rafmacalaba/datause-encoder-data huggingface dataset:
In Figure 7, I plot the ratio of the (dollar) aluminum price to the prices of respectively copper, lead, nickel and zinc over the period July 1993 to May 1995. normalized so that in each case the average for the three months September-November 1993 (i.e. just before the start of the price rise) is equal to 100. This graph shows that the behaviour of the aluminum price over 1994 and the first part of 1995 was remarkably similar to that of copper, lead and nickel but that the zinc price has fallen relative to other non-ferrous metals prices. This comparison suggests that, if special factors are to be invoked, these are required to explain why the zinc price has failed to share in the 1994 non-ferrous metals price surge rather than to explain any particular feature of the aluminum history.

This conclusion contrasts strongly with the dominant view in the aluminium industry which attributes a significant effect to the MoU. Aluminum analysts tend to point to the high level of aluminum stocks relative to those in copper - total commercial stocks amounted to 3.6 months consumption at the end of 1993 compared with 1.8 months in copper. However, the level of aluminum stocks is comparable with those in nickel (4.2 months consumption) so it is not clear that these stock levels would have resulted in depressed price growth in the absence of the MoU. In general, the effect of high stocks should primarily be seen in the level of prices, rather than in the response of prices to a shock, and it is true that the aluminium price remained beneath estimated long run production costs for most of 1994.26 It is also true that "western world" production of aluminum fell by 4.4% in 1994 from its 1993 level, and this may be taken as indicative of the effects of the MoU. At the same time, refined copper production (on the same basis) fell by 2.1%.27 It is unclear how what proportion of the fall in aluminum production can be attributed to the MoU rather than simply to closure of expensive capacity.

26 Econometric analysis suggests that demand shocks will typically have a much larger effect on aluminum prices than supply shocks, essentially because demand shocks are sustained over time while supply shocks have an effect which decays relatively rapidly. Focusing solely on the supply-demand balance, which gives equal weight to supply and demand shocks, may be misleading - see Gilbert (1995). For data sources see footnote 24.

27 World Bureau of Metal Statistics, _World Metal Statistics,_ June 1995 (Ware, Herts.).

36
derived from a combination of current survey and geospatial data with older, traditional censusbased small area estimates. These are important knowledge gaps given that subnational monetary poverty estimates play a significant role in resource allocation in many countries.

In addition, little is known about how the accuracy of predictions compares for target areas – the area at which we wish to produce estimates – that are covered by the sample and those target areas that lie outside the sample. This distinction is important for two reasons. First, in some contexts, the official household survey used for poverty measurement may not cover all target areas. If predictions for non-sampled areas are inaccurate, this would strengthen the case for both exercising caution when utilizing out-of-sample estimates for policy and for expanding survey data collection to cover all target areas. Second, comparing the accuracy of non-sampled and sampled areas sheds some light on the benefits of using Bayesian or Empirical Bayesian methods, which utilize the sample data as a prior to inform the estimates, rather than relying only on purely synthetic predictions.

Finally, the optimal statistical method to use when combining survey and satellite data remains unsettled. Bayesian methods effectively combine sample and big data in a well-established framework (Steele et al., 2017; Steele et al., 2021; Pokhriyal and Jacques, 2017). There are significant barriers, however, to their widespread use. These include computational complexity, a lack of familiarity among many practitioners, and required distributional assumptions on priors and hyperpriors that introduce additional complications. An appealing alternative is to use Empirical Best Predictor (EBP) models – a name coined by Jiang and Lahiri (2001) and Jiang et al. (2002) – in part because it is a well-established method in the existing literature on small area estimation.<sup>5</sup> This paper makes four main contributions. First, it assesses the gain in accuracy and precision, relative to direct survey estimates, from combining survey data with geospatial indicators to generate small area estimates of monetary poverty. Second, it compares the performance of different methods for generating estimates for both in-sample and out-of-sample municipalities –

> 5 EBPs are also known as EBLUP (Empirical Best Linear Unbiased Predictor) for linear mixed models. See Battese, Harter, and Fuller (1988), Jiang and Lahiri (2008), Molina and Rao (2010), and Van der Weide (2014) for more on their use in small area estimation.

4
- 2 -

As a result, SSA has fewer roads per km<sup>2</sup> than Asia oe Latin America, and a larger percentage of these roads is unpaved. Still, it has the highest ratio of roads per dollar of output (Table 2). Crude estimates of the stock of capital embodied in roads, based on a sample of 85 developing countries, indicate that this capital-output ratio (stock per dollar GDP) is substantially higher in Africa than in the other two regions (World Bank, 1988). If one excludes Nigeria, the maintenance of this stock imposes a burden which is twice as high in the Sub-Sahara as in the other two regions. The difference between the regions in the burden imposed by the maintenance of unpaved roads is even higher (by a factor of 5).

Table 2. Basic Characteristics of Regional Road Networks

|Sub-<br>Sahara|Sub-Sahara<br>excluding<br>Nigeria|<br>East Asia<br>& Pacific|<br>South<br> <br>Asia|Latin<br>America|
|---|---|---|---|---|
|Number of countries<br>39<br>|38|7|6|19|
|Area (mil km<sup>2</sup>)<br>20.0|19.1|13.2|_5.1_|18.9|
|GNP (1983) (bill. US$) 155.9|85.5|600.4|257.5|583.9|
|Estimated total road|||||
|network ('000 km'<br>1,019|911|1,540|1,680|2,212|
|Main road network|||||
|('000 km)<br>335|306|451|217|512|
|Density per 100 km<sup>2</sup><br>1.7|1.6|3.4|4.2|2.7|
|Density per mil $ 2.1|3.6|0.8|0.8|0.9|
|Main paved roads|||||
|('000 km)<br>101|79|277|174|252|
|Density per 100 km<sup>2</sup><br>_0.5_|0.4|2.1|3.4|1.3|
|Density per mil $ 0.6|0.9|_0.5_|0.7|0.4|
|Replacement value of<br>main network (bill. US$):|||||
|Paved<br>25.2|19.9|83.1|31.4|65.5|
|Unpaved<br>9.3|9.0|7.0|1.7|10.4|
|Total<br>_34.5_|_28.9_|_90.1_|_33.1_|_75.9_|
|Replacement value / GNP:|||||
|<br>Paved<br>0.16|0.23|0.14|0.12|0.11|
|Unpaved<br>0.06|0.11|0.01|0.01|0.02|
|_Total_<br>_0.22_|_0.34_|_0.15_|_0.12_|_0.13_|

# Source: World Bank (1988), Table A-1.
**Figure 1.8: Wedges and Aggregate GDP**

<!-- Start of picture text --> 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>GDP l g k GDP za zna<br>Panel (a) Panel (b)<br>Source : authors.<br>Let’s start by interpreting the labor wedge, given by equation (1). As we argued above, can be<br>broadly interpreted as a time-varying tax on labor income. It can arise due to distortions affecting<br>ll,tt<br>both the demand and supply sides of the labor market. For instance, the labor wedge may reflect ττ<br>payroll taxes, distortions due to unionization, collective bargaining, hiring and firing costs, or<br>sticky wages. An increase in this tax would lead to a reduction in labor input. Based on this, if we<br>observe that is increasing over time it would mean that is falling and labor market<br>frictions are becoming less important. From panel (a) of Figure 1.8, this is exactly the pattern that<br>ll,tt ll,tt<br>characterizes the Paraguayan labor market during 1991-2010. The labor wedge 1 −ττ ττ has<br>increased over time, especially during the 2000s, reflecting increased employment. This may<br>ll,tt<br>suggest that structural improvements in the Paraguayan labor market, higher labor force 1 −ττ<br>participation, and greater flexibility by firms in adjusting their labor inputs may have taken place<br>during this time.<br>3<br>3<br>2.5<br>2.5<br>2<br>2<br>1.5<br>1.5<br>1<br>1<br>.5<br>.5<br><!-- End of picture text --> To gain some insights into the driving forces behind these changes we turn to the “Doing Business” survey by the World Bank, particularly, its “Employing Workers” section. It provides information on the right to collective bargaining, difficulty of hiring workers, the cost of advance notice requirements, severance payments, and penalties due when terminating a worker. It also contains information about minimum monthly wages of apprentice workers. Young workers are especially susceptible to the negative effects of labor market rigidities, since they typically lack experience and on-the-job training. In the presence of firing costs, potential employers are discouraged from hiring such workers.

Unfortunately, the historical data from the survey are available going back only to 2006, thus limiting our ability to explain the entire trend in the labor wedge. Nevertheless it provides us with some hints about the developments in the Paraguayan labor market in the recent years. Interestingly, we find no changes in the indicators of firing costs in Paraguay during 2006-2011 (see Table A1 in the Appendix). However, we also find that minimum apprentice wages have increased substantially in Paraguay during this time. Their growth at 9.8 percent per year on average was above 8.8 percent average growth of apprentice wages in the Latin American region
**Compensation schemes.** Schools offer either a performance-pay contract or a fixed wage contract. Under the former, the teacher receives a base wage of _W_ plus a bonus _B_ if her performance  ̄ exceeds a threshold _y_ . Under the latter, the teacher simply receives a base wage of _G_ . The outside sector offers a performance-pay contract with a low base wage (normalized to zero) and a bonus _β_ if performance exceeds a threshold _z_  ̄.

**The impact of management practices.** We assume that people management has two effects. The first relates to the _structure_ of compensation: good people management practices enable managers to observe, and contract on, the performance of their employees—i.e. to offer a performancepay contract. The second relates to teacher motivation: good people management practices enable managers to cultivate the intrinsic motivation of their staff—i.e. to increase ∆. We assume that operations management also has two effects. The first relates to the _level_ of compensation: good operations management practices free up resources and enable managers to offer a higher level of base pay. The second relates to household effort: good operations management practices help to create a stimulating environment for students and parents —i.e. to increase _γ_ .

We classify schools into three management types: _high_ (strong people and strong operations management), _intermediate_ (weak people but strong operations management), and _low_ (weak people and weak operations management). Performance metrics are indexed accordingly by _i_ = _H, I, L_ . We assume that high management schools are found exclusively in the private sector, while the public sector consists of a mix of intermediate and low management schools. This implies that performance-pay contracts are only offered by private schools (and the outside sector). Figure 6 provides evidence that this key assumption is well-supported in our PISA data. Here, we plot empirical cumulative distribution functions (CDFs) of the PISA-based people management score by sector and find that the private sector CDF (dashed blue plot) clearly first order stochastically dominates the public sector CDF (solid red plot).<sup>19</sup>

**Timing.** The timing of the game is as follows.

> 19We show empirical CDFs by country in Latin America in Figure B.4 in Appendix B. The private sector CDF first order stochastically dominates the public sector CDF in Colombia, Peru, Costa Rica and Mexico.

15
# **iii. DEBIT AND CREDIT CARDS**

Figure 33. Debit Card Ownership across Gender <!-- Start of picture text --> 2011 2014<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High IncomeRest of ECA Turkey BRIC Rest of High IncomeRest of ECA<br>Developing Developing<br>World World<br>male female<br>Source : FINDEX 2011 and 2014<br><!-- End of picture text --> Figure 34. Credit Card Ownership across Gender <!-- Start of picture text --> 2011 2014<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High IncomeRest of ECA Turkey BRIC Rest of High Rest of<br>Developing Developing Income ECA<br>World World<br>male female<br><!-- End of picture text --> _Source_ : FINDEX 2011 and 2014 40
:lh, 129 Flat at _tUm_ Cmtlaleut af AWJFN(-12 bum _- 1_ on mmIm _r_ egpegaon <!-- Start of picture text --> .uUSM<br>a1m 1973 1973 1.3 11<br><!-- End of picture text --> fJ _CU$tE IO_ FIGURE Jo

> ,IG _, .ii _uG_ _. c t: I,J

<!-- Start of picture text --> Flat of tl Coflmwut of EAIlNTt-1 bai co uw1_ on<br>IlMuDlln t - \ [<br>-.zzes2 lsn 197|1 - lse3 ssirs<br>11. 1973 1173 1983 1i85<br><!-- End of picture text -->

<!-- Start of picture text --> flat.. ' tim . e CuuttleIa.t r a XI OSf-ao -z; em if O 113u1a11t<br>09me-.<br>19m is" 1973Is<br><!-- End of picture text --> ~~~~~~~~~~~~~~~~~~~LMUL £2 hEPlM TItK IQUA1 ION <!-- Start of picture text --> too plut of t). Coaticimt d PCCC-ti -o _t<br>-. 11111A111=m-<br>_,,Z3 .,,7, r-.1<br>tam 193 i973 193 Is5<br><!-- End of picture text -->
**Table 9:  Probit results - child mortality rate**<sup>**a**</sup>

|**Protein Intake**|**Parameter**<br>**Estimates**|**Survey Month**|**Parameter**<br>**Estimates**|
|---|---|---|---|
|**Fish**|**-0.400**|**July**|**0.12**|
||**(3.62)****||**(0.24)**|
|**Eggs **|**-0.186**|**August**|**-0.022**|
||**(1.44)**||**(0.04)**|
|**Meat**|**-0.244**|**September**|**0.173**|
||**(1.48)**||**(0.33)**|
|||**October**|**0.217**|
|**Characteristics**|||**(0.41)**|
|**Female**|**-0.278**|**November**|**0.262**|
||**(3.10)****||**(0.48)**|
|**Mother’s Education**|**0.021**|**December**|**0.576**|
||**(1.45)**||**(1.01)**|
|**Household Assets**|**-0.021**|||
||**(0.69)**|**Survey Year 2014**|**2.809**|
|**Size at Birth**|**0.081**||**(7.98)****|
||**(1.42)**|||
|||**Birth Year**||
|**Peak-Catch Birth**|**-0.388**|**2006**|**5.347**|
||**(3.96)****||**(9.33)****|
|||**2007**|**5.032**|
|**Constant**|**-6.389**||**(9.54)****|
||**(6.92)****|**2008**|**5.081**|
||||**(9.66)****|
|||**2009**|**4.806**|
||||**(9.01)****|
|||**2010**|**3.963**|
||||**(7.63)****|
|||**2011**|**2.927**|
||||**(6.71)****|
|||**2012**|**2.127**|
||||**(5.47)****|
|||**2013**|**0.836**|
||||**(2.15)* **|

# **a Grid location results excluded for brevity**

**Observations:  4,761 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

22
discounted flows of future income. Any difference between book and fair values is reflected in unrealized gains. Thus, the CPPIB effectively deducts management expenses from investment income. The same approach is followed with trading commissions paid to brokers. The result of this approach is to understate both investment returns and operating costs.

Documenting the contribution of different strategies to excess returns deserves detailed consideration in view of the claims that active management, investing in alternative assets, as well as principal investing are all crucial for enhancing the investment returns and achieving excess returns over passive investment in public markets.

The operating costs of the CPPIB have increased in absolute dollar terms as a result of the huge growth of assets under management and the expansion of staff. Although they declined in relation to total assets up to fiscal 2006, they rose in 2007 (Table 3.6). However, external management expenses for public markets as well as private equity, real estate and infrastructure have been much higher relative to average total assets. Trading commissions paid to brokers were very high in 2006 because of the realignment of the portfolio of public equities.

At first sight, the operating costs of the CPPIB appear low and comparable to those of the most efficient pension fund management institutions around the world, like the TSP in the US, ATP in Denmark, or the NBIM in Norway. In reality, however, total operating costs are much higher than the costs of these other institutions. First, internal operating cost rose to 11 basis points in 2007, implying a higher cost function. Second, and more importantly, the costs of these other institutions include investment fees paid to external managers. Total external management fees and trading commissions of the CPPIB are deducted from investment income. They amounted to around 20 basis points in the last couple of years, but equaled nearly 35 basis points in 2003. Adding external management fees and trading commissions to total operating costs raises their level to between 28 and 42 basis points over the last 5 years.<sup>24</sup>

|Table 3.6: Operat<br>|ingExpe<br>|nsesin r<br>|elationto<br>|CPPIB<br>|Managed<br>|Assets, <br>|2000-20<br>|07<br>|
|---|---|---|---|---|---|---|---|---|
|Fiscal Years<br>in basis points|2000|2001|2002|2003|2004|2005|2006|2007|
|Operating Costs|27.5|12|9|7|7|7|7.1|11.1|
|Public Mrkts Ext Exp|2.5|3.1|2.8|1.1|0.3|3.6|4.1|1.0|
|Private Equity Fees*|||8.4|29.6|25.4|15.3|11.8|12.8|
|Private Real Ext Fees*||||0.2|1.7|1.0|0.8|1.5|
|Infrastructure Fees*|||||0.1|0.4|0.7|0.5|
|Total Extern Fees*|2.5|3.1|11.2|30.9|27.6|20.3|17.4|15.8|
|Trading Commissions*|||3.7|3.8|5.6|2.4|3.8|3.8|

Source: Own calculations on reported CPPIB data

> 24 It should be noted that reported operating costs of most pension funds do not include investment management fees charged indirectly for participations in investment funds.

44
off-farm activities rises in the IHPS relative to the IHS4 sample by a greater factor as compared to women, resulting in wider gender gaps for the IHPS sample.

|**Table 2. Compari**|**ng Individua**|**l-Level L**<br>|**abor Out**|**comes by S**|**urvey App**<br>|**roach and**|**Gender**<br>||
|---|---|---|---|---|---|---|---|---|
|||Men|||Women||Gender d|ifferences|
||IHPS<br>(1)|IHS4<br>(2)|T-test<br>(1)–(2)|IHPS<br>(3)|IHS4<br>(4)|T-test<br>(3)–(4)|IHPS<br>(1)-(3)|IHS4<br>(2)-(4)|
|**Labor activities (Y=1, N=0)**|||||||||
|Any crop work (7d)|0.26|0.32|**|0.29|0.35|**|-0.03|-0.03***|
|At least 1 crop mainly for sale (7d)<sup>†</sup>|0.42|0.39||0.33|0.35||0.09***|0.04***|
|All for HH own use (7d)<sup>†</sup>|0.44|0.53|***|0.59|0.58||-0.15***|-0.05***|
|Any crop work (12m)|0.69|0.72||0.74|0.75||-0.05***|-0.03***|
|Livestock (7d)|0.19|0.17||0.20|0.16|***|-0.01|0.01**|
|Livestock (12m)|0.33|0.30||0.31|0.29||0.02|0.01|
|Management of NFE (7d)|0.14|0.11|***|0.13|0.08|***|0.01|0.03***|
|Management of NFE (12m)|0.20|0.15|***|0.20|0.13|***|0.005|0.02***|
|Working in NFE (7d)|0.04|0.03|**|0.04|0.03|***|-0.001|-0.001|
|Working in NFE (12m)|0.09|0.05|***|0.08|0.06|***|0.01*|0.01**|
|Wage employment (7d)<sup>‡</sup>|0.18|0.15|**|0.06|0.06||0.12***|0.09***|
|Wage employment (12m)<sup>‡</sup>|0.22|0.18|**|0.08|0.07||0.15***|0.11***|
|_Ganyu_employment (7d)|0.22|0.23||0.17|0.16||0.05***|0.07***|
|_Ganyu_employment (12m)|0.47|0.46||0.36|0.37||0.11***|0.09***|
|**Log weekly hours, among employed**|||||||||
|Any crop work|2.39|2.27||2.19|2.24||0.40***|0.03*|
|Livestock|1.53|1.40|*|1.23|1.24||0.30***|0.16***|
|Management of NFE|3.12|3.04||2.73|2.74||0.39***|0.30***|
|Working in NFE|2.41|2.43||2.46|2.35||-0.05|0.08|
|Wage employment|3.53|3.44|**|3.24|3.37||0.29***|0.07*|
|Ganyu employment|2.56|2.47||1.94|2.11|***|0.62***|0.56***|
|**Log annual hours, among employed**|||||||||
|Wage employment|6.82|6.87||6.44|6.75|**|0.38***|0.12**|
|Ganyu employment|5.53|5.72|***|5.15|5.33|***|0.38***|0.39***|
|**Log annual earnings, among employed**|||||||||
|Wage employment|12.63|12.80||12.40|12.53||0.23|0.27***|
|Ganyu employment|10.72|10.90|**|10.02|10.16|*|0.70|0.74***|
|Observations<br>**Notes:**|1,966|8,163||2,411|9,270||||

IHPS = individual interviews; IHS4 = business-as-usual survey approach. Significant differences are indicated with * (p<0.1), ** (p<0.05) and *** (p<0.01). Estimates are weighted with the response weight.

† indicates that means are calculated among those involved in crop agriculture (1,215 individuals in the IHPS and 5,724 in the IHS4).

‡ Wage employment excludes _ganyu._ 10
_jure_ policies<sup>2</sup> . We argue for a different interpretation of firms’ complaints. Firm profitability is not directly affected by “policy” but rather by policy _actions_ taken by agents of the state in the course of policy implementation. In the weak organizational and institutional environments for policy implementation which are common across Africa, policy does not predict policy implementation _actions_ at the firm level. Even when policy is unchanging and even when there is macroeconomic stability, firms face massive risk and uncertainty about their operations and profitability due to variability in policy actions that result from the weak capability for _implementation_ of regulatory and economic policy.

The evidence of variation in the implementation of policy is striking. We show for a number of specific policy outcomes – getting an operating license, getting a construction permit, clearing goods through customs – that the variation within country is much greater than the variation across countries. Studies that focus on measures of country averages are thus missing where most of the action is.

This variation in implementation in turn is associated with greater activities on the part of firms to influence the outcomes (e.g. paying bribes or spending time with officials). Rather than coping with the application of (more or less favorable) rules, the evidence indicates firms in Africa face _deals_ . The policy actions actually taken are influenced by actions (e.g. bribes) and characteristics (e.g. political connections) of the firm. However deals do not assure a certain outcome. Deals themselves create _ex ante_ or _ex post_ uncertainty to a differing extent depending on whether deals are ordered or

> 2 Another view is that firms are complaining about the inter-temporal uncertainty and volatility in overall economic conditions created by poor macroeconomic policy—but “macroeconomic instability” is a separately identified obstacle.

4