rafmacalaba/datause-encoder-data huggingface dataset:
# 3.2 Respondent-Specic Characteristics and Recall Bias

We estimate separate linear probability models for male and female respondents to identify the determinants of measurement error Y (false negative/positive reporting): The dependent variable is equal to one if an individual's subjective outcome does not coincide with the true, objective outcome, and is otherwise zero. Standard errors are always clustered at the community level to allow for within-community correlation of unobserved factors that inuence false reporting.<sup>11</sup> We include several variables in vector X to examine which individual characteristics drive misreporting. Demographic and wealth characteristics<sup>12</sup> are included to evaluate the extent to which socioeconomic characteristics aect false reporting. We also include the number of plots in the household<sup>13</sup> and the number of males of working age (age 15-55) to proxy for farm productivity or capacity constraints. The eect of these factors on misreporting is a priori ambiguous. For example, farmer productivity may improve the precision of responses, as high productivity may correlate with sharper knowledge. Alternatively, productivity may correlate with the higher diversication of responsibilities and farming practices within the household and across plots. This may dilute reported knowledge of the techniques by any one respondent.

Building on previous studies, we also account for the role of exposure to training and recall decay in misreporting by including ve binary variables which reect when the interviewer rst

> 11For all specications, the covariates are jointly correlated with the outcomes of interest according to the F test (p-value _≤_ 0.001).

> 12Controls include: dummies for age categories (26-40 years, 41-55 years, and greater than 55 years), marital status of the respondent, completion of primary school, the number of children in the household, and the total household landholdings.

> 13Our paper survey instrument allowed for the documentation of the number of plots managed by men and women, which would have provided a total number of plots variable that varied for each male and female respondent in the household. Erroneous skip patterns in the electronic version of our questionnaire prevented female respondents from reporting having managed plots other than their main plot. In the end, we are left with self-reported measures of the number of plots managed by mennot women. We instead use the total number of plots managed by the household, as it is inclusive of all plots owned by the household and is asked prior to the erroneous skip patterns.

10
than other surveys where respondents are tracked and re-interviewed irrespective of where they move within the sample area (e.g., as in the Indonesia Family Life Survey, see Thomas, Frankenberg and Smith 2001).

We include only the children who were successfully followed up _in the same barangays_ in Rounds 2 and 3 in the analysis because those who were in Round 1 but not in the two other rounds (with the exception of the few refusals or incomplete interviews) died or experienced changes in treatment status when they moved. Table 4 shows the sample sizes for children who remained in the same sample barangay for all three surveys and were not missing critical data in Round 3. This group totals 6,693 children. The sample in non-program areas thus includes: 1) children living in barangays in regions 6 and 7 who did not receive the program by Round 3 and 2) all children who reside in region 8. As can be seen from Table 4, very few sample children residing in regions 6 and 7 were not living in program barangays by Round 3. Thus, our measure of treatment is essentially (though not identical to) whether a child resides in regions 6 or 7.

**Table 4. Sample Sizes for Program and Non-Program Children by Region, Philippines ECD** **<u>Study</u>**

|Region and Program or Non-program Areas|N|
|---|---|
|_Program Areas_||
|Region 6|1,682|
|Region 7|2,458|
|Total|4,140|
|_Non-Program Areas_||
|Region 6|145|
|Region 7|49|
|_Total_|194|
|Region 8|2,359|
|Total|6,693|

For the purposes of this evaluation, a central question is whether attrition is selective with respect to treatment status. Several studies on the U.S. and developing countries show that non-random attrition generally does not have a significant effect on multivariate estimates of socioeconomic status, including estimates of how family background affects a child’s nutritional status and cognitive, social, motor and language development (see Fitzgerald, Gottschalk and Moffitt 1998 and Alderman et al 2001a for reviews and further discussion).

Appendix Table B.2 gives estimates of the relationship between remaining in the same sample barangays for all three survey rounds and the treatment status, baseline family background variables and child’s age, and interactions between these variables and treatment status. The results indicate that children in non-program areas are significantly less likely to remain in the sample. The interactions with treatment status are significant in 9
about fi ve consumption categories, namely food, clothing, housing, schooling, and health care. We investigate whether responses to these questions vary systematically with distance to markets and cities.<sup>1</sup> Our econometric investigation leads to a robust fi nding: isolation is associated with lower subjective welfare. This result obtains after we control for consumption expenditures, suggesting that the relationship between isolation and welfare is not only due to lower monetary consumption. Controlling for household mobility and adding various controls leaves results unchanged. We quantify the di ff erence in subjective welfare associated with isolation and fi nd it to be large, particularly for housing, schooling and health care. Surprisingly, the reduction in subjective welfare associated with isolation is largest for households already close to markets. These results should be interpreted as indicative of a strong empirical relationship between geographical isolation and subjective welfare. Better data is needed to ascertain the causal e ff ect of geographical isolation on welfare.

The paper is organized as follows. The conceptual framework is discussed in Section 2. Section 3 describes the data and its main characteristics. Econometric estimation results are presented in Section 4 while in Section 5 we quantify the reduction in subjective welfare associated with geographical isolation. Section 6 discusses the results and Section 7 concludes the paper.

# 2. Conceptual framework

For the sake of this paper, let us de fi ne isolation as distance from urban centers: a household located at a large distance d from the nearest urban center is deemed to be more isolated than a household located closer. We are interested in the relationship between welfare and local

> 1 Other surveys have also asked what is typically referred to as the subjective well-being question, namely, ’Do you feel generally happy with life?’. Unfortunately, this question was not asked in the NLSS.

2
enjoy stronger growth during 2006-2008, with the poor shrinking by 9 percent (= (11-10)/ 11) and the middle class remaining almost unchanged, while the vulnerable category expand by 6 percent over this period. However, while these changes are favorable for the lower income groups, the population still remain largely immobile with roughly 80 percent of the population (i.e., the sum of the cells on the diagonal) staying in the same income categories, and around 20 percent experience (upward or downward) mobility. This result based on our classification of the different income groups for the US may thus add another angle to the various discussions on economic inequality for this country.

A qualitatively similar situation happens in Vietnam during the same period (Table 4). Specifically, the poor and middle class categories shrink respectively by 8 and 5 percent during 2006-2008; but the vulnerable category expands slightly over the two years by around 7 percent. The overall population in Vietnam is, however, more mobile with approximately 70 percent of the population remaining in the same income categories.

# **3.1.3. Welfare Dynamics over Different and Longer Time Periods**

To illustrate the scenarios where there is interest in comparing the same vulnerability index (or vulnerability line) over different time periods, we fix the vulnerability index at 10 percent in the period 2006-2008 in 2006 prices for both the US and Vietnam, and adjust the associated vulnerability lines backward and forward to two adjacent pairs of periods, 2004-2006 and 20042008 using consumption deflators.<sup>22</sup> By definition, the vulnerability index for 2004-2006 measures the movement of those who were in the vulnerable group in 2004 but fall back into

> 22 Note that the assumption of a weaker correlation of household consumption over time in Proposition 5 is satisfied with data for both the US and Vietnam. For example, this correlation coefficient is 0.55 and 0.47 respectively for the US in the period 2004-2006 and 2004-2008. Using longitudinal earning data from the Social Security Administration between 1937 and 2004, Kopczuk, Saez, and Song (2010) also finds that the (rank) correlation of earnings decreases over longer time intervals. This assumption also holds for data from other countries such as India (Chaudhuri and Ravallion, 1994) and Peru (Dang and Lanjouw, 2013).

21
2 adjustment packages. Considering not only these analytical issues but also the difficulty of obtaining - reliable information on poverty and income distritution - especially before and after adjustment (and the emotionally charged nature of the topic of income distribution), it is understandable that we were poorly informed about the likely distributional effects of adjustment.

The OECD project was designed to remedy this situation. A two-pronged approach was used that combined country studies and simulation analysis. For the country studies, countries were selected to provide wide geographical coverage and variation in initial conditions, to give a relatively detailed picture of the variety of adjustment experiences. Authors were asked to draw on all available information on distributional shifts and poverty indicators during adjustment in their interpretations of events. Country authors were also encouraged to rely on simulation methods and counterfactual analysis to compensate for the general lack of information on distributional indicators during adjustment and to provide a more informed and dispassionate assessment of each country's adjustment experience. These methods could also be used to isolate the effects of various policies. This symposium brings together a selection of the country studies commissioned by the project. In this introduction to the symposium, we first discuss the channels through which the stabilization/structural adjustment programs are likely to have affected the distribution of income. This analytical discussion is essential for understanding how adjustment is likely to get translated into distributional shifts across households and how the linkages between stabilization and structural adjustment policies were modeled in the simulation analyses. We then discuss the six country studies presented in the volume,<sup>3</sup> recalling in section 3 the initial conditions and diversity of disequilibria facing each country. In section 4, we synthesize the main conclusions from the inerpretative description in the country studies. Then in section 5, we turn to the lessons from the simulation exercises used extensively in all but one country study (Chile) to evaluate the effects of the selected
**Table 5. Impacts on household consumption, imports and exports of goods and services in 2030 (% change from the baseline)**

|**Sector**|**Household con**<br>|**sumption**<br>|**Impor**<br>|**ts**<br>|**Expo**<br>|**rts**<br>|
|---|---|---|---|---|---|---|
||Not-Linked<br>Case|Linked<br>Case|Not-Linked<br>Case|Linked<br>Case|Not-Linked<br>Case|Linked<br>Case|
|Agriculture, Forestry, Animal Husbandry<br>and Fishery|‐1.0|‐0.2|‐5.3|‐1.5|5.6|1.7|
|Coal|‐2.2|‐0.5|‐9.9|‐5.9|‐59.0|‐26.8|
|Oil and naturalgas|‐7.7|‐2.6|‐12.4|‐4.2|‐13.2|‐4.7|
|Metal and nonmetal|‐6.0|‐1.9|‐2.8|‐0.6|‐9.5|‐3.3|
|Food, tobacco, textile, leather, fur,<br>feather, timber, furniture, paper, printing|‐8.7|‐2.6|‐4.2|‐1.4|3.7|1.7|
|Petroleum,coke,nuclear fuel|‐1.0|‐0.2|‐3.0|‐1.0|‐28.6|‐11.1|
|Chemicalproducts|‐2.2|‐0.5|6.5|2.2|‐15.9|‐5.5|
|Non-metallic mineralproducts|‐7.7|‐2.6|29.7|8.1|‐25.2|‐8.5|
|Metals|-|-|10.2|2.7|‐21.5|‐6.7|
|Other manufacturing goods|‐3.7|‐1.0|0.3|0.1|‐5.3|‐1.6|
|Electricityand heat|‐20.6|‐7.3|44.8|12.8|‐39.8|‐15.8|
|Processedgas|‐11.3|‐2.7|**-**|**-**|‐|‐|
|Tapwater|‐3.2|‐1.0|**-**|**-**|‐|‐|
|Construction materials|-|-|2.6|0.7|‐3.0|‐1.1|
|Transport,storage andpostal services|‐3.9|‐1.0|‐0.4|‐0.3|‐4.6|‐0.9|
|Other services|‐1.5|‐0.4|‐2.8|‐0.8|5.9|1.7|

20
|Infrastructure Spending|Infrastructure spending<br>per capita (mean = 0.83)<br>(7)<br>(8)<br>0.082<br>0.105<br>(0.035)*<br>(0.034)**<br>0.039<br>(0.010)**<br>0.136<br>(0.028)**<br>-0.031<br>0.002<br>(0.059)<br>(0.062)<br>-0.478<br>-0.104<br>(0.114)**<br>(0.042)*<br>0.060<br>0.130<br>(0.061)<br>(0.074)+<br>0.238<br>0.467<br>(0.229)<br>(0.240)+<br>-0.268<br>-0.542<br>(0.300)<br>(0.346)<br>54532<br>54532<br>4612<br>4612<br> simulated revenue and its<br> All specifcations include<br>evenue, per capita transfer<br>nd pre-primary enrollment<br>erage per capita income is<br>0,000s.|
|---|---|
|t of Revenue on Education and|Infrastructure spending<br>(% of revenue) (mean = 0.09)<br>(5)<br>(6)<br>-0.002<br>-0.001<br>(0.003)<br>(0.003)<br>0.004<br>(0.001)**<br>0.013<br>(0.003)**<br>-0.009<br>-0.006<br>(0.006)<br>(0.006)<br>-0.042<br>0.003<br>(0.010)**<br>(0.003)<br>-0.002<br>0.004<br>(0.005)<br>(0.005)<br>0.011<br>0.032<br>(0.028)<br>(0.028)<br>0.011<br>-0.014<br>(0.034)<br>(0.035)<br>52480<br>52480<br>4609<br>4609<br>raction with these covariates using <br>clustered at the municipality level. <br>e-reform, 1997 levels of per capita r<br>) schools, pre-primary enrollment, a<br>pita revenue is 9.61, the mean of av<br>s 0.39. Population is measured in 10|
|Table 10: IV Results, Showing How Income and Inequality Moderate the Efec|Dependent Variable:<br>Education spending<br>Education spending<br>(% of revenue) (mean = 0.30)<br>per capita (mean = 2.62)<br>(1)<br>(2)<br>(3)<br>(4)<br>Revenue<br>0.008<br>0.009<br>0.287<br>0.295<br>(0.002)**<br>(0.002)**<br>(0.036)**<br>(0.035)**<br>Revenue x avg income<br>-0.007<br>-0.133<br>(0.001)**<br>(0.011)**<br>Revenue x med income<br>-0.028<br>-0.606<br>(0.002)**<br>(0.036)**<br>Revenue x gini<br>-0.013<br>-0.017<br>0.120<br>0.034<br>(0.005)*<br>(0.005)**<br>(0.079)<br>(0.075)<br>Average income<br>0.069<br>0.001<br>1.422<br>0.119<br>(0.009)**<br>(0.002)<br>(0.142)**<br>(0.037)**<br>Population<br>0.002<br>-0.005<br>0.016<br>-0.130<br>(0.003)<br>(0.002)*<br>(0.043)<br>(0.062)*<br>0-6 population<br>-0.060<br>-0.089<br>-0.657<br>-1.264<br>(0.021)**<br>(0.026)**<br>(0.306)*<br>(0.409)**<br>7-15 population<br>0.045<br>0.074<br>0.599<br>1.214<br>(0.018)*<br>(0.022)**<br>(0.329)+<br>(0.448)**<br>Observations<br>58690<br>58690<br>58612<br>58612<br>Municipalities<br>4733<br>4733<br>4733<br>4733<br>_Notes:_ An observation is a municipality - year. I instrument for revenue and its inte<br>interaction with the same covariates. Robust standard errors are in parentheses, and <br>municipality and year fxed efects, as well as a linear time trend interacted with the pr<br>revenue, the fraction of public primary school students in state or federal (non-municipal<br>squared. ** indicates p_<_.01; * indicates p_<_.05; + indicates p_<_.10. The mean of per ca<br>0.64, the mean of 2000 median per capita income is 0.22, and the mean Gini coefcient i<br>_Sources:_ Author’s calculations based on data from IBGE, INEP, and Tesouro Nacional.|

48
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# 1. **INTRODUCTION**

Ghana's past cocoa t and pricing policy has caused cocoa exports to decilne steadlly over the past two decades. As part of the Economic Recovery Program the price paid to cocoa farmers has recently been significantly Increased, although In the 198788 season It was stlll less than 40 percent of the export price. The fIrst part of this Interku study examines the way In which Ghana's tax system and agrlcultural pricing policy has Influenced the composition and level of output of her agricultural sector, particularly In the cocoa growing regions. It also examines the consequences of this policy for Goverrnent revenue. The Government must decide on Its future price policy towards cocoa and other crops. These policies will affect future levels of agricultural output, forelgn exchange earnings, government tax revenue, and the standard of living of cocoa farmers. The final part sets out the case for ralshg the producer prico of cocoa as quickly as possible to at least 55 percent of the export price.

In 1986, the latest year for which esthiates are available, foreign exchange earnings In real terms were less than half their 1970 level, despite a considerable kIprovement over the recent past. Cocoa accounts for about two-thirds of Ghana's export earnings. World Bank projections show other sources of foreign exchange rlsirg from 32 to 37 percent of total export earnings by 1989, whilst debt service and Imports of necessities will place heavy demands on available foreign exchange. In the medium term it may be possible to diversify Ghana's exports, though most of the alternative agricultural exports are, like cocoa, tree crops with a lengthy gestation period. Despite the heavy discrh,inatlon against cocoa, there has been little evIdence In the past of any significant switch to alterative export crops, and It would therefore seem unwise to rely on any spontaneous Increase in earnings from these sources in the **short** to medium term. Export earnings from cocoa will remain central to Ghana's neods In thG foreseeable future.

Cocoa production will continue to declhe as trees age and yields fall, unless the rate of planting of new trees rises significantly compared with the past two decades or the productivity of existing trees Is -alsed, or both. Future world cocoa prices are
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