rafmacalaba/datause-encoder-data huggingface dataset:
Misreporting is positively associated with a greater number of plots for men. Recall decay appears prominent among men but not women for knowledge outcomes. Findings from cost-eectiveness and regression analyses always favor the collection of objective measures of knowledge. Given the lowest rate of accuracy for adoption was around 80 percent, objective adoption measures are recommended for a subsample in regions with heterogeneous farm sizes.

# 2 Data

## 2.1 Study Region

The research study was conducted in Mozambique's Zambezi Valley, and covered districts across all but one of its four provinces (Sofala, Tete and Zambezia). The valley spans 5.5 million hectares of arable land, accounting for 15% of Mozambique's overall arable land (FAO, 2007). The tropical climate provides abundant rainfall and the majority of the nation's water reserve. Economic activity stagnated following the end of the civil war in the 1990s. The national government currently targets agricultural investments in this region to encourage growth given its potential (World Bank, 2007).

There are two farming seasons each year in the Zambezi: a rainy season from October to March and a dry season from April to September. Farming is the main source of food and income, and farmers grow maize, cassava, beans, sorghum, and rice as main food crops, and sesame, cotton, cashew nuts, sugar, and tobacco as main cash crops. Agricultural productivity is low in the area, in part attributable to the lack of advanced technologies. Smallholders, who represent 98% of Mozambique's farmers, use low-yield seed varieties and traditional farming methods (Sachs, Toledano, and Maples, 2011). In addition, more than 95% of women are engaged in agricultural activities compared to 66% of men (Farnworth, 2010). Despite the high participation rate and the essential role in growing food crops for families, female farmers are disadvantaged by their lack of accesses to farming inputs, resources, extension services, and land security in Mozambique (Uaiene and Arndt, 2009).

We use data from the 2012 Smallholder's Survey, which were collected in the context of a technology adoption randomized controlled trial (RCT) on a large government-World Bank investment 4
# **Table 7. Tariffs and Nontariff Barriers on Mercosur's Imports**

||1988-94 Change in<br>Mercosur Intra-Trade<br>($000)|Avera<br>External Ta|ge<br>riff (%)|Nontariff Ba|rrier Ratio|Regional Orientation<br>Index Change|
|---|---|---|---|---|---|---|
|Decile Range||1988|1996|Frequency<br>Ratio|Trade<br>Coverage||
|First Decile Products|682,680|54.4|18.1|41.9|83.5|9.63|
|Second Decile Products|2,708,326|46.9|16.7|24.5*|36.6*|2.63|
|Third Decile Products|815,205|56.1|17.5|42.1|51.8|0.47|
|Fourth Decile Products|1,001,244|46.0|9.6|17.7|28.7|0.17|
|Fifth Decile Products|488,863|46.0|10.3|20.4|8.3|0.05|
|Sixth Decile Products|672,327|43.6|16.7|17.1|4.3|-0.21|
|Seventh Decile Products|307,604|38.4|10.9|18.5|27.5|-0.37|
|Eighth Decile Products|246,657|39.1|11.7|27.1|31.9|-0.77|
|<br>Ninth Decile Products|142,627|38.4|9.0|4.0|5.4|-2.15|
|Tenth Decile Products|287,531|40.7|10.9|8.2|28.2|-21.65|

*Includes Brazil's recent restrictions on automobile imports and domestic automobile assembly regulations.

Source: Computed from SMART and the WTO Integrated Data Base.
The main outcome variable that we analyze in this paper is total household income per capita.<sup>11</sup> To reduce potential mismeasurement due to outliers, we trim one-quarter of a percent of the data at both the top and the bottom of the income distribution and only keep individuals with a positive income level. But we also examine several other definitions of income, as well as consumption, for robustness checks.

# **IV. Estimation Results**

We start in this section with a discussion of the overall trends in income and inequality over the period 1994-2015. We subsequently turn to investigating mobility in the short term and in the medium term, before examining mobility in the longer term, its decomposition into growth and distribution, and its relationship with short-term and long-term inequality.

# **IV.1. Overall Trends of Income and Inequality**

As earlier discussed, despite a temporary decline in the late 1990s, income per capita has been rising in Russia; furthermore, this positive trend is accompanied by a continuous decrease in inequality throughout the period (Figure 1).<sup>12</sup> To further examine whether it is lower-income households or higher-income households that experienced more decrease in inequality, we plot in Figure 2 the 90<sup>th</sup> /50<sup>th</sup> and 50<sup>th</sup> /10<sup>th</sup> ratios of the income percentiles. The latter (red dotted line)

> 11 We use only those individuals that have data at the household level and drop 124 individuals who do not have household data. We focus on household income rather than household consumption since changes to consumption items in the survey questionnaires could render the latter variable incomparable over time. For example, 14 percent of total household consumption was comprised of items that were found in 2015 only. Furthermore, comparing household consumption between 1994 and 2015, 12 percent of total household consumption in 1994 is accounted for by consumption items that are more disaggregated than 2015; the corresponding figure for 2015 compared with 1994 is 11 percent. Still, when we re-plot Figure 1 using household consumption per capita, estimation results shown in Figure 1.1 (Appendix 1) indicate similar patterns.

> 12 The downward sloping trend of the Gini coefficient is consistent with the findings in other studies that use earlier data from the RLMS, including Gorodnichenko _et al._ (2010) and Denisova (2012). We also restrict our analysis to 1994, when the RLMS was first implemented. See Milanovich (1998) for a study that analyzes data from Russia for earlier periods.

15
**Figure 4.33. Residential mobility by the poorest and richest quintiles in other urban settlements**

<!-- Start of picture text --> Poor mobility Rich mobility<br>40<br>30<br>20<br>10<br>0<br>Mobility by the poorest and richest quintiles in other urban settlements Albania Armenia Azerbaijan Belarus Georgia Kazakhstan Tajikistan Turkmen<br><!-- End of picture text --> Source: see Table 2.1 If residential mobility is an adjustment mechanism to enable households to satisfy their housing needs and to improve their labor opportunities, then low income households in ECA cities, especially those in the capitals, are trapped in part due to housing sector conditions. Rigid housing markets make it difficult for households, especially lower income ones, to move to other cities for jobs, which contributes to unemployment. At the same time, decreasing real incomes have negative impacts on the housing demand. A study in Poland estimates that as much as 25% of its unemployment rate in 1992 occurred because workers could not find housing near the available jobs (Coricelly et. al 1995). High income groups are much more mobile and are approaching average mobility rates for some of the less mobile countries in Europe.

**Trapped in deteriorated living conditions—the development of urban slums.** Although residential differentiation by neighborhood in centrally planned economies was much less than in market societies, some differentiation did exist.  Development of real estate markets in transition countries has provided a mechanism for increasing the spatial concentration of different socio-economic groups.

Under central planning, housing was centrally built, allocated, owned and maintained. Citizens were constitutionally entitled to housing. Housing rents and maintenance fees were low and largely uniform so access to housing was not determined by ability to pay. Since housing was provided at little or no cost, demand for housing far out stripped supply and people waited for decades to receive a new unit (Smith, 1996).

The different types of housing differed by location and amenities. In urban areas, some families lived in privately-owned single-family houses, which commonly lacked access to basic infrastructure services and were of lower quality. Enterprises built multi-story apartment buildings of slightly lower quality for their employees, who lived in close proximity to one another and were more homogenous than the population at large. Cooperative housing, which required some self-financing, was inhabited by somewhat higher earners. Better quality housing was used to “reward” especially meritorious workers ranging from skilled specialists to Party nomenklatura, which also led to some residential differentiation. The elites were often located in central parts of the city in higher quality housing stock. Additionally, housing quality varied significantly by age of construction. Since a large area (micro-rayon) in a city was usually developed at in Kazakhstan during the 1990s and the Impact of Nationality Population Patterns: Results from the Recent Census of Kazakhstan.” Post-Soviet Geography and Economics 42, no. 8, pp. 571-614.

51
training dropped from 39 % in 1993 to 24 percent in 1998. Most<sup>of thereductioninhigher</sup> education and training budget came from reduction in state budget for technical<sup>andvocational</sup> training, and not from reductions in spending on universities.

**Table 7: Trends in Government expenditure on** education, 1992-1998

||1992|1993|1994|1995|1996|1997|1998|
|---|---|---|---|---|---|---|---|
|Percapitaexpenditure('000VNDat<br>constant1994prices)|39.2|58.1|71.3|82.8|80.4|100|115|
|%ofdiscretionarybudget|9.3|10.0|11.7|13.4|12.3|14.1|17.4|
|%ofGDP|1.8|2.6|2.9|3.0|2.7|3.2|3.5|
|_Shareoftotalpubliceducation_<br>_expenditureon:_||||||||
|Education|63.9|60.7|60.4|72.3|71.9|72.6|75.7|
|Primnaryschool|40.0|32.6|29.6|29.1|33.2|34.3|36.4|
|Lowersecondaryschool|14.8|17.6|17.7|19.9|19.2|19.2|17.6|
|Uppersecondaryschool|6.0|6.9|8.6|7.4|9.0|8.6|8.3|
|Highereducationand training|36.1|39.3|39.6|27.7|28.1|27.4|24.3|

_Source._ Author's estimates based on public expenditure data (from MOF) The allocation of public spending on education by level determines the<sup>availabilityof</sup> resources for primary education. Public funding for primary education<sup>iscriticalfor providing</sup> basic education skills and opportunities for the poor children to continue<sup>onto highereducation.</sup> The previous bias against primary education in the allocation of<sup>resourceswithinthe sectorasa</sup> whole in Vietnam is demonstrated by estimating how many primary<sup>studentscouldbe financed</sup> by the cost of one student in either secondary or tertiary education.<sup>In1993,alowersecondary</sup> student in Vietnam was 1.7 times as expensive as a primary-level<sup>student,anuppersecondary</sup> student in Vietnam was 4.3 times as expensive and a post secondary<sup>studentwas26.5timesas</sup> expensive. In 1998, the situation has been significantly imnproved,<sup>butbias against lowerlevelof</sup> education still exist. A lower secondary student in Vietnam was only<sup>0.9timesas expensiveasa</sup> primary-level student, an upper secondary student in Vietnam was<sup>1.5timesas expensive anda</sup> post secondary student was 6 times as expensive (Table<sup>8).</sup>

**Table 8: Share of public spending on education and** of enrolments,<sup>**1993and1998 by**education</sup><sup>**levels**</sup>

||Prima|ry<br>|Lower<br>Seconda|<br>ry|Upp<br>Secon|er<br>dary|Postseco|ndary|Total<br>leve|of4<br>ls|
|---|---|---|---|---|---|---|---|---|---|---|
||1993|1998|1993|1998|1993|1998|1993|1998|1993|1998|
|_Publiceducationspending_|||||||||||
|billions|1,152|4104|477|2096|179|968|953|2381|2,761|9549|
|%oftotal|42|43|17|22|6|10|35|25|100|100|
|_Enrolments(gross)_|||||||||||
|million|11.3|10.6|2.8|6.3|0.4|2.1|0.4|1.0|14.2|18.3|
|%|76|53|19|31|3|10|2|5|100|100|
|Perstudentpublicspending|105|388|173|346|448|568|2779|2287|||
|Primary student equivalentof<br>perstudentpublic spending|1.0|1.0|1.7|0.9|4.3|1.5|26.5|5.9|||

10
# _2.3. Ordering investment in low- and zero-carbon capacity_

In this subsection we study the various phases through which the system may go during the transition from coal to renewable. Three particular dates play a pivotal role.

To comply with the carbon budget constraint, all production must eventually switch to renewable power, at a first date that we denote _T_ (Fig. 2). At one point, polluting coal production is thus completely phased out, at a second date _Th_<sup>≤</sup><sup>_T_.Similarly,whilegasplantsmaybeusedduringthetransition,atone</sup> point gas production has to decline, possibly faster than the speed at which its capacity depreciates. This may result in an underutilization of gas capacity, at a third date _Tγ_ .

In Appendix B, we systematically list the various possible transition shapes, and identify three possible sequences of investment in gas and renewable power:

**Proposition 1.** _Depending on the parameters, investment phases may be ordered in three ways:_

_1. Two successive transitions. Gas first completely replaces coal, then renewable power replaces gas. In this case, investment in renewable power starts after coal is phased out, but before production from gas is phased out (Fig. 2a)._ _2. Gas and wind simultaneously replace coal. In this case, investment in renewable starts before production from any fossil resources is phased-out (Fig. 2b)._ _3. Starting with investment in renewable power. In this case, investment in renewable power starts before investment in gas power (Fig. 2c)._ _Proof._ See Appendix B.

# **3. Assessing investment in competing technologies**

In this section, we study optimal investment decisions. We first derive a general investment decision rule, showing that all future prices and the future resale value of capital are required to determine the optimal investment strategy. We then discuss the levelized cost of electricity and investigate consequences of failing to anticipate all future prices.

# _3.1. General investment decision equation_

During a time interval ( _τi_<sup>+</sup><sup>_,τ_-</sup> _i_<sup>)whenproductionandinvestmentarestrictly</sup> positive, the optimal marginal investment cost _c_<sup>′</sup> _i_<sup>(</sup><sup>_xi,t_)isthesolutionofthe</sup> differential equation (10). In general, such solution reads:<sup>12</sup>

> 12 Equation 15 is the textbook solution of equation 10 (e.g., Wikibooks contributors, 2014)

9
# IV. Results

The current annual rate of hurricanes striking the US is 2.3/year. These storms cause annual average damages of $9 billion/year (Nordhaus 2010). The damages per storm given the current level of annual damages and the current climate are calibrated so that annual damages are equal to this observed amount.

The economic conditions over the next century are then projected. GDP is assumed to grow 6 fold between 2010 and 2100 (GDP grows at 2%/yr). The US population is expected to increase by 50% (United Nations 2004). We use the historic growth of income per capita and population in each county between 1960 and 2000 to predict how income and population in each coastal county will grow compared to US average income and population. This yields a future baseline of population and income for each county in 2100.

Table 2 reports hurricane damages for both the current and future baseline given the current climate. The analysis shows how baseline damages would change just because of income and population growth but without climate change. According to the minimum pressure model, the increase in income and population in the coastal counties is expected to triple hurricane damages from an expected value of $9 billion/yr to $27 billion/yr. The wind speed model, however, has a much higher elasticity of income and so it predicts that baseline damages will increase to $55 billion, a six fold increase. Note that the aggregate results from the wind speed model are similar to the results by Pielke et al 2008 but the minimum pressure results are much lower.

The current and future baseline damages are not evenly distributed across the Eastern seaboard. The bulk of the current damages are in the Gulf region (56 percent) and Florida (33 percent). Most of the remaining damages are in the Southeastern states (11 percent). The Middle Atlantic and New England regions bear only 0.4 percent of national damages. With the minimum pressure model, the relatively high growth rates along the Florida coast compared to the Gulf coast reduce the future share of damages in the Gulf to 44 percent but increases Florida’s share to 44 percent. With the wind speed 15
Table S5: Performance using one, two or three predictor datasets

|**Data Sources**|**AUC**|
|---|---|
|Assets|0.73 (0.025)|
|Consumption|0.71 (0.000)|
|CDR|0.68 (0.028)|
|Assets + Consumption|0.76 (0.017)|
|Assets + CDR|0.76 (0.025)|
|Consumption + CDR|0.75 (0.016)|
|Assets + Consumption + CDR|0.78 (0.019)|

_Notes_ : AUC scores for targeting methods using a single data source, pair of data sources, and all three data sources together (in our 535-household matched sample). Standard deviations are calculated from 1,000 bootstrapped samples of the same size as the original sample, drawn with replacement.

Table S6: Targeting simulation results for one train-test split

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Targeting Method**|**AUC**|**Accuracy**|**Precision**|**Recall**|
|Random|0.50|0.48|0.28|0.28|
|Asset Index|0.68|0.63|0.33|0.33|
|Consumption|0.74|0.74|0.53|0.53|
|CDR|0.75|0.70|0.47|0.47|
|Combined|0.82|0.74|0.53|0.53|

_Notes_ : Reproduction of main results (Table 2 Panel A) to using a single train-test split (for our 535-household matched sample, with 10% of the observations in the test set).

41
# Table 3

# Durables Ownership Share Engel Curve Regressions

|VARIABLES|(1)<br>Computer|(2)<br>Television|(3)<br>Washing Machine|(4)<br>Cell Phone|
|---|---|---|---|---|
|log(income)|0.180***|0.133***|0.0806***|0.110***|
||(0.0120)|(0.0336)|(0.0265)|(0.0153)|
|Constant|0.0903***|0.591***|0.516***|0.666***|
||(0.0104)|(0.0295)|(0.0276)|(0.0130)|
|Mean Y|0.294|0.7685|0.6395|0.7303|
|Nation/Year Fixed Effect|Yes|Yes|Yes|Yes|
|Countries Included|116|89|74|113|
|Observations|3,050|780|730|2,720|
|R-squared|0.926|0.909|0.971|0.918|

The unit of analysis is a nation/year/income quintile. The dependent variable is the share of households who own the durable. Robust standard errors in parentheses. The standard errors are clustered by country. *** p<0.01, ** p<0.05, * p<0.1 The regressions are weighted by the nation's population in the sample year.

26
# **5. Conclusion**

Prior to the mid-1980s, the conventional wisdom was that the abundance of natural resources contributed positively to economic growth. More recent studies have shown that the opposite may be true.

A logical channel for this deleterious effect is that the incentive to build good economic institutions through growth enhancing reforms is low in natural resource abundant countries. The present study puts this idea to the test by using data on microeconomic reforms over the period 2003-2008. The findings confirm yet another form of a “natural resource curse.” That is, the proclivity to improve the general business environment is significantly lower in natural resource abundant countries.

16
<!-- Start of picture text --> Contribution to Labor Productivity growth<br>©<br>2<br>E<br>°<br>2<br>Ivory Coast Kosovo Moldova South Africa Vietnam<br>a « BE «n<br>Kosovo is output per worker<br><!-- End of picture text --> <!-- Start of picture text --> Estimated Coefficients and Confidence Intervals<br>South Africa Ivory Coast Kosovo<br>10-19 2 10-19 10-19<br>20-49 eH 20-49 20-49<br>Qo @Q Oo<br>NN 50-249 al NN 50-249 50-249<br>ao on on<br>250-499 oe 250-499 250-499<br>500+ HoH 500+ 500+<br>-1 -05 0 05 4 15 -1 -05 0 05 1 «15 -1 -05 0 05 1 15<br>Labor Growth Labor Growth Labor Growth<br><!-- End of picture text --> <!-- Start of picture text --> Vietnam Moldova<br>10-19 10-19<br>20-49 20-49<br>q 50-249 a an g 50-249<br>7) a<br>250-499 250-499<br>500+ —e— 500+<br>-1 -05 0 05 1 15 -1 +05 0 05 1 15<br>Labor Growth Labor Growth<br>Regression controls for Time and Location of the Firms:<br>Base Category: Size 1-9<br><!-- End of picture text -->
<!-- Start of picture text --> 90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>00<br>SMEs Share in Total Number of Firms (%) SME Share in Total Employment(%)<br>mNon-FCS FCS<br><!-- End of picture text --> <!-- Start of picture text --> Proportion of investment financed by banks (%) =<br>Proportion of working capital financed by banks (%) Ez<br>Percent of firms using banks<br>to finance working capital i<br>8 10 2030 4080 69708090 100<br>@FCS BNon-FCS<br><!-- End of picture text --> <!-- Start of picture text --> Political instability<br>Access to electricity<br>Access to finance<br>00 50 10.0 15.0 200 25.0<br>TM Large Enterprises © @Medium Enterprises @ Small Enterprises<br><!-- End of picture text --> <!-- Start of picture text --> Proportion of investment financed internally (%)<br>Percent of firms not needing a loan<br>Pr f (3%)<br>0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0<br>FCS mNon FCS<br><!-- End of picture text -->
lobbyists, often on an issue-by-issue basis, so that informational lobbying by those lobbies is influential on the margin). Hence, informational lobbying of Agriculture committee members is _not_ influential on the margin in the formation of coalitions.

This view of Agriculture committee members possessing a strong continuing link with PACs, whose preferences have been stable over time, is endorsed in Parker and Parker (1998). They factor-analyze data on voting within committees, where leadership is less partisan and more attuned to the issues and lobbying interests surrounding those issues, versus voting on related issues after those issues have left the committee for the floor, where floor leaders are more partisan and seek to establish coalitions based on party and ideology. Parker and Parker’s aim is to differentiate committees in the House in which voting by their members do not change as issues move from committee to the floor from committees in which voting does change. Agriculture and Ways and Means are the two highest ranked in terms of the stability of coalitions as their issues move from committee to the floor.<sup>3</sup> Parker and Parker take this to imply that the influence of special interests on forming preferences of committee members on these committees is strong. In other committees, members often change their votes on the floor, indicating that their preferences while serving on committees were weak to begin with.

In sum, evidence on the relevance and effectiveness of informational lobbying, at least in the context of agricultural policy, is an open issue and deserves further investigation. Hansen finds for informational lobbying, while Parker and Parker do not. Hansen’s is a longterm exercise and identifies threshold points in time during which legislators sought better information because they recognized it to be marginally effective in increasing electoral strength. Shorter run studies do not find this to be the case.

# C. _Quid Pro Quo Lobbying_

In contrast to Hansen (1991), Grossman and Helpman (1994) put forth a theory of how policy responds to lobbying contributions. In their model, money causes votes and is the source of electoral strength. A stylized fact that supports this view is that lobbying contributions account for a significant share, up to 80%, of a congress person’s total campaign

> 3 This finding about Ways and Means is at odds with Wright’s findings from his survey of lobbies.

11