rafmacalaba/datause-encoder-data huggingface dataset:
A final concern with the use of significant tests for imbalance is their potential for abuse. For example, Schulz and Grimes (2002) report that in the clinical trials literature, researchers who use hypothesis tests to compare baseline characteristics report fewer significant results than expected by chance. They suggest one plausible explanation is that some investigators may not report some variables with significant differences, believing that doing so would reduce the credibility of their reports. We have no evidence to suggest this is occurring in the development literature, but one interpretation for the repeated randomization methods discussed earlier in this paper is a desire by researchers to show no significant differences between groups when such tests are used.

So how should we interpret such tables? The first question of interest in practice is, given that such a test shows a statistically significant difference in baseline means, does this make it more likely that there is also a statistically significant difference in follow-up means in the absence of treatment? The answer is yes, provided that the baseline data have predictive power for the follow-up outcomes. Figures 7 and 8 illustrate this for the cases of Sri Lankan microenterprise profits, which have little predictive power for future profits, and Mexican labor income which have more predictive power. Appendix 4<sup>24</sup> shows similar figures for the other variables. For each dataset, these graphs are based on the 10,000 simulations of a single random draw (without any balancing). The x-axis shows the p-value for a test of difference in baseline means for the outcome of interest. The y-axis shows the same p-value for a test of difference in follow-up means in the absence of treatment. We divided the values on the x-axis into 100 bins and calculated that 10<sup>th</sup> , 50<sup>th</sup> , and 90<sup>th</sup> percentile of the follow-up p-value within each bin.

For the Sri Lanka data, the values of the percentiles are flat across the whole x- axis, suggesting that there is no relationship between p-value at baseline and p-value at follow-up. For the other datasets, however, as illustrated by the Mexican labor income, where the outcome variables show more persistence over time, the percentiles display an upward-sloping pattern. That is, a statistically significant difference in baseline means makes it more likely to also see a statistically significant difference in follow-up means.

The follow-up question of interest in practice is then: If we observe statistical imbalance at baseline, but control for baseline variables in our analysis, are we any more

> 24 Appendix 4 is available on the author’s website.

- 30 -
<!-- Start of picture text --> 50<br>National PLI<br>40 6.85 (USD 2017 PPP)<br>; aN<br>20<br>3.65 (USD 2017 PPP) Revised<br>10 national PLI<br>—,<br>2.15 (USD 2017 PPP) ==.<br>0<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->
firms. The AIA policy therefore slows down cloud diffusion the most amongst firms which benefit the most from its invention.<sup>46</sup>

**Table 9:** Heterogeneous effects of AIA and Cloud, Big data and AI adoption, by firm size

|Regressions|(1)|(2)|(3)|
|---|---|---|---|
|Dependent Variable|Cloud|Big Data|AI|
|AIA Dummy|-0.141***|-0.171**|-0.026**|
||(0.042)|(0.034)|(0.010)|
|AIA Dummy*Emp_<_50|-0.228**|-0.184*|-0.019**|
||(0.103)|(0.095)|(0.007)|
|Observations|2,220|1,746|1,746|

Note: Regression 1 use data for 2007 and 2013, regressions 2 and 3 use data for 2007 and 2019. The employment interaction is =1 if the firm had fewer than 50 employees in 2007. The dependent variables cloud, big data and AI are a dummy variables. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. All regressions use a binary AIA eligibility and are estimated via difference-in-differences. All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age, not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.

We also explore heterogeneity in the effects of AIA policy by firms in knowledge intensive industries. The literature has shown, for example, that firms who possess considerable amounts of intangibles including data, intellectual property or high skilled employees disproportionately adopt digital technologies (Autor et al., 2003; Bloom et al., 2012; Haskel and Westlake, 2018; Eckert et al., 2020; DeStefano et al., 2022). We explore this question by including an industry interaction term identifying firms in knowledge intensive sectors (Eurostat, 2014).<sup>47</sup> The results in Table A14 are mixed, with some evidence that firms in less knowledge intensive sectors are more affected by the AIA policy. In column 1, we find that the negative effect of the AIA policy on cloud is only present in less knowledge intensive sectors, for

> 46See for instance Jin and McElheran (2017); DeStefano et al. (2023).

> 47In Table A12 we consider the robustness to a measure of skill intensive sectors (Eckert et al., 2020) and in Table A13 we use an industry-level measure of R&D intensity.

33
results are comparable over time. The survey is described in more detail in Grootaert (1986, 1993).

The years 1985-88 are of particular importance in the recent economic history of C6te d'Ivoire. Throughout the eighties the country experienced an economic recession. The downturn is attributed to the collapse of the world prices of coffee and cocoa-the country's two main export crops-in the late seventies, and to unsustainable macro-economic policies (Demery, 1994). Of the decade, 1988 was one of the worst years. Between 1987 and 1988, GDP per capita fell by 5% in real terms, but private consumption fell by almost 17%, and the poverty rate rose from 35% to 46% (Grootaert, 1995). At the same time, the labor market underwent drastic changes. As a result of the recession, employment in the formal sector (including the public sector) shrunk by 14%. Many of the workers who were laid off as well as the vast majority of labor market entrants, had to find jobs in the informal sector. Between 1980 and 1990, employment in the informal sector more than doubled, and unemployment nearly tripled. The informal sector was characterized by underemployment, low productivity work, and low earnings -on average, one-fifth of earnings in the formal economy. The incidence of poverty among informal sector workers was hence high and rose rapidly during the 1980s (Grootaert, 1996).

7
Asdrubali and Kim (2008) obtain higher coefficients of partial risk sharing, namely 0.77 for a group of 15 European Union countries and 0.82 for all OECD countries, both over the period 1960-2004. Kose et al. (2009b), using conventional risk-sharing regressions, and Flood et al. (2012), using a different measure of imperfect risk sharing, also find that advanced countries exhibit higher degrees of risk sharing than developing countries.

Figure 3: Histogram of estimated λ’s The figure displays histograms of the estimated coefficient of partial risk sharing λ<sup>ˆ</sup> i for the groups of advanced and developing countries, using the unobserved factor model to forecast the income processes.

<!-- Start of picture text --> 7<br>6<br>5<br>Advanced<br>4<br>3<br>Developing<br>2<br>1<br>0<br>-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9<br>Coefficient of partial risk sharing<br>Frequency<br><!-- End of picture text -->

# **4.3 Patterns of consumption risk sharing over time**

Since the mid-1980s, the world has seen a large increase in the degree of financial integration, facilitated by the removal of barriers to international capital movements (as illustrated in Figure 1), and reflected in a steady rise in cross-border asset and liability positions (Lane 23
With the school database, we then used a probit model to determine the propensity score (p-score) for each school. The probit estimates the probability that a school will receive CONAFE support given a vector of the selected context variables: where CONAFE is a dummy variable indicating whether school _i_ in year _t_ received CONAFE support; and _Qi,t_ is an index function based on a vector of school background characteristics _Zi,t_ , an error term ε , and a series of constants φ . This probit gave us the p-score of each school observation.

At this point we associated students with the p-scores of their schools, so we had a database of students and each student had a p-score. We then ranked student observations by ascending p-scores and divided observations into groups so the difference in mean p-score between CONAFE and non-CONAFE schools for a given group was not significantly different at the 0.05 level. One group, for example, included all students with p-score between 0.0 and 1.1.We then tested that within each group, the difference in mean value for each background variable was not significantly different between CONAFE and non-CONAFE schools. When we could not satisfy that balancing property, as explained in the third criterion of variable selection, we increased the number of groups; when such increase could not satisfy the balancing property, we excluded the variable from construction of p-scores.

Since not every disadvantaged school receives CONAFE support, the p-score effectively selects CONAFE and non-CONAFE schools with similar backgrounds. A p-score of 1 indicated that a school had a typical CONAFE profile (i.e., student’s parents are illiterate, school is small 14
As Basel I has been applied around the world with different basic capital requirements – 8% and higher – there is a natural question as to whether the calibration of Basel II is appropriate for developing countries where credit and other risks are generally higher. In a series of recent papers with coauthors from the Central Bank of Argentina, and in a project sponsored by the World Bank including the Central Banks of Argentina, Brazil and Mexico, a set of related methodologies have been developed that may be applied to test the calibration of Basel II’s, IRB approach in the context of a developing country<sup>47</sup> .

The different approaches have two elements in common, (a) the estimation of loan default probabilities typically using historical data on loan default rates and econometric techniques such as a logit or probit analysis and (b) the application of a credit risk portfolio model<sup>48</sup> . An alternative is to use the Carey (2002) sampling methodology to estimate credit risk directly. Clearly a pre-requisite in each case is the availability of reliable and appropriate data.

This data may exist at the level of the bank. However in some countries, extensive loan databases have been built up. These are reviewed in a recent book, Miller (2003). While the origins of these databases are various and their uses multiple, one increasingly common use is to monitor provisions. In other words, the databases are used to check which loans are past due and to set and monitor bank provisioning policies.

Some countries have gone further than this and asked banks to provide a forward-looking rating for borrowers according to a standardized scale. In this case the ratings of different banks for the same borrower can be compared very easily and discrepancies investigated. These databases have then become important tools of banks’ reserving policies.

These types of public credit registries are also an extremely useful source of information to estimate probabilities of default and other parameters to feed in to credit risk models. For each of the techniques described above, combined with other assumptions (e.g.: on underlying correlations and on loss given

> 47 See Falkenheim and Powell (2003), Balzarotti, Falkenheim and Powell (2002), Balzarotti, Castro and Powell (2003) all on historical Argentine data and Marquez (2002) on Mexican data.  See also xxxx for a very similar study on Italian data.

> 48 Such models include Creditrisk+ (often referred to as an actuarial model and focused more on risk than on pricing, developed by Credit Suisse Financial Products a part of Credit Suisse First Boston) and Creditmetrics (developed by JP Morgan offshoot, Riskmetrics, perhaps simpler conceptually than Creditrisk+ but more taxing in terms of data and focused on pricing as well as on risk).

40
Table 3. Disttibulion of Total Flow of Capital to Canada, 1900-1914, (millions of dollams)

||AR1<br>countries|Grea<br>Bnitaia|Unite<br>Sutesw|torw<br>countries|
|---|---|---|---|---|
|MeipFal_govew__|260|200|60|-|
|Rtairoads|767|670|_S0_|47|
|Jnduatrial|630|420|180|30|
|LandSId finber|305|80|145|80|
|Mining|125|65|60|-|
|Insumnco|a2|32|_50_|-|
|Other|198|111|81|6|
|Totl|_2,546_|1,753|630|163|

Source: Buckley 1955, p.90.

Table 4. Gross Construction Outlays in Major Tansport Mdds in Canada, 1901-1930

|Period|R-ailways|<br>Hlighways<br>Can|als and harbors|Total|
|---|---|---|---|---|
|||V*luo (milhons of dollars)|||
|1901-05|124.3|3.3|32.1|159.7|
|1906-10|380.7|11.7|48.0|440.4|
|1911-15|537.4|38.5|93.7|669.6|
|1916-20|_252.5_|39.4|59.7|351.6|
|1921-25|253.2|100.4|109.8|463.4|
|1926-30|389.4|172.4|138.2|700.0|
|||P}etage<br>distnrbution|||
|1901-05|77.8|2.1|20.1|100.0|
|1906-10|86.4|2.7|10.1|100.0|
|1911-15|80.3|_5.7_|14.0|100.0|
|1916-20|71.8|11.2|16.9|100.0|
|1921-25|54.6|21.7|23.7|100.0|
|1926-30|_55.5_|24.6|19.7|100.0|

Source Buckldy 1955, p.<sup>3 2</sup> .

27
while only 6 percent is from Northern Ghana. The share of the sample from the other three countries is roughly the same – about 20 percent each.

# Table 2: Summary statistics

|Variable|Observations|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Height-for-age|32958|-119.36|182.99|-600|600|
|Weight-for-age|32958|-130.47|151.11|-592|600|
|Height-for-age (round 1)|13166|-115.61|174.75|-599|600|
|Height-for-age (round 2)|19792|-121.84|188.24|-600|600|
|Weight-for-age (round 1)|13166|-132.55|150.86|-592|600|
|Weight-for-age(round 2)|19792|-129.08|151.27|-591|599|
|Shock|54366|-.092|.217|-.79|.351|
|Rainfall levels(avg. mm/h)|62981|0.11|0.05|0.01|0.40|
|Urban|63911|0.26|0.44|0|1|
|Number of HH members|63911|8.73|5.96|1|74|
|Primary education|63911|0.17|0.37|0|1|
|Secondary education|63911|0.12|0.33|0|1|
|Higher education|63911|0.02|0.13|0|1|
|HH has toilet|63911|0.60|0.49|0|1|
|HH has electricity|63911|0.26|0.44|0|1|
|HH owns radio|63911|0.74|0.44|0|1|
|HH owns TV|63911|0.25|0.43|0|1|
|HH owns Refrigerator|63911|0.08|0.28|0|1|
|HH owns bicycle|63911|0.48|0.50|0|1|
|HH owns motorcycle|63911|0.25|0.43|0|1|
|HH owns car|63911|0.04|0.21|0|1|
|HH owns phone|63911|0.04|0.19|0|1|
|Dwelling has good floor|63911|0.44|0.50|0|1|
|Age of HH head|63777|43.29|13.66|13|97|
|Male headed HH|63911|0.89|0.31|0|1|
|Twin|63911|0.04|0.18|0|1|
|Male|63911|0.51|0.50|0|1|
|Burkina Faso|14154|0.22|0.42|0|1|
|Ghana|3874|0.06|0.24|0|1|
|Mali|13803|0.22|0.41|0|1|
|Nigeria|19023|0.30|0.46|0|1|
|Senegal|13057|0.20|0.40|0|1|

Source: Authors’ calculation using DHS data (various years), TRMM and NDVI data.

3.2 Predicted Normalized Difference Vegetation Index as a measure of shocks 11
|Bura Irrigation Project|
|---|
|Bura Irrigation Project|
|Wildlife and Tourism Project|
|Small Scale IndustryProject(01)|
|Small Scale IndustryProject(01)|
|Small Scale IndustryProject(01)|
|Education Project(04)|
|Sites and Services Project(02)|
|Sites and Services Project(02)|
|Sites and Services Project(02)|
|Sugar Rehabilitation Project|
|Smallholder Coffee Improvement Project|
|Smallholder Coffee Improvement Project|
|Smallholder Coffee Improvement Project|
|HighwaySector Project(01)|
|HighwaySector Project(01)|
|HighwaySector Project(01)|
|RURAL WATER SUPPLY I|
|RURAL WATER SUPPLY I|
|Integrated Agricultural Development Project(02)|
|Integrated Agricultural Development Project(02)|
|Land Development and Settlement Scheme Project|
|Land Development and Settlement Scheme Project|
|Land Development and Settlement Scheme Project|
|Land Development and Settlement Scheme Project|
|Fisheries Project|
|Fisheries Project|
|Fisheries Project|
|Fisheries Project|
|Industrial Development Bank Project(04)|
|Industrial Development Bank Project(04)|
|Industrial Development Bank Project(04)|
|Industrial Development Bank Project(04)|
|Olkaria Geothermal Power Project|
|Olkaria Geothermal Power Project|
|Agricultural Credit Project(04)|
|Agricultural Credit Project(04)|
|Agricultural Credit Project(04)|
|Agricultural Credit Project(04)|
|Education Project(05)|
|Education Project(05)|
|Education Project(05)|
|Education Project(05)|
|Education Project(05)|
|Education Project(05)|
|Education Project(05)|
|Education Project(05)|
they were pushed into operating an HE as they could not find adequate income-earning opportunities in either wage or agricultural employment. Although a combination of both may be at work, analysis of the ILFS data shows that push factors are more common, especially when an HE is a secondary activity (Table 2.5).

**Table ‎2.5:  Main Reason for Having an HE, by Economic Activity**

||_Primary_<br>|_Activity_<br>|_Secondary_<br>|_Activity_<br>|
|---|---|---|---|---|
|**PUSH FACTORS**|_2000_<br>%|_2006_<br>%|_2000_<br>%|_2006_<br>%|
|Can‟t find other work|44.9|37.2|21.2|18.5|
|Released from employment|3.6|1.7|1.1|1.1|
|Retirement from employment|1.1|0.9|1.2|0.5|
|Family need for more income|24.7|31.6|43.5|55.9|
|**PULL FACTORS**|||||
|Good business opportunities|6.6|11.8|8.5|9.0|
|Does not require much capital|7.8|7.4|10.2|7.2|
|Low production cost|0.6|0.4|0.3|0.5|
|Desire for independence|2.1|2.0|0.9|1.4|
|Free choice of work hours and place|2.3|1.6|3.2|1.4|
|Can combine business w/ housework|2.4|3.2|5.0|2.9|
|Traditional line of business of family|2.3|1.4|3.0|1.5|
|Other|1.7|0.9|2.0|0.4|
|Total|100|100|100|100|

_Source: Calculations based on the ILFS 2000/01 and 2006 data_ 18. For those engaged in HEs as their secondary activity, the primary reason for doing so was their family‟s need for additional income. This is not surprising as one would expect this need to be a strong motivation for seeking a second job generally. Informal sector arrangements are also more conducive to part-time work, which requires lesser time commitment than a primary occupation. The share of households driven into the HE sector as a secondary source of income rose by 12 percentage points during the 2000-2006 periods, indicating its increasing role as a source of livelihood.

19. More than two-thirds of HEs in the urban areas were formed because of lack of any other job opportunities, in particular wage employment providing adequate remuneration. The dominant factor in the rural areas, on the other hand, was the need for a secondary source of income by households whose main activity was farming (Box 2.1).

10
11 review of several literature sources<sup>6</sup> . The list of these episodes is presented in Appendix 3, together with the list used in WW.

Second, we note that although dropped from fixed effects estimates in SW and WW, income per capita does change over time and in many countries it multiplied significantly in a period of 20 years (e.g. East Asian countries). If, for instance, a country has significantly narrowed its income gap with respect to developed nations and unconditional convergence holds, then the exclusion of this variable would lead to a downward bias in the estimation of POSTLIB. We thus include the logarithm of income per capita of each country relative to the one in the U.S. for each year throughout our period of estimation (one-year lagged). The inclusion of this variable also should take into account the evolution of other determinants that are endogeneous to GDP per capita (institutional quality, human capital, and life expectancy) and thus evolve in parallel with the latter.

Third, we consider growth determinants suggested by the Solow growth model (X-variables in Equation (6)). Investment in human capital is usually approximated in growth regressions through the school enrollment rates. But although conventional wisdom and micro studies suggest a positive effect of education on economic growth, cross-country growth econometrics using enrollment has not confirmed this hypothesis. Several reasons have been proposed for this apparent contradiction (Pritchett, 1996; and Krueger and Lindahl, 2002). Recognizing these obstacles and the fact that investment in human capital generally has followed an upward trend in recent decades, we approximate its impact on growth by including year dummies or a time trend in our fixed effects estimates.

It is similarly complicated to control for the impact of changes in physical capital investment on growth. Because trade liberalization is expected to boost investment, this variable is usually considered as an outcome variable in empirical analysis. Thus, including it in the multivariate specification of Equation (2) would generate endogeneity in our estimates. If we exclude this variable, then our estimates of the coefficient of POSTLIB would be biased upwards (downwards) only if this omitted

> 6 Such sources include World Bank and IMF country documents, WTO Trade Policy Reviews, and country reports from the Economist Intelligence Unit.
Table 1 continued. Average health facility and citizen characteristics, pre-treatment.

||Treatment<br>group|Control<br>group|Di¤erence|
|---|---|---|---|
|Catchment area statistics:||||
|Number of villages per health facility|23.2|24.6|-1.3<br>314|
||||(.)|
|Number of villages per health facility|2.6|1.8|0.80*|
|in strata 1|||(0.45)|
|Number of villages per health facility|8.9|9.5|-0.64|
|in strata 3|||(1.7)|
|Number of villages per health facility|11.7|13.2|-1.5|
|in strata 5|||(1.69)|
|Number of households per health facility|2140|2224|-84<br>(275)|
|Number of households per village|93.9|95.4|-1.42<br>(8.2)|
|Health facility characteristics:||||
|Piped water|0.04|0.04|0|
||||(0.00)|
|Rain tank/Open well|0.52|0.36|0.16<br>(0.14)|
|Borehole|0.44|0.60|-0.16|
||||(0.14)|
|Drinking water|1.76|1.48|0.28<br>(0.20)|
|Separate maternity unit|0.16|0.16|0<br>(0.00)|
|Distance to nearest Local Council I|0.72|0.85|-0.13<br>(0.26)|
|Distance to nearest public health provider|8.68|7.76|0.92<br>(1.90)|
|Number of days without electricity|18.3|20.4|-2.12|
|in the last month|||(4.14)|

The results are catchment area (health facility) averages. Robust standard errors in parentheses. Signi...cantly di¤erent from zero at 99 (***), 95 (**), and 90 (*) percent con...dence. Description of variables: Catchment area statistics are determined from UBOS maps and census data; Piped water, Rain tank and Borehole are dummy variables indicating the health facility’s watersource; Drinking water is a dummy variable indicating whether the health facility has drinking water available; Separate maternity unit is a dummy variable indicating whether the health facility has a separate maternity unit; Distance to nearest Local Council I and distance to nearest public health provider is measured in kilometers; Number of days without electricity in the last month is measured out of 31 days.

42