rafmacalaba/datause-encoder-data huggingface dataset:
|**Super-area variables**||**Super-area variables**||
|---|---|---|---|
|Colombo|2.47***|Morogoro|0.18**|
|Kalutara|1.06***|Simiyu|-0.21**|
|Hambantota|-0.49|||
|Jaffna|-0.46|||
|Mannar|-0.83***|||
|Mullaitivu|-1.74***|||
|Kilinochchi|-0.69**|||
|Batticaloa|1.00***|||
|Trincomalee|-0.14|||
|Puttalam|7.33***|||
|Anuradhapura|2.47***|||
|Badulla|1.06***|||
|Moneragala|-0.49|||
|Rathnapura|-0.46|||
|Sector||Sector|-0.34***|
|Urban|2.65***|Rural||
|Estate|-2.02***|||
||||-1.29***|
|Constant|-4.49***|Constant||
|R<sup>2</sup>|0.251|R<sup>2</sup>|0.321|
|Of which: sub-area variables|0.060|Of which: sub-area variables|0.134|
|area variables|0.058|area variables|0.059|
|Super-area and sector dummies|0.129|Super-area and sector dummies|0.104|
|Number of observations|19,570|Number of observations|9,393|

_Notes: Stars indicate statistical significance at 5, 1, and 0.1 percent levels with robust standard errors. Subarea variables are aggregated across GN Divisions in Sri Lanka and villages in Tanzania. Area variables are populationweighted averages of sub-area variables aggregated to the are level. Super-area variables are district dummies in Sri Lanka and regional dummies in Tanzania. Sector dummies are a rural dummy in Tanzania and urban and estate sector dummies in Sri Lanka. See Table 2 for sources of remote sensing indicators._ For Tanzania, the lasso model selected several building footprints and built-up area measures, as well as measures of night-time lights that capture building and population density. Building counts, night- time lights, and built-up area are all positively associated with welfare. Several climactic variables were also selected, reflecting the dependence of rural areas on favorable rainfall patterns. Higher variance in the NDVI vegetation index, reflecting areas that contain a mix of built-up area and green space, is also positively related to welfare. The Sri Lanka model contains measures of built-up area, rainfall, as well as texture features such as the Fourier Transform and Line Support Region Mean at the subarea level, and other features such as the standard deviation of the Gabor filter and Histogram of Ordered Gradients at 22
relative ratings measure with the fixed GDP weights, and re-estimate the baseline regression. The results reported in Table 6 suggest that the baseline result indicating greater relevance of relative ratings for private capital flows in the post-crisis period compared to the pre-crisis period is robust to the use of fixed GDP weights.

# _6.3 Use of gross capital inflows_

As a robustness test for our main results obtained using net capital inflows, we use gross capital flows as the dependent variable instead of gross flows for our baseline specification. The results are reported in Table 7. The baseline results reported in Table 3 are robust to this alternative definition of the dependent variable. The coefficient of the absolute ratings is smaller in the post-crisis period while that of relative ratings is larger. The larger coefficient of relative risk ratings for gross capital flows, compared to that for net capital flows in the baseline results may be explained by foreign investors being more sensitive to relative ratings compared to resident investors. These additional results for gross capital flows, which have been widely used in the literature, substantiate our baseline results for net capital flows.

**Table 5: Use of lagged explanatory variables**

||**Abs**<br>|**olute rating**<br>|**Rela**<br>|**tive to peers**<br>|
|---|---|---|---|---|
|Net Inflows|Pre-Crisis<br>(1)|Post-Crisis<br>(2)|Pre-Crisis<br>(3)|Post-Crisis<br>(4)|
|L.Net Inflows|0.044|0.113***|0.092**|0.110***|
||(0.037)|(0.038)|(0.037)|(0.038)|
|L.S&P rating|0.512***<br>(0.094)|0.162*<br>(0.086)|||
|L.Relative rating|||0.170*<br>(0.088)|0.202**<br>(0.080)|
|L.Growth diff.|0.159|0.190**|0.218**|0.189**|
||(0.113)|(0.079)|(0.109)|(0.079)|
|L.Interest rate diff.|0.087|0.406***|-0.009|0.429***|
||(0.112)|(0.132)|(0.113)|(0.132)|
|L.VIX|-0.008|0.073**|-0.093**|0.064*|
||(0.041)|(0.036)|(0.040)|(0.035)|
|Country fixed effects|Y|Y|Y|Y|
|No. of obs.|769|859|769|859|
|No. of countries|26|26|26|26|
|Adj. R-squared|0.311|0.238|0.293|0.240|

Notes: All regressions have net capital inflows as the dependent variable and are estimated using a dynamic panel data model with a lagged dependent variable. All the independent variables are subjected to a one-period lag. The sample includes unbalanced quarterly panel data for 26 emerging and frontier market economies for the 19982017 period. The pre-crisis period includes Q1 1998-Q2 2008 and the post-crisis period includes Q2 2009-Q4 2017. Heteroskedasticity-consistent robust standard errors are presented in parenthesis. * indicates significance at 10 percent, ** at 5 percent, and *** at 1 percent.

20
conversely, less likely to say were overpaid) when the occupation was in the white-collar category compared to the blue-collar category. However, including a dummy variable for the occupation being in the white-collar category did not affect the main coefficient of interest (the coefficient for the gender of the randomly assigned vignette subject) to the third decimal place. An insignificant interaction term between the vignette subject being assigned male gender and the occupation being white-collar (or, conversely, the subject being assigned female gender and the occupation being blue-collar) suggests that these two effects are not correlated (column 2). Although respondents were significantly less likely to think workers in blue collar professions were underpaid, this effect did not vary between male and female vignette subjects. The results highlight large differences between occupations (i.e., doctor) vs. positions (i.e. manager) which may suggest power in a work-related responsibilities is a relevant factor, and biases related to views on women’s decision-making may be correlated with views on wages and gender.

**Table 7: Including Interaction between occupational categories and sex of the vignette subject**

|**Across all countries, by blue/**|**white collar occ**|**upations**||
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
|**_Random assignment sex/white collar occupation question_**||||
|Male|0.094***|0.098***|0.098***|
||(0.007)|(0.010)|(0.010)|
|White collar|0.192***|0.196***|0.196***|
||(0.007)|(0.010)|(0.010)|
|Male*White collar||-0.009|-0.009|
|||(0.014)|(0.014)|
|**_Random assignment age_**||||
|Age 35|0.037***|0.037***|0.037***|
||(0.010)|(0.010)|(0.010)|
|Age 45|0.055***|0.055***|0.055***|
||(0.010)|(0.010)|(0.010)|
|Age 55|0.051***|0.051***|0.051***|
||(0.010)|(0.010)|(0.010)|
|**_Random assignment pay_**||||
|Pay Group 2|-0.563***|-0.563***|-0.563***|
||(0.012)|(0.012)|(0.012)|
|Pay Group 3|-0.980***|-0.980***|-0.980***|
||(0.012)|(0.012)|(0.012)|
|Pay Group 4|-1.251***|-1.251***|-1.251***|
||(0.013)|(0.013)|(0.013)|
|Pay Group 5|-1.466***|-1.466***|-1.466***|
||(0.015)|(0.015)|(0.015)|
|Female Respondent|||0.002<br>(0.013)|
|Constant|4.079***|4.077***|4.076***|
||(0.018)|(0.018)|(0.020)|
|Sample|All|All|All|
|Observations|70,224|70,224|70,224|
|Adjusted R2|0.341|0.341|0.341|

13
Panel D. GDP deflator (Percent)

||1970-2016|1970-85|1986-2000|2001-16|
|---|---|---|---|---|
|Factor||All cou|ntries||
|Global|12.6<br>(18.7, 3.2 - 30.5)|13.7<br>(25.9, 5.5 - 43.0)|7.4<br>(16.7, 3.1 - 28.8)|33.4<br>(32.4, 16.2 - 50.0)|
|Group|12.1<br>(16.0, 4.6 - 25.5)|12.7<br>(18.6, 5.8 - 28.3)|7.1<br>(11.7, 3.4 - 18.8)|4.1<br>(11.3, 0.8 - 10.9)|
|Global + Group|35.3<br>(33.8,19.1 - 45.0)|45.3<br>(43.3,19.9 - 63.2)|25.0<br>(27.6,11.8 - 37.8)|44.8<br>(42.6,30.7 - 58.0)|
|Factor||Advanced|economies||
|Global|13.4<br>(19.1, 6.5 - 31.5)|14.8<br>(27.1, 9.4 - 42.1)|12.8<br>(18.1, 3.4 - 28.9)|35.7<br>(34.4, 19.5 - 48.0)|
|Group|12.6<br>(15.9, 3.5 - 25.0)|9.3<br>(13.8, 5.0 - 23.0)|7.2<br>(11.5, 3.4 - 15.9)|2.3<br>(3.2, 0.6 - 5.2)|
|Global + Group|33.6<br>(37.3,18.1 - 45.8)|37.8<br>(39.3,19.9 - 59.3)|29.7<br>(28.4,15.9 - 42.8)|39.2<br>(36.1,22.1 - 47.6)|
|Factor||EM|DEs||
|Global|9.3<br>(18.0, 2.2 - 26.1)|9.8<br>(23.7, 1.9 - 44.5)|3.5<br>(14.2, 3.1 - 15.5)|19.7<br>(28.7, 10.3 - 50.0)|
|Group|<br>9.5<br>(16.2, 6.2 - 25.5)|<br>19.6<br>(27.5, 9.1 - 44.5)|<br>7.1<br>(12.0, 3.4 - 18.8)|<br>25.5<br>(26.3, 10.4 - 44.4)|
||<br>33.7|<br>54.9|<br>22.8|<br>56.7|
|Global + Group|(34.2,22.2 - 37.4)|(51.2,32.5 - 70.1)|(26.2,10.5 - 35.9)|(55.0,53.0 - 65.5)|

Notes: The contribution of global and group inflation factors is estimated over the three subsample periods with the dynamic factor model (two-factor model) for each of the five different inflation measures: import prices, PPI, headline, GDP deflator, and core CPI. The data set includes 38 countries (25 advanced economies and 13 EMDEs) except import prices for 21 countries (17 advanced economies and 4 EMDEs). The first argument in the first row indicates the unweighted median across countries. The first argument in the second row (in parentheses) is the mean variance share across countries. The second and third arguments in the second row (in parentheses) indicate the interquartile range (25th and 75th percentiles) of variance shares. CPI = consumer price index; EMDEs = emerging market and developing economies; GDP = gross domestic product; PPI = producer price index.

31
Figure 1 into foreign direct investment and portfolio equity investments. We do this to recognize the possibility that the problems of inefficient renegotiation discussed in the text may not apply to the minority ownership stakes captured in the portfolio equity investment data. This in turn raises the issue of whether we should measure the foreign investments in the theory as foreign direct investment alone, or as the sum of foreign direct investment and portfolio equity investments. In the data, however, portfolio equity investments are a very small part of total foreign investments, and so this distinction matters little empirically. The portfolio equity liabilities of the South average 0.2 percent of South wealth, as compared with direct investment liabilities averaging 2.9 percent of wealth. Although portfolio equity investments in the South have increased significantly in the 1990s, they remain small at 0.5 percent of South wealth.

The second panel of Table A1 presents the same information as before, but instead defining the South as countries with per capita GDP in 1985 less than $5000 US at market exchange rates. Although this alters somewhat which countries we identify as “North” and “South”, it does not change our basic observation that capital flows are small and are financed primarily through loans. In this alternative sample, the net foreign assets and net lending of South average -9.8 and –7.3 percent of South wealth, as opposed to -10.3 and -7.5 percent in the baseline.

Lane and Milesi-Ferretti (2001) also present estimates of stocks of foreign assets for a large sample of countries over the period 1970-1998. Their data is also based on the accumulation of flow balance of payments data, but the authors make a number of different assumptions, primarily regarding the valuation of these stocks. The bottom panel of Table A1 verifies that our stylized facts also hold in this alternative dataset. This alternative dataset shows smaller gross and net positions than in our data, in part reflecting some differences in country coverage between the two datasets. South’s net foreign assets average –5.7 percent of wealth, with net lending averaging –3.6 percent of wealth. On average, this dataset shows that net lending is 63 percent of South’s net foreign assets, while in our dataset net lending is 75 percent of net foreign assets. We conclude from this that our main stylized facts of 27
Table 2: Transition from Non-Statutory to Statutory Right (Residential, Probit)

|Dependent variable: Transi|tion NS to|S|||
|---|---|---|---|---|
|Distance to CBD (km)|-0.041<sup>_∗∗∗_</sup>|-0.047<sup>_∗∗∗_</sup>|-0.056<sup>_∗∗∗_</sup>||
||(0.010)|(0.011)|(0.014)||
|Distance to road (km)||-0.080<sup>_∗∗∗_</sup><br>(0.019)|-0.081<sup>_∗∗∗_</sup><br>(0.021)||
|Log(area) (m2)|||0.25<sup>_∗∗_</sup><br>(0.080)|0.23<sup>_∗∗_</sup><br>(0.071)|
|Water dummy|||-0.41<br>(0.44)||
|Electricity dummy|||1.09<br>(0.98)||
|South bank dummy|||-0.42<sup>_∗∗_</sup><br>(0.15)||
|Log(land price) (CFA/m2)|||0.039<br>(0.071)|0.13<sup>_∗_</sup><br>(0.056)|
|Observations|777|777|777|777|

Note: Standard errors in parentheses.<sup>_<u>∗</u>_</sup> _p <_ 0 _._ 05 _,_<sup>_<u>∗∗</u>_</sup> _p <_ 0 _._ 01 _,_<sup>_<u>∗∗∗</u>_</sup> _p <_ 0 _._ 001 _._ Year dummies included. Data: survey on Bamako, Mali, 2009-2012. The table reports the probit regression of log of land prices on distance to city center. The sample is restricted to the residential plots that were Non Statutory at the time of transaction.

# 6.3 Prices

Next, we estimate the dierential land price gradients for residential plots with statutory and non-statutory rights. We restrict our attention to the land plots that have statutory rights at both dates of transaction and survey (S to S), and to those that are transformed from non-statutory to statutory rights between transaction and survey dates (NS to S). This restriction allows us to separately identify the land purchases by those among urban workers who purchase formal land and by brokers who purchase land from customary owners before formalizing it as described in our model.

Table 3 displays OLS regressions of the log of the sales price on various sets of price determinants. The rst column excludes the determinants associated with distance to the CBD and tenure. Controls include plot area, distance to paved main road, dummies for water connection, electricity connection and South bank location, and year dummies to control for land price ination. All coecients have the expected sign and are signicant at the 1 percent level (except for access to electricity, which is not signicant). The second column adds the eect of distance to the CBD. As in the 25
Table 8: Sibling Correlations across Caste groups and Regions

||Upper C|aste|Lower|Caste|Upper|Caste|Lower|Caste|
|---|---|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|1993|2006|
|||Rura<br>|l<br>|Panel<br>|A<br>|Urb<br>|an<br>||
|All children|0.587***|0.563***|0.534***|0.543***|0.659***|0.629***|0.642***|0.591***|
||(55.625)|(48.229)|(41.471)|(32.664)|(55.026)|(59.619)|(33.773)|(30.884)|
|Brothers<br>(Men)|0.553***|0.562***|0.537***|0.546***|0.633***|0.641***|0.661***|0.656***|
||(35.411)|(31.366)|(29.315)|(22.032)|(37.140)|(43.496)|(26.207)|(27.689)|
|Sisters<br>(Women)|0.767***|0.707***|0.682***|0.654***|0.722***|0.656***|0.769***|0.561***|
||(52.282)|(35.779)|(26.875)|(20.919)|(33.904)|(27.628)|(25.883)|(12.762)|
|||<br>Less Dev|<br>eloped|<br>Panel|<br>B|<br>Devel|<br>oped||
|All children|0.621***|0.642***|0.548***|0.573***|0.646***|0.579***|0.601***|0.592***|
||(36.302)|(52.480)|(25.682)|(24.866)|(79.575)|(58.267)|(51.655)|(42.165)|
|Brothers<br>(Men)|0.604***|0.633***|0.580***|0.615***|0.603***|0.593***|0.588***|0.600***|
||(25.264)|(34.683)|(21.031)|(20.156)|(47.321)|(40.425)|(33.918)|(28.148)|
|Sisters<br>(Women)|0.807***<br>(39.031)|0.756***<br>(34.100)|0.658***<br>(9.729)|0.672***<br>(14.554)|0.754***<br>(59.210)|0.656***<br>(35.342)|0.741***<br>(38.422)|0.636***<br>(22.358)|

Robust t statistics in parentheses. Standard errors corrected for clustering at family level * significant at 10%; ** significant at 5%; *** significant at 1% 43
Table 3: Excess female under-5 mortality as a function of ultrasound availability and firstborn sex

|**Outcome: Death before age 5**|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Panel A:**||||||
|_Firstborn girl_ * _Female_|2.957***<br>(0.607)|2.821***<br>(0.598)|2.768***<br>(0.588)|2.785***<br>(0.593)|2.563***<br>(0.588)|
|_Firstborn girl_ * _Female_ * _Post1_|-1.646**|-1.527*|-1.525*|-1.536*|-1.602|
||(0.773)|(0.776)|(0.768)|(0.774)|(0.974)|
|_Firstborn girl_ * _Female_ * _Post2_|-1.998**|-1.953**|-1.996**|-2.022**|-1.841**|
||(0.744)|(0.749)|(0.744)|(0.745)|(0.734)|
|_Firstborn girl_ * _Post1_|1.422**<br>(0.576)|1.455**<br>(0.538)||||
|_Firstborn girl_ * _Post2_|1.785***<br>(0.636)|1.793***<br>(0.623)||||
|_Female_ * _Post1_|0.303<br>(0.478)|0.225<br>(0.489)||||
|_Female_ * _Post2_|0.141|0.0209||||
||(0.578)|(0.585)||||
|**Panel B:**||||||
|_Firstborn girl_ * _Female_|2.957***|2.821***|2.775***|2.784***|2.564***|
||(0.607)|(0.598)|(0.586)|(0.593)|(0.587)|
|_Firstborn girl_ * _Female_ * _Post_|-1.741**|-1.642**|-1.647**|-1.662**|-1.659*|
||(0.711)|(0.710)|(0.695)|(0.702)|(0.828)|
|_Firstborn girl_ * _Post_|1.519**|1.546***||||
||(0.560)|(0.526)||||
|_Female_ * _Post_|0.261|0.173||||
||(0.445)|(0.455)||||
|N|227,129|227,129|227,129|227,129|227,129|
|Baseline mean|0.124|0.124|0.124|0.124|0.124|
|_Xijt_||x|x|x|x|
|_Firstborn Girl_ x Birth year FE|||x|x|x|
|_Female_ x Birth year FE|||x|x|x|
|_Female_ x State FE|||x|x|x|
|_Female_ x Birth order FE|||x|x|x|
|Birth order x Birth year FE|||x|x|x|
|Birth order x State FE|||x|x|x|
|State x Birth year FE|||x|x|x|
|_Firstborn girl_ x State FE|||x|x|x|
|_Firstborn girl_ x Birth order FE|||x|x|x|
|_Female_ x _Xijt_||||x||
|Mother FE|||||x|

NOTES: Sample of second- and higher-order births. In Panel B, instead of splitting the post-ultrasound period into two sub-periods, we only use one indicator, _Post_ , to divide the sample into pre- and postultrasound years. Each column is a separate OLS regression. The outcome is an indicator for death before age 5. We drop children that are less than 5 years old to allow each child in the sample full exposure to the risk of under-5 mortality. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE in columns (1)-(4). The vector _Xijt_ comprises mother’s age at birth and, except in column (5), household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Baseline mean refers to the average likelihood of under-5 mortality in the pre-ultrasound period. Data: NFHS. *** 1%, ** 5%, * 10%.

41
# **6. Discussion on possible coping strategies**<sup>**19**</sup>

There are several channels in the literature through which refugees might affect the welfare of households in host communities. For instance, some argue that influx of refugees induces a supply of cheap agricultural labor for commercial farmers and also fosters competition for agricultural wage workers within the hosting communities (Maystadt & Verwimp, 2014). While others suppose that refugees create a market for agricultural goods and thus benefit those involved in the production of these goods (Alix-Garcia & Saah, 2009). In the literature, therefore, the majorly discussed channels of refugees’ influence on household welfare are the labor market and the agriculture sector.

**Occupation choice.** The above findings (Sub-section 5.3) generally suggest that presence of refugees is more beneficial to the rural households, initially involved in subsistence farming.<sup>20</sup> One possible explanation is that subsistence farmers, who are mainly consuming what they produce and are relatively poor, sell off their home production to diversify and improve their well-being. Further investigation reveals such dynamics where some households change occupation over time. That is, a household whose initial main source of income is subsistence farming could switch to rely mainly on another more lucrative source of income in the consecutive years given the refugee situation.

In this investigation, firstly we correlate welfare and refugee presence given the time varying main occupation for households. The initial main sources of income are included as controls. We do not introduce household fixed effects in these estimations since that would correspond to looking at the change of a change in occupation in an heterogeneous framework. Due to a lack of statistical power, the analysis on the coping strategies also does not involve the instrumental variable approach. The analysis should receive a correlational interpretation, not a causal one (naturally, the change of occupation may be a result of the change in welfare). Table 8<sup>21</sup> shows that the few households who rely on commercial farming seem to benefit more.

> 19 Estimates in this section are estimated without instrumentalization due to the weakness of the corresponding instrumental variables when interaction terms are used and hence, two endogenous variables need to be dealt with. The corresponding Kleibergen-Paap rk LM statistics are indeed below one. Caution needs to be made not to interpret causally these results.

> 20 Further investigation reveals that indeed rural households emerge as beneficiaries compared with urban households (Appendix 4, Tables A27 & A28). However, the sample sizes when restricting the analysis to urban households is too limited to draw inference.

> 21 Details in Appendix 4; Table A29.

28
- 29 -

TablA 8: Estimated slopes<sup>(gama) and their standard *rrors obtained by regressing estimated Bayes residuals</sup> from contextuol effects<sup>model on between-unit variables</sup>

|Variables|Gamsm|Standard<br>Error|Gamma||Standard<br>Error|
|---|---|---|---|---|---|
|1. Related<br>to achievement.||Scienco||Mathematics||
|Teacher post-secondary<br>science|0.86<br>|0.40<br>|n.s.<br>||n.s.<br>|
|Laboratories<br>|0.73<br>|0.39<br>|0.90<br>||0.30<br>|
|Instructional planning|1.27<br>|0.41<br>|1.27<br>||0.32<br>|
|Student motivation (reverse)|-1.57|0.55<br>|-1.30<br>||0.43<br>|
|Student active learning|-1.15<br>|0.46<br>|-1.11<br>||0.36<br>|
|Local studant control|-0.44|0.21|-0.41||0.16|
|2. Related to positive attitudes||Science||School||
|Teacher post-secondary science|n.s.<br>|n.s.<br>|-0.07<br>||0.04<br>|
|Laboratories|0.10|0.05|n.s.<br>||n.s.<br>|
|Student practice|n.s.|n.s.<br>|0.08<br>||0.04<br>|
|Instructional<br>planning|0.35<br>|0.05<br>|0.29<br>||0.03<br>|
|Student motivation (reverse)|-0.24|0.07<br>|-0.20<br>||0.05<br>|
|Textbooks|0.12|0.05<br>|0.07<br>||0.04<br>|
|Groups|0.14|0.05|0.14||0.04|
|3. Related to nexative attitudes||Science||School||
|Instructional<br>planning|-0.25<br>|0.04<br>|-0.22<br>||0.04<br>|
|Student motivation (reverse)<br>|0.35<br>|0.06<br>|0.15<br>||0.06<br>|
|Student active learning|0.17|0.05|n.s.<br>||n.s.<br>|
||013|004|-011||004|
|Groups|-.|.|.||.|

n.s. not significant 56. The exploratory analyses<sup>yield both anticipa-ed</sup> and unanticipated results. A few material and non-material<sup>inputs were important in boosting achievement.</sup> Science achievement was<sup>higher in schools with more scientifically educated</sup> teachers (students whose<sup>teachers studied post-secondary</sup> science scored nearly one point higher on the<sup>science test than students whose teachers lacked post-</sup> secondary science training),<sup>and both science and mathematics achievement were</sup> higher in schools where<sup>teachers had access to science laboratories (also, nearly</sup> one point higher). However,<sup>most of these variables--class size, the teacher's</sup> experience, use of textbooks,<sup>use of small groups, student time on experiments-</sup>