rafmacalaba/datause-encoder-data huggingface dataset:
- 2.80 -

# **XVI.** PRICE STABIUZATION

The remaining Issue to resolve In setting the producer price of cocoa Is how to respond to medium run variations In the world price level. (Short run fluctuations are already dealt with by announcing and keeping to one price for the forthcoming season, though, as remarked in the main paper, It is Important to adjust the domestic price In line with exchange rate changes even wlthin the season to achieve a constant real price of cocoa). There are two separate Issues. The first Is how quickly to adjust the domestic price In response to a permanent change in the expected level of world prices -- caused, for example, by a permanent switch (or at least, a long perlod switch, In the level of supply of other producing countries), and how to respond to a deviation over the next year or so from the expected long run level. In the case of a permanent change, the sooner the domestic producers are confronted with the correct producer price corresponding to the world price the better. If the adjustment Is downwards, nothing Is gained by postponing the downward adjustment of the domestic price, as the short-run supply elasticity will be lower than the longrun elasticity, arguing for the shortrun exploitation of the revenue potential of lowering prices (assuming that the longrun consequences of this price change are what Is desired). If the adjustment Is upward, although this argument might appear to work against a rapid Increase In the price, the long run supply response Is unlikely to begin unless current prices Increase.

In the case of a shortrun deviation of the world price from trend, there are good reasons for buffering the domestic producers from these transient shifts. MIrrlees (1988), In a recent paper for the World Bank, estimates that In such cases It would be appropriate to take a five year moving average of export prices (including futures prices In the formula). In other words, current producer prices should only respond 20 percent to changes In the export price level. Mirrlees assumed no correlatlon between price and supply, and so his case Is somewhat overstated, If there were a strong correlation
|P154149|2015|2016|6/28/2016|3 Closed|Vanuatu|
|---|---|---|---|---|---|
|P154149|2015|2017|12/23/2016|4 Closed|Vanuatu|
|P154149|2015|2017|5/20/2017|5 Closed|Vanuatu|
|P154149|2015|2018|11/19/2017|6 Closed|Vanuatu|
|P154149|2015|2018|4/16/2018|7 Closed|Vanuatu|
|P154149|2015|2019|10/28/2018|8 Closed|Vanuatu|
|P154149|2015|2019|5/3/2019|9 Closed|Vanuatu|
|P154149|2015|2020|12/26/2019|10 Closed|Vanuatu|
|P154185|2017|2017|12/28/2016|1 Active|Malawi|
|P154185|2017|2017|6/27/2017|2 Active|Malawi|
|P154185|2017|2018|11/3/2017|3 Active|Malawi|
|P154185|2017|2018|5/7/2018|4 Active|Malawi|
|P154185|2017|2019|11/13/2018|5 Active|Malawi|
|P154185|2017|2019|5/1/2019|6 Active|Malawi|
|P154185|2017|2020|11/20/2019|7 Active|Malawi|
|P154185|2017|2020|6/10/2020|8 Active|Malawi|
|P154213|2016|2020|7/26/2019|1 Closed|Chile|
|P154219|2016|2016|6/15/2016|1 Closed|Bulgaria|
|P154219|2016|2017|2/10/2017|2 Closed|Bulgaria|
|P154219|2016|2018|7/18/2017|3 Closed|Bulgaria|
|P154219|2016|2018|2/11/2018|4 Closed|Bulgaria|
|P154219|2016|2019|8/9/2018|5 Closed|Bulgaria|
|P154219|2016|2019|3/10/2019|6 Closed|Bulgaria|
|P154219|2016|2020|9/15/2019|7 Closed|Bulgaria|
|P154253|2017|2018|9/14/2017|1 Active|St. Vincent and the Gr|
|P154253|2017|2018|4/17/2018|2 Active|St. Vincent and the Gr|
|P154253|2017|2019|11/7/2018|3 Active|St. Vincent and the Gr|
|P154253|2017|2019|5/28/2019|4 Active|St. Vincent and the Gr|
|P154253|2017|2020|12/30/2019|5 Active|St. Vincent and the Gr|
|P154253|2017|2021|7/6/2020|6 Active|St. Vincent and the Gr|
|P154255|2016|2017|10/14/2016|1 Active|Pakistan|
|P154255|2016|2017|4/3/2017|2 Active|Pakistan|
|P154255|2016|2018|11/7/2017|3 Active|Pakistan|
|P154255|2016|2018|6/26/2018|4 Active|Pakistan|
|P154255|2016|2019|12/22/2018|5 Active|Pakistan|
|P154255|2016|2019|5/16/2019|6 Active|Pakistan|
|P154255|2016|2020|10/29/2019|7 Active|Pakistan|
|P154255|2016|2020|2/4/2020|8 Active|Pakistan|
|P154255|2016|2020|6/16/2020|9 Active|Pakistan|
|P154263|2016|2016|4/19/2016|1 Active|North Macedonia|
|P154263|2016|2017|10/13/2016|2 Active|North Macedonia|
|P154263|2016|2017|4/27/2017|3 Active|North Macedonia|
|P154263|2016|2018|11/2/2017|4 Active|North Macedonia|
|P154263|2016|2018|5/31/2018|5 Active|North Macedonia|
|P154263|2016|2019|12/30/2018|6 Active|North Macedonia|
|P154263|2016|2020|8/6/2019|7 Active|North Macedonia|
|P154263|2016|2020|3/10/2020|8 Active|North Macedonia|
# **<u>Table 10: Post-vignette self-assessed subjective welfare positions</u>**

|(1)<br>PCE|(2)<br>PCE +<br>vignettes||(3)<br>PCE +<br>household<br>controls||(4)<br>PCE +<br>household<br>controls +<br>vignettes||
|---|---|---|---|---|---|---|
|Coef.<br>se|Coef.|se|Coef.|se|<br>Coef.|se|
|Log expenditure per capita<br>0.560*** (0.037)|0.573***|(0.037)|0.652***|(0.041)|0.673***|0.042|
|_Household Demographics_|||||||
|Female headed household|||-0.178***|(0.052)|-0.173***|(0.053)|
|Age of household head|||-0.005|(0.009)|-0.008|(0.009)|
|Age of household head squared|||0.000|(0.000)|0.000|(0.000)|
|Household size|||0.060***|(0.013)|0.068***|(0.013)|
|Number of children|||0.008|(0.017)|0.002|(0.018)|
|Number of older adults|||-0.039|(0.046)|-0.053|(0.046)|
|Number of migrants|||0.070|(0.043)|0.054|(0.043)|
|_Ethnicity (Reference: Tajik)_|||||||
|Uzbek|||0.064|(0.046)|0.070|(0.046)|
|Russian|||-0.412**|(0.161)|-0.435***|(0.166)|
|Other|||-0.650***|(0.159)|-0.664***|(0.149)|
|_Education (Reference: No Education)_|||||||
|<br>Primary|||-0.013|(0.113)|-0.112|(0.115)|
|<br>Basic|||-0.184*|<br>(0.109)|-0.220**|(0.111)|
|General Secondary|||-0.086|(0.105)|-0.129|(0.107)|
|<br>Special Secondary|||-0.021|<br>(0.113)|-0.072|(0.114)|
|<br>Technical Secondary|||-0.067|<br>(0.115)|-0.123|(0.116)|
|<br>Higher Education|||0.189*|<br>(0.112)|0.157|(0.114)|
|<br>Graduate School|||0.511|<br>(0.399)|0.612*|(0.368)|
|_Employment Characteristics of Household, Head’s_<br>|_Occupation (_|_Reference_|_for Occupation_<br>|_: Unemplo_<br>|_yed)_<br>||
|Number of employed|||0.013|(0.018)|0.007|(0.019)|
|Agriculture / Fishing / Forestry|||0.063|(0.063)|0.056|(0.064)|
|<br>Manufacture / Mining|||0.054|(0.092)|0.082|(0.095)|
|<br>Services|||0.447***|(0.120)|0.391***|(0.118)|
|Construction|||-0.032|(0.077)|-0.071|(0.077)|
|Public Administration / Education / Health|||0.094|<br>(0.067)|0.100|(0.067)|
|Sales and Services|||0.236***|(0.063)|0.241***|(0.063)|
|Other|||-0.142|(0.108)|-0.141|(0.107)|
|_Agriculture (Reference for holdings: No Land)_|||||||
|Small Holding|||-0.039|(0.064)|0.006|(0.066)|
|<br>Medium Holding|||0.055|<br>(0.078)|0.089|(0.080)|
|Large Holding|||0.088|(0.077|0.138*|(0.078)|
|<br>Any livestock, poultry, etc|||-0.017|<br>(0.056)|-0.021|(0.057)|
|<br>_Geography (Reference: Dushanbe)_|||||||
|<br>Sogd Urban|||-0.016|(0.072)|-0.087|(0.072)|
|<br>Sogd Rural|||-0.090|(0.078)|-0.170**|(0.079)|
|<br>Khatlon Urban|||-0.092|<br>(0.089)|-0.334***|(0.088)|
|Khatlon Rural|||-0.471***|<br>(0.073)|-0.560***|(0.074)|
|RRP Urban|||-0.286***|<br>(0.107)|-0.343***|(0.108)|
|RRP Rural|||-0.243***|(0.076)|-0.306***|(0.077)|
|Gbao Urban|||0.571***|(0.115)|0.657***|(0.120)|

22
**Urbanization rate.** The 1872 urbanization rate (share of cities 5,000 or greater) is 16.2 percent, interpolated between 1850 and 1900 from Bairoch (1988: Table 26,3, p. 423). The figure refers to all Latin America, of which Brazil was 33% in 1870 (Maddison 2004).

> **Mean income in $PPP.** From Maddison (2004).

# **REFERENCES**

- Banks, Arthur S. (1976), _Cross-National Time Series,1815-1973_ , [Computer File] ICPSR ed. (Ann Arbor, Michigan: Inter-University Consortium for Political and Social Research.

- Bértola, Luis, C. Castelnovo, E. Reis and H. Willebald (2006), "Income distribution in Brazil, 1839-1939" Paper presented at Session 116 A Global History of Income Distribution in the Long 20th Century, _XIV International Economic History Congress_ , Helsinki-Finland 21-25 August.

- Maddison, Angus (2004), _World Population, GDP and Per Capita GDP, 1-2001 AD,_ available at http://www.ggdc.net/Maddison/content.shtml.

44
6 clearly demonstrates that the highest rates of underemployment are found off Java (and largely off Sumatra), but because of the population density there, approximately twothirds of the underemployed are found on Java.

Because the _SAKERNAS_ data were not collected with the intention of estimating statistics at the kabupaten level, any disaggregation at this level must be treated with considerable caution. Such estimates of kabupaten underemployment rates (not shown here) describe a pattern similar to that seen at the province level; while it is possible to identify the kabupatens with higher than average rates, these statistics demonstrate no discernible pattern of seasonality.

There is another possible explanation for the lack of seasonality in underemployment in the informal sector as measured using SAKERNAS data. Recalling that demand for agricultura **l** abor has both a direct and an indirect effect on the local labor market, if the direct effect of seasonal labor demand on underemployment is immediate while the indirect effect (via consumer demand) on rural non-agricultural demand for labor operates with a lag, real seasonal fluctuations in underemployment for agricultura **l** abor might remain hidden from the simple statistical searches given in Tables 5 and 6. The figures provided below in Table 7 track quarterly variation in underemployment only among farm workers.<sup>8</sup> Table 7. Percent of **Farm Workers Underemployed by Quarter and Gender**

|I_____|T_**1st**|**2nd**|3rd|4th|Total|
|---|---|---|---|---|---|
|Male|13.1|13.6|12.8|11.7|12.8|
|Female|13.6|14.7|14.3|13.9|14.1|
|Total|13.3|14.0|13.4|12.6|13.3|

Compared to the underemployment figures of all workers in the informal sector shown in Table 5, the numbers in Table 7 indicate that agricultural workers do face greater fluctuations from quarter to quarter than are shown in the aggregate statistics. Full employment rates for male farm workers decline by 14 percent from the peak quarter of labor demand to the quarter of weakest demand, while rates for female farm workers drop by 7.5 percent; for all workers in the informal sector, such quarterly fluctuations are only 8.9 and 6 percent, respectively.

a This group represents 45.8 percent of the total workforce in the informal sector. Other agricultural occupations, such as livestock, fisheries and plantations, were not included, because these activities either have no obvious seasonality or have seasonal patterns that differ from those seen in crop-based farming activities.

11
two intraseasonal drought periods. Village "folklore" suggests that the timing of the monsoon is the most important aspect of weather (and uncertainty). We therefore first regressed total (real) profits from crop production and total farm profits (crop profits plus profits from animal products) on the monsoon onset date and then on all six weather variables, using all farm households in the ten ICRISAT villages.

Reduced-form random effects estimates of the influence of the rainfall variables on profits are presented in Table 1.7 The initiation date of the monsoon does significantly affect both crop and total profits--a one standarddeviation delay (16 days) in the start of the monsoon reduces crop (total) profits by 222 (150) rupees, or by approximately 6 (3) percent (average sample profits from crop production, in 1983 rupees, is 3700). We could not reject the hypothesis that the five other measures of rainfall, included in the specification reported in the second column for each profit variable, do not add to the explanatory power of the profit regressions. Moreover, inclusion of the other rainfall variables does not appreciably alter the effect of the onset variable. Of the other rainfall measures, the only potentially important candidate is the fraction of days with rain. Accordingly, we use both this measure of rainfall and the onset date as weather variables in our estimation of (8), and undertake additional tests of their importance. That the quantity of rainfall is far less important than its timing is consistent with the well-known difficulties experienced by researchers using rainfall quantities to explain yield (Herdt, 1972) or the allocative behavior of farmers (McGuirk and Boissert, 1988) based on aggregate Indian data.<sup>8</sup> The village-level rainfall variables explain a small proportion of the variability in individual profits. This might suggest that an investigation of the influence of the riskiness of these variables on farmer behavior would not 12
|**Table A2: Baseline Airtel Dat**|**a on Mobile Money Transa**<br>Low priority<br>|**ctions by Ai**<br>High|**rtel Priority R**<br>priority<br>|**ating**<br>P-value of<br>|
|---|---|---|---|---|
||||||
|Number of monthly transactions|Mean<br>anar<br>deviation|Mean|anar<br>deviation|erence<br>in means|
|(June to December 2016)|(1)<br>(2)|(3)|(4)|(5)|
|Any transaction|0.737<br>3.829|2.304|22.162|0.297|
|Send P2P transfer|0.020<br>0.271|0.054|0.420|0.224|
|Receive P2P transfer|0.041<br>0.317|0.049|0.335|0.224|
|Cash in|0.114<br>0.588|0.256|1.805|1.000|
|Cash out|0.149<br>0.808|0.211|0.997|0.224|
|Bill pay|0.013<br>0.191|0.078|0.911|0.484|
|Airtime or data top up|0.344<br>1.955|1.643|21.234|0.292|
|Observations|1589|17|43|3332|
|Number of months|7||7|7|
|Number ofphone numbers|227|2|49|476|

Notes: This table displays data for 476 Airtel phone numbers reported by our survey respondents. We do not include other phone numbers in the Airtel transactions data since we do not have the necessary information to map these to our study locations. Column 5 shows the p-value of the difference in treatment and control group means, clustered at the EA cluster level and conditional on strata and month dummies.

30
spouses of agrarian reform beneficiaries were asked some additional demographic information that had previously been collected for the beneficiaries.<sup>5</sup> The survey contains three different types of decision-making questions: i) D1: questions on who is generally responsible for decisions in [activity/topic]: the husband, wife, both (joint decision-making), or other household members; ii) D2: questions on who would make the final decision in the case of a disagreement, eliminating the “joint” option; iii) D3: questions on the decision-making process, including whether there was a conversation and who was involved in the conversation.

The first type of decision-making question (D1) is asked for a variety of agricultural and nonagricultural decisions. The second and third types of decision-making questions (D2 and D3) are only asked about major decisions on the parcel received through the Comprehensive Agrarian Reform Program (referred to as the “parcel of interest”), since practical limitations made it impossible to ask all types of questions about each domain, every household in our sample owned one of these parcels and all couples theoretically had equal rights to the parcel.<sup>6</sup> In practice, most respondents regard the parcel as primarily if not solely owned by the agrarian reform beneficiary, but are aware of its status as conjugal property: 96.1 percent stated that both spouses would have to agree to sell the parcel, while 90.1 percent stated that both would have to sign any relevant paperwork. Appendix Table 2 contains details on the decision-making questions and their answer options.

# **3.3. Measuring Spousal Response Discrepancy**

> 5 This demographic information had already been collected for the Agrarian Reform Beneficiary during the first part of the baseline survey in 2016 and 2017 and was not expected to change.

> 6 Under the 1987 Family Code of the Philippines, all property (including land) acquired or received after marriage is considered as conjugal property when owned by a married Filipino, giving spouses equal rights over that property, including spouses in common-law marriages. Parcels received before marriage do not legally count as conjugal property, but semi-structured interviews revealed that parcels were considered by spouses as “conjugal” even if received before marriage.

10
Table A2: Balance of baseline outcomes and covariates for those completing the short-term survey

||_Mean_|_Mean_|_Mean_|_Norm.Dif._|_Norm.Dif._|_Dif=0_|_Dif=0_|
|---|---|---|---|---|---|---|---|
||Drama<br>N=258<br>(1)|Document.<br>N=128<br>(2)|Control<br>N=220<br>(3)|Drama<br>vs.Control<br>(4)|Document.<br>vs.Control<br>(5)|_(p-value)_<br>Drama<br>vs.Control<br>(6)|_(p-value)_<br>Document.<br>vs.Control<br>(7)|
|**Panel A: Outcomes**||||||||
|Baseline index|0.130|0.085|0.049|0.110|0.049|0.091|0.534|
|Father determines sex|0.267|0.312|0.277|-0.016|0.054|0.810|0.490|
|Stricter control daugthers|0.439|0.520|0.533|-0.134|-0.018|0.044|0.822|
|Women should be virgin|0.689|0.728|0.751|-0.099|-0.037|0.153|0.657|
|Justify beating if unfaith|0.286|0.306|0.296|-0.015|0.017|0.821|0.836|
|Women wear whatever|0.976|0.959|0.956|0.076|0.011|0.260|0.894|
|Ban kitchen during period|0.284|0.291|0.318|-0.053|-0.043|0.435|0.603|
|Tell anyone if friend beat|0.866|0.913|0.917|-0.115|-0.010|0.087|0.902|
|Climate change is a threat|0.663|0.641|0.695|-0.049|-0.082|0.446|0.299|
|Work in the future|0.752|0.750|0.732|0.032|0.029|0.618|0.709|
|**Panel B: Controls**||||||||
|Age (years)|20.938|21.062|20.950|-0.004|0.040|0.946|0.616|
|English self-assess (0-10)|5.651|5.656|5.441|0.049|0.049|0.452|0.532|
|Survey in english|0.399|0.352|0.341|0.085|0.016|0.188|0.841|
|Female|0.287|0.258|0.264|0.037|-0.009|0.572|0.905|
|Primary|0.062|0.102|0.059|0.009|0.110|0.894|0.174|
|Secondary|0.368|0.422|0.459|-0.131|-0.053|0.045|0.501|
|University|0.535|0.453|0.464|0.101|-0.015|0.121|0.850|
|HH-head primary|0.209|0.250|0.182|0.049|0.117|0.450|0.143|
|HH-head secondary|0.318|0.289|0.355|-0.055|-0.099|0.399|0.205|
|HH-head university|0.372|0.367|0.323|0.073|0.066|0.259|0.403|
|Hindu|0.810|0.797|0.814|-0.006|-0.030|0.921|0.706|
|Muslim|0.147|0.148|0.150|-0.005|-0.003|0.934|0.969|
|Christian|0.000|0.000|0.009|-0.096|-0.096|0.157|0.158|
|Sikh|0.016|0.047|0.018|-0.015|0.114|0.822|0.169|
|General caste|0.531|0.570|0.495|0.050|0.106|0.439|0.177|
|OBC caste|0.271|0.273|0.305|-0.052|-0.048|0.426|0.537|
|SC caste|0.171|0.125|0.145|0.049|-0.042|0.453|0.589|
|Student|0.612|0.727|0.655|-0.062|0.110|0.341|0.158|
|Employed|0.159|0.102|0.114|0.093|-0.027|0.149|0.725|
|Self-employed|0.078|0.062|0.095|-0.045|-0.086|0.490|0.261|
|Live in Delhi|0.593|0.602|0.573|0.029|0.041|0.655|0.599|
|Member of organization|0.136|0.117|0.145|-0.020|-0.059|0.760|0.447|
|Currently dating|0.357|0.344|0.300|0.085|0.066|0.189|0.403|
|Married|0.070|0.039|0.064|0.017|-0.079|0.789|0.303|
|With sisters|0.756|0.734|0.705|0.082|0.047|0.210|0.550|
|Daily freq. social media|16.426|14.747|15.975|0.029|-0.079|0.675|0.354|
|Daily freq. watch videos|2.356|2.275|2.493|-0.097|-0.151|0.137|0.058|
|Male friend beating|0.163|0.133|0.118|0.091|0.031|0.160|0.694|
|Female friend beated|0.147|0.133|0.177|-0.057|-0.087|0.378|0.263|

Notes: Table shows sample means at baseline for respondents who completed the short-term survey. The normalized difference in columns 4 and 5 is the difference in the sample means of treatment and control groups divided by the square root of the sum of the sample variances.

27
were more successful in converting job applications into interviews, and then into offers, when applying for permanent jobs. However, each of these three results are only marginally significant, and not robust to our multiple hypothesis correction.

# **5 Why did we not create more hires?**

Why did our fairs not generate more hires, and what can we learn from this about matching in this labor market? We explore two main types of explanations for our results: first, that the fairs did not generate enough interaction between workers and firms to allow for hiring to happen (suggesting that fairs are not an appropriate mechanism for screening workers, or that they were badly executed events in our case), and second, we look at whether there simply were not good matches available at the fairs.

## **5.1 Did the fairs provide enough scope for interaction?**

We find that 454 job-seekers (75% of those attending) interacted with at least one firm at the job fair, according to job-seekers’ reports. Of these, 69 job-seekers (11%) were then formally interviewed after the job fairs. The same job-seeker would typically contact multiple firms and would sometimes be interviewed by more than one firm. In total, we record 2,191 contacts between firms and workers and 105 interviews. Finally, we find that 76 job offers were made at the fairs (to 45 job seekers); in total, 14 job-seekers (2%) were hired. Overall, the jobseekers who attended a fair secured one interview every 21 informal inquires with the firms, one job offer every 1.4 interview and one job every 6.2 interviews approximately. Between our baseline and endline interview, on the other hand, job-seekers in our sample obtained an interview every 3.5 job applications, an offer every 1.9 interviews, and a job every 3.3 interviews. From these numbers, two conclusions emerge. First, there was rich interaction between firms and job-seekers at the fairs. Second, this interaction lead to surprisingly few good matches. In this section, we combine data from job-seekers and from firms to explore this second finding.

We begin by exploring whether the more suitable worker-firm pairs attending the fair — according to the rankings we created and the matching algorithm we ran — were likely to meet. We interpret this as a basic descriptive test of coordination: _do participants’ rankings predict meetings?_ And, following this, _do participants’ synthetic rankings also predict meetings?_ To 14