rafmacalaba/datause-encoder-data huggingface dataset:
|Normalized agricultural asset index + Normalized land holdings|[0.242]<br>19.665***|[0.790]<br>22.493***|[0.281]<br>23.349***|[0.097]<br>27.503***|[0.138]<br>32.530***|[0.759]<br>32.966***|[0.912]<br>46.026***|
|---|---|---|---|---|---|---|---|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.001]|[0.002]|[0.006]|
|Household was affected negatively by some income Shock During the last 12 months|0.662|0.474|1.783|0.960|4.360+|6.643+|5.915+|
|(0/1)||||||||
||[0.593]|[0.644]|[0.167]|[0.647]|[0.131]|[0.115]|[0.110]|
|Household has a migrant network (0/1)|-0.970|-1.881+|-1.215|0.127|0.220|2.956|2.504|
||[0.340]|[0.121]|[0.396]|[0.948]|[0.938]|[0.361]|[0.583]|
|Rain - EA level CoV of Dec-Jan rainfall from 1983/84 - 2012/13|-13.093|-22.098|-28.994|-39.669*|-60.347+|-71.742|34.300|
||[0.515]|[0.348]|[0.247]|[0.087]|[0.141]|[0.187]|[0.677]|
|Average 12-month total rainfall (mm)|0.019|0.013|0.011|0.019|0.026|0.025|0.014|
||[0.443]|[0.634]|[0.733]|[0.561]|[0.508]|[0.566]|[0.857]|
|Annual Mean Temperature (∞C * 10)|-0.096|-0.076|-0.022|-0.003|0.201|0.528|1.146*|
||[0.406]|[0.537]|[0.886]|[0.988]|[0.683]|[0.244]|[0.077]|
|HH Distance in (KMs) to Nearest Road|-0.100|-0.151|-0.148|-0.034|-0.189|-0.365|-0.212|
||[0.640]|[0.382]|[0.437]|[0.859]|[0.694]|[0.483]|[0.772]|
|HH Distance in (KMs) to Nearest Population Center with +20,000|0.052|0.062|0.026|-0.109|-0.045|-0.043|-0.555+|
||[0.635]|[0.642]|[0.817]|[0.431]|[0.758]|[0.870]|[0.116]|
|Price of fertilizer in the EA|<br>-0.006|<br>-0.012**|<br>-0.011*|<br>-0.014*|<br>-0.018|<br>-0.017|<br>-0.010|
||[0.390]|[0.038]|[0.064]|[0.086]|[0.173]|[0.295]|[0.731]|
|Price of maize grains in the EA|<br>0.056**|<br>0.033|<br>0.026|<br>0.073***|<br>0.082***|<br>0.097**|<br>0.195**|
||[0.024]|[0.154]|[0.323]|[0.009]|[0.003]|[0.011]|[0.010]|
|Time dummy (year 2010=1)|0.005|-3.124+|-3.459*|-2.524|-3.625|-2.797|4.653|
||[0.998]|[0.138]|[0.075]|[0.341]|[0.297]|[0.388]|[0.571]<br>|
|Time average of explanatory variables|||||||[.]|
|District dummies||||||||
|Constant|36.094**|33.481**|30.597**|44.849***|62.689*|65.833+|34.729|
||[0.011]|[0.016]|[0.028]|[0.002]|[0.079]|[0.100]|[0.489]|
||5,321|5,321|5,321|5,321|5,321|5,321|5,321|

Source: Generated by authors using LSMS data Note: PSLC=Primary School Leaving Certificate. JCE=Junior Certificate Examination. MSCE=Malawi School Certificate of Education Examination. ***, **, *, and + indicate that the corresponding regression coefficient is statistically significant at the 1%, 5%, 10%, and 15% levels, respectively. The table presents quantile partial effects of explanatory variables from 10<sup>th</sup> to the 90<sup>th</sup> percentile. P-values between.

53
supply and are a major determinant of the relatively high rates of female labor force participation observed in Scandinavian countries.

# **7.2  Community Association Programs in Kenya**

The poor have lower rates of participation in community associations and some social programs aim to stimulate participation in such organizations. Increasingly, the World Bank has included a community based component in many of its programs. Gugerty and Kremer (2008) examine the effects of a social program, sponsored by a Dutch nongovernmental organization, that supported 80 Kenyan women‟s community associations through outside funding, training, and in-kind agricultural transfers (seeds, fertilizer, pesticides). In Kenya, rural women‟s groups engage in many activities, such as group farming and rotating savings activities, in which the members of the group contribute regularly to a pot that is given to each member in turn. They also engage in civic action promoting certain causes, such as prevention of violence against women, or holding fund-raising activities to raise money for local public goods. The social program gave about $674 to each group, half of which was in-kind agricultural transfers. The program‟s goals were to strengthen women‟s community organizations and improve agricultural output and practices. The program implementation was gradual and random assignment was used to decide which groups received the program first.<sup>27</sup> Gugerty and Kremer (2008) analyze the impacts of the program using information gathered on one baseline survey and two follow-up surveys. They find that the program induced enrollment effects, with the number of applicants to groups being 40% higher in treatment than in comparison groups. However, the program had only small effects on levels of output from the group-farmed plots; the in-kind farming subsidies appear to be have been distributed to individual group members rather than used to farm the group plots. Within groups, the younger, more educated members seem to have been the ones that best availed themselves of the training opportunities. The study also finds little evidence that the treatment groups did more to provide local public goods, though they did receive more visits from government officials. Treatment groups were more likely to experience a change in leadership, with men or better educated women taking on leadership roles. In summary, the program intervention did induce statistically significant differences between the treatment and comparison groups in enrollment rates and in

> 27 To be precise, the groups were ordered alphabetically within geographic region and every other group was assigned to receive the program first. The comparison group received the program two years later.

54
6 Table 1. Characteristics of Banking Crises

|Countrv|T_Reofdistress|Corporatesectordistress|
|---|---|---|
|Argentina<br>(1980-82)|Privatesectordebt|Thepeakrateofincreaseofbusinessfailureswas76%<br>in1980,thefirstyearofthecrisis(Balino1991).|
|Chile<br>(1981-86)|Private sector debt|Estimatedincreaseinnumberofbankruptcies from81<br>in1975to810 in1982(Velasco1991).|
|Hungary<br>(1991-95)|Debtsowedbystate-owned<br>enterprises,mainly,tostate-<br>ownedbanks.|Gross enterpriselosses increasedfromlessthan1%of<br>GDPin1988to14%in1992.By1993,50%of70,000<br>enterprises (mainlyinindustry)were lossmakers.The<br>largestloss-makers accountedfor60%oftotal<br>enterpriselossesand50%ofoverduebankdebt(World<br>Bank1997a).|
|Japan<br>(1991-ongoing)|Privatesectordebt|Corporatebalancesheets deterioratedoverthe1990s<br>andROEsdroppedfrom7.5%inlate1980stoan<br>averageof2.8%inthe1990s(InternationalMonetary<br>Fund2000).|
|Norway<br>(1987-93)|Privatesectordebt(corporate<br>andhousehold)|Bankruptcyratesroseby40%ayear during1986-89.<br>Banklossesconcentratedinprimarysectors(14%),<br>mining&export-orientedmanufacturing (15%)and<br>realestate(13%)(Norway'sRoyalMinistryof<br>FinanceandCustoms1992).|
|Poland<br>(1992-95)|Debtsowedby state-owned<br>enterprises,mainly,tostate-<br>ownedbanks.|In1992, 67%ofenterprises remaininginstatehands<br>werelossmakers,mostlyinheavyindustry(World<br>Bank1993).|
|Thailand<br>(1997-ongoing)<br>Source:Compilationbya|Privatesectordebt (realestate)<br>uthors.|In1997,32.6%ofcorporationscouldnotservicetheir<br>debts(Claessens, Djankov, Klingebiel1999).|

# C. Assessment of the Incentive Framework at the Onset of the Crisis

We analyze the incentive framework at the onset of the crisis by evaluating (i) banks' capital positions; (ii) accounting framework and (iii) legal framework; and then (iv) existing ownership links between banks and corporations.

> _a. Banks_ **_'_** _capital positions._ To engage in meaningful corporate restructuring banks need to be adequately capitalized as they need capital to write down loans. To determine the level of capitalization in our sample crises countries, or in other words, the incentives of banks at the

> initial stage of the crisis to engage in any meaningful corporate restructuring, we classify the

> crises countries into four categories according to the risk adjusted capital level of their financial institutions. Since the level of non-performing loans in the banking system is highly correlated

> with the level of capital, we also use the level of non-performing loans (NPLs) if information on capital adequacy is not available. We classify financial institutions' capital base as follows. A

> four indicates that banks are well capitalized as their capital adequacy ratio is beyond eight percent and non-performing loans in the banking system are beyond five percent of total banking system loan. At the other end of the spectrum a one denotes a system in which banks are clearly
**Table 9: Pre-vignette self-assessed subjective welfare positions**

|(1)|(2)|(3)||(4)||
|---|---|---|---|---|---|
|PCE|PCE +<br>vignettes|PCE +<br>household<br>controls||PCE +<br>household<br>controls +<br>vignettes||
|Coef.<br>se|Coef.|se<br>Coef.|se|<br>Coef.|se|
|Log expenditure per capita<br>0.630***<br>(0.037)|0.631***|(0.037)<br>0.727***|(0.043)|0.736***|(0.043)|
|_Household Demographics_||||||
|Female headed household||-0.151***|(0.053)|-0.148***|(0.053)|
|Age of household head||-0.014|<br>(0.010)|-0.017*|(0.010)|
|<br>Age of household head squared||0.000|(0.000)|0.000*|(0.000)|
|<br>Household size||0.076***|<br>(0.013)|0.082***|(0.013)|
|Number of children||-0.007|(0.018)|-0.011|(0.018)|
|Number of older adults||-0.030|(0.047)|-0.037|(0.048)|
|Number of migrants||0.049|(0.043)|0.034|(0.044)|
|_Ethnicity (Reference: Tajik)_||||||
|Uzbek||0.020|(0.046)|0.019|(0.046)|
|Russian||-0.337**|(0.157)|-0.350**|(0.165)|
|Other||-0.693***|(0.159)|-0.718***|(0.150)|
|_Education (Reference: No Education)_||||||
|Primary||0.066|(0.116)|0.001|(0.118)|
|Basic||-0.118|(0.109)|-0.129|(0.111)|
|General Secondary||0.028|(0.107)|0.007|(0.109)|
|Special Secondary||0.143|(0.114)|0.111|(0.115)|
|Technical Secondary||0.103|(0.117)|0.068|(0.119)|
|<br>Higher Education||0.304***|<br>(0.115)|0.285**|<br>(0.117)|
|Graduate School||0.545|(0.368)|0.617**|(0.305)|
|_Employment Characteristics of Household, Head’s O_|_ccupation (R_|_eference for Occupation:_|_Unemploy_|_ed)_||
|Number of employed||0.035*|(0.018)|0.032*|(0.018)|
|Agriculture / Fishing / Forestry||0.009|(0.064)|-0.003|(0.065)|
|<br>Manufacture / Mining||-0.023|<br>(0.094)|-0.009|<br>(0.096)|
|Services||0.363***|(0.132)|0.307**|(0.131)|
|Construction||-0.063|(0.080)|-0.093|(0.080)|
|Public Administration / Education / Health||0.186***|(0.070)|0.190***|(0.071)|
|Sales and Services||0.310***|(0.064)|0.314***|(0.065)|
|Other||-0.158|(0.107)|-0.156|(0.105)|
|_Agriculture (Reference for holdings: No Land)_||||||
|Small Holding||-0.063|(0.066)|-0.037|(0.067)|
|Medium Holding||-0.103|(0.079)|-0.087|(0.080)|
|<br>Large Holding||0.020|(0.081)|0.051|(0.082)|
|Anylivestock, poultry, etc||-0.007|(0.058)|-0.008|(0.058)|
|_Geography (Reference: Dushanbe)_||||||
|<br>Sogd Urban||-0.011|(0.075)|-0.067|(0.076)|
|<br>Sogd Rural||-0.106|<br>(0.079)|-0.159**|<br>(0.080)|
|<br>Khatlon Urban||-0.184*|(0.095)|-0.344***|(0.097)|
|Khatlon Rural||-0.509***|<br>(0.074)|-0.570***|<br>(0.075)|
|RRP Urban||-0.158|(0.108)|-0.200*|(0.110)|
|RRP Rural||-0.263***|(0.075)|-0.319***|(0.077)|

20
Table **5** Contagion Trading Results **by** Individual Crisis

||Mexico During<br>MexicanCrisis|AsiaDuring<br>Asian Crisis|RussiaDuring<br>RussianCrisis|**U.S.**During<br>MexicanCrisis|**U.S.**During<br>Asian Crisis|**U.S.**During<br>RussianCrisis|
|---|---|---|---|---|---|---|
|Statistic|||||||
|WholeFund<br>**LOC**<br>T-statistic|**3.89***<br>**1.88**|**0.27**<br>0.40|22.0***<br>**3.55**|**_-3.59***_**<br>**-3.71**|**-0.90****<br>**-2.23**|-0.22<br>**-0.33**|
|Manar**Ol**<br>LOC|**1.89***|**1.66**|**6.03**|**-1.73*****|-0.41|**0.10**|
|T-statistic|**1.80**|**_135_**|**1.18**|**-3.91**|**_-1.48_**|**0.86**|
|esterly<br>LOC|**3.23****|_0.42_|24.0***|**-1.86***|**-0.17**|**0.17**|
|T-statistic|2.00|_0.47_|**_5.53_**|-2.44|-0.42|**0.30**|

**LOC** denotes lag-0 contagion trading. Whole-Fund contagion tests whether the mean _of(AQ/_ qt)Rfis<sup>zero,</sup> where<sup>Rn</sup> is<sup>the returnon foreignindex</sup><sup>**_f_**</sup><sup>_from_</sup><sup>**t-Itot,**with fe (Mexico,Asia,Russia,</sup><sup>**U.S.).**per</sup>

equation (4). Manager-Only contagion controls for investor redemption effects as in equation **(5).** Investor-Only<sup>contagion reflectsonly investor redemptioneffects as inequation</sup><sup>**(6).All**t-statistics are</sup> corrected for heteroskedasticity across funds. The Mexican Crisis portion of the sample<sup>is December1994-June</sup><sup>**1995.**The Asian Crisis portion of the sampleisJuly1997-March</sup><sup>**1998,**The RussianCrisis</sup> portion of the sample is August 1998-October **1998.** Asia is the **IFC** Asia Stock Market<sup>Index.Note that Mexican equities are excluded from the calculation of LOC for Mexico.</sup>

- Statistically Significant at **the** 10-percent level

- ** Statistically Significant at the 5-percent level

- ** Statistically Significant at the I-percent level

**30**
- 2.45 -

- If alternative sources of revenue can be found or If less revenue Is needed (or expenditure Is cut) then lower cocoa taxes will be Indicated. The other two assumptions are that 20 percent of extra Income Is spent on taxed goods, and that the (compensated) price elasticity of demand is 1.25. These assumptions are also discussed In the section on the taxatlon of consumer goods below. Again this assumption biases the case In favour of high cocoa taxes -- If the price elasticity of their demand Is less than 1.25, then the optinal cocoa tax rate will be correspondingly reduced.

Perhaps the most useful way of structuring the discussion is to examine whether the World Bank's recommendation of setting the producer price of cocoa at 55 percent of the export price Is soundly based as a formula, and whether the Implied level of cocoa prices Is roughly right. Here the work of Meeraus, Asenso Okyere and O'Mara is particularly relevant. Their finding was that below a critical level of about 140 (1987)cedis/kg there would be little Incentive for the farmers to plant cocoa. Above this level there appears to be a strong response to price Incentives, summarised by a medium to long run price elasticity of supply of one or higher. The AES survey data shows that there was considerable replanting over the past four years once prices began to Increase, though we do not know what price expectations the farmers then held. If they based their planting decisions of current prices, In the full knowledge of the effect of Inflation on their eventual return, then the critical price below which planting will not occur may be somewhat lower than 140 cedis/kg, but If they planted In the expectation that the Government would continue to attempt to raise the real price of cocoa, so that they were looking ahead to more promising prices, then the figure might be an underesthnate. For the moment let us take It at face value. The formula for the optimal tax on cocoa In the Appendix knpiies a ratio for the producer to the export price which appears to be Independent of the world price level, lending superficial support to the World Bank formula. In fact the tax rate Is particularly sensitive to the supply elasticity, which rises rapidly as the real domestic price falls towards the critical level (of 140
reduces their income and by using this income transfer for augmenting the productivity for all factors in the recipient countries. The fourth scenario, called “Infrastructure”, involves infrastructure improvements, widespread throughout the economy. This is simulated by an AfT transfer by donors countries, which reduces their income and by using this income for increasing investment in the recipient countries. Table 4 reports the details of the shocks applied in these three scenarios in the recipient countries. The shocks in terms of AfT transfer for the donor countries are unchanged and equal to those reported in table 3.

**Table 4.  Effectiveness** **<u>of Aid  for Trade on Different Scenarios (w.r.t. baseline)</u>**

|_Region/Country_|_Institutional Reform_<br>_(IR) scenario:_|_Technical_<br>_assistance &_<br>_capacity building_<br>_scenario:_|_Economic_<br>_Infrastructure_<br>_scenario:_|
|---|---|---|---|
||_Export tax Revenues_<br>_(US $ million)_|_Factor_<br>_Productivity (%)_|_Investment (%)_|
|Eastern Europe|-3.85|0.08|0.05|
|Former Soviet Union|-342.94|0.08|0.09|
|Middle East|-1408.37|0.63|0.74|
|Central America|-1273.37|0.19|0.21|
|South America|-1635.11|0.07|0.07|
|South Asia|-2959.25|0.61|0.54|
|Southeast Asia|-1684.83|0.09|0.07|
|China|-482.96|0.01|0|
|North Africa|-492.5|1.29|1.11|
|Sub-Saharan Africa|-0.06|10.2|12.43|
|Rest of the World|-34.24|1.26|1.53|

Source: Modeling results based on OECD.STAT and GTAP data base

# **6. Modeling  results**

Effectiveness requires that AfT policy achieves its stated goals. Following the WTO/OECD report (WTO/OECD, 2011), the common priorities between donor and recipient countries are to expand trade and, jointly, to alleviate inequalities. We use trade balance, computed as total exports less total imports as trade indicator; whereas, welfare and income indicators are

**17**
# **Table A.3.3. Conditional correlations of absenteeism with aspired and expected education under a Tobit model**

||**Number of**<br>|**school days**<br>|**missed in the**<br>|**past month**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Aspired education in years<br>|**-0.524*****<br>(0.07)||||
|Aspired education in years<sup>2</sup>|**0.014*****<br>||||
||(0.00)||||
|Expected education in years||**-0.701*****<br>(0.07)|||
|Expected education in years<sup>2</sup>||<br>**0.021*****|||
|||(0.00)|||
|Diff. in years between aspirations and expectations|||**-0.085*****<br>(0.01)||
|Diff. in years between aspired and expected edu.<sup>2</sup>|||**0.007*****<br>||
||||(0.00)||
|Have lower expectations than aspirations (dummy)||||**-0.298*****<br>(0.05)|
|Self-esteem|-0.127***|-0.120***|-0.136***|-0.137***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Sense of belonging|-0.241***|-0.251***|-0.252***|-0.256***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Perseverance|-0.068**|-0.076***|-0.088***|-0.089***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Growth mindset|-0.243***|-0.258***|-0.272***|-0.276***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Effort beliefs|-0.003|-0.014|-0.013|-0.013|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Learning orientation|-0.249***|-0.267***|-0.284***|-0.284***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Performance-avoidance orientation|0.112***|0.114***|0.120***|0.120***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Age (with decimals)|0.340***|0.345***|0.377***|0.381***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Being a girl (dummy)|<br>-0.919***|<br>-0.995***|<br>-0.995***|<br>-0.996***|
||(0.05)|(0.05)|(0.05)|(0.05)|
|Living in urban area (vs. rural)|-0.278***|-0.273***|-0.368***|-0.377***|
||(0.08)|(0.08)|(0.08)|(0.08)|
|Living in Java (vs. in Sumatra)|-0.002|0.013|0.071|0.068|
||(0.08)|(0.08)|(0.08)|(0.08)|
|Constant|-0.511|0.179|-5.730***|-5.704***|
||(0.70)|(0.70)|(0.47)|(0.47)|
|Observations|55,039|55,014|55,009|55,009|

_Source_ : Authors’ calculations based on the 2018 survey “Getting to Know 8th Grade Students.” _Note_ : The Tobit model estimates linear relationships when there is either left- or right-censoring in the dependent variable. We use censoring from below 1 to that around half of students are never absent (0 days of absence). Yellow and blue highlights show significant correlations at least at the 95 percent level for the variables of interest and covariates, respectively. Grades are standardized at the school level, with a mean of zero and a standard deviation of one. Measures of socioemotional skills are standardized for the entire sample. Standard errors are clustered at the school level.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 37
These administrative data on taxes and transfers are complemented with information of the Cambodian tax code and laws to understand the design of each fiscal intervention.

Additional information on public education and health care services was collected to allow for a more precise assignment of in-kind education and heath transfers to households by the type of service received. The Ministry of Education, Youth and Sport provides enrollment and spending for each level of public education, allowing for an estimation of the spending per student by level of education. For health care, the costing of various health care interventions from Flessa et al. (2018) and Jacobs et al. (2019) is used to assign the monetarized value of health care per person by intervention. We apply this valuation, adjusted by inflation, to health care interventions reported in the 2019/20 CSES data (see Technical Appendix for more details).

# **Empirical Approach and Allocation Overview**

There are broadly five steps involved in the empirical approach of a CEQ.

**Step 1:** Identify the fiscal interventions that can plausibly be studied using the household survey.

**Step 2:** Allocate the gains and losses from fiscal interventions to individuals and households. In the case of taxes on income, allocations are made to individuals and then aggregated at the household level. In the case of taxes or subsidies on consumption, allocations are made to the household. This process involves (a) identifying who pays taxes and social security contributions, based on the source of income and imputed formality of employment; (b) estimating how much each household paid in taxes and received in transfers using laws, codes, program rules, plausible assumptions, and household survey responses. Step 2 is the most complex and laborintensive component of the exercise.

**Step 3:** Test the quality of the model by validating the simulated fiscal policy elements against administrative statistics.

**Step 4:** Build the income aggregates according to CEQ concepts.

**Step 5:** Construct poverty and inequality measures for each income concept.

In the analysis fiscal policy elements are allocated to individuals or households based on simulation and imputation. Household data usually do not focus specifically on the value of individual taxes paid by households and often collect aggregate information on direct transfers. In the CSES 2019/20 case, the only exception is property tax which is recorded in the data. This means that most of the modeled fiscal interventions need to be simulated using other data provided in the survey. The most important simulation assumptions which have been applied are the following (the Technical Appendix provides a detailed description of the assumptions applied in the analysis):

- A) Income taxes, health, and social security insurance values have been simulated using the detailed regulations based on the information on employment earnings and self-employment income; this information is combined with an imputation of employment status to ensure that taxes are only levied on formal employment. The tax on property rental is simulated based on the regulations

12
Table A5: IAT: Robustness check for priming with survey questions

||Dependent|variable:|
|---|---|---|
||IAT D|-Score|
||Advances<br>(1)|Safety<br>(2)|
|Order: advances IAT; advances questions; safety IAT; safety questions|-0.071|0.046|
||(0.065)|(0.064)|
|Order: advances questions; advances IAT; safety questions; safety IAT|0.033|-0.109*|
||(0.056)|(0.063)|
|Order: safety questions; safety IAT; advances questions; advances IAT|-0.029|-0.059|
||(0.056)|(0.058)|
|Constant|0.256***|0.215***|
||(0.038)|(0.042)|
|(Omitted category: Order: safety IAT; safety questions; advances IAT; advances questions)|||
|Observations|299|301|
|Sample mean|0.242|0.177|

_Notes_ : Unit of observation is one respondent. Sample in column (1) includes all respondents who completed the Provokes Advances test. Sample in column (2) includes all respondents who completed the Safety test. Robust standard errors in parentheses.