rafmacalaba/datause-encoder-data huggingface dataset:
Figure 10: Accounting for TFP Gaps in the Data <!-- Start of picture text --> Belgium<br>France<br>Portugal<br>SpainChile<br>FinlandRomania BulgariaItaly Peru<br>Malaysia<br>Hungary<br>ColombiaKenya<br>Latvia<br>IndiaSalvador<br>Ghana<br>Ethiopia<br>2 4 6 8 10 12<br>TFP−USA/TFP_i<br>1.0<br>0.8<br>0.6<br>0.4<br>Fraction of TFP gap closed<br>0.2<br><!-- End of picture text --> Note: The figure shows the TFP gains from dismantling _τi_<sup>_E_</sup> and _γi_ in the model relative to the productivity gap between the US and each country’s TFP in the data. Formally, we plot TFP gapclosed = _Model T F P_ _<u>{</u>_ 0 _<u>,</u>_ 0 _<u>}</u>_ <u>�</u> _T F PT F P {τi_<sup>_USAE,γi}_</sup> <u>�</u> _Data_ against the observed gaps; � _<u>T F PT F P</u>_<sup>_USA_</sup> _i_ � _Data_ . TFP in the data is read from _ctfp_ in <u>�</u> _T F Pi_ <u>�</u> Penn World Table version 9.0 for the year 2014, with the exception of Ethiopia, Ghana, and El Salvador for which _ctfp_ is missing. In these cases, TFP was computed as _K_<sup>1</sup><sup>_/_3</sup> _<u>YL</u>_<sup>2</sup><sup>_/_3,using</sup><sup>_rgdpna_astherealGDP,</sup> _rkna_ as the real capital stock, and _emp_ as the labor input.

ranges between one sixth and one-tenth of TFP in the US.

**Decomposition** The theory can be used to decompose the total productivity gains into those stemming from dismantling allocative distortions and those from reversing entry barriers. We show the results of this decomposition in Figure 11. As reference, the left panel of the figure reproduces the total gains. The middle panel shows the gains from only removing allocative distortions, which we accomplish by setting _γ_ = 0 while holding the entry tax rate fixed at the estimated level. The right panel reports the gains from removing the entry barriers, setting _τ_<sup>_E_</sup> = 0 and preserving the idiosyncratic distortion at the estimated value of _γ_ .

Figure 11 reveals that allocative distortions play a slightly more prominent role in the least developed economies, whereas the gains are more evenly distributed between allocative distortions and entry barriers in the most ad45
potential effects.<sup>1</sup> Another strand or work connected to ours explored PTAs and their potential effects.<sup>2</sup> . Third, recent papers have explored the trade effects of subsidies, including those by the WorldBank (2023) and Rotunno and Ruta (2024). Finally, studies have investigated the interconnections between industrial and trade policies. Lashkaripour and Lugovskyy (2023) show that when unilateral adoption of corrective industrial policies is associated with trade barriers, countries risk _immiserizing growth_ and a global race to the bottom. Ju et al. (2024) provide a quantitative evaluation of the US-China trade and industrial policy competitions.

The rest of the paper is structured as follows: Section 2 illustrates the data and the empirical specification for the analysis. Section 3 illustrates the main findings. Finally, Section 4 concludes.

# **2 Data and Empirical Strategy**

## **2.1 Data**

We use three different datasets. The dependent variable consists of bilateral, product-level (HS 6-digit) trade data from Comtrade-BACI Gaulier and Zignago (2010), covering the period 2012-2022.<sup>3</sup> There are two key explanatory variables: a measure of new industrial policy announcements as catalogued by the Global Trade Alert database; and measures of preferential trade agreement membership and depth, as documented in the World Bank’s Deep Trade Agreements Database (Mattoo et al., 2020).

### **2.1.1 Global Trade Alert Database**

Established in response to the 2008 financial crisis, the Global Trade Alert (GTA) is the only comprehensive database and independent monitoring platform that provides timely information on state measures likely to affect world trade. The database includes measures that are traditionally considered protective, such as tariffs and quotas, as well as subsidies and other mechanisms that governments use to support domestic industries (see Figure 4). The database also classifies the measures into “restrictive” (red), “neutral” (amber) and “liberalizing” (green).

> 1See for instance Kalouptsidi (2018), Criscuolo et al. (2019), Liu (2019), Bartelme et al. (2019), Choi and Levchenko (2021), Juhasz et al. (2022), Lane (2022). Juhasz et al. (2023) offer an extended critical review of the literature.

> 2See for instance Freund and Ornelas (2010), Fernandes et al. (2021), Limao (2006), Limao (2007), Lim ̃ao (2016),Mattoo et al. (2021), Mattoo et al. (2022), Mattoo et al. (2021) and Romalis (2007)

> 3To eliminate possible distortions from outliers, we exclude bilateral product-level flows accounting for less than USD 1 million yearly. We still cover 96 percent of world trade.

5
# **Do Behavioral Interventions Enhance the Effects of Cash on Early Childhood Development and Its Determinants? Evidence from a Cluster-Randomized Trial in Madagascar**

Saugato Datta<sup>1a</sup> , Joshua Martin<sup>1</sup> , Catherine MacLeod<sup>1</sup> , Laura B. Rawlings<sup>2</sup> , Andrea Vermehren<sup>2</sup> 25 June, 2021<sup>3</sup> JEL classification: O15 Keywords: Behavioral economics, Early childhood development, Human capital

- 1 ideas42, 80 Broad Street, NY NY 10004

a. Corresponding Author: Email: sdatta@ideas42.org; Phone: 617-417-8626; Address: 80 Broad St, NY NY 10004

> 2 World Bank, 1818 H St NW, Washington, DC 20009

> 3 **Funding** :  This work was supported by the Wellspring Philanthropic Fund and the Strategic Impact Evaluation Fund at the World Bank.

**Acknowledgments** : <mark>The trial was pre-registered at the American Economic Association’s RCT Registry as RCT ID AEARCTR0000957. We gratefully acknowledge helpful comments and suggestions from Alaka Holla, Jeffrey Tanner, and Benedicte Leroy De La Briere at the World Bank; Keetie Roelen at the Institute of Development Studies (IDS) at the University of Sussex; and Lawrence Aber at New York University. Participants at the World Bank’s Social Protection and Jobs Seminar Series and IDS’s seminar on “Putting Children First” where earlier versions of this paper were presented gave helpful feedback and suggestions. We thank Atila Cidam for his help with analysis. Any remaining errors are ours. This work does not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.</mark>
Another geographic aspect of migration is to look at the issue of whether inmigrants are from same province or other provinces. Table 7 suggests that in Zhejiang, about 41 percent of towns in-migrants were from other provinces. In contrast, in Henan and Sichuan, majority of migrants were from same province, and there was only about 8 percent from other provinces.

Table 7 Town in-migration by province and region: 2000

|||Zhejiang|Henan|Sichuan|
|---|---|---|---|---|
|||(%)|||
|From same p|rovince|59.08|91.93|92.68|
|From other p|rovinces|40.92|8.06|7.32|
|||100.00|100.00|100.00|
|Of which:|coast|11.03|38.33|22.79|
||central|60.74|34.00|15.47|
||west|28.23|27.67|61.74|
||total|100.00|100.00|100.00|

________________________________________________________________________ Sources: calculated from Tabulation on the 2000 Population Census of the P. R. China, Table L703b, and Table L704b.

# Note: regional classification:

Coast includes following provinces: Heilongjian, Jinin, Liaoning, Beijing, Tianjian, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujiang, Guangdong, Guanxi and Hainan.

Middle includes following provinces: Nei Mongol, Shanxi, Henan, Anhui, Hubei, Hunan and Jiangxi. West includes following provinces: Xingjiang, Qinghai, Gansu, Ningxia, Shaanzi, Sichuan, Guizhou, Yunnan and Tibet.

Table 7 further shows that among those of inter-provincial in-migrants in Zhejiang, majority (89%) came from central and western regions. Thus, many inmigrants in fact crossed provincial lines and traveled a quite long distance to seek economic opportunities in towns in Zhejiang. Whereas in Henan and Sichuan, even though the inter-provincial in-migrants only accounts for less than 10 percent, many of them (61.7% in Henan and 77.2% in Sichuan respectively) were from the western and central regions.

Migration was largely motivated by economic opportunities. Indeed, when census long-form asks about the reasons for migration into towns (Table 8), more than half (60 percent) of in-migrants in Zhejiang responded that purpose of migration into towns was the engagement in production and business, job transfer or assignment. In contrast in Henan and Sichuan, there were only 37 percent and 34 percent of in-migrants who came for the purpose of engaging in production and business, job transfer or assignment.

8
session, that is, they pay a price to increase the speed of their trades to comply with their investment style. Meanwhile, other investors with more discretion over their flows appear to time their transactions to take advantage of the common order flow by passive funds.

Our results need to be interpreted in the context of the market microstructure of the Colombian stock market. For example, average trade size, market impact, and bid-ask spreads constraint the speed at which managers can execute their trades. Depending on the liquidity of the underlying asset, managers with private information would optimally split their trades in multiple transactions, perhaps even across several days (Kyle, 1985; Kyle and Obizhaeva, 2016). Passive fund managers with little discretion over flows, however, have a natural time limit to execute their trades, namely, the end of the day. Consequently, managers end up paying higher transaction costs in order to meet their “deadline” and generate lower returns.

Our evidence is related to an extensive empirical literature that studies the relative value of active versus passive management. Numerous papers document a poor track record for active funds, with average returns for investors significantly below those of passive benchmarks (e.g. Wermers, 2000; Bollen and Busse, 2001; P ́astor and Stambaugh, 2002; Avramov and Wermers, 2006). More recently, Cremers and Petajisto (2009), using a new measure of active management (Active Share), find that managers with more active trading strategies exhibit relatively more skill than less active managers. We also use Active Share to asses the level of active management of the funds in our sample. We find that Active Share predicts fund performance among foreign funds. More importantly, we contribute to the literature by examining in intra-day trading patterns by level of active management and by documenting the potential costs of passive investing.

In our paper, instead of trying to identify informational advantages of local versus foreign investors,<sup>2</sup> we document a particular type of disadvantage —the cost of index investing in a small equity market. We would expect for similar findings in other developing countries, or in small (illiquid) stocks in developed countries. Overall, our results challenge the conventional wisdom that passive funds are, unconditionally, the best alternative for investors. While this could be the case for U.S. households investing in their home country (French, 2008), it is possible that for global strategies, index investing might deliver disappointing results.

> 2Other examples in this literature rely on lower frequency data, e.g. monthly or quarterly holdings. Most recently, Ferreira et al. (2015) use a sample of 32 countries to compare the performance of institutional investors in stocks of their home country to the performance of money managers located in other countries.

4
Measuring Poverty Rapidly Using Within-Survey Imputations Utz Pape<sup>1</sup> Keywords: Poverty and inequality measurement, survey methods JEL: C83, D63, I32 1 Corresponding author: Utz Pape (upape@worldbank.org). The findings, interpretations and conclusions expressed in this paper are entirely those of the author, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent. The author would like to thank Johan Mistiaen for discussions and support in pursuing the idea and Chris Elbers for help with the statistical properties of the new methodology, as well as Kathleen Beegle, Tomoki Fujii, Kristen Himelein, Dean Jolliffe, Peter Lanjouw, Emmanuel Skoufias, Shinya Takamatsu, Roy Van der Weide and Nobuo Yoshida for discussions. The author is also grateful to the Kenyan National Bureau of Statistics for implementing the methodology in a pilot survey.
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# Summary findings

[lie presence and persistence of substantial wage differentials between industries has been documcnted. l)iffcrcnecs between wages in different industries could result from (1) the normal functioning of competitive labor markets (compensaring differential levels of human capital), (2) institutional factors, such as the prcsence of a union, and (3) efficiency wages paid in some industries (employers finding they can increase profits by paying workers above-market wages).

Using a testable model of endogenous growth, Household Survey to estimatc the external effects (Jf education.

First, he estimates a wage equationi and filters out the internal effects of education. Then, to isolate extenal effects, he rcgresses the resulting wage premiums in industry on average human capital as well as on industryspecific characteristics.

Stronger conclusions cannot be drawn, but the analysis does not reject the hypothesis that external effects are present.

Sakellariou analyzes microdata from the Guatemala This paper - a product of the Education and Social Policy Department - is part of a larger effort in the departmnent to apply economic analysis in the education sector. Copies of the paper arc available free from the World Bank, 1818 H Street NV, Washington, DC 20433. Pleasc contact Ian Conachy, room S10-022, extension 33669 (17 pages). April 199S.

_Worksng Paper Sere Policy ResPh_ **_e_** _arch s disseminates the fidings of work in progress to encourage the exchange of icas about development issucs. An obfectiu of the series is to get the findings out quickly. even if I_ ,c _prcewntations are Icss than ful y poltsbed. Thce pape-rs carry the namecs of the authors and should be used and cited accordingly. Thc findings, interprcetaions, and conclusions are the 4authors *own and should not be attributed to th: World Bank, its E-xecutive Boa-rd of Directors. or any of its member countries.|_

Produced by the Policy Research Dissemination Center
to-food efficiency in animal production, decreased food wastage and dietary changes towards less land-demanding foods would help to reduce agricultural land use.

Animal production competes for land directly, for instance, for grazing and fattening, and indirectly through the need to produce animal feeds.<sup>11</sup> Gerbens-Leenes et al. (2002ab) show that foods associated with affluent lifestyles, especially animal products, oils and fats, and beverages, tend to require more land for their production than foods associated with less affluent lifestyles. For China, following Gerbens-Leenes et al. (2002ab)’s methodology, Li et al. (2013) find that China’s urbanization appears to have increased pressure on limited arable land resources, since urban residents consume more animal based and other land-demanding food than their rural counterparts.

The approach adopted in this paper is based on the methodology developed by Rask and Rask (2004, 2011) which converts crop and animal products into cereal equivalents. The CE coefficients for crop-based products are computed very simply by matching their caloric content to those of an equal weight of cereals, assuming broadly similar efficiencies across commodities but taking into account the greater resource use associated with producing foods that contain more calories per unit of weight (e.g., vegetable oils) relative to those that have, for instance, a higher water content (e.g., starchy roots). For animal products, the CE coefficients reflect the feedstuff used to produce one unit of animal products in terms of the dietary energy equivalent of a unit of corn, considering not only grains consumed but also other types of feed such as protein supplements, forages (including pasture) and other feeds.<sup>12</sup>

> 11 Livestock is the world’s largest user of land resources, with grazing land and cropland dedicated to the production of feed crops and fodder representing about 70 percent of all agricultural land. About 33 percent of arable land is used to produce livestock feed (Steinfeld et al., 2006).

> 12 The CE coefficients developed by Rask and Rask (2004, 2011) are based on a study published by the USDA (1975). The USDA study is unique in covering all types of feeds including forage crops and pasture in calorie equivalents of corn over the period 1964-1973. Wirsenius (2003) challenges the view that the use of non-edible feeds is “free” arguing that it involves opportunity costs such as production of feedstock for biofuels, preservation of

9
Competition from other dynamic East Asian economies is adding to the pressure to change in Singapore. Like Singapore, these economies are also looking for ways to enhance their own innovative capacity. Statements from Singapore officials often reflect worries that the region may not be able to support more than one major innovation hub and that the island state may lose out if it does not respond in time and effectively to the competition.<sup>14</sup> In this paper, we shall try to put in perspective Singapore’s efforts to transform the citystate from an efficient economy to an innovative economy. A basic question here is: Does an innovative economy require a very different set of supporting infrastructure from an efficient economy? If so, has the government made the necessary changes to facilitate the transition? More generally, one may ask whether there is a contradiction between Singapore’s twin objectives of becoming an innovation hub and sustaining its status as one of the world’s most efficient cities for businesses. The issues will be discussed within the context of the competition posed by other dynamic East Asian economies. The paper will draw on information gathered from interviews with individuals working in innovation-related fields. These include policy makers, intellectual property lawyers, participants within the creative industries, foreign chamber of commerce representatives, opinion makers, etc.

The paper is organized as follows. The next section summarizes the essential ingredients that were put in place to enhance business efficiency in Singapore since its independence. In section III, we look at the rationale behind the re-orientation of the innovation policy in the past few years. Section IV describes the various measures that have been implemented in the past few years to facilitate the transition to an innovative economy. Section V discusses Singapore’s comparative advantage in the innovation businesses, taking into account the constraints it faces as a small sovereign city-state. Section VI turns to the country’s efforts to enhance efficiency in its services sector. The last section summarizes by taking a critical look at the effectiveness of Singapore’s modus operandi and considering some alternative growth strategies.

# **_II. The Quest for Business Efficiency_**

Singapore is a densely populated city-state with 4.2 million people and a land area of only 685 square km. A British colony for many years, Singapore attained self-government in 1959, joined Malaysia in 1963 and became an independent city-state in 1965. The People's

> 14  See for example, ERC Main Report, 2003, report by the Services Sector Sub-committee.

4
# **V. Estimation results and discussion**

Due to the small size of the SSA sample (508 observations compared to 2,278 for the global sample), different specifications (see results from Table 3-5) were run separately to assess the effects of skills and migration on GVC participation. These specifications were estimated while considering identification problems that might affect our estimated impacts. Moreover, we report robust standard errors to obtain unbiased standard errors under heteroscedasticity. _Table 3_ presents the fixed effects panel estimation results for our baseline model with key explanatory variables that are expected to drive GVC participation. Estimated results on backward and forward GVC participation are also presented side by side with the GVC participation estimates. For comparison purpose, we also estimate our model for the rest of the world using a sample of 115 countries. This is done to assess how SSA countries performs compared to the rest of the world. For this analysis, our variables of interest are skilled and unskilled labor content in exports, the human capital index, and net migration as share of the population.

The results presented in Table 3 show that skilled labor content in exports has the greatest positive impact on both backward and forward GVC participation, and these effects are stronger for SSA countries compared to the rest of the world. According to our estimates, a 1% increase in skilled labor content in exports could globally increase forward participation by less than 3% but increases forward integration in manufacturing GVC by more than 20% in SSA. Similarly, a 1% increase in skilled labor content of exports could increase global backward GVC participation by more than 5% and increases SSA’s backward GVC participation by 24%. This suggests that increasing the skill component of SSA’s exports will tremendously help these countries in incorporating foreign inputs into their exports. This is because with higher skills, SSA countries can import more sophisticated products from foreign countries and incorporate these products into their production processes to produce and export high value-added products. At the same time, higher skill will help SSA countries in supplying high value-added inputs into the exports of more advanced countries. Importantly, higher-skills provides new trading opportunities to further increase the participation of SSA countries in GVC and to diversify their exports away from the export of raw materials to value added goods. From these empirical results, we can say that skilled labor is the most important determinant of backward and forward GVC participation in SSA even stronger in comparison with the rest of the world. On the contrary, for all the specifications we ran ( _Table 3-_ 24
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