rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 17. Innovation by Region (% of all firms in each category)</u>

|**Country**|**Region**|**Product**|**Process**|**Product or Process**|**Organizational**|
|---|---|---|---|---|---|
||<br>Dhaka|44.2%|64.4%|79.3%|<br>21.6%|
||Chittagong|43.5%|59.0%|80.9%|41.3%|
||Khulna|46.1%|37.4%|66.9%|31.1%|
|Bangladesh|Rajshahi|41.0%|44.4%|56.3%|12.7%|
||Punjab|8.8%|9.5%|16.2%|23.2%|
||Sindh|14.3%|11.7%|23.3%|19.5%|
||Khyber-Pakhtunkhwa|4.1%|4.7%|8.8%|0.4%|
||Balochistan|0.0%|0.0%|0.0%|0.0%|
|Pakistan|Islamabad|1.1%|3.4%|3.4%|9.3%|
||Andhra Pradesh|53.5%|77.3%|90.5%|73.8%|
||Arunachal Pradesh et al|23.8%|82.1%|91.4%|38.0%|
||Assam|37.4%|79.9%|88.8%|48.6%|
||Bihar|68.9%|71.7%|93.9%|94.8%|
||Chhatisgarh|72.8%|57.4%|89.9%|57.0%|
||Delhi|48.9%|52.9%|76.9%|41.4%|
||Goa|61.9%|61.0%|82.6%|24.3%|
||Gujarat|33.8%|30.7%|57.8%|50.5%|
||Haryana|63.5%|71.7%|88.9%|51.0%|
||Himachal Pradesh|59.8%|63.2%|83.0%|53.5%|
||Jammu & Kashmir|29.7%|70.2%|76.6%|72.8%|
||Jharkhand|56.3%|30.7%|69.8%|87.2%|
||Karnataka|74.7%|27.1%|80.8%|75.6%|
||Kerala|56.6%|24.1%|68.9%|13.5%|
||Madhya Pradesh|29.0%|77.5%|89.9%|65.5%|
||Maharashtra|36.2%|25.7%|57.0%|61.1%|
||Orissa|68.5%|54.6%|86.6%|24.1%|
||Punjab|64.8%|86.0%|96.6%|68.1%|
||Rajasthan|58.6%|82.2%|94.8%|31.4%|
||Tamil Nadu|73.6%|27.2%|79.5%|73.8%|
||Uttar Pradesh|39.3%|71.9%|84.9%|49.2%|
||Uttaranchal|54.5%|73.3%|90.4%|66.0%|
|India|West Bengal|46.3%|66.1%|88.3%|51.4%|
||West|16.3%|19.8%|32.7%|72.5%|
||Central|13.2%|8.8%|21.1%|26.8%|
|Nepal|East|1.4%|3.2%|4.4%|44.2%|

Source: Author’s elaboration from Enterprise Survey (2014) In all the SAR, large cities with more than 1 million population display a larger proportion of firms introducing new products than smaller cities (Table 18). A similar pattern is observed for process and organizational innovation except in India and Nepal. The innovation map in terms of product and organizational innovation is different from that observed in ECA, where innovation rates are higher in small cities. This contrasts the innovation picture portrayed in Africa, which presents similarities to the South Asia region.

23
WPS3747 rates provided by the CGE model for each labor-market category. Secondly, changes in agricultural and nonagricultural self-employment income in the micro-simulation module must be equal to changes in the corresponding income per worker provided by the CGE model. And finally, changes in the number of wage workers and self-employed workers by labor-market category in the micro-simulation model must match those same changes generated by the CGE model.

# **3. Implementation and Analysis of Trade Policy Shocks**

Various scenarios are examined in order to inform the Doha Development Agenda negotiations. These scenarios are built upon the work laid out in chapters 2 and 3 of this volume and entail shocks to Indonesia’s import prices, export prices and tariffs.<sup>4</sup> In the case of Indonesia, the pre-experiment outlined in Chapter 3 was also quite important. Recall that is was designed in order to take into account China's accession to the WTO, the completion of the UR-ATC commitment to abolish textiles and apparel quotas, as well as EU’s enlargement to 25. This simulation (PRESIM) is given explicit attention in this chapter, as it generates a new base from which all subsequent liberalizations simulations will start.

The employment and earnings impacts generated by the CGE model are fed into the microsimulation model.<sup>5</sup> As described above, the microsimulation is used to generate a new distribution of income which can then be compared to the base distribution. Both poverty and inequality indicators are presented and poverty indicators are computed using official poverty

> 4 More details of the shocks fed into the CGE model are given in Annex B of the World Bank Policy Research Working Paper version of this paper. Since the Indonesia model assumes export prices are exogenous and fixed, we diverge from the approach outlines in Chapter 3, and simply shock export prices. It should be noted that this specification gives this model a zero optimal tariff, unlike that implicit in the GTAP simulations. 5 A total of 32 variables generated by the CGE model are fed into the microsimulation module.

9
<mark>Gluckman, Peter D., and Hanson M. 2004. “Living with the past: Evolution, development, and patterns of disease.”</mark> _<mark>Science</mark>_ <mark>305 (5691): 1733-1736.</mark> <mark>Gregg, Paul, Elizabeth Washbrook, Carol Propper and Simon Burges (2005), “The Effects of a Mother's Return to Work Decision on Child Development in the United Kingdom”,</mark> _<mark>The Economic Journal</mark>_ <mark>115(501):48-80.</mark> <mark>Hill, Jennifer, Jane Waldfogel, Jeanne Brooks-Gunn and Wen-Jui Han. 2005. “Maternal Employment and Child Development: A Fresh Look Using Newer Methods.”</mark> _<mark>Developmental Psychology</mark>_ <mark>41(6): 833850.</mark> <mark>Kibreab. Gaim, 1985. African Refugees: Reflections on the African Refugee Problem. Trenton: Africa World Press.</mark> <mark>Landau, Loren B. 2003</mark> _<mark>.</mark>_ <mark>“Beyond the losers: transforming governmental practice in refugee-affected Tanzania.”</mark> _<mark>Journal of Refugee Studies</mark>_ <mark>16(1): 19-43</mark> <mark>Landau, Loren B. 2004. “Challenge without Transformation: Refugees, Aid and Trade in Western Tanzania.”</mark> _<mark>The Journal of Modern African Studies</mark>_ <mark>42(1): 31-59</mark> <mark>Leroy Jef .L. and Edward A. Frongillo. 2019. “Perspective: What Does Stunting Really Mean? A Critical Review of the Evidence.”</mark> Advances in Nutritio <mark>n 10(2): 196–204.</mark> <mark>Mabiso, Arthur, Jean-François Maystadt, Joachim Vandercasteelen, and Kalle Hirvonen. 2014. “Refugees and food security in host communities: a review of impacts and policy options to enhance resilience.” Unpublished, wired at:</mark> <u><mark>h</mark> ttp://www.ifpri.org/publication/refugees-food-security-andresilience-host-communities-transitioning-humanitarian</u> <mark>Maccini, Sharon, and Dean Yang. 2009. “Under the weather: Health, schooling, and economic consequences of early-life rainfall.”</mark> _<mark>American Economic Review</mark>_ <mark>99 (3): 1006-1026.</mark> <mark>Maluccio, John, John Hoddinott, Jere Behrman, Reynaldo Martorell, Agnes Quisumbing, and Aryeh D. Stein. 2009. “The impact of improving nutrition during early childhood on education among Guatemalan adults.”</mark> _<mark>Economic Journal</mark>_ <mark>119 (537): 734-763.</mark> <mark>Maystadt, Jean-François, and Phillipe Verwimp. 2014. “Winners and losers among a refugee-hosting population.”</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>62: 769–809.</mark> <mark>Maystadt, Jean-François, and Gilles Duranton. 2018. “The development push of refugees: evidence from Tanzania.”</mark> _<mark>Journal of Economic Geograph</mark>_ <mark>y 19(2):1-36.</mark> <mark>Montalvo, Jose G., and Marta Reynal-Querol. 2007. “Fighting against malaria: prevent wars while waiting for the “miraculous” vaccine.”</mark> _<mark>Review of economics and statistics</mark>_ <mark>89(1): 165–177.</mark> 25
# **<u>Table 5: Wage levels in Industry clusters in three areas</u>**

|<br>Industryclusters|<br>Median<br>income<br>from<br>HIES09/10|<br>Wage<br>rate<br>index in<br>2009/10|Cross-<br>time<br>Change in<br>wage<br>(09/06) in<br>Table 4|Wage<br>rate<br>index in<br>2006/7|
|---|---|---|---|---|
|Urban|||||
|Public|25000|100.0|1.06|105.8|
|Agriculture, employed|10651|42.6|0.80|34.0|
|Industry, employed|13872|55.5|0.83|46.0|
|Service, employed|14500|58.0|0.77|44.5|
|Agriculture, self-employed|9400|37.6|0.80|30.0|
|Industry, self-employed|17100|68.4|0.83|56.7|
|Service,self-employed|22200|88.8|0.77|68.1|
|Rural|||||
|Public|22166|88.7|1.06|93.8|
|Agriculture, employed|9679|38.7|0.80|30.9|
|Industry, employed|12500|50.0|0.83|41.5|
|Service, employed|12180|48.7|0.77|37.4|
|Agriculture, self-employed|11767|47.1|0.80|37.6|
|Industry, self-employed|13500|54.0|0.83|44.8|
|Service,self-employed|16710|66.8|0.77|51.3|
|Estate|||||
|Public|8886|35.5|1.06|37.6|
|Agriculture, employed|7500|30.0|0.80|23.9|
|Industry, employed|8580|34.3|0.83|28.5|
|Service, employed|8000|32.0|0.77|24.5|
|Agriculture, self-employed|5390|21.6|0.80|17.2|
|Industry, self-employed|10000|40.0|0.83|33.2|
|Service,self-employed|10500|42.0|0.77|32.2|

Source: Authors’ calculations using HIES 2006/7 and 2009/10 data.

The results of including the wage index are mixed (see the fourth column of Table 3). The poverty projection for rural areas shows significant improvement. It declines from 12.3 percent to 10.8 percent, which is much closer to the direct estimation of 8.9 percent. But it still only explains about 60 percent of the actual fall in poverty. For the estate sector, the poverty projection shows a negligible reduction toward the actual poverty rate (31.5 percent from 31.7 percent) and the difference from the direct estimation (10.4 percent) remains very large. For urban areas, the poverty estimation declines from 4.6 percent to 4.1 percent, further away from the direct estimation of 6.1 percent. As a whole, the imputed poverty rate at the national level declines from 12.3 percent to 11.1 percent, but it is still 2.5 percentage points higher than the direct estimation of 8.6 percent.

Given the magnitude of the differences between the projected and direct estimates and the large size of the two household surveys, it is likely that these differences are statistically significant. To verify this, we conduct two types of tests. First, we examine whether the 95 percent confidence interval of the direct poverty estimates contains the corresponding projections. Second, we test whether a difference between a projected estimate and a direct estimate is statistically significant. In this second case, it is important to be careful in interpreting the results of these tests. In particular, the test could fail to reject the null 11
# **Appraising Cross-National Income Inequality Databases: An Introduction**

**Francisco H. G. Ferreira, Nora Lustig and Daniel Teles**<sup></sup>

**JEL Codes:** D31, I32

**Keywords:** Inequality comparisons, inequality databases, international inequality

>  Ferreira is at the World Bank and a non-resident research fellow at the Institute for the Study of Labor (IZA, Bonn). Lustig is at Tulane University and is a nonresident fellow of the Center for Global Development and the Inter-American Dialogue. Teles is a doctoral student at Tulane University. For full disclosure we should note at the outset that Ferreira is a Senior Adviser at the World Bank’s Research Department (which hosts PovcalNet), and Lustig is the Director of the Commitment to Equity (CEQ) Institute, whose data is used in this review. The authors are grateful to both the reviewers and producers of the inequality databases for engaging in a frank and constructive dialogue about the strengths and weaknesses of those data sets. We would also like to thank Kathleen Beegle, François Bourguignon, Stephen Jenkins, Christoph Lakner and Tim Smeeding for comments on an earlier version of this introduction. All remaining errors are ours alone.
_For labor or for divorce?_

**Table 4 - Descriptive statistics of full and working sample pre and post Iftida’ reform in Jordan**

||Mean<br>JK<br>(1)|<br>Pre-reform<br>J<br>(2)|K<br>Post-reform<br>JK<br>(3)|<br>Control<br>(post-reform)<br>J<br>(4)|K<br>Treatment<br>(post-reform)<br>(5)|
|---|---|---|---|---|---|
|**Marital status**||||||
|Married|0.97|0.98|0.96|1|1|
|Divorced/seperated|0.019|0.018|0.19|0|0|
|**Demographics**||||||
|Age|33.9|32.8|34.12|37.88|33.18|
|Age at first marriage|20.74|19.3|21.02|20.12|22.4|
|<br>Number of children|3.77|4.66|3.59|4.69|1.14|
|Number of children under 5|1.18|1.53|1.11|0|0.07|
|Numberof householdmembers|5.77|6.66|5.59|6.64|3.17|
|**Educational attainment**||||||
|No Education|0.06|0.17|0.04|0.07|0.05|
|Primary|0.10|0.17|0.07|0.11|0.08|
|<br>Secondary|0.54|0.47|0.55|0.54|0.50|
|<br>Higher|0.29|0.17|0.32|0.26|0.36|
|**Outcome variable**||||||
|Currently working|0.15|0.12|15.72|0.14|0.15|
|**N**<br>Source: Authors Calculations - De|52070<br>mographic a|8695<br>nd Health Survey|43375<br>(DHS): Jordan (1990|6088<br>, 1997, 2002, 2007,|4719<br>2009, 2012, 2017)|

<mark>Source: Authors Calculations - Demographic and Health Survey (DHS): Jordan (1990, 1997, 2002, 2007, 2009, 2012, 2017) Note: This table displays full and working sample means for several variables both pre and post Iftida’ divorce reform in</mark> Jordan. For the first three columns, we present full sample means pre and post reform. The remaining columns present working sample means for treatment and control groups. While the table shows means values for treatment and control groups post "treatment", the empirical analysis employs a quasi-experimental framework that takes into consideration values for pre and post reform.

# **5. Effect of Unilateral Divorce Legislation on Women’s Employment**

In this section, we present evidence on the complex relationship between legislative reforms and women’s employment in MENA countries. Tables 5, 6 and 7 present regression results over two period windows with “currently working” as dependent variable. While these results capture correlations between the outcome variable, control variables, taking the influence of fixed effects into account, they still provide valuable insight. The control variables are selected based on data availability and relevant literature on the determinants of labor force participation. The connections to the legislation reform are further discussed below.

## _5.1.The immediate effect of the reform is limited as opposed to a lagged ‘structural’ effect_

Columns 1, 2 and 3 in each table present results restricted to the first window period capturing the “immediate” effect of the reform on women’s employment. In Morocco, over the first period spanning between 1992 and 2007, an increase in women’s employment is found, but that is not statistically significant. Moreover, column 3 displays an estimate from a specification that includes age and age at first marriage time trends which allows to 21
# **Annex A.  Variables and summary statistics**

**Table 13. Variable definitions and sources**

|**Variable**|**Definition**|**Source**|
|---|---|---|
|CPIA Cluster A|Assessment of the quality of a country’s<br>economic management|World Bank|
|CPIA Cluster B|Assessment of the quality of a country’s<br>structural policies|World Bank|
|Number of prior<br>actions|Count of prior actions germane to Clusters A &<br>B of the CPIA|World Bank|
|GDP per capita|GDP per capita, PPP|World<br>Development|
|Aid over GDP|Net Overseas Development Assistance and<br>official aid over GDP (international $)|Based on WDI|
|Political Rights|Measure for political rights|Freedom House|
|Economic Freedom<br>Index|An index of twelve elements of economic<br>freedom|Heritage<br>Foundation|

30
# Table A1.11: Products for which the EEC Price-based SSG system was operational, by marketing year <u>(‘X” indicates products for which the system was operational)</u>

|||||||Mark|etin|g yea|r||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|HS code|Product description|1996/1997|1997/1998|1998/1999|1999/2000|2000/2001|2001/2002|2002/2003|2003/2004|2003/2004|2004/2005|2005/2006|
|0207 12 10|Chicken carcasses,70%,frozen|||||||||||**X**|
|0207 12 90|Chicken carcasses,65%,frozen||||**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|0207 14 10|Boneless cuts of fowls of the species_gallus_<br>_domesticus_,<sup>a</sup>frozen|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|0207 14 50|Chicken breasts,frozen||||||||**X**|**X**|**X**|**X**|
|0207 14 60|Chicken legs,frozen||||||**X**|**X**|**X**|**X**|**X**||
|0207 14 70|Other chicken cuts,frozen||||**X**|**X**|**X**||||||
|0207 25 10|Turkeycarcasses,80%,frozen||||||**X**|**X**|**X**|**X**|**X**||
|0207 27 10|Boneless cuts of turkey|**X**|**X**|||||||||**X**|
|0207 27 10|Boneless cuts of turkey||||||**X**|**X**|**X**|**X**|**X**||
|0207 36 15|Boneless cuts of ducks andguinea fowls,frozen||||||**X**|**X**|**X**|**X**|**X**||
|1602 32 11|Preparations of uncooked fowl of the species_gallus_<br>_domesticus_<sup>a</sup>|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1701 11 10|Cane or beet sugar and chemically pure sucrose, in<br>solid form|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1701 11 90|“|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1701 12 10|“|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1701 12 90|“|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1701 91 00|“|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1701 99 10|“|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1701 99 90|“|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1702 90 99|Other sugar|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1703 10 00|Cane molasses|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|
|1703 90 00|Other molasses|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|**X**|

Source: Tabulated from national notifications of SSG actions, available on the WTO documents website Footnote;<sup>a</sup> _gallus domesticus_ is chicken.

60
# **1 Introduction**

There is a growing body of literature on the impacts of labor-replacing technologies. Most of this literature is focused on developed economies, since they are at the frontier of adoption of new technologies, and concerns are rising about the negative impacts on workers. The findings of this empirical literature show that while these technologies can bring about significant efficiency gains (Graetz & Michaels 2017), they can also have negative impacts on employment outcomes (Acemoglu & Restrepo 2017).

However, evidence for developing countries is scarce. This is driven not only by data constraints, but also because poorer countries in general have not been as successful as their richer counterparts at adopting new technologies at such a large scale. This article argues that developing countries can still experience some disruptive effects by being exposed to automation in developed economies. For example, automation in the US may reduce the demand for Mexican goods if it allows to reduce marginal costs and prices of US products. If Mexico’s exports to the US decline as a result, this could in turn displace Mexican workers. In other words, automation in the US can substitute away workers in Mexico by disrupting trade.

Concerns about the destruction of jobs in developing countries that were originally off– – shored from high-income economies a process known as reshoring are rising. Several media outlets have reported that firms in the United States and Europe have started to repatriate some of their production processes.<sup>1</sup> However, empirical evidence about the existence and magnitude of this phenomenon is scarce. De Backer et al. (2016) report that even though company surveys and anecdotal evidence suggest that the reshoring movement is gaining steam, empirical evidence does not support such claims. In contrast, Allen (2017) argues that economic history suggests that some of the concerns about reshoring are warranted. While the first industrial revolution brought progress to Western economies, many

> 1See, for instance, Coming home (The Economist, January 19th 2013) and Reshoring boosts manufacturing hopes (Financial Times, March 21st 2012).

1
documents), in 832 cases the firm had closed and neighbors had said this was a permanent closure, in 871 cases the owner was unable to be contacted on three visits at different times and days, 699 cases the owner said they were too busy and/or refused outright to be interviewed, there were errors in records for 209 firms (such as them being listed twice), and 1,353 firms were interviewed. Thus the interview rate was 25 percent of all listed firms, and 48 percent of non-formal, non-closed firms without listing data errors.

Firms which appear in the baseline survey are almost evenly split between commerce and services, with less than 5 percent in manufacturing. The most common types of firms were hairdressers/salons (20%), bars (14%), automobile mechanics (8%), clothing (4%), and grocery stores (4%), with a wide range of other types of firms such as restaurants, bookstores, photocopying, flower shops, laundromats, and dance and language schools. The average owner is 44 years old and has run the business for 8 years, with 37 percent of the owners being female, and 42 percent having completed high school education or higher. The mean firm has 1.3 employees (not including the owner), reports annual revenues of R$52,000 (US$26,000) and monthly profits of R$2,000 (US$1,000).

# **4.4. Randomization to Treatment Status at the Individual Level**

Given that only one-quarter of the listed firms answered the baseline survey, it was decided to focus the communication treatments just on this subgroup, since there was little point in trying to provide information on the process of formalization to firms which were already formal, closed, or where the owner could not be found to talk to. We therefore randomly chose half of the firms which had responded to the baseline survey in each communication block to receive the communication treatment, and the other half to receive the free cost treatment. They would then be directly comparable to the firms in the control block which had answered the baseline survey. This gives a sample of 1,348 firms split up as 689 control firms, 331 communication firms and 328 free cost firms for use in evaluating the effectiveness of the communication and free cost treatments (Table 1).<sup>10</sup> The first three columns of Table 2 show that randomization succeeded in generating comparable firms across the different treatment groups. The first column shows the control group mean, while the second and third columns show the coefficients on the communication and free cost assignment to treatment dummies in the following regression for firm _i_ in geographic block _s_ : BaselineVariablei,s = α+ βCommunicationi,s + γFreeCosti,s + ∑δsds + εi,s (1)

> 10 Due to data coding issues, seven of the firms which answered the baseline were not assigned to the control, communication or free cost groups, while two firms which were not in the baseline were still assigned to the communication treatment. We work with the 1,348 observations which were assigned for treatment or control.

13