rafmacalaba/datause-encoder-data huggingface dataset:
-9 -

Securities markets in developed countries, where IT has had a longer history, have tended to gradually automate functions as the need to do so arose, either from market or regulatory pressures, and as commercial funding of the developments became practicable. The approach taken has varied from country to country but perhaps a general pattern can be identified in which flow-of-trades and market data systems are developed first, then clearance and settlement systems, and lastly trade-related and market mon:toring systems. This is the pattern of developments in the UK and USA for example.

In part, the pattern of evolution in each place is determined by the prevailing legal and institutional structure, and commercial custom and practice. A well established stock exchange and stock broking industry may resist changes which will diminish their profit making ability and power. The UK and USA provide two examples of such resistance. Such conflicts are perhaps inevitable when rights and obligations are altered. This is dealt with more fully in subsequent sections.

# Box 3 IT Introduction in Developed Markets - UK and USA

The London Stock Exchange introduced a distributed quote display in broker's offices in 1969; an inter-broker net settlement system in 1979; computer assisted trading in 1986; and automated trade execution for small orders in 1988. The initial motivation for the enhancements came from the supervisory authority, but the Exchange itself soon took up the iaitiative. But opposition to fully automatic execution of trades continues and the great majority of trading is conducted by manual execution. Similarly a proposal to centralize and automate transfer and registration of securities has also been subject to much debate and delay over the last six years.

In the USA transfer and registration has been centralized in four deposiLries which immobilize the securities and maintain automated account records. This is much more advanced than in the UK. There are several trading systems: gL, trading on the distributed computer network of the National Association of Securities Dealers (NASD) is computer assisted, although only small orders are executed automaticaily by the system; on the New York Stock Exchange the principal trading sessions each day are floor based computer assisted trading with small order automatic execution and after hours computer network trading.

Because there are many exchanges, the US Securities and Exchange Commission (SEC) proposed a national market system to ensure that clients obtain the best prevailing price for an order. The initial SEC proposal was for a broker to market universal message switch that would automatically route orders to whichever market offered the best price. That proposal was abandoned after strong opposition from stock exchanges. The stock exchanges proposed a compromise which provides a market to market link via the Inter-market Trading System (ITS) and this was accepted by the SEC.

Annex IU describes in more detail the principal features of IT on the London Exchange in the UK and the National Association of Securities Dealers Automated Quotation (NASDAQ) market in the USA.
Policy Research Working Paper 10806

# **Abstract**

This paper explores the effects of industrial policy on trade, focusing on the role of preferential trade agreements. The analysis uses data for the period 2012–2022 on detailed product-level bilateral trade, industrial policy announcements, and rules on subsidies in different preferential trade agreements. The introduction of a new industrial policy measure in a destination market reduces export growth to that market on average by about 0.28 percent. However, exports from fellow members of preferential trade agreements are not adversely affected and may even be positively affected if the agreements have deep disciplines on subsidies. These findings suggest that preferential trade agreements have a shielding effect against the trade distorting effects of industrial policies.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at abarattieri@worldbank.org, amattoo@worldbank.org, and dtaglioni@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|MekongIntegrated Water Resources Management Project- Phas|e IIVirak Chan|
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|StrengtheningThe AccountingProfession in Indonesia|Christina I. Donna|
|StrengtheningThe AccountingProfession in Indonesia|Christina I. Donna|
|StrengtheningThe AccountingProfession in Indonesia|Christina I. Donna|
|Institutional Reform and Transformation of MAF|Jan Joost Nijhoff|
|Institutional Reform and Transformation of MAF|Jan Joost Nijhoff|
|Institutional Reform and Transformation of MAF|Jan Joost Nijhoff|
|Institutional Reform and Transformation of MAF|Jan Joost Nijhoff|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|Corredor Seco Food SecurityProject|Augusto Garcia|
|BuildingTechnical Capacityof CILSS/CERPOD for Demographyin t|heAissatou Diack|
|CapacityAugmentation of the National Waterway- 1(JAL MARG|VIKArnab Bandyopadhyay|
|CapacityAugmentation of the National Waterway- 1(JAL MARG|VIKArnab Bandyopadhyay|
|CapacityAugmentation of the National Waterway- 1(JAL MARG|VIKArnab Bandyopadhyay|
|CapacityAugmentation of the National Waterway- 1(JAL MARG|VIKArnab Bandyopadhyay|
|CapacityAugmentation of the National Waterway- 1(JAL MARG|VIKArnab Bandyopadhyay|
|CapacityAugmentation of the National Waterway- 1(JAL MARG|VIKArnab Bandyopadhyay|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|EmergencyWater and ElectricityServices UpgradingProject|Oumar Diallo|
|CapacityBuildingof China Food and DrugAdministration|Paavo Eliste|
|CapacityBuildingof China Food and DrugAdministration|Paavo Eliste|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
|NI Caribbean Coast Food SecurityProject|Augusto Garcia|
The inclusion of all the key factors of production described in Tables 1 and 2, while keeping region fixed effects, show a statistically significant gender gap of 21 percent with female-headed households having lower productivity (Table 8), compared with 31 percent in 2008.

The gender gap in agricultural productivity is entirely driven by lower food crop productivity among female-headed households in 2016. The coefficient of the dummy variable capturing whether the household farms export crops is positive and statistically significant (Table 8, column 2). Indeed, the gender gap decreases by 9 percentage points, from 21 to 12 percent, and becomes non-significant when introducing the export crops adoption variable.

As in 2008, the logarithm of land area cultivated is negatively correlated with agricultural productivity. The number of hired laborers and household male labor, the use of fertilizer and use of pesticide are all associated with higher productivity. The household head having tertiary education is associated with lower productivity.

Moreover, certain explanatory variables are only related to either food- or export-crop productivity, while controlling for female headship. The positive relationships between number of fields managed and household male labor use with productivity are driven by food crop productivity. The total number of crops produced is positively related to export crop productivity, and negatively correlated to food crop productivity. The household head having secondary education is significantly negatively related to food crop productivity, while having tertiary education is significantly negatively related to export crop productivity specifically – perhaps because of the opportunity cost of learning on-the-job agricultural skills.

**Table 8. OLS regressions of agricultural productivity with control variables (2016)**

||**Log**<br>|**[Self-reported**<br>|**Productivity (XO**<br>|**F/ha)]**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops<br>|All crops<br>|Export crops<br>|Food crops<br>|
|Female-headed households|-0.235**|-0.132|-0.064|-0.212*|
||(0.101)|(0.099)|(0.112)|(0.114)|
|Log [Total land area cultivated in ha]|-0.294***|-0.378***|-0.308***|-0.295***|
||(0.029)|(0.032)|(0.039)|(0.038)|
|Household head age (years)|0.003|0.003|0.002|-0.001|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Married|0.166|0.146|0.106|0.135|
||(0.101)|(0.099)|(0.099)|(0.128)|
|Divorced/Separated|0.184|0.160|0.068|0.272|
||(0.159)|(0.151)|(0.160)|(0.233)|
|Widowed|0.098|0.030|-0.004|0.135|
||(0.161)|(0.155)|(0.174)|(0.182)|
|Household head has Primary education|-0.031|-0.044|-0.048|-0.073|
||(0.051)|(0.051)|(0.053)|(0.067)|
|Household head has Secondary education|-0.107|-0.107|-0.095|-0.188*|
||(0.087)|(0.086)|(0.075)|(0.104)|
|Household head has Tertiary education|-0.706***|-0.664**|-0.760***|-0.416|
||(0.264)|(0.262)|(0.236)|(0.343)|
|Household head disability|0.061|0.042|0.014|0.085|
||(0.061)|(0.060)|(0.064)|(0.081)|

18
# Social Mobility

An observation that is increasingly being made for the entire Europe and Central Asia region is that despite improvements in objective measures of welfare, a substantial share of the population in several countries appears to harbor dimmer views of how they currently fare, relative to their situation pre-transition. This perceived decline in living standards appears to coincide also with the widespread perception that inequality is on the rise. There is no clear trend in the data to back this view and if anything inequality appears to be on a decline in most countries in the region (EBRD, 2016).<sup>17</sup> This apparent disconnect between what is captured by ‘traditional’ indicators visà-vis those that measure individuals’ perceptions regarding welfare and mobility has been posited as a puzzle for the region and there are several efforts currently underway to examine the possible explanations.

<!-- Start of picture text --> Figure 11: Benchmarks when evaluating present economic circumstance<br>100%<br>7 10 10<br>90% 20<br>80%<br>21<br>70% 26<br>14<br>60% 9 46<br>50%<br>19 24<br>27<br>40%<br>30% 9<br>20<br>20% 19 36 15<br>10%<br>11 15 12<br>0%<br>Armenia Azerbaijan Georgia Other CIS<br>Don't Know Parents<br>Own Family Pre 1989 Friends and Neighbors<br>Domestic Elites Neighboring Transition Countries<br>Western Europe<br><!-- End of picture text --> _Source_ : LiTS (2016) _Note_ : The bars for each country may not add up to 100 percent because responses that were “Don’t know” and “Not applicable” are not plotted here. The plotted figures are responses to the question: “When thinking about <u>your current economic situation, what is your benchmark?”</u> 17 The share of individuals surveyed in 2015-2016 (LiTS III) who believe that inequality increased over the previous four years is greater than those who believe it fell, though the data suggest that the number of countries where it has declined is similar to that where it increased (EBRD, 2016).

24
A final note of caution around the estimation results is that there is most likely measurement error in our variables that could potentially lead to downward bias in the estimates. This is not dealt with in the current draft.

We now present the data and regression results, followed by the statistical analysis and robustness testing.

**3. Presentation of results of the derivation of vulnerability relationships for the impact of drought on poverty in Ethiopia**

This section discusses the results of the vulnerability relationships estimated. We first present the descriptive statistics, and then the tables of results. The next section discusses the statistical learning methods attempting to validate the regression results.

- **a. Data**

The data used by Hill and Porter (2014) to establish vulnerability to poverty relationships is used as the main dataset for the initial regression results. We use the data collected in the 2005 and 2011 rounds of the nationally representative Household Income and Consumption Expenditure and Welfare Monitoring Surveys (HICES/WMS). These contain information on just under 25,000 households in each year. The key information recorded in the HICE used to calculate vulnerability is expenditure on food and other items. The WMS records household assets and characteristics as well as a fairly detailed module on self‐reported adverse events (referred to as shocks throughout). In both years they were administered by Ethiopia's Central Statistics Agency (CSA).

The advantage of using the HICES‐WMS for vulnerability analysis is that they are relatively large, nationally representative, comparable across years and allow measures of vulnerability to be estimated at the household level. This allows us to look at the relative importance of geographic and household factors in determining vulnerability, and it also allows us to examine how vulnerability varies across certain groups of households.

As noted above, we exclude pastoral areas of Ethiopia from the preliminary analysis as we believe the relationship between rainfall and consumption to be structurally different to that of other regions. The descriptive statistics for the two datasets are shown in table 3.1.

**Table 3.1: Descriptive statistics**

|Variable|2005|2011|
|---|---|---|
|Lnadult monthlyexpend|7.27|7.28|
||(0.50)|(0.50)|
|LEAP crop loss|16.33|11.58|
||(18.53)|(13.25)|
|Femalehead|0.23|0.23|
||(0.42)|(0.42)|
|Agehhead|43.24|44.43|
||(15.73)|(15.74)|
|Head notagri|0.16|0.14|
||(0.37)|(0.35)|
|Headschool|0.25|0.30|
||(0.43)|(0.46)|
|Lotsplots|0.57|0.53|
||(0.49)|(0.50)|
|Cattle|0.66|0.67|
||(0.47)|(0.47)|

16
|annual)|||||(0.206)|
|---|---|---|---|---|---|
|Inflation (GDP deflator)|||||-0.279**<br>(0.115)|
|Rule of Law|||||-6.725<br>(4.206)|
|Control of Corruption|||||5.506<br>(3.791)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|
|Constant|70.593***|44.578**|40.802**|41.381**|46.291|
||(14.050)|(19.378)|(18.905)|(19.163)|(32.466)|
|Number of observations|23,852|23,852|23,852|23,852|23,852|
|R-squared|0.150|0.155|0.158|0.160|0.166|
|**Panel B: Estimated**|**impact of Ethnic Fra**|**ctionalization**|**on SMEs and**|**Large firms**||
|SMEs|-19.756***|-15.596***|-15.468***|-15.132***|-14.498***|
||(4.734)|(4.919)|(4.930)|(4.840)|(4.666)|
|Large (>=100 workers)|-10.205**|-6.991|-6.121|-5.215|-3.844|
||(4.758)|(4.775)|(4.820)|(4.929)|(4.693)|

Huber-White robust standard errors clustered on country in brackets. Significance is denoted by *** (1%), ** (5%), * (10%) 53
improving local schools will cost more the larger a city’s population. Enhancing efficiency in the largest cities will therefore require more resources than in the smallest cities.

**Table 3. Urban policies in China for constant population-weighted characteristics**

||**W**<br>|**elfare differences(perce**<br>|**nt)**<br>|
|---|---|---|---|
|**Urbanpolicies**|**Efficiency**|**Amenities**|**Excessive frictions**|
|Improve in worst cities<br>by20percent|2.7|8.2|0.0|
|Improve in smallest<br>cities by20percent|0.8|2.2|0.0|
|Improve in medium-<br>large cities by20percent|1.9|1.3|0.0|
|Improve in largest cities<br>by20percent|0.0|2.0|0.1|

_Source:_ Authors.

To remove the effect that certain policies benefit more people than others (and are therefore likely to cost more than others to implement), table 3 analyzes the same urban policies as before, but now keeps the ex-ante population-weighted characteristics constant. In other words, we leave ex-ante aggregate efficiency unchanged but change the distribution of efficiency across cities, benefiting some cities at the cost of others.<sup>11</sup> We apply the same methodology to amenities and excessive frictions. Policies that target cities by their characteristics rather than their size seem much more effective. Implementing such policies requires a methodology to rank cities by their characteristics. Our model provides a framework to do so. Note that within the class of size-based policies, no clear picture emerges of which size should be targeted. For example, focusing on the smallest cities is more effective for amenities, whereas improving conditions in medium-size cities has a greater impact for efficiency. Which type of city benefits the most depends on the distribution of the different characteristics across cities of different sizes.
Table 5. System GMM estimation with overall Gini (dependent variable = per capita income growth at a given percentile of state income distribution; 1960-2010; five 10-year periods)

||dlny5|dlny10|dlny25|dlny50|dlny75|dlny90|dlny95|dlny99|
|---|---|---|---|---|---|---|---|---|
|Gini|-0.306|-0.294*|-0.166*|0.0116|0.118**|0.143***|0.172***|0.204***|
||(-1.57)|(-1.75)|(-1.70)|(0.20)|(2.47)|(3.26)|(3.68)|(2.92)|
|edu_ms_age2139|0.0561|0.0983|0.115*|0.109**|0.0936*|0.0994**|0.0987**|0.106*|
||(0.75)|(1.23)|(1.87)|(2.04)|(1.83)|(2.18)|(2.32)|(1.92)|
|edushort1518|-0.131***|-0.0956***|-0.0526***|-0.0362***|-0.0410***|-0.0343***|-0.0254**|-0.0242|
||(-4.27)|(-4.86)|(-4.05)|(-3.23)|(-3.56)|(-3.05)|(-2.34)|(-1.41)|
|age015|0.000659|-0.105|-0.202*|-0.214**|-0.209**|-0.228***|-0.239***|-0.225**|
||(0.00)|(-0.88)|(-2.00)|(-2.29)|(-2.44)|(-3.28)|(-3.35)|(-2.61)|
|age65|-0.0609|-0.00419|0.00420|-0.0186|-0.0467|-0.0597|-0.0466|-0.00804|
||(-0.34)|(-0.03)|(0.05)|(-0.23)|(-0.58)|(-0.87)|(-0.77)|(-0.12)|
|olf_female|-0.183***|-0.150***|-0.106***|-0.0982***|-0.0915***|-0.0686**|-0.0592**|-0.0506|
||(-2.82)|(-2.84)|(-2.97)|(-3.02)|(-2.94)|(-2.68)|(-2.18)|(-0.94)|
|lnyxx|-0.0987***|-0.103***|-0.0938***|-0.0810***|-0.0712***|-0.0649***|-0.0537***|-0.0573***|
||(-4.47)|(-4.63)|(-5.19)|(-4.88)|(-4.43)|(-4.70)|(-4.40)|(-3.75)|
|constant|1.133***|1.181***|1.074***|0.919***|0.822***|0.766***|0.644***|0.685***|
||(4.58)|(4.89)|(5.42)|(5.10)|(4.65)|(5.15)|(4.95)|(3.99)|
|N|245|245|245|245|245|245|245|245|
|Hansenp|0.590|0.461|0.110|0.202|0.227|0.109|0.191|0.114|
|ar1p|0.000744|0.0000457|0.0000362|0.00000606|0.0000219|0.0000261|0.00000819|0.000127|
|ar2p|0.817|0.172|0.0726|0.0162|0.0643|0.235|0.957|0.318|
|instruments|33|33|33|33|33|33|33|33|

Note: All right-hand side variables estimated at time=0. Income is household per capita. Gini calculated across individuals ranked by their household per capita income. _t_ statistics in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Time-period dummies are included but not reported.

25
Policy Research Working Paper 9892

# **Abstract**

This paper uses a “mystery client” approach and visits the websites of national statistical offices and international microdata libraries to assess whether foundational microdata sets for countries in the Middle East and North Africa region are collected, up to date, and made available to researchers. The focus is on population and economic censuses, price data and consumption, labor, health, and establishment surveys. Following the exercise, a new microdata access indicator that measures the degree of opennes of microdata and the ease with which microdata users can understand and navigate the websites of national statistical offices is presented. The results show that about half of the expected core data sets are being collected and that only a fraction is made available publicly. As a consequence, many summary statistics, including national accounts and welfare estimates, are outdated and of limited relevance to decision makers. Additional investments in microdata collection and publication of the data once collected are strongly advised. National statistical offices in the region should make considerable improvements to the outlook of their websites to make them more user friendly. Specifically, microdata libraries and updated survey calendars should be a standard feature of the websites to ensure easy access to available microdata.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at uekhator@worldbank.org and jhoogeveen@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team