rafmacalaba/data-use-mentions-extended huggingface dataset:
data from Brazil, Dix-Carneiro (2014) shows that the slow reallocation of workers and capital toward export-oriented industries leads to substantially lower gains from trade compared to traditional frictionless models. He estimates 11 to 26 percent lower gains from trade compared to a situation where reallocation occurs immediately. In addition, he estimates that adjustment costs are heterogeneous across the population. In particular, older, less educated, and female workers face substantially higher barriers to mobility across sectors. These workers in import-competing sectors experience substantial losses following liberalization, so governments willing to compensate the losers from trade should pay particular attention to workers with these characteristics. Dix-Carneiro and Kovak (2017) investigate how Brazilian region-specic labor market outcomes have responded to local shocks induced by the tari cuts, tracking the evolution of these eects over time. They show that regions facing larger negative shocks induced by liberalization experienced declines in formal sector employment relative to regions facing smaller shocks. These eects gradually increased following the beginning of liberalization and only stabilized fteen years afterward. These results indicate that formal sector employment adjustment in response to trade liberalization was large but slow. They also document that wages in harder-hit locations steadily declined for years and never recovered. The slow reallocation of capital led to a steady amplication of the initial local labor demand shock, making workers in harder-hit regions even less productive over time compared to those in more favorably aected regions. Agglomeration economies also amplify the labor market eects of trade liberalization. As rms in harder-hit regions leave the market, the productivity of remaining local rms gradually declines, further reducing local wage and employment growth. This evidence focused on how aggregate formal sector and regional-level labor market outcomes responded to liberalization. In another study, DixCarneiro and Kovak (2019) analyzed how individual labor market trajectories responded to the trade-induced labor demand shocks. Following individual workers over time using the Relação Anual de Informações Sociais (RAIS) data, they show that tradable sector workers initially employed in harder-hit locations were more likely to switch to
Czech Republic, Bulgaria or Poland.

**Self-employment is underdeveloped.** Self-employment in Latvia is concentrated primarily in agriculture (see Box 2 and Appendix Table 2). It is less common than in the EU-15 in all sectors (35.8% vs 52% in agriculture, 4.7% vs 11% in industry, and 6.5% vs 13% in services). As shown in Figure 9, selfemployment is also is less common than in most Central and Eastern European countries.

**Too few part-time jobs or too few full-time ones?** The proportion of part-time workers has declined from 13 percent in 1997 to less than 10 percent of all workers employed in 2002. In the EU-15 this fraction is 18 percent.  While the decline of part-time employment was particularly strong for men (see Appendix Figure 4 and Appendix Table 3 for more details), the contrast between Latvia and the EU-15 is especially sharp for women. Only 12 percent of women workers are part-time in Latvia, while 33.5 percent are in the EU-15. In other Central European countries, except Romania and Poland, the proportion of part-time workers is even lower than in Latvia, in some cases substantially. Latvia also has one of the highest shares of temporary and seasonal employees among the accession countries; in 2002 this share has reached 14 percent (Figure 10 displays a lower proportion, 12 percent, for the second quarter).

While the proportion of part-time workers among employed females has been stable during the last 5 years, for males this fraction has dropped from 12.5 percent to 7.7 percent. Part-time employment is an important way of enhancing labor market flexibility, so the relatively low percentage of part-time workers, together with low youth activity rates might suggest that there are too few part-time jobs on offer for young people. Part-time work, however, does not only indicate labor market flexibility. According to LFS 2002 data, 45 percent of part-time workers in Latvia would have preferred to work full
10 (0.89)|
|Financial Inclusion Index|0.15 (1.27)|0.34 (1.39)|0.15 (1.32)|-0.05 (0.79)|
|Psychological Well-being Index|0.35 (1.01)|0.38 (1.00)|0.43 (0.97)|-0.02 (1.07)|
|CWR Group|0.62 (0.90)|0.89 (1.02)|0.62 (0.88)|0.26 (0.66)|
|_Panel B: Correlations Between _|_Outcomes_||||
|Ultra-Poor _←→_Asset Index|-0.32|-0.30|-0.27|-0.14|
|Ultra-Poor _←→_Consumption|-0.39|-0.30|-0.39|-0.26|
|Asset Index _←→_Consumption|0.37|0.34|0.34|0.15|
|_N_|2,814|535|1,807|472|

_Notes_ : Table reports average characteristics, with standard deviations in parentheses, of TUP survey respondents. Each column represents a different sample of respondents: (1) all respondents in the TUP survey; (2) Just those respondents who own a phone, where the phone number matches to the CDR obtained from the mobile phone operator; (3) Respondents who report owning a phone, but whose phone number does not match to the CDR obtained from the operator; (4) Respondents who report they do not own a phone.

24
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
anywhere else in transitional economies. In fact, considering the parallel growth in wage arrears and the loss of non-wage benefits for workers, the slow decline in employment is probably fully explained by these factors alone. It could be argued that losses in real wages absorbed most of the output shock and explain labor retention in the CIS-7. From an initial situation of labor hoarding in enterprises combined with excess of labor in the market, the recessional period contributed to increase the excess supply of labor within and outside enterprises. And this phenomenon was larger in the CIS-7 than anywhere else in transitional economies.

**Table 1.3 The Output-Employment Gap During the Recession**

||GDP growth (%)|Empl. Ratio growth (%)|O-E diff (*)|Real wages growth (%)|
|---|---|---|---|---|
|CIS-7 (1989-1995)|-53.6|-11.1|-42.5|-75.4|
|Other CIS (1989-1996)|-44.2|-7.5|-36.7|-54.8|
|Non CIS (1989-1993)|-34.0|-15.2|-18.8|-36.3|

Source: Unicef TransMONEE database (2002). (*) Output-Employment growth difference Since the second half of the 1990s and following the stabilization period, the situation has partly reversed. Output growth has been significant and consistent for all transitional economies including the CIS-7 and the employment ratio growth continues to be negative, although less negative. The result is increased productivity. The productivity gains made in recent years have, in turn, allowed wages to grow and the growth has been larger the larger had been the fall during the recession period. The CIS-7 in this respect have outperformed other transitional economies although the starting point was the lowest (table 1.4 and figure 1.3)
our analysis. Among 15 to 19 year olds, the intra-household correlation of the years of schooling attained ranges from 0.70 to 0.95 across these surveys, with a mean of 0.86. This suggests a very high degree of correlation among siblings in school attainment.

# **3) Results**

# **3.1)  Aggregate trends in adult mortality**

Figure 3 summarizes the aggregate trends in under-5 (solid lines) and adult mortality (dashed lines) for countries from the Sub-Saharan African region and countries from other regions.<sup>12,13</sup> Under-5 mortality (which are obtained from the World Bank’s World Development Indicators database) is shown for two sets of countries.<sup>14</sup> First “all” countries for which under-5 mortality data are available and for which GDP per capita never exceeds $10,000 (in real 2000 dollars) during the periods under consideration; and second, restricting the set of countries to those for which we also have adult mortality data (referred to as “overlapping” countries). A comparison of adult mortality trends to under-5 mortality trends in overlapping countries provides comparison of these two mortality trends in the same set of countries.

Comparing under-5 mortality between the full and “overlapping” samples suggests that our DHS sample is fairly representative of the regions as a whole: indeed, the levels and trends in these mortality estimates are very similar. This is consistent with the fact that the countries surveyed in Sub-Saharan Africa cover a high share of the population in that region. It also suggests that while the countries outside of Sub-Saharan Africa are not “representative” of developing countries, they do have similar levels and trends of under-5 mortality.

> 12 When there are multiple estimates of adult mortality for the same country and same period (based on different rounds of the DHS) the estimates are averaged, weighting each estimate by the inverse of the variance of the estimate so that more precise
sum of up to 12 including, but not limited to, the countercyclical capital buffer and loan-to-value ratios. F. Distance to frontier score for strength of insolvency resolution. A higher index indicates reforms that improve the business climate. EAP, ECA, LAC, MNA, SAR, and SSA include 22, 22, 32, 19, 8, and 46 economies, respectively. Advanced economies include 36 economies. Based on World Bank Doing Business report.

29
that is explained by global shocks. It is expected that if inflation expectations are well anchored, then the variance of domestic core inflation is more likely to be explained primarily by its own domestic core price shocks, and that relative price shocks (global or domestic) will have more modest effects. The values of the IRFs and variance decompositions are then projected on institutional and policy characteristics of each country. This allows us to determine the characteristics associated with relatively high versus low response rates of domestic core inflation to various global inflation innovations, and to assess which characteristics are more closely associated with well-anchored inflation expectations in LICs.

# 4.2. Database

A monthly panel data set was used, covering 104 countries, including 25 advanced economies, 61 non-low-income-country (LIC) emerging market and developing economies (EMDEs), and 18 LICs for 1970M2-2016M12.<sup>11</sup> The panel data set is unbalanced, with the number of observations varying across countries. Various sources were used to construct the monthly series on headline, food, and energy inflation. The main sources for headline consumer price index (CPI) inflation include Haver Analytics, the International Monetary Fund (IMF) International Financial Statistics, and OECDstat. Similarly, food and energy inflation data are covered by OECDstat, the Economic and Statistical Observatory for Sub-Saharan Africa, and Haver Analytics. And, for some countries, data were obtained from national sources. The NEER data were obtained from the International Financial Statistics.

Core inflation is obtained by subtracting the contributions of volatile components of the CPI, such as food and energy inflation. First, a measure of core inflation is obtained by using official core data from OECDstat and Haver Analytics. For the countries for which official core inflation was not available, it was estimated by deducting food and energy inflation multiplied by their corresponding weights from headline CPI inflation and dividing this contribution from the core by the weight of core inflation in the total CPI. The following formula for calculating core inflation was utilized: where π ,
:|11.23|1.49|1.77|

Notes: The outcome in all columns is the difference in the count of self-reported knowledge, adaptive behaviors, or health symptoms of pollution exposure from each household in the survey. The omitted group is the control group. The survey asked about adaptive actions and health symptoms during the 30 days prior to each survey round. The actions we ask about are: avoiding busy traffic areas, avoiding high levels of outdoor activity on high pollution days, using a ventilation fan while cooking, opening windows to vent the house. The symptoms we ask about are: eye irritation, sore throat, headache, cough, wheezing, sinus pain, or shortness of breath. Heteroskedasticity robust standard errors clustered by census block - the sampling unit- are in parentheses (* p _<_ .10 ** p _<_ .05 *** p _<_ .01). We report results controlling for household wealth and housing age in Table A2.

20
making research on this issue a priority.

Several studies explore long-term effects of foreign cash crop plantations using discontinuity designs. They often find negative and persistent effects on human capital as in South America (Dell 2010) or Puerto Rico (Bobonis & Morrow 2014) due to labor coercion. In the Democratic Republic of Congo, individuals located inside the perimeter of a historical rubber concession exhibit significantly worse outcomes in terms of education, wealth, and health whereas their village chiefs are elected less competitively and provide fewer public goods (Lowes & Montero 2020). Positive impacts of a large banana plantation in Costa Rica on a range of welfare indicators highlight the importance of labor mobility; in the case studied, labor was highly mobile and had ample alternative sources of employment so it could be retained by the monopsonist only by offering amenities (Mendez-Chacon & van Patten 2021).

> 8 Most of the land available for expansion in Africa is concentrated in a few countries (Deininger & Byerlee 2012), with poor access to infrastructure, low levels of profitability (Jayne _et al._ 2014b), and often weak governance (Arezki _et al._ 2015) that are outweighed by the advantages of resource access (Lay & Nolte 2018) and institutional proximity (Raimondi & Scoppola 2018).

5
is particularly relevant as experts agree that a significant part of Ukraine’s agriculture sector operates in informality, but little is known on either its size or the nature and profitability of its operations.

# **2.2 Sample construction and level of informality**

To obtain information on changes in welfare, production, and productivity in the small and medium farm sector between 2021 and 2022, a nation-wide phone survey of such farms in areas controlled by Ukraine, was conducted in cooperation with the Ministry of Agricultural Policy and Food (MAPF), from October to December of 2022.<sup>9</sup> The original intent was to construct a sample frame using data from the State Statistics Service of Ukraine (SSSU), complemented with the company registry. However, these sources cover only registered legal entities. To capture informal farms that, based on expert estimates, cultivate 32% of Ukraine’s agricultural area (Nivievskyi _et al._ 2021), a decision was taken to use the State Agrarian Registry (SAR) as a sample frame instead.

> 8 - <u>https://downloads.usda.library.cornell.edu/usda esmis/files/3t945q76s/cc08jp14g/cr56p755q/wasde0822.pdf</u> 9 The survey was implemented by the Kyiv International Institute of Sociology (KIIS) with financial support from the European Commission.

6
<!-- Start of picture text --> Exports of goods and services 30%<br>(constant 2000 USD)<br>1,200<br>Exports (% of Global total)<br>25%<br>1,000<br>Annual % growth<br>20%<br>800<br>15%<br>600<br>10%<br>400<br>200 5%<br>0 0%<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>Source: World Bank GDF and WDI central database Sep 2009<br>Billions<br><!-- End of picture text -->

# **Terms and Definitions**

As we begin our discussion, some clarifications on the terms and definitions would be helpful. In particular, we need to differentiate between the various types of economic zones and industrial clusters.

# Special Economic Zones

_Special economic zone_ is a generic term that covers recent variants of the traditional commercial zones. The basic concept of a special economic zone includes several specific characteristics: (a) it is a geographically delimited area, usually physically secured; (b) it has a single management or administration; (c) it offers benefits based on physical location within the zone; and (d) it has a separate customs area (dutyfree benefits) and streamlined procedures (World Bank 2009). In addition, an SEZ normally operates under more liberal economic laws than those typically prevailing in the country. SEZs confer two main types of benefit, which explain in part their popularity: “direct” economic benefits such as employment generation and foreign exchange earnings; and the more elusive “indirect” economic benefits, which are summarized in table 1.1.

Table 1.1 Potential Benefits Derived from SEZs

||Direct<br>benefits|Indirect benefits|
|---|---|
<!-- Start of picture text --> % firms 'finance no obstacle'<br>0.5<br>0.4<br>0.3<br>All firms<br>0.2<br>Large Firms<br>0.1<br>0<br>2002 2005 2008/9<br><!-- End of picture text -->

**Figure 1**

Source: Authors‘ calculations from full BEEPS samples in 2002, 2005, and 2008/9.

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>East Asia & Pacific East Europe & Central Asia<br>Latin America & Caribbean Middle East & North Africa<br>South Asia Sub-Saharan Africa<br>% of total foreign claims relative to GDP<br><!-- End of picture text --> Figure 2 Source: Data reported by Bank for International Settlements (BIS) 39
# **Online Appendix**

# **A. Timeline**

November 2012-February 2013: Communication campaign and application window October 2013-December 2013: Baseline survey April 2014: Training interventions May 2014-August 2014: Once a month mentoring sessions _Short-term follow-up survey rounds:_

- September 2014: First follow-up survey    ( _4 months after training_ )

- January-February 2015: Second follow-up survey _(9-10 months after training)_

- August-September 2015: Third follow-up survey _(16-17 months after training)_

- August-September 2016: Fourth follow-up survey ( _28-29 months after training)_

_Long-term follow-up survey rounds:_

- March-April 2021: Phone follow-up surveys ( _7 years after training)_

- September-October 2021: In-person follow-up survey ( _7.5 years after training_ )

# **B. Data Definitions**

To adjust for inflation we converted all nominal values of financial variables to real September 2021 CFA using the consumer price index published at the Institut National de la Statistique et des Etudes Economiques et Démographiques (INSEED-TOGO), a public establishment attached to the Togolese Ministry in charge of statistics. For ease of interpretation of magnitudes, we then converted these to USD at the exchange of 550.5 CFA per USD. We combined the phone and in-person surveys into a single longterm survey round, taking the in-person response where available, and the phone survey response for those who did not answer the in-person. Table A5 shows results are robust to the inclusion of survey month fixed effects.

The main outcome variables in Table 1 are defined as follows:

- <u>Real last month profits:</u>

- <u>Main business: Profits in the last full month in the main business operated by the</u> entrepreneur. This variable was winsorized at the 99th and 1st percentiles by survey wave, and coded to 0 for individuals who are not running a business.

- <u>All businesses: Profits in the last full month for the main business added to those in any</u> other businesses
This paper proposes a novel approach to estimating firm-level DVAR, based on an export revenue function, which relies solely on customs transaction data and does not require matching with industrial census data, significantly expanding its applicability. The comparison with previous estimates of Kee and Tang (2016) using both customs data and industrial census data confirms the validity of our new approach. Our estimations based on China’s customs transaction provide micro-level evidence of China’s rising DVAR during 2000 and 2016 and the increase are mainly driven by processing exporters.

HSR is the passenger-dedicated transportation infrastructure and mainly used for business purpose, which promotes the face-to-face communication between domestic buyers and sellers and reinforces domestic economic interactions and integration. To capture this IO linkages, this paper proposes a novel approach to measure firm-level IO linkages that only requires customs transaction data, by matching import product codes of exporting firms with export product codes of other domestic firms, implying that the latter can be viewed as the potential suppliers of the former. Empirical results based on this new measure of firm-level IO linkages, reveal that the rising DVAR can be attributed to HSR expansion, which enhances access to domestic materials by linking exporting firms with potential domestic suppliers. The effects of HSR connections are particularly significant for processing firms, foreign-invested firms, and firms in the electronics industry. Additionally, firms that import more homogeneous products and those connected to domestic suppliers over medium to long distances experience greater impacts from HSR.

In addition, this paper presents a two-sector theoretical model illustrating how HSR expansion affects firm DVAR by increasing the likelihood that exporting firms access domestic suppliers at lower communication costs. As the HSR network more domestic become accessible to grows, suppliers exporting firms, facilitating greater access to domestic materials. This improved access encourages the substitution of domestic materials for imported ones, thereby increasing firm-level DVAR. The findings of this paper indicate that, in addition to trade and FDI liberalization—previously recognized as key drivers of China’s rising DVAR—enhancing domestic transportation infrastructure can also play a critical role in