rafmacalaba/datause-encoder-data huggingface dataset:
# **Incorrect coding of the consumption of home production data**

In the process of working with the 1993 data, we discovered that the data, as it was originally processed, showed a dramatic decline in consumption of home production. The reason was that in 98 percent of the 25,570 cases, the number of months of consumption of home production were coded as one month, in contrast to 2 percent of the cases in the 1995 survey (table 22). In examining a few questionnaires it was evident that the data for the number of months of consumption of home-produced items had been incorrectly entered on the computer file. Unfortunately, INS was not able to reenter the data for 1993.

**Table 22 CMte d'Ivoire: Number of Months of Consumption of Home Production 1993 and 1995**

|_Reported Months_|_Unrevised 1_|_993 survey_|_1993 correc_|_ted survev_|_19_|_95_|
|---|---|---|---|---|---|---|
|_of Consumption_|_Frequency_|_Percent_|_Frequency_|_Percent_|_Frequency_|_Percent_|
|1|25107|98.3|320|1.3|57|1.9|
|2|1|0.0|774|3.0|169|5.6|
|3|||1110|4.3|184|6.1|
|4|||1076|4.2|185|6.1|
|5|4|0.0|752|2.9|123|4.1|
|6|2|0.0|1696|6.6|285|9.5|
|7|||532|2.1|82|2.7|
|8|||886|3.5|128|4.2|
|9|||322|1.3|61|2.0|
|10|124|_0.5_|3442|13.5|117|3.9|
|11|288|1.1|175|0.7|11|0.4|
|12|3|0.0|14444|56.6|1612|53.5|

Fortunately, however, a mistake was made in filling out the questionnaires that allowed us to make a first-order correction. Even in cases where households had purchased none of a particular food item that they produced at home, they still filled out the number of months of consumption question under the entry for the number of months a purchased good was consumed. In the small number of original questionnaires that we were able to examine, the number of months of purchased consumption corresponded in most cases to the number of months of consumption of home production. Hence we used the number of months of purchased consumption (in the case where no purchased value was declared) to correct what was assumed to be the incorrectly coded number months of consumption of home production (almost always one month). Other procedures were used to correct the cases where purchased food value was nonzero. These are described in more detail in Appendix C.

Once the corrections were made, the frequency of months of consumption between 1993 and 1995 are fairly similar, though there is a higher frequency of 10 months of home consumption in 1993 data compared to the 1995 data (table 22). The mean number of months in the corrected 1993 sample is 9.7, compared to 8.9 in 1995. Whether this difference is a result of the cleaning procedure or reflects actual reported consumption patterns cannot be ascertained. However, it does suggest that there may be some upward bias in the 1993 consumption of home production and thus that it would be desirable to reenter the incorrectly entered 1993 data, rather than rely on the imputed number of months.

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Figure 10: Average number of students, by level <!-- Start of picture text --> 856<br>571<br>442<br>327<br>Public Private Public Private<br>Primary Secondary<br><!-- End of picture text --> Source: Authors' calculations using 2015 Morogoro data.

Consequently, average primary and secondary class sizes are significantly smaller in private schools than public schools. Note that both public and private schools are gender-balanced.

Based on national data, in primary grades, private schools are at 70 percent of their student capacity while public schools are currently at 130 percent of capacity (MoEST, 2016). In lower secondary schools, private schools have even greater excess capacity; private schools are only 57 percent filled while public schools are at 107 percent of capacity.

# 6.2 Teachers

Public school teachers in our 2015 Morogoro data sample have higher levels of education. Among the total teachers in the 142 schools surveyed, 62 percent are public school teachers and 38 percent are private school teachers. Nearly 42 percent of public school teachers have a bachelor’s degree compared to 22 percent of teachers in private schools. This is driven largely by the high number of private pre-primary teachers and high number of public lower secondary teachers in the sample. Looking exclusively at lower secondary schools, the percentage of teachers with a bachelor’s degree is not statistically significant between public and private schools, at 67 and 71 percent, respectively. Also, private schools are significantly more likely to have uncertified teachers. Nearly, 21 percent of the private schools have full time teachers without certification whereas the number is 13 percent for public schools. Accordingly, teacher salaries in private schools are significantly lower, possibly driven by the lower qualifications required for pre-primary teachers.

Based on the 2013 IE data, we find that compared to public schools, teachers are less likely to be absent and more likely to be in class and teaching in private schools. As a part of data collection, schools were visited in February-March 2013 for headteacher and teacher surveys and again in October 2013 for unannounced visits to measure teacher attendance. During unannounced visits, nearly 20 percent of public-school teachers were absent, as compared to only 7 percent of private school teachers. The likelihood of a teacher being in school but out of class was higher in public schools than private schools (41 and 30 percent, respectively). Similarly, the likelihood of a teacher being in class but not teaching was higher in public schools relative to private schools (13 and 6 percent, respectively). All these differences are statistically significant. In addition, when data are disaggregated by level, a public preprimary/primary school is likely to have 30 more students per teacher than a private preprimary/primary school. However, there is no discernable difference between public schools and private schools at the secondary level (Table 2).

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indicators in WMO. Specifically, we drop data sources that have limited country coverage (Clark, Dollar and Micco (2001) and Transparency International), but include Kaufmann, Kraay and Zoido-Lobaton (2002) (henceforth KKZ) which has a wider country coverage. Therefore, we rely on three sources--World Economic Forum Global <u>Competitiveness Report 2001-2002</u> (henceforth GCR), IMD Lausanne, World <u>Competitiveness Yearbook 2002</u> (henceforth WCY), and KKZ. See the Appendix for a more complete description of the sources and each of their methodologies for collecting and preparing data about a country.

Because the survey scales of the sources differ, we must put all survey data from the three sources on comparable basis. In contrast to WMO, we index each observation of a raw series (which is an observation representing a country) to the maximum of all the countries’ value for the raw series (e.g. global best practice). WMO used the mean of all countries as a benchmark for each of the indexes. We use the maximum as a benchmark since this easily indicates how far a country’s performance is from the best practice country whose indexed value is 1.0.

Two survey data inputs are used to form each of the trade facilitation measures. We use multiple survey inputs into each trade facilitation indicator to avoid depending too heavily on any one survey question or source.

The next step in creating the trade facilitation indictors involves collecting these indexed inputs into the four specific trade facilitation indicators. A simple average of the two indexed inputs is used for transparency of method, and also because there is no specific argument (theoretical or statistical) to choose a different aggregation method. Therefore:

- Port efficiency for each country _J_ is the average of two indexed inputs from GCR: o Port facilities and inland waterways o Air transport

- Customs environment for each country _J_ is the average of two indexed inputs from GCR:

- Hidden import barriers

- Irregular extra payments and bribes

- Regulatory environment for each country _J_ is constructed as the average of indexed inputs from WCY and KKZ:

- Transparency of government policy is satisfactory (WCY)

- o Control of Corruption (KKZ)

- Service sector infrastructure for each country _J_ is from GCR:

- Speed and cost of internet access

- Effect of internet on business

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# ~~<u>ks</u>~~

|Output (billions of dollars)||
|---|---|
|Direct|$2.64|
|Indirect|1.32|
|Tourism Related|2.42|
|Total Output (billion)|$6.38|
|Employment (number of jobs)||
|Direct|19,437|
|Indirect|19,826|
|Tourist Relabed|14,995|
|Estimated Total Number of Jobs|54258|
|Wages||
|Direct|$722|
|Taxes [millions of dollars)||
|Sales|$24.04|
|Residential Property|45.07|
|Tourism Related|7.00|
|TotalTaxes(million)|$76.11|

~~<u><mark>[|</mark></u>~~
attrition effect for household-level BREGS participation or when estimating the impacts of the RCT for the information campaign.

In both the baseline and follow up surveys, individuals were asked whether they had heard of the scheme and if so, they were asked 12 questions aimed at testing their knowledge of the scheme’s functioning, as well as of their rights under the Act. Table 1 provides the mean of correct responses for each of the questions by gender and survey round.<sup>10</sup> We find knowledge of the details of the scheme to be low and even lower for women. Most men and three-quarters of women had heard about the program by R1, but many were unaware of their precise rights and entitlements under BREGS. As an overall measure of knowledge about the scheme’s employment aspects we estimate a “work score” as the number of correct answers to the 8 employment related questions. The average “work score” is 2.6 for men and 1.5 for women out of a maximum of 8.

A second measure can be created for knowledge of the facilities and amenities that the scheme mandates must be provided at work sites (daycare, drinking water, shade and first aid kits). Respondents were asked to identify what facilities were supposed to be provided. The mean number of correct answers was 1.4 and 1.0 for men and women respectively out of a maximum of 4. We call this the “facilities score.” As can be seen in Table 1, knowledge of entitlements and BREGS procedures improved modestly over time. The average employment awareness score increased to 3.1 for men and 2.1 for women between Rounds 1 and 2; while the facilities score rose to 1.6 and 1.1, respectively.

Focusing on specific aspects of the scheme, knowledge is particularly low on entitlements such as the maximum time period of 15 days since demand within which work should be provided, the requirement to pay wages within two weeks, or the fact that contractors are not permitted. These are critical provisions of the scheme guidelines to ensure that rights are met. Knowledge that work needs to be demanded is higher (based on R2 information) but in practice it is not yet widespread. Only a quarter of male (and only 16% of female) workers in our sample had actually demanded work at the last worksite at which they worked in R1. The rest had obtained work through village leaders (including the Mukhiya<sup>11</sup> ), scheme functionaries

> 10  Note that the relevant means in Table 1 treat those who had not heard of the scheme as missing observations rather than including them as zeroes.

> 11  The Mukhiya is the elected leader of the Gram Panchayat (the local self-government) and is responsible for the implementation of development programs at the panchayat level.

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agricultural output in poor countries. Non-LAC developing countries exhibit a predominant resource-pull effect reflected in a negative and statistically significant coefficient on the lagged non-agricultural output variable. The results under column 3 suggest that for all developing countries agricultural output has a positive effect on non-agriculture, whereas this is not true for high-income countries. In contrast, the results under column 4 indicate that for developing countries there is an effect going from non-agriculture to agricultural growth for poor non-LAC and high-income countries, whereas this effect is not significant for LAC countries.<sup>13</sup> When the agricultural sector includes the food industries, the results are a bit different. The estimates under column 5 of Table 3(b) show that the net positive effects from agriculture to non-agricultural output are larger in LAC countries when including the food industries than for commodity agriculture. This last fact can be explained by noting that in LAC the share of agriculture in GDP shows a very significant increase once the food processing industry is added (See Appendix A). Finally, model 6 suggests that the expanded definition of agricultural output is not affected by past non-agricultural output in all countries.

In sum, we found evidence that agriculture might have net positive causal effects on nonagricultural growth in developing countries. These positive effects in LAC are also higher when agriculture includes the downstream industries associated with food processing. The evidence concerning the cross-sector effects are considered in the calculations of welfare elasticities discussed in the conclusions, for they reflect each sector’s contribution to national GDP per capita as well as their indirect effects on the other elements of the welfare function.

# **IV. The Impact of Agricultural Productivity on Quintile Incomes**

> 13 This analysis and the following paragraph are based on the p-values of the F-tests reported at the bottom of table 3(b).

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Table A33: Robustness of impact on finance index to differential attrition

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|---|
|||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
|||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1|(Safe Spaces)|0.03|0.04|-0.04|0.01|0.02|0.03|0.03|
|||(0.12)|(0.13)|(0.11)|(0.13)|(0.12)|(0.12)|(0.13)|
|T1|+Leaders|0.03|0.11|-0.17**|-0.04|0.01|0.05|0.03|
|||(0.10)|(0.10)|(0.08)|(0.10)|(0.10)|(0.10)|(0.10)|
|T2|(Safe Spaces + IGA)|0.05|0.10|-0.13|0.00|0.04|0.07|0.05|
|||(0.09)|(0.10)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|
|T2|+Leaders|0.15|0.26**|-0.09|0.06|0.12|0.12|0.14|
|||(0.09)|(0.10)|(0.07)|(0.09)|(0.09)|(0.09)|(0.09)|
|T3|(Safe Spaces + Husbands Clubs)|-0.05|0.04|-0.25***|-0.15|-0.08|-0.01|-0.04|
|||(0.12)|(0.12)|(0.08)|(0.12)|(0.12)|(0.12)|(0.12)|
|T3|+Leaders|0.19|0.21*|0.08|0.15|0.18|0.19*|0.19*|
|||(0.12)|(0.12)|(0.09)|(0.12)|(0.11)|(0.12)|(0.11)|
|T4|(control spillover)|-0.13*|-0.12*|-0.13*|-0.13*|-0.13*|-0.12*|-0.12*|
|||(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|
|N||2995|2994|2994|2996|2996|2996|2995|
|Con|trol Mean|0|0|0|.003|.001|-.001|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

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- 32 v

and by features -- including the limited size of the jrivate sector -- that continue to distinguish them from a more conventional market economy. The model elaborated below necessarily simplifies and is based on the standard efficiency bargaining model developed in the context of a capitalist economy 4.

# 5.2 A cooperative game between Manager and Workers

There are two players, the Manager and the Workers. There is one firm directed by the Manager and a given number of Workers (m) seeking employment in the state owned sector. The individual Worker has the utility function defined in section 3.2. Of the m Workers seeking employment in the state sector only n (nrm) are employed. The effort delivered by Workers in the state sector is assumed to be exogenous and equal to Z. **"** The utility for a Worker employed in the state industry is: UW - u(Z) + V(W).

Unemployment amounts to m-n. The utility for an unemployed Worker is: Uw - u(B) + v(O), where B denotes exogenous unemployment benefits. Involuntary unemployment requires that u(Z) + v(W) > u(B) + v(O).

Using a utilitarian formulation of the utility function ^ for the group of Workers seeking employment in the state industry we have: Us - nv(W) + (m-n)v(B) + nu(Z) + (m-n)u(O) The remuneration of the Manager is formulated as in section 4.2. That means that if a, - a2 the state firm actually acts as a profit maximizer, but other choices of a, and a2 will induce other incentives for the Manager. The efficiency locus is found as the first order conditions to the following problem:

- - Max ( [UM UM][Us Us]' ) W,n

(5.1] In the R2E the threat point U8 is the remuneration the Worker can obtain by being employed outside the state industry. (5.1] can be rewritten using the specific 4 McDonald and Solow (1981), Nickell (1982), Ellis and Fender (1985). For an excellent survey, see Oswald (1985).

" Notice, n is now endogenous while the effort Z is exogenous. The explanation for the latter assumption is that the Workers delivering a low effort are laid off in the R2E.

a This way of aggregating utility functions is also used in Oswald (1985) and McDonald and Solow (1981).
Tables 7 and 8 summarize the spread of investment costs under the sensitivity scenarios. Generally, shifting to higher efficiency irrigation systems and significant climate impacts, such as those from RCP 8.5 increases the investment costs. Sustainable consumption and weaker climate impacts such as those from RCP 2.6 decrease the irrigation investment requirement.

Table 7. Maximum and minimum difference in the cumulative irrigation investment costs of the sensitivity scenarios compared to baseline investment scenarios by region

|||MaxI<br>|nvest<br>|||Inv<br>|est<br>||
|---|---|---|---|---|---|---|---|---|
||Minimum differe<br>irrigation investme<br>compared to_Max_|nce in<br>nt costs<br>_Invest_|Maximum diffe<br>irrigation investm<br>compared to_M_|rence in<br>ent costs<br>_axInvest_|Minimum differe<br>irrigation investme<br>compared to_Inve_|nce in<br>nt costs<br>_st_2050|Maximum diff<br>irrigation invest<br>compared to_In_|erence in<br>ment costs<br>_vest_2050|
||2050 (%)||2050 (%)||(%)||(%)||
|Region|||||||||
|AFR|RCP2p6 NOR|‐50%|WatrEff High|9%|HealthySustDiet|‐14%|WatrEff High|9%|
|EAP|RCP2p6 NOR|‐18%|WatrEff High|9%|WatrEff Low|‐11%|RCP8p5 IPSL|12%|
|ECA|RCP2p6 NOR|‐55%|RCP8p5<br>HadGem noCO2|33%|HealthySustDiet|‐23%|TradeOpen|56%|
|LCR|RCP2p6 NOR|‐60%|WatrEff High|12%|HealthySustDiet|‐22%|WatrEff High|16%|
|MNA|RCP2p6 NOR|‐26%|WatrEff High|21%|RCP2p6 NOR|‐21%|WatrEff High|16%|
|SAR|RCP8p5 HadGem<br>noCO2|‐18%|WatrEff High|8%|RCP8p5<br>HadGem noCO2|‐16%|WatrEff High|7%|
|WLD|RCP2p6 NOR|‐28%|WatrEff High|10%|WatrEff Low|‐9%|RCP8p5 IPSL|14%|

Table‐8. Maximum and minimum difference in the irrigated area of the sensitivity scenarios compared to baseline investment scenarios by region

|||Max<br>|Invest<br>|||In<br>|vest<br>||
|---|---|---|---|---|---|---|---|---|
||Minimum differe<br>irrigated areas co<br>to_MaxInvest_20|nce in<br>mpared<br>50 (%)|Maximum differ<br>irrigated areas co<br>to_MaxInvest_20|ence in<br>mpared<br>50 (%)|Minimum differe<br>irrigated areas co<br>to_Invest_2050|nce in<br>mpared<br>(%)|Maximum diffe<br>irrigated areas c<br>to_Invest_205|rence in<br>ompared<br>0 (%)|
|Region|||||||||
|AFR|RCP2p6 NOR|‐17%|WatrEff High|5%|RCP8p5 NOR|‐23%|WatrEff High|5%|
|EAP|RCP2p6 NOR|‐46%|TradeRestr|5%|HealthySustDiet|‐11%|SSP3|5%|
|ECA|SSP1|‐10%|RCP8p5<br>HadGem noCO2|9%|HealthySustDiet|‐12%|RCP8p5 IPSL|9%|
|LCR|RCP2p6 NOR|‐46%|RCP8p5<br>HadGem noCO2|33%|HealthySustDiet|‐30%|RCP8p5 IPSL|33%|
|MNA|RCP2p6 NOR|‐54%|TradeOpen|12%|HealthySustDiet|‐17%|RCP2p6 MIROC|12%|
|SAR|RCP8p5 HadGem<br>noCO2|‐13%|TradeRestr|4%|RCP8p5<br>HadGem noCO2|‐12%|RCP2p6 NOR|4%|
|WLD|RCP2p6 NOR|‐15%|TradeOpen|7%|HealthySustDiet|‐8%|RCP2p6 MIROC|7%|

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**Table 5: Effects of Agent Rollout on Self-Employment and Food Security** **<u>(Follow-up Survey)</u>**

||Self-employed<br>(non-farm)|Had to reduce<br>number of meals<br>in last 7 days|Food security<br>index is very low|
|---|---|---|---|
|Panel A: ITT for all clusters|(1)|(2)|(3)|
|Agent|0.011*|-0.033|-0.026|
||(0.006)|(0.022)|(0.022)|
|R-squared|0.013|0.041|0.050|
|Control group mean|0.032|0.480|0.594|
|Panel B: ITT byAirtelpriority||||
|Agent*High priority|0.014|-0.068**|-0.076**|
||(0.009)|(0.033)|(0.033)|
|Agent*Low priority|0.009|0.003|0.025|
||(0.008)|(0.029)|(0.029)|
|R-squared|0.013|0.042|0.052|
|F-test p-value (Agent*High = Agent*Low)|0.665|0.108|0.021|
|Control group mean - High priority|0.040|0.487|0.598|
|Control group mean - Low priority|0.024|0.474|0.590|
|Observations|8816|8844|8844|

Note: Panel A shows results from OLS regressions of the dependent variables on a dummy variable that is equal to 1 if the cluster was randomly selected for the agent rollout and equal to zero otherwise. Panel B shows results from similar regressions, where the agent rollout dummy is interacted with two dummy variables indicating Airtel priority rating. In clusters with higher priority, the professional services firm spent more time rolling out agents and a larger fraction of these clusters received an agent than clusters with low priority. The randomization was stratified by priority rating. All regressions include randomization strata dummies and household head age. Control group means are means of the dependent variables. Standard errors clustered at the cluster level in parenthesis. Statistical significance levels: * p<0.10, ** p<0.05, *** p<0.01. To deal with multiple hypothesis testing, we calculated sharpened q-values and indicate statistical significance levels: † q<0.10, †† q<0.05, ††† q<0.01.

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