rafmacalaba/datause-encoder-data huggingface dataset:
doing this, since many potential uses of the data will not be known at the time of collecting the survey.

# **HOW MUCH IMPROVEMENT DOES GPS GIVE OVER SELF-REPORTS, AND IS A STRAIGHT LINE GOOD ENOUGH?**

A subset of the uses of GPS detailed above involves measuring distances from households and communities to other households, communities, or infrastructure. The natural question which then arises is whether we need to use GPS to measure these distances, or whether distances can be obtained directly through self-reports in household surveys. A follow-up question is then whether a simple straight-line (crows-fly) distance is sufficient, or whether the GPS coordinates should be integrated with GIS information on transport routes and topography to measure travel distances and travel times.

The consequences of mis-measuring distance depend on how distance is going to be used, how badly it is mis-measured, and on the nature of the mis-measurement. If measurement errors are classical, then when distance is used as a regressor, as in the studies of access, the effect will be an attenuation bias which understates the impact of distance. Using distance as an instrument with classical measurement error will lower the power of the instrument, potentially giving rise to weak instrument concerns, but will still result in consistent estimates.

However, there are strong reasons to believe that measurement errors are not random. Entwisle et al. (1984) note as an example that if people are asked to report travel times to a health provider, those who currently use the source will have more accurate knowledge than those who do not. Thus the measurement error is likely to be correlated with usage patterns, a problem if one wishes to investigate the impact of distance on usage. Indeed, Andrabi et al. (2007) report that in their survey in Punjab, Pakistan, many households do not even know the name of their nearest school, let alone its location. If the measurement error is correlated with socioeconomic variables which also affect the outcome of interest, then the mis-measured distance will also give inconsistent instrumental variable estimates.

At present there are very few studies which systematically compare self-reports of distance and travel times to GPS measurements, particularly in developing countries. However, the two studies of which we are aware suggest errors can be large, and correlated with socioeconomic variables, although it should be noted that neither explicitly considers physical

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Participants also reported that many clients who sought sex work attempted to negotiate on the price, arguing that they could not afford the prices that they were previously paying prior to COVID-19. Some participants shared that they were initially reluctant to negotiate however ultimately opted to accept a lower price rather than forgo the work.

_“Clients bargained on the price that we gave them because they complained on being broke. So instead of paying 10,000 [TZS] for instance, they’d pay 5,000 [TZS]. Therefore, the price decreased compared to how it was before the pandemic.”_ _“I had to change how I charged my clients. This was because most of them complained on being broke because of the pandemic, so I always had to bargain on my charges. I had to agree with them because if I did not, they left for other women and my biggest fear by then was to go back home from work without having earned anything. Before the pandemic, I used to charge them 30,000 TZS but during the pandemic it dropped up to between 10,000 to 5000 TZS.”_ While negotiating was still common for some, most participants explained how prices had begun to return to pre-pandemic levels. Survey data indicate that there was a decrease in the average price per client from 6.6 USD in 2018 to 6.2 USD in 2021, though the difference was nonsignificant.

Participants also reported a significantly lower average number of clients per week in 2021 (6.2) than in 2018 (8.6). The lowest levels were experienced in 2020, with participants reporting an average of 4.8 per week. However, most interviewees explained that they started to observe higher demand for work when restrictions on gatherings and workplace closures were lifted.

_“In the first wave of the pandemic, there were less clients but then as time went on, the demand started getting back to normal, but they have not yet reached the normal level.”_ _“The biggest changes happened during the time when the government issued a declaration of closure of schools, banning gatherings...it has at least started getting back to normal but not yet the same as it was before the pandemic. In late June to July 2021, I started noticing that the work was at least improving.”_ Many described June and July 2021 as a key turning point when they began to notice their work returning to normal, which coincides with the shift in the government's approach to addressing COVID-19. Some participants now viewed the pandemic as being “over” and felt that their income was no longer affected.

12
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<!-- Start of picture text --> Figure 3.3: Turkey -- FX Intervention and nominal effective<br>0.6% exchange rate (2000-12) 45<br>ρ= 0.61<br>0.5%<br>0.4%<br>40<br>0.3%<br>0.2%<br>0.1% 35<br>0.0%<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>-0.1%<br>30<br>-0.2%<br>-0.3%<br>-0.4% 25<br>6-month moving average of monthly net FX interventions in % of GDP NEER of Turkish Lira (right axis)<br>Sources: IMF World Economic Outlook, Central Bank of the Republic of Turkey, and WB Global Economic Monitor.<br><!-- End of picture text -->

# **3.4.   India**

India operates a floating exchange rate regime. The Reserve Bank of India (RBI) has no fixed or preannounced target or band for the exchange rate. The RBI intervenes in the interbank FX market in a discretionary manner, with the aim of modulating excessive volatility and maintaining orderly market conditions. As persistent unidirectional movements of the exchange rate tend to amplify expectations of one-way movements, FX intervention aims at times to moderate speculative expectations of such movements (Gokarn, 2012). The RBI intervenes in both the spot and forward interbank FX markets depending on market conditions. It deals only with authorized dealers and publishes monthly intervention data.

The nominal effective exchange rate of the rupee followed a moderately depreciating trend over 2000-07. The rupee depreciated substantially as a result of “flight to safety” at the onset of the 2008-09 global financial crisis and, again, during the turmoil unleashed by the Eurozone crisis in mid-2011 (Figure 3.4). Overall, there is a significant positive correlation between the effective exchange rate and FX intervention, indicating that the central bank has been intervening with the objective of smoothing exchange rate fluctuations and doing so successfully. Intervention in the FX market in India did not attempt to reverse fundamental exchange rate trends. Most often, during the first sub-period of relative exchange rate stability (2000-07), the RBI conducted net 14
equal their (perceived) marginal revenue, which coincides with the demand price under perfect competition or with the industry's marginal revenue under perfect collusion. This model allows for an easy to use test statistic and a direct relationship to a natural measure of excess capacity. Specifically, a parameter can be estimated which provides a measure of the degree of imperfect competition, varying between perfect competition or full market power. One empirical advantage is that one only needs to use industry aggregate data to estimate this parameter, although using firm-specific data is possible as well.

The alternative approach is Rosse and Panzar (1977), expanded by Panzar and Rosse (1982) and Panzar and Rosse (1987). This methodology, abbreviated here to the PR model, uses firm (or bank)-level data. It investigates the extent to which a change in factor input prices is reflected in (equilibrium) revenues eamed by a specific bank. Under perfect competition, an increase in input prices raises both marginal costs and total revenues by the same amount as the rise in costs. Under monopoly, an increase in input prices will increase marginal costs, reduce equilibrium output and consequently reduce total revenues. The PR model also provides a measure ("H-statistic") between 0 and 1 of the degree of competitiveness of the industry, with less than 0 being a collusive (joint monopoly) competition, less than 1 being monopolistic competition and 1 being perfect competition. It can be shown, if the bank faces a demand with constant elasticity and a Cobb-Douglas technology, that the magnitude of H can be interpreted as an inverse measure of the degree of monopoly power, or alternatively, as we do, as a measure of the degree of competition.

The advantage of the PR model is that it uses bank-level data and allows for bank-specific differences in production function. It also allows one to study differences between types of banks (e.g., larges versus small, foreign versus domestic). Its drawback is that it assumes that 7
113 years an individual would enroll in school if he or she enrolled at the average rates from age 7 to age 25 reported in a particular year. There are many reasons to suspect that these enrollment rates exceed attendance rates, ant the actual time and effort applied to schooling undoubtedly differs greater across countries- and over time within countries. There may also be differences in these *real" investmnt rates per year of enrollment between boys and girls, but they are not reflected ln any of these UNESCO statistics.

T/There are few quantitative studies that seek to test **the** efficacy of public policy that varies (1) the mix of women and men hired to extend new agricultural teb..ologies, (2) the sex of the agricultural worker these personnel have contacted, (3) the reported source of innovative practices or now productive inputs, and finally (4) the effects on the hourly productivity of male and female workers and managers, as well as their overall earnings. One description of the agricultural extension program in Malawi (Spring et al., 1083) shows the growing predominance of women full-time and part-time workers and managers. Female headed households and wives, however, benefit from relatively fewer extension visits than do oen. But women attribute to extension agents their sources of modern agricultural practices more than do men. One interpretation of these data from Lilongwe Rural Development Project is that women agricultural workers **are** more responsive to the extension inputs they received. But they received fever such inputs. Unfortunately, the education of the male and foeale farm managers and their size of holdings are not reported, and these characteristics of the farmers might help to explain the reported patterns. None of 50 extension evaluation studies reviewed by Evenson (1988) or Birkhaeuser et al. (1988) note the differential effective-
|**Table 2: N**<br>|**et Pres**<br>|**ent Valu**<br>|**e of an A**<br>|**dditional Y**<br>|**ear of Sc**<br>|**hooling, b**<br>|**y Years o**<br>|**f Schooli**<br>|**ng Completed**<br>|**, 2017 Doll**<br>|**ars ($)**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Years of|Probab|ility of a|dvancing|Public|P|rivate costs||Wa|ge-employed w|orkers, ages 1|5–45|
|schooling|an ad<br>|ditional<br>schoolin<br>|year of<br>g<br>|costs<br>($)||($)<br>||<br>|All<br>|Men<br>|Women<br>|
||All|Male|Female||All|Male|Female|One-|Forward-|Forward-|Forward-|
|||||||||year<br>NPV|looking<br>NPV|looking<br>NPV|looking<br>NPV|
|||||||||($)|($)|($)|($)|
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|0|0.87|0.91|0.84|0||||--|--|--|--|
|1|0.99|0.99|0.99|367|70|72|69|2,696|954|<br>708|752|
|2|0.98|0.99|0.98|367|70|72|69|3,484|1,016|<br>745|814|
|3|0.98|0.98|0.97|367|70|72|69|2,893|1,050|<br>750|886|
|4|0.97|0.98|0.97|367|70|72|69|–1,778|1,084|<br>798|969|
|5|0.95|0.95|0.95|367|70|72|69|–1,096|1,258|<br>997|1,033|
|6|0.91|0.93|0.90|367|70|72|69|817|1,492|<br>1,304|1,134|
|7|0.91|0.92|0.90|790|133|137|130|105|1,680|<br>1,454|1,559|
|8|0.90|0.91|0.89|790|133|137|130|1,363|1,977|<br>1,780|1,881|
|9|0.60|0.64|0.56|790|133|137|130|1,982|2,208|<br>2,086|2,184|
|10|0.86|0.87|0.84|1,015|749|697|812|277|2,546|<br>1,974|3,801|
|11|0.90|0.90|0.90|1,015|749|697|812|1,639|3,153|<br>2,490|4,932|
|12|0.48|0.48|0.47|1,015|749|697|812|2,015|3,586|<br>2,909|5,508|
|13|0.92|0.92|0.92|1,881|1,940|1,965|1,904|–1,382|5,742|<br>4,753|6,883|
|14|0.99|0.99|1.00|1,881|1,940|1,965|1,904|13,338|6,866|<br>5,996|8,024|
|15|0.85|0.85|0.85|1,881|1,940|1,965|1,904|17,929|7,377|<br>6,416|8,664|
|16|0.74|0.77|0.68|1,881|1,940|1,965|1,904|7,683|5,891|<br>4,379|8,823|
|17|0.43|0.44|0.39|1,881|1,940|1,965|1,904|1,302|5,646|<br>3,685|7,404|
|18|0.00|0.00|0.00|1,881|1,940|1,965|1,904|12,551|12,551|<br>8,481|14,282|

_Source_ : Authors’ estimations based on data from the 2016/17 GLSS and the Ghana Ministry of Education.

_Note_ : NPV = net present value. Discount rate for the calculations is 8 percent. Calculations assume a working life from age 15 to 60, except when the individual is in school; they also assume that students have no labor earnings. The probability of advancing an additional year of schooling is estimated for all individuals ages 15–45. We use advancement probabilities and private costs for each gender in the gender-specific results, and the “all” columns for the results that combine both genders.

22
would be possible to value changes in natural areas (including biodiversity) as long as a key proviso noted in the previous section applies – it must be the case that stated willingness to pay varies systematically with changes in the properties of the natural area. If this were the case, then in principle the relevant portion of the utility function for the users of natural areas could be estimated, yielding marginal values of the different properties of the natural area.

While we concluded the previous section by noting the likely difficulties in arriving at any additive decomposition of the values of natural areas into their component properties, including biodiversity, production function techniques offer a way to measure marginal changes in the flow of benefits from natural areas associated with marginal changes in the properties of the natural area. In principle this opens the door to adjusting the measure of genuine saving to reflect losses of biodiversity, but the practical difficulties should not be underestimated. These difficulties in measuring genuine saving spill over to measuring net income, which is defined as the sum of consumption and net (genuine) saving.

We now turn to an analysis of the World Bank’s estimates of the value of protected areas.

# **Valuing conservation in national accounts: Empirical estimates**

As the foregoing has emphasized, biodiversity can provide a range of functions of value to human wellbeing. In theory and in practice this contribution to wellbeing can be valued, but what are lacking are comprehensive estimates of these values both within and across countries – the literature on valuing biodiversity is generally concerned with specific ecosystems in specific countries.

As a proxy for biodiversity values, we analyze the values of terrestrial protected areas across countries, exploiting figures published in _The Changing Wealth of Nations_ (World Bank 2011). This is clearly not the same as valuing biodiversity, but there is at least a link: protected areas harbor much, but certainly not all, of the world’s biodiversity. However, just as there are no comprehensive measures of biodiversity values across countries, there is a similar lack of crosscountry estimates of the value of natural areas based upon the value of the environmental services provided by these areas. The World Bank therefore falls back upon quasi-opportunity costs as the basis of valuation.

Starting from estimates of the extent of terrestrial protected areas from the World Conservation Monitoring Centre (as published in the _World Development Indicators_ ), World Bank (2011) assumes that the best alternative use for protected land is in agriculture. It further assumes that areas subject to protection are not the most productive agricultural lands – first, because most such conversions to agriculture would already have occurred over the course of history, and second, because it is unlikely that a government would choose to declare prime agricultural land 10
Note: each figure depicts the association between the estimated probability of vulnerability across municipalities pertaining to the respective vulnerability group, that is, low (categories 1 to 3) and high (categories 4 and 5) There are several possible explanations for the weak association between agricultural expenditure and vulnerability to food insecurity. One is that agricultural spending alone has a weak direct impact on the reduction of vulnerability to food insecurity in Bolivia. A second possibility is that composition effects within agricultural spending may not be captured through an aggregate measure of sectoral spending. The correlations between categories of agricultural per capita spending and VAM are -0.18 for restricted and 0.43 for extended agricultural spending, respectively (see Annex 2 for definitions of each category). This suggests that the composition of spending may be important for reducing vulnerability to food insecurity, and that the two categories of spending may be working in different directions, offsetting their individual effects (which would explain the weak correlation for total spending). A third explanation may be that higher agricultural spending is poverty-driven (i.e. it is a proxy for lower development levels) rather than a variable able to pick up impacts on vulnerability to food insecurity. Per capita agricultural spending correlates positively with malnutrition rates (Figure 8a) and negatively with human development at the municipal level (Figure 8b).<sup>8</sup>

**Figure 8. Per Capita Agricultural Expenditure and Welfare Measures**

<!-- Start of picture text --> a. Total Agr pc Exp and Malnutrition rate b. Total Agr pc Expenditure and DHI<br>0 10 20 30 40 0 10 20 30 40 .4 .5 .6 .7 .8 .4 .5 .6 .7 .8<br>Malnutrition rate 2006 Malnutrition rate 2006 DHI 2005 DHI 2005<br>Tot Agr pc exp 2003 Fitted values Tot Agr pc exp 2007 Fitted values Tot Agr pc Exp 2003 Fitted values Tot Agr pc Exp 2007 Fitted values<br>250 600<br>200<br>400<br>150<br>100<br>Tot Agr pc Exp 2007 200<br>50<br>0 0<br>250 600<br>200<br>400<br>150<br>100<br>Tot Agr pc exp 2007 200<br>50<br>0 0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database. Note: Per capita expenditure in Bolivianos.

In addition to these simple correlations, the proposed econometric estimation strategy consists of predicting the probability of pertaining to categories 4 and 5 of VAM in a given year (V), determined by per capita agricultural spending, our key policy variable of analysis (A). Note that this analysis aims at understanding whether and how agricultural spending affects high and very high food insecurity risks. It does not answer the question of whether and how agricultural

> 8 Other explanation mentioned in the context of public spending and welfare levels in Bolivia, see Inchauste (2009), refers to the misalignment between central and local government decisions. Although our paper does not test explicitly this hypothesis, the enormous quantity of agricultural programs, not always clearly delimited in terms of objectives or responsibilities, may be consistent with Inchauste‘s explanation.

11