rafmacalaba/datause-encoder-data huggingface dataset:
-9 -

Table 8. Determinants of Pakistan's Manufacturin, Growth Rate. 1974-1992

||Constant Term|Growth of<br>Capital Stock|Growth of<br>Labor|Measure of trade<br>policyr|Structural change<br>Summary|
|---|---|---|---|---|---|
||C|GMK(-2)al|GML(-I) bl/|(X+M/GDP)-I bh|D|
|Value of coefficient|-9.64|0.31|0.16|0.25|6.27|
|t-statistic|(-1.31)|a-36)|(1.41)|(1.23)|(3.82)|
||A|djustedR2=0.65;|D.W. = 1|.89||

31 The best estimates of capital appeared with a two-period lag. bf The best estirnates of labor and trade variable appeared with a one-period lag.

10. The main results of the statisdcal analysis are as follows:

- Physical capital accumulation played a very important role in Pakistan's growth performance-overall and in the manufacturing sector-over the past two decades.

- :Labor's contibution to overall growth showed up with the right sign and was also statistically significant at the _5%_ level. However, in the case of manufacturing, the coefficient was 'significant only at the 20% level.

- Trade policy, as measured by the ratio of total value of exports and import to GDP, came up with the right sign and a significaht coefficient (at the 5% level) in the case of overall GDP; however, the variable was significant at only the 25 % level in the case of growth of maTnfacturing output.

- The strctural change dummy, included to captare the impact of the change in the policy regime m Pakistan from the socialist-orientation of the 1972-77 period to the market-oriented economy since 1978, came up with a significant coefficient in both theequations.

# Interoretation of the Regression Results

11. The results of the regression analysis are intaitively appealig and consistent with the predictions,of economic analysis. Although Pakistan's investment rate remains low, by ipternational standards, the t&tal and fiked invesunent rates grew stadily during the 1970s and then stabilized at around 19% and 17% of GDP respectively in the mid-1980s (see Graph 2). This allowed the capital stock, economy-wide and 'in the manufactring sector, to expand fairly rapidly, which in turn supported a substantial increase in the capital labor ratios and labor productivity (see Table 9).

> 3 This measure of die openness of trade policy has been frequendy used in regression analysis, despite its limitations because of the ease of consruction. More sophisticated indicators, such as the one used by Leaner (1988), are aLw problematic. See Harrison (1992) fbr a critical rview of the literature on openness and growth.
in the ASP. Moreover, the estimated impacts are important in terms of magnitude. For example, the increase in time to do homework was up to 17% and the reduction in absenteeism was almost 25%. Moreover, teacher reports of student misbehaviors indicate a reduction of almost 0.14–0.17 standard deviations (intensive margin) and 6–11 percentage points in the probability of having a bad behavior report (extensive margin).

In terms of emotional regulation (panel B), estimations indicate that most of the statistical differences come from the comparison between the HM and C groups. I estimate a reduction in the overreaction to stimuli (valence), particularly to positive ones (positive valence difference), and a move toward internal locus of control, which is a belief that their own actions determine the rewards they receive. The HT and C groups are also different in the overreaction to positive stimuli. The rest of the estimated coefficients are not statistically different from zero.

Finally, academic performance can be improved by any type of group composition, compared to no treatment. As panel C shows, the HT composition of peers can increase the probability of a passing grade by almost 4 percentage points (column 1), while being assigned to similar peers can also reduce the probability of failing at least one grade by almost 3 percentage points, both compared to the C group (column 2).

Does one particular group composition improve the ASP’s effectiveness more than another? Overall, the estimated results indicate that the HT composition of peers has a better impact on most of the outcomes than tracking by violence. As presented in panel A, students assigned to the HM groups show a reduction of 0.16 standard deviations, on average, in positive attitudes toward school, compared to students assigned to heterogeneous groups. They also increase their probability of having a bad behavior report at school by 5.5 percentage points.

As shown in panel B, the only statistical difference between the HM and HT group compositions is in participants’ stress. I find that when students are treated in the HM groups, their stress 25
**Table 1. Inventory of Socioemotional Skills in the 2012 Colombia STEP Household Survey**

|**Skill /**<br>**Personality trait**|**Definition**|**Questionnaire item**|
|---|---|---|
||Appreciation for<br>li|Do you come up with ideas other people haven't thought of before?|
|Openness to<br>experience|art, earnng,<br>unusual ideas, and<br>variety of<br>|Are you very interested in learning new things?|
||experience|Do you enjoy beautiful things such as nature, art, and music?|
||Tendency to be|When doing a task, are you very careful?|
|Conscientiousness|organized,<br>responsible, and<br>|Do you prefer relaxation more than hard work? R|
||hardworking|Do you work very well and quickly?|
||Sociability,<br>tendency to seek|Are you talkative?|
|Extraversion|stimulation in the<br>company of<br>|Do you like to keep your opinions to yourself? Do you prefer to keep quiet<br>whenyou have an opinion? R|
||others,<br>talkativeness|Are you outgoing and sociable⎯for example, do you make friends very<br>easily?|
||Tendency to act|Do you forgive other people easily?|
|Agreeableness|<br>in a cooperative,<br>unselfish manner|Are you very polite to other people?|
|||Are you generous to other people with your time or money?|
||Predictability and<br>consistency in|Are you relaxed during stressful situations?|
|Emotional<br>stability|emotional<br>reactions, with<br>|Do you tend to worry? R|
||absence of rapid<br>mood changes|Do you get nervous easily? R|
|||Do you finish whatever you begin?|
|Grit|Perseverance with<br>long-term goals|Do you work very hard? For example, do you keep working when others<br>stopto take a break?|
|||Do you enjoy working on things that take a very long time (at least several<br>months)to complete?|
|||Do you think about how the things you do will affect you in the future?|
|Decision makin|Way in which<br>individuals|Do you think carefully before you make an important decision?|
|g|approach decision<br>situations|Do you ask for help when you don’t understand something?|
|||Do you think about how the things you do will affect others?|
|Hostile|Tendency to<br>i htil|Do people take advantage of you?|
|attribution bias|perceve ose<br>intents in others|Are people mean/not nice to you?|

_Source_ : Authors’ elaboration based on Almlund _et al._ (2011); John and Srivastava (1999); World Bank (2014).

_Note:_ For each item, response categories range from 1 to 4: (1) almost never; (2) sometimes; (3) most of the time; (4) almost always. The score of each trait domain (e.g. extraversion) is the average of the individual scores on items of this trait. "R" refers to items that are reversely coded for the aggregation.

7
# Table 1

Deposits, Savings, Certificates of Deposits and Bank Debentures (trillion yen)

||TOTAL|Banks|Trust<br>Fund<br>Bureau|Insur<br>Comp|Agric<br>Coop|Other|
|---|---|---|---|---|---|---|
|1945|0.3|0.1|0.05|0.01|0.02|0.01|
|1950|1.8|1.2|0.2|0.06|0.1|0.05|
|1960|18.8|13.2|1.9|0.9|0.8|0.4|
|1970|107.7|67.2|13.7|6.6|6.2|3.5|
|_1980_|499.3|272.4|97.0|30.9|27.5|16.5|
|1988|1094.3|559.7|209.3|108.5|47.3|36.8|

- Notes: Totals include overlapping accounts, i.e., interfinancial institutions deposits.

- Banks include city banks, regional banks, trust banks, long-term credit banks, sogo banks and shinkin banks. Trust fund bureau includes postal savings.

- Other includes the Shokochukin and Norinchukin banks. The series is discontinued after 1988.

- Source: Bank of Japan, _Economic Statlstics Annual,_ 1989 (pp. 163-166) and 1988 (pp 161-164).

Table 2 Size of the Financial Sector relative to GNP

||1960|1970|1980|1988|
|---|---|---|---|---|
|TOTAL (tn yen)|18.8|107.7|499.3|1094.3|
|GNP (tn yen)|16.7|75.2|245.4|379.2|
|% GNP|113|143|203|289|

Source: Bank of Japan, _Econnmic Statistics Annual,_ 1991 p. 10 and Table 33
ESTIMATES OF COSTS AND BENEFITS 75 Table **5.3:** Controlled and 'Uncontrolled' Rents Paid by Sector (Cedis)

||+---|Gross|Rent|--- +|+-Gr|ossRent/Co|nsumption+||
|---|---|---|---|---|---|---|---|---|
||Q3|Median|Q1|N|Q3|Median|Q1|N|
|AllRenters|||||||||
|Tenement|300|300|300|403|**.03**|**.02**|**.02**|383|
|Indigenous|300|300|**200**|351|**.04**|**.02**|**.02**|339|
|Both|300|300|202|754|**.03**|**.02**|**.02**|**722**|
|"Controlled" Renters|||||||||
|Tenement|300|300|300|359|**.03**|**.02**|**.02**|341|
|Indigenous|300|250|200|318|**.03**|**.02**|**.02**|307|
|Both|300|300|200|677|**.03**|**.02**|**.02**|**648**|
|"Uncontrolled' Renters|||||||||
|Tenement|866|500|**500**|44|**.05**|**.04**|**.03**|42|
|Indigenous|929|600|500|33|**.09**|**.07**|**.04**|32|
|Both|875|600|500|77|**.08**|**.05**|**.03**|74|

Note: Unveighted statistics, high cost and government excluded.

Figure **5.3:** Rents by Sector (Cedis) The striking results are the lack of variation in rents in both indigenous and 1000 tenement controlled sectors, and the much larger variation in "uncontrolled" rents. 800 The difference between sectors is dominated by differences between controlled and 600 uncontrolled units within sectors. Recall that the indigenous sector has more single 400 rooms and more houses built in swish. Their controlled rents are somewhat lower as a 200 result (see Table 2.5, above).

<!-- Start of picture text -->
--  - - ----<br>400<br>200  .-.  e  ...<br>lOndig Cont  Inoig  Uncont  Tenmt  Cont  lenmt Uncont<br><!-- End of picture text -->

# D. Estimating PmOm With a Cross Country Model of Housing Demand

This section presents a variant of the cross-country housing demand model of Malpezzi and Mayo (1985, 1987a, 1987b) which can be used to estimate market rr;nts in the absence of controls. These estimates can be used to predict market -;ents in cities where no uncontrolled sector exists for comparison; to test for bias and to adjust rents in the uncontrolled sector if such rents have been affected by controls as in Fallis and Smith's model; and as an independent check on other methods.

First _we_ will describe Malpezzi and Mayo's model generally, then we will present new estimates from a variant of that model. Malpezzi and Mayo first
**Table 4:  Length and percentage of federal road network not meeting standards (2009)**

||Length (km)|Percentage|
|---|---|---|
|Failing to meet minimum riding quality requirements|28,500|57.1|
|Failing to meet minimum grip requirements|12,200|24.4|
|Failing to meet minimum strength requirements|24,900|49.9|
|Failing to meet minimum defects requirements|35,100|70.3|

Source: Avtodor, Long-Term Program of the Russian Motor Roads Public Company (2010-2015), December 31, 2009.

**Given these findings based on descriptive statistics, the question arises whether they can be corroborated by econometric analysis and to what extent infrastructure deficiencies affect firm productivity.** This is the question we take up by explicitly modeling changes in TFP productivity growth using a vector of infrastructure variables from the BEEPs surveys, combined with firm characteristics.

**Since BEEPs surveys are infrequent, and in this case were conducted in 2004 and 2008 only, results in this section use data for 2004 and 2008 only.** Following Escribano and Gausch (2004) and Alam et al (2008), we postulate the general regression model, with changes in productivity being the dependent variable, and include controls for firm characteristics.

For each of Russia’s regions, and for each sector (2-digit NACE classification), we calculate firm size (micro, small, medium, and large) and sector responses to each of the following infrastructure variables: (a) _Electricity_ : number of days of power outage, percentage losses from power outage, and response to the question: Did you experience power outage?

(b) _Water_ : frequency of insufficient water supply, and a dummy on whether the firm experienced insufficient water supply.

(c) _Transportation:_ The proportion of firms reporting that transportation is a major or severe constraint, derived from the question: “How much of an obstacle is transportation?” 18
**_<u>Figure 7. Infrastructure, Credit, Benchmarking and Returns to Investment</u>_**

<!-- Start of picture text -->
1. Growth Impact of Infrastructure 2. Composition of Domestic Credit Stock<br>7% 100%<br>90%<br>6%<br>80%<br>5%<br>70%<br>4%<br>60%<br>3% 50%<br>2% 40%<br>30%<br>1%<br>20%<br>0%<br>10%<br>2000-2013 Early Late 2000s Early<br>-1%<br>2000s 2010s 0%<br>2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14<br>Telephone Mobile Roads SOEs CG credit Private credit<br>3. Simulated Income Levels (SSA benchmark) 4. Simulated Income Levels (LMIC benchmark)<br>Counterfactual income % income change Counterfactual income % income change<br>5. Public and Private Investment (% of GDP) 6. Estimated Marginal Product of Capital (%)<br>0.25 0.5<br>0.45<br>0.2 0.4<br>0.35<br>0.15 0.3<br>0.25<br>0.1<br>0.2<br>0.15<br>0.05<br>0.1<br>0.05<br>0<br>0<br>1985 1990 1995 2000 2005 2010<br>1985 1990 1995 2000 2005 2010<br>Public Investment Private Investment Public Capital Private Capital<br>2000 200 2000 150<br>1500 150 1500<br>100<br>1000 100 1000<br>50<br>500 50 % income change 500 % income change<br>(Counterfactual) income level (Counterfactual) income level<br>0 0 0 0<br>actual infrastructure govt consumption credit/GDP openness inflation schooling banking crises actual infrastructure govt consumption credit/GDP openness inflation schooling banking crises<br><!-- End of picture text -->

Source: 7.1: Author’s calculation. 7.2: National Bank of Ethiopia. 7.3 and 7.4: Author’s calculations. Figure 7.5 and 7.6: Eden and Kraay (2014ab).

21
**Table 7: Quality and Trade Cost Effect by Tenth Parts (Average Unit Value of Product Group)**

|Tenth Parts|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Wei. Dist.|0.236***|0.179***|0.175***|0.177***|0.192***|
||(0.024)|(0.022)|(0.019)|(0.020)|(0.019)|
|Observations|85779|85849|85595|86023|343246|
|R-squared|0.224|0.079|0.053|0.040|0.361|
||(6)|(7)|(8)|(9)|(10)|
|Wei. Dist.|0.198***|0.176***|0.204***|0.194***|-0.279***|
||(0.021)|(0.025)|(0.030)|(0.018)|(0.083)|
|Observations|85694|85999|85735|686315|85709|
|R-squared|0.034|0.028|0.025|0.468|0.157|

Notes: All regressions are run with importing and exporting country fixed effects as well as product fixed effects, but for which the coefficients are not reported; Wei. Dist. stands for the log of weighted distance; ***,**,* denotes significance at the 1 %, 5 %, and 10 % level, respectively; robust standard errors, which are in parentheses, are corrected for clustering by country pair.

# **3.4 Market Size Effect**

Our model predicts that market size is positively correlated with the average price of exports (Result 3). To test this result empirically, a measure of market size needs be included in the econometric specification of (13). Excluding the bilateral variables adjacency, language and currency, we obtain the following equation: In equation (14) _msi_ and _msj_ stand for the market size of the exporting ( _i_ ) and importing ( _j_ ) country, respectively. The other variables are identical to equation (13).

A country’s GDP might constitute the most appropriate variable to measure its market size.

The population size also holds information on the size of an economy. The predictions of our model are tested using the ratios between the exporting and importing country.<sup>31</sup> Table 8 reports the estimation results for the market size effect. Column (1) and (3) of Table 8 include the GDP ratio and column (2) and (4) the population ratio in the regression.

> 31 Other studies (e.g. Hallak, 2006) use the difference of per capita income as a variable to explain quality trade. The underlying thinking of these models is that different consumption preferences shape the quality content of trade flows (Linder Hypothesis). However, in our model the economies are assumed to be identical in consumption and per-capita income differences are neglected.

30
**Table 1 Decomposition of area, yield, and production growth by crop**

||**Maize**|**Wheat**|**Rice**|**Soybeans**|**Big-4**|**Other**|**All**|
|---|---|---|---|---|---|---|---|
|**A. 1961-63 average**||||||||
|_Area (million hectares)_|105.8|206.0|118.3|24.0|454.1|517.5|971.6|
|_Yield (MCal/ha)_|7.1|3.8|5.4|3.8|5.0|3.9|4.4|
|_Production (million MCal)_|747.8|786.0|643.3|91.8|2,268.9|2,039.1|4,308.0|
|_Area (share, percent)_|10.9|21.2|12.2|2.5|46.7|53.3|100|
|_Production (share, percent)_|17.4|18.2|14.9|2.1|52.7|47.3|100|
|**B. 2019-21 average**||||||||
|_Area (million hectares)_|200.1|218.1|163.0|126.0|707.2|715.6|1,422.7|
|_Yield (MCal/ha)_|20.8|11.7|13.2|9.4|14.2|7.6|10.9|
|_Production (million MCal)_|4,166.2|2,551.6|2,155.8|1,187.0|10,060.6|5,427.5|15,488.2|
|_Area (share, percent)_|14.1|15.3|11.5|8.9|49.7|50.3|100.0|
|_Production (share, percent)_|26.9|16.5|13.9|7.7|65.0|35.0|100.0|
|**C. Growth from 1961-63 t**|**o 2019-21**|||||||
|_Area (log change)_|63.7|5.7|32.0|165.7|44.3|32.4|38.1|
|_Yield (log change)_|108.1|112.0|89.0|90.2|104.7|65.5|89.8|
|_Production (log change)_|171.8|117.7|120.9|256.0|148.9|97.9|128.0|
|**D. Contribution to growt**|**h from 19**|**61-63 to 2**|**019-21**|||||
|_Area (percent)_|37.1|4.9|26.5|64.7|29.7|33.1|29.8|
|_Yield (percent)_|62.9|95.2|73.6|35.2|70.3|66.9|70.2|
|_Production (percent)_|100|100|100|100|100|100|100|
|<br>**E. Contribution to growt**|**h from 196**|**1-63 to 2**|**019-21 (p**|**roduction-a**|**djusted sh**|**are)**||
|_Area (percent)_|8.2|0.8|3.8|3.2|17.5|13.6|29.8|
|_Yield (percent)_|13.9|16.5|10.6|1.7|41.3|27.6|70.2|
|_Production (percent)_|22.1|17.4|14.4|4.9|58.8|41.2|100|

**Notes:** “All” includes 144 commodities. “Big-4” is the sum of maize, wheat, rice, and soybeans. “Other includes” the remaining 140 commodities. Yield is measured as Mcal/hectare. The changes from 1961-63 to 2019-21 are calculated as logarithmic changes. The contribution of growth in panel E is evaluated at the average production shares of the two sub-periods. For example, the contribution of maize area is calculated as (all numbers are taken from the first column): 8.2 = 0.221*37.1, where 0.221 = 0.5*(0.174 + 0.269). Numbers may not add up due to rounding.

**Source** : Authors’ calculations based on FAO data.

_— 19 —_
representative listing for the name and size of the firms where they are employed. From this list, we selected an additional 210 firms with 5 to 250 workers.

The survey took an average of one hour and a half to complete, and collected rich data on the characteristics of the firm, and of the firm owner. In addition to standard operating data, the survey had detailed modules on the education and employment background of the owner, the owner’s ability and personality traits, members of the owner’s household, use of finance and loans, informality, the competitive environment facing the firm, and most importantly for this study, a detailed module on innovation. Tables 1 and 2 summarize the key variables used in our study. These variables will be introduced and explained in later sections of the paper.

# **3. Innovation in Micro, Small and Medium Firms**

# **3.1 Defining Innovation**

Our survey follows the recommendations of the Oslo and Bogota Manuals for measuring innovation (OAS, 2001, OECD, 2005). The OECD’s Oslo Manual defines an _innovation_ as “the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations” (OECD, 2005, p.46). It notes that the minimum requirement for an innovation is that the product, process, marketing method, or organizational method must be new or significantly improved to the firm. This includes both innovations that the firm in question is the first to develop, as well as those adopted from other firms or organizations. In developing countries incremental changes, acquisition of embodied technology, and applications or adaptations of existing products or processes are thought to be the most frequent forms of innovation.

This general definition of innovation can be split into four subcomponents of innovation, defined in the Bogota and Oslo manuals as:

- 1) _Product innovation:_ the introduction of a good or service that is new or substantially improved.

- 2) _Process innovation:_ the introduction of a new or significantly improved production or delivery method.

- 5 -
**<u>Table 4: Reasons for commercial farms’ failure to cultivate all of the area allocated to them</u>**

||**Total **|**Tigray**|**Afar **|**Amhara**|**Orom **|**Som.**|**B'gl **|**SNNP **|**Gmbla**|
|---|---|---|---|---|---|---|---|---|---|
|**Incidence & reasons for**<br>|**cultivating**<br>|**less than**<br>|**the total a**<br>|**llocated area**<br>|<br>|||||
|Cult. <100% of area|48.22|16.62|13.51|26.95|59.71|40.52|88.22|70.88|69.54|
|Technology|30.91|43.73|96.55|29.48|39.74|37.76|14.57|33.55|41.01|
|Manpower|29.77|4.23|0.00|11.21|24.93|0.00|50.60|30.54|37.26|
|Land dispute|21.09|26.33|1.90|18.76|19.80|10.32|31.57|15.83|22.95|
|Land clearing need|18.07|24.12|0.00|40.25|11.82|6.87|24.05|6.97|15.15|
|Infrastructure|17.79|10.20|0.00|10.23|24.22|3.46|23.26|17.50|17.74|
|Credit|17.71|7.46|1.90|17.85|11.25|54.09|22.24|17.38|23.92|
|Delayed machinery|9.87|8.67|2.95|2.25|3.13|23.74|9.37|12.24|44.07|
|Lack of irrigation|6.26|3.75|1.56|2.15|6.55|31.47|7.07|7.44|4.31|
|Lack of skilled workers|3.88|3.88|0.00|5.67|4.84|0.00|6.04|1.11|7.61|
|Input shortage|3.67|0.92|0.00|2.19|5.56|7.45|7.07|0.83|13.34|
|Marketing|3.47|6.53|37.20|3.16|3.85|0.00|3.39|1.26|10.08|
|Foreign exchange|1.06|0.00|0.00|0.69|3.70|0.00|0.00|0.84|3.73|
|**Scope for expansion**||||||||||
|Tried to expand|38.75|23.72|37.63|60.52|38.33|3.04|38.62|26.98|18.05|
|If yes, faced problems|64.50|39.49|47.30|68.82|68.61|0.00|70.01|64.40|72.01|
|Gave advice to farmers|63.79|79.54|32.99|50.20|68.13|34.16|55.40|77.84|66.72|

_Source:_ Own computation from 2013 CSA large farm survey 18
**Figure 12. Change in GDP (%) relative to the baseline in 2020 – by region**

<!-- Start of picture text --> Rest of SSA<br>South Africa<br>MENA<br>Rest of ECA<br>Russia<br>Rest of LAC<br>Brazil<br>Argentina<br>Rest of SA<br>India<br>Rest of EAP<br>Thailand<br>Malaysia<br>Indonesia<br>China<br>Rest of EU & EFTA<br>UK<br>Spain<br>Italy<br>Germany<br>France<br>United States<br>Canada<br>Japan<br>Aus‐NZ<br>‐0.4 ‐0.3 ‐0.3 ‐0.2 ‐0.2 ‐0.1 ‐0.1 0.0 0.1 0.1 0.2<br>AT ET<br><!-- End of picture text --> <!-- Start of picture text --> AT<br><!-- End of picture text -->

# **5. Conclusions**

In this study we analyze the impacts of a large scale expansion of biofuels –reflected in the announced targets of numerous countries, both developed and developing- on agriculture, landuse and food supply. The first core finding is the moderate long-term impact on commodity prices induced by a large expansion of biofuels under both scenarios (Announced Targets, and Enhanced Targets more or less doubling the former). This finding is conditioned on yield gains taking place as a response to increases in commodity and land prices. In addition, and for both 42