rafmacalaba/datause-encoder-data huggingface dataset:
WPS3831

# **Exploring the Linkages between Poverty, Marine Protected Area Management, and the Use of Destructive Fishing Gear in Tanzania**

# **Patricia Silva**

## **Abstract**

Coastal resources in Tanzania have come under increasing pressure over the past three decades, which has led to a significant decline in the biodiversity and productivity of coastal ecosystems. The livelihoods of coastal communities that directly depend on these resources are consequently under increasing threat and vulnerability. Marine Protected Areas (MPAs) are one tool for managing coastal and marine resources that have been increasingly used in Tanzania. Promotion of alternative income generating activities (AIGA) is often a component of MPA management strategies to reduce fishing pressure and address poverty concerns. However, empirical evidence on whether these AIGA are successful in reducing pressure on fisheries, or their impact on poverty, is scarce and inconclusive. This paper seeks to contribute to this debate by investigating the linkages between household characteristics, MPA activities, and household choice of fishing gear. The empirical analysis is based on household survey data from a sample of villages located along the coast of mainland Tanzania and Zanzibar. We find that some aspects of poverty increase the likelihood of using destructive fishing gear. MPAs do not directly affect household choice of fishing gear. However, households participating in AIGA are less likely to use destructive fishing gear, suggesting that MPA support to these activities in Tanzania has a positive influence on household choice of fishing gear. We also find the use of destructive fishing gear is associated with higher consumption levels, whereas participation in AIGA does not significantly affect household consumption levels.

## **Acknowledgements**

We are grateful to the Trust Fund for Environmentally and Socially Sustainable Development for generous financial support. Thanks to Sushenjit Bandyopadhyay, Robert Deacon, Paavo Eliste, Kirk Hamilton, Indumathie Hewawasam, Sergio Margulis, and Jack Ruitenbeek, for useful discussion and valuable comments provided. We also thank the participants of the workshop on Marine and Coastal Conservation and Management, held in Dar es Salaam on June 27, 2005, for the many insightful comments and suggestions.

## World Bank Policy Research Working Paper 3831, February 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1
The enterprise sample in Moldova was built from the database of all former and still state-owned enterprises complied by the Ministry of Economy (our counterpart) in March 1997. 1he database comprised about 800 companies located throughout Moldova, including in the T ransnistria region. We used the methodology described for the Georgian sample to select a smnaller group of enterprises which was representative of the overall distribution of economic acEivity across manufacturing sectors. Letters with the questionnaire were sent to 320 companies (excluding companies in Transnistria). Those were followed by phone calls and some on-site visits. in total, about half of the companies (149) answered the questionnaire. Most companies wvere located in Kishinev (113), with some companies from other regional centers: Balti (14), Soroca (12), Drochia (4), Nisporeni (4), and Dobrugea (2). As in the Georgian sample, the largest iiuinber of companies (38) came from the food and beverage sector, followed by textiles and apparel (25), and machinery (24). The rest of the sample consisted of companies in the construclion imaterials (18), metals (15), chemicals (12), pharmaceuticals (9), and wood and furniture (8) sectors.

The data from the collected surveys were then entered in spreadsheets by our counterpart anencies and sent to the World Bank for further analysis. In both sample, a number of managers had given implausible (for example, profit rate of 120% of sales) or incomplete answers. Given l!. already small size of the samples, we chose to carry on further phone interviews with those ianiagers and attempt to clarify their reports rather than exclude the companies from the surveys. This approach may have introduced some bias in the survey, to the extent that the data on those firrmis were subject to more scrutiny and corrections. Since there were no other reliable sources of inrduLstrial data in either Georgia or Moldova to make comparisons possible at the time of our sLurveys, we could not conjecture which way the bias would have gone.

We first study the ownership structure of the sampled firms. We distinguish between two eroups of privatized firms, since the primary focus is on the differences between firms bought by mna.agers, and firms which were either given to managers for free or were _defacto_ controlled by i-ailagers since ownership was not exercised (Table 1). Only a tenth of the firms in the Georgian ,;arple can be classified as management buy-outs (MBOs), while about a quarter of the firms in 8
# 5.3. Heterogeneous treatment effects

Analysis of two crucial heterogeneous treatment effects makes a valuable contribution for policy decisions. The first is the impact of moving out of camps on female-headed households. The estimate shows that female-headed households are in general more vulnerable than male-headed households, whether they live in or out of camps. Table 3 shows that, on average, female-headed households earn less, are more likely to be at risk of deprivation in basic needs, and own fewer household assets. They are also less likely to report increased life satisfaction if they move out of camps. The gendered disaggregated DiD analysis, based on common-support regions, estimates that moving out of camps increases the steps in the Cantril ladder by 0.76 point for male-headed households, but only 0.67 point for femaleheaded households. In general, after controlling for pre-crisis differences, female-headed households living in camps earn JD4.34 less than male-headed counterparts. One positive result is that women headed households are less likely to live in overcrowded houses both in and out of camps. This result may be because of absence of the male head. About 78 percent of female-headed households in camps are at risk of abject poverty compared to 61 percent of male-headed households. Poverty nevertheless reduces for both groups when they move out of camps. Moving out-of-camp also tends to decrease female-headed household poverty more than for male-headed households: the share of households below the abject poverty level reduced by 47 percent for female-headed households who moved out of camps, but the reduction is only 31 percent for relocating male-headed households.

_Table 3: Heterogeneous treatment effect on gender of the household head_

||Female|-headed ho|useholds|Male-h|eaded house|holds|
|---|---|---|---|---|---|---|
||Camp|Out-of-<br>camp|ATT|Camp|Out-of-<br>camp|ATT|
|Life satisfaction|||-0.67(0.35)*|||-0.76(0.14)***|
|Risk of overcrowded dwelling|0.76|1.07|-0.31(0.07)***|0.69|1.03|-0.34(0.03)***|
|Asset index|4.25|7.20|-2.96(0.22)***|4.57|7.34|-2.78(0.11)***|
|Satisfaction with overall access to<br>service including water and electricity|2.57|3.09|-0.51(0.09)***|2.51|3.13|-0.63(0.04)***|
|Income per capita per month|30.06|44.26|-14.20(3.92)***|34.48|47.24|-12.76(2.07)***|
|Share of household in abject poverty|0.78|0.31|0.47(0.07)***|0.60|0.29|0.31(0.03)***|
|Share of households that can lend out<br>150 J|0.04|0.08|-0.03(0.04)**|0.10|0.18|-0.09(0.02)***|
|Number of observations|152|191||1056|974||

N/B the Table shows the heterogeneous treatment effect by gender of the household head. It can be used to compare the quality of life for female (male) headed households living camp with that living out-of-camp. It also shows the differences 22
INTRODUCTION **XXV** Table 6 Long-Run and Stationary Population, Momentum, Doubling Year, and Year When Net Reproduction Rate Reaches 1 foi the World, Less Developed and More Developed Countries, and Continents

||________|Popula<br>___________|tion<br>(mill<br>____________|ions)<br>_|Station<br>ary over|-<br>Popu-|Year<br>when||
|---|---|---|---|---|---|---|---|---|
|Region|1985|<br>2050|<br>2100|Station<br> <br>ary|1985<br>- popula<br>tion|lation<br>- mo-<br>mentum|popula<br>tion<br>doubles|- Year<br>when<br>NRRu1|
|World|4,844|10,035|11,330|11,514|<br>2.4|<br>1.4|<br>2044|2060|
|Loss developed|3,666|8,716|10.020|10.200|<br>2.8|<br>1.5|<br>2028|2060|
|More developed|1.179|1.319|1.310|1.314|<br>1.1|<br>1.1|<br>(a)|2030|
|Africa|559|<br>2.275|2.962|3,049|<br>5.5|<br>1.6|<br>2009|2060|
|Ameica|667|<br>1,146|1,192|1,201|<br>1.8|<br>1.4|<br>(a)|2030|
|Asia|2.823|5.728|6.288|6,374|<br>2.3|<br>1.5|<br>2047|2045|
|Europe<br>and U.S.S.R.|771|<br>843|<br>842|<br>846|<br>1.1|<br>1.1|<br>(a)|2030|
|Oceania|25|<br>42|<br>44|<br>45|<br>1.8|<br>1.3|<br>;a)|2030|

Note: "More developedu comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia. New Zealand, and Japan. uLess developed" comprises the rest of the world.

a. Population will not double with projected rates.

Africa is growing much faster, at over 3 percent annually, as opposed to only 1.9 percent for Asia. Africa currently has a smaller population than Asia, Europe and the U.S.S.R., and the Americas, but by the year 2000 will be second in size only to Asia. Annual growth rates in Africa are pro,ected to stay above 2 percent at least up to 2025, by which year the continental population will have almost tripled.

America, with 14 percent of world population, includes both more developed and less developed countries and is growing as a whole more slowly than Africa and Asia. By 2025, its population will have increased by half, but its share of world population will have fallen to 12 percent. The small Oceania group is projected to experience growth essentially similar to America.

Europe and the U.S.S.R., now second in size to Asia, will be only fourth among these continents by 2025. Its share of world population will shrink from 16 to 10 percent. This projection assumes that fertility, currently below replacement level for the group and for many countries, will rise to replacement level by 2030. Should this not hanpen, the projected 10 percent increase in the population by 2025 could be smaller, or could even become a decrease.

Long-run trends. The long-run implications of the projection assumptions are illustrated in Table 6, which shows projections up to 2100 as well as other indicators. If every country and economy reaches replacement-level fertility by or before 2060, world population will be over 11 billion by the end of the next century. Another 300 million will be added before the population becomes stationary, i.e., before the growth rate falls to zero.

Table 6 shows two ratios, that of the projected stationary population to the 1985 population, and that for population momcntum. The latter is the ratio to the 1985 population of
We can summarize the results by writing out the composition of global inequality in the early 19<sup>th</sup> and 21<sup>st</sup> century as follows: <u>Early 19th century: Inequality between individuals in the world: Gini around 43.</u> = 35 percent due to differences in average country incomes (15 Gini points) + 65 percent due to within-national income differences (28 Gini points).

<u>Early 21</u><sup>st</sup> <u>century: Inequality between individuals in the world: Gini around 70.</u> = 85-90 percent due to differences in average country incomes (60-63 Gini) + 10-15 percent due to within-national income differences (7-10 Gini points).

These two simple relationships describe the change that has occurred globally during the last two centuries: inequality between individuals is much higher today than 200 years ago, but –more dramatically—its composition has totally reversed: from being predominantly driven by within-national inequalities (that is, by what could be called “class” inequality), it is today overwhelmingly determined by the differences in mean country incomes (what could be called “location” or citizenship-based inequality). This latter, “locational”, element was “worth” 15 Gini points in the early 19<sup>th</sup> century; it is “worth” 60-63 Gini points today.

The evolution of the share of between inequality in global inequality is shown in Figure 1. The calculations are based on Milanovic’s estimates of global inequality for 1820 and 2002, and Bourguignon and Morrisson’s for all points in-between. With BFLZ estimates of global inequality, which are for the 19<sup>th</sup> century lower by about 3 Gini points than Bourguignon and Morrisson’s (see Table 2), the share of between-country inequality in the 19<sup>th</sup> century would be slightly higher. The trend would not be affected however.

13
the Chinese government fully opened export and import rights to all firms since mid-2004.<sup>26</sup> Under the new system, all firms can export regardless of their sales, ownership types, and ages. For this reason, we use only 2005 data in our analysis, not the data before 2005.

In the data, China Customs does not directly distinguish the production firms from the intermediary trading firms, but firms’ Chinese names include the words like “Jinchukou”, “Jingmao”, “Maoyi”, “Waimao”, “Kemao”, “Waijing”, and “Gong Mao”. Ahn, Khandelwal, and Wei (2011) classify all of them except “Gongmao” as trading firms, while Tang and Zhang (2012) include also “Gongmao”.<sup>27</sup> Following them, we also use these keywords to identify trading firms and create two measures: a conservative measure as in Ahn, Khandelwal, and Wei (2011); and a liberal measure as in Tang and Zhang (2012). In 2005, the share of indirect exports among total exports in China is about 20% based on the conservative definition of indirect exports (21%, based on the liberal definition). Because the average size of shipments tends to be smaller for indirect exports than that for direct exports, the corresponding shares are larger in terms of the number of shipments: 37% and 38% based on the conservative and liberal measures of trading firms respectively. In sum, indirect exports constitute a large percentage of Chinese exports in 2005.

There are 16 types of trade regimes in the trade data from China Customs, but we consider only normal trade and processing trade with imported materials.<sup>28</sup> We leave out the processing trade with supplied materials for reasons discussed in Section 3.1 and all of the other 13 trade regimes when calculating the shares by trade regimes.<sup>29</sup> This is acceptable because the export values under most of the other regimes are very small, and

> 26 See Circular [2003] 1019 , “Forwarding the Notice of Adjusting the Export and Import Eligibility Criteria and Approval Procedures by Ministry of Finance” issued by the State Administration of Taxation of China in 2003. The registration method started from July 1, 2004.

> 27 Gongmao firms, also involved in the design and production of their products, are not pure intermediary trading companies.

> 28 The other 16 trade regimes are: entrepôt trade by bonded areas, international aid, donation by overseas Chinese, compensation trade, goods on consignment, border trade, equipment for processing trade, goods for foreign contracted project, goods on lease, equipment investment by foreign-invested enterprise, outward processing, barter trade, duty-free commodity, warehousing trade, equipment imported into EPZs, and others.

> 29 The results are very similar when only normal and processing exports are used for the dependent variables.

17
POLICY RESEARCH WORKING PAPER 2369

# Summary findings

Using historical data on sovereign and individual borrowers, Ferri, Liu, and Majnoni assess the potential impact on non-high-income countries of linking capital asset requirements for banks to private sector ratings, as the Basel Committee has proposed.

They show that linking banks' capital asset requirements to external ratings would have undesirable effects for developing countries.

First, ratings of banks and corporations in developing countries are less common, so capital asset requirements would be practically insensitive to improvements in the - quality of assets widening the gap between banks of equal financial strength in higher- and lower-income countries.

Second, bank and corporate ratings in developing countries (unlike their counterparts in high-income countries) are strongly linked to the sovereign ratings for - the country and appear to be strongly related (asymmetrically) to changes in the sovereign ratings. A sovereign downgrading would bring greater changes in capital allocations than an upgrading, and would call for larger capital requirements at the very time access to capital markets was more difficult.

Under the new guidelines, capital requirements in developing countries would thus be exposed to the cyclical swings associated with the revision of sovereign ratings in recent crises.

Ultimately, linking banks' capital asset requirements to private sector ratings would reduce the credit available to non-high-income countries and make it more costly, limiting economic activity. Bank capital needs in developing countries would be more volatile than those in high-income countries.

These findings suggest that the Basel Committee should reassess the role it proposes assigning to external ratings, to minimize the detrimental impact of the regulatory use of such ratings on developing countries.

- - This paper a product of the Financial Sector Strategy and Policy Department is part of a larger effort in the department to study the impact of financial regulation on economic development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Elena Mekhova, room MC9-622, telephone 202-4585984, fax 202-522-2031, email address emekhova(%worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may be contacted at lliu2@worldbank.org or gmajnoni@worldbank.org. June 2000. (31 pages)

_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 toget 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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
Table II.1 presents descriptive statistics on individual and household characteristics and the local labor market, between 2002 and 2018. During this period, FLFP increased from 26% to 32%, respectively. There were increases in several areas including level of women’s education, share of females that is household heads, the share of women with a working spouse or reporting more income stability, measures of water and sanitation at home, or share living in urban areas. On the other hand, the period 2002 to 2018 saw reductions in the share of married women, reductions in the number of children. The number of people older than 12 years old and the presence of women’s mother or mother-in-law in the household remained unchanged over this period. The structure of (male) employment in each municipality became less concentrated in agriculture, while the shares of service sectors rose. The main 2018 census sample includes 1,696,260 women of working age (25 to 49 years old) living in households were they or their partner are the household heads. Our final sample covers a total of 333 municipalities (of 340 total) and 22 departments (of 22).<sup>17</sup> Table II.1 Descriptive statistics at the individual and municipality level in 2002 and 2018

||**2002**|**2018**|
|---|---|---|
|Panel A: Individual and household characteristics|||
|Labor force participation|0.26|0.32|
|Age|35.77|36.55|
|No education|0.40|0.25|
|Primary level|0.41|0.43|
|Secondary level|0.15|0.26|
|University level|0.04|0.07|
|Indigenous|0.38|0.41|
|Married|0.91|0.87|
|Number of children 0-3|1.30|1.17|
|Number of children 4-6|1.22|1.12|
|Number of children 7-12|1.77|1.47|
|Number of people > 12|3.29|3.28|
|Presence of grandmother|0.05|0.05|
|Household head|0.20|0.22|
|Spouse works|0.87|0.89|
|Income stability|0.52|0.56|
|Household Home & WASH index|0.00|0.04|
|Urban|0.50|0.57|
|Panel B: Municipality structure of male employment|||
|Share of male employment in primary activities|0.62|0.46|
|Share of male employment in construction|0.10|0.10|
|Share of male employment in manufacturing|0.08|0.08|
|Share of male employment in white collar services|0.03|0.11|
|Share of employment in other services|0.17|0.25|
|Observations|1,176,160|1,696,260|

Sources: 2002 and 2018 National Census of Guatemala.

Notes: Statistics in Panel A for women ages 25 to 49 who either have spouses that are household heads or that are household heads themselves and who live in municipalities appearing in both censuses (331 municipalities). Table A1 in the paper provides the definition of each variable.

> 17 We exclude 7 municipalities due to missing data.

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