rafmacalaba/datause-encoder-data huggingface dataset:
# 5 Measuring the “pours” component

Kaminsky et al. (2004) coined the expression “when it rains, it pours”to capture the amplification effect of procyclical fiscal policy on output. Consider, for example, a fall in commodity prices. Such a shock will have a direct negative effect on output through standard channels, which is the “rain”component.<sup>35</sup> The fall in output and consumption will reduce the tax base, thus increasing the primary fiscal deficit and forcing the fiscal authority to reduce government spending and/or increase taxes to restore fiscal balance. This, in turn, will have an additional negative output effect (the “pours”component). The total effect of the fall in commodity prices can be thus decomposed into a rain and a pours component: The purpose of this section is to quantify the “pours” component. This will be done in four steps:

- Panel regression (with country fixed-effects) to quantify the effects of changes in commodity prices on output. (This will be the total output effect)

- Panel regression (with country fixed-effects) to quantify the effects of changes in commodity prices on government spending. This is the fiscal response.

- Computation of the average fiscal multiplier.

- Computation of the pours component.

## 5.1 Data

Our sample consists of 15 commodity-exporting countries: 11 EMDE and 4 AE. The EMDE countries are Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Indonesia, Russian Federation, South Africa, and Ukraine, and the 4 advanced economies are Australia, Canada, New Zealand, and Norway.<sup>36</sup> 37 The data is an-

> 35On the output effects of terms of trade shocks, see, for example, Mendoza (1995).

> 36The number of countries in our sample was limited by the need to have quarterly data for the purposes of computing an average fiscal multiplier using the Blanchard-Perotti identification.

> 37We should note that results are robust to the exclusion of Russian Federation and Argentina.

19
# TALE 6.2

_Seaegal_ (All ISaul) OLS Edinuze of Angutd Cob-ougla fodicion Funihioi -Depedet Vriable - LAg of Conta Price Value Added **(Values** in Parentheses **re** Standard Errors)

||Bas<br>in|ed on Value-A<br>Domestic Pric|dded<br>es|Base<br>i|d on Value-<br>n World Pric|Added<br>es|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|Intercept<br>(% Senegalese Private)|1347*<br>(0559)|086<br>(0519)|4.042<br>(0.481)|0.171<br>(0.606)|0.062<br>(0.557)|_-0.831e_<br>(0517)|
|% Senegalese State|0.264<br>(0256)|0.145<br>(0.241)|4.016<br>(0.244)|0.158<br>(0.269)|0.242<br>(0252)|-0.049<br>(0252)|
|% French|0.115<br>(0.107)|0.089<br>(0.095)|0.132<br>(0.097)|0.016<br>(0.112)|4.038<br>(0.099)|0.006<br>(0.101)|
|% Syro-Lebanese|4.498*<br>(0.121)|4.489<br>(0.111)|_4.469e_<br>(0.119)|4.73*<br>(0.127)|-0541^<br>(0.11|_.0.486e_<br>(0.22)|
|% Other Foreign|0.047<br>(0.127)|0.005<br>(0.116)|0.062<br>(0.118)|4.079<br>(0.134)|4.138<br>(0.121)|4079<br>(0.021)|
to 80 percent of the explained variation in measurement error. Asymmetric rounding of selfreported plot areas at half-acre increments accounts for the second major source of measurement error, accounting for 18 percent of the explained variation. Further, focal point bunching around simple rounded values is highly asymmetric, with farmers more likely to round up than down, especially at smaller plot sizes.

Perhaps most strikingly, NCME persists within the treatment group in follow-up (postharvest) interviews conducted three to four months after the information treatment during the postplanting period. Even though plot size is directly observable and farmer subjects witnessed GPS measurement and were directly provided the true plot area and were informed of the error in their prior self-report of that variable, updating was far from complete. Further, farmers' updates of their self-reports are asymmetric. Upward corrections are more common than downward ones. The magnitude of updating in self-reported plot areas also varies by true plot area as well as with the magnitude and direction of the pre-treatment error in self-reported information. Finally, receiving information on true plot area and the extent of measurement error in self-reports affects subsequent farmer reporting on plot-level fertilizer and labor inputs, indicating that the effects of measurement error and updating spill over across variables and that survey data on non-land agricultural inputs may likewise contain significant NCME that correlates with the measurement error in plot area, with direct implications on the estimates of marginal returns to these inputs and a range of behavioral and policy parameters of interest.

We cannot identify the causal mechanisms behind these observations, which clearly merit further investigation. The absence of quick correction of demonstrably mistaken beliefs seems surprising given that rural households in Malawi depend heavily on land to generate income. Farmers have strong motives to know the true size of their plots. The fact that NCME is so widespread in such a readily observable, important input and that farmers learn and correct their errors incompletely and asymmetrically strongly signals likely behavioral anomalies that matter for (a) the design and implementation of information-based interventions that aim to ameliorate mistaken beliefs/persistent learning failures and improve agricultural and welfare outcomes, and (b) survey data collection and statistical inference. Regarding the latter, in view of the effects of the information treatment on subsequent reporting on agricultural inputs and the insights from existing research on correlated NCME in survey-based measures of agricultural production and inputs, methodological research should accelerate to develop improved survey data methods for 30
||Motor vehicle damage<br>Catastrophe<br>Retirement savings<br>Investment|Comprehensive motor vehicle<br>Flood,<br>earthquake,<br>and<br>others<br>(sometimes<br>compulsory)<br>Tax-supported long-term savings<br>Investment-linked contracts|
|---|---|---|
|Industrial|Tort exposure<br>Households<br>Financialplanning|General and professional liability, pollution, and<br>others<br>Multicoverage householders insurance<br>Complex Life insuranceproducts|

The distribution strategies associated with this growth model have historically been oriented to brokers, agents, and direct sales. Agency-based channels in particular have proved to be highly expensive to establish and can take decades to reach profitability. They have also been subject to high wastage and potential loss of consumer trust. In highly developed markets (aside from the United States, for path-dependent reasons) the agency distribution system is gradually being replaced by the following:

- Direct sales for commodity products

- Bancassurance for simple savings and

- Credit-related insurance products and certified financial advisors for complex needs.

India, where the insurance market was opened to private insurers only a decade ago, provides a good example of the relative cost structures of long-established agency insurers (LIC), recently established agency insurers, and the recently established Bancassurers (ICICI and SBI; table 2.3). Recent studies of European insurers demonstrate a distinct cost advantage for Bancassurance, even where agency-based insurers are more developed (Davis 2007, chap. 8).

**Table 2.3 Relative Expense Ratios: Indian Life Insurers 2009–2010**

**_percent gross premium_**

||LIC|SBI|ICICI<br>Lombard|Others|
|---|---|---|---|---|
|Acquisition cost ratio|6.5|7.5|3.6|8.9|
|Administrative cost ratio|6.6|6.5|15.5|25.3|
|Total|13.1|14.0|19.1|34.2|

_Source:_ IRDA annual report, 2010.

32
# 6.1 Autonomy

Self-employment is often associated with greater independence. To assess whether the higher levels of job satisfaction and the distribution associated with being self-employed and employing workers stem from greater autonomy, individual’s self-perceived control is included. Again, sample size affects the precision of the panel estimates, in this case eliminating the signficance of the random effect. However, the “control” variable has virtually no impact on either the mean utility premium or the standard deviation in either self- employment category. This suggests that differences in autonomy are not the explanation for the observed satisfaction premia or its variance.

# 6.2 Job Characteristics

We examine how three job characteristics may affect the observed premia. First, for wage workers we add dummies indicating whether or not the individual in question was engaging in manual labor and working in the manufacturing sector<sup>28</sup> while for the self-employed we include a dummy for being engaged in trading. Columns 3 and 4 present the results. Note that sample selection effects again affect the precision of the estimates. Only occupation has explanatory power,<sup>29</sup> and its inclusion appears to raise the premium for self- employment with employees. The other dummmies are not significant and there is virtually no change in the significant standard deviation variables for either self-employment sector of civil employment, suggesting that differences in job characteristics are not a major determinant of the observed preference heterogeneity.

# 6.3 Risk Aversion

Third, we assess the possibility that the distribution of job satisfaction results from the distribution of risk preferences, which have been demonstrated to be a predictor of sector choice (Falco et al., 2010), using an experimentally solicited measure of risk aversion. Since

> 28Our survey identifies whether wage workers are manual laborers, managers, professions or clerical workers - yet there is not sufficient variation in the latter three groups, which is why we pool them to construct a single indicator.

> 29For example, workers engaging in manual labor have lower significantly lower levels of job satisfaction than workers who do not. This effect loses significance when fixed effects are controlled for, yet the coefficient estimate remains positive.

28
# **APPENDIX I: THE SURVEY RESULTS, SECTION BY SECTION**

# **Section 1: Entry into Banking**

This part of the survey contains 18 questions covering bank licensing and entry requirements, the existence of entry restrictions for foreign entities, and the number of applications for bank licenses received, accepted, and denied during the five years prior to the survey. More than 90 percent of the questions in this part of the survey are similar to those included in the 2007 survey, making temporal comparisons readily possible.

We observe changes between the 2011–12 and 2007 surveys along several fronts (Table A1). First, the percentage of countries requiring more than one license for entry into banking declined significantly from 47 percent in 2005 to 16 percent in 2010.<sup>7</sup> Second, a slightly higher percentage of countries (an increase from 92 percent in 2005 to 97 percent in 2010) legally required that the source of funds that are to be used as entry capital be explicitly identified. Third, between 2005 and 2010, a higher percentage of EMDEs prohibited entry of foreign entities through joint ventures and to some degree through acquisition of domestic banks and via the establishment of subsidiaries. While restrictions on the entry of foreign banks through the establishment of branches have declined in EMDEs, it is still, however, by far the most common form of entry restriction among EMDEs. In contrast, there are no prohibitions to the entry of foreign banks in advanced economies. Fourth, between the two rounds of the survey, the median of the minimum capital required for entry increased significantly for EMDEs (from 5.9 to 11.5 million dollars), while it has remained almost unchanged among the advanced economies. Fifth, relative to advanced economies, EMDEs have become a more popular destination for foreign banks. While the number of foreign bank applications increased by 74 percent in EMDEs between 2005 and 2010, this figure declined by 51 percent for the advanced economies. Note that if we concentrate on a “matched sample” – those countries that responded to both survey – we observe a smaller contrast between advanced economies and EMDEs. Among the latter, foreign bank applications increased by 19 percent and they declined by 21 percent for advanced countries. Finally, both the number of total license applications and rejections increased between 2005 and 2010. The increase in rejections was mainly driven by a 15 percentage points increase in the rejection rate for domestic bank applications, which was in turn mainly due to an increase in rejection rates from 9 to 26 percent in EMDEs.

> 7 If we limit our analysis only to those countries that have responded to both surveys, the drop is slightly larger from 49 percent in 2005 to 16 percent in 2010.

24
# **Table 1: Survey names, dates and sample sizes**

||**BRAZIL**|**COLOMBIA**|**ECUADOR**|**GUATEMALA**|**PANAMA**|**PERU**|
|---|---|---|---|---|---|---|
|Survey|PNAD 1996|ECV 2003|ECV 2006|ENCOVI 2000|ENV 2003|ENAHO 2001|
|Sample selection<br>criteria|30-49 head or<br>spouse|30-49|30-49|30-49|30-49|30-49 head<br>or spouse|
|Original sample size|85,692|22,517|12,650|6,956|6,339|13,947|
|Observations with<br>earnings and<br>circumstances|50,560|16,575|9,671|4,661|4,127|9,830|
|(share of original<br>sample)|0.590|0.736|0.765|0.670|0.644|0.704|
|Observations with<br>income/consumption<br>and circumstances|71,688|22,436|12,643|6,865|5,653|13,649|
|(share of original<br>sample)|0.837|0.996|0.999|0.984|0.889|0.979|

34
France, and the U.K., negative effects dominate but most of them are not statistically significant.<sup>21</sup>

[Table 11 near here] Finally, results in Table 12 revisit the learning-by-exporting question by looking at productivity premia three years after new exporters entered the export market. The flimsy support using sales per worker as dependent variable is mirrored by the estimates in the first column, and support is not overwhelming for the other two dependent variables either. Still, for some countries the support gets slightly stronger using _VA/L_ or _TFP_ as dependent variable, although the significance tends to be quite low. Positive and marginally significant results remain for Italy most years and weak support gets a tad stronger for Belgium, France, Ireland and the UK. The negative coefficient estimates for China for _S/L_ become much smaller for _TFP_ and even turn positive in one year. Results for Colombia are never significant. In contrast with the results in the previous two Tables, the _TFP_ results are not uniformly weaker. A caveat is in order though. We measure _TFP_ assuming the same importance of capital for all firms in an industry.<sup>22</sup> If exporters and non-exporters operate with a different technology, as Van Biesebroeck (2006) argues, they would face a different capital-labour trade-off. Especially in countries or industries where the majority of firms do not export this will lead to an overestimate of productivity for exporters as they accumulate capital.

[Table 12 near here]

> 21 Negative estimates would suggest that firms invest in new capital equipment prior to entering the export market, as shown by Van Biesebroeck (2005) using data on African firms. The exercise in this paper does not allow examining this idea as we do not follow the same groups of entrants over time.

19
**Table Ila. Transparency and Autocracy**

||_DATASET:_|_KAUFMANN,KR_|_AAYANDZO_|_IDO-LOBA_|_TON_||
|---|---|---|---|---|---|---|
||Howauthoritie<br>rep|sareselectedand<br>laced|Capacityoft<br>implementso|hestateto<br>ndpolicies|Respectofth<br>the citizensf|estateand<br>ortherules|
||Voiceand<br>Accountability|PoliticalInstability<br>andViolence|Government<br>Effectiveness|Regulatory<br>Burden|Ruleof<br>Law|Graft|
|SocialistLegalCode|-.11|-.01|-.56***|-.62***|-.47***|-.<br>73***|
||(-.74)|(--07)|(-3.89)|(-3.89)|(-.36)|(-5.37)|
|FrenchLegalCode|.12|-.07|-.04|-.03|-.26**|-.29**|
||(1.08)|(-.49)|36)|(-.38)|(-2.33|(-2.44)|
|GermanLegalCode|.47***|.33*|.26|-.24|.34**|-.04|
||(2.66)|(1.67)|(1.07)|(-1.62)|(2.19)|(-.18)|
|ScandinavianLegalCode|.42***|.44***|.56***|-.09|.43***|.68***|
||(4.26)|(3.07)|(4.22)|(-.85)|(3.46)|(4.24)|
|Income|.14**|.38**|.34***|21***|.47***|.37***|
||(2.24)|(4.59)|(4.46)|(3.31)|(7.27)|(5.04)|
|Transparency|-.07|-.07|-.13**|-.16***|-.05|-.09*|
||(-1.34)|(-1.04)|(-2.17)|(-2.76)|(-1.04)|(-1.77)|
|Autocracy|-.13***|-.05**|-.04***|-.03*|-.03*|_-.05***_|
||(-8.72)|(-2.44)|(-2.15)|(-2.20)|(-1.81)|(-2.88)|
|Constant|-.14|-2.76***|-2.18**|-.93|-3.41***|2.36***|
||(-.23)|(-3.21)|(-2.86)|(-1.4)|(-5.18)|(-3.21)|
|#obs|128|121|122|125|125|121|
|RT_|<br>.69|.56|.64|.56|.69|.72|

**Table llb. Transparency and Autocracy**

_*DATA SET: ICRG, Averaged over the period 1984-1997_

||ICRG|Corruption|Ruleof<br>Law|Bureaucracy<br>Quality|Contract<br>Repudiation|Expropriation<br>Risk|
|---|---|---|---|---|---|---|
|SocialistLegalCode|1.00***<br>(3.84)|1.39***<br>(4.7)|1.79***<br>(6.62)|.67**<br>(2.3)|.83**<br>(2.53)|I11***<br>(4.63)|
|FrenchLegalCode|-.18|.02|-.05|-.47**|-.07|-.22|
||(-1.12)|(.10)|(-.23)|(-2.01)|(-.41)|(-1.6)|
|GermanLegalCode|.79**|.63**|1.02***|.87***|1.06***|.63***|
||(4.08)|(2.33)|(2.98)|(3.77)|(4.36)|(3.31)|
|Scandinavian LegalCode|.65***|1.21***|1.01***|.64**|.48*0*|.25**|
||(4.30)|(5.46)|(4.2)|(2.59)|(3.51)|(2.14)|
|Income|.41***|.39***|.64***|.50***|.39***|.35**|
||(4.55)|(3.05)|(5.38)|(4.06)|(4.30)|(4.73)|
|Transparency|-.22*0*|-.23**|-.18|-.24*|-.22***|-.22***|
||(-3.19)|(-2.01)|(-1.83)|(-2.19)|(-2.97)|(-3.89)|
|Autocracy|-.12**|-.14***|-.12***|.14***|-. ***|-.09***|
||(4.89)|(4.35)|(-3.74)|(-4.27)|(4.09)|(4.79)|
|Constant|1.85**|1.6|-.39|1.05|2.00**|2.74***|
||(2.52)|(1.48)|(-.38)|(1.02)|(2.62)|(4.28)|
|#obs|91|91|91|91|91|91|
|R'|.76|.65|.68|.68|.71|.74|
|_*indicatessignificanceat._|_Ilevel,_**_at.05_|_leveland_***|_at.01level._||||

The transparency index developed here does not correct for the _quality_ of the data produced. An index covering developing countries only and constructed by the World Bank 30
Table 4 -- Treatment Effects on Normalized Test Scores across Test Administrations

||AllStudents|Boys|Girls|
|---|---|---|---|
||<br>(1)|(2)|(3)|
|Treatment x 2012 Pre-program test|-0.000|0.008|-0.008|
||(0.088)|(0.094)|(0.090)|
|Treatment x 2012 Post-program test|0.288***|0.295***|0.281***|
||(0.084)|(0.088)|(0.088)|
|Treatment x 2013 Pre-program test|0.006|0.029|-0.018|
||(0.087)|(0.096)|(0.087)|
|Treatment x 2013 Post-program test|0.133|0.144|0.122|
||(0.100)|(0.105)|(0.099)|
|Observations|66,888|33,404|33,484|
|R-squared|0.297|0.298|0.299|
|Mean ofdep var in control|-0.001|0.028|-0.030|
|P-value: T x 2012 Pre-program test= T x 2012 Post-program test|0.000|0.000|0.000|
|P-value: T x 2012 Post-program test= T x 2013 Pre-program test|0.013|0.035|0.008|
|P-value: T x 2012 Post-program test = T x 2013 Post-program test|0.226|0.270|0.207|
|P-value:Tx2013Pre-programtest=Tx2013Post-programtest|0.073|0.102|0.075|

‘Notes: Robust standard errors in parentheses, clustered by village. *** p<0.01, ** p<0.05, * p<0.1. All analyses are restricted to testtakers, defined as students who were present for pre- and post-program tests in the year the test was administered (2012 or 2013). Observations are at the student-subject-level. All specifications include controls for 2011 (pre-program) enrollment by gender, cohort fixed effects, subject (Math, English, and Hindi) fixed effects, and test administration (2012 pre-program test, 2012 post-program test, 2013 pre-program test, and 2013 post-program test) fixed effects.
|SITC_12|661|Cement|Yes|52|48|Algeria, Angola, Benin, Burundi,<br>Cameroon, Chad, Democratic Republic<br>of Congo, Ethiopia, Gabon, Kenya,<br>Liberia, Malawi, Mauritania, Morocco,<br>Mozambique, Niger, Nigeria, Republic<br>of Congo, Senegal, Sierra Leone,<br>Somalia, South Africa, Sudan,<br>Tanzania,Togo,Tunisia,and Zimbabwe|
|---|---|---|---|---|---|---|
|SITC_13|662|Diatomite|Yes|2|2|Kenya and Mozambique|
|SITC_14|681|Platinum|Yes|8|7|Botswana, Ethiopia, South Africa, and<br>Zimbabwe|
|SITC_15|683|Nickel|Yes|1|1|Zimbabwe|
|SITC_16|686|Zinc|Yes|2|2|Algeria and South Africa|
|SITC_17|687|Tin|Yes|6|6|Nigeria, Rwanda, and Uganda|
|SITC_18|971|Gold|Yes|35|26|Algeria, Burundi, Cote d’Ivoire,<br>Ethiopia, Kenya, Liberia, Mali,<br>Republic of Congo, South Africa,<br>Tanzania, Uganda, Zambia, and<br>Zimbabwe|
|SITC_19|282**|Steel|No|16|14|Kenya, Mozambique, Nigeria, South<br>Africa,Sudan,andUganda|
|SITC_20|289**|Gemstones|No|13|10|Botswana, Ethiopia, Madagascar,<br>Mozambique,and Rwanda|
|SITC_21|321**|Coal|No|37|22|Botswana, Burundi, Ethiopia, Malawi,<br>Morocco, Mozambique, Niger, Nigeria,<br>South Africa, Tanzania, Zambia, and<br>Zimbabwe|
|SITC_22|525**|Uranium|No|2|2|Namibia and Niger|
|SITC_23|663**|Chromite|No|8|6|South Africa, Sudan, and Zimbabwe|
|SITC_24|684**|Aluminium, ammonia,<br>and bauxite|No|5|5|Cameroon, Mozambique, Nigeria, and<br>South Africa|
|SITC_25|728**|Asbestos|No|4|3|South Africa|
|Subtotal||||376|304||
|Base|999|No commodities|n.a.|1806|349||
|Total||||2182|653||

* Names assigned by authors for purposes of analysis and ease of understanding of results. ** These commodities were dropped from the regressions that included mineral prices; this explains the differences in the numbers of countries in the regression tables.

Source: authors’ construction (based on USGS data).

Table 3: Mining sites by districts in Africa, 2001–2012

|Status of mining activiti|es<br>||||||
|---|---|---|---|---|---|---|
||Status|No mine|Active mines|Closed mines|Total||
|Mineral deposits|No|1806|0|0|1806|82.8%|
||Yes|0|333|43|376|17.2%|
||Total|1806|333|43|2182|100%|
|||82.8%|15.3%|2.0%|100%||
|Scale of mining operatio|ns<br>||||||
||Status|No mine|Large-scale|Small-scale|Total||
|Mineral deposits|No|1806|0|0|1806|82.8%|
||Yes|0|354|22|376|17.2%|

25