rafmacalaba/datause-encoder-data huggingface dataset:
page 23 However, for Ghanaian assumptions on the health impacts of vaccinations and encounters, the degree of complementarity plays an important role. If e and v can be produced as perfect joint products so that the isocost curve looks like Figure ld, then B is very large _(6_ approaches zero) and Strategy F is preferable for a > .9. If, on the other hand, the opportunity cost of superuviung VH Ws from a frxed center is substantial in terms of foregone vaccinations so that the Strategy F iscoat curve resembles Figure la, then B approaches one (d approaches infinity) and Strategy M is preferable for s < 1.0.

Based on these illustrative parameter esatmates, the choice of primary health care integration strategy seems to be quite sensitive to the particularities of the epidemiologLcal situation and the costs of production in a specific region. Where the relative impacts of vaccination and basic health services and the **relative** costs of the two strategies resemble the West African data and assumptions used to generate rows (A) and (B) of column (4), the degree of complementarity of the joint production of vaccinatdons and encounters in fixed centers is an imporcant input to the strategy choice.

# V. Concluding Remarks

With only two parameters for the objective function and three from each cost function, the model presented here is extremely parsimonious. The advantages of this parsimony are that the coefficients of the model are relatdvely easy to estimate and that the model can be relatLvely easily understood by decision-mnakers. Of course, the parsimony is purchased at the expense of several strong assumptions. Most important among these is the assumption that the choice between the fixed and mobile integration strategLes is the important policy decision and is separable from other government policies and programs within and without the health sector. A second critical assumption is that the units of analysis can be the "average encounter" and the "average vaccination" and that the chosen integration strategy is independent of the mix or impacts of these average events. A third assumption is that the national health objective in rural areas is to maximize healthy-life-days.

Given these assumptions and the additional assumption that the effects of **diseases** and health interventions are additive, the parameters of the objective function can be "guess-timated" from fundamental epidemiologiLal data organized according to the pattern of the Ghana Health Assessment Team study, as is done here in Tables 1 and 3. Since each of the objective function parameters (a and b) represents the net impact of an intervention **oi** an index of overall health status, rather than its impact on any single disease, it would be feasible to estimate the objective function for a region by setting up two experimental groups, one with only the vaccination program and
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25
all tiers to offer interest on deposits only on longer term accounts and to eliminate any interest offerings on current accounts.

This change is clearly borne out in the data, most dramatically in tier 3 banks where interest bearing accounts dropped precipitously to nearly zero percent of portfolio in response to the interest rate caps, down from a previous average of 36%. Correspondingly, non-interest bearing accounts increased by a similar magnitude to an average share of nearly 75%, up from a precaps average of 38.9%. These changes are highly statistically significant in companion regressions.

Overall, these changes in loan and deposit books highlight the strategic response of formal banks to the interest rate caps to minimize any potential financial losses, and to ensure sustainability of their best-risk clients.

# **_Impacts on the Non-Bank Financial Sector_**

Outside of the formal banking sector, although SACCOS were not directly affected by the policy change, the indirect effects of the legislation on these institutions is also important. Data analysis on aggregate SACCO-level data provided by SASRA demonstrates that there were no noticeable changes in ongoing trends when it comes to loan portfolios, investments, deposits, or other indicators aggregated across the industry. These results largely confirm insights from conversations within the industry. Since SACCOs require new members to save for at least six months before they are eligible for loans, it is still too early to distinguish shifts in demand from consumers or changes in strategic behavior by the institutions.

SACCO membership requirements also impede these institutions from serving the small and riskier borrowers locked out from the traditional banking sector. However, it is notable that most of the large SACCOs have reduced their rates in line with the caps to stay competitive and to retain their customers. Similarly, SACCOs are turning to technology/mobile solutions to reduce costs and are also looking to non-funded income to supplement reduced funded income.

The same trends also apply to deposit-taking microfinance institutions (DTMFIs). The vague language contained in the Act resulted in customers expecting compliance from the DTMFIs with respect to the rate caps. Several institutions interviewed as part of this study reported that customers demanded lower rates and were unwilling to pay more than 14%, which they could receive from banks. As a result, loans were re-priced from 17-18% for SMEs and 22% for micro loans. On the funding side, deposits, which accounted for a large proportion of funding for DTMFIs before the enforcement of the Act, were drastically reduced. This resulted in lower liquidity and forced offshore borrowing at higher rates.

15
**Table 1:  Surviving Wild Tiger Populations** *

|Bengal<sup>a</sup>||Indochinese||Malayan||Sumatran||
|---|---|---|---|---|---|---|---|
|India|1,706|Thailand|200|Malaysia|500|Indonesia|325|
|Bangladesh|440|Myanmar|85|(Peninsular)||(Sumatra)||
|Nepal|155|Vietnam|20|||||
|Bhutan|75|Cambodia|20|||||
|||Lao PDR|17|||||
|Total|2,376||342||500||325|

* Midrange estimates a No current estimate for Myanmar Source: Dasgupta, et al. (2012a)

- (2) Habitat Quality (Q).  This includes two components:  Habitat area (H) and the degree of

- fragmentation (F).  For the latter, we use the percentage of habitat area not subject to clearing by August, 2011.

- (3) Effectiveness of protection (P):  For all tiger habitat areas within a country, we total original and remaining forest area and compute the shares of original forest that were subjected to clearing by 2000 and August, 2011, respectively.  The increase in share from 2000 to 2011 is a proxy for the risk of protection failure during the period.

- (4) Protection project Cost (C).  This includes two components:

- a. Habitat land opportunity cost (L):  In Dasgupta, et al. (2012a), we specify a model that relates local forest clearing to the profitability of alternative uses for the land. By implication, the best single measure of the opportunity cost of forested land in a locality is the intensity of recent clearing.  Accordingly, our proxy for opportunity cost in each habitat is the increase in the percent of its area cleared from 2000 to August, 2011.

- b. Labor cost (W):  We incorporate relative protection project costs using differential wages, with income per capita as the proxy and an exponential weight

8
account deficits and higher reliance of foreign capital, has seen private flow decline to below 2005 levels in 2009 (see Figure 2). Despite this, however, the financing needs of developing countries have not declined. Overall, net private capital flows to developing countries in 2009 are estimated to have fallen by $795 billion relative to their 2007 peak, while total external financing needs, measured by current account deficits and maturing private debt, is measured at $1.2 trillion. LICs will suffer the most from this decline as their already small 2.6 percent of total private capital flows will fall to almost zero in 2010. Though small in absolute terms, these flows represent a significant share of national income, investment, and budgetary support, and this loss will certainly have a severe impact on the ability of LICs to meet their financing needs in the short to medium term.<sup>10</sup> According to the World Bank, countries eligible for soft loans and grants from the International Development Association (IDA) may require as much as $35 billion to $50 billion in additional funding in 2010 just to maintain 2008 program levels, on top of the resources necessary to fund additional demands brought upon by the crisis.<sup>11</sup> <!-- Start of picture text --> Figure 4: Projected GDP Growth: 2007, 2009, and Figure 5: External Financing Needs as a Share of<br>2010 (Index, 2006=100) GDP 2010<br>Advanced Apr '07<br>165 EDME Apr '07 16 Current Account<br>Advanced Oct '09 14 Maturing foreign debt<br>155<br>EDME Oct '09<br>12<br>145 Advanced Apr'10<br>10<br>EDME Apr '10<br>135<br>8<br>125<br>6<br>115<br>4<br>105<br>2<br>95 0<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 ECA SSA EAP MNA LCR SA<br>Source: IMF WEO, EDME—Emerging and developing market economy Source: World Bank GEP<br>Percent<br><!-- End of picture text --> Tighter regulation in high‐income countries and the need for multinational banks to conserve capital will also impede foreign bank lending in developing countries. In some regions, a growing participation by foreign banks in domestic financial systems supported the rapid rise in domestic financial intermediation. Indeed, the extent of the expansion in domestic credit in developing countries is directly related to GDP growth and the extent to which foreign banks increased their market shares (see Figure 6). Foreign direct investment (FDI) is usually less volatile than debt flows, and should be less affected by the crisis; however, parent firms will face higher capital costs, and these are likely to reduce their ability to finance individual projects. The real‐side consequences of such a decline could be serious because FDI represents an important share of fixed investment in developing regions, and LICs in particular (see Figure 8).<sup>12</sup> Remittances, also an important and resilient source of capital for LICs, have declined sharply (see Figure 7) and are not projected to regain their pre‐crisis levels until 2012.<sup>13</sup>

# **Figure 6: Local Currency Lending as a Percent of**

> 10 World Bank GEP (2010)

> 11 Protecting Progress (WB, 2009)

> 12 See World Bank GEP (2010)

> 13 Mohapatra and Ratha (2010)

5
Table 3: Matched sample balance: SBM subsidy eligibility criteria (SBM data)

||.<br>**All**|**N**|**Control**|**N**|**Treatment**|**N**|
|---|---|---|---|---|---|---|
|HH owns BPL card|0.29<br>(0.45)|959|0.34<br>(0.037)|551|0.23<sup>_∗∗_</sup><br>(0.035)|408|
|Landless with homestead|0.076<br>(0.27)|959|0.094<br>(0.032)|551|0.051<br>(0.017)|408|
|Physical disability|0.0021|959|0.0036|551|0|408|
||(0.046)||(0.0025)||(0)||
|Scheduled Caste|0.34<br>(0.47)|959|0.33<br>(0.049)|551|0.36<br>(0.059)|408|
|Scheduled Tribe|0.030<br>(0.17)|959|0.033<br>(0.014)|551|0.027<br>(0.015)|408|
|Small and Marginal Farmers|0.23<br>(0.42)|959|0.19<br>(0.031)|551|0.29<sup>_∗_</sup><br>(0.050)|408|
|Female Headed|0.014|959|0.0091|551|0.020|408|
||(0.12)||(0.0044)||(0.0072)||
|VG|0.80|959|0.77|551|0.85|408|
||(0.40)||(0.052)||(0.038)||
|HH owns a toilet at SBM baseline|0.22<br>(0.41)|959|0.20<br>(0.044)|551|0.25<br>(0.037)|408|
|SBM eligible|0.64<br>(0.48)|959|0.63<br>(0.058)|551|0.66<br>(0.046)|408|

Note: Standard deviations in parenthesis in first column (All); Standard Errors in parenthesis in other columns, clustered at the gram panchayat; _* p <0.10, ** p <0.05, *** p <0.01_ 31
UCT programs. Columns 1 and 3 estimate differences between UCT and TUP programs, Columns 2 and 4 allow for heterogeneity with respect to years since last transfer, while Column 5 allows for heterogeneity with respect to TUP costs.

Table 8: Multifaceted graduation programs and persistence

||(1)<br>|(2)<br>|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Panel A: Efect on consum**|**ption per unit **|**of transfer**||||
|_β_1: TUP|0.111|-0.056|0.464|0.324|0.099|
||(0.114)<br>|(0.170)<br>|(0.170)<br>|(0.193)<br>|(0.129)<br>|
||{-0.122, 0.329}|{-0.401, 0.273}|{ 0.116, 0.789}|{-0.070, 0.693}|{-0.160, 0.350}|
|_β_2: TUP *||0.078||0.090||
|Years since last transfer||(0.056)<br>{-0.032, 0.190}||(0.050)<br>{-0.007, 0.190}||
|_β_3: TUP * (Cost per<br>unit of transfer _≥_4)<br>|||||0.031<br>(0.165)<br>{-0.302, 0.349}|
|_στ_: Std. dev. of efects|0.277|0.280|0.235|0.223|0.283|
||(0.060)|(0.064)|(0.057)|(0.059)|(0.063)|
||{0.171, 0.409}|{0.168, 0.419}|{0.136, 0.362}|{0.123, 0.354}|{0.173, 0.419}|
|**Panel B: Efect on consum**|**ption per unit **|**of cost**||||
|_β_1: TUP|-0.150|-0.317|-0.018|-0.130|-0.089|
||(0.054)|(0.077)|(0.062)|(0.068)|(0.059)|
||{-0.258, -0.045}|{-0.473, -0.170}|{-0.152, 0.094}|{-0.279, -0.008}|{-0.205, 0.028}|
|_β_2: TUP *||0.082||0.076||
|Years since last transfer||(0.028)<br>{ 0.027, 0.138}||(0.022)<br>{ 0.033, 0.122}||
|_β_3: TUP * (Cost per|||||-0.130|
|unit of transfer _≥_4)<br>|||||(0.062)<br>{-0.258, -0.013}|
|_στ_: Std. dev. of efects|0.123|0.114|0.070|0.056|0.116|
||(0.027)|(0.024)|(0.030)|(0.025)|(0.026)|
||{0.077, 0.183}|{0.075, 0.166}|{0.015, 0.135}|{0.010, 0.110}|{0.073, 0.173}|
|Controls||||||
|Years since last transfer||X||X||
|Africa|||X|X||
|Transfer duration|||X|X||
|# of RCTs|17|17|17|17|17|
|# of observations|38|38|38|38|38|

_Notes:_ Estimates of the model of impacts of UCT and TUP interventions on household consumption per unit of transfer or per unit of cost in Equation 5 are presented in this table. Columns 1 through 5 report Bayesian posterior means of each parameter, with posterior standard deviations in parentheses and 95% credible intervals in curly brackets.

First, in Column 1 of Panel A, we compare the effects of TUP and UCT on consumption per unit of transfer across our sample. Consistent with our estimates in Table 2, TUP have a 0.11 larger impact on consumption per unit of transfer than UCT. However, 95% credible 42
# Figure 1: Kernel Density of Change in Log Income between Periods 1 and 2 by Attrition Status

<!-- Start of picture text --> 2<br>1.5<br>1<br>.5<br>0<br>-1 -.5 0 .5 1<br>Change in Log Income<br>Non-Attritors Drop out after period 2<br><!-- End of picture text --> Kernel Densities plotted in STATA using the Epanechnikov kernel with a bandwidth of 0.01.
_B_<sup>_′_</sup> , and _C_ (a new district containing part of _A_ and of _B_ ), then all of _A_ , _B_ , _A_<sup>_′_</sup> , _B_<sup>_′_</sup> , and _C_ would be a single constructed district, although splits this complicated are rare.<sup>12</sup> The empirical strategy of this section is to compare the heights of rural children under 3 years old in the NFHS rounds 1 and 2, using district fixed effects.<sup>13</sup> In particular, I regress child height on the fraction of households reporting open defecation at two levels of aggregation: districts and villages (or more precisely, rural primary sampling units). Because open defecation has negative externalities on other households, it is necessary to test for effects of community-wide sanitation coverage, rather than simply comparing households that do and do not have latrines; section 4.1 considers the econometric implications of these negative externalities in more detail.

Therefore, the regression specification is: _heightidvt_ = _β_ 1 _open defecation_<sup>_d_</sup> _dt_<sup>+</sup><sup>_β_2</sup><sup>_open defecationv_</sup> _dvt_<sup>+</sup><sup>_αd_+</sup><sup>_γt_+</sup><sup>_Xidvtθ_+</sup><sup>_Aidvtθ_+</sup><sup>_εidvt,_(2)</sup> where _i_ indexes individual children, _d_ are districts, _v_ are villages (rural PSUs), and _t_ are survey rounds 1 and 2. The dependent variable, _heightidvt_ is the height of child _i_ in standard deviations, scaled according to the WHO 2006 reference chart. As recommended by the WHO, outliers are excluded with _z_ -scores less than -6 or greater than 6. The independent variables _open defecation_<sup>_d_</sup> _dt_<sup>and</sup><sup>_open defecationv_</sup> _dvt_<sup>are computed fractions 0 to 1 of households</sup> reporting open defecation in the child’s district and village, respectively. Fixed effects _αd_ and _γt_ are included for districts and survey rounds. The vector _Aidvt_ is a set of 72 indicators for age-in-month by sex, one for each month of age for boys and for girls.<sup>14</sup> Controls _Xidvt_ are at the household or child level: electrification, water supply, household size, indictors for being

> 12The NFHS was not constructed to reach all districts, so households are only included in the sample if they are members of districts that appear in both survey rounds, to permit district fixed effects.

> 13Although district fixed effects are used, the NFHS did not survey the same villages in the two survey rounds; thus there remains an important cross-sectional component to the heterogeneity studied.

> 14Panagariya (2012) has recently argued that height-for-age _z_ score reference charts are inappropriate for Indian children; because age-in-months-by-sex is the level of disaggregation used to create height-for-age scores, these controls fully and flexibly account for any deviation between the mean height of Indian children and the reference charts.

16
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

# **LIST OF TABLES**

|Table 1: Summary of key PSPF indicators, 2003/04........................................................ 12|
|---|
|Table 2: Summary policy considerations under alternative reform options / 1................ 15|
|Table 3: The Uganda pension system............................................................................... 21|
|Table 4: Government pension arrears............................................................................... 23|
|Table 5: Benefit Types under CAP 286............................................................................ 27|
|Table 6: Summary of data on the stock of employees, 2003/04....................................... 29|
|Table 7: Summary of data on the stock of old age pensioners (2003/04)......................... 33|
|Table 8: Main pension system indicators (2003/04)......................................................... 36|
|Table 9: Projected total pension expenditure (baseline: variant 1)................................... 42|
|Table 10: The impact of lower covered population growth rates on end of simulation<br>period results............................................................................................................. 48|
|Table 11: Alternative scenarios under the pure DC and hybrid reform options............... 49|
|Table 12: Projected average replacement rates* for the stock of retirees (average over<br>2070 – 2075) ............................................................................................................. 67|
|Table 13: Summary policy considerations under alternative reform options / 1.............. 72|
|Table 14: Summary of the key outputs for alternative scenarios...................................... 76|
|Table 15: Macroeconomic assumptions............................................................................ 80|
|Table 16: Assumed average wage growth for the covered population............................. 80|
|Table 17: Projected life expectancy* for the general population ..................................... 82|
|Table 18: Projected life expectancy for civil servants...................................................... 83|
|Table 19: Medium term active population growth assumptions....................................... 84|
|Table 20: Public debt, pension spending and implicit pension debt for 35 low and middle<br>income countries for various years during the end-1990s and 2000 ........................ 94|
|Table 21: Estimated IPD for 35 Low and Middle Income Countries - detailed results.... 96|
|Table 22: Projected Internal Rates of Return.................................................................. 105|

- 10 -
a monthly fee is the sample mean, i.e., US$4.5.<sup>30</sup> It would take even more time, because the PCMs associated with future investments are likely to be lower than the current minimum profit margin, 2.6. In principle less profitable markets remain to be developed. In addition, this calculation _does_ ignore non-incremental operation and maintenance costs. However, less than 10 years appear commercially acceptable. Obviously, sound business environment needs to be established for supporting long-term private investments in the telecommunications sector.

<u>Table 10. Implied Price Cost Margins</u>

||Average|Max|Min|
|---|---|---|---|
|Fixed-line|3.832|4.848|3.283|
|Mobile|3.866|7.984|2.624|
|Prepaid|3.820|7.984|2.624|
|Contract|3.914|7.980|3.092|

Note that there are four negative price cost margins calculated from the estimated demand paramters; those cases are excluded from the above figures.

Source: Author’s calculations.

The implied PCMs appear irrelevant to the intensity of market competition (Figure 12). This is good news and bad news for policy makers. The good news is that intensified market competition does not erode the profit margins of telephone operators. Thus, more private investment in the telecommunications sector could be expected in the region. The bad news is that market competition may not be working effectively, possibly due to insufficient deregulation and explicit and implicit price control by the authorities. In theory more competition must reduce the profit margins in equilibrium, unless products/services are significantly differentiated. Thus, price and entry liberalization may need to be accelerated.

> 30 This does not sound prohibitively long, given the fact that in Latin America, incumbent telecommunications operators were granted exclusivity periods of four to ten years in the 1990s (World Bank, 2004).

32