rafmacalaba/datause-encoder-data huggingface dataset:
13 expenses, in the first two years, and returns of 4.46% p.a. by the third year. Compared to inflation of 15-20% p.a. during this period, returns were significantly negative in real terms.

Table 6: **Pension Fund Returns in Peru**

|||**(in thou**|**sands of new**|**soles)**|||
|---|---|---|---|---|---|---|
|Year'|Annual<br>Contribution|Annual<br>Commissions<br>Collected|Annual<br>Outflows of<br>Affiliate|Cumulative<br>Contribution|Year-End<br>Assets<br>Managed|Annual<br>Internal Rate<br>of Return<sup>2</sup>|
|1994|382939|47924|430863|430863|430236|-0.32%|
|1995|_<br>610398<br>|1<br>122872|733270|1164133|1154906|-0.92%|
|1996|751649|204808|956457|2120590|2241167|4.46%|

- Notes: ' Months covered for year 1994 are August 1993- September 1994; October-September for years 1995 and 1996.

> 2 IRR is calculated using the monthly flow through the end of the corresponding period. Sources: Superintendency of Pension Fund Administrators, Peru.

Table 7 compares the returns on investment in CDs and Peruvian stock market versus the gross and net returns from pension funds, and is indicative of the opportunity costs of not being able to invest more flexibly. For example, an affiliate able to invest in bank CDs at the average rates reported below, without paying the typical AFP fees, would have obtained average returns of around 15-17% p.a., compared to 4.5% p.a. earned in pension fimds.

Table 7: Performance of Financial Instruments in Peru

||180-365 CD|IFCG Total<br>Local Currency|Gross<br>Pension|Net<br>Pension|
|---|---|---|---|---|
|Year|Rate|Index|Fund IRR|Fund IRR|
|(a)|(b)|(c)|(d)|(e)|
|1994|16.9%|80.4%|27.8%|-0.3%|
|1995|17.2%|55.7%|18.0%|-0.9%|
|1996|_15.0%_|17.3%|20.3%|4.5%|

- (a) Months covered for 1994 are Aug 93- Sep 94; Oct-Sep for years 1995 and 1996.

- (b) Interest rates for 180-360 day deposits for September of indicated year. 1996 figure is for August.

- (c) IFC Global Index in local currency with dividends reinvested. Annual change in index December to December figure for the preceding year.

- (d) Annual internal rate of return through the corresponding date without considering fees and commissions.

- (e) Annual internal rate of return through the corresponding date considering fees and commissions.

Sources: IFC Emerging Markets Database; Peruvian Superintendencia de Admninistradoras Privadas de Fondos de Pensiones.

Marketing Expenses. Analysis of the financial results of the system and individual AFPs reveal the same pronounced pattern of marketing expenses. The direct
incidence and behavior at the individual or household level, as opposed to using a _representative_ household to capture the behavior of an aggregate grouping of households.

A great many papers fit into the partial-equilibrium/cost-of-living analysis category.<sup>3</sup> One example is Levinsohn, Berry and Friedman (2000), who examine how the 1997-1998 Indonesian economic crisis affected poor households in that country. Although their study does not involve trade liberalization, the approach can be used for such a purpose, and it serves to highlight the general methodology of many studies within this category. The authors combine 1993 consumption data for 58,100 households from the Susenas survey, along with price changes contemporary with the crisis, to compute household-specific cost-of-living changes. The salient finding is that very-low-income households were not insulated from the international shocks, and in fact tended to be hurt the most. Regardless of urban or rural orientation, households at lower expenditure levels experienced larger cost-of-living increases. Additionally, the consumer price impacts of the crisis were greater for urban than rural areas, and greatest overall for the urban poor.<sup>4</sup> The beauty of the Levinsohn, Berry and Friedman analysis is its simplicity and high level of household disaggregation. However, from a methodological perspective, this work has several drawbacks. Firstly, the analysis does not isolate the financial crisis from other phenomena, including the El Nino drought and widespread forest fires occurring in the same period. To do so would require a more complete model of macro-economic activity, such as that offered by Robilliard, Bourguignon, and Robinson (discussed in the micro-macro synthesis section) who provide an alternative assessment of the Indonesian crisis that provides such a decomposition.

A second methodological limitation of the Levinsohn, Berry and Friedman (2000) study is that it abstracts from changes in the households’ pattern of consumption in the wake of the financial crisis. In terms of a household’s demand schedule for a given good, there are no movements along or shifts 3 A few of the studies in this general category are: Deaton (1989); Ravallion (1990); Ravallion and van de Walle (1991); Levinsohn, Berry, and Friedman (2000); Case (2000); Deaton (2003); Minot and Goletti (2000); Dercon (2001); McCuloch and Calandrino (2001); Fofack, Monga, and Tuluy (2001). Summaries are provided in the tables of Reimer (2002).

4 This finding is reinforced by the study of Suryahadi et al. (2000) who estimate that the poverty headcount in Indonesia rose by 152% in urban areas, vs. just 57% in rural areas, during the 1996-99 period.

9
prices (values divided by quantities) reported<sup>in the Susenas database (for a given</sup> reference group). Either of these detailed food<sup>price series can be used to construct</sup> an inflation rate for food using expenditure<sup>shares for items within the food basket</sup> based on a sample of poor consumers.9 However for non-food prices only CPI prices exist - there is no Susenas equivalent. So, this gives<sup>a combination of six possible</sup> inflation rates in the poverty line: three methods<sup>of choosing weights to combine</sup> food and non-food inflation and two estimates<sup>of food price inflation.</sup> Since it is well known from consumer theory that<sup>a fixed weights (Laspeyres)</sup> price index _overestimates_ the change in welfare<sup>from a given change in prices (since</sup> the fixed weights do not allow for substitution<sup>effects due to relative price changes)</sup> we begin by using two Laspeyres indices, with<sup>the only issue being the weight on</sup> food and non-food inflation (both from the CPI).

The first deflator, we call method I, uses the actual<sup>expenditures of poor</sup> households. This would be the natural deflator<sup>in defining household's real</sup> expenditure, as it uses their actual consumption<sup>shares. In order to do this we</sup> estimated an Engel curve: a,, = ar +,B * ln(e ) + ej Based on the predicted values of the food share<sup>from this regression we created a</sup>

# deflator for each household:

ri _j (i *nr F + (1 9j6) * NFv_

> 9The expenditure shares were taken from the consumers in the 100 Village Survey, whose food share was near that of the bottom 30 percent of consumers from<sup>the Susenas.</sup>

14
# **III Firm Size and Age in India**

# **_A. Lifecycle in the Formal Manufacturing Sector_**

To examine the relationship between firm size and age, we pool repeated cross-section data from the Indian census. We begin with some descriptive statistics of our data. For each census year separately, we first compute the mean employment in each age bin reported by that census. We then compute an index for each year of data where the index takes the value 1 for firms less than five years old. For all other age bins, the index is the ratio of the mean employment in that age bin to the mean employment for firms under five years of age reported in the same census. Figure 1 plots the mean index for each age bin using data for five repeated cross-sections – 1983/84, 1989/90, 1994/95, 2000/01, and 2004/05. The figure shows that the average 40+ year old plant is 3 to 5 times the size of the plants under five years of age. The firms in the 40+ age bin are typically large firms (mean size is 291 employees compared to sample mean of 121 employees). Many of these firms (62%) were born during the colonial era before India achieved independence from Britain in 1947, so the early growth opportunities of the latter subsample will have occurred in a distinct business and regulatory environment. Khanna and Palepu (2005) detail how British merchants set up large trading houses in India which were transferred to Indian owners in the 1950s.

Figure 2 presents data using weighted employment and shows that for the average 40+ year old plant, the ratio varies from 4.8 times in 1983 to 2.2 in 1994. We also see that in recent years, the average 30 year old plant is not that much larger than plants younger than five years old but after that, older plants are larger than the young plants.

To mitigate concerns about sampling issues we present data only using the census sector in Figure 3 and find that even the 20 year old plant in most years is twice as large as plants 17
loans incurred while migrating) and therefore unlikely to send remittances immediately after arrival in the host country.<sup>4</sup> Modeling the evolution of variables such interest rate differentials, official and parallel market exchange rates and other variables over the medium term is fraught with similar difficulties.

The model-based remittances therefore rely primarily on the latest available information on bilateral migrant stocks (Ratha and Shaw 2007, World Bank 2010a) and the World Bank and IMF’s medium term projections of nominal incomes in the host countries and in the home country.<sup>5</sup> Changes in exchange rates are captured to some extent since projections of nominal GDP factor in plausible assumptions about the evolution of nominal exchange rates.

The forecasts for remittance flows are based on stocks of migrants in different destination countries, incomes in the host country which can influence remittances sent by these migrants, and to some extent incomes in the origin country. Therefore, remittances received by country i from country j can be expressed as where _Mij_ is the stock of migrants from country _i_ in country _j_ , _yj_ is the nominal per capita income of the migrant-destination country, and _yi_ is the per capita income of the remittance-receiving country. The bilateral remittance estimates are calculated using the methodology described in Ratha and Shaw (2007) based on migrant stocks in different destination countries, incomes of migrants in the different destination countries, and incomes in the source country (see Annex 1). We assume that migrant stocks will remain unchanged. This is not an unreasonable assumption in the short term. We prepare the forecasts for remittance flows by examining the effects of income changes in destination countries worldwide.

> 4 In some of the simulations, we try to capture the vintage effect examining a low case scenario where recent migrant inflows of the last one or two years are forced to go back as the economic crisis deepens in the major destination countries, an unlikely but high-impact scenario.

> 5 The World Bank’s projections up until April 2010 have used the bilateral migration matrix of Ratha and Shaw (2007). This is an updated version of the bilateral migration matrix developed by Parsons, Skeldon, Walmsley and Winters (2005, 2007).

6
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# Table of Contents

|1<br>Introduction ................................................................................................................................................ 4|
|---|
|2<br>Methodology, Data Sources and Analysis ....................................................................................... 5|
|2.1<br>Study Area ........................................................................................................................................... 5|
|2.2<br>Preparing GIS Database ................................................................................................................. 7|
|2.3<br>Identifying Public Land for the Study ...................................................................................... 8|
|2.4<br>Use of Public Land ......................................................................................................................... 14|
|2.5<br>Identifying Potentially Marketable Public Land ............................................................... 16|
|2.6<br>Extent of Potentially Marketable Public Land ................................................................... 19|
|2.7<br>Alternative Methods for Estimating Potentially Marketable Land in Partially|
|Developed Land ......................................................................................................................................... 23|
|2.8<br>Valuation of Potentially Marketable Public Land............................................................. 24|
|3<br>Conclusions: Significance of Revenue Potential ........................................................................ 29|
|4<br>Conclusions: Further Applications ................................................................................................. 29|
|ANNEX I ............................................................................................................................................................. 32|
|ANNEX II ............................................................................................................................................................ 37|

2
significant (see Table A5).

Despite these caveats, we think that this and the subsequent exercises do have significant empirical content. This holds even more so for the next two experiments since the effects of both short birth intervals and BCG-vaccination turned out to be large and highly significant in the survival model estimations. The simulated policy changes correspond to all children receiving the BCG vaccine and that preceding birth intervals never fall below 24 months. Obviously, the results are driven by the same forces as above. First, the strength of the effect, which varies much more for short birth intervals than for the BCG-vaccine; and, second, the differences in the scope of the policy change. Here, the differences are smaller with regard to short birth intervals (ranging from a minimum reduction of 12.1 percent for Guinea to a maximum reduction of 25.4 percent for Uganda) than they are for BCG where current coverage varies between 56.7 percent in Niger and 86.9 in Uganda.

Results from these two policy experiments are reported in the third and fourth column of Table 2 and indicate that the impact is significant but varies somewhat across countries. Concerning the elimination of short birth intervals, the impact on U5M varies from -6.2 per thousand points for Senegal to -28.0 per thousand points for Mali. This difference is explained to some extent by the fact that the incidence of short birth intervals is higher in Mali (see Table A2) but mainly by the fact that its effect on child survival appears to be very strong in this country (Table A5). This effect is also fairly strong for Niger and Guinea, where the elimination of short birth interval would reduce U5M by 19.4 and 12 per thousand points, respectively. In Madagascar and Mozambique, the policy would have similar effects with a corresponding decrease in U5M rates by 14 per thousand points. Finally, in Kenya and Uganda, the reduction would be close to 10 per thousand points.

Universal BCG vaccination would also permit a significant reduction of U5M in most countries, with impacts ranging from -7.6 per thousand points for Senegal to -31.1 per thousand points for Mali. Again, policy leverage depends on the estimated effect and the prevalence of the problem in each country. In Kenya, for example, the large reduction of 26.9 per thousand points is driven by a strong coefficient<sup>11</sup> , while the effect in Mali is due to the relatively strong expansion of BCG coverage of almost 35 percentage points.

We have seen above that these three policies that we have simulated separately are, in fact, closely related. More precisely, better educated mothers are less likely to give birth in very short intervals and more likely to have their children being vaccinated. In order to

> 11 A possible reason for the differences in coefficients in the case of vaccines could be the capacity of the health system to deliver vaccines safely and timely.

37
wealth quintiles increases the odds of child survival by around 35 percent. Overall, these findings confirm the general perception of pronounced rural-urban disparities in many WestAfrican countries, while East African urban agglomerations are often considered as poor as the rural areas in the respective country. A surprising finding – and one that is difficult to resolve – is the lack of significantly lower odds of dying for children from rich urban households in Guinea, Uganda, Kenya, and Mozambique.

In sum, the findings from this first specification confirm findings from other studies. We can identify some robust proximate determinants of child mortality, with birth spacing as an important choice variable of parents with a very strong effect on child survival. With the exception of mother’s education, socio-economic variables present ambiguous results. This is likely to be related to measurement problems. Based on this rudimentary specification, we are not able to detect any considerable changes in coefficients over time. Thus, the candidates for explaining the observed changes in child mortality rates are changes in the observables (and/or unobservables). Finally, the stability of estimates gives us some confidence in using cross-sectional parameter estimates for some illustrative forward-looking simulations.

The estimation results of the specification that includes proxies for BCG, DPT, polio and measles vaccination are reported in Table A5. As mentioned earlier, this specification can only be estimated on data from the final survey year. The results demonstrate the potentially large beneficial effect of vaccines for small children. Having received a BCG vaccine consistently reduces the odds of dying. The reduction ranges from around one third in Niger to more than 60 percent in Kenya. It should be noted that these estimates appear to be fairly precise judged by their standard errors. In half of the countries, the measles vaccination is associated with similar reductions; yet, the effects of this vaccination are smaller and/or not significant for Senegal, Mali, Niger, and Madagascar. For the other two vaccines, our results suggest that DPT and polio never significantly increase the chances of survival. On the contrary, they actually often appear to increase mortality risk – in three cases significantly. These results are difficult to interpret and certainly deserve more scrutiny. A review of the medical literature on the impact of DPT and polio vaccines in West-Africa, suggests that there are indeed doubts with regard to their beneficial effects on child health (Kristensen et al., 2000; Aaby et al., 2004).<sup>9</sup> The odds ratios and the corresponding standard errors for the other explanatory variables hardly change with the inclusion of the vaccination proxies. In some countries, for

> 9 There is some evidence for so-called ‘non-specific’ adverse or beneficial effects of vaccinations in low-income countries. See e.g. Folb et al. (2004) and the literature cited therein.

31
TABLE 6 Variance decompositions: Tradables and nontradables (percent)

||Nontradable sector||Tradable sector|
|---|---|---|---|
|Factor||All countries||
|Global|13.2<br>(19.3,4.3 - 31.6)||40.1<br>(36.7,14.5 - 53.6)|
|Group|5.9<br>(10.9, 0.7 - 15.4)||3.8<br>(7.2, 1.7 - 9.8)|
|Global + Group|30.2<br>(30.2,13.3 - 41.8)||45.7<br>(43.9,24.1 - 62.8)|
|Factor||Advanced economies||
|Global|17.9<br>(21.5, 9.3 - 32.1)||43.4<br>(41.7, 29.7 - 55.2)|
|Group|9.4<br>(11.3, 2.8 - 16.6)||3.9<br>(7.4, 1.3 - 10.1)|
|Global + Group|33.7<br>(32.8,20.0 - 43.3)||52.4<br>(49.0,34.4 - 63.7)|
|Factor||EMDEs||
|Global|7.8<br>(15.2, 2.5 - 21.0)||15.3<br>(25.6, 8.6 - 40.4)|
|Group|1.2<br>(10.0, 0.2 - 8.5)||3.4<br>(6.7, 1.9 - 7.1)|
|Global + Group|13.3<br>(25.2,8.7 - 39.8)||24.1<br>(32.3,12.7 - 52.4)|

Notes: The global and group inflation factors for tradable and nontradable goods and services are estimated with the baseline two-factor dynamic factor model for 1970-2016 (Annex 2.1). The common factor from three measures of domestic inflation (import prices, PPI, and headline CPI) is used as a proxy variable for the common component for tradable goods. Similarly, common factors for headline CPI, core CPI, and the GDP deflator are extracted as a proxy for the global inflation factor for nontradable goods. The sample includes annual inflation in 38 countries (25 advanced economies and 13 EMDEs) for 1970-2016. The long-term trend (15-year moving average) is eliminated from annual inflation rates. For each pair of rows, the number in the first row indicates medians across countries. The first number in the second row (in parentheses) is the unweighted mean across countries. The second and third numbers in the second row (in parentheses) indicate the interquantile range (25th and 75th percentiles). CPI = consumer price index; EMDEs = emerging market and developing economies; GDP = gross domestic product; PPI = producer price index.

33