rafmacalaba/datause-encoder-data huggingface dataset:
_“It started improving in July to August, and by September, October 2021 it had reached to normal up to date._ _“Since June 2021, last year. I started noticing that the pandemic was over because I earned a lot of money.”_ Similar results emerged from the survey data, which indicate that sex work income has recovered from the initial price shocks that occurred at the beginning of 2020. Among participants who reported sex work as their primary source of income, the total monthly income from sex work reported in surveys significantly increased from 84 USD to 127 USD per month from 2020 to 2021. Total income also recovered by 2021 and was similar to income levels prior to the pandemic.

# **Discussion**

This study found that by late 2021 FSW in Dar es Salaam, Tanzania had mostly recovered from the economic insecurity following the initial phase of the COVID-19 pandemic in 2020. The restrictions introduced in early 2020 resulted in dramatic decreases in sex work income, leading to extreme financial vulnerability, food insecurity, and challenges in meeting other basic needs such as paying rent. However, participants reported that the demand for sex work substantially increased in summer 2021. Notably, despite decreases in the average number of unique weekly clients, the price per client and the total monthly sex work income had returned to pre-pandemic levels. This may potentially be explained by an increased number of repeat clients, which started to represent a larger proportion of all clients during COVID-19, even after restrictions were lifted. Additionally, this study revealed that the pandemic changed the landscape of sex work in many ways, including the places to meet new clients and ways of meeting and engaging with clients. In 2021, participants were still not meeting clients as regularly in bars and brothels and were more likely to meet clients on the street.

Our results are consistent with findings from studies in rural and urban Kenya which demonstrated sharp decreases in transactional sex activity, income, and numbers of clients in the early phases of the pandemic.<sup>23,24</sup> Other research conducted in India, Ukraine, Kenya, and Zimbabwe also found that many FSW struggled to meet basic needs such as pay rent and experienced increased food insecurity during this period.<sup>3,25</sup> In addition, our findings are supported by previous studies which have highlighted how patterns of work have shifted over the pandemic, with fewer FSW going to bars and nightclubs, and more working in street based settings or independently in apartments.<sup>3,13</sup> This study advances our understanding of how the landscape of sex work evolved during the first two years of the COVID-19 pandemic. Using a mixed-methods approach, we triangulate data from quantitative and qualitative sources to quantify shifts in income, demand, and client frequency, and describe FSW perspectives on their current work environment. While previous studies have 13
<!-- Start of picture text --> ©<br>e<br>DBwo e e<br>sia e -<br>c<br>2 e e<br>BeoO y ° C »<br>st<br>>e<br>o<br>5<br>EO e6.°<br>fo}<br>a<br><<br>—]<br>fo}<br>EN<br>x<br>fo}<br>0 2 4 6 8 10<br>Migration Cost / Annual HH income<br>95% Cl<br>——— Fitted curve (polynomial smoother)<br>e Average share of borrowings relative to migration costs<br><!-- End of picture text -->
2

# **Distortions to Agricultural Incentives in Australia Since World War II**

## **I. Introduction**

During the first four decades after World War II, Australia’s GDP per capita grew slowly relative to that in other high-income countries apart from the United States and New Zealand. In the past 20 or so years, by contrast, the Australian economy has outperformed most other high-income countries, with its per capita income growing half as fast again as the OECD average (Figure 1). This marked difference between recent and earlier relative performance has been attributed to major economic policy reforms that have occurred since the 1970s and, in particular, to the belated opening of the Australian economy to the rest of the world. Having been more protectionist than all other OECD countries except New Zealand for most of the twentieth century (Anderson and Garnaut 1987), and having largely stood aside from the industrial trade policy reforms agreed to by other Contracting Parties to the General Agreement on Tariffs and Trade (GATT) in the first seven rounds of multilateral trade negotiations (1947 to 1979), Australia has since undergone a remarkable degree of domestic microeconomic reform and opening up of its current and capital accounts. This liberalization has reversed the downward trend in its trade share of GDP, although no more so than for the average of other high-income countries – including the United States, whose share has trebled in the past three decades (Figure 2).
observed (post-SWP) levels of government spending in SWP and non-SWP villages respectively (per capita of the relevant population). We assume that in the absence of the SWP there would be no difference in the level of the government’s spending between these two types of villages. The amount of displacement of non-SWP spending in SWP villages that is attributed to the SWP is then ( _GOVNSW_ − _GOVSW_ )(1 − _w_ .)40 The bias in the double-difference estimate is _RNSW_ (<sup>_GOV_</sup> _NSW_ − _GOVSW_ ) where _RNSW_ is the income rate of return to the government’s projects.41 The true impact is thus: * _DD_ = _DD_ + _RNSW_ ( _GOVNSW_ − _GOVSW_ ) (8)

* * * On noting that _DD_ = _RSW_<sup>_AID_</sup> _SW_ where _RSW_ is the true rate of return to the SWP and the external aid-financed investment is _AIDSW_ per capita in the SWP villages, we can then derive the following formula for the proportionate bias:

and where _k_ ≡ _GOVSW_ /<sup>_GOV_</sup> _NSW_ and _AID_ = _wAIDSW_ . There will be no bias if there is no displacement ( _k_ =1), or the SWP is negligible in size ( _w_ =0) or the rate of return to the displaced government investment is zero ( _RNSW_ = 0 ).

However, this is still not a usable formula for determining an upper bound for the bias since the measured rate of return to SWP spending will also be contaminated by the spillover effect. (We assume that the bias due to the local-spending spillover effects induced by the external aid only contaminates estimates of the rate of return to that aid.)

* * The true rate of return is _RSW_ = _RSW DD_ / _DD_ .  Substituting into (9) and solving we have:

> 40 Note that if _S_ is the per capita government spending displaced from SWP villages then _Sw_ /(1 − _w_ ) is the corresponding gain (per capita) in the non-SWP villages. _GOVSW_ = _GOV_ − _S_ and _GOVNSW_ = _GOV_ + _Sw_ /(1 − _w_ ) .

> 41 Note that Δ _YSW_ = Δ _YSW_ * − _RNSW S_ and Δ _YNSW_ = Δ _YNSW_ * + _RNSW Sw_ /(1 − _w_ ) are the measured income gains where the * denotes the values without the spillovers. Also note that * * * _DD_ = Δ _YSW_ − Δ _YNSW_ and _DD_ = Δ _YSW_ − Δ _YNSW_ . The following result is then easily derived.

27
20 The dynamics are more interesting. Between 1976 and 1996, the earnings-education profile changed shape. After rising in the late 1970s, the linear component fell substantially from 1981 to 1996. Meanwhile, the coefficient of squared years of schooling fell to 1981 but then more than doubled to 1996, ending the period substantially above its initial 1976 value. Overall, the relationship became more convex, suggesting a steepening of marginal returns to education at high levels. However, plotting the parabola which models the partial earnings-education relationship from equation (2), the lowering of the linear term dominates. The profile shifts up from 1976 to 1981, and again to 1985, before falling precipitously (while convexifying) to 1996. See figure 4. The net effect across the entire period was a fall in the cumulative returns to education (from zero to t years) for the entire range. This co-existed with increasing marginal returns at high levels of education. The implications for poverty and inequality are clear, with the education price effect leading to an increase in the former and a decline in the latter.

|**Year**|**1976**|**1981**|**1985**|**1996**|
|---|---|---|---|---|
|Intercept|4.350<br>(0.0001)|4.104<br>(0.0001)|3.877<br>(0.0001)|4.256<br>(0.0001)|
|Education<br>|0.123<br>(0.0001)|0.136<br>(0.0001)|0.129<br>(0.0001)|0.080<br>(0.0001)|
|Education<sup>2</sup><br>(x 100)|0.225<br>(0.0001)|0.181<br>(0.0001)|0.283<br>(0.0001)|0.438<br>(0.0001)|
|Experience|0.075<br>(0.0001)|0.085<br>(0.0001)|0.087<br>(0.0001)|0.062<br>(0.0001)|
|Experience<sup>2</sup><br>(x 100)|-0.105<br>(0.0001)|-0.119<br>(0.0001)|-0.121<br>(0.0001)|-0.080<br>(0.0001)|
|Gender|-0.638<br>(0.0001)|-0.590<br>(0.0001)|-0.635<br>(0.0001)|-0.493<br>(0.0001)|
|_______________|0.525|0.538|0.547|0.474|

Source: Authors' calculations based on the "Pesquisa Nacional por Amostra de Domicilios" (PNAD). P-values in parentheses.

# **[See Figure 4 in Appendix 4]**
the pre‐crisis upward trend for developing economy users, the 40% increase may have taken place even under more “normal” macroeconomic conditions had the 2008‐2009 crisis not occurred.

I address this question more closely in table 3. In addition to summarizing figure 3, table 3 also provides a breakdown, by policy‐imposing G20 economy, of the increase in the stock of product coverage of TTBs between 2007 and 2009 using the method defined in both equation (1) [column (2)] and equation (2) [column (4)]. The economies are ordered in the table by which had the largest percentage change in TTB product coverage between 2007 and 2009 using the _count_ method of equation (1). Three major emerging economies – India, Indonesia, and Argentina – lead the list with the largest increases in the stocks of products covered by TTBs during this period, again reflecting the information presented in figure 2.

# [TABLE 3 HERE]

The main piece of new data presented in table 3 are economy‐by‐economy _forecasts_ of the 2009 level of TTB coverage based on predictions from the historical data. Motivated by figure 3b, the thought experiment is simply to regress the 1997‐2007 import share data on a liner time trend, use the estimated coefficient from the regression to _predict_ the (out of sample) import share for 2009, and then to compare the prediction for 2009 with the realized data for 2009. I report in column (3) the prediction which uses the _count_ import share defined in equation (1), and I report in column (6) the prediction which uses the _value_ share defined in equation (2).

A table 3 comparison of column (2) with (3) and column (5) with (6) makes it difficult to conclude that the change in product coverage taking place between 2007 and 2009 is a substantial deviation from historical trends. First note that in columns (2) and (5), I make **bold** all realizations of the 2009 import share data that were _larger than_ the 2009 predicted import share stemming from the simple linear regression model. According to the _count_ measure, ten economies (five developing and five developed) had a larger share of 2009 imports become subject to TTBs than was predicted from the models. Only four economies (China, South Africa, Mexico and South Korea) had less product coverage in 2009 than was predicted. On the other hand, using the _value_ measure of equation (2) and comparing column (5) with (6) gives different results; only six economies (two developing and four developed) had a higher‐than‐predicted share of 2009 imports become subject to TTBs. While these economies (India, Indonesia, Australia, Canada, U.S., Japan) did experience increases in the share of imported products subject to TTBs during the economic crisis (see column (4)), the simple linear time trend model predicted this. Therefore, under such an exercise it is only the small _difference_ between the realized 22
WPS4098

# **Does Insurance Market Activity Promote Economic Growth? A Cross-Country Study for Industrialized and Developing Countries**

**Marco Arena**<sup>♣</sup> **The World Bank**

## **Abstract**

Insurance market activity, both as a financial intermediary and a provider of risk transfer and indemnification, may contribute to economic growth by allowing different risks to be managed more efficiently and by mobilizing domestic savings. During the last decade, there has been faster growth in insurance market activity, particularly in emerging markets given the process of liberalization and financial integration, which raises questions about its impact on economic growth. This paper tests whether there is a causal relationship between insurance market activity (life and non-life insurance) and economic growth. Using the generalized method of moments for dynamic models of panel data for 56 countries and for the 1976-2004 period, we find robust evidence of a causal relationship between insurance market activity and economic growth. Both life and non-life insurance have a positive and significant causal effect on economic growth. High-income countries drive the results in the case of life insurance. On the other hand, both high-income and developing countries drive the results in the case of non-life insurance.

### World Bank Policy Research Working Paper 4098, December 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._

> ♣ Operations and Policy Department (OPD), Financial Sector Vice-Presidency (FSEVP). I would like to thank the advice and support of Rodney Lester, Senior Advisor Capital and Risk Markets Group, in the preparation of this paper. I am also graceful to Thorsten Beck, César Calderón, Vidhi Chhaochharia, Stijn Claessens, and Kostas Tzioumis for their comments, feedback and suggestions on the preliminary results of this paper. Comments to the author are welcome: marena@worldbank.org
12

- Analysis of the political economy of public expenditures<sup>on health</sup> -- the determinants and composition of public spending<sup>on health,</sup> including of the poor allocation of public resources<sup>between</sup> largely private goods, especially high-cost hospital<sup>care, and the</sup> largely public good activities that are more cost-effective<sup>in</sup> improving health, such as basic health services, disease<sup>control,</sup> nutritional and epidemiological surveillance;

- Analysis of alternative financing mechanisms for<sup>health care, and</sup> alternative mechanisms for controlling costs;

- New work on concepts, methods and data needs for<sup>study of adult</sup> health, including not only adult mortality but<sup>adult morbidity.</sup>

Work on the cost-effectiveness of alternative health enhancing inputs (e.g. doctors vs. nurses, vaccines vs. drugs) is critical<sup>for the design of</sup> health programs, and could certainly comprise another<sup>category. Such work is</sup> an input (in effect a tool) for work in other categories,<sup>as will become</sup> evident below.

Household level studies. Studies of the effects<sup>of various measures of</sup> development on health at the cross-national level can generate hypotheses, but cannot be conclusive, since they cannot capture variations<sup>in the endowments</sup> and the price environment of individuals and households<sup>which ultimately are</sup> what matter. The potential for breaking new ground<sup>is thus at the household</sup> level, where, despite a large literature using increasingly<sup>sophisticated</sup> methodology,'5 the potential for policy-relevant results has been limited 15 It is of some concern that this line of research,<sup>increasingly</sup> sophisticated in methodology, is carried out by a relatively<sup>small group of</sup> economists, most of whom are located in a limited number<sup>of centers (at Yale,</sup> Pennsylvania, North Carolina, Minnesota). The requiremencs in terms of training and know-how for success in this area of research are relatively<sup>stringent, and</sup> low-skilled workers in this vineyard tend to produce<sup>work which because of</sup> methodological problems is not useful. The Cebu group based<sup>at North Carolina</sup> (see footnote above) have begun to bridge this gap by<sup>publishing their results</sup> in epidemiology journals, and by pointing out<sup>the links between the approach</sup> economists have taken and such approaches as that taken<sup>by Mosley and Chen</sup> (1984); see Cebu Study Team (1990).
|grains crops|||0.005|||-0.011**|
|---|---|---|---|---|---|---|
||||[0.364]|||[0.033]|
|legumes crops|||0.003|||-0.009*|
||||[0.638]|||[0.088]|
|tubers crops|||-0.003|||-0.014**|
||||[0.714]|||[0.039]|
|oil crops|||0.024**|||0.002|
||||[0.023]|||[0.829]|
|Horticulture crops|||0.004|||-0.005|
||||[0.498]|||[0.386]|
|cotton crops|||-0.007|||-0.010|
||||[0.343]|||[0.163]|
|tobacco crops|||0.022***|||0.005|
||||[0.000]|||[0.377]|
|Total value of crop sales (MKW)|||0.000|||-0.000|
||||[0.439]|||[0.665]|
|Time dummy (year 2010=1)|-0.048|-0.050|-0.128|-0.048|-0.048|0.000|
||[0.763]|[0.700]|[0.339]|[0.765]|[0.714]|[1.000]|
|Normalized wealth index|2.175***|2.298***||2.082***|2.207***||
||[0.000]|[0.000]||[0.000]|[0.000]||
|District dummies|included|included|included|included|included|included|
|Time average of explanatory variables|included|included|included|included|included|included|
|atrho21|||-0.107***|||-0.111***|
||||[0.000]|||[0.000]|
|atrho31|||-0.157***|||-0.068|
||||[0.004]|||[0.191]|
|atrho32|||-0.100*|||-0.037|
||||[0.089]|||[0.513]|
|Likelihood ratio test of joint significance of rhos:|||28.09***|||16.13***|
|Chi2(3)|||||||
||||[0.000]|||[0.001]|
|Constant|-0.796|-2.522**|2.460**|-0.816|-2.553**|0.558|
||[0.564]|[0.025]|[0.022]|[0.555]|[0.023]|[0.583]|
|Observations|5,018|5,018|5,018|5,018|5,018|5,018|

Source: Generated by authors using LSMS data 63
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_ facility is the largest smartphone factory in the world, producing 120 million units per year.<sup>12</sup> The new smartphone production facility will be set up in the Saigon Ho-Tech Park just outside Ho Chi Minh City. Samsung currently employs 75,000 production workers and software engineers, hiring an average of 2,000 people a week for its production facilities. The company will reach 100,000 employees in Vietnam by the end of the year. Vietnam is now responsible for 1/3 of Samsung’s global output.

**Figure 4. Vietnam’s Electronics Exports and Inward FDI, 1995-2009**

<!-- Start of picture text --> 1800 60<br>1600<br>50<br>Foreign value added<br>1400<br>1200 40 Re-imported domestic<br>value added<br>1000<br>30 Indirect domestic value<br>800 added (originating from<br>domestic intermediates)<br>600 20 Direct domestic value<br>added<br>400<br>FDI Stock<br>10<br>200<br>0 0<br>1995 2000 2005 2009<br>USD Million USD Billion<br><!-- End of picture text --> _Source:_ OECD-WTO TiVA Dataset; UNCTAD FDI Database Samsung relies heavily on Korean suppliers that have co-located in Vietnam to produce intermediate inputs. Of the company’s 67 suppliers in the country, only four are Vietnamese. They include Goldsun Packaging and Printing JSC (corrugated paper packaging materials), the Thang Long Packaging Production Export–Import JSC (thin-film packaging), Viet Hung Packaging Company Ltd (corrugated paper packaging) and Nam A Company Ltd (packaging). The prominence of packing companies mentioned here is no accident. Packaging is one of the lowest value added inputs to any manufacturing operation. Fifty-three of Samsung’s suppliers are from Korea, seven are from Japan, one is from Malaysia, one is from Singapore and one is from the United Kingdom.<sup>13</sup> Thus, while the TiVA data states that 37 percent of the value of production for export is domestic, most of that accrues to global suppliers in Vietnam, with Vietnamese suppliers making up a negligible portion of total local content.

Efforts to increase local content by local firms have largely failed. Last year, the Ministry of Industry and Trade announced that Samsung would source 91 parts for the Galaxy S4 and 53 parts for tablets from local suppliers. Targeted components included relatively simple parts: batteries, earphones, USB storage devices, insulation tape and parts of data transmission cables among others. Samsung held a workshop with the Vietnamese government and 200 local firms to

> 12 Bloomberg News article available from <u>http://www.bloomberg.com/news/2012-11-07/vietnam-luringtech-companies-narrows-trade-gap-southeast-asia.html.</u>

> 13 Details on Saumsung’s supplier network in Vietnam in Viet Nam News: <u>http://vietnamnews.vn/economy/261534/samsung-chooses-few-vietnamese-suppliers.html</u> and <u>http://vietnamnews.vn/economy/264165/local-firms-to-make-144-samsung-components.html.</u>

15
Table 2 reports the first stage estimates relating changes in export revenue originated from each labor market to changes in external demand directed to the labor market, as defined in equation (2). The econometric results reveal that our instrument provides a suitable source of variation for examining the impact of plausibly exogenous export shocks on labor market outcomes. The coefficient of interest is 0.797 indicating that a 10% increase in external demand directed to the labor market leads to a 7.97% increase in exports. This relationship is precisely estimated, with a Kleibergen-Paap rk Wald F-stat of 26.10, which is indicative of a strong instrument.<sup>14</sup> Table 2: First stage estimates

|Dependent variable:|∆Exports|
|---|---|
|∆<sup> ̄</sup>_Z_|0.797|
||(0.104)|
|F-stat|26.10|
|Observations|3815|
|Year efects|Y|

_Notes_ : Table reports first stage estimates. For each dependent and independent variable, we take log before computing the first differences. Observations are weighted by exports per worker in the labor market in 2003. Standard errors clustered by microregion and year are presented in parentheses.

We proceed by examining the causal effects of export shocks on various labor market outcomes. Table 3 presents the instrumental-variables estimates of (1), using the strategy discussed above. The first two columns show that a 10% increase in exports leads to a 2.4% increase in employment and 3.3% increase in average residual wages. The estimates in columns (3)-(5) reveal that export shocks have important implications for gross worker flows between and within labor markets. A positive export shock leads to a decrease in the number of workers leaving the labor market (gross out-flows); an increase in the number of workers entering the labor market (gross in-flows); and a significant increase in the number of workers switching 6-digit detailed occupation within the labor market. The last result implies that a positive export shock increases internal churning, which is the key motivation of our model framework where workers can move between jobs in response to a trade shock.

# **3 Model**

In this section, we first introduce a new dynamic labor mobility model with an endogenous number of job options. We define a labor market by a pair of a region and a sector consistently with the reducedform analysis. Each labor market offers a different number of job options as well as a different wage. We then introduce international trade to this framework to examine how wages and the number of job options, and eventually between- and within-market labor mobility, are impacted by trade shocks.

> 14Since our specification requires taking log of exports, we use only observations with positive exports. All results (including the structural estimates in subsequent sections) are robust to alternative specifications such as adding one to exports before taking log or using inverse hyperbolic sine, changing by less than 10% for every variable of interest.

10