rafmacalaba/datause-encoder-data huggingface dataset:
**8**

**Table 1: Agricultural and non-agricultural growth rates by decade and continent between 1960 and 2003.**

|**Average annual**<br>**growth rate (%)**|**1960**<br>|**-1969**<br>|**1970**<br>|**-1979**<br>|**1980**<br>|**-1989**<br>|**1990**<br>|**-2000**<br>|**2000**<br>|**-2003**<br>|**T**<br>|**otal**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Agr.**|**Non-**<br>**agri**|**Agr.**|**Non-**<br>**agri**|**Agr.**|**Non-**<br>**agri**|**Agr.**|**Non-**<br>**agri**|**Agr.**|**Non-**<br>**agri**|**Agr.**|**Non-**<br>**agri**|
|Sub-Saharan<br>Africa|2.7|5.0|2.5|5.5|2.6|3.4|2.7|3.0|2.7|3.6|2.6|3.8|
|South Asia|2.9|5.7|1.7|4.7|3.6|6.4|3.2|6.2|3.0|5.9|2.9|5.8|
|East<br>Asia<br>&<br>Pacific|4.0|7.7|3.2|7.4|3.0|4.9|1.7|5.1|0.1|5.0|2.3|5.7|
|East Europe &|-1.4|7.0|1.7|7.0|1.3|3.3|-0.7|0.0|3.4|6.7|0.8|2.6|
|Central Asia|||||||||||||
|Europe, others|1.2|6.0|1.7|3.5|2.0|2.6|1.7|2.5|-0.8|2.3|1.5|2.9|
|Latin America &|2.8|5.2|2.3|5.0|1.5|1.6|1.9|3.3|2.2|2.0|2.0|3.3|
|the Caribbean|||||||||||||
|Middle East &|1.3|6.1|6.0|7.3|4.8|3.0|3.9|4.2|4.4|3.7|4.4|4.7|
|North Africa|||||||||||||
|North America|-|-|-0.3|3.7|3.2|2.7|2.7|2.7|-1.8|3.2|1.7|3.0|
|Total|2.7|5.7|2.6|5.3|2.5|3.2|2.0|3.1|2.1|4.0|2.3|3.9|

Both annual agricultural and non-agricultural growth rates are based on GDP expressed in constant 2000 US$. Non-agricultural growth is defined as the sector weighted sum of GDP growth in industry and services. Source: Authors’ calculations based on World Bank data (2005c) Despite the powerful appeal of this view throughout the development economics literature<sup>6</sup> and among policy makers, there are few comparable estimates available of productivity growth in agriculture and industry, especially for developing countries.<sup>7</sup> In a first simple step to explore this further, we decompose the sectoral GDP growth rates reported in Table 1 into their (labor) productivity and population growth components (see Table 2).<sup>8</sup> Table 2 presents the average sectoral growth rates and their (labor) productivity and population growth components during the 1960-2003 period.

Contrary to the widely held assumption that improvements in agricultural productivity cannot match those in non-agriculture, the results in Table 2 suggest that over the past 4 decades labor productivity in agriculture has on average been growing faster. With the exception of South

> 6 The powerful dual economy models of development for example, critically assume a stagnant, traditional  rural sector from which labor and resources flow to the dynamic, modern industrial sector.

> 7 Productivity estimates for the economy as a whole or for individual sectors on the other hand abound.

> 8 To see this, denote GDP by _Gk_ , and population in sector k by _Lk_ ,. Through total differentiation of

_dGk d_ ( _Gk_ / _Lk_ ) _dLk Gk_ = _Lk_ ( _Gk_ / _Lk_ ) , it can readily be shown that = + . _Gk_ ( _Gk_ / _Lk_ ) _Lk_
schools, particularly in small municipalities. Additionally, for the RDD analysis, we constructed a more balanced (trimmed) sample using the distribution of the p-score and dropping units with a propensity score below 0.1 or above 0.9 to make estimates more precise and less sensitive to changes in specification (Crump et al. 2009).

To test the identifying assumption of the RDD approach formally, we carried out a number of continuity checks on baseline characteristics at the individual and household level –all of them available from the census of the poor and used for the _Sisben_ proxymeans test – that could be associated with the outcomes of interest. Figure 3 and Table 9 show graphical and regression analysis on the relationship between the _Sisben_ poverty index score and these variables. Because of the large size of the sample used in the analysis, these differences are very precisely estimated. In general, there are not remarkable statistically significant differences in pre-program characteristics between the two groups on each side of the cutoff that could be argued to drive the results. In cases where the differences are statistically significant, the magnitude of the discontinuity is either relatively small in economic sense to drive the results (on average 0.046 more children in households right above the threshold of eligibility) or the direction of the difference (smaller household size or larger home ownership) is unlikely to introduce a bias that could affect the interpretation of our results.

Another potential concern in the RDD is the possibility that individuals could manipulate the assignment variable (the _Sisben_ poverty index score) and generate nonrandom sorting around the threshold. For example, the RDD approach may be invalid if more motivated and education-driven people seek to influence the value of their scores by taking actions such as hiding assets, ̳borrowing‘ children from other families to increase the size of the household, or bribing local authorities and program administrators. This may undermine the comparability of people on each side of the cutoff. While it is impossible to fully rule out this type of behavior in the context of FA, there are several reasons to believe that direct manipulation of the assignment mechanism is not a major concern for identification. First, an examination of the density of the _Sisben_ score, presented in Figure 4, itself shows that there are no jumps in the distribution at the 28
**36**

# **Table** _i._

# **State and Local Pension Plan Characteristics**

|**1. Public Pension Plan Ch**|**aracteristics Through**|**Time**||
|---|---|---|---|
|||Year||
||**1980**|**1985**|**1989**|
|Total Participants|NA|15,234|16,684|
|(000)||||
|Active Participants|NA|10,364|11,357|
|(000)||||
|Total Assets|$162|374|629|
|($B current)||||
|Total Contributions|$21|37|44|
|($B current)||||
|Total Benefits|$11|22|33|
|($B current)||||
|Number of Plans|NA|2,589|2,387|

Source: Piascentini and Foley (1992).

Olivia S. Mitchell - Public Pension Governance and Perfornance
randomly drawn from the blocks within these specific selected areas, conditional on the presence of dirt floors in 2000.

Finally, we imposed the following eligibility criteria on all (i.e., both treatment and control) surveyed households: (i) households had to own the house where they reside, since homeownership is an eligibility requirement for the _Piso Firme_ program; (ii) the family groups must have resided in that specific dwelling since 2000; (iii) the houses must have had at least one room with a dirt floor in 2000 (since, otherwise, the household could have not received the program benefits); and (iv) households must have at least one child younger than the age of 6 at the time of the interview. This last requirement was based on the fact that the treatment was expected to render its main health benefits to individuals in this age group.

# **4. Data**

We collected household information for both treatment and control groups through a cross-sectional household survey conducted by the Mexican National Institute of Public Health during the spring of 2005. The survey’s target sample size was 3,000 households, equally split between treatment and control groups. Response rates were very high. In the treatment area, the response rate was 92.6% or 1,390 completed surveys, while in the control area the response rate was 92.9% or 1,393 completed surveys. Of these 2,783 households, we have complete geographical location information for 2,755 of them (99%). This group constitutes the final sample used in this analysis.

We collected detailed information on household demographic structure, socioeconomic status, housing infrastructure (including detailed information on the type of floor in each room of the house), health outcomes and cognitive development of children under 6 years of age, as well as happiness and mental-health indicators for their mothers. Table I provides a description of all the outcomes used in this study.

The child health and development outcomes include: (1) maternal-reported cases of diarrhea in children in the four weeks prior to the survey, (2) stool samples to determine the prevalence of parasites in fecal matter, (3) height and weight anthropometric statistics to measure stunting and wasting, (4) blood from a finger prick to assess hemoglobin levels and anemia, and (5) measures of language and communication ability.

9
- **Amihud, Yakov** , “Illiquidity and stock returns: cross-section and time-series effects,” _Journal of financial markets_ , 2002, _5_ (1), 31–56.

- **Barca, Fabrizio and Marco Becht** , _The control of corporate Europe_ , Oxford University Press, 2001.

- **Bolton, Patrick, Von Thadden et al.** , “Blocks, liquidity, and corporate control,” _The Journal of Finance_ , 1998, _53_ (1), 1–25.

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- group affiliation: Evidence from East Asia,” _Emerging Markets Review_ , 2006, _7_ (1), 1 26.

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- **Copeland, Thomas and Dan Galai** , “Information Effects on the Bid-Ask Spread,” _The Journal of Finance_ , 1983, _38_ (5), 1457–1469.

- **Faccio, Mara and Larry HP Lang** , “The ultimate ownership of Western European corporations,” _Journal of financial economics_ , 2002, _65_ (3), 365–395.

- **Glosten, Lawrence R and Paul R Milgrom** , “Bid, ask and transaction prices in a specialist market with heterogeneously informed traders,” _Journal of financial economics_ , 1985, _14_ (1), 71–100.

- **Gul, Ferdinand A., Jeong-Bon Kim, and Annie A. Qiu** , “Ownership concentration, foreign shareholding, audit quality, and stock price synchronicity: Evidence from China,” –

- _Journal of Financial Economics_ , 2010, _95_ (3), 425 442.

- **Holderness, Clifford G.** , “The Myth of Diffuse Ownership in the United States,” _Review of Financial Studies_ , 2009, _22_ (4), 1377–1408.

23
activity pays capital rent and activity taxes (8‐9). Private activity output sales are total activity output sales in Macro SAM I less government sales (10); it could also have been defined as the total production cost (column total) of the private activity. The remaining entries, all to or from the private commodity, are identical to the payments to or from the aggregate commodity in Macro SAM I (11‐18).

**Table 3.4. Macro SAM II for archetype low‐income country in 2015 (percent of GDP)**

||act‐prv a|ct‐gov c|om‐prv co<br>|m‐gov|f‐lab f‐cap|hhd gov row t|ax‐act ta|x‐com ta|x‐imp ta|x‐exp t|ax‐dir cssoc|sav‐inv dstk|total<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|act‐prv|||153.1||||||||||153.1|
|act‐gov||||11.7|||||||||11.7|
|com‐prv|63.6|8.4||||80.8<br>19.8||||||19.2 ‐6.2|185.6|
|com‐gov||||||11.7|||||||11.7|
|f‐lab|49.1|3.3||||0.1|||||||52.6|
|f‐cap|40.0|||||0.2|||||||40.2|
|hhd|||||52.3 37.8|1.6<br>2.8|||||||94.5|
|gov||||||1.8<br>0.9|0.3|5.7|1.4|0.0|4.0<br>0.0||14.1|
|row|||25.3||0.2<br>2.4|0.5<br>0.1|||||||28.5|
|tax‐act|0.3||||||||||||0.3|
|tax‐com|||5.7||||||||||5.7|
|tax‐imp|||1.4||||||||||1.4|
|tax‐exp|||0.0||||||||||0.0|
|tax‐dir||||||4.0|||||||4.0|
|cssoc|||||0.0||||||||0.0|
|sav‐inv||||||7.4<br>0.8<br>4.9|||||||36.4|
|dstk||||||||||||‐6.2|‐6.2|
|total|153.1|11.7|185.6|11.7|52.6 40.2|94.5 14.1 28.5|0.3|5.7|1.4|0.0|4.0<br>0.0|36.4 ‐6.2||

Source: Authors’ elaboration.

‐15‐
16 service. In other words, citizens notice when the activities of civil servants differ from the standards set by the legislation and codes of ethics. Corruption, unethical conduct and maladministration are the most common realities faced by citizens. The only way to increase public trust is to provide more information on anticorruption achievements and to seek transparency, efficiency and effectiveness in delivering services to various societal groups” (Palidauskaite, Pevkur & Reinholde, 2010; p. 68).

_Table 1_ :

# **Tax Morale Development over Time in Eastern European Countries that Joined the EU**

|**EU Country**|**Tax Morale**<br>**1999**|**Joined**|**Tax Morale**<br>**2008**|**Prob >**<br>**|z|**|**N**|
|---|---|---|---|---|---|
|**Bulgaria**|2.316|1-Jan-07|2.391|0.397|2441|
|**Czech Republic**|2.209|1-May-04|1.923|0.000|3660|
|**Estonia**|1.563|1-May-04|1.259|0.000|2468|
|**Hungary**|2.252|1-May-04|2.536|0.000|2482|
|**Latvia**|2.113|1-May-04|1.561|0.000|2474|
|**Lithuania**|1.433|1-May-04|1.546|0.044|2370|
|**Poland**|2.228|1-May-04|1.809|0.000|2548|
|**Romania**|1.97|1-Jan-07|1.775|0.001|2514|
|**Slovakia**|2.181|1-May-04|1.925|0.000|2780|
|**Slovenia**|2.122|1-May-04|2.265|0.017|2359|

Hanousek and Palda (2008) explore tax evasion using survey data (2000, 2002, 2004, and 2006) from the Czech Republic to measure its development for the years 1995 to 2006. Their results indicate that the number and percentage of evaders increased until the early millennium and started to decrease. The authors called the inverse-U shape “an evasional Kuznets curve” (p. 3) and suggested this could indicate that hysteresis “may not be a feature of evasion in a transition economy” (p. 3). However, our results on the development of the Czech Republic and Slovakia between 1999 and 2008 actually indicates a decay of tax
-10-

# I _L_ 2 Mea.

Is extreme-poverty mostly a rural phenomenon? Political considerations aside, which regions should be targeted for poverty alleviationi programs? Policy makers need to know the number of people livinz in moderate and extreme-poverty. But they also need to know how poor are the poor, where they are located, what the regional composition of national poverty is, and how much of total poverty is accounted for by the moderately-poor and the extremely-poor. One often finds large numbers of indicators quoted in povertdiscussions (life expectancy, literacy rates, child mortality rates, access t_ piped water, number of hospital beds per region, etc.). This information is useful as evidence of poverty, but is less useful for policy. To the extent that these indicators are highly correlated with each other, any one would suffice. To the extent that they are not, policy makers .: -d to know which should guide resource allocation. In addition, many of these indicacors confuse inputs with outputs: we do not really care about the number of hospital beds per region but about health status; longer life expectancy is a result of good nutrition and healthy ives.

|tsultiple indicators of poverty have been used in previous studies of poverty in Mexico. In particular the influential study by the Coordinacion General del Plan Nacional de Zonas Deprimidas y Grupos Marginados (henceforth Coplqmzr), computes an 'indice de ma Slnalizacionl 6 from a list of 19 diffe-ent socioeconomic indicators. For four rea'ons this is a doubtful procedure. One, the list of indicaters is to some extent arbitrary, and mixes issues of lack of infrastructure with evidence of extreme-poverty. Two, the weights on the 19 indicators calculated at the state level are not equal to the regional weights, which in turn are not equal to the municipal weights (see Coplamar, 1985c, pp. 30-56). Three, the sign of the weights varies as the geographical 8 level of aggregation changes. Fourth, the index of marginalizaUion provides no information about the depth or distribution of poverty, and cannot be used 9 to rank regions to allocate resources in a poverty alleviation program

8The weights for the marginalization index are obtained using the method ef principal components, i.e., the weights are the elements of the eigenvector associated with the dominant root of the square matrix formed from the product of the region/indicator matrix times its transpose; there is no guarantee that this eigenvector is non-negative (and indeed it is not).

> 9To the extent that Coplamar's indice de marginalidad is used by current government programs for poverty alleviation, there is a risk of mis-targeting the population; I return to this point in section VII.2.
# **Figure 2: The relationship between the ratio of tax to GDP and per capita incomes: an international comparison**

Data for 2006 or 2007 (1)

## **Tax revenue including social security contributions**

<!-- Start of picture text --> Tax revenue<br>(% of GDP)<br>50<br>DNK SWE y = 0.31x + 26.44<br>R2 = 0.27<br>45 BEL<br>ITA FIN NOR<br>SCG FRA AUT<br>40 BIH HRV HUN SVN ESP ISL NLD<br>PRT CZE NZL GBR<br>35 POL DEU CAN<br>ZAF RUS LVA GRC AUS IRL<br>30 MKD SVK CHE<br>LTU JPN USA<br>25 BRA ARG TUR EST KOR<br>20 CHN MEX<br>IND<br>15<br>10<br>0 10 20 30 40 50 60<br>GDP per capita (thousand USD)(2)<br><!-- End of picture text -->

## **Tax revenue excluding social security contributions**

<!-- Start of picture text --> Tax revenue<br>(% of GDP)<br>50<br>DNK y = 0.26x + 19.04<br>R2 = 0.25<br>45<br>40<br>ISL<br>35 NZL SWE NOR<br>FIN AUS<br>3025 BIHSCGZAFRUS HRV HUN PRT SVNITA ESPGBRFRA BELAUT CANNLD IRL<br>ARG POL LVA KOR DEU CHE USA<br>20 MKD TUR LTU EST CZE GRC<br>CHN BRA MEX SVK JPN<br>15 IND<br>10<br>0 10 20 30 40 50 60<br>GDP per capita (thousand USD)(2)<br><!-- End of picture text -->

- (1) 2004 for Argentina and Serbia and Montenegro.

- (2) Calculated using current purchasing power parities.

_Source:_ OECD (2008), National Accounts of OECD Countries - online database, February, IMF (2008), Government Finance Statistics, International Monetary Fund, December; World Bank (2009), World Development Indicators - online database, February; World Bank (2008), and FYR Macedonia - Public Expenditure Review, Report No. 42155-MK, February; Indian Ministry of Finance (2008), Indian Public Finance Statistics 2007-2008; CEIC database for China.

11
|**Table 5: Impact on Employment**|Firm|Data||||PILA Data||||
|---|---|---|---|---|---|---|---|---|---|
||Jan 2013|‐Dec 2017|||Jan 2013‐|Dec 2017||Jan 2013|‐Dec 2018|
||||Firm|Cond|itional|Uncon|ditional|Cond|itional|
||Level|I.H.S.|Survival|Level|I.H.S.|Level|I.H.S.|Level|I.H.S.|
|Individual Treatment*During Intervention|‐3.012|‐0.018||‐1.987|‐0.058*|‐1.048|0.001|‐2.056|‐0.063*|
||(2.912)|(0.040)||(2.339)|(0.035)|(2.279)|(0.045)|(2.129)|(0.033)|
|Individual Treatment*Post Intervention|‐2.150|0.040|0.019|1.222|0.027|2.563|0.113|||
||(3.741)|(0.052)|(0.049)|(4.253)|(0.066)|(4.103)|(0.094)|||
|Group Treatment*During Intervention|3.837*|0.101**||4.685|0.097*|3.081|0.081|4.815|0.104**|
||(2.268)|(0.039)||(3.053)|(0.050)|(3.386)|(0.094)|(3.155)|(0.052)|
|Group Treatment*Post Intervention|5.874**|0.121**|0.019|6.806*|0.164**|4.044|0.087|||
||(2.848)|(0.049)|(0.049)|(3.746)|(0.068)|(4.199)|(0.133)|||
|Individual Treatment* Year 1 Post||||||||0.104|‐0.008|
|||||||||(3.071)|(0.043)|
|Individual Treatment* Year 2 Post||||||||1.662<br>(4.534)|0.049<br>(0.071)|
|Individual Treatment* Year 3 Post||||||||1.698<br>(5.224)|0.014<br>(0.098)|
|Group Treatment* Year 1 Post||||||||7.516*<br>(4.016)|0.173**<br>(0.076)|
|Group Treatment* Year 2 Post||||||||6.545<br>(4.832)|0.169*<br>(0.093)|
|Group Treatment* Year 3 Post||||||||5.549|0.158|
|||||||||(5.324)|(0.096)|
|Sample Size (N*T)|7299|7299|159|8553|8553|9076|9076|10225|10225|
|Number of Firms|145|145|159|146|146|156|156|146|146|
|P‐value: Individual=Group During|0.058|0.033||0.036|0.013|0.258|0.412|0.041|0.011|
|P‐value: Individual=Group Post|0.072|0.190|1.000|0.112|0.044|0.734|0.844|0.322|0.128|
|P‐value: Individual Year 1 = Year 2 = Year 3||||||||0.801|0.269|
|P‐value: Group Year 1 = Year 2 = Year 3||||||||0.744|0.943|
|Control Mean in 2013|56.077|4.360|0.937|59.291|59.291|56.219|56.219|59.291|4.420|
|Control S.D. in 2013|51.328|0.864||51.950|51.950|51.190|51.190|51.950|0.890|
|Notes:<br>Fixed effects regressions with firm and tim<br>*, **, *** denote significance at the 10, 5, a<br>Level denotes monthly level of employme<br>Firm data are taken from firm records, PILA<br>Conditional is for the group of surviving fir|e fixed ef<br>nd 1 perc<br>nt; I.H.S. is<br>data are f<br>ms, uncon|fects. Stan<br>ent levels<br>inverse h<br>ormal emp<br>ditional co|dard error<br>respective<br>yperbolic s<br>loyment d<br>des emplo|s clustered<br>ly.<br>ine transf<br>ata from<br>yment as|at the fir<br>ormation.<br>administra<br>zero once|m level are<br>tive record<br>firm dies.|in parent<br>s.|heses.||

39
DSM had a measurable impact on the use or adoption of fresh teff seed and its productivity. We estimated the impact of DSM on teff following the same strategy as a falsification test. Indeed, the aggregation of average treatment effects based on cohorts and survey years (Appendix Table A2), as well as event-study aggregation (Appendix Figure A1), provides no evidence that DSM influences the amount of teff seed farmers purchase, the quantity of seed purchased per hectare, or teff yield.

# **6 Discussion**

This study finds that the DSM approach increased maize yields by enabling more farmers to purchase seed and by increasing the quantity of seed sown per hectare. This, in itself, is a desirable productivity outcome for Ethiopia and offers encouraging evidence in support of the DSM approach and, more broadly, the role of competitive markets in Ethiopia’s agricultural transformation. However, the study also finds no impact of DSM on the quantity of wheat seed purchased or on wheat yields. We explore several factors that might explain these differential effects.

The primary explanation for these crop-specific differences in findings is likely linked to the wellknown differences in the reproductive biology of maize (particularly hybrid maize) and wheat. Typically, for farmers to realize the full productivity benefits conferred by hybrid vigor (heterosis), which results from crossing inbred parent lines, they must purchase fresh seed each season—seed that is the first-generation (F1) progeny of these parent lines. This recurring need for fresh seed sustains demand and provides private firms with an opportunity to sell seed each season. This unique characteristic associated with hybrid vigor also allows seed companies an opportunity to recoup a share of their investments in breeding, production, and marketing from repeat seed sales. In contrast to hybrid maize, OPV maize and self-pollinating wheat present limited opportunities for profit-maximizing firms because farmers can use saved seed or rely on farmer-to-farmer exchanges of seed for several years without significant genetic deterioration that might affect yields or output (Belay 2004; Benson, Spielman, and Kasa 2014).

The unique profitability function associated with the crop reproductive biology of hybrid maize is a key factor explaining the large levels of global private investment in maize breeding and seed marketing relative to other major field crops (Fuglie et al. 2011; Morris 1998). The same is true for Ethiopia: the profitability of hybrid maize incentivizes private sector entry into the maize seed 22