rafmacalaba/datause-encoder-data huggingface dataset:
_14_ _Price Support at Any Price?_

# **3. Price Support: Timing and** Costs

The analysis of the likely effects of various agricultural policy mixes for Poland presented in chapters 3-5 is based on simulation analysis performed with the use of the CGE model POLAGR.

The model is solved for the 19-year period 1991-2010 (in fact, the agricultural policy .... variables are not changed beforepolicy the year 1995). The base solution of the model (or the steady case) represents a steady growth path of 4.4 % (yearly average). _In the base case, no active agricultural policy is applied_ (the base run assumes a dynamic continuation of the situation from 1991, with the PSE level close to zero). The simulation experiments assume certain agricultural policy mixes (namely price supporting, various forms of income supporting, investment incentives etc.).. _Deviationsfrom the base run calculatedfor every experiment represent an evaluation of the agricultural policy effects (costs and benefits)._ <!-- Start of picture text --> .... Box 2. Policy cost measures: :ia the POLAGR imodelt<br>cost measures<br>comparable with the:OECD methdology (OECD,1994,Annex lII)<br>Cost for taxpayers is defined as anet :costof exporting<br>% (erodution surpluses (export subsidies minus:revenue from import<br>.+ E . . > ;;E: ::E:<br>levies) +direct payments to farmers + policy costs not connected<br>with price or income suppo rt(including investment incentives for:i.<br>job creation but excluding social security payments):<br>:<br>where: E-exports, P ;,,,domestic producer price, jP,=world<br>Trice, M -jimports, DP diiect payments, PCNPIs --Poicy<br>costsnotconnectedwiwth prceor income support.<br>Cost for consumers is derived from: a Consumer Subsidy<br>Equivalent (CSE) conceptuand is equal to:<br>.....<br>c 0 =Qc*( )<br>t:where: QG=volume of consumption.<br>The cost is therefore equal to _________i________<br>coserelaeossaud<br>a be)by higher PDO,: prices they have<br>~~~~to pa for food (dotte area ABDE):.<br>Please note, that the cost is smaller ; -<br>that the total welfare Eloss, caused<br>both bY higber preand the volume Ofconisumtip*on y from e _________ _<br>to c (area ABCDE).<br><!-- End of picture text --> Agricultural policy cost measures used the model are shown in Box 2. Benefits are measured by the income relation (a ratio of per capita income of farmers, both from farming and from other sources, to the average wage), decrease of the number of the macroeconomic impact.

;;; **Producer Subsidy Equivalent (PSE) ii** defined as:

## PSE= (QP(xPw)+DP+oS)*1oO/(Q*PD+DP)

where: **0Q.** volume of production. fPSE informs what percent of the producers' income from farmingin f is due to the price support or direct income support. . .. .. ...

This section evaluates the effects of various strategies of targeting EU prices level. The question we try to answer is: what is the best strategy of approaching EU prices, that allows to avoid excessive costs and that is coherent with the URA commitments of Poland?
**Figure A2** Sample page from SECC

SECC डाफट सूची - गामीण

||राजय<br><br>**:**|RAJASTHAN|िजला<br>Ajme<br>**:**|r|तहसी|ल<br>Ajmer<br>**:**|A<br>शहर/गाम**:**|jaysar|वाड कोड नबर|(कवल शहर|0000<br>क िलए)<br>**:**|गणन बलॉक|0158<br> -उप खड<br>**:**|_0|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|घरल सखय|ा<br>: 0003|घर कपकार|:साधारण|गाम पच|ायत|:-AJAYSAR|आिदम जनजाित|वग स ह<br>वध<br>:नही|ािनक रप स|छडाया गया|बधवा मजदर|हाथ<br>:नही|स मला साफ करन वा|ल<br>:नही|
|सखया|||नाम|||मिखया स<br>सबध|िलग<br>जनमितिथ|िपता का नाम<br>माता का नाम||ववािहक<br>िसथित#|वयवसाय/<br>गितिविध|अन.जाित/<br>जनजाित/अनय|िवकलागता<br>|िशका|
|001||||||मिखया|परष<br>1953|||2|मजदर|अनय|कोई िनःशकता नही|िनरकर|
|002||||||पतनी|सी<br>1955|||2|मजदर|अनय|कोई िनःशकता नही|िनरकर|
|003||||||पत|परष<br>1989|||1|मजदर|अनय|कोई िनःशकता नही|पव माधयिमक|
|भाग|1िववरण|:आवासीय/िन|वासीय||भाग3रो|जगार और आ|य िवशषताओ|भाग4 :िववरण|समपितया|भाग5अ:भ|िम सवािमतव(ए|कड म)<br>भ|ाग5ब:अनय भिम|सवािमतव|
|मकान क दीवार की पमख<br>सामगी<br>#|मकान की छत की पमख सामगी<br>#|मकान का मािलकाना हक की<br>िसथित|िनवास क कमरो की सखया|िनयिमत वतन पान वाला कॊई<br>पिरवार का सदसय|आयकर या वित कर दाता ह|सवय की/सचिलत ऐसी ससथा<br>जो शासन दारा पजीकत ह|पिरवार क सबस अिधक कमान<br>वाल सदसय का मािसक आय<br>पिरवार की आय का मखय सोत|रिफिजरटर<br>टलीफोन/मोबाईल फोन|दो/तीन/चार पिहया या मछली<br>पकडन की नाव पजीकत|सवािमतव की भिम(वास भिम<br>को छोडकर)|कल अिसिचत भिम<br>2फसलो वाली िसचाई भिम|अनय िसिचत भिम<br>यतीकत तीन/चार वहीलर किष<br>उपकरण|िसचाई उपकरण(नलकप.बोर.<br>डीजल/िमटटी क तल/िवदत पप<br>सट.फववारा/िडप िसचाई आिद<br>समत)|िकसान किडट काड की सीमा<br>50000रपए या अिधक ह।|
|6|6|सवय|4|नही|नही|नही|1<br>10,000या अिधक|हा<br>कवल<br>मोबाइल|दो पिहया|हा|1.0<br>3.0|1.0<br>नही|हा|नही|

Notes: This is a sample page taken from a PDF file that was scraped from secc.gov.in. Individual-level variables are name, relationship with head of household, gender, date of birth, parents’ names, marital status, occupation, caste category, disability and education. Householdlevel variables are wall material, roof material, house ownership, dwelling room count, salaried job, payment of income tax, ownership of registered enterprise, monthly income, source of income, asset ownership (refrigerator, telephone, vehicle, mechanized farm equipment, irrigation equipment, Kisan credit card), and land ownership.
## **Appendix B: The Survey Instrument**

# **<mark>In-Service Teacher Training Survey Instrument (ITTSI)</mark>**

|**In**<br>1<br>2|**troduction**<br>What is the name of the in-service teacher training program under discussion?<br>What is your full name?|
|---|---|
|3<br>**Ov**<br>|What is your role in this program?<br>**erarching aspects**<br>|
|4|By the end of this training what is it that you expect teachers to be able to do differently?|
|5|How many years has this program been running?|
|6|At what scale is this program implemented?<br>1.<br>National<br>2.<br>Multiple states or regions<br>3.<br>One state or region<br>4.<br>Less than one state or region|
|7|What kind of organization designed this teacher training program?<br>_(Select all that apply.)_<br>1.<br>Government<br>2.<br>Non-governmental organization<br>3.<br>Private company or social enterprise<br>4.<br>Researchers|
|8|What kind of organization is implementing this teacher training program?<br>_(Select all that apply.)_<br>1.<br>Government<br>2.<br>Non-governmental organization<br>3.<br>Private company or social enterprise<br>4.<br>Researchers|

43
households formerly attached to the formal labor market, tend to increase income inequality in most countries, non-contributory pensions have consistently served to decrease inequality (see for instance Lustig, Lopez-Calva and Ortiz-Juarez, 2013, and several Latin American country studies under the Commitment to Equity project). This section delves deeper into each of these income sources to identify how each has contributed to observed inequality trends in the region.

Figure 7. Decomposition of annual Gini change by income source in LA, regions and selected countries.

<!-- Start of picture text --> Uruguay urban Ecuador Argentina urban Colombia Dominican Republic Brazil Panama Peru Costa Rica Mexico<br>Annual Gini points<br>2003-10 2010-12 2003-10 2010-12 2003-10 2010-12 2003-10 2010-12 2003-10 2010-12 2003-10 2010-12 2003-10 2010-12 2003-10 2010-12 2003-10 2010-12 2003-10 2010-12<br><!-- End of picture text --> Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). Note: Dashed gray lines represent changes in Gini points between 2003-2010 and 2010-2012, or the nearest years in case data for these years are not available (see the Table A2 for details). The figure shows the Shapley Decomposition of inequality changes (Barros et al. 2006 and Azevedo et al. 2012). Detailed decomposition results are reported in Table A3 in the annex. Note that due to data limitation, public transfers are not fully captured in the Brazilian data. Instead, some of these are captured in “other non-labor income.” Changes in Gini are decomposed by changes in income sources using a non-parametric decomposition technique developed by Azevedo et al (2012) and based on the property that changes in inequality can be expressed as a function of changes in each income source along the distribution of total income (Barros et al 2006) (Figure 7). This decomposition allows us to see which income sources were the key drivers of inequality reductions and which income sources were behind the inequality trend reversals seen in the later period. One note of caution in interpreting results from this decomposition: it relies only on _changes_ in income sources to explain _changes_ in inequality. For example, in Argentina the full effect of transfers on inequality is not captured since transfers were already large prior to the first year of data included in the analysis. Thus, even as transfers may have maintained inequality lower than it would otherwise be, the decomposition shows that they have not contributed to further gains in inequality. Figure 7 shows the decomposition for the two periods for ten selected countries, including both upper-middle and lower-middle income countries with different inequality trends (see Table A3 for the decomposition results for all countries).

The first stylized fact is that, in almost all countries, inequality reduction coincided with a reduction of labor income inequality while increases in labor income inequality were seen during periods of 11
Table 12. Regression results for Nigeria for July floods in Nigeria and large conflict buffers

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||T-1 to t+1|T-1 to t+1|T-1 to t+1|T-1 to t+1|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|
|Treated|-0.0522<sup>***</sup>|-0.0522<sup>***</sup>|-0.000604|0.000655|
||(0.0109)|(0.0109)|(0.0105)|(0.0104)|
|PostPeriod|-0.0322<sup>***</sup>|-0.0392<sup>***</sup>|-0.0322<sup>***</sup>|-0.0322<sup>***</sup>|
||(0.00488)|(0.00784)|(0.00488)|(0.00488)|
|Treated*PostPeriod|-0.0120<sup>**</sup>|-0.0119<sup>**</sup>|-0.0120<sup>**</sup>|-0.0120<sup>**</sup>|
||(0.00547)|(0.00554)|(0.00547)|(0.00547)|
|PopDens|0.000505<sup>***</sup>|0.000505<sup>***</sup>|0.000408<sup>***</sup>|0.000413<sup>***</sup>|
||(8.49e-05)|(8.48e-05)|(7.93e-05)|(7.95e-05)|
|cf_cvgBuff05||0.00148<br>(0.00132)|||
|Urban_Suburban|||0.112<sup>***</sup><br>(0.0156)|0.112<sup>***</sup><br>(0.0157)|
|Fatalities||||-3.88e-05<sup>*</sup><br>(2.16e-05)|
|Constant|0.439<sup>***</sup>|0.433<sup>***</sup>|0.382<sup>***</sup>|0.382<sup>***</sup>|
||(0.00633)|(0.00832)|(0.00826)|(0.00821)|
|Observations|13,330|13,330|13,330|13,330|
|R-squared|0.674|0.674|0.682|0.682|
|Treatment Obs|5342|5342|5342|5342|
|Control Obs|7988|7988|7988|7988|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 0.14 to 16.64.

Table 13. Regression results for July floods in Nigeria and large conflict buffers at different periods post July floods

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||T-1 to t+1|T-1 to t+2|T-1 to t+3|T-1 to t+4|T-1 to t+5|T-1 to t+6|
|VARIABLES|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|avg_radBuff05|
|Treated|-0.0522<sup>***</sup>|-0.0554<sup>***</sup>|-0.0553<sup>***</sup>|-0.0600<sup>***</sup>|-0.0564<sup>***</sup>|-0.0531<sup>***</sup>|
||(0.0109)|(0.0113)|(0.0111)|(0.0119)|(0.0122)|(0.0117)|
|PostPeriod|-0.0322<sup>***</sup>|0.0163<sup>***</sup>|0.0705<sup>***</sup>|0.0633<sup>***</sup>|0.0533<sup>***</sup>|-0.0445<sup>***</sup>|
||(0.00488)|(0.00593)|(0.00689)|(0.00841)|(0.0108)|(0.00963)|
|Treated*PostPeriod|-0.0120<sup>**</sup>|-0.00657|-0.00898|-0.0101|-0.0232<sup>**</sup>|-0.0204<sup>**</sup>|
||(0.00547)|(0.00713)|(0.00680)|(0.00877)|(0.0107)|(0.00957)|
|PopDens|0.000505<sup>***</sup>|0.000485<sup>***</sup>|0.000555<sup>***</sup>|0.000580<sup>***</sup>|0.000634<sup>***</sup>|0.000602<sup>***</sup>|
||(8.49e-05)|(8.18e-05)|(8.59e-05)|(9.22e-05)|(9.82e-05)|(9.32e-05)|
|Constant|0.439<sup>***</sup>|0.441<sup>***</sup>|0.438<sup>***</sup>|0.438<sup>***</sup>|0.434<sup>***</sup>|0.434<sup>***</sup>|
||(0.00633)|(0.00630)|(0.00655)|(0.00705)|(0.00792)|(0.00752)|

26
rigidity. For example, models developed by Dunlop (1944), Shishter (1943) and Oswald (1986) assume that the unions try to maximise the total wage payments of their members, not taking into account the negative effect that excessive wage increases can have on employment. As a result, wages are downward rigid. The structure of wage setting is also likely to play an important role. One might expect unions negotiating at the firm level to be more flexible at the time of accepting wage cuts in exchange for the maintenance of employment when business conditions turn bad. In the theoretical model of Holden (2002), employment protection legislation (EPL) increases wage rigidity. Holden discusses that in the case of collectively negotiated wage agreements, wage cuts need the mutual consent of employers and employees. Such cuts are less easily obtained if the threat of lay-off is more difficult to implement for the firm, e.g. because of strong EPL.

In the empirical literature on wage rigidity, the above-described labour market institutions have been cited as the cause of differences in downward wage rigidity across countries. The studies by Dickens et al. (2007) and Holden and Wulfsberg (2007, 2008) find that higher wage rigidity is associated with higher union density. The former study finds a significant positive correlation between union density and real wage rigidity, whereas the latter studies imply that a positive relationship exists for both types of wage rigidity. Du Caju et al. (2009) in the case of Belgium and Messina et al. (2009) using individual data for four European countries also find that bargaining coverage is positively associated with real wage rigidity, but the latter finds no effect on DNWR. There is also some controversy in the literature regarding the role of EPL. On the one hand, Dickens et al. (2007) find that EPL indices are not significantly correlated with the country-level incidence of wage rigidity. On the other, Holden and Wulfsberg (2007, 2008) indicate a positive relationship between EPL and wage rigidity.

# **4. Typology of firms subject to wage rigidities and institutional characteristics of the sampled countries**

# **_4.1. The incidence of downward nominal and real wage rigidity in the sampled countries_**

The survey used in the current article allows us to examine the extent of wage freezes in 15 European Union member states. The data on wage indexation is available for 14 countries.<sup>9</sup> Table 1 shows that indexation is much more prevalent in our data (17% of firms are affected) than wage freezes (only 10% of firms are affected), which is consistent with other evidence

> 9 The national questionnaire for the Netherlands did not include the section related to wage indexation.

12
LAC in 1999,<sup>25</sup> which are for Jamaica with 19.1 and 31.8 percent, respectively, of male and female youth being unemployed. Clearly unemployment is a problem specific to youth because the unemployment rate drops markedly to 34.5 and 20.2 for the age groups 25–34 and 35–44 years, respectively. Thus, youth unemployment appears to follow the pattern from other LAC countries where the average rate is far higher than the adult unemployment rate (8 and 16.6 percent, respectively, on average in LAC), but the level is far higher in Haiti. Moreover, unemployment is higher in Port-au-Prince, where a large proportion of youth reside, than outside the metropolitan area. The large group of inactive youth should not be forgotten, but in Haiti few can afford to be truly inactive and many girls have hard, unpaid work in a household, and boys may be engaged in illegal activities, which in the household data are considered as inactivity. Under all circumstances, the Haitian labor market is not easily accessible and leaving school does not simply happen because of job offers, but to a large extent because of costs or need for support in the household.<sup>26</sup> Moreover, in Port-au-Prince some youth may be able to afford inactivity or unemployment because of remittances from relatives abroad.

<!-- Start of picture text --> Figure 4.2: School, Employment, and Unemployment by Age Cohort, 2001 (percent)<br>90.0<br>School<br>80.0<br>Employed<br>70.0 Unemployed/<br>inactive<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30<br>Age<br>Source: Own calculations based on ECVH 2001.<br>Percent<br><!-- End of picture text -->

> 25 Of countries with available data in 2003 (i.e., not including Haiti), St. Lucia had the highest rate of youth unemployment between 1996 and 1998 with 30.6 percent being unemployed, according to the World Bank (2003).

> 26 For example, one person often needs to stay in the household to claim it because many do not have a deed.

16
Table 4: Summary statistics of pass-through estimates

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Average pass-through|0.543|0.295|0.369|0.396|0.552|0.377|
||(0.751)|(0.646)|(2.568)|(0.882)|(1.247)|(0.426)|
|% of estimates below 1|83.2|82.4|94.2|84.0|76.7|94.3|
|% of estimates below 0|15.1|25.5|20.5|21.2|24.0|18.2|
|Distance to origin location|0.082|0.115|-0.012|-0,075|0.029|-0.099|
||(0.057)|(0.149)|(0.128)|(0.091)|(0.057)|(0.081)|
|Observations|232|51|832|368|562|88|

_Notes_ : This table presents summary statistics of pass-through estimates. The third row includes estimates of a linear relationship between the pass-through rate and (log) distance to origin. For average pass-through rates, standard deviations indicated in parenthesis. For the coefficient on distance to origin coefficient, standard errors indicated in parentheses.<sup>_∗∗∗_</sup> significant at 1%,<sup>_∗∗_</sup> at 5%, and<sup>_∗_</sup> at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **4.3 Estimates of trade costs using pass-through-adjusted price gaps and distance**

We now turn to the estimation of trade costs under the more general case of imperfect competition. With imperfect competition, price gaps can vary across trading pairs not only because trade costs vary with distance, but also because intermediaries can charge different markups in different locations, i.e. there is spatial variation in markups. Atkin and Donaldson (2015) emphasize three mechanisms by which markups can vary with distance:

1. More remote locations face higher trade costs, which, all else equal (i.e. demand conditions), induces traders to choose lower markups since they face higher marginal costs.

2. More remote locations are characterized by fewer traders and less competition, which induces traders to choose higher markups.

3. Traders in more remote locations may face different demand conditions, which induces correlation between distance and markups.

In their results, Atkin and Donaldson (2015) find that ignoring variation in markups can considerably underestimate intra-national trade costs. Their methodology to account for spatial variation in markups in the estimation of trade costs is based on using the pass-through rates estimated above, _ρ_ ˆ<sup>_k_</sup> _od_<sup>,toadjustpricegapsacrosstradingpairs,as</sup> explained in section 3.2. Assuming a linear relationship between distance and trade costs, 14
# **_Costs Technique_**

2.75 The costs method estimates normal profit as a percentage of production costs. Although profit is usually not measured as a proportion of costs, this benchmark has in fact been used in the forest sector in the Komi Republic, which is why the method is replicated here. All variables are calculated as in the equity method, except normal profit. Here, normal profit = production costs * a coefficient of 15%. 15% is the rate that was used in Komi in 1999.

2.76 Table 18 gives the aggregated results for Russia as a whole (see the Annex for the details per region). Again using the regional data set as lower bound estimate and the NIPEILesprom as upper bound estimate, the interval for appropriated rent on round wood was in the range of US$ 1.6 - 8.5 per cubic meter in 1999, and total annual foregone revenues amounted to US$ 191 - 1,032 million, to which, as above, 35% of the unrealized rent should be added to obtain the likely total foregone revenues. Table 17. Timber Rent in Russia - Costs Technique (US$/m<sup>3</sup> )

|Dataset|Regio<br>Domestic|ns<br>Export|NIPIElLes<br>Domestic|prom<br>Export|
|---|---|---|---|---|
|Revenues(CIFprice)|21.01|52.56|21.01|52.56|
|Costofsales|17.00|34.22|10.21|27.42|
|Normalprofit*|1.92|1.92|0.90|0.90|
|Profittaxtake (notional)|2.34|8.90|3.24|9.82|
|Appropriatedrent|-0.25|7.53|6.66|14.43|
|Unrealizedrent|12.00|0.00|12.00|0.00|
|Weightedaverageappropriatedrent|1.5|7|8.4|9|
|Totalrentappropriated (US$mn)|19|1|1,0|32|

a 15% return on costs 2.77 Table 18 distinguishes between the various components of rent averaged across domestic sales and exports. The only difference compared to Table 16 is the distribution between normal and excess profit.

Table 18. Total Timber Rent in Russia - Costs Technique (US$/m<sup>3</sup> )

||Regions|NIPIEILesprom|
|---|---|---|
|Unrealizedrent(implicitsubsidy)|9.69|9.69|
|Entrepreneurialrent(normal profit)|1.92|0.90|
|Capturedrent(notionaltaxtake)|3.85|4.76|
|Appropriatedrent(excessprofit)|1.57|8.49|
|Totalrent|17.03|23.82|

32
# **Table 11 (a): Baseline Open Economy Money Demand Model Cointegration Analysis with Johansen**

# **Test: sample 1959–2013**

||eigenvalue|Log likihood for|Trace test|Max test|Trace test|Max test|
|---|---|---|---|---|---|---|
|rank||rank|||||
||||[ Prob]|[ Prob]|(T-nm)|(T-nm)|
|0||30.84816|165.81<br>[0.000]**|68.82<br>[0.000]**|111.55<br>[0.013]*|46.30<br>[0.008]**|
||||96.99|36.22|65.25|24.37|
|1|0.71387|65.25920|||||
||||[0.001]**|[0.030]*|[0.283]|[0.509]|
||||60.77|24.04|40.88|16.17|
|2|0.48240|83.36951|||||
||||[0.010]*|[0.177]|[0.432]|[0.732]|
||||36.73|16.86|24.71|11.34|
|3|0.35405|95.38802|||||
||||[0.032]*|[0.251]|[0.423]|[0.720]|
|4|0.26405|103.8194|19.87|15.02|13.37|10.10|
||||[0.055]|[0.067]|[0.343]|[0.336]|
|5|0.23897|111.3292|||||
|6|0.084384|113.7536|||||

P-values are in brackets *** Significance level at 1%; , ** Significance level at 5%; * Significance level at 10% 67