rafmacalaba/datause-encoder-data huggingface dataset:
# **The Verdict on Structural Change versus Within-Sector Productivity Growth**

So what do these findings on individual countries add up to in terms of broad trends in structural change? We believe this book provides a worthwhile insight in that, although we are only examining seven country studies, these countries together represent about 22 percent of developing country GDP and 30 percent of developing country population. Moreover, within their respective regions, some of these countries matter greatly—like Nigeria (19 percent of Sub-Saharan Africa’s GDP and 19 percent of the region’s population), India (82 percent of South Asia’s GDP and 75 percent of the region’s population), and Brazil (35 percent of Latin America and the Caribbean’s GDP and 34 percent of the region’s population).

Overall, our country sample shows that the past two decades have seen extraordinary growth and rapid catch-up convergence in developing countries— underpinned by increases in labor productivity—although the patterns of within-sector versus structural change increases vary widely (Table O.3). Nonetheless, a few themes emerge at the regional level.

**Africa.** Labor productivity rose in all four countries during the 2000s, with the second half of the period characterized by a resurgence of structural change as a driver of productivity growth in Nigeria and Zambia, and to a lesser extent in Ghana. Botswana, the only upper-middle-income country in the African sample, resembles a Latin America country in the sense that most of the productivity stems from within-sector growth rather than from structural change. These results are consistent with McMillan and Harttgen (2014), who show that structural change was growth enhancing in Africa post-2000.

**Asia.** In this region, India and Vietnam represent stark contrasts in terms of what has driven labor productivity increases. In Vietnam, structural change has been a strong driver throughout the period. However, in India, most of the productivity growth has come from within-sector productivity. In fact, the contribution of structural change decreased in the 2000s from the 1990s, down from 1.3 percent to 0.3 percent—a definite worrying sign for a country that still has a large portion of the population working in the agriculture sector.

**Latin America.** Brazil exemplifies an upper-middle-income country that has already undergone a deep structural transformation, moving a large share of workers from agriculture to manufacturing by the end of the 20th century. Over the past two decades, however, the country has strongly relied on within-sector productivity change—in fact, gains in structural change are minimal.

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# 1 Updating pre-conflict poverty estimates

Official poverty estimates for Syria are available for 1997, 2003 and 2007 and produced with UNDP technical assistance. Poverty measurement builds on nationally representative household budget survey data from the Household Income and Expenditure Surveys (HIES) conducted by the Central Bureau for Statistics (CBS – the official statistical agency in Syria).<sup>6</sup> CBS conducted another round of the HIES in 200910, but no poverty estimates were released. Since the conflict erupted in 2011, no new rounds of HIES were conducted.

The methodology adopted by UNDP to compute official poverty estimates builds on the construction of “household-specific poverty lines”, reflecting caloric requirements which vary by household composition and regional variation in the cost per calory and non-food allowance.<sup>7</sup> Two different poverty thresholds are considered, one reflecting basic needs (referred to as the “extreme” or “lower” poverty line – henceforth LPL) and a more generous one reflecting a “reasonable” level of basic needs (referred to as the “moderate” or “upper” poverty line – henceforth HPL). The LPL – once expressed in USD using the 2011 PPP conversion factor – is close to the international poverty line for lower-middle-income countries ($3.65 at 2011 PPP), while the UPL is close to the one for upper-middle-income countries. Table 1 below summarizes the available poverty estimates.

_Table 1: Official poverty rate and national poverty lines_

||**1997**|**2003**|**2007**|
|---|---|---|---|
|||**_Poverty rate (%)_**||
|**Lower Poverty line (LPL)**|14.26|11.39|12.3|
|**Upper Poverty line (UPL)**|33.22|30.13|33.6|
||**_National average_**|**_of household specific poverty_**|**_lines - SYP per month_**|
|**Lower Poverty line (LPL)**|NA|1458|2183|
|||_($3.6 daily, 2011PPP)_<br>_($3.9 daily, 2017PPP)_|_($4.2 daily, 2011PPP)_<br>_($4.6 daily, 2017PPP)_|
|**Upper Poverty line (UPL)**|NA|2052<br>_($5.1 daily, 2011PPP)_|3037<br>_($5.8 daily, 2011PPP)_|
|||_($5.5 daily, 2017PPP)_|_($6.4 daily, 2017PPP)_|

_Source:_ Abu-Ismail et al. (2011); El Laithy, Abu-Ismail (2005). World Bank staff estimates for 2011 PPP conversion. Notes: SYP value of the poverty lines is expressed in current prices. Daily 2011 PPP value of the PL provided in parenthesis for reference _._ At present, the World Bank Poverty and Inequality Platform (PIP) only includes poverty estimates at international poverty lines for 2003, the only year for which microdata are available in the Global Poverty Monitoring Database. However, building on tabulations available in Abu-Ismail et al. (2011) and on the CBS website, it is possible to gather all relevant information to compute grouped-data poverty estimates at international poverty lines for 2007 and for 2009, hence to update the available pre-conflict baseline poverty series (Box 1).

> 6 Information from the HIES 1997 was collected between October 1996 and September 1997; for the HIES 2003 information was collected between July 2003 and June 2004; for the HIES 2007, information was collected between November 2006 and October 2007.

> 7 El Laithy, Abu-Ismail (2005).
snowball survey in which households are asked to provide referrals to other households with migrant members; and iii) an intercept point survey (or time and space sampling survey), in which individuals are sampled during set time periods at a pre-specified set of locations where households in the target group are likely to congregate.

We apply these methods in the context of a survey of Brazilians of Japanese descent (Nikkei). There are approximately 1.2-1.9 million Nikkei amongst Brazil’s 170 million population. Many of these Nikkei have migrated to Japan to work after a Japanese law change in 1990 allowed third-generation Nikkei unrestricted access to Japanese labor markets (Tsuda, 1999, Higuchi, 2006). The estimated 265,000 migrants send approximately $US2 billion in annual remittances (Beltrão and Sugahara, 2006). We compare the performance of the three different survey methods in collecting data from Nikkei households in Brazil with and without migrants in Japan.

Whilst our application involves surveying Nikkei households (an ethnic minority), with and without migrants abroad, the methodologies employed are equally applicable to attempts to survey migrants in their destination countries. More generally, the problem of surveying migrant households is one of surveying “rare elements” (Kish, 1965, Kalton and Anderson, 1986). The results of the survey experiment are therefore also informative for surveys of other rare populations, such as ethnic minorities and the homeless.

The remainder of the paper is structured as follows. Section 2 outlines the different methodologies which have been developed and used in previous studies to survey migrants and their families. Section 3 describes our experiment and the Brazilian setting, while Section 4 describes how the different methodologies were applied in practice. Section 5 compares the results of the three survey methods, and Section 6 provides a cost comparison across the different methods. Section 7 concludes.

3

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coastal Bangladesh during the dry season. For example, salinity increased from 2ppt to 20ppt at Mongla in the Pussur River from 1962 to 2008.<sup>10</sup> A map displaying the distribution of maximum river salinity from field measurements during the 2010-2011 dry season (October 2010 to May 2011) is shown in Table 1.

> 10 Salinity is measured in parts per thousand (ppt), grams of salt per 1,000 grams of water.

5
OPTIMAL SIMPLE FISCAL RULES 17 <!-- Start of picture text --> baseline baseline baseline<br>2.5<br>3<br>2.7 2.48<br>2.46<br>2.6 2.8<br>2.44<br>2.5 2.42 2.6<br>2.4<br>2.4 2.4<br>0.1 0.2 0.3 0.4 -1 -0.5 0 0.5 0 0.2 0.4 0.6 0.8<br>θ θ θ<br>a y p<br>ρ p=0 ρ p=0 ρ p=0<br>0.08<br>0.16<br>0.07 0.14 0.15<br>0.12<br>0.06<br>0.1<br>0.1<br>0.05 0.08<br>0.06<br>0.04 0.05<br>0.04<br>0.1 0.2 0.3 0.4 -1 -0.5 0 0.5 0 0.2 0.4 0.6 0.8<br>θ θ θ<br>a y p<br>ss ss ss<br>Welfare loss % of C Welfare loss % of C Welfare loss % of C<br>ss ss ss<br>Welfare loss % of C Welfare loss % of C Welfare loss % of C<br><!-- End of picture text --> Figure 3. **Welfare loss around the neighborhood of the OSR-Equal:** vertical line indicates OSR Equal. Top row: Baseline Calibration. Bottom Row: Transitory commodity price shocks ( _ρp_ = 0).

are relatively similar to those of HtM HHs. Figure 9 in section B.3 shows how the welfare loss changes as we change the fiscal rule coefficients one at a time around the OSR.

**Equal Allocation OSR** In Column 7 of Table 2, we also calculate the optimal fiscal coefficient assuming that the government cannot target transfers separately at HtM and Ricardian HHs (i.e. _θa_<sup>_′_=</sup><sup>_θ_</sup> _a_<sup>_′′_,</sup><sup>_θ_</sup> _y_<sup>_′_=</sup><sup>_θ_</sup> _y_<sup>_′′_,</sup><sup>_θ_</sup> _p_<sup>_′_=</sup><sup>_θ_</sup> _p_<sup>_′′_),sothefiscalauthorityjusthastochooseatransfer-basedrulefor</sup> all households _{θa, θy, θp}_ (without primes). Despite this substantial restriction, the OSR-equal delivers _almost the exact same welfare as the fully OSR_ . The reason is that the unrestricted OSR coefficients discussed above are quite similar for HtM and Ricardian HHs. Although Lemma 2 doesn’t hold exactly (as _θa_<sup>_′′_=0),welfareisgenerallylesssensitivetovariationintransfersto</sup> Ricardian HHs than to the HtM HHs. The policy implication is that for stabilization purposes, — it doesn’t matter if the government can target transfers at the HtM HH they should just set the fiscal rule that is relatively optimal for HtM HHs, and this will be close to optimal for the Ricardian HHs as well.<sup>21</sup>

In the top row of Figure 3 one can see how the welfare loss changes in the neighborhood of the OSR-Equal rule. Most important, we find that the welfare loss increases substantially as the government saves more commodity revenue (i.e. _θp <_ 0 _._ 68). For _θa_ , the welfare loss increases sharply below the optimal value of _θa_ = 0 _._ 09, because this causes SWF assets to become highly volatile. As above, the welfare loss increases slowly as the response to non-resource income shocks become more pro-cyclical above the optimum of around _θy_ = _−_ 0 _._ 8.

> 21In additional results (not reported) we show that if the government is restricted to equal transfers, the fiscal rule that maximizes the welfare of the HtM HH is almost identical to the rule that maximizes total welfare.
Table 1 displays the results of regressing individual monthly earnings against employer size for our LATAM sample (self-employment without employees is the left-out category). It shows that the close connection between individual earnings and employer size remains after controlling for individual characteristics. Regression coefficients for size categories increase monotonically with size, with or without controls. The average earnings in the 50+ size bin roughly double those of the self-employed with no workers group, in regressions without additional controls. Evaluating these numbers at average individual characteristics, the gap is barely reduced when incorporating controls for gender, age, education, or economic sector. The additional regressor (other than employer size) with the highest explanatory power is years of schooling. It increases the regression R2 from 0.06 to 0.16 in regressions that already control for age and sex, and reduces the mean predicted earnings gap between the self-employed and the 50+ categories to a factor close to 1.8, as opposed to the 2.1 obtained in the unconditional regression.

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they cannot export their goods, thereby gaining access to domestic markets. As a consequence, trade integration reduces incentives for capital to flow to capital-scarce countries. Recent theoretical work on international investments argues, however, that trade integration can foster capital flows and that the South exports capital to the North (Antras and Caballero, 2009; Ju and Wei, 2011; Jin, 2012). Part of these effects might be rooted in firm-level motives to export and invest abroad (Greenway and Kneller, 2007; Alfaro and Charlton, 2009).

On the empirical front, some papers use data from the early 2000s to understand whether financial flows are explained by gravity models, where aggregate trade is one of the key variables capturing distance and transaction costs and, thus, explaining capital flows.<sup>3</sup> The most disaggregated level at which the links between financial and trade flows have been studied so far is the country-pair level, generally pooling both exports and imports. However, the empirical relevance of the interaction between trade and capital flows is not yet fully understood. In particular, little is known about the cross-country sectoral allocation of capital and how that is related to the sectoral composition of exports. This paper expands this strand of the literature by analyzing to what extent LAC’s financial integration is related to its trade flows. In particular, the paper analyzes the sectoral allocation of both financial and trade flows and studies if the comparative advantage of LAC countries has helped in attracting financial flows.

To shed light on all these topics, the paper uses a novel dataset encompassing cross-border portfolio investments, syndicated loans, mergers and acquisitions (M&A), and greenfield flows, and analyzes in detail how LAC has been integrating financially with the rest of the world. In addition, it uses sector-level trade data to study the relation between trade and financial flows.

Some of the main patterns that arise from the analysis can be summarized as follows. First, accompanying the trends in the South, LAC countries appear to be increasingly more connected with the rest of the world in terms of both cross-border portfolio holdings and capital flows. The largest increases took place in LAC’s investments abroad, even though the investments of the rest of the world in LAC have also increased across all types of financial flows. Second, despite these increases in recent years, cross-border investments into LAC countries far outweigh foreign investments by LAC countries. That is, LAC is more important as a receiver than as a sender of investments. Third, LAC’s connections with other South countries grew more rapidly than with North countries, especially during the second half of the 2000s. This growth has increased the participation of South countries as

> 3 See, for example, Aviat and Coeurdacier (2007), Stein and Daude (2007), Daude and Fratzscher (2008), Lane and MilesiFerretti (2008), Dailami et al. (2012), and Okawa and van Wincoop (2012).

3
over a given period in order to compensate for the observed change in inequality over the same period. We then document the size of this compensation, and its relationship to average income growth, in a large dataset of episodes of growth and changes in inequality covering 117 countries between 1970 and 2012.

A simple example helps to illustrate our approach. The World Bank has recently made a major public commitment to the goal of promoting “shared prosperity”, defined as growth in average incomes of those in the bottom 40 percent of the income distribution in each country in the developing world.<sup>2</sup> As a matter of simple arithmetic, growth in average incomes in the bottom 40 percent is the sum of growth in average incomes, and growth in the share of income accruing to the bottom 40 percent. In China, for example, between 1990 and 2009 average incomes grew at 6.7 percent per year. At the same time, inequality increased in the sense that the income share of the bottom 40 percent declined from 20.2 percent to 14.4 percent, corresponding to an average annual rate of -1.7 percent per year. Combining these two observations, average incomes in the bottom 40 percent grew more slowly than overall average income, at 5 percent per year. From the standpoint of promoting shared prosperity, therefore, the growth “cost” of the increase in inequality in China over this period is about 1.7 percentage points of growth per year. Or put differently, had inequality not increased in China during this time, a growth rate of 5 percent per year (instead of the 6.7 percent that actually happened) would have generated the same improvement in social welfare according to the “shared prosperity” metric of the World Bank.

This example illustrates the two key ingredients in our approach. First, in order to understand how much inequality changes “matter”, we need to specify a social welfare function that assigns weights to individuals in a country based on their incomes. In the case of the World Bank’s “shared prosperity” target, the implicit social welfare function weights everyone in the bottom 40 percent of the income distribution in proportion to their incomes, and assigns zero weight to everyone in the upper 60 percent of the income distribution. In our empirical work, we will consider a variety of very standard social welfare functions corresponding to different preferences over how income is distributed across individuals in a country. Second, growth in social welfare between two points in time will reflect growth in average incomes, and growth in the relevant inequality measure implied by the particular social welfare function under consideration. In the case of the World Bank’s “shared prosperity” target, the relevant inequality measure is the income share of the bottom 40 percent. For all of the social welfare

> 2 See World Bank (2013).

3
- Devereux, Stephen. 2002. “Social Protection for the Poor: Lessons from Recent International Experience”. Brighton, UK.

- Devereux, Stephen, and Paul Jere. 2008a. “‘Choice, Dignity and Empowerment?’Cash and Food Transfers in Swaziland: An Evaluation of Save the Children’s Emergency Drought Response, 2007/08”. Brighton, UK, and Lilongwe, Malawi.

- ———. 2008b. “Cash and Food Transfers in Swaziland: An Evaluation of Save the Children’s Emergency Drought Response, 2007/08”. Brighton, UK, and Lilongwe, Malawi.

- Devereux, Stephen, Catherine Mthinda, Fergus Power, Patrick Sakala, and Abigail Suka. 2007. “An Evaluation of Concern Worldwide’s Dowa Emergency Cash Transfer Project (DECT) in Malawi, 2006/07”. Brighton, UK, and Lilongwe, Malawi.

- Devereux, Stephen, Peter Mvula, and Colette Solomon. 2006. “After the FACT : An Evaluation of Concern Worldwide ’ S Food and Cash Transfers Project in Three Districts of Malawi , 2006”. Brighton, UK, and Zomba, Malawi.

Edmonds, Eric. 2002. “Reconsidering the Labeling Effect for Child Benefits: Evidence from a Transition Economy.” _Economic Letters_ : 303–309.

European Commission. 2013. “The Use of Cash and Vouchers in Humanitarian Crises.”

- Evans, David K., Stephanie Hausladen, Katrina Kosec, and Natasha Reese. 2014. _Community-Based Conditional Cash Transfers in Tanzania: Results from a Randomized Trial_ . Washington, DC: World Bank: © World Bank.

- Farrington, John, and Rachel Slater. 2009. “Lump Sum Cash Transfers in Developmental and PostEmergency Contexts: How Well Have They Performed?” London.

- Fiszbein, Ariel, and Norbert R. Schady. 2009. _Conditional Cash Transfers: Reducing Present and Future Poverty_ . Washington, DC: The World Bank. doi:10.1596/978-0-8213-7352-1.

- Galárraga, Omar, and Paul J. Gertler. 2009. “Conditional Cash & Adolescent Risk Behaviors: Evidence from Urban Mexico.”

- Gangopadhyay, Shubhashis, Robert Lensink, and Bhupesh Yadav. 2013. “Cash or Food Security Through the Public Distribution System? Evidence from a Randomized Controlled Trial in Delhi, India.” _Evidence from a ..._ .

- Gilligan, Daniel O., and Shalini Roy. 2013. “Resources, Stimulation ,and Cognition: How Transfer Programs and Preschool Shape Cognitive Development in Uganda”. Washington, DC.

Gitter, SR. 2006. “Women and Targeted Cash Transfers in Nicaragua.” _Aae.wisc.edu_ .

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11 payments) obtained In the formal market exceeds<sup>that of a loan from the Informral market. A</sup> study in Bangladesh, for example, has shown<sup>that the average effective cost of a loan smaller</sup> than 1000 Takas varied between 146 percent and<sup>169 percent In the formal market, while It</sup> varled from 57 percent to 86 percent In the Infomal<sup>market.10Only for loans above this</sup> amount were effective loan costs cheaper for<sup>credit from formal markets. Another study In</sup> Bolivla showed that transaction costs which borrowers<sup>had to Incur before they even knew</sup> whether their loan would ever be approved amounted<sup>to over 18 percent of the amount applied</sup> for.<sup>11</sup> The same costs amounted to slightly over<sup>8 percent In the Informal market. A cross-</sup> sectlonal study found that transaction costs as<sup>a percentage of officlal Interest rates could be</sup> as high as 245 percent (Bangladesh) II r small loans,<sup>while It was between 3 percent and 56</sup> percent for large loans.<sup>12</sup> The hlgh borrower transactlon<sup>costs for small loans obtalned from</sup> official lenders explain why small farmers who<sup>require modest loans may avold using the formal</sup> market.

17. The effect of controls on Interest rates.<sup>Officlal ceilings on onlnding Interest rates</sup> have frequently forced lending at rates which<sup>do not cover transactlon and risk costs. For</sup> example, the typical spreads of 3 to 6 percent<sup>do not provlde for full cost recovery when</sup> lenders' administrative costs for small or medlum<sup>sized loans can reach up to 20 percent of the</sup> sum lent. Even specialized rural financlal<sup>Institutions with a good loan portfolio and excellent</sup>

> 10 **Z** Ahmed, **'Efhfeove** coot of rural loan In Bangladsh', World Development, 17.3, 1989. 1 **J.** Ladman, 'Loan **Transactions** Costs, Credi Ratoning and Market Structure: The Case<sup>of Bc'ivia', in 0. Adams et al.</sup> Underminina Rural Development wth Cheap Credit, Westview Press, 1984. 12 C. Cuevas, 'Transaction Costs of Borrowing and Credit Rationing in Developing Countries, ESOP No. 1470, The Ohio State UniversIty, 1988.