rafmacalaba/datause-encoder-data huggingface dataset:
Given the scope of this paper, we focus explicitly on within product quality gains as a result of robot diffusion. The literature on export quality finds evidence of quality gains accruing both from within product quality improvements (Atkin et al, 2017) and introduction of new high-quality products (Goldberg et al 2009). Our fixed effects specification does not allow for the assessment of the latter channel, and therefore we leave this to future research.

# **_i. Instrumental variable construction_**

To address the endogeneity of robot adoption, we use the diffusion of robots in (initial) trade partners as an instrument for home country-industry robot adoption. This instrument is motivated firstly by the FDI literature that finds broad consensus of knowledge spillovers to industries that supply multinationals through backward linkages (Javorcik, 2004; Havránek and Iršova, 2011). Lead firms tend to demand better quality inputs from suppliers and may directly share knowledge, technology and encourage the adoption of new practices to achieve this, for example, obtaining ISO 9000 certification (Javorick and Sawada, 2018). Secondly, our motivation borrows from the trade literature, which finds evidence that trade can be an import channel for technology diffusion (e.g. Bustos, 2011; Bas and Strauss-Kahn , 2015; Bloom et al., 2016).

Specifically, we instrument a country-industry’s robot adoption using weighted average robot diffusion of their (pre-existing) export customers, using their initial export network in 2000 to define these weights: define these weights: <u>j</u> (4) r p 0 1 r p � pp→ j,pp,pp=2000 eexxpp pp jjp pp qqll r qq = αα + αα pp,pp,pp=2000 ∗qqll r qq + εε reflects the exports from the home country qqrrq qq_eexxpp to foreign customer of sector in year 2000 which is scaled by the total quantity of exports of country pp→jj,pp,pp=2000 of sector in the same year eexxpp qq jj ss . These weights are multiplied by the robot stock of supplier in sector at qq ss time , pp,pp,pp=2000r . qqrrq qq_eexxpp jj ss jjp We consider only export flows and robot adoption in countries located in other world regions in qq qqll r qq constructing the instrumental variable. We do so out of a concern of potential correlated regional shocks that may jointly affect robot diffusion at home and in their (initial) foreign customers.

20
- 32 -

of productivity in developing countries do not rely on innovation but on inporting foreign plant and equipment and on borrowing foreign technology. Second, although econometric theory shows that M-R-W's OLS assumption would produce a biased result, for all practical purpose, OLS regression results are as good as panel regression results. However, one thing to note is that in all our regressions, the F-tests demonstrate that although the heterogeneous intercept and homogeneous slopes specification is a better model than the simple OLS, it should be rejected in favor of models allowed for hetergeneity of intercept and slopes. That is, the data call for individual country regressions. However, since we do not have enough observations for each country to allow individual country regressions, ibis is a candidate for future study of the relationhip between imcome growth and foreign technology imports.
Other interventions have provided consulting instead of or in addition to training. The evidence is more positive for this type of interventions, especially for larger firms. For example, Bloom et al. (2013) provided consultancy services to large indian textile firms (with an average size of 273 employees) to improve management practices. Doing so increased productivity by 17 percent in the first year and many of these improvements lasted even after nine years (Bloom et al. 2018). Bruhn, Karlan & Schoar (2018) show a similar result of improving management practices as the result of consulting with 432 small and medium enterprises in Mexico (with an average size of 13.7 employees). There have been few consulting interventions with smaller enterprises. Karlan, Knight & Udry (2015) provided consulting services to microenterprises, which improved management practices, but did not lead to higher profits. Additional sets of interventions targeting improvements in management practices and skills are promising but yet to be rigorously evaluated in developing economies. These include outsourcing and insourcing of talent<sup>3</sup> , improvements in mid-level operational management, improving mechanisms of identifying talent, among others.

# _Mozambique_

Earlier evidence suggests that management practices are poor in Mozambique. Using 2014 data from manufacturing firms, the World Management Survey ranks Mozambique as the country with the lowest average score in the adoption of good management practices, lower than other African countries including Ethiopia, Tanzania, Ghana and Zambia (Lemos & Scur 2014: Figure 1).

In comparison with countries with high management score - United States, Sweden, Japan, and Germany – we see that more than 75 percent of Mozambique’s firms scored below the worst 10 percent firms from these countries (Figure 2). Less than 1 percent of Mozambique’s firms reach the score of the top quartile of firms in these developed countries.

Manufacturing firms in Mozambique score particularly low when it comes to improving production processes (operations) (Figure 3), and score at similar levels as Ethiopia and Ghana on people management and target-setting. Nevertheless, there is a large degree of heterogeneity: firms with low management practices co-exist with those with better managerial practices. There are

> 3 Outsourcing refers to contracting certain services outside of the firm. Insourcing refers to implanting outside knowledge within the firm to better manage the firm.

7
indicates whether the poor are catching up or being left behind. For example, in DHS 2000, children from the bottom 40% had an almost equal chance of being stunted, wasted and underweight (Table 4a). However, in DHS 2014, the chance of being undernourished for this group is 27 to 40 percent higher than those of children from the top 60%.

**Table 4a.Trends in relative gaps MNCH between poor and rich households, 2000-2014**

||Ratio: Bot<br>Earlier<br>Survey|tom40%/<br>Latest<br>Survey|Top60%<br>P-value|Ratio: Poo<br>Earlier<br>Survey|rest/Ric<br>Latest<br>Survey|hest<br>P-value|
|---|---|---|---|---|---|---|
|Stunting|1.08|1.27|**0.077**|1.24|1.71|**0.096**|
|Wasting|1.03|1.40|0.991|1.37|1.58|0.861|
|Underweight|1.12|1.34|**0.090**|1.41|2.02|0.475|
|NMR|||||||
|IMR|0.78|1.31|NA|0.88|1.34|NA|
|U5MR|0.99|1.41|NA|0.99|1.52|NA|
|Measles Vaccination|0.51|0.80|**0.038**|0.35|0.59|**0.008**|
|Full Immunization|0.42|0.60|0.397|0.21|0.34|0.825|
|Contraceptive|0.32|0.72|0.425|0.12|0.54|**0.028**|
|ANC4+|0.29|0.30|0.158|0.12|0.14|0.257|
|SBA|0.13|0.21|**0.026**|0.03|0.08|**0.037**|

Source: Authors’ compilation from the DHS (2000, 2005, 2011 & 2014) data. Note: The p-values are for the F-test of the equality of rate ratios. The test in the first part of the table: _Is [(bottom 40%, in the Latest Survey) / (top 60%, in the Latest Survey)] equal to [(bottom 40%, in the Earlier Survey) / (top 60%, in the Earlier Survey)] ?_ Similarly, in the second part of the table the quality of RR tests are: _Is [(bottom 20%, in the Latest Survey) / (top 20%, in the Latest Survey)] equal to [(bottom 20%, in the Earlier Survey) / (top 20%, in the Earlier Survey)] ?_ Test is not applicable for IMR and U5MR.

On the services side, over the study period, relative health inequalities declined for coverage of immunization services. For example, for measles vaccination, the RR in the latest survey is significantly different to that of the RR in the earlier survey. The RR changed from 0.51 to 0.8 [p <0.05] in favor of the bottom 40% and from 0.35 to 0.59 [p<0.01] in favor of the bottom 20% (Table 4a). Other services where wealth-related 22
Table B5: Full variance decomposition for TZ

|||Absolute<br>|contributi<br>|ons(in s.d.<br>|units)<br>|||R<br>|elative sha<br>|res(in %)<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(a)<br>π = 0|(b)<br>π = 0.5|(c)<br>π = 1|(d)<br>UBH|(e)<br>UBS|(f)<br>MLM|(a)<br>π = 0|(b)<br>π = 0.5|(c)<br>π = 1|(d)<br>UBH|(e)<br>UBS|(f)<br>MLM|
|σ��<br>�|0.23|0.23|0.23|0.22|0.27|0.19|5.06|5.38|5.51|4.84|7.11|3.63|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.08)|(0.08)|(0.08)|(0.07)|(0.09)|(0.06)|
|σ�<br>�|0.59|0.42|0.36|0.58|0.30|0.43|34.31|17.61|12.80|34.11|9.25|18.55|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.26)|(0.19)|(0.16)|(0.26)|(0.14)|(0.19)|
|σ�<sup>�</sup>|0.25|0.36|0.55|0.12|0.53|0.42|6.23|12.91|30.74|1.53|27.90|17.24|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.17)|(0.25)|(0.39)|(0.09)|(0.37)|(0.29)|
|2Σ��|-0.08|0.26|-0.09|0.10|0.16|0.28|-0.72|6.78|-0.84|0.92|2.63|7.74|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.05)|(0.14)|(0.05)|(0.05)|(0.09)|(0.15)|
|2Σ���|0.18|0.16|0.13|0.21|0.10|0.11|3.16|2.41|1.71|4.29|1.03|1.15|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.07)|(0.06)|(0.05)|(0.08)|(0.04)|(0.04)|
|2Σ���|0.06|0.09|0.13|0.04|0.15|0.16|0.37|0.87|1.67|0.16|2.22|2.53|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.03)|(0.04)|(0.06)|(0.02)|(0.07)|(0.07)|
|σ�<sup>�</sup>|0.72|0.74|0.70|0.74|0.71|0.70|51.59|54.04|48.39|54.15|49.85|49.17|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.24)|(0.25)|(0.23)|(0.25)|(0.24)|(0.23)|
|σ�<br>�|1.00|1.00|1.00|1.00|1.00|1.00|100.00|100.00|100.00|100.00|100.00|100.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|.|.|.|.|.|.|
|ρ��|-0.02|0.22|-0.02|0.06|0.08|0.22|.|.|.|.|.|.|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|.|.|.|.|.|.|

Note: following equations (3b) and (3c), the table sets out the absolute and relative variance shares attributable to each component of the test score; absolute shares are in standard deviation units; different models/estimators are indicated in the columns – (a) to (c) refer to results from a partitioned iterative algorithm for different choices of initialization scalar _π_ , (d) is the household upper bound, (e) is the school upper bound, and (f) is a mixed linear model; _ρhs_ is the estimated correlation coefficient between household and school effects; standard errors are reported in parentheses, calculated using the asymptotic approximation due to Ahn and Fessler (2003).

Source: own calculations.

50
# Table of Contents

|Acknowledgments ............................................................................................................................................................... 3|
|---|
|1.<br>Introduction ................................................................................................................................................................. 4|
|2.<br>Analytical framework and research design ......................................................................................................... 7|
|How many developing countries have launched RIA reforms? ............................................................................. 7|
|What are the main design features of RIA reforms in developing countries? ................................................... 8|
|Were the RIA reforms successful or not? Identifying operational RIA systems .................................................. 8|
|Can particular design features be associated with successful and unsuccessful RIA reforms? ....................... 9|
|Can we identify key factors causing certain RIA reforms to be unsuccessful? ................................................ 10|
|3.<br>Findings ..................................................................................................................................................................... 13|
|RIA Reforms in Developing Countries ...................................................................................................................... 13|
|Case studies ................................................................................................................................................................. 18|
|Uganda (2004-2007) ........................................................................................................................................... 18|
|Kenya (2009-2013) .............................................................................................................................................. 20|
|Cambodia (2008 - ongoing)................................................................................................................................ 21|
|Botswana (2013-2014) ........................................................................................................................................ 23|
|Cross-cutting observations .................................................................................................................................... 26|
|4.<br>Conclusions and policy implications .................................................................................................................... 30|
|5.<br>Bibliography ............................................................................................................................................................ 33|
|Annex 1: Data points for mapping of RIA reforms and methodology for scoring ............................................. 36|
|Annex 2: Semi-structured survey questionnaire ......................................................................................................... 38|

2
Page 45

# **_Table IV.5 Comparison of Key Specifications for GDCR and Modified GCDR Specification for More Affordable Housing_**

|**BYLAWS**||**DESIGN**<br>**AS**<br>**GDCR**|**PER**|**DESIGN WITHOUT GDCR**|
|---|---|---|---|---|
|<br>PARKING||<br>25%||<br>N.P|
|<br>COP||<br>10%||<br>N.P|
|<br>DISTANCE<br>BETWEEN<br>BUILDINGS|TWO|<br>MIN.15'0"||<br>MIN. 7'6"|
|<br>STAIRCASE WIDTH||<br>MIN.4'0"||<br>2'9"|
|<br>CORRIDOR WIDTH||<br>5'0"||<br>N/A|
|<br>GROUND COVERAGE||<br>45%||<br>55%|
|<br>FRONT MARGIN||<br>15 FT.||<br>15 FT.|
|<br>SIDE MARGIN||<br>MIN. 10'0"||<br>MIN. 10'0"|
|<br>FSI||<br>1.2||<br>2.36|
|<br>ROAD WIDTH||<br>MIN. 25'0"||<br>12'0"|
|<br>HEIGHT OF BUILDING||<br>(G+2) 30'0"||<br>(G + 4) 45'0"|
|<br>LENGTH OF BUILDING||<br>MAX 450 FT.||<br>MAX 450 FT.|

**Source: Environmental Policy Collaborative and SEWA Mahila Housing Trust**

Note that these specifications respect all perimeters with respect to neighboring plots, such as side and front margins.
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|---|---|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|SN-Elec. Serv. for Rural Areas(FY05)|Awa Seck|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Disaster VulnerabilityReduction Project - APL2|Eric Dickson|
|Support to the AccountancyProfession|Robert Wallace DeGraft-Hanson|
|Support to the AccountancyProfession|Robert Wallace DeGraft-Hanson|
|Support to the AccountancyProfession|Robert Wallace DeGraft-Hanson|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
|Tanzania Agricultural Sector Development Project|Sarah Anne Simons|
O.A.M.D.G.

# **C) Bi-variate Probit**

An econometric analysis of household portfolios has to take into account that many households do not hold certain asset categories at all. Further, the decision process to hold assets may be different from the choice of shares.<sup>36</sup> Given the context we are studying, it is possible that households focus on whether to hold assets rather than choosing their respective portfolio shares. This is especially true for the poorest fringe of the population. From the bi-variate analyses we have seen that only about 10% of people do not hold both assets, with 3% holding none. - A bi-variate probit in this context picks up the responsiveness of the poorest to violent threats. Note, however, that this empirical set-up only measures participation without valuing the assets, and therefore misses a lot of the investment dynamic. Nonetheless, it may offer some hints as to whether the poorest react differently.

The estimated model reads: _P_ ( _Fin_ > 0) = Φ( _X in_ β+ _Cn_ γ + _Vn_ δ + _vn_ + _uin_ ) _P_ ( _M in_ > 0 = Φ( _X in_ φ + _Cn_ φ+ _Vn_ ι+ ω _n_ + ε _in_ ) where F and M are =1 if the household participates in fixed and mobile assets, respectively and 0 otherwise, and the other controls as before. _v_ respectively<sup>ω</sup> is a random village level factor; and _u_ and<sup>ε</sup> are at the individual level correlated error terms.

> 36 Also see Bertaut and Starr-McCluer 2002

37
exchange rate. Finally, given total interest payment on domestic debt and the average interest rate ic, it is straightforward<sup>to</sup> calculate outstanding government debt Bb using the formula<sup>ic-Bb</sup> = interest payments. The part of public debt held<sup>by the banking</sup> system (Bbd) can readily be identified from the banking<sup>system</sup> data, hence, Bbp, the part held by the private sector is<sup>obtained</sup> as the residual. In order to close the fiscal account,<sup>transfers</sup> to the private sector (Tbp) are left as the residual.

The capital account of the budget can be written as: (Al.ll) Sb + dCRb + dBbd + dBbp + E*dFGb = = Ib + E*dFXbc + KTbo + KTbp + KTbd Investment data are taken from fiscal accounts and are consistent with the national accounts data. All data on capital transfers<sup>and</sup> savings are also obtained from fiscal data. The Central Bank accounts determine dFXbc and dCRb, while balance-of-payment<sup>data</sup> determine E*dFb and the domestic banking system accounts dBbd. Consequently, to close the accounts, borrowing from the private sector (dBbp) is left as the residual.

25
**–** **<u>Table 7 Countervailing Duty Initiations and Measures 1995 June 2004</u>**

||1995|1996|1997|1998|1999|2000|2001|2002|2003|Jan.–<br>June<br>2004|Totals<br>1995–<br>J2004|Per<br>Year<br>1995–<br>J2004|Per<br>Year<br>1996–<br>J2004|Measures<br>in place as<br>of June 30,<br>2004|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Initiations_|||||||||||||||
|Argentina|1|1|1|0|0|0|0|0|1|0|**4**|0.4|0.4|n.a.|
|Brazil|0|0|0|0|0|0|1|0|1|0|**2**|0.2|0.2|n.a.|
|Chile|0|0|0|0|4|0|0|0|0|0|**4**|0.4|0.5|n.a.|
|Costa Rica|0|0|0|0|0|0|0|0|1|0|**1**|0.1||n.a.|
|Mexico|0|0|1|0|0|0|0|0|1|0|**2**|0.2|0.2|n.a.|
|Peru|0|0|1|0|0|1|0|1|0|0|**3**|0.3|0.4|n.a.|
|_Measures_|||||||||||||||
|Argentina|0|2|0|2|0|0|0|0|0|0|**4**|0.4|0.5|3|
|Brazil|5|0|0|0|0|0|0|0|0|0|**5**|0.5|0.0|0|
|Chile|0|0|0|0|0|2|0|0|0|0|**2**|0.2|0.2|0|
|Costa Rica|0|0|0|0|0|0|0|0|0|1|**1**|0.1|0.1|0|
|Mexico|7|0|0|0|0|0|0|0|0|0|**7**|0.7|0.0|1|
|Peru|1|0|0|0|0|0|1|0|1|0|**3**|0.3|0.2|1|

50
2

# **Agricultural Distortions in Sub-Saharan Africa: Trade and Welfare Indicators, 1961 to 2004**

In the 1960s and 1970s, governments of many Sub-Saharan African countries adopted macroeconomic, sector, trade and exchange rate policies that directly or indirectly taxed farm households seeking to export their way out of poverty. This antiagricultural, anti-trade, welfare-reducing policy stance, which was also prevalent in numerous other developing country regions up to the early 1980s (Krueger, Schiff and Valdes 1988), has since begun to be reformed. How far has that reform effort gone in altering the trade- and welfare-reducing characteristics of farm and food policies in Sub-Saharan Africa? This matters greatly for economic development and poverty alleviation, because 60 percent of Sub-Saharan Africa’s workforce is still employed in agriculture, nearly 40 percent of the population is earning less than $1/day, and more than 80 percent of the region’s poorest households depend directly or indirectly on farming for their livelihoods (World Bank 2007, Chen and Ravallion 2008).

There are important questions to be addressed about future agricultural policy reform in Africa because African agriculture is currently the subject of several new agricultural development assistance programs, as well as being important in ongoing multilateral and preferential trade negotiations. A first step in considering possible future policies is to examine the impacts of past policy choices, and in particular to ask by how much are the policies still reducing national economic welfare and trade?

Economy-wide models are able to address that question, but such models are not available for many of Africa’s poorer countries. Even where they are, typically they depend on myriad assumptions about parameter (for lack of econometric estimates) and they apply to just one particular previous year and so are unable to provide trends in effects over time.

This paper provides a partial-equilibrium alternative to economy-wide modeling, by drawing on a modification of so-called trade restrictiveness indexes to provide theoretically precise indicators of the trade and welfare effects of agricultural