rafmacalaba/datause-encoder-data huggingface dataset:
While the data cover the years 2000 to 2015, we focus on the most recent 10‐year period, between 2006 and 2015. Years prior to 2006 show several missing values for two of our variables of interest. Approximately 25% and 30% of city GDP per capita growth rate and city employment growth rate values, respectively, are missing for the year 2001. These percentages decrease for the subsequent years with only two missing observations for each variable for the year 2006. The period we chose has then the double advantage of being the most recent and complete.

Using the information in the data set, and following the classification provided in World Bank Report (2016), cities can be classified in three broad categories depending on their mix of economic sectors: Market towns, Production Centers, and Creative and Financial Services. Market towns are cities with a GDP per capita below $2,500 and where consumer services account for a large proportion of the economy; Production Centers include cities with a GDP per capita between $2,500 and $20,000 with their economy mostly focused on manufacturing, construction, and mining industries; and Creative and Financial Services refer to cities with a GDP per capita above $20,000 where high‐end services account for an increasing share of their economies.

This distinction of cities by major economic activity can help us understand how different the challenges could be for different types of cities. Figure 1 shows the distribution of these different types of cities by region. Blue represents Market Town cities, Orange corresponds to Production Centers, and Creative and Financial Services are shown in green. It is clear from this figure that the distribution of city types varies widely across regions. On one extreme, in South Asia and Sub‐Saharan Africa, cities are either Market Towns (blue) or Production Centers (orange). These cities are often starting their industrialization process, attracting population, and growing fast. They often struggle with the natural consequences of fast growth, such as how to accommodate a growing population or how to manage increasing pollution levels. In these regions we can expect to find cities that are facing high pollution levels coming from early industrialization and poor living conditions. On the other extreme, we find that most cities in OECD countries fall in the Creative and Financial Services category (green), and there are no Market Town cities. Creative and Financial Services cities face different challenges, such as increasing housing prices in downtown or improving public transportation systems. We expect for these cities to show lower levels of pollution which is usually the case for more developed countries that have succeeded at managing pollution levels without affecting growth. In the rest of the regions, the majority of the cities are Production Centers (orange) with likely a variety of pollution levels, mostly associated with different types of industrial activity and the city level of development.

5
INTRODUCl ION XXXiX made, which were specified above. The assumed trends in vital rates partly reflect the efforts that have been made throughout the developing world to restrain fertility, as well as to reduce mortality, in effect actually taking into account some exceptional past experiences at reducing growth. Altering these trends and producing more rapid deceleration of population growth is possible, but would probably require at least as much, and probably much more, demographic intervention as in the recent past.

# REFERENCES

- Arnold, Fred. 1989. Revised estimates and projecticns of international migration. Policy, Planning, and Research Working Papers, No. 275. Washington, D.C: World Bank.

- Bos, Eduard, and Rodolfo A. Bulatao. 1989. Projecting fertility for all countries. Policy, Planning, and Research Working Papers. Washington, D.C.: World Bank. Forthcoming.

- Blum, Alain, and Roland Pressat. 1987. Une nouvelle table de mortalitd pour l'URSS (19841985). _Population_ 42(6):843-862.

- Bulatao, Rodolfo A., and Eduard Bos. 1989. Projecting mortality for all countries. Policy, Planning, and Research Working Papers. Washington, D.C.: World Bank. Forthcoming.

- Bulatao, Rodolfo A., and Ann Elwan. 1985. Fertility and mortality transition: Patterns, projections, and interdependence. World Bank Staff Working Paper No. 681. Washington, D.C

- Coale, Ansley J., and Paul Demeny (with Barbara Vaughn). 1983. _Regional Model Life Tables and Stable Populations._ 2nd ed. New York: Academic Press.

- Eurostat. 1987. _Demographic Stati&tics._ Luxembourg: Statistical Office of the European Communities.

Hill, Kenneth. n.d. ProjPC-II: User's manual. Washington, D.C: World Bank.

- Jamison, Ellen, Peter D. Johnson, and Richard A. Engels. 1987. World Population Profile: 1987. Washington, D.C: Bureau of the Census, U.S. Department of Commerce.

Monnier, Alain. 1988. "La conjoncture ddmographique: L'Europe et les pays ddveloppds d'outremer,a _Population_ 43(4-5):893-904.

- United Nations. Department of International Economic and Social Affairs. 1984. _Population and Vital Statistics Report. 1984 Special Supplement._ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. 1989. _World Population Prospects 1988._ New York. United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. _Demographic Yearbook._ New York: United Nations.

United Nations. Department of International Economic and Social Affairs. Various years. _Population and Vital Statistics Report._ New York: United Nations.

- U.S. Bureau of the Census. 1983. _World Population 1983--Recent Demographic Estimates for the Countries and Regions of the World._ Washington, D.C.

- U.S. Bureau of the Census. 1985. _World Population: Recent Demographic Estimates for the Countries and Regions of the World._ Washington, D.C.

- World Bank. 1989. _World Development Report 1989._ New York: Oxford University Press. Zachariah, K C., and My T. Vu. 1988. _World Population Projections: 1987-88 Edition._ Baltimore and London: The Johns Hopkins University Press.
**Appendix Figure D. WTO Membership Use of China-Specific Transitional Safeguards (CSG), 2002-1Q 2009**

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4 CSG Initiations<br>3<br>2<br>1<br>0<br>2002 2003 2004 2005 2006 2007 2008 1Q-2009<br><!-- End of picture text --> Sources: data taken from WTO member reports to the Committee on Safeguards and comprehensively covers the full WTO membership to the extent that members report all initiated investigations. Also supplemented with some information from national government reports.

28
# **Table A16: Self-reported fertilizer price change compared to last agricultural season.**

||BFA|ETH|TZA|UGA|
|---|---|---|---|---|
|Much higher|22.1|35.4|10.7|2.5|
||(3.14)|(3.09)|(1.84)|(1.27)|
|Higher|41.4|45.1|31.7|29.1|
||(3.84)|(3.25)|(3.04)|(6.43)|
|About the same|26.3|14.2|38.8|45.8|
||(3.43)|(2.24)|(3.07)|(6.53)|
|Lower|8.4|5.2|17.0|22.7|
||(2.06)|(1.39)|(2.35)|(5.62)|
|Much lower|1.7|0.0|1.8|0.0|
||(0.71)|(0.01)|(0.96)||
|_N_|382|572|546|118|

Note: Reported change in fertilizer price compared to last agricultural season. Standard error in parentheses. Survey timing: BFA (01/24), ETH (02/24), TZA (11/23), UGA (10/23).

37
**Table 8. Average Pre- and Post-Uruguay Round Protection Levels, by Importing Region**

|_Importing Region_|_Pre-R_<br>_Tari_|_ound'_<br>_ff_**_(%)_**|_Post-R_<br>**_Tari_**|_Qund?_<br>**_ff (%)_**|_Average_<br>**_Pce_**_Cha_|_Import'_<br>_nges_**_(%)_**|
|---|---|---|---|---|---|---|
||_Food_|_Mnfcs_|_Food_|_Mnfcs_|_Food_|_Mnfcs_|
|US & Canada (USC)|11.7|4.3|11.0|2.8|-0.6|-1.4|
|European Union (EU)|26.5|6.5|26.0|3.9|-0.3|-2.4|
|Japan (JPN)|87.8|4.9|56.1|2.1|-8.1|-2.7|
|Korea (KOR)|99.5|16.1|41.1|8.2|-17.9|-6.8|
|Taiwan (TWN)|0.0c|O.c|.00c|<br>0.Oc|0.0c|0.0c|
|Hong Kong(HKG)|0.0c|0.0c|0.0c|0.fc|0.0c|0.0c|
|China (CHI)|0.0c|0.Oc|0.0c|0.0c|0.0°|0.0c|
|Indonesia (IND)|21.9|14.2|15.5|13.5|-4.2|-0.6|
|Malaysia (MYS)|87.9|11.0|34.3|7.7|-14.9|-2.9|
|Philippines (PHL)|86.9|23.9|33.4|21.5|-15.3|-1.8|
|Thailand (THA)|59.8|36.2|34.5|27.6|-10.8|-5.9|
|Latin America (LTN)|2.3|17.1|1.5|14.9|_-0.5_|-1.6|
|Sub-Saharan Africa (SSA)|15.6|9.5|12.4|9.4|-1.7|-0.1|
|South Asia (SAS)|-3.5|51.9|-4.3|37.1|-0.7|-9.4|
|Rest of World (ROW)|15.7|10.6|14.1|9.1|-1.2|-1.3|

'Source: Integrated Data Base, GATT h Change in tariff rate divided by the power of the initial tariff rate. This is the average of the disaggregate price cuts, and therefore differs from the price cut computed from the average tariffs.

'Data for Taiwan, Hong Kong, and China were not available from the Integrated Data Base.

27
Appendix Table 1. Weighted Percentages of the Top Five Occupations for Employed Women by Less/More Forested Regions and Year, Nepal DHS, 1996-2006

|_1996_||**Less Forested Reg**<br>_2001_|**ions**|_2006_||
|---|---|---|---|---|---|
|_Occupation_|_Weighted %_|_Occupation_<br>|_Weighted %_|_Occupation_|_Weighted %_|
|agriculture self-employed|77.7|agriculture self-employed|90.1|agriculture self-employed|75.6|
|agriculture employee|11.1|sales|4.4|agriculture employee|9.1|
|sales|4.1|skilled manual|2.4|sales|5.6|
|skilled manual|3.4|prof., technical, managerial|1.7|skilled manual|4.3|
|unskilled manual|1.7|unskilled manual|0.5|services|2.2|
|||**More Forested Re**|**gions**|||
|_1996_||_2001_<br>||_2006_||
|_Occupation_|_Weighted %_|_Occupation_<br>|_Weighted %_|_Occupation_|_Weighted %_|
|agriculture self-employed|91.8|agriculture self-employed|95.1|agriculture self-employed|87.8|
|agriculture employee|3.8|sales|2.1|agriculture employee|3.5|
|sales|1.5|prof., technical, managerial|1.5|sales|3.3|
|unskilled manual|1.1|skilled manual|0.8|services|1.7|
|skilled manual|0.8|services|0.3|prof., technical, managerial|1.2|

Notes: Weighted to national level with weights provided by the Nepal DHS in each year.

42
# **Accounting for Gender Production from a Growth Accounting Framework in Sub-Saharan Africa**<sup>1</sup>

Hippolyte Fofack The World Bank Group .

Keywords: Gender, growth and growth accounting framework.

JEL Classification: F43, J16, N17, N30, O40

> 1 This research is partly supported by grants from the World Bank GAP Trust Fund. I would like to thank Emmanuel Akyeampong, Nihal Bayraktar, Shanta Devarajan, Raj Nallari, Leonce Ndikumana, Yaw Nyarko, Susan Opper, Pierella Paci and Xiao Ye for their comments and suggestions on an earlier version of this paper.
# Figure 4. Demographics

A. Impact of 1 percentage point higher B. Working-age population working- age population share on per capita GDP growth <!-- Start of picture text --> Percent<br>3<br>2<br>1<br>0<br>Aiyar and Bloom and Bloom and Bloom et Ahmed<br>Mody Williamson Canning al. (2000) and Cruz<br>(2011) (1998) (2004) (2016)<br>C. Working-age population<br>Percent of total population<br>75 EAP ECA LAC<br>MNA SAR SSA<br>70<br>65<br>60<br>55<br>50<br>1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029<br><!-- End of picture text --> <!-- Start of picture text --> Percent of total population<br>70 AEs World EMDEs<br>65<br>60<br>55<br>D. Potential growth<br>Percent Percent<br>Baseline Demographic trends Other factors<br>2.8 5.6<br>2.6 5.2<br>2.4 4.8<br>2.2 4.4<br>2.0 4.0<br>1.8 3.6<br>World EMDEs (RHS)<br>1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029<br>2011-21 Factor 2022-30 2011-21 Factor 2022-30<br><!-- End of picture text --> Sources: United Nations World Population Prospects: The 2022 Revision. World Bank.

Note: AEs = advanced economies; EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A. The sample of each study differs. Aiyar and Mody (2011): Indian states,1961-2001; Bloom and Williamson (1998): 78 countries, 1965-90; Bloom and Canning (2004): over 70 countries, 1965-95; Bloom et al. (2000): 70 countries, 1965-90; Amer and Cruz (2016): 160 countries, 1960-2010.

B.C. Population weighted averages. The working-age population is defined as people aged 15-64 years. D. GDP-weighted arithmetic averages. Derived using production function-based potential growth. “Other factors” reflects declining population growth, convergence-related productivity growth, policy changes, cohort effects, and a slowdown in investment growth relative to output growth. “Factor” reflects the percentage-point changes between the averages of 2011-21 and 2022-30.
# **Table S1.4: Robustness: different enumerator sample**

||(1)<br>DP|(2)<br>FDC|(3)<br>NRM|(4)<br>UPC|
|---|---|---|---|---|
|Enumerator gender (male)|-0.522***|-0.306**|-0.086***|-0.451***|
||(0.120)|(0.136)|(0.027)|(0.099)|
|Enumerator education|0.150*|0.186*|-0.119***|0.146|
||(0.089)|(0.101)|(0.040)|(0.089)|
|Enumerator age|-0.031*|0.003|0.002|-0.039***|
||(0.016)|(0.019)|(0.003)|(0.012)|
|Enumerator from urban locality|0.444***|0.577***|-0.030|0.443***|
||(0.157)|(0.177)|(0.036)|(0.129)|
|Observations|6,761|6,868|7,175|6,854|
|R-squared|0.475|0.440|0.227|0.464|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Work experience as enumerator_ is the number of months previously worked as an enumerator. _Want people to know how good can be at work; Strongly motivated by the wage can earn; Enjoy handling new problems; Enjoy trying to solve complex problems; Curiosity is a driving force for your actions_ are categorical variables taking value from 1 to 4 (decreasing). Each regression includes community fixed effects and a constant. For all regression, the sample includes enumerators who have completed at least 24 interviews. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

37
4 income support programs. This long downward trend In lifetime work comes despite longer life expectancies in many developed nations, and without any evidence of system-wide deterioration in health. How retirement systems have contributed to this long-term downward trend In work at older ages Is ex_-nilned in the next Section.

# Trends to Earlier Retirement

Defining what constitutes "retirement" differs not only from nation to nation, but from one person to the next. In many developed nations, a transition to retirement is taken to mean the point where one accepts a public pension; this may or may not co.ncide with labor force withdrawal. In Sweden for example, many retirees are partially employed after acceoting a public pension, while In the Unltea States pension acceptance and work cessation are virtually synonymous (Fields and Mitchell 1984b). In other countries, accepting a privately-provided retirement payment, rather than a public pension, constitutes retirement; in Japan, for instance, many workers retire from their ca er job with a lump sum pension, and often move to some other, usually lower paying employment, for several years (Rebick 1993).

Yet a different view of what constitutes retirement is gamered by surveys of older persons who self-report" their retirement status using any definition they wish; usually such surveys produce higher rates of retirement than objectively defined measures which equates retirement with complete labor force withdrawal. Additionally, retirement is not an absorbing state in many nations, in that many older persons move fluidly between full and part-time wage jobs, self employment, unemployment, leisure 2 activities, and perhaps in some countries work In the "grey or "underground" economy.

These different definitons of retirement are more than semantic, since altemative measures produce very distinct estimates of the size of the country's actual and potentia **l** abor force. In general, labor economists prefer to define retirement using an objective labor force participation measure, in lieu of a self-reported definition or one which relies on pension acceptance. Even here, however, there are cross-country differences in the definition of labor force participants making these
<!-- Start of picture text --> Exports Imports<br>Mp op op | 1o7} i} |<br>’ =! B!<br>eS e: &: o & 7948: 8! Ss} ve<br>uw 49 oS a) 1 ’ on wi ao! a! e<br>= Bz 8 5: / = 2: 8B: x “<br>f 3: @ oi “ 2 2! 6! 3! Y<br>S zs! 8! a “ S 6512! ! o: a<br>o =i a wi a 1 i 1 ’<br>$387 =| wh a ae & r it “<br>QD ' ' 1 o 1 ' \ a“<br>= i 1 ’ = 1 1 yr<br>x : ioe @ 51 ' ' } #<br>= t 1 ly = ' 1 ' ’<br>GA fe S 1 of a<br>} i } —_ 2019 237 1 fet —— 2019<br>H H ------- 2020 yoo: ------- 2020<br>5t111'1<br>9<br>13 5 7 9 11 1 3 5 7 9 11<br>Month Month<br><!-- End of picture text -->
Table A.1: Descriptive relationship between self-employment and migration, baselines and first follow-ups

||**Any**<br>**migration**|**Internal**<br>**migration**|**International**<br>**migration**|**Any**<br>**migration**|**Internal**<br>**migration**|**International**<br>**migration**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Self-employed|-0.023***|-0.021***|-0.002***|-0.024***|-0.021***|-0.002**|
||(0.003)|(0.002)|(0.001)|(0.003)|(0.002)|(0.001)|
|Aged 25 to 34|-0.071***|-0.067***|-0.004***|-0.070***|-0.067***|-0.004***|
||(0.005)|(0.004)|(0.001)|(0.005)|(0.004)|(0.001)|
|Aged 35 to 44|-0.112***|-0.104***|-0.007***|-0.111***|-0.104***|-0.007***|
||(0.004)|(0.004)|(0.001)|(0.004)|(0.004)|(0.001)|
|Aged 45 to 54|-0.120***|-0.112***|-0.008***|-0.120***|-0.112***|-0.008***|
||(0.005)|(0.004)|(0.001)|(0.005)|(0.004)|(0.001)|
|Aged 55 to 65|-0.123***|-0.115***|-0.007***|-0.123***|-0.116***|-0.007***|
||(0.005)|(0.005)|(0.001)|(0.005)|(0.005)|(0.001)|
|Years of education|0.004***|0.003***|0.000*|0.004***|0.003***|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Female|0.004|0.007***|-0.003***|0.004|0.007***|-0.003***|
||(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.001)|
|arcsinh(income)||||-0.001*|-0.000|-0.000**|
|||||(0.000)|(0.000)|(0.000)|
|Wage worker mean|0.101|0.092|0.008|0.101|0.092|0.008|
|Sample size|64,911|64,911|55,671|64,766|64,766|55,526|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the CFPS 2010 and 2016, ELMPS 1998 and 2006, IHDS 2005 and 2011/12, IFLS 2000 and 2007/08, MxFLS 2002 and 2005/06, LSMS-ISA 2010/11 and 2012/13, KHDS 1991/94 and 2004, and PSID 2001 and 2007. We drop respondents who are not selfemployed or paid workers in the baseline wave of each survey. Refer to online appendix section B for more details on variable construction in the different surveys. Each column reports marginal effects from a probit regression of an indicator for migration on an indicator for self-employment at baseline. Each regression controls for country fixed effects. Columns (4)-(6) additionally control for the inverse hyperbolic sine (arcsinh) of the respondent’s income in the baseline wave.

40
_Simulating the Multipillar Reform with a 8 percent Contribution to the Second Pillar_ The reluctance of the Government to raise the contribution rate to 8 percent, allegedly because of the larger transitional deficit, raises the question of what would be the impact of this measure on the balances of the system and on the economy. This analysis is summarized in Figures 13 and 14, where it is assumed that the contribution to the second pillar is raised to 8 percent in 2003. The number of switchers is assumed to be the same as in Figures 11 and 12.

Figure 13 reveals the obvious fact that the increase in the second pillar contribution from 6 to 8 percent would imply an additional loss of revenues to the PAYG and slightly larger PAYG deficits. The difference between the two lines would be 0.3-0.4 percent of GDP in the next decades. However, these larger deficits would be offset by larger private surpluses, leading essentially to the same overall pension balance, as shown in Figure 14 (by comparison with the overall balance in Figure 12). Therefore, the increase in contribution rates to the second pillar would not produce any adverse impact on key macroeconomic variables such as inflation and the current account.

<!-- Start of picture text --> Figure 14: Public, Private and Total Pension Balaces with<br>8% Contribution to 2nd Pillar (in % of GDP)<br>4<br>3<br>2<br>0<br>1997 2003 2009 2015 2021 2027 2033 2039 2045 2051 2057 2063 2069<br>Public B alance (M ultipillar) Net Private B alance<br>- -_- Total Pension System B alance<br><!-- End of picture text --> The difference between the two scenarios depicted in Figures 11-14 is clearly artificial, as it assumes that the number of switchers is the same. If a large number of workers switches back to the PAYG, the scenario with a 6 percent contribution could actually prove worse than the 8 percent contribution scenario at the end of the projection period, as it would imply a smaller reduction of accrued rights, a larger implicit pension debt, and possibly larger PAYG deficits in the long-run.

The failure to restore the original contribution rates could also imply some efficiency losses. A smaller second pillar implies a weaker link between contributions and benefits with possible adverse 19
# **Table 1: Comparison of Evidence Gap maps and other Scoping and Mapping methodologies**

|**Methodology**|**Broad**<br>**thematic/**<br>|**In**<br>|**cluded evidenc**<br>|**e**|**Systematic Search**|**Critical**<br>|**Analysis**|**Visualization using**<br>**intervention-**<br>|**Access to**<br>**user-friendly**|
|---|---|---|---|---|---|---|---|---|---|
||**sector**<br>**focus**|**Systematic**<br>**Reviews**|**Impact**<br>**Evaluations**|**Other**||**appraisal**||**outcome**<br>**framework**|<br>**summaries**|
|**3ie gapmap**<sup>**5**</sup>|Yes|Yes|Can do|No|Yes*|Yes|No|Yes|Yes|
|**Evidence map (GEM, Headspace)**<sup>**6**</sup>|Yes|Yes|Yes|Yes|Yes|No|Yes (a)|No|Yes|
|**Scoping study**<sup>**7**</sup>|Yes|Can do|Can do|Can do|May be limited|No|Yes (a,b)|No|~~No~~|
|**Systematic map**<sup>**8**</sup>|Yes|Can do|Can do|Can do|Yes|Limited|Yes (a)|No|No|
|**Rapid evidence assessment**<sup>**9**</sup>|No|Can do|Yes|Can do|Yes (but may be<br>~~limited)~~|Limited|Yes (a,c)|No|No|
|**Systematic reviews**<sup>**10**</sup>|No|No|Yes|Can do|Yes|Yes|Yes (a,d)|No|Can do|
|**Overviews of systematic reviews**<sup>**11**</sup>|No|Yes|No|No|Yes|Yes|Yes (c)|No|No|
|**Evidence-Based policing matrix**<sup>**12**</sup>|Yes|No|Yes|No|Yes|Yes|No|No<sup>13</sup>|Yes|

* Systematic search and critical appraisal of SRs. The search for IEs may be more restricted. The IEs may not be critically appraised.

(a) summary of characteristics of the identified studies (reporting on categories such as study design, population, context, outcomes reported etc.) (b) descriptive summary of findings (c) analysis of results (d) synthesis of findings 5 Gaarder (2010) 6 Parkhill et al. (2011); Bragge et al. (2011); http://www.evidencemap.org 7 Arksey & O'Malley (2005); Anderson et al. (2008);Davis et al. (2009); Grant et al. (2009); Levac et al. (2010) 8 Clapton and Coren (2007); Oakley et al., ( 2005); Bates, Peersman (1996) 9 Ganann et al. (2009); Khangura et al. (2012), Government Social Research REA Toolkit (2008) 10 Cooper et al (2009);Higgins and Greene (2011); Lavis (2009); Waddington et al (2012).

11 Becker et al.(2011)

## 12 Lum et al. (2011)

13 The evidence-based policing matrix includes a visualisation of the existing evidence in a matrix based on three key dimensions of crime prevention interventions. The number of studies, as well as direction of effects is also displayed in the matrix.

6