dominican Republic, Ecuador, El Salvador, Guyana, Panama, Rwanda, Senegal, Seychelles, Turkey and Venezuela. It is well known that the inclusion of compensation of employees is quite relevant for the Philippines.

> 24 As an example, in column [7] of Table 3, reserves need to go above 89 percent of GDP, a value that in our sample is greater than the 99<sup>th</sup> percentile of the distribution in terms of reserves/GDP. In the same way the external debt must reach 216 percent of GDP to invert the relationship, a value again around the 99<sup>th</sup> percentile. Admittedly, these turning values occur at lower percentiles in the estimates reported in Table 5.
|Table 4. Nonresi|dential tariff reg|imes|||||
|---|---|---|---|---|---|---|
||Commercial|||Industrial|||
||Tariff type|Fixed<br>charge/month?<br>yes/no|Demand<br>charge?<br>yes/no|Tariff type|Fixed<br>charge/month?<br>yes/no|Demand<br>charge?<br>yes/no|
|Benin|Linear|No|No|Linear|No|No|
|Botswana|Linear|No|No|Linear|Yes|Yes|
|Burkina Faso|TOU|Yes|Yes|TOU|Yes|Yes|
|Cameroon|DBT|No|Yes|TOU|No|Yes|
|Cape Verde|Linear|No|No|Linear|No|No|
|Chad|IBT|No|Yes|TOU|No|Yes|
|Congo, Dem.<br>Rep. of|DBT|No|No|DBT|No|No|
|Congo,Rep. of|Linear|Yes|No|Linear|Yes|Yes|
|Côte d’Ivoire|DBT|Yes|No|TOU|Yes|No|
|Ethiopia|TOU|Yes|No|TOU|Yes|No|
|Ghana|IBT|Yes|No|Linear|Yes|Yes|
|Kenya|Linear|Yes|No|DBT|Yes|Yes|
|Lesotho|Linear|No|Yes|Linear|No|Yes|
|Madagascar|Linear|Yes|Yes|Linear|Yes|Yes|
|Malawi|Linear|Yes|Yes|Linear|Yes|Yes|
|Mali|Linear|No|No||||
|Mozambique|Linear|Yes|Yes|Linear|Yes|Yes|
|Namibia|Linear|Yes|Yes|Linear|Yes|Yes|
|Niger|Linear|Yes|Yes|Linear|Yes|Yes|
|Nigeria|IBT|Yes|No|IBT|Yes|Yes|
|Rwanda|Linear|No|No|Linear|No|No|
|Senegal|TOU|Yes|No|TOU|Yes|No|
|South Africa|IBT/Linear|Yes|No|TOU|Yes|Yes|
|Tanzania|Linear|Yes|Yes|Linear|Yes|Yes|
|Uganda|TOU|Yes|No|TOU|Yes|Yes|
|Zambia|Linear|Yes|No|DBT|Yes|Yes|
|Zimbabwe|Linear|Yes|Yes|Linear|Yes|Yes|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Note:_ IBT – increasing block tariff; DBT – decreasing block tariffs; TOU – time of use tariff. _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database Time-of-use tariffs (TOUs) are only practiced in a minority of cases, and are twice as prevalent among industrial tariff structures as among commercial tariff structures. TOUs allow power consumption to be associated with hours (peak/off-peak/night) and/or seasons (summer/winter, dry/regular), creating incentives for more efficient use of the power network. One-third of industrial and one-fifth of commercial tariff regimes in Africa incorporate TOUs.

12
Table 4. Treatment Effect on Social Capital

||Positive<br>Reciprocity<br>Index|Negative<br>Reciprocity<br>Index|Altruism|Trust|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|I(Treatment)|0.019|-0.023|-0.017|-0.036|
||(0.061)|(0.063)|(0.061)|(0.061)|
|R-squared|0.029|0.000|0.022|0.013|
|Observations|1,040|1,040|1,040|1,040|
|Mean (Control Group)|0.000|0.000|0.000|0.000|

_Notes:_ This table reports an OLS estimate. The variable in Column (i) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the statement: _when someone does me a favor, I am willing to return it_ , and the answer of the hypothetical money the respondent may give to a stranger as a thank-you for helping him on the street; (ii) is an index constructed using the methodology of Kling et al. (2007) and the reported answer on a 1 to 5 scale of the approval of the following statements: _How willing are you to punish someone who treats you unfairly, even when there are risks to you of personal consequences_ ; _How willing are you to punish someone who treats others unfairly, even when there are risks to you of personal consequences_ ; and _If I am treated very unfairly, I will take revenge on the first occasion, even if I have to pay a cost for it_ ; (iii) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _How willing are you to donate to charitable causes without expecting anything in return_ ; (iv) is the standardized reported answer on a 1 to 5 scale of the approval of the statement: _I always assume that people have only the best intentions_ . All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

Table 5. Treatment Effect on Altruism Towards Migrants

||Money will<br>kept by<br>their-self (log)|Money will<br>share with<br>vulnerable<br>Venezuelan (log)|Money will<br>share with<br>vulnerable<br>Colombian (log)|
|---|---|---|---|
||(1)|(2)|(3)|
|I(Treatment)|0.011|-0.024|-0.043|
||(0.063)|(0.044)|(0.032)|
|R-squared|0.028|0.010|0.017|
|Observations|332|434|796|
|Mean values (Control Group)|8.077|7.737|8.131|

_Notes:_ This table reports an OLS estimate. Dependent variables are the logarithm of the answer of the respondent about the distribution of 5,000 Colombian pesos between him (column (i)), a vulnerable Venezuelan migrant (column (ii)), and a vulnerable Colombian (column (iii)). All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

37