rafmacalaba/datause-encoder-data huggingface dataset:
27 vertical relations with large finms emerge as a crucial source of external capability in all dtree subsectors: chemical companies and (to a lesser extent) trading houses are crucial sources of technological capability for woven textile SMEs; and silverware and auto parts SMEs receive important technologicial support from their immediate principals in the multi-tier subcontracting chain.

More broadly, the Japan study highlights the power of inter-firm flows of information within industrial districts as an important source of technological capability. All three subsectors dominated the economic base of a narrow geographical area - silverware in Tsubame city, synthetic woven textiles in Fukui-Ishikawa prefecture and auto parts in Ohta city. Vertical linkages represent one example of inter-firm information flows in these industial districts. In addition, Table 12A identifies similar firms and public agencies (organized, as discussed further below, at the district level) as useful sources of capability. And equipment - suppliers many of which are themselves located within the specialized, product-centered industrial districts - emerge in all three subsectors as an external source of technological capability second only to subcontracting principals.

Indonesia. Interactions with the intemational marketplace emerge in Table 12B as the most important source of technological capability for Indonesian SMEs. While both pnbumi and non-pribumi firms have thus gained technologically from export expansion, Table 12B also points to distinctive advantages enjoyed by non-pnbumi fins. In both the rattan and garments subsectors, expatriate employees play a disproportionately important role as a source of technological capability for non-pribuin finns vis-a-vis their pribumi counterparts. While half of the non-pnbumi users recruited expatriates from Hong Kong and Taiwan, both Chinese centers, no_pnbumi users drew expatriates from these sources. Pribumi firms rely disproportionately on substitute channels of technical leaming. For furniture finns (both rattan producers and those from Jepara), technical literature has played an important role. As for pribumi gdrment firms (whose role in the garment industry is an exceedingly small one), Table 12B suggests that they have not reaped any direct technological benefits from Indonesia's export success, but instead rely on the local diffusion of technological capability by private
Table D.1: Cross-firm Variance in Technology Sophistication, Robustness

|||MOST|||GAP|||EXT||
|---|---|---|---|---|---|---|---|---|---|
||ABF|GBF|SSBF|ABF|GBF|SSBF|ABF|GBF|SSBF|
|_V ar_(_Tc −T_)|0.04|0.05|0.03|0.00|0.00|0.01|0.06|0.07|0.07|
|_V ar_(_Tj,c −Tc_)|0.04|0.05|0.07|0.03|0.04|0.08|0.07|0.08|0.12|
|_V ar_(_Tj,Brazil −TBrazil_)|0.07|0.08|0.10|0.03|0.04|0.08|0.08|0.09|0.13|
|_V ar_(_Tj,V ietnam −TV ietnam_)|0.03|0.04|0.07|0.03|0.04|0.10|0.05|0.05|0.11|
|_V ar_(_Tj,Senegal −TSenegal_)|0.03|0.04|0.05|0.04|0.05|0.06|0.08|0.10|0.12|
|Contribution within|0.51|0.48|0.73|0.95|0.98|0.91|0.55|0.54|0.64|
|Contribution within with controls|0.49|0.52|0.27|0.05|0.02|0.09|0.45|0.46|0.36|

Note: Technology measures are weighted by the sampling weights. Contribution within with controls is estimated after controlling for size group small, medium and large), sector (agriculture, manufacturing and services), age (0-5, 6-10, 11-15, and 16 years or more), export and foreign ownership status.

Table D.2: Within-firm Variance in Technology Sophistication, Robustness

||ABF EXT|ABF MOST|ABF GAP|
|---|---|---|---|
|_V ar_(_Tf,j,c −Tf,c −Tj,c_)|0.14|0.12|0.13|
|_V ar_(_Tj,c −Tc_)|0.07|0.04|0.03|
|_V ar_(_Tf,j,Brazil −Tf,Brazil −Tj,Brazil_)|0.16|0.18|0.15|
|_V ar_(_Tf,j,V ietnam −Tf,V ietnam −Tj,V ietnam_)|0.12|0.12|0.13|
|_V ar_(_Tf,j,Senegal −Tf,Senegal −Tj,Senegal_)|0.14|0.07|0.11|
|_V ar_(_Tj,Brazil −TBrazil_)|0.08|0.07|0.03|
|_V ar_(_Tj,V ietnam −TV ietnam_)|0.05|0.03|0.03|
|_V ar_(_Tj,Senegal −TSenegal_)|0.08|0.03|0.03|

Note: Technology measures are weighted by the sampling weights.

80
Two other areas of concern are violent child discipline and work (including domestic work). There are some disparities by background in both these outcomes. For instance, children from richer families are less likely to be violently disciplined but somewhat more likely to engage in work and domestic work. It is not possible to assess the change in the impact of different characteristics over time because data are not available on these outcomes from any years other than 2006/07.

# _4.3 Inequality in ECD over Time_

Inequality in early childhood is of particular concern for three reasons. First, it is morally objectionable; children have no control over their circumstances. Second, it acts as a key mechanism for adult inequality because disparities tend to persist and are even compounded. Third, inequality in early childhood plays a key role in the intergenerational transmission of poverty. This section assesses how inequality has been evolving over time in Morocco; the tables underlying the quantification of inequality (dissimilarity index) and the different factors that contribute to inequality (Shapley decomposition) are presented in the annex (Table 4 and Table 5).

# 4.3.1 Health and Survival

Young children in Morocco face widely disparate development opportunities in terms of health and survival. Inequality in access to early health care remains substantial. Around 7–8 percent of the opportunities for prenatal and delivery care would have to have been distributed differently for equality of opportunity to have prevailed in 2012. However, these levels are substantially lower (by more than half) compared to 2003/04 (Figure 10). There has been a slight increase in inequality in immunizations and infant mortality inequality is quite low; both outcomes potentially having disparities due to chance (not statistically significant).

**Figure 10. Inequality (Dissimilarity Index) in Health and Survival by Year**

<!-- Start of picture text --> 25<br>20<br>20<br>15<br>14<br>15<br>12 2003/4<br>2011<br>10 8<br>7 2012<br>4<br>5 3<br>1 1<br>0<br>Prenatal Delivery Fully Infant mortality<br>Immunized<br>Dissimilarity index<br><!-- End of picture text --> Source: Authors’ calculations based on DHS 2003/04, ENPSF 2011, and ONDH panel 2012.

These inequalities translate into very different chances for the most and least advantaged children in society (Figure 11). The most advantaged child is almost certain to have good early 16
_d_ failure at the midpoint of the interval, we can write<sup>_n_</sup> _j_<sup>as</sup> _n j_ = _N j_ − 2 _<u>j</u>_ and the lifetable estimator of the survivor function as Figure 1 illustrates the life table survival function for the entire sample. The plot shows clear evidence of negative duration dependence throughout the lifetime of the trade flows. In other words, the probability that an export flow will disappear falls the longer is the duration of the flow. Particularly during the first five years after a given trade flow first appears, hazard rates are high. Only about a third of all spells survive this initial period while a fifth survives the first twenty years. This finding is in line with previous results in Besedes and Prusa (2006a) and Blyde (2008), among others and highlights why the intensive margin of trade is so important in the process of export growth in developing countries.

Table 3 presents the estimated rates of survival for three broad income groups and a selected set of exporters. For each of these countries the presence of negative duration dependence is immediately obvious. The estimates clearly suggest that countries at higher stages of development are more likely to see their export flows survive for longer periods of time. For high-income countries 59% of trade flows survive for more than one year. For low-income countries only 39% of flows survive for more than a year. A similar finding is illustrated in Figure 1, showing that the survivor function for OECD countries lies consistently above the survivor function for the remaining countries.

Within these income groups, 70% of US export flows survive for more than one year. In contrast, less than 30% of the export flows of Burkina Faso have spells of more than one year. Whereas some 41% of US export flows survive the first 10 years after they are initiated, only 17% of Argentine, 12% of Egyptian and just 4% Burkina Faso export flows survive for a decade. Table 4 contains additional evidence showing that the average survival rates for the different World Bank defined regions are longer for exports in more developed parts of the world. In all of these cases the Log-Rank test rejects the Null of 7
resort to (statistical) inference based on previous experience is unavoidable.

It must be remarked that India’s system of public finances has a potentially decisive influence on the program’s social profitability. First, PMGSY contains no provisions for road maintenance beyond the first 5 years following initial construction. Thereafter, maintenance becomes the responsibility of the state in question. Since the states’ fiscal condition is a parlous one, there could be a dramatic fall in the quality of these roads, and hence in the benefits they generate, if no system of secure, long-term financing is put into place. Second, since PMGSY roads are part of a wider network, their social profitability depends on the condition of that network. The district roads and state highways are often in an unsatisfactory, and sometimes deplorable, state; so that the larger fiscal question arises here, too. Much of past investment in PMGSY will be wasted, and further investments will be markedly less profitable, if the wider policy framework is Given PMGSY’s sheer scale, it is natural to ask whether there are extant evaluations of its social profitability. Neither a search of the literature nor presentations at some of India’s leading research institutions uncovered any. There are certainly general investigations into the effects of investments in rural roads on villagers’ well-being, broadly interpreted. Two well-known studies of the sub-continent are Binswanger _et al._ (1993), which is largely concerned with output and investment in rural India, and Fan _et al._ (2000), which addresses welfare more directly, in that it also investigates the effects of government spending on rural poverty. More directly connected to the central question posed in this paper is Khandker _et al._ (2009), which analyzes two panels of data in connection with Bangladesh’s Rural Roads and Market Improvement and Maintenance Projects, which were undertaken in the late 1990s. Transport costs were much reduced, output and net prices were boosted, as were agricultural wages and per capita household consumption. Boys’ and girls’ schooling rose, too. Where consumption is concerned, the distributional pattern turned out to be favorable, or at least broadly neutral, so that there was a substantial associated reduction in poverty. There is evidence that providing all-weather roads also reduces poverty in south-east Asia. Between 1997-98 and 2002-03, the headcount ratio in rural Laos fell by 9.5 percentage points: Warr (2010) estimates that 13 percent of this decline is attributable to the conversion of dry- into all-weather roads. Geographically farther afield, Escobal and Ponce (2002, pp. 37-8) estimate that the rehabilitation of motorized rural roads in Peru’s rugged terrain yielded the beneficiaries $120 each annually, or 35 percent of the average per capita household income in the control group.

5
TABLE A19: Impact of leaders’ exposure on individual intermediary outcomes: micronutrients, ECD attitudes and observed hygiene

|**I**|**TT Leaders**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Micronutrients_**||||||
|**Micronutrients index**|-0.16|( 0.06)|0.02|0.13|2,059|
|Received Vitamin A|-0.06|( 0.03)|0.05|0.76|2,059|
|Received deworming medecine|-0.05|( 0.03)|0.05|0.80|2,059|
|Received iron supplementation|-0.04|( 0.03)|0.15|0.55|2,059|
|**_ECD Attitudes of main caregiver_**||||||
|**Attitudes index**|-0.00|( 0.06)|0.97|0.08|1,970|
|Should always answer child’s questions|-0.01|( 0.02)|0.63|0.89|1,970|
|Boys can play with dolls|0.02|( 0.03)|0.62|0.39|1,970|
|Girls can play with cars (toy)|0.01|( 0.03)|0.82|0.53|1,970|
|Early language develops from stimulation|0.01|( 0.02)|0.67|0.68|1,970|
|Brain develops from very early on|-0.03|( 0.01)|0.04|0.88|1,970|
|**_Hygiene (observed by test administrator)_**<br>||||||
|**Hygiene index**|-0.11|( 0.06)|0.09|0.06|2,068|
|Child has clean face|-0.03|( 0.03)|0.25|0.70|2,068|
|Child has clean hair|-0.05|( 0.02)|0.02|0.82|2,068|
|Child has clean hands|-0.07|( 0.03)|0.01|0.61|2,068|
|Child has clean clothes|-0.06|( 0.03)|0.03|0.61|2,068|
|Child is wearing shoes|0.03|( 0.02)|0.20|0.30|2,068|
|Child does not cough|0.01|( 0.02)|0.64|0.82|2,068|
|Child has clean nose|0.00|( 0.02)|0.94|0.83|2,068|
|Child does not have skin problems|0.00|( 0.02)|0.81|0.92|2,067|

Every row corresponds to a separate estimation (equation 3). The vitamin index is based on three variables: the child has received vitamin A, ferrous sulfate and deworming medicine during the last six months (yes/no questions). The hygiene index captures the hygienic condition of the child observed by the test administrator. The Attitudes index is calculated based on answers by the caregiver to the following questions: 1. Do you answer your child’s questions? 2. Do you think boys can play with dolls? 3. Do you think girls can play with cars? 4. Do you think children start talking by nature? 5. Does the child’s brain develop from gestation or when the child starts going to school? Each outcome variable is standardized using the mean and standard deviation for control households. The rows shows ITT estimates. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age, gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. Five villages without leaders were excluded from the estimation. The standard errors (in parentheses) are clustered at village level. p<0.1, p<0.05, p<0.01 67
Table 2: OLS estimates of IT use vs. Chinese competition

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable:Computers-per-worker|||||||
|Chinese competition|0.315***|0.260**|0.278**|0.268**|0.263***|0.217*|
||(0.114)|(0.129)|(0.111)|(0.104)|(0.099)|(0.114)|
|_R_<sup>2</sup>|0.77|0.77|0.81|0.81|0.82|0.82|
|Dependent variable: Share of labor with Internet|||||||
|Chinese competition|0.340***|0.274**|0.303***|0.274***|0.267***|0.148|
||(0.101)|(0.113)|(0.104)|(0.091)|(0.088)|(0.108)|
|_R_<sup>2</sup>|0.71|0.71|0.75|0.76|0.76|0.77|
|Dependent variable: Share of labor with computer|||||||
|Chinese competition|0.222**|0.195|0.232*|0.196*|0.193*|0.183|
||(0.103)|(0.124)|(0.122)|(0.110)|(0.103)|(0.125)|
|_R_<sup>2</sup>|0.73|0.73|0.76|0.77|0.77|0.77|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Robust standard errors in parentheses * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects.

Source: Authors’ calculations using data from ENTIC, INEGI 16
# **4. Empirical Specification**

# _Rainfall shock variables_

Let Rjtm be the amount of precipitation in cluster _j_ in month _m_ of year _t_ , and denote by R<sup>�</sup> jm corresponding historical monthly averages.<sup>9</sup> Now, since the monsoon season (JuneSeptember) consists of _m =_ 6, 7, 8, and 9, the rainfall shock in _the last completed_ monsoon, in terms of log deviations, is The _current_ monsoon rainfall shock is slightly more complicated because it depends upon the month Mijt in which child _i_ was interviewed and weighed. Thus, define where the additional _i_ subscript reminds us that ∆<sup>�</sup> ijt can vary across children in the same survey round and cluster.

# _Weight-for-age_

The basic regression for weight-for-age is then where WAZijt is the weight-for-age z-score of child _i_ in cluster _j_ in survey round _t_ . Controls consist of normal monsoon rainfall in the cluster (∑9k=6 R<sup>�</sup> jm) along with gender, age, year of birth, month of birth, birth order of the child, fixed effects (φr) for the 15 regions and fixed effects (μt) for the three survey rounds. An augmented set of controls includes characteristics of the mother (age, education level, occupation) and her partner (education level and occupation), and the ethnicity of the household head.

> 9More precisely, each of these variables is the exponential of the log rainfall prediction using DHS clusterlevel explanatory variables and coefficients from the regression of the corresponding log rainfall variable on the same set of explanatory variables (see section 3) at the weather station-level.

9
|**Table 10: Compensating variation o**|**f travel time to ma**<br>**Evaluated at me**|**rkets**<br>**an travel time**||**Evaluated at 90%**|**travel time per**|**centile**|
|---|---|---|---|---|---|---|
||**CV**|**(95% confiden**|**ce interval)**|**CV**|**(95% confiden**|**ce interval)**|
|Food consumption|32.4%|14.3%|50.6%|4.1%|1.4%|6.9%|
|Clothing|26.1%|7.0%|45.1%|3.2%|0.5%|5.9%|
|Housing|50.9%|22.2%|79.6%|7.4%|1.5%|13.2%|
|Children's schooling|50.0%|27.8%|72.3%|7.2%|2.8%|11.6%|
|Health care|[54.3%-66%]|[44.6%-56.1%]|[64%-75.9%]|[8.1%-11%]|[6%-8.2%]|[10%-13.8%]|
|Total income|21.3%|-3.9%|46.5%|2.5%|-0.8%|5.9%|
|Weights equal to consumption share|[34.1%-34.8%]|||[4.38%-4.48%]|||

All figures expressed in percentage of average consumption expenditures.
Table 2

# CENTRAL AMERICA: EXTERNA **L** EGAL TARIFF

# (percentages)

||Pre-reform<br>Average<br>Tariff,|Average<br>Legal<br>Tariff,<br>1987|1991Range|1993 Range|1995 Range|
|---|---|---|---|---|---|
|Costa Rica|52|26"|10-50b|5- 30|5- 20|
|El Salvador|48|23|5 - 35|5 - 25|5 - 20|
|Guatemala|50|25|5<br>37c|5 - 20|5 - 20|
|Honduras|41|20|4<br>35<sup>d</sup>|5 - 20|5 - 20|
|Nicaragua|54|21|5 - _200_|5 - 20|5 - _29_|

SOURCES: "Politicas Industriales en Centroamerica y Panama", ECLAC, LC/MEX/R.310, August 5, 1991; World Bank, Trade Liberalizatioa **n** d Economic Integration in Central America, Report No. 7625-CAM, 1990, p. 29 and 32; Ministry of Economy, Industry and Comerce, Costa Rica.

- Ad valorem equivalent of the average external tariff before the reform.

- a Include **s** urcharges.

- **b** Excludes a temporary surcharge of 2% and a levy of 1% on extra-regional imports. The Central Bank of Costa Rica also requires prior import deposits, currently 30% of the CIF value of the imports.

- a Excludes a surcharge of 3% on extra-regional imports.

- d Excludes a general surcharge of 5% and an additional 10% on final products. The surcharges cover all imports, but Central America will be exempt as of 1992.

- Excludes a stamp tax of 3% and selective consumption taxes of up to 75% that act as import tariffs. Such taxes will be reduced gradually to 15%.
firms overall are not too different from those obtained from the estimation of sophisticated structural trade models by Bernard et al. (2003) or Spearot (2013).

**<u>Table 6. Tariffs and Imported Intermediates</u>**

||**Import**<br>**Growth**<br>**(1)**|**Log (Import**<br>**Value)**<br>**(2)**|**Import**<br>**Growth**<br>**(3)**|**Log (Import**<br>**Value)**<br>**(4)**|**Import**<br>**Growth**<br>**(5)**|**Log (Import**<br>**Value)**<br>**(6)**|
|---|---|---|---|---|---|---|
||**A**|**ll**|**Agrib**|**usiness**|**Ap**|**parel**|
|**Change in Applied Tariff Rate**|‐4.072***||‐6.789***||‐1.974||
||(0.690)||(2.302)||(1.509)||
|**Log (Applied Tariff Rate)**||‐3.286***||‐1.777||‐1.956|
|||(0.637)||(1.998)||(1.260)|
|**Year Fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Firm Fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Main HS 2‐digit Fixed Effects**|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|14,062|22,522|792|1,278|1,485|2,345|
|**R‐squared**|0.303|0.912|0.409|0.790|0.346|0.880|

Notes: Robust standard errors in parentheses. *** indicates significance at the 1% confidence level. Only exporter-importers are included in all the regressions.

Second, we consider the correlations between NTMs and Peruvian firms’ import choices. The data on NTMs is taken from the Trade Analysis and Information System (TRAINS) developed by the United Nations Conference on Trade and Development (UNCTAD). For Peru, the data on NTMs is available for year 2012 only, which precludes the analysis from having a time-series dimension. NTMs are available by HS tariff line and are categorized according to the 2009 classification whose categories are; A - Sanitary and phytosanitary measures; B - Technical barriers to trade; C - Pre-shipment inspection and other formalities; D - Price control measures; E - Licenses, quotas, prohibitions and other quantity control measures.<sup>14</sup> For the purpose of this analysis, given the exposure of the particular sectors we are looking at, we only consider the first three categories in our definition of NTMs. We make the simplifying assumption that if at least 1 HS tariff line within an HS 6-digit product is subject to an NTM as of 2012 (in one of the first three categories mentioned above), the entire HS 6-digit product is categorized as being subject to an NTM. For each firm in 2012, we calculate the share of its intermediate imports that is accounted for by HS 6-digit products subject to an NTM and we define a dummy variable equal to 1 if more than 40% of the firm’s imports are subject to an NTM. We estimate a cross-sectional variant of Eq. (3) where the tariff measure is replaced by one of the firm-level NTM measures just described, year and firm fixed effects are dropped, and the dependent variable is either import value, the

> 14 See Cadot and Malouche (2012) for more details on the classification and for a thorough review of the role of NTMs for trade.

19
Table 5. IV regressions (dep = log of fertility rate) Developing countries

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Constant|1.319|1.388|1.529|1.422|
||(5.35)***|(6.02)***|(5.62)***|(6.42)***|
|Log of fertility at dest|0.375<br>(3.45)***|0.360<br>(3.70)***|0.219<br>(1.91)*|0.229<br>(2.12)**|
|p0:Log of fertility at dest|-0.695<br>(1.14)||0.041<br>(0.10)||
|log of p0|0.048|0.005|-0.070|-0.069|
||(0.62)|(0.12)|(1.24)|(2.22)**|
|Selection ratio (sec+tert)|0.016<br>(0.44)||-0.024<br>(0.71)||
|Log of remittances|||0.015|0.024|
||||(1.07)|(1.67)*|
|Urbanization|-0.004|-0.003|-0.005|-0.004|
||(2.06)**|(1.41)|(2.95)***|(2.62)***|
|GDP per capita|-0.070|-0.099|-0.118|-0.107|
||(1.81)*|(3.29)***|(3.28)***|(3.69)***|
|Adult’s education|-0.413|-0.482|-0.318|-0.321|
||(2.55)**|(3.01)***|(1.83)*|(1.82)*|
|East Asia & Paci...c|0.425|0.396|0.364|0.334|
||(3.93)***|(3.72)***|(3.33)***|(3.26)***|
|Sub-Saharan Africa|0.445|0.378|0.502|0.451|
||(4.43)***|(4.08)***|(4.99)***|(4.97)***|
|Latin Am. & Carib|0.353<br>(4.78)***|0.276<br>(3.96)***|0.390<br>(5.62)***|0.361<br>(5.62)***|
|Mena|0.098||0.179||
||(0.88)||(1.47)||
|Muslims (% of pop)|0.004<br>(4.31)***|0.004<br>(5.20)***|0.004<br>(3.51)***|0.004<br>(5.61)***|
|Catholic (% of pop)|0.002<br>(2.22)**|0.002<br>(2.87)***|0.003<br>(3.38)***|0.003<br>(3.35)***|
|Partial Corr First Stage|0.150|0.336|0.183|0.367|
|F-stat First Stage|9.46|19.00|8.03|25.40|
|Hansen J Test (p-value)|0.298|0.420|0.803|0.623|
|R-squared|0.755|0.758|0.829|0.824|
|Observations|142|142|118|118|

Robust t statistics in parentheses. Instruments for ln(p0): island, ln(size), ln(distance to main destination) * signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% 26