rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5. Effect of Shocks on Expenditures**

||(1)<br>Total<br>Expend.|(2)<br>Food<br>Expend.|(3)<br># of Meals<br>Respondent|(4)<br>Private<br>Expend.|(5)<br>Medical<br>Expend.|(6)<br>Non-Medical<br>Shared Expend.|
|---|---|---|---|---|---|---|
|Mean of Dependent Variable^|588.85|181.08|2.80|65.17|13.16|431.21|
|**Panel A. Household Sickness**|||||||
|Somebody in Household (other|63.05|-14.08|-0.02|10.59|18.28|84.56|
|than respondent) Sick|(18.086)***|(5.45)**|(0.010)|(3.51)***|(3.35)***|(22.19)***|
||[0.107]|[-0.078]|[-0.007]|[0.163]|[1.389]|[0.196]|
|Respondent Sick|52.11|-0.73|-0.02|2.43|26.65|17.99|
||(18.570)***|(5.390)|(0.020)|(3.720)|(3.14)***|(25.150)|
||[0.088]|[-0.004]|[-0.007]|[0.037]|[2.025]|[0.042]|
|Observations|12288|12270|12109|12269|12252|5463|
|Number of women|192|192|192|192|192|108|
|**Panel B. Sexually Transmitted Infe**|**ction(STI)**||||||
|STI|-10.77|-15.94|0.00|-8.92|26.55|-88.57|
||(40.001)|(12.610)|(0.040)|(7.430)|(8.88)***|(48.48)*|
||[-0.018]|[-0.088]|[0.000]|[-0.137]|[2.017]|[-0.205]|
|First Day After STI|139.80|-3.48|0.06|44.28|27.46|-44.06|
||(80.904)*|(14.490)|(0.070)|(19.25)**|(10.99)**|(93.720)|
||[0.237]|[-0.019]|[0.021]|[0.679]|[2.086]|[-0.102]|
|Observations|10391|10374|10239|10373|10359|4422|
|Number of women|192|192|192|192|192|108|

Note: All regressions are fixed effects regressions with controls for the month and for the day of the week. Clustered standard errors (at the individual level) in parentheses.

All regressions include controls for the round of data collection.

Private expenditures include alcohol, soda, cigarettes, meals in restaurants, clothing, health and beauty products, and other privately consumed categories such as airtime for cellular phones.

The exchange rate was approximately 70 Kenyan shillings to $1 US during the data collection period. ^Means of dependent variables are means when all shocks are equal to 0.

* significant at 10%; ** significant at 5%; *** significant at 1%
Figure 3: Economic Growth and Rate of Return to University Education in South Africa <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011<br>-10<br>Economic Growth Rate Rate of Return to University Education<br><!-- End of picture text --> Source: Computed from Psacharopoulos and Patrinos (2018); Montenegro and Patrinos (2014).

The second pattern in all three countries is that returns to university education rise during economic crisis years. In short, we find support for Schultz’s thesis during the financial crises of the 1990s and the late 2000s Global Recession. In Indonesia, returns increased from 12.3 percent to 15.6 percent, a 3.3 percentage point increase or 27 percent increase, during the 1998-2002 recession. Pakistan experienced low (but not negative) growth during the 2008-10 recession, and returns increased from 15.3 percent to 16.2 percent, a 0.9 percentage point increase and 6 percent increase. Finally, South African returns increased from 27.2 percent to 35.9, an 8.7 percentage point and 32 percent increase, during the 200709 recession.

After the crises, returns to university education narrowed to pre-crisis levels in Indonesia and Pakistan. In South Africa, however, the returns to university education continued to widen.

# **4. Random Effects GLS Regression Analysis and Projections**

We use a random effects GLS regression model to examine the relationship between economic crises and rate of return to university education. The advantage of random effects GLS regression is that it allows generalizations beyond the sample. In our case, we can make predictions after the data period, extending into the current COVID-19 period and beyond for the three countries.

Table 2 presents the random effects GLS regression results where we regress the rate of return to university education on economic growth and economic growth-squared. Consistent with the Schultz’s disequilibria thesis, the results show that the rate of return to university education increases during an economic crisis. In other words, the rate of return to university education has a negative and statistically significant relationship with economic growth rates.

6
Table 1: Stratification of Afar Region

|Stratum|Description|Radius<br>(km)|Points<br>Selected|Total area<br>(km<sup>2</sup>)|Percent of total<br>landscape|
|---|---|---|---|---|---|
|1|High likelihood: towns|0.1|10|33|<1%|
|2|Almost no possibility: settled<br>agricultural areas/commercial farms|0.5|15|930|2%|
|3|High likelihood: within 2 km of major<br>river or swamps|1|60|3,538|6%|
|4|Medium likelihood: within 10 km of<br>major river or swamps|2|30|6,921|12%|
|5|Low likelihood: all land not in another<br>stratum|5|10|45,152|80%|
|Total|||125|56,574<sup>a</sup>|100%|

> ~~a~~ The total area in the table does not match the total area of Afar due to exclusion of Zone 2 from our study.

# **3.2 Survey Implementation**

To develop the framework protocols for the RGCS approach in Afar, two pretests were conducted, the first in December 2011 and the second in June 2012. The first focused on equipment and field practices, and qualitative research into seasonal migration patterns. The second finalized the protocols and tested the survey instrument.

The resulting methodology was designed to be relatively straightforward to implement in a lowcapacity field environment. Each interviewer was given a GPS device to which the selected points, and the circles around them, had been preloaded. In addition to the usual zoom and pan features, the device always displayed where the interviewer was in relation to selected area and was set to sound an alarm when the interviewer entered the circle. The interviewer teams were to drive as close as possible to the circle and then travel the rest of the way on foot, if necessary. Figure 3 shows an example of a point and circle. The selected circle has a radius of one kilometer and includes both land and water (on the eastern edge). To assist in locating the area, each interviewer was also provided with printed maps like that shown in Figure 3 in the appendix.

Once inside the circle, the team was assigned to canvas the area and interview all livestock holding households. The device recorded the interviewer’s path of travel within the circle so that he could navigate back to the starting point.

When a team member encountered a household (or a group of people travelling together) inside the circle, they attempted to complete three questionnaires. The first was a household roster, completed with a household informant, which captured basic demographic information about each member of the household, such as name, age, schooling, and health information. The second questionnaire, also for the household informant, gathered data about the goats, cattle, and camels currently travelling with the household (those away for a day to graze were included in this roster). This questionnaire also asked who owned the livestock and whether the owner was currently travelling or staying with the group. The third questionnaire was administered to each individual livestock holder in the household and contained more information about the animals in his or her possession. Following the completion of each selected circle, the supervisor filled out a cover sheet indicating how many persons or households were found in the circle.

5
ˆ Let u and P<sup>ˆ</sup> denote the preferred indicators, respectively for individual well-being ˆ and social well-being. Let u<sup>ˆ</sup> and P<sup>ˆˆ</sup> denote the benchmark indicators, respectively for individual well-being and social well-being. In this case, the benchmark indicators are those obtained from the AF methodology while the preferred indicators are those obtained from one of the two solutions presented above, or a mixture of the two. The individual well-being indicator is whether i is identified as multidimensionally poor and the social well-being indicator is the multidimensional poverty measure, such as the head-count ratio.

The evaluation should quantify the deviations between inter-personal comparisons (Objective 1) or between cross-regions comparisons (Objective 2), as defined in Section 2.6. In the case of interpersonal comparisons, we thus check how reliably we have u > ˆˆ ˆ u(di) when u > u(di). In the case of cross-regions comparisons, we thus check how reliably we have P<sup>ˆˆ</sup> (dR) > P<sup>ˆˆ</sup> (dR′) when P<sup>ˆ</sup> (dR) > P<sup>ˆ</sup> (dR′).

There are different ways in which these quantifications can be done. One way is to compute the frequency of “reversals”, i.e., the fraction of all pairwise comparisons for which the two indicators yield opposite comparisons. One drawback of this approach is that the frequency of reversals depends on how similar are the units being compared. Indeed, you mechanically find more frequent reversals when contrasting any two countries in Sub-Saharan Africa than when contrasting any two countries in the world. For this reason, it is desirable to check robustness by also using another method that avoids this drawback when quantifying the difference between two indicators. We present here one such other method, which interprets the two indicators as-if they have cardinal meaning.<sup>33</sup> Intuitively, the idea relies on the distribution of a counterfactual budget across the regions being compared, where the distribution is made proportionally to the poverty in each region. One can then compare how differently the two indicators would distribute the counterfactual budget across regions, say by computing the fraction of the total budget that would need to be re-allocated across regions when moving from one indicator to the other. These two types of quantifications are for instance used in Decerf and Fonton (2023). There exist other quantification methods, e.g., comparing the profile of individuals in the bottom of the well-being distribution, as in Decancq and Neumann (2014).

# **3.3 Hybrid indices that integrate longevity**

In this section and the next, we consider solutions to add more variables, which are not yet taken into account.

The case for integrating mortality into well-being measurement seems strong. From a conceptual perspective, mortality determines longevity, which arguably has high intrinsic

> 33Such cardinal interpretation is meaningful for the poverty head-count ratio.

21
difference between the logs of output and factor income, with inputs (labor and capital) weighted by their income shares, i.e., log _TFP =_ logY _- a_ log _L -_ (1-a) log _K,_ with a equal to labor's share. The capital stocks are derived from investment series using the perpetual inventory model with a 5% depreciation rate.

The R&D flow data are taken from the ANBERD 2000 (OECD) database (DSTI/EAS Division). The database covers 15 OECD countries from 1973 to 1998 at either the two-, three- or four-digit level.<sup>7</sup> From this, we construct R&D flow data for the 16 manufacturing industries at the two- or three-digit level (according to the United Nations International Standard Industrial Classification (ISIC) Revision 2). R&D flows cover all intramural business enterprise expenditures. Cumulative R&D stocks are derived using the perpetual inventory method with a 10% depreciation rate.

The import input-output matrix is not available, and instead, the national inputoutput matrix is used as a proxy, which is derived from JTAP (1998). Bilateral openness shares are derived from the World Bank database "Trade and Production 1976-1998" (Nicita and Olarreaga, 2001). For each industry and year, the shares are measured as the ratio of industry imports over value added. Trade data were collected at the 4-digit level and input-output data at the 3-digit level for the period 1981-98 (the available years for

> Professional Goods. The 10 low R&D-intensive industries are: (1) 31-Food, Beverage & Tobacco; (2) 32Textiles, Apparel & Leather; (3) 33-Wood Products & Furniture; (4) 34-Paper, Paper Products & Printing; (5) 355/6-Rubber & Plastic Products; (6) 36-Non-Metallic Mineral Products; (7) 371-Iron & Steel; (8) 372Non-Ferrous Metals; (9) 381-Metal Products; and (10) 39-Other Manufacturing. Industry R&D intensity is defined as the ratio of R&D spending over its value added. The R&D intensity of U.S industries was used to group them into low and high R&D-intensity industries.

> 7 The 15 OECD countries are: Australia, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Norway, Spain, Sweden, United Kingdom, and United States.

8
population, the area, the length and the condition of the district road network<sup>38</sup> that depends on weights such as: where i represents each district.<sup>39</sup> Table 16 presents the correlation coefficients between the actual district roads maintenance fund allocation and alternative allocation methodologies that take into account agriculture potential among other variables. When the function assigns more weight to the agriculture potential, the correlation between the two allocations is lower, which again demonstrates that agricultural potential is not a major variable for defining road allocation.

> 38 - **Actual district roads maintenance fund (2006).** Funds released by Ugandan government for district/feeder/secondary roads maintenance by district (vote 501-577, program 7). Data in Ugandan Shillings. 2006 data refers to FY 2006/07. Source: Draft estimates of revenue and expenditure FY 2006/2007, Ministry of Finance, Planning and Economic Development, 2007.

> - **Agriculture potential.** Total potential cash crop area multiplied by the total potential production of the winner cash crop. Winner cash crop refers to the crop with higher potential yield (in Ugandan Shillings). Cash crop prices account for the price farmers sell direct to the market. Cash crops are coffee, maize, bananas, groundnuts and cotton. Sources: GAEZ (potential data) and household surveys (for cash crop prices).

- **Area.** Total area by district measured in kilometers squared. Source: UBOS.

> - **Network length** . Number of kilometers of district/feeder/secondary roads by district. Source: Ten year district, urban and community access roads investment plan, Ministry of Public Works and Transport, 2008.

> 39 During the last decade Uganda has been increasing the number of districts by dividing the original districts from 2002. Therefore, we aggregated the values of the divided districts to match the 2002 sample (56 districts).

41
Repeating evaluations across different settings and at different scales can help address these concerns, although it will not always be feasible to do a sufficient number of trials to span the relevant domain of variation found in reality. The scale of a randomized trial needed to test a large national program could be prohibitive. Nonetheless, varying contexts for trials is clearly a good idea, subject to feasibility. The failure to systematically plan locational variation into their design has been identified as a serious weakness in the randomized field trials that have been done of welfare reforms in the US (Moffitt, 2003).

Important clues to understanding impacts can often be found in the geographic differences in impacts and how this relates to the characteristics of locations, such as the extent of local inequality, “social capital” and the effectiveness of local public agencies; again, see the example in Galasso and Ravallion (2005). This calls for adequate samples at (say) village level. However, this leads to a further problem. For a given aggregate sample size, larger samples at local level will increase the design effect on the standard error of the overall impact estimate.<sup>60</sup> Evaluations can thus face a serious trade off between the need for precision in estimating overall impacts and the ability to measure and explain the underlying heterogeneity in impacts. Smallarea estimation methods can sometimes improve the trade off by exploiting Census (or larger sample survey) data on covariates of the relevant outcomes and/or explanatory variables at local level; a good example in the present context can be found in Araujo et al (2006).

Another lens for understanding impacts is to study what can be called “intermediate” outcome measures. The typical evaluation design identifies a small number of “final outcome” indicators, and aims to assess the program’s impact on those indicators. Instead of using only final outcome indicators, one may choose to also study impacts on certain intermediate indicators of behavior. For example, the inter-temporal behavioral responses of participants in anti-poverty programs are of obvious relevance to understanding their impacts. An impact evaluation of a program of compensatory cash transfers to Mexican farmers found that the transfers were partly invested, with second-round effects on future incomes (Sadoulet et al., 2001). Similarly, Ravallion and Chen (2005) found that participants in a poor-area development program in China saved a large share of the income gains from the program (as estimated using the matched

> 60 The design effect ( _DE_ ) is the ratio of the actual variance (for a given variable in the specific survey design) to the variance in a simple random sample; this is given by _DE_ = 1 + ρ ( _B_ − )1 where ρ is the intra-cluster correlation coefficient and _B_ is the cluster sample size (Kish, 1965, Chapter 5).

52
**Table 7** : Estimated loss of productive time due to funeral attendance, M.Agr, 1990-2000 (UNDP 2002)

|**Number of D**|**eaths**|**Total**<br>**Funeral**<br>**Attendants**|**Total #**<br>**of Days**<br>**Lost**|**Total # of**<br>**Months**<br>**Lost**|**Average Salary per**<br>**Funeral Attendance**<br>**(MK)**|**Total Salary Lost**<br>**(MK)**|
|---|---|---|---|---|---|---|
|Total|1,613|32,260|64,520|2,933|3,000|8,799,000|
|HIV-related|158|3,160|6,320|287|3,000|861,000|

# <u>6.4. Role of policies and practices regulating human resource management</u>

In addition to morbidity and mortality, the human resource policy environment has a bearing on the ability of target institutions to effectively respond to the HIV/AIDS pandemic and mitigate its impact. For the public sector, the following policies are of particular relevance:

- _Recruitment_ :  The centralized human resources system in the public sector ensures standardization of practice in the entire service, but makes agencies more vulnerable to HIV/AIDS (for example by slowing down the process of filling vacancies).

- _Retirement_ : A civil servant qualifies for retirement on medical grounds. In practice, this provision is rarely implemented because there are no mechanisms for monitoring HIV/AIDS-related illnesses in the public service. This state of affairs affects such organizations like Ministry of Agriculture which has officers spread all over the country that are highly affected by HIV/AIDS-related morbidity, especially where there are prolonged cases of illnesses.

- _Absenteeism and Sick Leave_ : Civil servants who are absent during normal working hours without permission from responsible officer or controlling officer, or without a valid excuse, is guilty of misconduct. If the period exceeds five consecutive days, it amounts to abscondment and results in dismissal. However, this regulation is defeated in practice, as some officers do not have line supervisors and there is currently no rigorous system in place for monitoring and recording absenteeism.

28
**Table 10** Determinants <u>of TFP growth</u>

||Depe<br>|ndent variable:<br>Model<br>|TFPt<br>|
|---|---|---|---|
||1|2|3|
|Independent variables||||
|Constant|-0.089|0.05|-2.07***|
|CREDIT|(-0.56)<br>0.064***|(0.34)<br>0.038***|(-2.3)<br>0.024***|
||(3.36)|(2.58)|(2.37)|
|EXCHG|-0.0074***|-0.0065***|-0.0034***|
||(-3.56)|(-3.95)|(-2.33)|
|TAXES|0.0049***|0.0051***|0.0054***|
||(2.34)|(3.3)|(4.54)|
|TRADE|0.0038|-0.0035|-0.0036|
||(1.32)|(-0.93)|(-0.90)|
|DUM94|-0.43***|-0.24**|-0.43***|
||(-3.93)|(-1.69)|(-2.48)|
|CAPITAL||0.045***|0.03**|
|||(2.8)|(1.83)|
|ODA|||0.11***<br>(2.45)|
|R-square (adj)|0.74|0.82|0.85|
|Durbin-Watson|1.95|2|2|
|Estimation period|1971-2003|1971-2003|1971-2003|

t-statistics in parentheses

** and *** denote significance at 10 % or 1%

The tax variable (TAXES) exerted a positive and significant effect on TFP. Revenues are the product of imports and tax rates, but while tax rates declined due to trade liberalization, import volume increased, thus increasing international trade revenues after 1995. The positive connection between revenue and productivity suggests that revenues collected on taxes from international trade were used to finance productive projects, which led to an improvement in total factor productivity.

The variable TRADE, which reflects the degree of openness to outside economies, surprisingly did not appear to have a significant impact on TFP, with its 25
35

**Appendix 1: Data and Methodology**

# _Macroeconomic Data._

All macroeconomic indicators reported in this paper are based on original data from the archives of the Brazilian Statistical Institute (IBGE). GDP and GDP per capita figures reported in Section I come from the series shown below in Table Al. This series was constructed from the current GDP series (A), which was revised in 1995 and backdated to 1990; and from the old series (B), from 1976 to its final year: 1995. The series reported below comprises the values of series A from 1990 to 1996, and the values of series B scaled down by a factor of 0.977414 from 1976 to 1989. This factor is the simple average of the ratios A/B over the years from 1990 to 1995. The series is expressed in 1996 Reais, using, the IBGE GDP deflator.

Table Al: Real GDP and GDP per capita, Brazil 1976-1996, annual (constant 1996 prices)

|Year|GDP (reais)|Population (,OOOs)|GDP per capita (reais)|
|---|---|---|---|
|1976|434,059,220|107,452|4040|
|1977|455,477,123|110,117|4136|
|1978|478,113,823|112,849|4237|
|1979|510,432,394|115,649|4414|
|1980|562,395,141|118,563|4743|
|1981|538,474,976|121,213|4442|
|1982|542,971,306|123,885|4383|
|1983|527,054,370|126,573|4164|
|1984|555,515,747|129,273|4297|
|1985|599,129,793|131,978|4540|
|1986|644,002,821|134,653|4783|
|1987|666,708,887|137,268|4857|
|1988|666,304,312|139,819|4765|
|1989|687,391,828|142,307|4830|
|1990|651,627,236|144,091'|4522|
|1991|658,339,124|146,408|4497|
|1992|654,759,303|148,684|4404|
|1993|687,004,026|150,933|4552|
|1994|727,213,139|153,143|4749|
|1995|757,918,030|155,319|4880|
|1996|778,820,353|157,482|4945|

The GDP per capita growth rates plotted in Figure 1 are derived from this series. Annual inflation and unemployment rates also come from the relevant IBGE series.
<!-- Start of picture text --> —*— Floating ER (NK Model) ---—~—- Floating ER (Empirical)<br>—~*— _ Fixed ER (NK Model) —---- Fixed ER (Empirical)<br><!-- End of picture text --> <!-- Start of picture text --> Panel A. EMERGING MARKETS AND DEVELOPING ECONOMIES<br>= r<br>& 95% confidence interval<br>s<br>a=) © cs) ==<br>zeaa sae ae al<br>33° ; te<br>— ad<br>Sox ae<br>5 5 j= tLe<br>aon % al weeee ee eo<br>as<br>c | aa<br>Seo a.<br>5 o<br>cs 4<br>co<br>0 1 2 3 4 5 6 7 8 9 10. =6fi 12 13 #14 #15 #16 #17 «618 «619 «620+<br>BWA ZMB ZWE TGO MEX VNM PER NOR TTO IDN AUS GUY NER GHA QAT BFA GIN<br>NAM CHL PNG ZAF VEN SDN EGY MYS Moz PNG MLI<br>UGA CMR KWT SEN SEN DZA TGO BOL<br>Half-life of the commodity price index (years)<br><!-- End of picture text -->

Panel B. Sub-Saharan Africa