rafmacalaba/datause-encoder-data huggingface dataset:
Two further points relating to the Bigsten et al. (2004) paper are worth stressing. The first is that the finding that firms seem to learn from exporting is not the typical finding in studies looking at this issue for other regions. One possible explanation is that the potential gains from exporting are large in Africa because of high trade restrictions in the past and a large technological gap to developed countries. In such economies exporting offers the maximum scope for the increased discipline of competition, and contact with foreign customers provides the maximum scope for learning opportunities. Arguably, if exporting induces efficiency in any environment, it should do so in these economies. The second point is that a productivity gain of 7-8 percent in an output production function corresponds to productivity gains in terms of value-added of 20-25 percent in the short run and up to 50 percent in the long run. These estimates are in line with the results reported by Van Biesebroeck (2005a). The quantitative effect of exporting, thus, is far from negligible.

# **Box 2: Macroeconomic Policy Reforms in Cameroon**

In 1994 Cameroon implemented major policy reforms which included tariff reduction and rationalization, increased domestic taxes as well as attempts to reduced corruption and evasion. Gauthier, Soloaga, and Tybout (2002) use data for Cameroon on prices and quantities of the firms’ major inputs and output to compute the effects of tariff changes and tax reforms. Before the reforms that tariff structure was highly diversified with rates ranging from 0 to 500 percent. The structure encouraged evasion and stimulated attempts to get special treatment by authorities. This case-by-case treatment was hardly beneficial for efficiency. With the reform more than half the firms lost their privileges after 1992/93. For firms operating under the normal regime, though, average customs rate fell from 66.8 percent in 1992/93 20.2 percent in 1994/95. This means that in the case of Cameroon was a leveling of the playing field, which would tend to improve resource allocation in the economy.

The average effect of the reforms was to drive up the unit cost per unit of revenue by 8 percent. This was mainly due to the fact that tariff reductions on outputs fell faster than the protection rates on inputs. The impact varied quite a lot across firms, and the signals thus differed across categories. In the decomposition of the various effects it actually turns out that productivity gains reduced unit costs by as much as 8.5 percent. The average nominal tariff rate fell from 68 percent to 27 percent. It is also shown that exporters did much better than non-exporters because they managed to increase their productivity by as much as 19.1 percent.

33
24 surat is issued. Anecdotal<sup>evidetuce suggests that it is used very little. As</sup> a method of price discrimination,<sup>the surat may be promising but more needs to</sup> be known about its operation<sup>in practice and what the costs are to</sup> participants. For many categories<sup>including Drugs prescribed by the Doctor,</sup> Doctors, and Birth Control, it<sup>seems very unlikely thatthe</sup> poor are paying lower prices. Rather, it seems<sup>fair to assume that these are indeed luxury</sup> goods.

# 6. Who Benefits from Public Exgenditurgs<sup>in the Education and Health</sup> Sectors?

In this section, an estimate<sup>is made of the distribution of the</sup> benefits of public expenditures<sup>in the education and health sectors. Changes</sup> in the distribution of benefits<sup>since 1978 are also examined.</sup> Following common methodology,<sup>program expenditures are treated as</sup> proxies for aggregate benefits,<sup>and benefits then attributed to households</sup> based on household level utilization<sup>information from the unit record data.</sup> This approach to incidence<sup>analysis became popular in the late 70s, spurred in</sup> part by increased availability<sup>and improvements in household level surveys.</sup> The best known applications<sup>for developing countries are the studies of</sup> Malaysia by Meerman (1979) and<sup>of Colombia by Selowsky(</sup> 1979<sup>).*5 It is also</sup> the approach followed by Meesook<sup>(1984). Replication of the riethodology thus</sup> allows the paper to make a<sup>comparison of the distribution of access and</sup> utilization across income groups<sup>over the two dates.</sup> It is notoriously difficult<sup>to measure the benefits from publicly</sup> provided goods and services. And<sup>indeed, much of the standard analysis of</sup> benefit incidence is crude<sup>and it is clear that it could be improved upon.</sup> The problems associated with<sup>it are well documented (for example, see Selden</sup> and Wasylenko, 1991) and will<sup>not be repeated here. However, it may be useful</sup> '5 For a detailed review of the<sup>past and present state of benefit incidence</sup> analysis see Selden and Wasylenko,<sup>1991.</sup>
# **Table 7: Effect of the distance between enumerator and respondent characteristics**

||DP|FDC|NRM|UPC|
|---|---|---|---|---|
|Enumerator and respondent both male|-0.077|-0.006|0.007|-0.079|
||(0.096)|(0.093)|(0.033)|(0.094)|
|Enumerator and respondent both female|0.132**|0.119**|0.056***|0.122**|
||(0.055)|(0.056)|(0.017)|(0.050)|
|Respondent education level|0.216***|0.278***|-0.050|0.286***|
||(0.075)|(0.059)|(0.042)|(0.089)|
|Enumerator high education|0.333**|0.239*|-0.009|0.313**|
||(0.129)|(0.129)|(0.031)|(0.133)|
|Enumerator high education*Respondent education level|-0.185**|-0.180***|0.006|-0.244***|
||(0.073)|(0.060)|(0.043)|(0.087)|
|Enumerator and respondent both young|-0.031|-0.007|-0.025|-0.040|
||(0.066)|(0.062)|(0.017)|(0.053)|
|Enumerator and respondent both old|0.067|-0.013|-0.055**|-0.029|
||(0.043)|(0.055)|(0.022)|(0.044)|
|Respondent marital status (single)|0.054|0.087|-0.034|0.045|
||(0.059)|(0.058)|(0.026)|(0.060)|
|Enumerator from urban area|0.500***|0.672***|-0.066|0.490***|
||(0.131)|(0.115)|(0.041)|(0.095)|
|Work experience as enumerator|0.007***|0.007***|0.000|0.006***|
||(0.001)|(0.001)|(0.000)|(0.001)|
|Observations|6,245|6,332|6,552|6,319|
|R-squared|0.495|0.474|0.241|0.486|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely DP, FDC, NRM, and UPC. _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university or teaching institute, or has master. _Enumerator young_ ( _old_ ) is a dummy that takes value 1 if the enumerator’s age<=27 (>27) and 0 otherwise. _Respondent young_ ( _old_ ) is a dummy that takes value 1 if the respondent’s age<=32 (>32) and 0 otherwise. _Enumerator high education_ is a dummy that takes value 1 if the enumerator has completed university, teaching institute, or master. _Work experience as enumerator_ is the number of months previously worked as an enumerator. Each regression includes as additional control variables: _Want people to know good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ (for the definitions see Table 5). Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

32
While our results indicate that there are inequities in the deliberation process, it is important to keep in mind that we cannot say whether these inequities extend to actual outcomes - which is a subject for future work<sup>20</sup> .

> 20 However, we have evidence that the topics of discussion in the gram sabha are related to subsequent public goods outcomes. We conducted village level facility surveys recording the quality of roads in the village in November 2001 and again in 2005. Using the transcript data from the ...rst round, to limit the potential for reverse causality, we ...nd that villages where discussion about roads dominate the gram sabha also experience a greater improvement in the quality of roads between 2001 and 2005. The quality of roads is measured on a scale from 1 to 6, 1 being a mud road and 6 being an asphalt road. The improvement in roads is measured as the fraction of roads, by length, that has moved upward in quality between 2001 and 2005. In estimating the relationship between discussion about roads and improvement we control for initial road quality, a wide range of village level variables, and block ...xed e¤ects. We also perform a falsi...cation test, by estimating the relationship between discussions about water and road improvement, and we ...nd no relationship. These ...ndings are available upon request.

21
Table 3

# **Number of** Sampled Patients with Selected Diagnoses for Quality of Care Measures by Hospital Service

|Diagnoses|Total<br>Samplea|Number of<br>Outpatients|Number of<br>Emergency<br>Patients|Number of<br>Inpatients|Number of<br>Surgeries|
|---|---|---|---|---|---|
|Hernia|56|28|8|17|3|
|Acute Tonsilitis|<br>101|32|69|0|0|
|**Appendicitis**|23|8|9|4|_2_|
|Cataract Surgery|<br>34|27|0|3|4|
|Hypertension|204|144|60|0||
|Diarrhea|83|10|73|0||

# Source: The Urban Institute

- a. Each diagnosis sample consists of all patients presenting with that dz,Src's: **during the** period of the survey.
3 millimeters of rainfall a year. The river flow through this region is strongly seasonal: 25 of the 53 main rivers dry up entirely during the dry season from May to December. As a result, the average availability of surface water in the coastal area is estimated at 2,885 M3per capita, compared to an estimated world average of 8,500 M<sup>3</sup> (Macroconsult 1996).

Pollution aggravates the scarcity problem. The major source of Lima's water supply is the Rimac River, which is contaminated by heavy metals from nearby mines as well as untreated sewerage. About a third of Lima's water comes from wells, which depend on a shrinking supply of groundwater. The aquifer is increasingly polluted by salinity when the water table near the ocean drops because of increased pumping in the dry season (World Bank 1994).

ii. Demand and Water Tariffs. The potential demand for water was increasing rapidly in the period before reform. Lima was growing by an average of about 2.7 percent a year from 1981-1992 as terrorism and economic factors drove rural populations to move to low income communities on the outskirts of the city. These so-called _pueblos jovenes_ accounted for 58 percent of Lima's population of 6.5 million by 1991.

Throughout the 1980's and 1990's water and sewerage tariffs, which are combined in Lima, were well below the opportunity cost of supply in an arid area, nor did they reflect the social costs of pollution. Under the populist policies of President Alan Garcia (1985-1990), the water and sewerage tariff was allowed to decline in real terms to the point that by 1989 the average tariff was about US$0.17 M<sup>3</sup> , less than half of the 1985 price in constant (1989) soles. Even if tariffs had been higher they would have done little to curb consumption, since only about a third of connections were metered in 1991 and
# Figure 3. Global waves of debt

# A. Change in total debt

# B. Average annual change in total debt

<!-- Start of picture text --> Percentage points of GDP<br>First wave<br>80<br>Second wave<br>60 Third wave<br>Fourth wave<br>40<br>20<br>0<br>-20<br>EMDEs EMDEs excl. Advanced<br>China Economies<br>C. U.S. policy interest rates<br>Percent<br>13 Nominal Real<br>10<br>7<br>4<br>1<br>-2<br>Start Crisis Start Crisis Start Crisis<br>First wave Second wave Third wave<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points of GDP First wave<br>8 Second wave<br>Third wave<br>6<br>Fourth wave<br>4<br>2<br>0<br>-2<br>EMDEs EMDEs excl. Advanced<br>China Economies<br>D. Capital flows to EMDEs<br>Percent of GDP<br>6 FDI Portfolio investment<br>Other investment Total<br>4<br>2<br>0<br>-2<br>First wave Second wave Third wave<br>Start Peak Trough Start Peak Trough Start Peak Trough<br><!-- End of picture text --> Source: Haver Analytics; International Monetary Fund; World Bank.

A. B. Cumulative (A) or average annual (B) change in total debt from the start to the end of each global wave of debt. The first wave runs from 1970-89, the second wave from 1990-2001; the third wave from 2002-09; and the fourth wave from 2010. Average annual change calculated as total increase in debt-to-GDP ratios over the duration of a wave, divided by the number of years in a wave.

C. Start of a wave defined as the first three years of the wave. Crisis defined as the year before, and year of, widespread crises. First wave: 1970-72 and 1981-82; second wave: 1990-92 and 1996-97; third wave: 2002-04, and 2008-09. Real interest rates are deflated by the GDP deflator.

D. Net capital inflows to EMDEs. The start of each wave is the first year, the peak is the year of peak capital inflows before the start of crises, and the trough is the year of lowest capital inflows after the crisis. First wave: 1970, 1978, and 1988; second wave: 1990, 1995, and 2000; third wave: 2002, 2007, and 2009.

23
<!-- Start of picture text --> — 2<br>=a |<br>i ‘¢ <= ~ veeSa82<br>, ran ae - he<br>= Vode os ee<br>qr » Myst ns +R. ..<br>ge ‘i + tiem<br>be, i wl OPSoe aeTate:<br>thw ¢ Oa@ . Fan] CaS :<br>4 yee d aa ne j<br>+f Gioia * +4 ee ° én, he<br>oF : Fae gt<br>”wihe “iotage”ty ofthe-& +. °ggeng!ee2 ”° Ae ‘SeeSyneSe<br>(Se. os<br>» os<br>- . Hazard Seismic Index : :<br>i a, ooo ey<br>i HE 001. v0.22 J J<br>HB 170.99 238.8 Pe - Legend<br>HM| 3537-32454 © Low lecome ResidenceHousing<br><!-- End of picture text -->

<!-- Start of picture text --> é<br>:&<br>Fi)<br>= 8<br>; = ~ e , o cre :<br>Sa ; Ry, t 0@?)° B82Zo 83iecaBeaos @e°oeSS 5Hap<br>be, o.oo<br>> a> See eB<br>£s es: a<br>a ye | bay VS a 3 % ae OT<br>ts Leo aah iA >. poe & onTM ;<br>O 4 = ae ry %<br>Fj rw ag * € ‘os<br>, 2 Pave Se ea<br>e Bee2 hetfee<br>Avss 2 a<br>Ee User hotiary<br>‘ me sg<br>I had Legend j } ° |<br>Hazard Seismic Index nad ae 5<br>200 Oo.<br>afl HB 0.01. 170.02 j .<br>: HM 17003-2926 E Legend<br>—— eeen —ewcomme ¢ Hous<br><!-- End of picture text -->
- 50 -

Mills-ratio, lambda. Lambda captures the probability to be included in the sample as a wage-earner, and the inclusion of lambda as a regressor in the earnings function corrects for the non-randomness of the selection into wage-earner status, so that consistent estimates are obtained for the earnings function.17/ To check for the presence of selection bias in the CILSS sample of wage-earners, we re-estimated the earnings equation with lambda included (the choice equation will be discussed in section 7.2). The results in Table 19 indicate the presence of a significant negative selection factor into the status of wage-earner. This factor diminishes as the specification is made more complete, which points at the role of the added variables-diplomas and demographic variables--as elements in the selection.

The coefficient of lambda represents the covariance between the error terms of the choice equation and the error terms of the earnings equation. A negative coefficient thus means that whichever unobserved factors exist that increase the probability to become a wage-earner also contribute to the likelihood that a person will earn a below average wage. If we accept that people choose their status of employment in a way which maximizes their earnings potential, then we should observe positive selection. One interpretation of the negative selection into employee status in Cote d'Ivoire is therefore that barriers to entry exist and/or 17/ For a further theoretical discussion of this two-step estimation method, see Maddala (1983). An application of the technique in labor market analysis can be found in Grootaert (1986b).
fall directly a¤ects people’s moral compass with respect to corruption, or determines children’s cognitive ability, after taking into account its e¤ects through income.<sup>13</sup> We report results from a falsi...cation exercise and a detailed discussion on the potential objections to our identi...cation strategy below in section (4.1).

As additional evidence, we use interaction of rainfall with exogeneous household characteristics such as age and religion of household head as identifying instruments.<sup>14</sup> This is motivated by the observation that the e¤ects of heavy rainfall are likely to vary across households; a younger household head is probably more equipped to deal with adverse weather shocks, for example, by temporary migration to the nearest town for work, and e¤ectiveness of informal risk sharing may vary across di¤erent religious groups. To strengthen the exclusion restriction imposed on the interaction of heavy rainfall, we follow Carneiro et al. (forthcoming) and control for possibly nonlinear direct e¤ects of age and religion in the IV regressions. The estimates from alternative instruments provide robust evidence on the e¤ects of household income on the propensity to bribe and amount of bribe payments. For an in-depth discussion of our approach to identi...cation, please see pp. 13-21 below.<sup>15</sup> The empirical results from the instrumental variables approach ...nd a statistically signi...cant e¤ect of income on propensities to bribe, but not on the amount of bribe paid. Income has a signi...cant and negative e¤ect on the probability of paying bribes, providing credible evidence that the rich are less likely to pay bribes, possibly because of their superior bargaining power. The evidence from IV regressions that ...nds no statistically signi...cant e¤ect of income on the amount of bribes suggests a lack of ‘price’ discrimination; thus the poor pay more as a share of their income. This conclusion is especially noteworthy, because the OLS regressions stand in sharp contrast, showing a signi...cant positive e¤ect of income on the amount of bribes paid. This seems to justify the worry that the OLS regressions may be susceptible to ...nding spurious progressivity

> 13 We use a binary indicator of ‘heavy rainfall’ as the main identifying instrument for household income. A Google Scholar and Econlit search on December 20 2012 for di¤erent combinations of keywords ‘rainfall’, ‘scholastic ability’, ‘ability’, ‘smart’, ‘corruption’, ‘corrupt’,‘attitude’returned no relevant entries. An advantage of a binary instrument is that it necessarily satis...es the monotonicity condition of Imbens and Angrist (1994).

> 14 Religion is determined by birth for almost everyone, as conversion is extremely rare.

> 15 Some of the potential objections to identi...cation are: (i) rainfall may a¤ect the child wage and thus demand for schooling, (ii) heavy rainfall may cause damage to schools and thus increase the demand for local resources, (iii) heavy rainfall may a¤ect health. We provide evidence on the (in)validity and/or irrelevance of these and other objections in section (4.1) below.

6
||PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|---|
|WPS74|Fiscal Stabilization<br>and Exchange Rate<br>Instability: A Theoretical<br>Approach<br>and Some Policy Conclusions<br>Using<br>Mexican Data|Andrew Feltenstein<br>Stephen Morris|August 1988|A. Ohalla<br>60359|
|WPS75|Welfare Dominance and the Design of<br>Excise Taxation in the Cote d'lvoire|Shlomo Yitzhaki<br>Wayne Thirsk|August 1988|A. Bhalla<br>60359|
|WPS76|On the Shadow Price of a Tax<br>Inspector|Shlomo Yitzhaki<br>Yitzhak Vakneen|||
|WPS77|<br>lncentive<br>Policies and Agricultural<br>Performance in Sub-Saharan<br>Africa|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS78|Economists,Institutions<br>and Trade<br>Restrictions: A Review Article|J. Michael Finger|||
|WPS79|Quantitative<br>Appraisal of<br>Adjustment<br>Lending|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS80|Emerging Issues of Privatization<br>and the Public Sector|Samuel Paul|September 1988|<br>E. Madrona<br>61711|
|WPS81|Reaching People at the Periphery:<br>Can the World Bank's Population,<br>Health, and Nutrition Operations<br>Do Better?|Richard Heaver|||
|WPS82|Microeconomic<br>Theory of the House-<br>hold and Nutrition Programs|Dov Chernichovsky<br>Linda Zangwill|September 1988|<br>S. Ainsworth<br>31091|
|WPS83|Welfare Costs of U.S. Quotas in<br>Textiles, Steel, and Autos|Jaime de Melo<br>David Tarr|September 1988|<br>C. Cabana<br>61539|
|WPS84|Black Markets for Foreign Exchange,<br>Real Exchange Rates and Inflation:<br>Overnight vs. Gradual Reform in<br>Sub-Saharan<br>Africa|Brian Pinto|||
Teaching Rrocesses. rnree classroom management and organizational practices are explored: grouping, testing and practical work. The indicator of small group work was the consensus of the students and teacher on frequency of use. If the teacher indicated that the class was "frequently divided into small groups of student who work together on the same assignment or different assignments, including practical/laboratory work", and at least 50% of the students in the class agreed that "often" the class "breaks into small groups cf students to do experiments during science lessons" the class was coded as a "high group work" class; 10% of all classes were so categorized. The indicator of testing was the consensus of the students and teacher on frequency of occurrence. If the teacher indicated that the class was "frequently" assessed by "teacher-made objective (short answer) tests", and at least 50% of the students in the class agreed that they "often" had "tests on what (they) had learned in science", the class was coded as a "high testing" class; 29% of all classes were so categorized. The indicator of Rlactical work was the teacher's report on the amount of "time students usually spend on practical activities on their own or in small groups; for example, doing experiments or fieldwork." If the teacher indicated that 50% or more of the student time involved practical work, the class was coded as a "high practical work" class; 57% of all classes were so categorized.

# Selection of Variables

The IEA data set contains a total of 242 variables: (a) 83 variables dea'ling with student attitudes, test scores and background 21