rafmacalaba/datause-encoder-data huggingface dataset:
Table 13: Effects of forecast errors and forecast growth on firm performance: positive versus negative demand shocks

|Dep. variable:|log sales|log exports|log (1+ inv.<br>fixed tangible<br>assets)|log (1+ inv.<br>intangible<br>assets)|log<br>employment|log value<br>added|log value<br>added per<br>worker|log avg.<br>worker pay|
|---|---|---|---|---|---|---|---|---|
|Weighted forecast error<br>|||||||||
|- positive shocks|0.0250|0.1333|0.1059|0.0257|0.0061|0.0516**|0.0427*|0.0040|
||(0.0217)|(0.1216)|(0.1546)|(0.1784)|(0.0142)|(0.0217)|(0.0225)|(0.0082)|
|- negative shocks|0.0826***|0.1077***|0.1128|0.1758**|0.0273***|0.0580***|0.0291***|0.0155***|
||(0.0126)|(0.0301)|(0.0858)|(0.0835)|(0.0067)|(0.0121)|(0.0111)|(0.0058)|
|Weighted forecast growth|||||||||
|- positive shocks|0.0321***|0.1030***|0.0977|-0.0308|0.0090*|0.0278***|0.0174**|0.0046|
||(0.0076)|(0.0273)|(0.0654)|(0.0830)|(0.0053)|(0.0087)|(0.0087)|(0.0036)|
|- negative shocks|0.0379***|0.0709***|0.0409|0.0921*|0.0114***|0.0248***|0.0117*|0.0060*|
||(0.0068)|(0.0209)|(0.0554)|(0.0552)|(0.0037)|(0.0070)|(0.0069)|(0.0032)|
|Period x region FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|Y|Y|
|N (obs.)|22199|22199|22199|22199|22199|22199|22199|22199|
|N (firms)<br>|9306|9306|9306|9306|9306|9306|9306|9306|
|Adj. R<sup>2</sup>|0.0729|0.0708|0.0157|0.0132|0.0537|0.0344|0.0141|0.0307|
|RSS|1578|22747|129156|291569|858|2581|2378|465|

Notes: In each column, the dependent variable is the change between the average of each 3-year period. Standard errors are clustered at the firm-level. *10% level of significance, **5% level of significance, ***1% level of significance.
# **References**

- Ahohounkpanzon, M., and M. Y. Zakariallou (2010). “Étude sur les Mécanismes de Fixation du Prix du Coton Graine et la Prise en Compte des Co‐produits du Coton au Bénin.” Report prepared for the Department of Agricultural Eco‐ nomics, Michigan State University, and the West African Cotton Improvement Program.

- Anderson, Kym, and Ernesto Valenzuela (2007). “The World Trade Organiza‐ tion’s Doha Cotton Initiative: A Tale of Two Issues,” _The World Economy_ 30: 1281‐1304.

- Argüello, Priscilla B. (2008). “Cottonseed: Derived Demand and Price Analysis.” Master’s Thesis, Department of Agricultural and Applied Economics, Texas Tech University.

- Auld, Dick, Efrem Bechere, Jim Davis, Lindy Seip, and Jack Brown (2006). “Lint, Cottonseed Oil, or Biodiesel?—Breeding Cotton for the Next Decade.” Paper presented at the ASA‐CSSA‐SSSA International Meetings. November 12‐16.

- Baffes, John (2010). “More on the Energy/Non‐Energy Commodity Price Link,” _Applied Economics Letters_ , DOI: 10.1080/13504850903120683.

- ________ (2009). “The ‘Full’ Potential of Uganda’s Cotton Industry,” _Development Policy Review_ 27: 67‐85.

- ________ (2007). “Cotton Developments in West Africa: Domestic and Trade Policy Issues and the WTO.” In J. Morrison and A. Sarris, eds., _WTO Rules for Agricul‐ ture Compatible with Development_ . Rome: Food and Agriculture Organization of the United Nations.

- ________ (2007). “Oil Spills on Other Commodities,” _Resources Policy_ 32: 126‐134.

- ________ (2005). “The Cotton ‘Problem’,” _World Bank Research Observer_ 20: 109‐ 144.

- ________ (2004). “Tanzania’s Cotton Sector: Reforms, Constraints, and Chal‐ lenges,” _Development Policy Review_ 22: 75‐96.

- Beltrão, Napoleao E. M., and Sebastiao Barbosa (2007). “The Development of Bio‐ Fuels in Brazil: Implications for Cottonseed.” Paper presented at the World Cotton Research Conference, Lubbock, Texas, US. September 10‐14.

- Banse, M., H. van Meijl, and G. Woltjer (2008). “The Impact of First and Second Generation Biofuels on Global Agricultural Production, Trade, and Land Use.” ‐

- GTAP Conference Paper, June 2008. https://www.gtap.agecon.purdue.edu/ re <u>sources/download/3693.pdf</u>

- Bayoulou, J. (2010). “La Prise en Compte des Co‐produits du Coton dans le Mécanisme de Fixation du Prix de Coton Graine au Burkina Faso.” Report

**_Page 24_**
- 2.6 -

they have now shlf ted to their second-best choices. If they have few alternatives to the taxed goods, then they are effectively compelled to pay the tax, and the efficiency of the tax will be high.

The case for uniform and broad based taxes, that Is, taxes which fall on a large fraction of consumer expenditure, Is that they provkid Uttle reason or opportunity for changing consumption patterns to avoid the tax, **and** hence are relatively efficient. Income taxes, or uniform taxes on consumption (such as a universal value added tax) are good examples where the mair source of inefficiency (assuming that evasion Is not a problem) Is the reduction In work effort since ielsura b untaxed). Land taxes are ahuiarly attractive as the SUppiy of lnd Is fixed, and **again** the tax camot be avoide by shIfting expenditure to other **goods.** Heavy taxes on a narrow range of goods are potentially distortionary unless these **goods** are helastkally demnded (le are ssentlals) or are helastbcaity supplied (ike land). Goods that **are** hieastlcafy demanded and essential are lkely to account for a larger fractlon of expenditure by the poor than the rlch, and heavy taxes on such goods are therefore lkely to be inequitable. Although taxing such goods may appear to score well on effciency, they score poorly on equity, strengthening the case for broader based taxes which are less inequtable.

The suiplest taxes to analyze are those whose incidence falls entirely on the tax payer. Value addod taxes which are only paid by final consumers are a good example - - they resut In an increase In the consumer prlce of the taxed good, but, under reasonable assumptions, no change In the pattern of r.oducer prices. When analyzing changes In taxes on fhal consumpt!oii goods one only needs to study the hnpact on consumers - supply consirations (and supply eiastthies) can be gnored. Taxes on agricutural Inputs and outputS have far more widespread repercussions, and It Is potentially misleading to concentrate only on the hedate tax payers. Further, the efficlency of such taxes wifi depend not only on demand elasticities but also on supply responses and supply elasttities.
- Estache, A. and A. Iimi (2009). Joint bidding, governance and public procurement costs: a case of road projects. _Annals of Public and Cooperative Economics 80_ (3), 393–429.

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- Flyvbjerg, B., M. K. Skamris Holm, and S. L. Buhl (2003). How common and how large are cost overruns in transport infrastructure projects? _Transport Reviews 23_ (1), 71– 88.

- Gertler, P. J., M. Gonzalez-Navarro, T. Gracner, and A. D. Rothenberg (2014). The role of road quality investments on economic activity and welfare: Evidence from indonesia’s highways.

- Gleditsch, N., P. Wallensteen, M. Eriksson, M. Sollenberg, and H. Strand (2002). Armed conflict 1946-2001: A new dataset. _Journal of peace research 39_ (5), 615–637.

- Graf Lambsdorff, J. (2005). Determining trends for perceived levels of corruption. Technical report, Passauer Diskussionspapiere V-38-05: Volkswirtschaftliche Reihe.

- Greene, W. H. (2003). _Econometric analysis_ , Volume 5. Prentice hall Upper Saddle River, NJ.

- Harris, I., P. Jones, T. Osborn, and D. Lister (2014). Updated high-resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. _International Journal of Climatology 34_ (3), 623–642.

- Huysentruyt, M. (2011). Development aid by contract: Outsourcing and contractor identity. mimeo.

- Hyytinen, A., S. Lundberg, and O. Toivanen (2007). Politics and procurement: Evidence from cleaning contracts. Discussion Paper No 233, Helsinki Center of Economic Re-

27
of the local county which may have a¤ected timing of ID distribution, and then identify the size of the village migrant labor force o¤ of non-linearities in the time that it requires for migrant networks to build up.

In this paper, we identify the village migrant network by further interacting the quartic in years-since IDs with land per capita held by households in period t � 2: Why might we expect that interacting with lpcit�2 might achieve this? We believe that the land per capita managed by households will likely pick up a dimension of proximity of di¤erent households within the village. Within villages in rural China, households are separated into smaller units of roughly 20 households known as village small groups (cun xiaozu), which were referred to as production teams during the Maoist period. These households are located in clusters and will have closer relationships with one another than with households of other small groups. Moreover, property rights to land in rural China typically reside with the small group, not with the village. Thus, when land reallocations take place they typically take place within but not across small groups. Small groups make more frequent small adjustments to household land as the land per capita available starts to become unequal with di¤erential changes in household structure across households within the small group, but there is much less ‡exibility in making adjustments across small groups. As a result, much of the variability of land per capita within villages occurs across small groups.<sup>15</sup> Interacting a village level instrument for the migrant network with land per capita will allow the importance of Mjt to vary across households, and much of the di¤erence across households occurs because of unobserved di¤erences in the small groups in which they reside and from which migrants refer to as home.

As period t � 2 lagged land per capita appears as an exogenous regressor and is also interacted with the quartic in years since IDs were distributed in the ...rst stage, our estimation approach must also eliminate bias introduced through likely serial correlation of the error term in both the ...rst stage regression. To this end, it is important to note that our two-step

> 15We do not know village small group membership in the RCRE survey prior to 2003 when a new survey instrument was introduced. If we regress land per capita on village dummy variables in 2003, we obtain an R-Squared of 0.503, while if we run a regression of land per capita on small group dummy variables, we obtain an R-Squared of 0.616. A Lagrange Multiplier test for whether the small group e¤ects add anything signi...cant over the village e¤ects, which is e¤ectively a test of whether small group coe¢ cients are constant within villages, yields an LM statistic of 310.67, which has a p-value of 0.0000.

23
Table 3: Effect of PVP on Women’s Atendance

|||||_Depend_|_ent variable_|_:_|||
|---|---|---|---|---|---|---|---|---|
|||Female Ate|ndance (Raw)|||Female At|endance (% of|Voters)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|I(PVP)|68.63<sup>_∗∗∗_</sup>|70.03<sup>_∗∗∗_</sup>|79.61<sup>_∗∗∗_</sup>|58.16<sup>_∗∗∗_</sup>|0.08<sup>_∗∗∗_</sup>|0.09<sup>_∗∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|0.07<sup>_∗∗∗_</sup>|
||(24.34)|(24.45)|(24.12)|(21.59)|(0.03)|(0.03)|(0.04)|(0.02)|
|Matched Pair FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Demographic Controls||✓|✓|||✓|✓||
|Infrastructure Controls|||✓||||✓||
|_p_-Score Control||||✓||||✓|
|Observations|96|96|96|96|95|95|95|95|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the block-pair, in parenthesis. Data are taken from the full sample of villages. Demographic controls include: number of households, percentage Scheduled Caste, percentage Scheduled Tribe. Infrastructure controls include indicators for the presence of a primary school, secondary school, health center, hospital, clinic, medicla shop, government hospital, and bank.

Table 4: Effect of PVP on Frequency of Women’s Speech

|||||_Depe _|_ndent varia_|_ble:_|||
|---|---|---|---|---|---|---|---|---|
|||P(Female),|All Speeches|||P(Female)|, Citizen Spee|ches|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|I(PVP)|0.07<sup>_∗∗_</sup>|0.06<sup>_∗∗∗_</sup>|0.05<sup>_∗∗∗_</sup>|0.07<sup>_∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|0.21<sup>_∗∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|0.22<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.02)|(0.03)|(0.05)|(0.04)|(0.04)|(0.05)|
|Matched Pair FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Demographic Controls||✓|✓|||✓|✓||
|Infrastructure Controls|||✓||||✓||
|_p_-Score Control||||✓||||✓|
|Observations|3,894|3,894|3,894|3,894|2,130|2,130|2,130|2,130|

_Note:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. Robust Standard Errors, clustered at the block-pair, in parenthesis. Data are taken from the full sample of villages. Demographic controls include: number of households, percentage Scheduled Caste, percentage Scheduled Tribe. Infrastructure controls include indicators for the presence of a primary school, secondary school, health center, hospital, clinic, medicla shop, government hospital, and bank.

the likelihood of having a female speaker is greater in treatment rather than control villages. Models (1) through (4) of Table 4 present results for all speakers (officials and citizens), while Models (5) through (8) focus on speeches only by citizens. Once again, we see that PVP has a substantial impact on the frequency of women’s speech. We see a roughly 6 to 7 percentage point increase in the incident of any women’s speech. Given a baseline frequency of 35 percent, this increase represents an 18 percent change. Te effect is even more pronounced when looking at citizen speeches alone. From a baseline rate of 38 percent, PVP increases female speech by 17 percentage points, which represents a 57 percent increase. Tese results hold to a variety of specifications, including those that control for demographic and infrastructural characteristics.

19
geographical levels.

In this paper, I estimate intergenerational mobility in education for LAC countries at a disaggregated regional level using data from 91 censuses. The analysis covers 24 countries spanning more than half a century (between 1960 and 2012). I rely on samples of co-residents (i.e., children living with their parents or older relatives). To minimize the impact of coresidence, I investigate mobility in education at the bottom of the educational attainment distribution by focusing on primary education, which can be measured with a high degree of confidence between ages 14 and 18. Furthermore, an important share of the population does not attain more than primary education in the period analyzed and this focus allows me to create indicators that are directly comparable to the estimates recently generated for 27 countries in Africa (see Alesina, Hohmann, Michalopoulos, & Papaioannou, 2021), a continent that shares the feature of having high levels of income inequality despite its lower levels of income and higher poverty rates.

The estimates of upward (and downward) mobility measured as the likelihood of finishing (or failing to finish) primary education, conditional on having parents who failed to finish (or who were able to finish) primary school, show wide cross-country and within-country heterogeneity. In LAC, the distance between the most and least upwardly mobile countries is similar to what has been recently documented in Africa, although the least mobile countries in Africa are less mobile than the least mobile country in LAC. I find only small differences by gender, but I do document a declining trend in the mobility gap between urban and rural populations. At the sub-national level, there is heterogeneity in mobility across districts/provinces, and some countries show lower levels of mobility in the northern regions (e.g., Brazil), whereas the opposite is true for Mexico. However, the variability is much lower in countries with lower number of regions and less population. The level of mobility at the sub-national level is highly positively correlated to the share of primary completion of the previous generation, which suggests a high level of inertia. In addition, geographical correlates do not appear to be highly correlated to mobility except for distance to the capital.

3
second concept follows from the first. Because the intensive use of a factor increases demand for it, and so too its price, increased trade will cause the relative price of each factor to rise in the country in which it is most abundant. So, to take the simplest example, a country with a lot of labor and little land might produce vegetables – which require laborintensive cultivation – most efficiently, while a country with a lot of land and not many people might specialize in wheat. Trade between the two would increase the demand for labor in the first (where labor is cheaper), raising its price relative to the price of land. Conversely, in the second country (where land is cheaper), the price of land relative to labor would rise. The factor prices would therefore tend to “equalize.” Transfer of technology through foreign direct investment (FDI) or imported inputs can also be channels through which trade liberalization improves productive efficiency, as was the case in our studies of Chile, Madagascar, Vietnam and China. Other studies have also found this effect to be important. For example, transfer of agricultural technology “embodied” in inputs has sometimes been blocked by regulatory requirements on seed, fertilizer or machinery imports, with adverse effects on farmers (Gisselquist, Pray and Nash, 2002).

Applying these concepts to developing countries, one would expect that since labor – especially unskilled labor – is abundant and cheap, these countries would tend to specialize in products that use a lot of this labor, which in the end would raise incomes of low-wage laborers and thereby reduce poverty. In the real world, of course, things are not so simple, and there are many intermediate steps between changes in the incentive structure triggered by trade policy reform and the consequent effects on growth and poverty reduction. Many of the most important lessons from this project and other research on the effects of trade reform concern the institutional and legal framework necessary to make sure that this tradepoverty linkage works well. It should also be recognized that the labor intensity of a product is not measured only by the direct labor use – for example, labor used on-farm in growing an agricultural commodity -- but also by labor required to distribute inputs, process the primary product and bring it to market. This was underscored by several of our case studies (more below).

Similar considerations apply to the linkage between trade and environment. Traditional trade theory would predict that countries that have a relative abundance of 16
account for workers heterogeneity (by for example estimating different costs for skilled and unskilled workers), to incorporate informality, or to better deal with the non-manufacturing sector.

# **References**

Attanasio, O., P. Golberg, and N. Pavcnik (2004). “Trade Reforms and Income Inequality in Colombia,” Journal of Development Economics, vol. 74, pp. 331-366.

Artuç, E. (2012). “A Tractable Method for Estimating Dynamic Discrete Models with Policy Shocks,” mimeo, World Bank.

Artuç, E. and J. McLaren (2010). “A Structural Empirical Approach to Trade Shocks and Labor Adjustment: An Application to Turkey,” in G. Porto and B. Hoekman (2010).

Artuç, E. and J. McLaren (2012). “Trade Policy and Wage Inequality: A Structural Analysis with Occupational and Sectoral Mobility,” mimeo World Bank and University of Virginia.

Artuç, E., S. Chaudhuri, and J. McLaren (2008). “Delay and Dynamics in Labor Market Adjustment: Simulation Results,” Journal of International Economics, 75(1): 1-13.

Artuç, E., S. Chaudhuri, and J. McLaren (2010). “Trade Shocks and Labor Adjustment: A Structural Empirical Approach,” American Economic Review, 100(3), pp. 1008–45.

Artuç, E., G. Bet, I. Brambilla, and G. Porto (2012). “Trade Shocks, Firm-Level Investment Inaction, and Labor Market Responses,” mimeo Universidad de La Plata.

Coşar, A. K. (2013). “Adjusting to Trade Liberalization: Reallocation and Labor Market Policies,” mimeo University of Chicago.

Coşar, A. K., N. Guner, and J. Tybout (2011). “Firm Dynamics, Job Turnover, and Wage Distributions in an Open Economy,” NBER Working Papers 16326, National Bureau of Economic Research, September.

Davidson, C. and S. J. Matusz (2000). “Globalization and Labour-Market Adjustment: How Fast and At What Cost?” Oxford Review of Economic Policy, 16: 42-56.

20
# **_Table 10: Perception of Female Financial Performance_**

||Women can run businesses<br>just as well as men|It is easier for men to<br>receive loans than|Women make better<br>financial decisions|
|---|---|---|---|
|Perceptions of women at e|ndline<br> <br>(1)|women<br>(2)|than men<br>(3)|
|_Treatments_||||
|Movie|‐0.00|0.07*|0.05|
||(0.020)|(0.038)|(0.030)|
|MFB|0.00|0.07*|0.04|
||(0.020)|(0.039)|(0.031)|
|Movie + MFB|0.00|0.07*|0.06*|
||(0.020)|(0.039)|(0.031)|
|_Gender disaggregated inter_|_action effects (female base)_|||
|Movie|‐0.01|0.04|‐0.02|
||(0.023)|(0.044)|(0.035)|
|MFB|0.01|0.05|0.01|
||(0.024)|(0.046)|(0.037)|
|Movie + MFB|0.00|0.03|0.01|
||(0.024)|(0.046)|(0.036)|
|_Gender disaggregated inter_|_action effects (male interaction)_|||
|Male|‐0.13***|0.09|‐0.48***|
||(0.034)|(0.066)|(0.052)|
|Male*Movie|0.04|0.13|0.25***|
||(0.045)|(0.087)|(0.069)|
|Male*MFB|‐0.04|0.06|0.15**|
||(0.046)|(0.088)|(0.071)|
|Male*(Movie + MFB)|‐0.01|0.16*|0.19***|
||(0.045)|(0.087)|(0.070)|
|p‐values|δ1+ γ1≠0<br>0.55|0.03|0|
|for F‐tests|δ2+ γ2≠0<br>0.54|0.13|0.01|
||δ3+γ3≠0<br>0.88|0.01|0|
|_Observations_|_1,261_|_1,261_|_1,261_|
|_R‐squared_|_0.09_|_0.08_|_0.19_|
|_Controls_|_YES_|_YES_|_YES_|
|_Restricted Model_|_YES_|_YES_|_YES_|
|_Control Mean:_|_0.936_|_0.342_|_0.751_|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 34
|Low risk||1.601*||1.362|
|---|---|---|---|---|
|Moderate risk||(0.829)<br>1.267||(0.986)<br>0.330|
|||(0.775)||(0.966)|
|High risk||1.659*||0.554|
|||(0.946)||(0.981)|
|Very high risk||1.167||0.397|
|||(0.880)||(1.122)|
|**Experienced flooding on business premises**: Yes|||2.059**<br>(0.948)|1.959**<br>(0.908)|
|Log (water usage, liters/month)|0.192*|0.195*|0.129|0.170|
||(0.100)|(0.102)|(0.091)|(0.107)|
|Log (costs of water, $/liter)|0.156**|0.140*|0.103|0.154*|
||(0.075)|(0.083)|(0.072)|(0.080)|
|Firm age (years)|0.017|0.018|0.012|0.021|
||(0.016)|(0.017)|(0.017)|(0.017)|
|Number of employees|0.002|0.004|-0.000|0.002|
||(0.008)|(0.009)|(0.007)|(0.008)|
|Log (total operational costs, $/month)|0.400***|0.396**|0.520***|0.507***|
||(0.151)|(0.154)|(0.182)|(0.171)|
|**Main water source**(central distribution)|||||
|Water vendor|-1.209|-1.221|-1.071|-1.173|
||(0.815)|(0.811)|(0.836)|(0.914)|
|Other|-1.288|-0.898|-0.868|-1.084|
||(0.816)|(0.783)|(0.762)|(0.805)|
|Constant|-2.995**|-3.638***|-2.969**|-4.717***|
||(1.373)|(1.215)|(1.319)|(1.579)|
|Observations|167|167|165|165|
|Sector fixed effects|YES|YES|YES|YES|

_Note:_ All coefficients refer to the linear predictor in a logistic regression model. Effects cannot be interpreted as a linear effect on probabilities but state the effect on log odds ratios. Reference levels of categorical variables in parentheses. Standard errors are clustered on the ward level in parentheses: *** p<0.01 ** p<0.05 * p<0.1

# **Table D.5: Regression results for generator ownership**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES||P (generator o|wnership = 1)||
|**Direct disaster risk perception**(no risk)|||||
|Low risk|-0.313|||-0.290|
||(0.346)|||(0.380)|
|Moderate risk|0.019|||0.202|
||(0.394)|||(0.418)|
|High risk|-0.483|||0.059|
||(0.434)|||(0.486)|
|Very high risk|0.390|||0.849|
||(0.515)|||(0.581)|
|**Indirect disaster risk perception**(no risk)|||||
|Low risk||-0.242||-0.175|
|||(0.496)||(0.515)|
|Moderate risk||-0.189||-0.210|
|||(0.503)||(0.527)|
|High risk||-0.512||-0.572|

43