rafmacalaba/datause-encoder-data huggingface dataset:
the income/consumption poverty literature, we often have clear nutritional-based “cost of basic needs” approaches to estimating poverty lines. When dealing with time poverty, the correct level for the time poverty line is less clear, at least if one wants to consider an allocation of time for leisure on top of what is strictly needed for rest from a health point of view. In practice, depending on the social context of the country for which the analysis is conducted, we may want to use relative as opposed to absolute time poverty lines together with some tests for the robustness of comparisons of time poverty obtained over time or across households groups to the choice of the time poverty line.

When combining time and consumption poverty, four groups of individuals can be defined. A first group, which is the focus of this paper, consists of the time and consumption poor—those who spend a lot of time on the fulfillment of basic needs and in other work activities but are unable to rise above the consumption poverty line. At the opposite end, a second group— not very interesting for policy purposes—is represented by the non time poor and non consumption poor. A third group consists of those who are overworked (time poor), but are not consumption poor. A question could be raised about whether individuals who work long hours in a well-paid job are actually ‘free to choose’, i.e. free to decrease the number of hours they work in a linear way. Maybe this is not possible and they are ‘forced to choose’ a combination of long hours and high income. We still believe that this group is less of a concern for the policy maker than the time and consumption poor. The fourth group consists of those individuals who are below the time poverty line but are consumption poor—the ‘underemployed’. In many SubSaharan African countries and in many other developing areas, lack of job opportunities and sluggish growth on the demand side and inadequacy of education on the supply side explain (among other reasons) why the consumption poor may not be able to increase their hours of work and remain underemployed.

# **_3.3 Data requirements_**

Time-use surveys have been implemented for many years in several developed countries, but in developing countries, their use had been more limited so far, with much of the evidence coming from small-scale village-level instruments or otherwise small samples. Recently, thanks to efforts by the United Nations’ statistics division, nationally representative time use surveys have been carried in India and Nepal in 1999, Benin in 1998, Nigeria in 1999, South Africa in 2000, 12
# **Organizing for prosperity:  Collective action, political parties and the political economy of development**

## Philip Keefer*

Prepared for the _Oxford Handbook of the Politics of Development_ , Carol Lancaster and Nicholas van de Walle, editors.

*Development Research Group, The World Bank

**Acknowledgements** :  I am particularly grateful to the comments of Cesi Cruz, and those of the editors.

**JEL code** :    D7

**Key words** :  collective action; political parties; clientelism; state-building; democratization.

**Disclaimer** : The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

**Sector** :  Public Sector Governance

**Contact information** :  Development Research Group The World Bank 1818 H St. NW Washington,  DC 20433 pkeefer@worldbank.org
40 availability to the family of a buffer mechanism for mitigating the effects of changes in the family's economic environment on investment in child health .

Unlike other variables used in the analyses family expenditure and savings raise methodological problems. They vary over time and may be affected by the outcomes of child health processes. And their measurement may be subject to systematic errors. Although we have no indication of how these drawbacks may affect the empirical results, it is important to keep them in mind when drawing inferences based on the data.

# Changes in Child Health: 1970-86

Infant mortality in Cote d' Ivoire started at relatively high levels in the 1970's.2 But in the early 1980s a declining trend that started around 1978 was interrupted (figure 5). Neonatal mortality fluctuated between 50 and 55 per 1,000 during the period without any discernable trends (figure 6). Postneonatal mortality, however, increased by 10 points between 1977-81 and 1982-86 --- from 41 per 1,000 to 51 per 1,000.

The data suggest that infant mortality increased between the expansionary period of the late 1970s and the economic crisis-adjustment period of the early 1980s. But these changes were apparently limited to the postneonatal ages. Among families above the median income (HIGH in figures 5 to 7), neonatal mortality remained fairly steady, while postneonatal mortality increased from 38 per 1,000 in 1977-81 to 68 per 1,000 in 1982-86. Among families below the 2The low levels in the early 1970s are probably due to distortions in the data. The levels of the middle and late 1970s, however, are consistent with IFS data mentioned earlier.
65 Table 13. African Transport Costs for Exports to the United States Compared wlth Competing Nations.

|||Average<br>|Average No<br>Rate<br>|minal Freight<br> (%)<br>|
|---|---|---|---|---|
|African Group and Major Supplier(s)'|Value of Exports<br>($ millions)|Tariff (%)|African<br>Group|Other<br>Countries|
|ALL SUB-SAHARAN AFRICA|1,737.6|_0.5_|8.7|7.2|
|Low Income East and Southem Africa|778.9|1.1|6.8|5.3|
|Kenya|92.3|0.6|8.8|6.5|
|Malawi|84.2|11.1|10.0|5.4|
|Zaire|240.7|_0.5_|5.4|4.9|
|Zimbabwe|142.3|3.2|9.2|6.0|
|Low Income West Africa|418.7|0.3|11.4|10.0|
|Benin|15.7|0.7|7.5|7.2|
|Ghana|208.5|0.0|4.7|4.4|
|Guinea|113.0|0.0|25.2|22.0|
|Middle Income East and Southem Africa|253.1<br>|12.8<br>|6.7<br>|5.6<br>|
|Mauritius|196.4|14.4|7.1|5.8|
|Middle Income West Africa|287.0|0.6|11.3|9.9|
|Cameroon|101.2|0.8|9.7|8.5|
|Senegal|7.5|2.6|12.1|7.4|
|Oil Exporting Countries|8,833.1|0.7|6.4|7.6|
|Nigeria|5,309.5|0.7|5.8|7.6|
|Republic of South Africa|1,851.0|1.6|4.5|4.3|

'The country group definitions are as follows: (i). The all sub-saharan group includes all countries listed below except the oil exporting countries (Angola, Congo, Gabon and Nigeria) and the Republic of South Africa.

(ii). Low income East and Southern Africa includes: Burundi, Comoros, Eritrea, Ethiopia, Kenya, Lesotbo, Madagascar, Malawi, Mozambique, Rwanda, Somalia, Sudan, Tanzania, Uganda, Zaire, Zambia, Zimbabwe.

(iii). Low income West Africa includes: Benin, Burkina Faso, Central African Republic, Chad, Equatorial Guinea, The Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Sao Tome and Principe, Sierra Leone and Togo.

(iv). Middle income East and South Africa includes: Djibouti, Namibia, Swaziland, Botswana, Mauritius, Mayote, Reunion, and the Seychelles.

(v). Middle income West Africa includes: Cameroon, Cape Verde, Cote d'lvoire and Senegal.

(vi). The oil exporting countries include: Angola, Congo, Gabon and Nigeria.

Source: Nominal transport costs computed from United States Department of the Census trade tapes. Pre-Uruguay Round tariff averages are from Erzan and Svedberg (1989).
**22**

TABLE 2

# Nominal and Real Interest **Rates**

|'ear|Average nominal Interest<br>Real<br>on Deposits|'nterest<br>Rate<sup>2</sup>|
|---|---|---|
|1980|4,54|**-4.6**|
|**1981**|4.58|0|
|**1982**|4.61|-2.3|
|'?83|4.72|-2.6|
|198"|5.04|3.3|
|1.985|4.92|-2.1|
|.386|5.06|-0.2|
|.987|6.30|-2.3|
|**?88**|!0.20|5|

-Source: eorLd Bank Staff estlmates.

**2Deflated bv** th.e C?1.
|A/L|0.15**|(0.06)|0.23***|(0.02)|0.24***|(0.06)|
|---|---|---|---|---|---|---|
|University degree|0.26***|(0.07)|0.35***|(0.02)|0.43***|(0.12)|
|Dependency ratio|‐0.10***|(0.03)|‐0.12***|(0.01)|‐0.22***|(0.05)|
|_HH assets (yes=1)_|||||||
|Computer|0.14***|(0.02)|0.11***|(0.01)|0.09*|(0.06)|
|Cooker|0.15***|(0.02)|0.19***|(0.01)|0.18***|(0.03)|
|Electric fan|0.06**|(0.02)|0.13***|(0.01)|‐0.06*|(0.03)|
|Refrigerator|0.09***|(0.02)|0.11***|(0.01)|0.07*|(0.04)|
|Washing machine|0.15***|(0.02)|0.11***|(0.01)|0.19**|(0.08)|
|Land phone|0.10***|(0.02)|0.03***|(0.01)|0.06**|(0.03)|
|Water pump|0.12|(0.18)|0.14***|(0.02)|0.44***|(0.11)|
|Motorcycle|0.01|(0.02)|0.13***|(0.01)|0.10*|(0.05)|
|Car/van|0.40***|(0.03)|0.39***|(0.01)|0.37***|(0.13)|
|Three‐wheeler|0.15***|(0.02)|0.14***|(0.01)|0.14***|(0.04)|
|Four‐wheel tractor|0.07|(0.19)|0.20***|(0.03)|0.00|(.)|
|_Housing quality and facilities_|||||||
|Bedrooms per person (DCS def’n)|0.23***|(0.03)|0.15***|(0.01)|0.17***|(0.06)|
|Have floor tiles/terasso|0.16***|(0.02)|0.12***|(0.01)|0.22**|(0.10)|
|Drinking water source: inside unit|0.13***|(0.02)|0.01|(0.01)|‐0.04|(0.05)|
|Electricity for lighting|0.04|(0.10)|0.08***|(0.02)|0.16***|(0.06)|
|Have wall of brick/kabok/cement|0.04|(0.04)|0.01|(0.01)|0.01|(0.04)|
|Have toilet within unit|0.10**|(0.05)|0.05**|(0.02)|0.29***|(0.06)|
|Constant|8.48***|(0.14)|8.32***|(0.05)|8.15***|(0.18)|
|Observations|3,42|9|17,39|4|933||
|R‐squared|0.56|5|0.51|4|0.40|3|

# Table A7. Targeting efficiency with separate rural/urban/estate PMT models

|Cutoff score|Under‐coverage|Leakage|Eligible share|
|---|---|---|---|
|Score: 856 (10th percentile)|89.5|12.2|3.0|
|Score: 868 (15th percentile)|77.4|17.4|6.8|
|Score: 878 (20th percentile)|63.5|24.1|12.0|
|Score: 887 (25th percentile)|50.1|29.8|17.8|
|Score: 895 (30th percentile)|37.9|35.4|24.0|
|Score: 902 (35th percentile)|27.8|40.6|30.4|
|Score: 909(40thpercentile)|20.9|45.8|36.5|

Note: Model 1 specification was used to generate this table. Target population assumed to be bottom 25% of consumption distribution.

30
Looking beyond India, the results could also hold considerable relevance for other parts of the developing world. In particular, in neighboring Pakistan as well as many countries of the Middle East and North Africa, substantial growth, fertility decline, and an expansion of female education also coexist with very low female labor force participation rates (e.g., World Bank 2003; Gaddis and Klasen 2014). The insights from this paper might also inform research and policy-makers in those settings.

39
IABLE 6.a : BALANCE OF PATNEMIS **(oit** USS) Ease Case Scenario

|||1988|<br>1989|**1990**|<br>1991|1992|1993|1994|1995|
|---|---|---|---|---|---|---|---|---|---|
||Stocks of Debt:|||||||||
|**Oft**|Total get<br>|39592.2<br>|40524.6<br>|40926.2<br>|40793.5<br>|40553.3<br>|40369.8<br>|40896.4<br>|42122.7<br>|
|O**Fb**<br>|**Budget**|19222.2<br>|19788.9<br>|20296.6<br>|20813.2<br>|21338.8<br>|21813.3<br>|22416.6<br>|22968.7<br>|
|**OfGb**|Eaternal Debt|20318.7|20885.4|21393.1<br>|21909.7<br>|22435.3<br>|22969.8<br>|23513.1<br>|24065.2|
|**D**fxbc|**FX**Deposits at Co|**1096.S**|1096.5|<br>1096.5|1096.5|1096.5|1096.5|1096.5|1096.5|
|**O Fo**|SEEs|4114.3|4459.6|4897.8|5362.3|5853.6|6313.9|6924.4|7506.5|
|**O FGo**<br>|External Debt<br>|4332.7|4678.1|5116.3|5580.5|6072.1|6592.4|7142.8|7125.0|
|0 fXoc<br>|**FX**Deposits<br>at**CB**<br> <br>|218.5<br>|218.5<br>|218.5<br>|218.5<br>|218.S<br>|<br>218.5<br>|218.5<br>|218.5<br>|
|**_D fp_**<br>|Pr**_ivate_**<br>Sector|-2277.3|-2179.8|-2268.8|-2187.4|-2106.2|-2022.7|-1932.1|-l834.0|
|**_D_**fGp|External<br>Debt|733.7|831.2|742.2|823.6|904.8|988.3|1078.9|1177.0|
|**D**fXpd|**FX**Deposits at OS|3011.0<br>|3011.0<br>|3011.0<br>|3011.0<br>|3011.0<br>|3011.0<br>|3011.0<br>|3011.0<br>|
|0**fc**<br>|Central**lank**<br> <br>|11981.4|11561.6|1038.3|9385.8|8533.8|7809.5|7193.9|6670.6|
|**_D_**fGc<br>|External<br>Debt<br> <br>|8276.0|7862.2|6682.8|5680.4|4828.3|4104.1|3488.5|2965.2|
|**OFXbc**|**FX**Deposits<br>of Budget<br>|1096.5|1096.5|1096.5|1096.5|1096.5|1096.5|1096.5|1096.5|
|**D Fxoc**|**FX**Deposits<br>of SEEs<br> <br>|218.5|218.5|218.5|218.5|218.5|218.5|218.5|218.5|
|**D**FXdc|**FX**Oeposits<br>of**SS**|2390.5|2390.5|2390.5|2390.5|2390.5|2390.5|2390.5|2390.5|
|**D Fd**<br>|Banking System<br> <br>|6551.7<br>|6888.2<br>|7612.3<br>|7419.7<br>|6933.4<br>|6335.7<br>|6293.6|6810.9|
|oFGd<br>|External<br>Debt<br> <br> <br>|5931.2<br>|6267.7<br>|6991.8<br>|6799.2<br>|6312.9<br>|5715.2<br>|5673.1<br>|6190.3|
|O Fxpd|**FX**Deposits<br>of Private<br>Sector|3011.0|3011.0|3011.0|3011.0|3011.0|3011.0|3011.0|3011.0|
|**D 5xdc**|**FX**Deposits<br>at CB|2390.5|2390.5|2390.5|2390.5|2390.5|2390.5|2390.5|2390.5|
|**D**RESc|foreign Reserves ofCB|3671.2|6128.9|6627.9|6674.5|6937.4|7764.9|9861.8|13276.5|
|D FNt<br>||33000.0|31182.6|30763.9|30231.1|29339.3|27900.5|25859.8|23154.0|
|**D**RESd||2921.0|3213.1|3534.4|3887.9|4276.6|4704.3|5174.7|5692.2|
Goetz, M. R., L. Laeven, and R. Levine. 2016. “Does the Geographic Expansion of Banks Reduce Risk?” _Journal of Financial Economics_ 120 (2): 346–62.

Ippolito, F., J.-L. Peydro, A. Polo, and E. Sette. 2016. “Double Bank Runs and Liquidity Risk Management.” _Journal of Financial Economics_ 122 (1): 135–54.

Iyer, R., and J.-L. Peydro. 2011. “Interbank Contagion at Work: Evidence from a Natural Experiment.” _Review of Financial Studies_ 24 (4): 1337–77.

Kashyap, A. K., and J. C. Stein. 2000. “What Do a Million Observations on Banks Say about the Transmission of Monetary Policy?” _American Economic Review_ 90 (3): 407–28.

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22
# **3.10 New Zealand (NZL)**

In New Zealand, antidumping investigations are conducted by the Trade Remedies Group at the Ministry of Economic Development. The Trade Remedies Group publishes initiations and outcomes of antidumping cases in the official New Zealand Gazette as well as on the Ministry of Economic Development’s website. The source of the data was the Ministry’s website which was combined with some information provided directly by the Trade Remedies Group. Information about New Zealand’s antidumping investigative process can be found on the Ministry’s website via the Trade Remedies link.

In addition to the standard data described in section 2, the file for New Zealand contains additional data described in the table below.

**Table 3.10.1 : Additional Data from New Zealand Included in AD-NZL-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-N**|**ZL-Master :**||
|**U.**|**P_AD_DUTY**|The preliminary anti-dumping duty for all non-named foreign exporting<br>firms imposed|
|**V.**|**F_AD_DUTY**|The final anti-dumping duty for all non-named foreign exporting firms or<br>range of duties imposed|
|**W.**|**INIT_GP**|The government publication containing the case initiation|
|**X.**|**P_GP**|The government publication containing the preliminary case outcomes|
|**Y.**|**F_GP**|The government publication containing the final case outcomes|
|**Z.**|**NOTES**|Comments|
|**In AD-NZ**|**L-Foreign-Firms:**||
|**D.**|**F_AD_MARGIN**|The trade-weighted average (or range of) firm-specific dumping<br>margin(s) found|
|**E.**|**F_AD_DUTY**|The ad-valorem equivalent of the firm-specific final antidumping duties<br>imposed|

In terms of missing or incomplete data, the New Zealand workbook does not contain preliminary outcomes because New Zealand’s antidumping procedures differ from those of other countries in that a preliminary outcome is rarely issued unless provisional measures are imposed. Furthermore, the data is 29
# **Tabe 2: Nonfarm Share of** Rural **and** Urban hcolr V: **hdba** 1067/68 to **19B1/2**

||||R U<br>|R A L|||Urban|
|---|---|---|---|---|---|---|---|
|Source of Income|1967/68|1968/69|1969/70|1970/71|1975/76|1981/82|1975/76|
|Own harm<br>**Wage**iaboL|62.8<br>(11.7)|54.9<br>19.9|61.2<br>17.7|60.5<br>17.4|55.8<br>13.7|53.3<br>(16.5-<br>1 1.9)|4.7<br>0.5|
|Totd agri-<br>**culture**<br>|(74.5)|74.8|78.9|77.9|69.5|(69.8-<br>65.2)|(5.2)|
|Nonfarm<br>||||||||
|Selfmploym nt<br> IbP|10.3<br>|8.4<br>|7.6<br>|9.0<br>|8.1<br>||26.4<br>|
|Wageao|(2.0)|3.4|2.1|3.0|8.6|(2.9-<br>75)|10.6|
|Salary<br>|10.5<br> <br>|8.0<br>|5.9<br>|5.8<br>|9.8<br>|.<br>-|49.1<br>|
|Rent&dividends|<br>2.7|5.4|5.5|4.?|4.0|-|5.6|
|Totalnonanrm|(25.5)|25.2|21.1|22.1|30.5|(30.2-<br>34.8)|-|
|TorM**hoome**|100.0|100.0|10|10.0|100.0|100.0|100.0|

a/ Excludes trasfer income from 1967/68 and 1975/76 to make income definition comparable with owt years.

b/ In _some_ yes, published asults fail to disaggregate farm and nonfarm wages. Figures in parenthese partition wages based on farm and nonfarm shares prevailing in other years. For 1967/68, the estimated wages breakdown takes nonfarm share of total wages at 15 percent, the I1 prevailing in 1968/691970/71. Since nonfarm share of wages appear to have risen over time, Xt 1961/82 stimate offers a range. The lower bound takes nonfarm wage share at 15 percent, the upper bound puts it at 38.7 percent, the level prevailing in 1975/6.

Not applicable. Wages disaggregated in those years.

## Breakdown not available

Source. National Council for Applied Economic Research (1972, 1975, 1980, 1986a).
INTRODUCTION **Iv** Table 12 Fenility and Mortality Rates by Geographic Region and by Income Group, 1985-90, 2000-05, and 2020-25

||||1985-90||||2|000-05|||||2020-25|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region<br>or||||||||||||||||
|income<br>group|CBR|<br>**TFR**|<br>**CDR**|e(O)|**INR**|<br>CUR|<br>TFR|<br>**CDR**|e(C)|IMR|CBR|<br>TFR|<br>**CDR**|e(O)|IMR|
|Wortd|27.1|3.43|9.7|64.9|70|22.6|2.91|8.3|68.2|48|18.7|2.48|8.4|70.6|35|
|Less developed (a)|31.0|3.92|9.6|62.1|78|25.0|3.13|7.9|66.2|52|19.9|2.55|7.8|69.3|38|
|More developed(a)|14.7|1.90|9.9|74.1|15|12.9|1.89|10.0|76.0|10|12.5|2.03|11.6|77.0|9|
|Africa|44.4|6.14|14.2|57.4|107|38.1|5.09|10.2|58.8|73|27.8|3.36|7.6|64.0|52|
|East Africa|46.1|6.31|14.4|53.0|110|<br>39.9|5.39|10.4|58.4|78|29.7|3.62|7.6|63.8|54|
|West Africa|47.n|6.52|16.1|50.2|113|<br>40.8|5.52|11.5|56.1|78|29.1|3.51|8.2|62.2|55|
|North Africa|37.1|5.19|10.4|59.4|84|29.6|3.80|7.4|64.6|50|20.7|2.46|6.6|68.5|36|
|America|23.4|2.84|8.1|70.2|44|17.9|2.28|7.1|73.0|27|15.3|2.11|8.2|74.6|19|
|Latin America<br>and||||||||||||||||
|Caribbean|28.6|3.57|7.4|66.6|56|20.6|2.46|6.1|70.5|33|16.6|2.15|7.1|73.0|23|
|Northern America|15.4|1.87|9.0|75.7|10|12.9|1.90|8.7|77.4|8|?2.5|2.03|10.5|78.0|7|
|Asia|27.7|3.47|8.9|63.9|71|22.0|2.76|7.7|67.8|45|17.7|2.32|8.1|70.7|32|
|East and Southeast Asia|22.6|2.66|7.1|68.4|40|17.5|2.21|7.0|71.8|21|15.1|2.10|8.5|73.9|16|
|South Asia|34.6|4.70|11.6|57.0|102|27.1|3.38|8.8|62.3|67|20.0|2.47|8.0|66.9|46|
|Southwest<br>Asia|36.6|5.11|<br>8.8|63.4|73|30.3|4.00|6.4|67.9|43|23.8|2.94|6.0|70.8|31|
|Europe and U.S.S.R.|14.9|1.96|10.7|72.8|18|13.1|1.91|10.6|75.0|12|12.7|2.04|11.7|76.2|10|
|Oceania|20.1|2.55|8.1|72.1|28|17.8|2.41|7.9|73.7|17|15.0|2.15|9.0|74.8|14|
|Income group (b)||||||||||||||||
|Low|30.8|3.89|10.2|61.1|84|25.2|3.18|8.5|65.3|58|20.1|2.58|8.2|68.5|41|
|Lower-middle|31.5|4.05|8.4|64.0|63|24.5|3.00|6.7|68.4|38|19.2|2.41|6.9|71.0|28|
|Upper-middle|26.7|3.39|8.1|67.3|53|21.6|2.73|6.9|70.9|32|18.0|2.39|7.5|73.0|23|
|High|13.9|1.79|9.4|76.1|12|12.7|1.90|9.6|77.5|10|12.6|2.10|11.6|77.9|9|
|Nonreporting**nonmemb**|**er**19.6|2.52|10.6|69.1|32|16.4|2.28|9.9|72.1|24|15.3|2.22|10.2|73.8|20|

Note: Crude birth rates **(CBR)** and crude death rates **(CDR)** are per 1000 population; total fertility rates CTFR) are per woman 15-49 years of **age; life** expectancy at bjrth, eCO), **is** given in years; and infant mortality rates (IMR) are per 1000 live births (See definitions in World Bank 1989.)

- a. "More developed" comprises Europe, U.S.S.R.. Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed" comprises the rest of the world.

- b. Criteria for income groups are given in World Sank 1989. See listing in Exhibit 1.
The definition of self-employment comprises owner/co-owner of a firm or organization and entrepreneurial work. Excluding self-employment is a common practice in this area of the literature since the earnings of the self-employed tend to be subject to higher measurement error and introduce too much noise on the results. As in the case of unpaid workers, excluding selfemployment wages is not expected to affect the results since barely 7 percent of the employed population reports to be self-employed in 2012.<sup>18</sup> We focus on full-time workers as this group is expected to obtain all their money from their primary occupation. Furthermore, in each round we exclude the top and bottom 0.25 percent of the hourly wage distribution.<sup>19</sup> These observations are considered as outliers.

Taking advantage of the richness of data in the RLMS, we include a wide set of variables that match the main factors outlined in the precious section: schooling variables, institutional characteristics (size and type of ownership of the firm), job characteristics to capture demand side factors (industrial affiliation and occupation), and demographic characteristics (gender, age, and geographic location). Further detail on the definition of the variables and their descriptive statistics can be found in Table A2.1and Table A2.2 in Annex A2.

# **4. Methodology: The RIF decompositions**

The decomposition methodology developed by Firpo, Fortin and Lemieux (2007) seeks to decompose any moment in a distribution while still providing a detailed account of the contribution of each variable used in the regression. The decomposition builds on the use of Recentered Influenced Functions—widely called RIF—that create a linear approximation for the moments of the distribution. RIF regressions were also developed by the same authors (Firpo, Fortin, and Lemieux 2009). Following, we review the basic steps behind this approach, noting the interpretation changes compared to the traditional Oaxaca-Blinder decomposition.

> 18 Including self-employed workers increases mean hourly wages by 2.2 percent, on average, between 2002 and 2012.  Even if the wages of the self-employed are higher, on average, than the earnings of wage workers, they follow the same trend during the period of analysis.

> 19 Lukiyanova (2006) also uses data from the RLMS and removes the top 0.25 percent of the monthly wage distribution in each round. However, for the bottom of the distribution, she adopts a different criteria, and removes all individuals reporting monthly earnings less than two thirds of 17,600 rubles (17,600 rubles was the mandatory minimum wage in November 1994).

20
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