rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9: Annual Contributions and Total Assets of Superannuation Funds**

|Year to June or end June|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|
|GDP (AUD billion)|711.3|759.0|809.7|859.7|933.7|1003.5|1083.8|
|Contributions (AUD billion)|50.1|51.6|53.5|60.8|68.9|85.1|122.6|
|Contributions (% of GDP)|7.0|6.8|6.6|7.1|7.4|8.5|11.3|
|Benefits (AUD billion)|30.6|32.6|33.2|30.7|32.6|37.7|41.1|
|Benefits (% of GDP)|4.3|4.3|4.1|3.6|3.5|3.8|3.8|
|Total Assets (AUD billion)|519.0|518.1|546.7|641.0|759.3|917.8|1143.2|
|Total Assets (% ofGDP)|73.0|68.3|67.5|74.6|81.3|91.5|105.5|

**Source: IFS and APRA, Annual Superannuation Bulletin, June 2007**

**Table 10** : **Superannuation Fund Assets by Type of Fund**

||1995|2000<br>percent of tot|2006<br>al assets|2007<br>|
|---|---|---|---|---|
|Corporate|21.4|13.8|5.7|6.1|
|Industry|4.4|10.1|16.6|17.3|
|Public Sector|22.7|21.1|16.7|15.5|
|Retail (incl RSAs and ERFs)|22.7|27.5|32.8|32.3|
|Small Funds|8.7|15.5|23.6|25.1|
|Balance of Statutory Funds*|20.1|12.0|4.8|3.7|
|Total|100.0|100.0|100.0|100.0|
|Total (AUD billion)|229|484|918|1143|
|Total(% of GDP)|49|74|92|106|

# **Source: APRA**

Of funds with more than four members in June 2006, some 66 percent were accumulation, or defined contribution, funds and another 28 percent were in funds offering a combination of accumulation and defined benefits. Only 7 percent were in pure defined benefit funds, compared with 22 percent ten years before. While the importance of defined benefit funds has declined sharply in the past decade, the reported assets of defined benefit funds understate the present value of their future payment obligations because some large public sector schemes are not fully funded.

The Productivity Award and Superannuation Guarantee developments were a large contributor to the trend toward defined contribution arrangements. Multi employer arrangements were established under the auspices of trade unions, industry associations and by other promoters on a defined contribution basis to assist in compliance with the SG requirements. In addition, there was a general transition where the extremely generous taxation benefits for superannuation were wound back. Legislation for both the new positive coverage arrangements and the taxation elements tended to be developed with a view to DC schemes first and defined in terms of contributions (reflecting the deferred pay approach) then adjusted to include how DB schemes could also comply with the regulation. DB compliance often required additional actuarial certification (for 37
# Appendix 3: Figures

<!-- Start of picture text --> Figure 1. Governance and per Capita Incomes in Latin America and the Caribbeana<br>Voice and Accountability<br>2<br>y=05336x-4 R2 =043 1189<br>1 ^ 05~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~,0* * ~~~~~~~~~~~~GUY * .| BLZM URt *<br>0 JAM a PAN<br>0~~~~~~~~~~~~~~~~~~~ * 55 6 * 4~~~~5 *7 ~~~ ~ECU* 85 *9 95* 10<br>5 -ij O U~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~DMU *aC 85 0 5<br>0<br>(9 -1 s e s.* a s 1<br>0~~~~~~~~~~~~~~~~~~<br>-15 | . 0<br>-2<br>-2 5<br>In(Real Per Capita GDP at PPP, 1996)<br>Political Stability<br>2 y =0 6089x - 4 8066<br>R= 0 5792<br>15 -<br>1 * CRIU UR? * f<br>0 5 * * *MB<br>S; o o~~~~~~~~~~~~~~~~ NIPHNO . .rr<br>05~~~~~~~~~~~<br>i 45 55 6 PR 5 9 9 5 10<br>-0 U~ ~ ~~~~~~~~~~~C ~ *<br>-10<br>CD~ ~~~~~~~~~~~~~~~~ O<br>-1 5<br>-2 0<br>.3<br>In(Real Per Capita GDP at PPP, 1996)<br><!-- End of picture text -->

Source: Authors' calculations as descnbed in text.

a. The govemance ratings on the vertical axis are based on subjective assessments from a variety of sources and are subject to margins of error and in no way reflect the official view of the World Bank, its Executive Directors, of the countries they represent.

<!-- Start of picture text --> 39<br><!-- End of picture text -->
15

**Table 2: Descriptive statistics of explanatory variables**

|Variables|All|TP|STP|SMP|MP|P-value|
|---|---|---|---|---|---|---|
|**Household head**|||||||
|Age|46.63|47.50|46.03|47.03|45.63|0.944|
|Sex (1=male)|0.92|0.88|0.93|0.91|1.00|0.070|
|Years of education|2.59|5.58|3.03|1.49|2.89|<0.001|
|Experience in rice farming (years)|17.81|20.19|16.99|17.81|20.05|0.750|
|Received training (1=Yes)|0.36|0.17|0.44|0.32|0.58|<0.001|
|Number of training participation|1.17|0.53|1.43|1.07|1.53|0.319|
|Plot visited by extension agent (1=Yes)|0.45|0.17|0.47|0.46|0.74|<0.001|
|<br>Number of plot visits by extension agent|1.16|0.39|1.38|1.12|1.45|0.325|
|Off-farm activity (1=Yes)|0.07|0.04|0.10|0.06|0.11|0.072|
|<br>Access to credit (1=Yes)|0.32|0.03|0.37|0.35|0.29|<0.001|
|Information seeking attitudes<sup>10</sup>(PCA)|-0.03|0.50|-0.11|-0.16|1.01|0.360|
|Risk attitudes (PCA)|-0.09|1.11|-0.33|-0.17|-0.04|<0.001|
|**Household**|||||||
|Household size|5.98|4.55|5.87|6.37|6.32|<0.001|
|Number aged 0-14|2.90|1.95|2.87|3.08|3.53|<0.001|
|Number aged 15-35|1.52|1.26|1.46|1.67|1.05|0.030|
|<br>Number aged 36-65|1.33|1.05|1.33|1.38|1.66|0.001|
|Number aged > 66|0.23|0.29|0.22|0.24|0.08|0.215|
|<br>Dependency ratio|0.49|0.45|0.50|0.49|0.53|0.054|
|Land cultivated with rice (ha)|0.98|0.59|0.92|1.10|1.34|<0.001|
|Total land owned (ha)|9.65|2.92|4.65|15.83|5.84|0.351|
|Wealth index (PCA score)|0.22|-1.00|0.32|0.38|0.56|<0.001|
|Total livestock units|1.67|0.04|1.60|2.03|2.76|0.010|
|Distance to plot (walking minutes)|49.7|46.7|54.7|46.2|48.6|0.121|
|Population density|287|386|279|274|256|0.357|
|<br>Labor availability (1=Yes)|0.60|0.50|0.62|0.60|0.63|0.164|
|**Instruments**|||||||
|Availability of tractor service (1=Yes)|0.79|0.02|0.80|0.96|0.97|<0.001|
|Availability of draft animal service|0.23|0.02|0.30|0.22|0.32|<0.001|
|(1=Yes)|||||||
|Number of agricultural cooperatives|1.78|1.18|1.62|2.05|1.84|<0.001|
|Proportion of households using chemical|0.80|0.16|0.84|0.89|0.99|<0.001|
|inputs|||||||
|**Region dummies**||||||<0.001|
|Poro|0.37|0.00|0.29|0.54|0.05||
|Tchologo|0.47|0.02|0.55|0.46|0.95||
|Tonkpi|0.16|0.98|0.15|0.00|0.00||
|Observations|1039|109|415|477|38||

Notes: Mean values of key variables are shown. P-values of continuous variables are obtained by one way anova test for equality of mean values and P-values of binary variables are obtained by Pearson’s _χ_<sup>2</sup> -tests for equal proportions.
Table 6 Percentage of Households that Grow Maize Conditional on Growing Cotton 1997 - 2002

|Province|1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Central|96.7|95.7|95.1|100.0|97.9|99.3|
|Eastern|100.0|98.4|98.7|99.7|99.5|100.0|
|Southern|97.6|90.5|96.8|97.4|93.1|92.4|
|Lusaka|100.0|100.0|88.9|100.0|100.0|100.0|
|Total|98.8|96.6|97.7|99.5|98.4|98.8|

Note: Own calculations based on the Post Harvest Surveys 1997-2002.

30
Policy Research Working Paper 10605

# **Abstract**

This paper analyzes gender gaps in the Colombian labor market, with a particular focus on the impact of the COVID-19 shock and the economic recovery. Using household survey and administrative data, the analysis finds significant and persistent gender gaps in favor of men in terms of participation, unemployment, and income. These gaps are heterogeneous at the regional level in Colombia, and are exacerbated among women with children, particularly young children, and those with low levels of education. The COVID-19 pandemic widened these gaps, including from higher female concentration in the most affected sectors and potentially associated with the disproportionate burden of care on women. Moreover, using decomposition techniques, the paper finds that the decline observed in the hourly gender wage gap results from two offsetting effects: the explained gap, which favors women due to their endowments, primarily education level, and the unexplained gap, which favors men and may be associated with discriminatory biases. Moreover, the gender wage gap widens significantly when considering monthly income, showing variations in hours worked between men and women. Given the poorer labor market outcomes among women with children, policies toward the reduction and redistribution of care work within households could contribute to increase women’s opportunities in the labor market.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mdavalos@ worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
hazards, such as structure reinforcement and afforestation at the seaward side of the embankments (World Bank, 2013). Afforestation plans included both planting of commercial species for economic purposes and mangrove planting for coastal protection. Bangladesh has large mangrove afforestation potential, as it is the home of 60% of the Sundarbans, the largest mangrove forest in the world, and it has considerable experience in mangrove afforestation, with 150,000 ha of planted mangroves since the 1960s (Saenger and Siddiqi, 1993). The modeling work of Dasgupta et al. (2017) showed that mangrove afforestation in Bangladesh would likely have a small effect on surge heights. Nevertheless, mangroves could significantly reduce the flow velocities and erosion near embankments (Dasgupta et al., 2017). Mangrove belts could also provide additional protection to coastal structures by attenuating short waves, and thus reducing the wave loads acting on them and the wave runup. Potential locations for mangrove afforestation seaward from embankments have been identified for Bangladesh (Gijón Mancheño et al., 2021), however, their effect on the wave loads acting on the structures and on wave runup has not been evaluated.

This paper quantifies the potential wave height reduction by a mangrove belt planted on the seaside of embankments, and how such wave attenuation would impact embankment designs. Wave energy attenuation by mangroves is calculated by integrating the energy dissipation caused by the trees, and by solving the wave energy balance as done by Mendez and Losada (2004). The wave model is applied using the hydraulic properties of _Sonneratia apetala_ , which is a mangrove pioneer species in Bangladesh, obtained from the analysis of field pictures and from a literature study. The potential failure of the trees by either overturning or trunk breakage is estimated for the design conditions using empirical expressions developed in agroforestry studies. Finally, the calculated wave heights are used as an input in the design formulas of embankments to evaluate how the presence of a mangrove belt would affect their height, slope protection needs, and the shear stresses at their toe.

# 2. Theoretical background

2.1. Coastal protection by mangroves Mangroves reduce coastal erosion and flooding through several mechanisms. During storms, strong winds push the sea water towards the coast, causing an increase of water level 2
influenza has had a significant but diverse impact on these products. Generally speaking, avian influenza appears to have had little impact on overall volumes of trade, but it has greatly restructured the international trade flows of poultry products, especially regarding Asian markets. What follows below is an analysis of the effects of avian influenza on the different poultry products.<sup>12</sup> 3.1 International Trade of Unprepared Poultry Parts Supported by development in the cold chain, meat processing, packaging and transportation, international trade in unprepared poultry has constantly increased during the last 10 years. Unprepared poultry trade increased from a value of about 4 billion US$ in 1995 to about 7 billion US$ in 2006. In values, trade flows of unprepared poultry have increased even during the avian influenza epidemic; however, when measured in weight, volumes have temporarily decreased in 2004 and 2005, by 2006 volumes were already higher than these observed pre-2004.<sup>13</sup> The avian influenza outbreaks have had a larger impact on the market shares of exporters than overall volumes. Because international trade in unprepared poultry is 12 This analysis utilizes yearly data from the United Nations COMTRADE database. Trade data follows the HS 88 classification: 0207 (unprepared poultry), 1602 (prepared poultry), 0407 (eggs) and 0105 (live poultry).

13 This is related to the increase of prices in 2004 and early 2005 on supply constraints. Prices generally declined in 2006. See Moore T. and N. Morgan (2006) for a discussion on the effect of HPAI on volumes and prices.

9
entities were allowed to purchase land in 2024, they accounted for about 20% of total land sales. Figure 3a shows, that sales prices for land for commercial agricultural use decreased to about 80% of the pre-war level after mid-2022. Once they were allowed to enter the market in January 2024, legal entities paid prices for such land that were about 30% higher than those by individuals, reaching or even exceeding the prewar price level. While a similar pattern emerges for personal agricultural land, prices paid for it are above those for commercial land and less affected by the war, consistent with the notion that such land is located closer to villages’ settled area, making it more suitable for non-agricultural uses including housing.

Although, as a result of the land privatization process, land supply is atomistic with individual landowners owning at most a few parcels, demand may be more concentrated. Appendix table 2 makes use of registry data to provide information on the mean and median areas acquired by individual tenants or buyers in each year, separately for land leases (panel A) and sales markets (panel B). For legal entities for which registry information could be accessed (73% of tenants and 67% of buyers post-2024), this is complemented with data on registered area cultivated.

In the lease market, legal entities not only account for the lion’s share of land rented but also pay prices that are significantly higher than those paid by individuals, as noted earlier. With a mean of 158 ha (from 95 ha in 2021 to 46 ha in 2024), the area rented by each legal entity over a period of four years is about eight times that (20 ha) by individuals (panel A). On average, legal entity tenants have registered rights to 1,031 ha of which they cultivated 895 ha.

The differential between legal entities and individuals in median (US$ 1,250 vs. US$ 969) or mean prices (US$ 1,650 vs. US$ 1,777) as well as the mean and median area purchased per buyer (8.5 and 1.5 ha for individuals vs. 37 and 6 ha for legal entities) is smaller in sales than in lease markets although in both cases, the distribution of sold area is heavily skewed with maximum areas bought by the same buyer amounting to 3,195 for individuals and 2,233 for legal entities. With 2,100 ha registered and 1,850 ha cultivated, legal entities that buy land are more than double the size of tenants, a fact that may be suggestive of higher barriers to sale market participation due to ill-functioning financial markets and possibly also speculative elements.

# **3. Descriptive evidence and econometric approach**

To characterize the subjects and objects of market transactions and market structure, we complement price data with information from cadaster, registry, and remote sensing. In addition to highlighting differences between tenants’ and buyers’ farms, this also allows us to identify fields as the basic unit of crop cultivation within a farm, thereby allowing to use within-field variation in formal land rights. We use this as a basis for discussing the model and expected coefficients.

10
global supply of liquidity, commodity price fluctuations, and technological shocks (see e.g. Forbes and Warnock, 2012; Burger and Ianchovichina, 2013), and μjis a vector of country dummies used to control for time-invariant country characteristics. These dummies absorb many important time-invariant determinants of FDI, including factor endowments, country size (Dunning, 1993; Markusen, 1995) and regulations that do not change over the sample period (Wang et al., 2012). The lagged dependent variable Fjis(t−1) is included to allow for adjustment dynamics and to tackle serial correlation. The main parameter of interest is αjs which tells us how the relationship between investment and political instability varies by sector and source country.

We allow the sensitivity of FDI to political risk to vary by source-country in addition to sector mainly as a robustness check. Some authors have speculated that investors from countries with strong institutions may be more sensitive to political instability perhaps because they are less apt at coping with it, face greater informational frictions, or are more concerned with corporate social responsibility (see e.g. Cuervo-Cazurra, 2006; Driffield et al., 2012).

# _2.1 Measuring Political Instability_

Political instability is an elusive concept that is both difficult to define and quantify. While some papers define political instability narrowly as regime or government change or the incidence of political upheaval and violence in a society, this paper instead takes a broader approach by using a proxy for political instability that also encompasses policy uncertainty (inter alia pertaining to the enforcement of contracts and property rights). We consider this broader measure of instability appealing given the complexity of political developments in the region over the period considered. Following earlier papers that have examined the relationship between investment and this broader concept of instability (e.g., Busse and Hefeker, 2007; Alfaro et al., 2008; Asiedu and Lien, 2011; Méon and Sekkat, 2012), we use the political risk index from the International 9
years. In this way, we can: (i) better isolate the effect of MDB participation on subsequent bank flows (as well as loan terms), (ii) run a series of additional tests to deal with the omitted variable bias to provide a convincing estimate of the mobilization effects, (iii) test for crowding-out effects to other flows, and (iv) estimate country-level mobilization effects. To the best of our knowledge, we provide the first assessment and quantification, fully based on loan-level data, of MDB mobilization effects, a key channel to catalyze the private sector and finance investment and growth in developing countries.

# **2 Data**

Our main source of data is the Dealogic Loan Analytics database, which contains micro data at the level of tranches of all syndicated loans to 127 developing countries from 1993 to 2017. These data are widely used for studying the international syndicated loan market (see, for instance, Esty and Megginson, 2003; Carey and Nini, 2007; Giannetti and Laeven, 2011) and their coverage is comparable to flows coming from aggregate statistics: for instance, Cerutti et al. (2015) compare syndicated loan exposures with loan claims as reported by the Bank of International Settlements, finding a very good match between the two series between 1995 and 2012.

Syndicated loans are provided by a syndicate, e.g., a group of lenders, that share risks by pooling together capital. They have been used for decades and are now becoming a dominant way to tap banks, finance companies and institutional investors (Miller, 2006). Their relevance has been expanding dramatically and they have become a key source of funding for corporations in both developing countries and advanced economies (Bruche et al., 2017; Cortina et al., 2018).

In line with existing studies (e.g., Nini, 2004; Carey and Nini, 2007; Cortina et al., 2018), we exclude loans to public authorities, as they are likely driven by different factors compared to loans to non-sovereign entities (private and public sector firms). As a result, we are left with 21,373 syndicated loans to 117 countries: 51 percent of these loan deals are destined to Asia, 27 percent to the Americas, 15 percent to Europe, 7 percent to Africa, and 0.3 percent to Oceania. The countries with the majority of syndicated loans across the period are India, Brazil, Russia, Indonesia, Turkey and Mexico, as shown by the largest bubbles in Figure 1. The countries with the greatest share of loans supported by MDBs are Afghanistan, Tajikistan, Vanuatu, Belize, Kyrgyzstan and Moldova, as shown by the darker bubbles in the same chart. Over time the number of syndicated loans has increased, despite some drops during major financial crises, as shown in Figure 2.

The information in the data is comprehensive, including signing date, nationality of the bor5
# Figure 1: CHILE

<!-- Start of picture text --> (a)<br>fli t<br>4 ...........................................................<br>0<br>2d -4<br>-6<br>- lrr t principai compDnent X<br>- - weighted a enage or prIncIpaE componer1tsj<br>(c)<br>rt 1<br>so- _<br>0 ,11111<br>-1D0 \ I<br>-200 \_1<br>-250 . ................................................. .<br><!-- End of picture text --> <!-- Start of picture text --> (b)<br>Money& Credit<br>-0 4<br>0.6<br>~~~~~~~~~~~~04 ...... . ..................... .... ......... ...................... ..... ............... %f°t ee q e t<br>0 .<br>-(M2,GNDI)t- - (pr-- ate credite GNDi)t|<br>(d)<br>(sly) t<br>0.25.. ............ .. [<br>0.2<br>0.05 -<br>1 3 5 7 9 11 13 15 17 19 21 23 25<br>- - adjusted - unadjusted<br><!-- End of picture text -->

_52_