rafmacalaba/datause-encoder-data huggingface dataset:
# **Exploiting Three Sources of Variation**

Table 6 reports the results from estimating a model for the log of tourist inflows into Nepal by country of origin, explained by the log of GDP per capita of the country of origin, and by country fixed effects. Results show that the model fits the data well, the F-stat is significant at 1 percent, and the coefficient on GDP per capita is statistically significant, and its size is plausible. Tourist flows into Nepal are highly elastic to income per capita. Figure 4 shows the goodness of fit of the model presented in Table 6 by plotting actual tourist inflows by origin, against the prediction of our estimated model.

Table 6: Predicting Tourist Flows by Origin Figure 4: Goodness of fit of the prediction.

|Variables<br>Log GDP PC<br>Constant|LogTourist Inflows<br>2.071***<br>-0.409<br>-10.11**<br>-3.89|9<br>10<br>11<br>12<br>ed Log Tourist Inflows||
|---|---|---|---|
|Country of Origin Fixed<br>Effect|Yes|Predict||
|Observations|169|8||
|Number of Countries|17|||
|||7||
|F-stat<br>R-squared|25.62<br>0.414|7|8<br>9<br>10<br>11<br>12<br>Actual Log of Tourist Inflows|

Table 7 reports the results of different specifications in which the three sources of variation are exploited: time, product, and country of origin of tourists/destination of exports. The first column reports the results when the prediction of the model whose results were presented in Table 6 was used as a proxy for actual tourist inflows by country of origin. Our results suggest that increases in inflows of tourists from country j in period t-1 are associated with increases in exports to that country j in period t, all other things equal. The magnitudes of these effects are bit smaller than those reported when tourist and export flows were pooled across origin. The second column reports results when the (log) expenditure per day of tourists and its interaction with the tourist product dummy are incorporated. Results are virtually unchanged and tourist expenditure per capita seems to exert no effect directly, or indirectly via its interaction with the tourist product dummy. The third column substitutes the predicted tourist inflow variable and its interaction with the tourist product dummy with the actual tourist inflow variable and its interaction, which implies restricting the sample to the period 2001-2011, where actual (rather than predicted) data on the origin of tourists are available. Here, also tourist expenditure and the interaction are included, and they gain statistical significance. The coefficient on the interaction of the actual tourist inflows with the tourist product dummy carries the expected sign, although it is not well-determined. The fourth column reports the results of the baseline specification now introducing year dummies, which implies that those regressors with time-variation only are dropped (the real exchange rate, and the per day expenditure of tourists). The spillover effect from tourism into greater exports of ‘niche’ or ‘traditional’ goods withstands the inclusion of the year dummies.

17
(Eds.) Decentralization and Coordination of Water Resource Management. KLuwer Academic Publishers pp. 429-447.

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Olson, M. 1965. _The Logic of Collective Action_ . Cambridge: Harvard University Press.

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Samad, Madar, 2005. Water institutional reforms in Sri Lanka. Water Policy, xxxxxxxxxx

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45
<!-- Start of picture text --> Figure 11: Model Fit (Calibration)<br>(A) Routine Share (% of x ) (B) Non-Routine Share (% of x ) (C) ICT Share<br>0.6 Calibration<br>0.58 0.54 Data 0.06<br>0.56 0.52 0.055<br>0.54 0.5 0.05<br>0.52<br>0.48 0.045<br>0.5<br>0.46 0.04<br>0.48<br>0.46 0.44 0.035<br>0.44 0.42 0.03<br>0.42 0.025<br>0.4<br>0.4 CalibrationData 0.38 0.02 CalibrationData<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 1970 1975 1980 1985 1990 1995 2000 2005 2010 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year Year Year<br>(D) Log of x (E) Routine Labor ( lr )<br>0.62<br>0.6<br>0.4<br>0.58<br>0.2 0.56<br>0.54<br>0 0.52<br>0.5<br>-0.2<br>0.48<br>0.46<br>-0.4<br>0.44<br>Calibration Calibration<br>-0.6 Data 0.42 Data<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Year Year<br>sR sNR sICT<br>lnx lr<br><!-- End of picture text --> _Notes:_ The blue dashed lines plot the data between 1968 and 2012, and the red solid lines are calibrated equilibrium paths, given aggregate labor and aggregate ICT capital. Note that all income shares are reported as a fraction of _x_ rather than _y_ in this exercise and therefore are not directly comparable to those reported in Figure 1.

mulation, was the main driver of productivity growth over the past few decades (e.g., Colecchia and Schreyer, 2002; Basu et al., 2003; Bloom et al., 2012; Acemoglu and Autor, 2011).

Table 3 presents the full set of calibrated core parameters. We find an elasticity of substitution between ICT capital and non-routine labor to be around 1.2. Interestingly, this magnitude is similar to the elasticity of substitution between aggregate capital and aggregate labor estimated by Karabarbounis and Neiman (2014). Relative to aggregate capital, ICT is more substitutable with labor, but relative to labor, non-routine labor is less substitutable with capital. The magnitudes are therefore not inconsistent. We calibrate the elasticity of substitution between routine labor and _xnr_ to be around 5.7.

27
# **Figure 21. Growth by Sectors in Central American Countries**

<!-- Start of picture text --> (a) SCCIs (b) WCCIs<br>0.07 0.07<br>0.06 0.06<br>0.05 0.05<br>0.04 0.04<br>0.03 0.03<br>0.02 0.02<br>0.01 0.01<br>0.00 0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Services Agriculture, forestry, and fishing Manufacturing Industry Services Agriculture, forestry, and fishing Manufacturing Industry<br><!-- End of picture text --> Note: The growth rates are measured over a ten-year moving window, as in the case of all terms of the growth accounting decompositions. Country groupings follow Table 2. _Source: WDI._ 42
_Controlling for pre-treatment outcomes_ . As an additional robustness check, we also present ANCOVA estimates obtained from estimating Equation (4) with an additional regressor equal to either the value of the outcome at baseline, where available, or to an available proxy of the outcome at baseline, when the outcome was not measured at baseline but a reasonable proxy exists. Using this ANCOVA approach is preferable to Difference-in-Differences even when the baseline outcome is available, as there is no loss of power when the correlation between pre- and post-treatment outcomes is low (McKenzie, 2012). Results in Table 9 show that all our conclusions so far are robust to the inclusion of these pre-treatment outcomes.

_Interpretation of our findings for the information treatment_ . Here we present further evidence in support of our interpretation of the effect of the information treatment as being due to an increase in the quality of parental monitoring rather than due to some generic “salience” effect of the treatment. For the girls surveyed in their households who were both (i) (reported by parents as being) enrolled in school in 2016 and (ii) could be matched to our independent school attendance records,<sup>30</sup> we can evaluate the quality of the parental monitoring technology by checking the predictive power of the number of child absences during October 2016 reported by the parent in the household survey on attendance at the last spot check carried out in schools, which took place between October 10 and November 3, 2016. Table 10 reports estimates from a regression of an indicator for whether the girl was absent at the last independent attendance check on the reported number of days absent during October 2016 and district fixed effects, experimental arm by experimental arm (Columns (1) to (4)). On the basis of 22 days of school, if the probability of being absent was the same in any given day, then an additional day absent during the month should increase the probability of being absent on the day of 30 77% of girls whose parents said were enrolled could be matched to names in official school records.

36
We include several control variables at the individual bank level. Specifically, Y, i, is the ratio of equity to total assets, Y2,;, is the ratio of net loans to total assets, and _Y3_ is iS the logarithm of total assets (to control for potential size effects). D is a vector of year dummies for the years 1995 through 2001 (we drop the year dummy for the year 1994). We take natural logarithms of all variables. We estimate model (1) both using OLS with time dummies and GLS with fixed bank-specific effects (in the latter case a = ai). The H-statistic equals _f,i + f82 + ,3_ . In what follows we refer to Hi as the H-statistic estimated using OLS, and to H2 as the H-statistic based estimated using GLS with fixed-bank effects.

# B. Equilibrium Test

Since the PR-model is only valid if the market is in equilibrium, we also estimate the following equation for each country: _ln(ROAi,) = a + ,,_ ln(WJ,) + **P2** _hn(W2JI)_ + **A3** ln(W3 JI) + where _ROA_ is the pre-tax return on assets (pre-tax profits to total assets). Because return on assets can take on (small) negative values, we compute the dependent variable as _ROA'=ln(l +ROA)_ where _ROA_ is the unadjusted return on assets. We define the equilibrium E- statistic as _f,i +_ **_,/2 +,83_** _._ We test whether E = 0, again using a F-test. If rejected, the market is assumed not to be in equilibrium. The idea behind this test is that, in equilibrium, returns on bank assets should not be related to input prices. This approach for testing whether the 11
**Table 4 Relative trade gains of European partner countries resulted from trade facilitation improvements of the new member and candidate member countries**

||EU|15|New m|embers|Candidat<br>cou|e member<br>ntries|
|---|---|---|---|---|---|---|
||Export gain|Import gain|Export gain|Import gain|Export gain|Import gain|
|**Port efficiency**|||||||
|New members|3.85%|11.31%|1.59%|4.95%|0.18%|0.97%|
|Candidate member countries|<br>0.76%|4.14%|0.17%|0.40%|0.09%|0.35%|
|**_EU8+3 candidate members_**|**_4.61%_**|**_15.45%_**|**_1.76%_**|**_5.35%_**|**_0.27%_**|**_1.32%_**|
|**Customs regimes**|||||||
|New members|3.50%|3.24%|1.15%|1.10%|0.13%|0.15%|
|Candidate member countries|<br>1.88%|3.04%|0.58%|0.30%|0.23%|0.20%|
|**_EU8+3 candidate members_**|**_5.38%_**|**_6.28%_**|**_1.73%_**|**_1.39%_**|**_0.36%_**|**_0.36%_**|
|**Regulatory policy**|||||||
|New members|2.31%|5.00%|0.74%|1.73%|0.11%|0.37%|
|Candidate member countries|<br>0.91%|3.99%|0.20%|0.28%|0.09%|0.20%|
|**_EU8+3 candidate members_**|**_3.22%_**|**_8.99%_**|**_0.94%_**|**_2.01%_**|**_0.19%_**|**_0.57%_**|
|**IT infrastructure**|||||||
|New members|5.40%|14.86%|1.74%|4.67%|0.24%|1.25%|
|Candidate member countries|<br>1.66%|8.20%|0.38%|0.69%|0.18%|0.54%|
|**_EU8+3 candidate members_**|**_7.06%_**|**_23.06%_**|**_2.12%_**|**_5.36%_**|**_0.42%_**|**_1.79%_**|
|**TOTAL**|**_20.27%_**|**_53.79%_**|**_6.55% _**|**_14.12%_**|**_1.24%_**|**_4.03% _**|

|**TOTAL**<br>**Table 5a Absolute trad**<br>**efficiency improvement (h**|**_20.27%_**<br>**e gains of E**<br>**alf-way to E**<br>**countr**|**_53.79%_**<br>**_6_**<br>**uropean par**<br>**U15) of each**<br>**ies (unit: mil**|**_.55% _**<br>**_1_**<br>**tner count**<br>**new mem**<br>**lion $)**|**_4.12%_**<br>**ries result**<br>**ber and ca**|**_1.24%_**<br>**ed from po**<br>**ndidate m**<br>|**_4.03% _**<br>**rt**<br>**ember**<br>|
|---|---|---|---|---|---|---|
||||||Candidat|e member|
|Port efficiency|EU|15|Newm|embers|cou|ntries|
||Export gain|Import gain|Export gain|Import gain|Export gain|Import gain|
|_New members_|||||||
|Czech Republic|1445.73|4254.22|228.14|928.23|16.64|102.77|
|Estonia|46.59|122.28|7.32|22.14|0.49|0.28|
|Hungary|1644.67|5442.06|165.69|536.00|46.82|216.90|
|Latvia|51.10|104.90|20.12|34.45|0.76|0.08|
|Lithuania|166.00|300.69|53.45|69.88|1.89|14.05|
|Poland|1880.95|4141.78|212.69|552.59|34.39|95.11|
|Slovakia|1068.83|3096.13|419.32|1235.44|14.21|99.19|
|Slovenia|249.54|625.13|28.39|87.41|4.43|17.19|
|**_subtotal_**|**_6553.43_**|**_18087.20_**|**_1135.11_**|**_3466.12_**|**_119.63_**|**_545.56_**|
|_Candidate member countries_<br>|||||||
|Bulgaria|345.14|977.98|39.71|56.93|30.13|94.94|
|Romania|260.93|719.21|39.33|47.92|13.37|35.79|
|Turkey|735.15|2198.22|27.75|119.22|21.53|69.61|
|**_subtotal_**|**_1341.22_**|**_3895.41_**|**_106.80_**|**_224.06_**|**_65.03_**|**_200.34_**|
|**TOTAL**|**7894.65**|**21982.62**|**1241.91**|**3690.19**|**184.65**|**745.90**|

19
weakened.61 By 1980, education'3 share of the public budget had fallen to 13 percent, representing 3.12 of GDP (Arregui, 1988).

Although the specific educational policies of Belaunde and Velasco differed, both targeted education as the key to economic development and income redistribution. Belaunde embarked on an ambitious program of school expansion to raise skill levels, and extend access to previously excluded groups. He ditected his attent<sup>4</sup> 0n towards achieving universal primary education, expanding universities, and improving the quality of instruction. He instituted a law requiring all elementary schools to offer the complete cycle of education if enrollment would justify doing so, began experiments and research activities in bilingual education to attract and retain Indian children in primary schools, and awarded all primary school teachers a 252 nominal pay increase per year to attract and retain teachers for the expanding system.71 6/ By the end of Velasco's tenure, inflation was running at 602 a year and a serious balance of payments problem was brewing. Export prices for Peru's primary export commodities plumeted resulting in a severe cutback in both public and private investment. Overall, economic performance throughout the 1970s **was** decidedly mediocre. GDP grew at 3.2Z annually; per capita incomes stagnated; and unemployment rose (World Bank, 1985). 71 These reforms took place within the traditional system of Peruvian education, where students followed a conventional ladder of 5 years each of primary and secondary schooling, followed by higher education. In secondary schools students chose between the general or technical streams. The former offered an academic curriculum with a specialization in humanities or the sciences during the last two years of the cycle. The technical stream offered 5 years of technical education with specializations in such areas as agriculture, business, electronics, etc. At the post-secondary level, vocational training was offered in technical, pedagogical, and other specialized institutes; courses were usually three or four years in length.

8
The number of household members is another possibly important correlate with input use if labor use is a function of household labor endowments, consistent with the hypothesis that multiple rural market failures cause rejection of the separation hypothesis for most African agricultural households (Dillon and Barrett in review). In Table 17, we show that larger households are indeed statistically significantly more likely to use improved inputs. As with all of the other correlates we describe, we emphasize that household size may be endogenous to input use in that households that successfully reap the benefits of larger harvests with improved input use may affect household composition decisions regarding fertility, fostering, marriage, and migration.

# <u>Cooperative farming arrangements</u>

We do not observe, in any country, which households belong to an agricultural cooperative. But, through the community-level surveys, we do know, in all countries except Malawi, in which communities an agricultural cooperative exists. Only in Niger do we observe whether or not these cooperatives explicitly provide inputs. Of those communities with a cooperative in Niger, 68 percent of households live near a cooperative that provides inputs in the village.

Table 18: Differences in input use between households where an agricultural cooperative exists in the community

||% using<br>organic<br>fertilizer|% using<br>inorganic<br>fertilizer|Avg total<br>inorganic<br>fertilizer<br>(kg/ha)|% using<br>improved<br>seed|% using<br>agro-<br>chemicals|% using<br>irrigation|% owning<br>any ag<br>equipment|
|---|---|---|---|---|---|---|---|
|Ethiopia||||||||
|Coop in village (n=212)|60|**60**|**55**|**34**|**39**|6|**84**|
|Other (n=2,880)<br>_significance_|**67**|55|44|21|30|**9**<br>_*_|75|
|Niger||||||||
|Coop in village (n=344)|53|14|**8**|**6**|7|**15**|72|
|Other (n=1,905)<br>_significance_|**55**|**18**|4|2<br>_**_|**8**|5<br>_**_|**79**|
|Nigeria||||||||
|Coop in village (n=813)|4|**57**|**188**|-|**42**|**6**|**10**|
|Other (n=1,603)|**5**|38|117|-|28|3|10|
|_significance_||_***_|_***_||_***_|_*_||
|Tanzania||||||||
|Coop in village (n=63)|8|**13**|**15**|-|2|<1|5|
|Other (n=169)|**13**|9|5|-|**5**|**3**|**6**|
|<br>_significance_||||||||
|Uganda||||||||
|Coop in village (n=70)|10|**4**|**2**|-|**27**|2|**16**|
|Other (n=1,508)<br>_significance_|**13**|3|1|-|10<br>_***_|**4**|13|

Note: “n” identifies the number of households. The average inorganic fertilizer statistics are unconditional and represent total, not nutrient, values. Community-level data are not collected for all surveyed areas of Tanzania, the reason for the smaller sample size. Community-level cooperative information is not available in Malawi. *, **, and *** denote that difference in mean between groups is statistically significant using t-values of 1.645, 1.96, and 2.58 respectively.

24
**Figure 5: Death probability in 1994 by level of education of the sister. From siblings' mortality: Demographic and Health Survey, 2000, Rwanda.**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1 to 5 6 to 10 11 to 15 16 to 20 21 to 25 26 to 30 31 to 35 36 to 40 41 to 45 46 to 50 Above 50<br>Age in 1994<br>Sister with no secondary education Sister with at least some secondary education<br>Death probability<br><!-- End of picture text --> 25
|Line of credit X State foreign<br>bank||||(0.106)|
|---|---|---|---|---|
|Ln(ae)|0.003|0.003|0.002|0.003|
|g|(0.008)|(0.008)|(0.008)|(0.008)|
|20-99 l|0.024|0.026|0.025|0.024|
|empoyees|(0.014)|(0.014)|(0.014)|(0.014)|
|>99 emloees|0.025|0.025|0.024|0.025|
|py|(0.017)|(0.018)|(0.019)|(0.017)|
|SOE|-0.154|-0.13|-0.131|-0.155|
||(0.082)|(0.072)|(0.072)|(0.083)|
|Fi ?|0.120***|0.120***|0.118***|0.119***|
|oregn company|(0.021)|(0.021)|(0.020)|(0.020)|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|R2|0.14|0.14|0.14|0.14|
|N|22956|22956|22742|22956|

Note: OLS regression. The dependent variable is whether the firm has introduced a new or improved product or service over the last three years ("innovation diffusion or new to the firm"). Robust standard errors clustered at the sector level are reported in brackets. ***, **, * represent significance at the 1%, 5%, and 10% level.

# **3.1. Addressing endogeneity concerns about access to credit**

The link from access to credit to innovation can be subject to possible reverse causality and endogeneity issues. We apply the following method to identify a weakly exogenous credit access shock at the firm level. First, we identify countries in our sample with more efficient banking markets.<sup>4</sup> Second, we estimate a market scoring model for those efficient markets using "Does the firm have a credit line" as the dependent variable and all other variables from the World Bank Enterprise Survey as potential explanatory variables. We use LASSO as the dimension-reduction technique to arrive at an estimate of a parsimonious credit scoring model. Third, we take the OLS estimated credit scoring model based on LASSO-selected covariates for efficient banking credit markets to the remaining countries in our sample and calibrate it for each firm. Fourth, if the calibration indicates that the firm belongs to the lowest 25<sup>th</sup> percentile of scored firms, we assume it is not creditworthy and should not have access to credit. Fifth, if any such not-creditworthy firm did get credit, we identify this as a positive credit shock (0/1 dummy). Finally, we use this positive credit shock instead of the credit line access variable in our estimations. The results are reported in Table 3. The estimation results reinforce the significance and magnitude of the baseline findings regarding the impact of state ownership and foreign state ownership variables on firm innovation. Using the credit shock helps better identify the positive effect of state ownership which is now significant at the 1 percent level—although controlling for bank soundness in column nuances this result. The effect of foreign state ownership remains highly significant and large in magnitude.

> 4 We take the two most developed countries in our sample based on GDP per capita, which are Spain and Italy, to represent the efficient banking market benchmark. We exclude them from the sample that we use for the robustness check involving the credit shock instead of the access to credit variable.

7