rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 2. Baseline Head Teacher Survey: Description and Balance</u>

||Control|Treatment|Treatment-<br>Control||Control|Treatment|Treatment-<br>Control|
|---|---|---|---|---|---|---|---|
|Panel A. School Characteristics||||Panel B. Teachers and Student||||
|School has a BOG|0.97|0.95|-0.023|Number of teachers|16.22|16.79|0.571|
||(0.16)|(0.22)|(0.04)||(4.62)|(5.89)|(1.16)|
|Frequency of BOG meeting|3.41|3.54|0.128|Number of female teachers|2.85|3.77|0.914*|
||(1.12)|(0.85)|(0.23)||(1.67)|(2.50)|(0.47)|
|BOG rarely meets|0.62|0.67|0.051|Percentage of female teachers|0.18|0.21|0.037|
||(0.49)|(0.48)|(0.11)||(0.11)|(0.10)|(0.02)|
|BOG monitors teachers absence|0.49|0.61|0.119|Percentage of permanent teachers|0.62|0.62|0.001|
||(0.51)|(0.50)|(0.12)||(0.28)|(0.22)|(0.05)|
|BOG monitors infrastructure|0.59|0.58|-0.016|Teacher is in class right now|0.11|0.17|0.058|
||(0.50)|(0.50)|(0.12)||(0.20)|(0.20)|(0.04)|
|BOG members|10.24|11.47|1.237|Teacher is absent|0.44|0.3|-0.138**|
||(3.77)|(3.34)|(0.82)||(0.32)|(0.31)|(0.07)|
|Number of toilets in the school|6.39|7.67|1.276|Number of Students in Grade 1|57.35|68.07|10.72|
||(3.79)|(4.55)|(0.92)||(43.55)|(44.25)|(9.65)|
|School has electricity|0.39|0.37|-0.018|Number of Students in Grade 2|42.08|48.81|6.739|
||(0.49)|(0.49)|(0.11)||(27.26)|(36.02)|(7.05)|
|School has a library|0.44|0.49|0.049|Number of Students in Grade 3|41.6|48.79|7.191|
||(0.50)|(0.51)|(0.11)||(31.16)|(38.85)|(7.77)|
|Source of drinking water|0.9|0.93|0.028|Number of Students in Grade 4|46.13|48.72|2.596|
||(0.30)|(0.26)|(0.06)||(37.13)|(38.26)|(8.29)|
|||||Number of Students in Grade 5|6.17|11.84|5.662|
||||||(12.65)|(20.83)|(3.82)|
|||||Number of Students in Grade 6|5.63|10.42|4.794|
||||||(12.51)|(18.05)|(3.43)|
|||||Number of Students|198.95|236.65|37.701|
||||||(128.48)|(158.93)|(31.87)|
|||||Number of Female Students|94.08|110.19|16.115|
||||||(63.82)|(85.60)|(16.74)|
|Number of observations|41|43|84||41|43|84|
|Date||||Dec. 2010/April 2011||||
|Test for jointly significance||||||||
|Chi2||||31,418||||
|Prob>Chi2||||0.113||||

Note. This table presents the coefficient of the OLS regression of the treatment indicator (equal 1 if treated; 0 otherwise) against each of the listed baseline characteristics, as reported by the head teacher.

19
**Table 6 (continued):**

|**Variable**|**All farms**|**Dryland**|**Irrigated**|
|---|---|---|---|
|Soil (geCU)|-631**|-287**|-540|
|Soil (ilqHS)|-387**|-156**|-1147**|
|Soil (loMH)|-2181**|-1959**||
|Soil (vcFU)|-1180**|-1006**|-1719**|
|Soil (lcMFU)|-295**|-241**||
|Soil (qc)|1633**|1726**||
|Soil (ql)|-482**|-188**||
|Soil (lcMS)|-2153||-6157**|
|Soil (nd)|214||7051**|
|Soil (lg)|-199**|-154**||
|Soil (frFHS)|1428**||3212|
|Soil (ykCMUH)|1071**|148||
|West Africa dummy|136**|208**|-285|
|North Africa dummy|457**||675*|
|East Africa dummy|-186**|-154**|-361|
|Heavy machinery dummy|51.8**|55.5**|-60.8|
|Animal power dummy|10.4|49.3**|-185.5**|
|Constant|-388|1081|-549|
|N|8459|7238|1221|
|R2|0.4|0.2|0.3|
|F|63.6|32.4|46.3|

_Notes:_ * significant at 5% level ** significant at 1% level _Soil definitions_ : Rhodic ferralsols with fine texture in hilly to steep areas (frFHS), eutric gleysols with coarse texture in undulating areas (geCU), lithosols hilly to steep slope (ilqHS), chromic luvisols with medium to fine texture in undulating areas (lcMFU), chromic luvisols in moderate to steep areas (lcMS), gleyic luvisols (lg), orthic luvisols in moderate to hilly areas (loMH), dystric nitrosols (nd), cambic arenosols (qc), luvic arenosols (ql), chromic vertisols with fine texture in undulating areas (vcFU), calcic yermosols with coarse to moderate texture and in undulating to hilly areas (ykCMUH), lithosols in hilly and steep areas (ilqHS), luvic arenosols (ql), dystric nitosols (nd), gleyic luvisols (lg).

32
with the first having the strongest interpretation as capturing amenities. The correlation with wages for this measure is 0.37, while the correlation with the INEGI’s welfare index is 0.77.

# **Trade and Investment Variables**

<u>Foreign Direct Investment : This is aggregate per capita foreign direct investment from</u> 1995-1999 reported to the government by the firms investing and was provided by the Central Bank of Mexico. The Federal District (DF)- Mexico City- shows vastly higher rates because much of the FDI destined for other states is registered at the headquarters of the firm in the DF. We include a dummy variable in the regressions to account for this measurement error. As figure 2 suggests, FDI is highly, although not exclusively concentrated along the northern border with the US.

<u>Maquila Value Added: This variable may be seen purely as a proxy for FDI, but since the</u> maquilas are primarily exporters, it can also be seen as a proxy for “maquila exports per capita” which brings it closer to being a trade variable. Since it is collected from industrial surveys, it does not suffer from the “headquarters” effect of the FDI variable. The two variables are moderately correlated (.64) and hence are it may be a serviceable proxy for FDI more generally. On the other hand, the government of Mexico tabulates 12 of the states in an “other” category and the loss of these state implies reduced information and potential selection bias. We run the regressions with the substantially reduced sample.

<u>Exports: This variable is provided by the Ministry of Finance (MOF). As with the FDI</u> data, the exports reported by firm are assigned to the location of the headquarters, often in the DF, which may not be the actual location of production. The MOF therefore reassigns the firm’s aggregate exports proportionally using data on the location of plants and employees of each firm found in the Mexican Institute of Social Security (IMSS) data base. The methodology does not include oil or electricity exports and may miss smaller firms which do not register with IMSS.

8
Results from t-tests and Kolmogorov-Smirnov tests (Table 1) indicate that there is no difference in the 2003 distribution of tobacco consumption between the households who purchased a mobile phone between 2003 and 2006 and those who did not.

Over the period 2003-2006, the average monthly tobacco consumption decreased to 30.4 PHP per adult over the age of 15. A different picture emerges once we separate households who owned and who did not own a mobile phone in 2006. Indeed, for households without a mobile phone, monthly tobacco consumption rose to 33.9 PHP while it decreased to 23.1 PHP for households who such a phone (cf. Figure 1).

# **_3. Estimation Strategy_**

## _3.1. Basic Setup_

5 Let _Ln_ ( _Cijt_ ) be household _i_ ’s (log) per adult (over 15) tobacco consumption. It is determined by: where α and β are coefficients to be estimated, _M ijt_ is a dummy equal to one if household _i_ in village _j_ owns a mobile phone at time _t_ , _X ijt_ is a vector of control variables that vary across households and time, _uij_ is a time-constant household effect, _v jt_ is an effect common across all households in village _j_ at time _t_ and, _wijt_ is the usual idiosyncratic error term.

' We can eliminate _uij_ by differencing equation (1). Rewriting _v j_ = ( _v j_ 1 − _v j_ 0 ) and ' _wij_ = ( _wij_ 1 − _wij_ 0 ) leads to:

> 5 The main results of the paper are basically unchanged if we run our regressions with a different age cutoff. Results available upon request.

4
The postwar experience of the Japanese banking system and its relevance to developing and transition economies has been receiving considerable attention recently.' This research has been motivated in part by the fact that the conditions in the defeated Japan of 1945 are similar to those in transition economies today, such as high inflation and the need to switch from a military to a civilian economy.

At the same time many of the postwar conditions in Japan were different from those faced by transition economies today. Prior to World War II Japan had spent more than sixty years experimenting with and developing modem financial institutions. This essay relates some highlights of that history, concentrating on the banks that sprung up from the large industrial conglomerates-zaibatsu.

How did the zaibatsu banks, the predecessors of the main banks of the postwar era, perform? They performed very well relative to their competitors and much differently relative to their postwar successors. Looking at the prewar setting also permits an exploration of banking stability. While the postwar Japanese system has been free of major bank failures, the prewar banking system experienced numerous banking panics and failures, even among zaibatsu banks.

# The Main **Bank System and Important Supplements**

The views expressed represen **t** hose of the author and not the Federal Reserve Bank of New York or the Federal Reserve System.

' See, for example, the World Bank working papers on the Japanese main bank system by Aoki, Patrick, and Sheard (1994).

1
suggests that the households had a low income elasticity of test scores, it is impossible to make any further conclusions without further information on what the household spent the extra cash on. Specifically, given the declining marginal benefit of spending on education in each particular period, the household may still have found it better to save this money for spending on future educational materials, rather than just spend it on goods and services this year. As we do not have information on what the households did with the extra cash available, we cannot explore this further.<sup>39</sup> We summarize this section by noting that while having further disaggregated data on both school and household expenditures would allow for an even more precise understanding of the mechanism for our test score results, the combination of the household spending results and the test score results are most parsimoniously explained by the theoretical framework laid out in this paper. We consider but end up rejecting several alternative explanations for these results and finding the same results in two contexts as varied as Zambia and India makes us confident that our results present evidence of differential household responses to anticipated and unanticipated school grants.

# **6 Conclusion**

Data on test-scores and household expenditures in the context of school grant programs in Zambia and Andhra Pradesh in India suggest that grants anticipated by households crowd-out private educational spending. Consequently, school grants that are fully anticipated have no impact on test-scores. Unanticipated grants elicit no household responses and do have positive impacts on learning.

These results have implications for common estimation techniques in the educational literature. The dominant technique for estimating the effect of school inputs on test scores is based on the production function approach, where achievement (or changes in achievement) is regressed on school inputs. Following Todd and Wolpin (2003), these estimates represent the policy effect of school inputs that combines both the effect of inputs on test scores through the production function, as well as household responses to such inputs. Our use of unanticipated

> 39 What we _do_ know from our evidence is that households did not spend it on other (non-education) inputs that may raise child test scores directly, such as child nutrition; if so, we could have had substantial crowding out, but still a positive impact on test-scores, which is rejected by our evidence.

27
fluid milk) experienced large price increases, as did commodities with no futures markets at all (apples, edible beans). Fourth, index funds rebalance as grain prices rise, reducing long positions to maintain portfolio shares, and thus stabilize prices somewhat like a more flexible variant of a price-band policy. Fifth, empirical work has shown no evidence that position changes by speculators help forecast price changes in these markets.<sup>36</sup> Finally, if long futures market positions exacerbated price spikes last year, they must have reduced consumption and increased commodity stocks. But stocks were around minimal feasible levels in 2007/08. To the extent that speculators might have influenced the market by increasing stocks in previous years, their unwinding of those positions last year should have increased consumption and moderated price, which are hardly undesirable effects.

# **Policies to prevent irrational or manipulative bubbles**

The reality that overall grain availability increased last year prompted a quite different rationalization of the crisis in the grain markets: there were irrational or manipulative bubbles attributable to “greedy” speculators that burst in the spring and summer of 2008. In 2007, one story goes, prices got out of line in the grain markets and supplies were withheld in anticipation of greater profits later. The sharp reversals of grain price trends in different months of 2008 are viewed as confirmation of this interpretation: the “bubbles” proved unsustainable, as bubbles always are, and burst at different times. Given the global disarray in financial markets at present, an explanation dependent on greed and irrationality can be both plausible and attractive.

Unfortunately, recent research on models of commodity markets like the one represented in Figure 12 but with slightly different, though hardly unconventional, demand behavior has shown<sup>37</sup> that irrational bubbles are difficult if not impossible to distinguish from normal rational investment behavior by nonmanipulative market participants, just as “greedy” investors are difficult to distinguish _ex ante_ from regular “profit maximizers.” There is another reason to discount the need to prevent bubbles. If a bubble occurred in a grain market last year, to affect price it must have increased stocks. But, as previously noted, stocks were at or close to minimum levels. Where were the increased stocks to be found? More

> 36 See the Granger causality tests in Sanders, Irwin, and Merrin (2008).

> 37 See Bobenrieth et al. (2000, 2008).

– 42 –
**Table 3**

**Aid and banking crises 1977‐2007**

|Equation|**1**|**2**|**3**|**4**|
|---|---|---|---|---|
|Sample/specification change|Ongoing<br>crisis obs.<br>dropped|Ongoing<br>crisis<br>variables<br>added|Non‐<br>systemic<br>crises<br>included|Countries<br>with<br>multiple<br>crises<br>dropped|
|Number of years from crisis start|‐0.041**|‐0.041**|‐0.044**|‐0.040*|
||(0.019)|(0.017)|(0.018)|(0.020)|
|Number of years from crisis start squared|0.002***|0.002***|0.001*|0.001|
||(0.0006)|(0.0004)|(0.0005)|(0.001)|
|No. of years from start of ongoing crisis|<br>|‐0.005<br>(0.030)|<br>||
|No. of years squared from start of ongoing crisis|<br>|0.001<br>(0.001)|<br>||
|Log per capita income|3.203***|3.140***|2.810***|2.843***|
||(0.745)|(0.706)|(0.652)|(0.672)|
|Log population|‐0.354|‐0.444|0.369|0.076|
||(1.291)|(1.287)|(1.109)|(1.300)|
|R<sup>2</sup>|.97|.97|.97|.95|
|No. of donors|24|24|24|20|
|No. of observations|650|692|692|598|

Dependent variable is log of net ODA disbursements. Note *p< .1, **p<0.05, ***p<0.01. All regressions control for country and year effects, not shown for space reasons. Standard errors are adjusted for clustering at the country level. Banking crises dates are from L&V. Ongoing crises are identified using crisis‐ending dates in CKLN. Non‐systemic crises are coded by CKLN. Countries dropped in equation 4 are Turkey, the UK, US and Iceland.

28
The disappointing economic outcomes from the resource boom left resource-rich Africa more exposed to the COVID-19 crisis and global economic slowdown. The need has never been greater for policy choices that learn the lessons of the past and of peers to harness resource wealth effectively and to diversify away from resource dependency.

# **Bibliography**

Bates, R., B. Ndulu, S.A. O’ Connel, and P. Collier. 2008. _The Political Economy of Economic Growth in Africa, 1960–2000._ Cambridge University Press.

Bellver, A., and D. Kaufmann. 2005. “Transparenting Transparency.” _MPRA Paper_ , no. 818. University Library of Munich, Germany: 1403–48.

Bhattacharyya, S., and P. Collier. 2014. “Public Capital in Resource Rich Economies: Is There a Curse?” _Oxford Economic Papers_ 1 (66): 1–24.

Bravo-Ortega, C., and J. de Gregorio. 2005. “The Relative Richness of the Poor? Natural Resources, Human Capital, and Economic Growth.” _World Bank Policy Research Working Paper_ , no. 3484.

Calderon, Cesar, and Albert Zeufack. 2020. “Borrow with Sorrow? The Changing Risk Profile of Sub-Saharan Africa’s Debt.” _Policy Research Working Paper, Office of the Chief Economist, Africa Region, World Bank Group_ 9137: 1–35.

Corden, W Max, and J Peter Neary. 1982. “Booming Sector and de-Industrialisation in a Small Open Economy.” _The Economic Journal_ . JSTOR, 825–48.

Crespo-Cuaresma, J. 2017. “Income Projections for Climate Change Research: A Framework Based on Human Capital Dynamics.” _Global Environmental Change_ 42: 226–36.

Crespo-Cuaresma, J., W. Fengler, H. Kharas, K. Bekhtiar, M. Brottrager, and M. Hofer. 2018. “Will the Sustainable Development Goals Be Fulfilled? Assessing Present and Future Global Poverty.” _Palgrave Communications_ 4 (29).

Cust, James, and Torfinn Harding. 2013. “Institutions and the Location of Oil Exploration.” OxCarre Research Paper. _OxCarre Research Paper_ 127 (December). University of Oxford, Department of Economics. https://ideas.repec.org/p/oxf/wpaper/oxcarre-research-paper127.html.

Cust, J, and D Manley. 2014. “Natural Resource Charter.” _NRGI. Available at: Https://Resourcegovernance. Org/Analysis-Tools/Publications/Natural-Resource-Charter2nd-Ed_ .

Cust, J., D. Manley, and G. Cecchinato. 2017. “Unburnable Wealth of Nations.” _Finance and Development_ 54 (1).

51
Table 7: Institutions and Export Survival Rate

||**_Full Set_**|**_OECD_**|**_Non-OECD_**|
|---|---|---|---|
|Rule of law|-0.062|0.030|-0.082|
|Rule of law x Inst-Int|0.318<sup>_∗∗∗_</sup>|0.287<sup>_∗∗∗_</sup>|0.196<sup>_∗∗∗_</sup>|
|lnH|-0.334<sup>_∗∗∗_</sup>|-0.461<sup>_∗_</sup>|-0.170|
|lnH x H-Int|0.037<sup>_∗∗∗_</sup>|0.043<sup>_∗∗_</sup>|-0.022|
|lnK|-0.183|0.105|-0.618<sup>_∗∗_</sup>|
|lnK x K-Int|-0.200<sup>_∗∗_</sup>|-0.252<sup>_∗_</sup>|0.602<sup>_∗∗∗_</sup>|
|NR|0.012<sup>_∗∗_</sup>|0.020<sup>_∗∗_</sup>|-0.010|
|NR x N-Int|0.006<sup>_∗∗∗_</sup>|-0.000|0.007<sup>_∗_</sup>|
|lnESTABLISH|0.028<sup>_∗∗∗_</sup>|0.016|0.034<sup>_∗∗∗_</sup>|
|lnGDPE|0.464<sup>_∗∗∗_</sup>|0.367|0.733<sup>_∗∗∗_</sup>|
|lnGDPI|0.030|0.115<sup>_∗_</sup>|-0.209<sup>_∗∗_</sup>|
|No. of Observations|632363|425766|201772|
|No. of Clusters|9003|5518|3441|
|_χ_<sup>2</sup>|34860.962|25055.355|16534.468|
|Prob _> χ_<sup>2</sup>|0.000|0.000|0.000|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. The dependent variable is _Pr_ ( _Survivaljist >_ 0) in all cases. All models include exporter, importer, industry and year fixed effects which are not reported. Standard errors are clustered by exporter-importer pair.

# **5.2 Evidence from cross-country firm-level data**

The results presented to this point have used disaggregated product-level data to study the trade effects of institutional quality. A potential drawback of the product-level data used so far is that they do not provide information on the actual number of exporting firms. More specifically, we have (for instance) used the number of 6-digit HS product groups traded within a 3-digit ISIC sector as a measure of the extensive margin of countries exports. This could potentially bias the relative contribution of the intensive and extensive margins since the mapping between products and firms is unlikely to be one-to-one.

In what follows, we re-examine our results using a more granular trade dataset – the World Bank’s Exporter Dynamics Database (EDD). The advantage of EDD is that it provides information on the actual number of exporting firms as well as export sales per exporter, which allows us to capture the extensive and intensive margins of exports more precisely.

The results are reported in Table 8. Results show that poor institutions restrict firm entry into foreign market as well as constrain the scale of firms’ sales in foreign markets. Despite the fact that our analysis relies on a relatively smaller set of countries, our results reaffirm our earlier findings.

20
for energy management practices, if energy requirements and associated costs are very small relative to total costs or sales and thus have little impact on profit. Generic management practices, by contrast, may be more universal in their potential to help diverse firms excel. Moreover, energy is a subset of the strategic and operational concerns of a firm; stronger generic management practices may thus correlate with a propensity to invest in specialized energy-centric management practices. However, the need for energy-specific practices may be endogenous to energy requirements implied by a firm’s capital equipment choices or a function of factors beyond a firm’s control, such as local energy system characteristics.

_Across high, upper middle, and lower middle income groups, average management scores are not statistically different, with high variation within country groups (see Appendix Table A4)._ Using the winsorized sample and computing generic management score as a straight average of non-missing entries, firms in the high income group average a generic management score of 0.491 (SD 0.209), upper middle a score of 0.481 (SD 0.211), and lower middle 0.477 (SD 0.200); differences are not statistically significant. Large standard deviations are consistent with the finding in (Bloom et al., 2012b) that score heterogeneity within countries is often greater than between them. All countries have roughly the same share of zeros, while high and upper middle income countries have a slightly larger share of firms scoring above 0.75 (4% versus 3%). For energy management, the high income group firms’ energy-centric management score average is 0.291 (SD 0.304), upper middle 0.237 (SD 0.273), and lower middle 0.365 (SD 0.296); the differences are statistically significant. Appendix Table A4 summarizes generic and energy-centric management scores by income group. Interestingly, firms in lower middle income countries have fewer zero entries for energy management score, and also have a higher share of high-scoring firms. This could be due either to the fact that these countries have higher shares of energy-intensive manufacturing firms, or firms in these settings may be more motivated to invest in energy management practices, e.g. to mitigate the impact of volatile prices or supply disruptions. Distributions of generic and energy management scores by country income group are shown in Appendix Figure A2.

15
**Table 4: Monthly wages and GDP per capita, Mexico and United States**

||Mex|ico<br>PPP adjusted b|US|
|---|---|---|---|
||Current $us|y<br>State CPI|Current $us|
|GDP per capita||||
|Average|5,393|8,349|29,451|
|Min.|2,368|3,617|20,856|
|Max.|15,226|20,268|40,870|
|Max./Min.|6.4|5.6|2.0|
|Average US/M ax. Mexico|1.9|1.5||
|Monthly wage||||
|Average|372|575|1,716|
|Min.|218|375||
|Max.|551|825||
|Max./Min.|2.5|2.2||
|Average US/Max. Mexico|3.1|2.1||

Notes (1) INEGI; (2) Author's estimation for male urban workers using ENEU (3) Bureau of Economic Analysis (http://www.bea.doc.gov); (4) Bureau of Labor Statistics (http://www.bls.gov/cps/cpsaat37.pdf) median earnings of Hispanic origin workers.

30