rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1:Superannuation Funds: Type, Number and Size - December 2005**

|Fund type|Number of funds|Total assets<br>($Ab)|Average<br>assets<br> ($Am)|
|---|---|---|---|
|Retail|194|272|1,402|
|Industry|86|137|1,593|
|Corporate|681|56|82|
|Public sector|43|142|3,302|
|Small funds|319,492|194||
|Other|127|45||
|**Total**|320,623|845||

_Source: APRA_ All classes of funds (other than ‘small funds’) have been consolidating for some time. Corporate funds have been declining particularly rapidly in both number and share of industry assets as costs of administration increase and the early-1990s introduction of near-universal employer superannuation has eroded any competitive benefits from offering in-house superannuation. This trend has been given substantial new impetus recently by the introduction of more stringent prudential regulations, including for fund governance and risk management, and the requirement that all funds be licensed by the middle of 2006. As a result, the number of funds, excluding ‘small funds’, at end-June 2006 was 872, less than half than two years earlier.

**Table 2: Superannuation Funds: Asset Allocation (percent)**

|Asset class|2004|2000|1995|
|---|---|---|---|
|Cash and deposits|8.3|6.5|6.8|
|Loans and placements|3.6|4.9|4.4|
|Interest-bearing securities|16.0|18.5|25.3|
|Equities and units in trusts|48.5|43.1|38.5|
|Land and buildings|5.2|5.3|6.9|
|Other domestic assets|1.8|2.7|4.1|
|**Total domestic**|83.3|80.9|86.1|
|Assets overseas|16.7|19.1|13.9|
|**Total**|100.0|100.0|100.0|

Source: APRA A new classification of investments was introduced for 2005 and shows: Australian equities (33 percent), international equities (23 percent), Australian fixed interest (13 percent), international fixed interest (5 percent), listed and unlisted property (8 percent), cash (7 percent) and other (10 percent).

5
# 2.2 Health System and Malaria in Senegal

Senegal is geographically divided into 14 health regions, under which there are 76 health districts. The main point of service for malaria cases is the health post. There are a total of 1,247 health posts in the country (PNLP, INFORM, LSHTM 2015). In addition, there are rural health huts and community health workers that provide care for those living far from a health post, and report the cases to the closest health post.

Since the establishment of the National Malaria Control Program (PNLP) in 1995, the program has coordinated a variety of measures and policies that have led to a reduction in deaths attributed to malaria from 12.93 per 100,000 people in 2000 to 8.26 in 2013 (PNLP, INFORM, LSHTM 2015). Currently, the north of the country has very low incidence and is at the level considered ready for elimination by the World Health Organization (1 case per 1000, known as the pre-elimination phase). In contrast, the south still has a high case load, with some districts as high as 270 cases per 1000.<sup>10</sup> The heterogeneity can be partly attributed to environmental factors because the rainy season is twice as long in the south as in the north, which allows for mosquitoes to breed and spread the disease for longer. Nevertheless, the mosquitoes required to spread the disease are also present in the low malaria areas (Ndiath et al. 2012). Given the two distinct zones in the country, the Government of Senegal strives to continue reducing the case load in the South, while aiming to eliminate it completely from the North. As potentially infected individuals travel from the South to the North, though, they can hinder elimination eorts in the North.

# 2.3 Population Movement in Senegal

Senegal has large ows of long term and permanent migration, with 27% of the population recorded as an internal migrant in 2004 (P. D. Fall, Carretero, and M. Y. Sarr 2010).<sup>11</sup> A large part of this migration is rural to urban due to irregularity of rainfall and degradation

> 10The Appendix contains a map of annual malaria incidence by district.

> 11This is comparable to the rest of Sub-Saharan Africa, where 50-80 percent of rural households were estimated to have at least one migrant (Deshingkar and Grimm 2005).

7
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14 highest to lowest price, and the marginal rate is that rate which fully exhausts the available (perhaps pre-announced) supply. In a competitive auction the marginal rate is the exchange rate and is paid by bidders regardless of their bids. In a Dutch or discriminatory auction, bidders pay their own price, and the exchange rate may be a quantity-weighted average of the bids, or the marginal price.

Auctions also offer various control variables to the authorities: the supply offered, a choice of pricing mechanism- (competitive or discriminatory), the use of a reserve price (below which the authorities will not accept bids), the possibility of revealing information about the bids, and the right to impose entry restrictions of various kinds. Thus, unless the operation of the auctions is fully transparent, adhering to published rules, the potential exists for government manipulation of the exchange rate. A recent statistical and econometric study using weekly data for foreign exchange auctions in- four SSA countries (Ghana 1986-92, Nigeria 1986-88, Zambia 1985-87 and Uganda 1992-3) found a clear distinction between two sets of countries in terms of design features, auction policies and outcomes (Aron and Elbadawi, -1993 a,b). Ghana and Uganda represent a set where auctions have been largely on target in terms of the three policy objectives of exchange rate unification, stabilization of the exchange rate and an efficient allocation of foreign exchange (Figure I b,c). On the other hand, the auctions in Zambia and Nigeria were subject to frequent policy interventions, with the consequence of unsustainable auctions, inefficieut allocation through ad hoc disqualifications (at least in Zambia), limited unification, and a rather volatile exchange rate (Figure 1 a,d). A distributional analysis found the distribution of the auction rate exhibits left skewness (tendency towards appreciation) in Nigeria and Zambia; while the opposite was observed for Ghana and Uganda.
has mainly explored household-level crime. (ii) A unique firm level data set with about 12,000 firms in 27 developing countries maintained by the World Bank’s Enterprise Analysis unit (Enterprise Surveys) is used that follows a consistent methodology across surveys and therefore allows for cross comparisons between economies. This alleviates the external validity issue faced by most survey level studies on crime.

Our results indicate that economic growth may actually reduce crime in developing countries. We find that an increase in real GDP per capita growth by 1 percent is associated with a 0.30 percent reduction in the losses due to crime for firms of all sizes in developing economies. More importantly, we find that this relationship is stronger for small and medium firms than large firms. That is, a 1 percent increase in real GDP per capita growth is associated with a 0.33 percent reduction in losses due to crime for small and medium firms as opposed to a 0.21 percent reduction for large firms. We also find that several macro-variables tend to amplify or reduce the effect of economic per capita growth on crime experienced by small and medium sized firms. Despite the improvements of this study over the literature in several aspects, there are some issues that need to be addressed which are typical of studies that are cross-country in nature. There is still the issue of time varying omitted variables, as well as the issue of reverse causality. Addressing these endogeneity issues is challenging given the data at hand, but we try and limit them in the following manner. We limit omitted variable bias by checking if our specification is robust to additional potential explanatory variables including ethnic fractionalization, security costs, corruption, human capital, government spending, and trade. In order to limit endogeneity, we also use the percent of land in the tropics and a malaria risk index as instruments and find that the results are retained, if not magnified.

4
in the same sector as the local firm), backward (with the MNE in a downstream industry supplied by the local firm), or forward (with the MNE in an upstream industry supplying to the local firm). In work such as Javorcik (2004), the most significant spillovers are typically backwards, although there is often some evidence in favour of horizontal spillovers. The subsequent work cited in the above surveys generally confirms this across countries and time periods. Second, the impact often depends on the characteristics of the local firm. Gorodnichenko et al. (2014) find that the size, age, and industry of the local firm has a significant effect. Damijan et al. (2013) similarly point out the role of the local firm’s technological advancement and absorptive capacity. Thus, although there is a sizable body of evidence indicating that inbound FDI increases the productivity and growth of local firms, these are conditional.

Building on this literature, we decompose our spillovers into horizontal, backward, and forward linkages. When using the standard approach where only net changes in FDI matter (i.e. where entries and exits have equal but opposite effects, which we refer to as symmetry), we find evidence that increases in net foreign ownership in a given firm, along with increases in net backward and forward spillovers, are associated with higher employment and capital growth in that firm. This symmetry, however, presupposes that all spillovers are transitory in that only the net stock of current multinationals not the history of foreign presence matters for growth. When we relax this assumption and decompose the spillover variables into the changes driven by increases in FDI and those caused by decreases, we firmly reject symmetry. In fact, we find that when FDI exits, this has a more sizable and significant effect on employment and capital growth in local firms than when FDI enters. This is particularly true for backward linkages. As such, this indicates asymmetry with a strong transitory component to the spillover effects since, if the foreign firm leaves, growth falls. In contrast, if the spillovers were permanent reflecting learning effects, then MNE exits should have no impact, since local firms have already adopted the knowledge necessary, for instance, to operate the foreign technology. Furthermore, we find that the impact of FDI on the local firm depends on its sector, size, and age. In particular, in line with Damijan et al. (2013), smaller firms are generally more affected by FDI. However, our results indicate that this is highly driven by reductions in the transitory spillovers resulting from MNE exits, especially via the backwards linkages (reflecting leaning effects of small suppliers to MNEs). This granular effect is something that they (and the rest of the existing literature) cannot identify as they consider only net spillover changes, i.e. as they impose a symmetry our estimates reject.

This evidence of transitory effects is consistent with the idea that spillovers arise from leaning effects due to higher domestic demand for locally sourced goods and services which 3
<!-- Start of picture text --> Fig 3.3a China’s rapid transformation in Fig 3.3b .... But slow transformation of the<br>export Structure: following the Comparative employment structure<br>Advantage<br>1.0 100<br>0.9 Misc. 90 Tertiary Sector<br>manufact<br>0.8 80<br>ured<br>0.7 articles 70 Secondary sector<br>0.6 Machinery 60<br>and<br>0.5 50<br>transport<br>0.4 Crude equipment 40 Primary Sector<br>0.3 Mfg'ed- 30<br>fuels and<br>material<br>0.2 Minerals 20<br>0.1 Food & 10<br>0.0 animals 0<br>Source : Justin Lin and Yan Wang 2008 and updated based on UN COMTRADE data<br>1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007<br><!-- End of picture text --> The evolution of China‘s export structure in the last three decades reflects the significant structural transformation in flying geese style that enabled the country to graduate from an exporter of labor-intensive products such as apparel, textiles and leather to more sophisticated ones such as home appliances, office machines and electric machinery (Figure 3.3). Such a transition from low- to middle and high value-added products could not have been possible had the original endowments of the Chinese economy remained unchanged during the last three decades. With annual savings of around 40 percent of GDP and dramatic improvement in education, its endowments of human, physical and financial capital have evolved significantly- a topic covered by many studies. Most importantly, its stock of technological capabilities has benefited from importing and adapting foreign technologies of production and mastering their use. Unlike the case of Korea where the channel of technology transfer was mainly through import of equipment and purchasing of licenses, foreign capital (FDI) played a critical role in China‘s industrial upgrading.

- **Inward FDI helps the industrial upgrading** . Many studies have provided evidence on the spill-over effect of inward FDI through 1) horizontal spillovers, and 2) vertical (forward and backward) linkages. Others pointed out that foreign investors are quick to identify a country‘s comparative advantage, and served as the most dynamic forces in the industrial development and upgrading (Harrison, 2010, Aghion, Dewatripont, Du, Harrison and Legros 2011).  Although the amount of FDI is not huge, it accounted for 4-5% of China‘s Gross National Investment, and the composition matters a great deal because, first,  foreign investors serve as _―identifiers of growth sectors‖_ ;  and (2) they provide advanced technology helping to reduce first-mover risks and transaction costs when firms

40
_Table 5. Estimation results for males_

||||ln(|CA)|||||ln(rev|enue)|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||OLS||Rand|om eff|ects||OLS||Ran|dom eff|ects|
||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||Coef.|SE|P>|z||
|can read|0.35|0.21|0.09|0.32|0.28|0.25|0.02|0.17|0.93|0.00|0.21|1.00|
|education>2|0.50|0.22|0.03|0.47|0.30|0.12|0.05|0.18|0.77|0.03|0.23|0.91|
|age 30-39|-0.02|0.14|0.86|-0.04|0.18|0.82|-0.27|0.11|0.02|-0.27|0.14|0.05|
|age 40-49|-0.12|0.20|0.55|-0.14|0.27|0.61|-0.10|0.17|0.56|-0.11|0.21|0.60|
|age>49|-0.25|0.48|0.61|-0.27|0.65|0.67|-0.74|0.48|0.12|-0.87|0.57|0.12|
|age unknown|0.11|0.22|0.63|0.08|0.29|0.80|0.00|0.18|0.98|-0.01|0.23|0.96|
|exp. 5-10|-0.01|0.22|0.95|-0.01|0.29|0.98|0.17|0.18|0.35|0.22|0.22|0.34|
|exp.>10|0.29|0.25|0.25|0.28|0.34|0.41|0.49|0.21|0.02|0.54|0.26|0.04|
|electronics|-0.38|0.12|0.00|-0.36|0.16|0.02|-0.68|0.10|0.00|-0.67|0.12|0.00|
|Abidjan|0.23|0.22|0.30|0.21|0.30|0.49|0.29|0.18|0.11|0.30|0.23|0.19|
|PB|0.29|0.15|0.05|0.33|0.15|0.03|-0.07|0.13|0.55|-0.05|0.13|0.69|
|PA|0.34|0.15|0.02|0.36|0.15|0.02|0.20|0.13|0.12|0.18|0.13|0.15|
|NPB|-0.01|0.13|0.96|0.01|0.05|0.85|-0.26|0.11|0.02|-0.24|0.07|0.00|
|constant|11.00|0.30|0.00|11.01|0.40|0.00|11.23|0.25|0.00|11.18|0.31|0.00|
|PB-NPB=0|3.96||0.05|4.36||0.04|2.24||0.13|2.26||0.13|
|PA-PB=0|0.09||0.77|0.22||0.64|3.96||0.05|6.39||0.01|
|PA-PB=-NPB|0.04||0.84|0.23||0.63|0.00||0.10|0.00||0.96|
|B&P LM-test||||30.83||0.00||||80.53||0.00|
|Hausman-test||||0.96||0.62||||2.12||0.35|
|N|696|||696|||696|||696|||

In the specification where ln(revenue) is the dependent variable again the tailors have significantly higher earnings than workers in the agricultural sector. Furthermore, it should be noticed that the participants after PAFPA earn more than the nonparticipants, and that the revenue of the participants has actually fallen during the period. So even though the participants do not have higher revenues after the program than they had before, the negative trend has been avoided. This can also be seen from the test of the DID-estimate showing that at a 7% level of significance it can be concluded that the female participants of PAFPA have experienced a positive impact on revenue.

17
guidelines and political patronage undoubtedly played a role, so that a village’s population relative to the threshold significantly influenced its likelihood of receiving a road but was not definitive. For instance, smaller villages could be connected if they were along the least-cost path between larger prioritized villages, and proximate villages could combine their populations to attain the eligibility thresholds. For more details, see Asher and Novosad (2017) and National Rural Roads Development Agency (2005).

# **II.B National Highways**

In 1999, the Indian government announced a plan to modernize its major highways, the National Highways Development Project. The first component of the project was the upgrading and widening of the Golden Quadrilateral highway corridor (henceforth, GQ), so named because it connected the four major cities in India: New Delhi, Mumbai, Chennai and Kolkata. The second component was a similar upgrading of the the North-South and East-West corridor (NS-EW), which would connect the furthest corners of the country from Srinagar in the north to Kanyakumari in the south, and from Porbandar in the west to Silchar in the east.

While the GQ and NS-EW projects were commissioned around the same time, the government prioritized the implementation of the GQ and construction of the NS-EW was substantially delayed. Construction on the GQ began in 2001; 80% was completed by 2004 and 95% by 2006. In contrast, by 2006 only 10% of the NS-EW corridor was completed, almost half of which was a set of highways which were shared with the GQ (Ghani, Goswami and Kerr, 2016). By 2010, 72% of the NS-EW was completed, and 90% was completed by 2015. The delay in the construction of the NS-EW allows us to use the NS-EW corridor as a counterfactual for changes in forest cover in the GQ corridor during and immediately following substantial completion of the GQ.

Before these highways were widened and upgraded, the GQ and NS-EW routes were already significant transportation corridors, but their road quality and congestion were highly variable. The upgrading of these networks dramatically improved their quality and reliability; 7
standard deviations) when comparing one variable to another. <mark>To modify this formula,</mark> we <mark>first calculate the sample size required to achieve the desired level of precision for a simple random sample (SRS). We then adjust this formula by the design effect (DEFF), which in this application measures the efficiency of a stratified clustered sample compared to SRS. We also derive two different formulas for estimating the sample size based on whether the targeted variable being studied is “continuous” or “dichotomic”.</mark> To determine optimal allocation of the sample across sampling strata, we also modify Neyman’s (1939) original allocation rule, which incorporates the survey design effect and can therefore optimally allocate for multi-stage stratified clustering. The derivation of this formula follows Neyman’s (1939) original closely and consists in solving an optimization problem that minimizes the stratified clustered sampling variance of the targeted variable of interest subject to a fixed pre-established sample size that is determined using the modified Cochran (1977) formula described above. The solution to this optimization problem gives us a more general optimal rule for the allocation of the sample across strata. This rule can be used both for multistage stratified clustered samples and for stratified simple random samples.

# **3. Sampling strategy**

# **3.1. Objective of the survey**

As mentioned before, the main objective of the HIES 2016/17 is to produce reliable poverty statistics, measured by the poverty headcount ratio, at three different levels: (i) annual poverty estimates at the district level; (ii) annual poverty estimates at the division level for urban and rural areas; and (iii) quarterly poverty estimates at the national level.<sup>6</sup> A secondary objective of the HIES 2016/17 is to understand the rapid urbanization occurring in Bangladesh, and to

> 6 The poverty headcount ratio is defined as the proportion of people living below the official national poverty line. In Bangladesh, there are two official government poverty measures: the poverty headcount ratio and the _extreme_ poverty headcount ratio. The poverty headcount ratio is based on a set of division-level poverty lines, defined separately for urban and rural areas, called “upper poverty lines”. In contrast, the extreme poverty headcount ratio is based on a set of division-level poverty lines, also defined separately for urban and rural areas, called “lower poverty lines”. Both upper and lower poverty lines include a food and non-food component, but they differ in how the non-food component is defined. For details on how the poverty lines and poverty estimates are produced, see World Bank (2013).

7
Firm Heterogeneity and Costly Trade: A New Estimation Strategy and Policy Experiments.

Ivan Cherkashin<sup>�</sup> Svetlana Demidova<sup>y</sup> The Australian National University McMaster University Hiau Looi Kee<sup>z</sup> Kala Krishna<sup>x</sup> World Bank The Pennsylvania State University and NBER Keywords: Rules of Origin, Firm Heterogeneity, Demand Shocks, Policy Experiments F12, F14, F17

> �Research School of Economics, College of Business and Economics, Australian National University, ACT, Australia, 0020. E-mail: ivan.cherkashin@anu.edu.au. Phone: +612 612 55637.

> yDepartment of Economics, McMaster University, Hamilton, ON, Canada L8S 4M4. E-mail: demidov@mcmaster.ca. Phone: (905) 525 9140, ext.26095. Fax: (905) 521 8232.

> zDevelopment Research Group –Trade, The World Bank, 1818 H ST NW (MSN MC3-303), Washington, DC 20433. E-mail: hlkee@worldbank.org. Phone: (202) 473 4155. Fax: (202) 522 1159.

> xDepartment of Economics, Pennsylvania State University, University Park, PA, 16802. E-mail: kmk4@psu.edu. Phone: (814) 865 1106. Fax: (814) 863 4775.