rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1 Summary of Theoretical Propositions***

||**Lan**|**d Rental Reg**|**imes**|
|---|---|---|---|
||_Rent-In_|_Autarchy_|_Rent-Out_|
|_Tenure Security Investment Demand Effect_||||
|_ak/aS_||+, >04||
|_Factor Price Equalization_||||
|Threshold Effects:aT'I/aM,||+**>04**||
|_aT,°/aM,_||+ <04||
|Non-Separability/Endowment Dependence:<br>_a/aTc ,ak/rTc_|-0|<0|=0|
|TransferRights Dependence:<br>f/lM,1 ,_ak/dM,_|<0|0|>0|
|_Investment Regret_||||
|Pure Regret: akIa_,aT/D_|< 0|< 0|=0|
|Non-Separability:_ak/DTc,a/aTc_|_<0_|<0|=0|
|_Investment Regret Mitigation_||||
|Transfer Rights Dependence:_aklaM 1 ,a_<br>_aM,_|_<0_|**0**|=0|
|_ak/aTcaM,a/aTcaMM_|_> 0_|_>0_|= 0|
|_ak/laaaM , ,a/aaaM,_|_,>0_|_>0_|=0|

_*_ Note that the shadow price of labor, 5, moves in the opposite direction of labor intensity, while the expected second period shadow value of investment, r2k, moves in the sarne direction as investment.

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<u>products/international‐macroeconomic‐data‐set.aspx (Accessed on September 27, 2015). There</u> was a relatively small (9 percent) difference in the total estimated shortages in 2030 based on the two methods (15.6 million with the main method we used, and 17.0 million using USDA numbers).

Second, we examine the possible upper and lower bounds of the predictions older than 65 resulting from high and low future population estimates. Because population is the largest driver of demand in our model, using the high and low variant estimates, we can obtain predicted total health worker deficits that may result from population growth among people older than 65 that is higher and lower than expected, compared to the median estimate that is presented in the main results. These alternative low and high estimates (shown in Annex Figure A1) indicate a tight band for the resulting predicted values.

# _Projecting supply_

The supply of health workers was projected to 2030 using historical data to predict the changes in health worker densities (per 1,000 population) for each country. We assume that the historical growth rate of health workers density for each country will continue into 2030 at the same rate. This assumes that supply growth is exogenous and only trends with time. This assumption may be plausible if the health labor market is relatively rigid, for example, due to the strong influence of professional associations and trade unions in the sector. Given that data are only available beginning from 1990, and that many countries only have a few data points, the growth rate method enables us to obtain projections using minimal empirical data inputs.

We separately estimate physician and nurses/midwives density for each country from time _t_ = {1990, ... 2013} using the following equations:

* (Eq 2) _Physicians per 1000 populationt_ = _α_ 0 + _α_ 1 _yeart_ + _εt_

* (Eq 3) _Nurses/midwives per 1000 populationt_ = _β_ 0 + _β_ 1 _yeart_ + _εt_

where _εt_ is the random disturbance term and α0, β0, α1 and β1 are unknown parameters, with the last two parameters representing the linear growth rates to be estimated from the model.

For countries where more than two historical data points are available for physician and nurse/midwife density, the future projections of worker density are predicted based on the model parameter estimates. More than two historical data points were available for physician and nurse/midwife density for 136 and 81 countries, respectively, out of the total of 165 countries in our sample.

For countries with insufficient historical data points or where linear regression prediction yielded implausible values, several alternative approaches were used to estimate future supply (see Annex A for details). Briefly, for 72 and 118 countries, respectively, for physicians and 7
# **S2.2 Using Administrative Matched Employer-Employee Data**

Table S2.12 Variance Decompositions, AKM Model: Brazil, Costa Rica, and Ecuador

|_a. Brazil (data available 1995-20_|_14)_||||||||
|---|---|---|---|---|---|---|---|---|
||1995-98<br>(1)|1998-2001<br>(2)|2001-04<br>2004-07<br>(3)<br>(4)|2007-11<br>(5)|2011-14<br>(6)|Change<br>1995-2001<br>(2)-(1)|Change<br>2001-14<br>(6)-(3)|Change<br>1995-2014<br>(6)-(1)|
|Variance of worker efects|49 (0.299)|50 (0.280)|52 (0.252)<br>54 (0.230|)<br>56 (0.217|)<br>59 (0.216)|36 (-0.020)|31 (-0.035)|34 (-0.083)|
|Variance of frm efects|31 (0.188)|29 (0.163)|28 (0.133)<br>25 (0.107|)<br>28 (0.087|)<br>21 (0.075)|46 (-0.025)|50 (-0.057)|46 (-0.112)|
|2xCov. Worker and frm efects|13 (0.077)|14 (0.076)|13 (0.064)<br>12 (0.052|)<br>12 (0.048|)<br>11 (0.042)|2 (-0.001)|19 (-0.022)|15 (-0.035)|
|Residual|7 (0.045)|7 (0.037)|7 (0.033)<br>8 (0.033|)<br>9 (0.033)|9 (0.033)|46 (-0.025)|0 (0.000)|5 (-0.012)|
|Variance of log earnings|100 (0.609)|100 (0.556)<br>|100 (0.482)<br>100 (0.42|2)<br>100 (0.38|5)<br>100 (0.366)|100 (-0.054)|100 (-0.115)|100 (-0.243)|
||_b. Costa _|_Rica (data avai_|_lable 2006-11)_||||||
||||2006-08<br>(1)|2009-11<br><br>(2)|Change 2006-11<br>(2)-(1)||||
||Variance|of worker efect|s<br>55 (0.233)|53 (0.238)|22 (0.005)||||
||Variance|of frm efects|29 (0.125)|32 (0.144)|84 (0.019)||||
||2xCov.|Worker and frm|efects<br>7 (0.029)|7 (0.030)|6 (0.001)||||
||Residual||9 (0.039)|8 (0.037)|-12 (-0.003)||||
||Variance|of log earnings|100 (0.426)|100 (0.448)|100 (0.022)||||
||_c. Ecuad_|_or (data availa_|_ble 2006-12)_||||||
||||2006-09<br>(1)|2009-12<br>(2)|Change 2006-12<br>(2)-(1)||||
||Variance|of worker efec|ts<br>54 (0.211)|58 (0.175)|41 (-0.036)||||
||Variance|of frm efects|39 (0.152)|36 (0.109)|50 (-0.043)||||
||2xCov.|Worker and frm|efects<br>-1 (-0.004)|-5 (-0.016)|14 (-0.012)||||
||Residual||8 (0.030)|11 (0.034)|-5 (0.005)||||
||Variance|of log earnings|100 (0.388)|100 (0.302)|100 (-0.086)||||

_Sources:_ Authors’ calculations based on data from Social Security Records: Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS) in Brazil, Caja Costarricense de Seguro Social (CCSS) in Costa Rica, and Instituto Ecuatoriano de Seguridad Social (IESS) in Ecuador. _Note_ : The table reports the results of the variance decomposition. Cells contain the share of the total variance explained by each decomposition element. The total variance explained by each decomposition element is shown in parentheses. AKM = Abowd, Kramarz, and Margolis (1999).

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1.40 -

-

Flgure I.C.1 COCOA SUPPLY RESPONSE UNDER FIXED RESOURCES <!-- Start of picture text --> 700- I<br>Suoo /<br>-s0o I .1 I I' -4<br>400 /<br>200 -<br>1987 1997 2007 2017 2027 2037<br>PERIOD MIDPOINTS<br>0 10 * Mo o 30 £ 0 X<br><!-- End of picture text -->
use.<sup>30</sup> The top panel shows results for the overall sample, the middle panel shows results for legal entities alone, while the bottom panel shows heterogeneous results for firms above and below the median risk score.

We find a significant impact of e-filing on compliance costs as shown in Table 5 (columns 1 and 2). Firms that e-file as a result of the program visit the tax authority 1.4 times fewer each month. In all, e-filing reduces the time firms spend on tax-related activities by 4.7 hours a month. As shown in Table 7 (column 6), this effect is concentrated in activities that involve visiting the tax authority office (submitting tax returns and obtaining the reconciliation act). As such, e-filing does fulfill the intended goal of reducing tax compliance costs of firms. The time savings corresponds to 15 percent of the overall 33 hours firms report spending on tax-related activities. A “naive” cost effectiveness analysis comparing program costs to the amount of money firms save from lower compliance costs<sup>31</sup> (using the average wage of an accountant in our sample) shows that potential benefits from compliance costs reduction more than compensate program costs after 7 months (Appendix C and Appendix Table A6 detail program costs and calculations).

Using our two measures of unofficial payments, we find no overall impact of e-filing, but observe different patterns by firm risk profile. For the survey question about how widespread unofficial payments are (Table 5 column 3), we find no overall impact either in the full sample or among legal entities. However, we find a decrease of 23.6 percentage points (only marginally statistically significant with p=0.103) among low-risk firms and a positive impact of 16 percentage points (not statistically significant) among high-risk firms and the difference between the two coefficients is significant (p=0.035).

Similarly, using the list experiment, we find opposite results for high- and low-risk

> 30 For simplicity, we only present the instrumental variables results. ITT results are qualitatively similar (scaled down by a factor of about 3) and available upon request.

> 31 A full cost effectiveness analysis would account for other benefits such as savings in tax administration costs.

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in the case of the Guangxi CDM project discussed above - or for working with local collectives raises the net payoffs for all participants, including poor households.

In the frontier areas where deforestation is most active, land tenure is often insecure. When coupled with credit constraints, this makes it very difficult for low income households to participate in such programs. On the other hand, by minimizing transactions costs, front-loading payments, and working to ensure title to the land, it is possible to increase the participation of the poor in such programs. Yet in the end, Pagiola, Arcenas, and Platais offer some sobering evidence on the potential for PES to reduce poverty. They point out that _even those programs which are explicitly designed to reduce poverty_ in developing countries have limited success. Citing (Coady, Grosh, and Hoddinott 2004), they note that the median targeting rate for poverty reduction programs across a large sample of countries is only 1.33 (i.e. the median program transfers only 33% more income to the bottom decline than would a uniform transfer program to all households in the economy). Any program with the primary objective of reducing carbon emissions cannot hope to do much better than this, and will likely do worse.

In addition to the direct impacts from payments for environmental services, mitigation policies can have indirect poverty impacts through commodity and factor markets (in much the same way as the climate change impacts discussed above). This potential for indirect effects is illustrated by the contribution of biofuels programs – motivated themselves in part by greenhouse gas (GHG) mitigation objectives – to the recent run-up of world food prices. Mitchell (2008, p.17) attributes as much as three-quarters of the rise in food commodity prices over the 2002-2008 period to biofuels and the related consequences of low grains stocks, speculative activity and export bans. Others suggest that the contribution of biofuels to this price spike was much more modest. However, any program which contributes to the majority of global 40
above all to cooking but also includes, for example, child caring, preparing the beds before going to sleep, and repairing clothes. The Pico‐PV lamp replaces above all wick lamps and is used by female adults that had not been using any particular lighting device before.<sup>14</sup> Male adults also use the lamp mostly for housework, although these are more diverse activities than for women. For male adults, the Pico‐PV lamp replaces wick lamps, ready‐made torches, and hand‐crafted LEDs and is also used by men who had not used any artificial lighting device before for housework activities.

**Table 7. Activity Using Pico‐PV Lamp, Adults and Children in Treatment Households (%)**

|||**First Activit**|**y**|**Second Activ**|**ity**|**Third Activ**|**ity**|
|---|---|---|---|---|---|---|---|
|Female adult >17 years old|N=149|Housework|87|Study|5|Eat|4|
|Male adult >17 years old|N=60|Housework|71|Recreation|10|Study|10|
|Children 6 to 17 years old|N=56|Study|75|Housework|16|Recreation|4|

_Note:_ Information on activities stem from an open question among treatment households at follow‐up, asking for the main activities the different lamp users are exercising while using the lamp.

_Source:_ Household data set 2011/2012.

Table 8 shows that housework is done primarily during daytime, also in the treatment group, and the total _time dedicated to domestic_ work per day does not change significantly. The _total time household members are awake_ per day does not change significantly, either. This reveals that the Pico‐PV lamp is also used during daytime for housework activities, which is in line with observations made during qualitative

> 14 We analyse lamp switching by comparing lamps used for the corresponding activities by treatment and control households. Detailed results of the analysis can be found in the supplemental appendix, Table S6.3.
have influenced those estimates of poverty, and relief generosity rose up to the early 19th century, falling by 1850. Thus these trends also must be treated with caution.

MacKinnon (1984, 1986) concentrates on long run trends in English indoor pauperism from 1860 to 1910. She is able to break up the data by region and type of pauper and, by using regression analysis, purges the data of some of its policy component. Wage data for this period suggests that working class incomes were sharply rising (Williamson, 1985), but, except for specific groups, there is no consistent fall in the policy adjusted indoor pauper rate proxying the experience of the extreme poor. This prompts MacKinnon toward tentative pessimism as regards late 19th century trickle down to the extreme poor: "...it seems highly probable that the very poor were little better off in 1910 thau in 1860" (MacKinnon, 1984, p.271).

Hannon's work on New York State starts earlier. Table 9 reports pauper rates for New York State at five year intervals. On the face of it, pauperism (and poverty) was on the rise throughou **t** he antebellum period just as was inequality. While Hannon reserves judgement on the postbellum figures, she views the trends up to 1860 as evidence of an increase in the demand for poor relief and thus of the distress of the poor, especially given that policy was becoming "increasingly stingy across the antebellum period" (1984b, p.1008). Although the long-run effect of early industrializati **on** those in extreme poverty can be debated, the effect of short run macro fluctuations is certain. For example, when English growth slows down dramatically after 1900.

42
Myers 2001, for example). In addition, a firm’s actual debt–equity ratio will be strongly influenced by the market valuation of its equity (Welch 2002).

# **4.2.3 Incorporating customers’ claims in the analysis**

Customers typically don’t receive a share of the operating profits of the firm, but for our purposes it is useful to think of them alongside other beneficiaries of the firm’s activities in an expanded stakeholder structure. Customers, like equityholders, debtholders, and governments, share in the value of the services the firm produces, receiving what economists call consumer surplus. By considering the present value of customers’ consumer surplus, we can interpret the stakeholder structure as the shares in the total value of the firm services received by equityholders, debtholders, government, and customers.

The value of each stakeholder’s share of the value of the firm’s activities is the present value of the cashflows and, particularly in the case of customers, other benefits (such as the services provided by the firm) the stakeholder expects to receive or give up, over the life the firm. The value therefore depends on the expected values of the cashflows (and other benefits) and on the risks surrounding the future cashflows.

The risks of each type of claim differ and those differences affect the values of the claims. For example, the payments to the firm’s debtholders are usually less risky than the payments to its equityholders, so the value of the debtholders’ claim is greater, per dollar expected, than that of equityholders. Put differently, debtholders discount their expected cashflows at a lower rate than equityholders. Although customers and governments typically do not attempt to calculate the value of their claims, these values also depend on the risk characteristics of the payments and other benefits.

# **4.2.4 Stakeholder strategies for claiming value**

All stakeholders are interested in increasing the value of their claims. Debtholders seek to increase the probability of receiving their promised return; equityholders seek to increase the firm’s profitability; governments seek to achieve their goals, while minimizing subsidies and tax exemptions; and customers seek higher-quality services and lower prices.

One strategy stakeholders can pursue to increase the value of their claim is to increase the total value added by the firm’s services and ensure they get a share of the increase. In the standard metaphor, the stakeholders can try to increase the size of the pie. (Because we’ve used bar charts rather than pie charts—as we want to show negative claims—we might refer to increasing the size of the “bar”). Equityholders, for example, may encourage managers to innovate or invest in ways that increase profits and provide new benefits to customers.

Another strategy a stakeholder group can pursue is simply to increase their share of the value—without increasing, or even at the cost of reducing, the size of the pie. In an idealized world in which there were no transaction costs and all changes had to be 42
30 availability and qualified doctors (appendix 2), factors which are largely beyond village control<sup>29</sup> .

Table 7: Correlation of social capital with indicators of parental participation in schools, school quality, and health facility quality

||Survey|Bivariate ra|nk correlations|
|---|---|---|---|
|||Social Capital<br>Index|Group Functioning<br>Sub-Component|
|Median parental participation<br>in schools|HRDS|.243<br>(.025)|.202<br>(.065)|
|Attendance at community<br>meetings|HRDS|.296<br>(.006)|.117<br>(.291)|
|School quality|HRDS|.176<br>(.108)|.238<br>(.029)|
|Health facility quality|HRDS|.132<br>(.228)|-.039<br>(.724)|
|Participation in joint efforts at<br>road repairs|SCPS|.147<br>(.182)|.272<br>(.012)|

Notes: p-levels in parenthesis.

_Social capital and village level cooperation_ Another possible channel for the impact of social capital is the management of resources that are treated as common property within the village (or perhaps among a few villages) such as improved water supplies, local irrigation capabilities, and local roads. Unfortunately on this question we have very little data, but in

**29** While we do confirm an association between social capital, community involvement and better public services, we should point out that strictly speaking this does not go far in "explaining" the income effect of social capital as a proximate determinant, as we have no evidence on the magnitude of the link from better schools to higher incomes and moreover, in the data above the link with income and the quantity of schooling is quite weak in tables 3 and 4.
TSLS estimates, as are the standard errors, but the fact that the difference between the coefficients estimated with the two methods is negligible reinforces the hypothesis that the instruments have enough predictive power. Indeed, if they were weak, the TSLS estimates would have been much closer to the OLS ones than to the LIML ones. Also, the Fuller specifications<sup>20</sup> with _α_ = 1 and _α_ = 4 produce lower threshold values for the _F_ statistic and thus are less biased than the TSLS coefficients, and their magnitude is almost identical to the TSLS.

**Table 12.** Robustness Checks: Weak Instruments.

||**Sh**<br>|**ares of hou**<br>|**sehold in**<br>|**come spen**<br>|**t on:**<br>||**Stock**<br>|**-Yogo we**<br>|**ak ID**<br>|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|**_F_**|**test**|**critical v**|**alues**|
||Food|Education|Health|Durables|Adult|**_statistic_**|**10%**|**20%**|**30%**|
|OLS|-0.104|-0.012|-0.019|-0.024|0.062***|||||
||(0.079)|(0.022)|(0.019)|(0.019)|(0.017)|||||
|TSLS|-0.293|-0.082|0.082|-0.017|0.149**|9.374|19.93|8.75|7.25|
||(0.337)|(0.102)|(0.135)|(0.020)|(0.074)|||||
|Wooldridge|-0.096<br>(0.251)|-0.079<br>(0.094)|0.074<br>(0.131)|-0.028<br>(0.020)|0.143**<br>(0.071)|18.61|16.38|6.66|5.53|
|IV|-0.153<br>(0.342)|-0.017<br>(0.126)|0.150<br>(0.158)|-0.053<br>(0.041)|0.186**<br>(0.087)|14.03|16.38|6.66|5.53|
|LIML|-0.299<br>(0.348)|-0.086<br>(0.107)|0.088<br>(0.143)|-0.017<br>(0.020)|0.154**<br>(0.078)|9.374|8.68|4.42|3.92|
|Fuller (_α_= 1)|-0.288<br>(0.329)|-0.081<br>(0.101)|0.082<br>(0.135)|-0.018<br>(0.019)|0.149**<br>(0.073)|9.374|10.89|9|7.49|
|Fuller (_α_= 4)|-0.262|-0.071|0.067|-0.019|0.136**|9.374|10.89|9|7.49|
||(0.282)|(0.087)|(0.116)|(0.016)|(0.062)|||||

**Notes:** Controls are size of household, rural household, education of mother, log total household expenditure. Standard errors robust to village level clustering in parenthesis. *** p <0.01, ** p <0.05, * p <0.1

In terms of exogeneity of the instruments, as those I am employing are time (of migration) and village specific, one may be concerned that some villages have unobserved characteristics that simultaneously affect the share of household income devoted to a certain commodity and the destination, or the wages at destination, of the migrants. For example, suppose a village has a very good nursing school, women there will mainly be nurses and thus obtain, when they migrate, a certain specific wage. Yet the fact that the

> 20. When errors are normally distributed and instruments are fixed, the Fuller- _k_ with _α_ = 1 is best unbiased to second order (Rothenberg 1984). While the critical values for the _F_ statistic are not significantly lower than those for TSLS, this estimator has been proved to yield more precise estimates than both TSLS and LIML when instruments are weak.

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