## 10. Impact of Gender Budgeting on Fiscal Spending, with GMM and Lagged Dependent Variable

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### Overview and data
- Focus: effect of gender budgeting efforts in Indian states on (a) gender equality (female-to-male school enrollment ratios) and (b) fiscal spending (real per capita total and sectoral spending).
- Data source: CEIC India Premium Database and Ministry of Women and Child Development (2015) (state-level panel).
- Coverage and samples:
  - Gender inequality sample: panel with up to 28 states; gender equality dependent variables: lower primary, upper primary, lower secondary, upper secondary school female-to-male enrollment ratios.
  - Fiscal spending sample: up to 27 states (Union territories dropped); expenditure variables measured in real per capita terms (thousand rupees, 2014=100).
  - 16 of 29 states adopted and sustained gender budgeting; 13 did not (Andhra Pradesh split; Telengana excluded because data only up to fiscal year 2014).
- Key covariates: gender budgeting dummy (GBit; equals 1 after adoption and thereafter), real transfers from center per capita (log), real income per capita (log), population (millions), agriculture/manufacturing/services shares of state GDP (percent of state GDP).
- Econometric approaches: OLS, fixed effects (one-way state FE and two-way state+year FE), generalized method of moments (GMM) with lagged dependent variable; GMM specifications use panel averaging (2-year or 3-year averages) to limit instruments.

### Main empirical specification
- Core reduced-form model:
  - Υit = β GBit + δ Xit + ηi + νt + εit
  - Υit: dependent variable (gender equality index or expenditure indicator)
  - GBit: gender budgeting dummy (contemporaneous or lagged (T-2))
  - Xit: vector of controls (real transfers per capita (log), real income per capita (log), population (millions), sector shares)
  - Fixed effects included; alternative specifications include lagged dependent variable and GMM.

### Findings — Gender equality (school enrollment ratios)
- Primary education gender equality:
  - Fixed effects (Table 4): gender budgeting indicator positive and significant for lower primary (panel one-way) 0.024* and for upper primary (panel one-way) 0.031*.
  - Lagged GB (T-2) (Table 5): stronger positive and significant effects:
    - Lower primary: 0.037** (panel one-way), 0.033** (panel two-way).
    - Upper primary: 0.034* (panel one-way), 0.029 (panel two-way).
  - GMM with lagged dependent variable (Table 7): consistent positive and significant GB coefficients with larger magnitudes:
    - Lower primary (System GMM, two-step): 0.046*** (column (a)); with time dummies included coefficient reported 0.049* (column (b)).
    - Upper primary and secondary specifications in GMM also show positive and in some cases significant GB coefficients (Table 7 shows 0.046*** across specifications for the GB indicator rows).
  - Magnitude interpretation: coefficients imply an improvement of roughly 2 to 3 percentage points in the primary-school female-to-male enrollment index in many specifications; GMM estimates somewhat larger.
- Secondary education:
  - No consistent significant effect on lower secondary and upper secondary school gender equality indices across specifications (Tables 4–7).
- Other covariates for gender equality:
  - Real transfers from center: generally not significant in fixed effects; some significance for upper secondary when GB is lagged (Table 5).
  - Real income per capita: positive and significant in some secondary-school regressions (Table 4 shows 0.156*** for lower secondary in panel one-way), but income is muted in many specifications.
  - Population: positive and sometimes significant for lower primary and lower secondary (e.g., Table 4 shows 0.002** in lower primary panel one-way).

### Findings — Fiscal spending
- Overall pattern:
  - Gender budgeting dummy generally not significant for most per-capita spending categories, except infrastructure.
- Infrastructure spending:
  - Fixed effects (Table 8):
    - One-way panel: gender budgeting indicator 0.128** (infrastructure real expenditure per capita, log terms).
    - Two-way panel: gender budgeting indicator 0.099* (infrastructure).
  - Lagged GB (T-2) (Table 9):
    - Infrastructure: 0.170*** (panel two-way) and 0.171*** (panel one-way).
  - GMM (Table 10): System GMM (two-step) shows positive and significant GB for infrastructure in one specification (infrastructure column (6) shows GB = 0.250* with Windmeijer-corrected SE); other GMM columns show mixed significance.
  - Interpretation: gender budgeting in Indian states appears to exert an expansive effect on infrastructure spending; qualitative literature suggests infrastructure priorities relevant to women (clean water, sanitation, safe transport).
- Total and sectoral spending elasticities:
  - Real transfers from center per capita (log) and real income per capita (log) are generally positive and often significant determinants of spending:
    - Table 8 (one-way panel, Total): transfers elasticity 0.293***; income elasticity 0.572***.
  - Population: often negative (economies of scale) but not uniformly significant; e.g., Table 8 panel two-way total shows population -0.009**.
  - Sectoral structure:
    - Agriculture share tends to dampen spending (negative coefficients occasionally significant, e.g., Table 8 agriculture GDP percent coefficient -0.008** for total in panel two-way).
    - Services share tends to increase spending (positive and sometimes significant, e.g., Table 8 services GDP percent 0.009** in one-way total).

### Robustness and econometric notes
- Fixed-effects choice:
  - Tests (F-test for fixed effects and Hausman) supported use of fixed effects over pooled OLS or random effects.
- Endogeneity and dynamics:
  - GMM estimators used to account for lagged dependent variables and potential endogeneity; panel averaging used to limit number of instruments (2-year averages for gender equality GMM; 3-year averages for spending GMM).
  - GMM results for gender equality confirm positive GB effects on primary education; GMM for spending shows more limited GB significance (infrastructure most robust).
- Measurement of GB:
  - Gender budgeting measured as a dummy (1 after adoption and thereafter); inability to construct a graded GB intensity index noted as limitation.

### Synthesis of empirical conclusions
- Gender budgeting in Indian states:
  - Positive and consistent association with improved gender equality in primary school enrollment (lower and upper primary).
  - No consistent significant association with secondary school gender equality.
  - Limited impact on aggregate fiscal spending levels, with the most consistent positive effect on infrastructure spending.
- Policy-relevant implications:
  - Gender budgeting can contribute to improvements in gender equality in education, particularly at the primary level.
  - Mechanisms may include focused spending within programs, changes in program structure or incentives, or investments in infrastructure that facilitate female enrollment rather than simply higher aggregate education spending.
  - Income growth and central transfers alone may be insufficient to equalize enrollment; targeted government actions (e.g., gender budgeting) appear important.
  - Further evaluation with more disaggregated fiscal and household-level data and a more detailed measure of gender budgeting intensity would help identify channels and refine policy design.

*Source: Authors’ estimates based on CEIC India Premium Database and Ministry of Women and Child Development (2015); chapter 10, “Impact of Gender Budgeting on Fiscal Spending, with GMM and Lagged Dependent Variable.”*

### References  ............................................................................................................

### _wp16227 - References  .............................................................................................................................33

### Figures
- 1.   Structure  of Government  in India.................................................................................... 8
- 2.   Spending  Trends in Gender Budgeting  vs. Non-Gender  Budgeting  States ...................13
- 3.   Revenue  Trends in Gender Budgeting  vs. Non-Gender  Budgeting  States.....................14
- 4.   Comparison  over Time in Gender Inequality  Indicators ................................................15
- 5.   Comparison  over Time in Fiscal Spending Indicators ...................................................16

### Tables
- 1a.  Summary  Statistics for Gender Inequality  Dependent  Variables (2002-2011) ............10 
- 1b.  Summary  Statistics for Fiscal Spending Dependent  Variables (2002-2014) ...............10 
- 2a.  Gender Budgeting  vs. Non-Gender  Budgeting  Sates (2002-2011)  ..............................11 
- 2b.  Gender Budgeting  vs. Non-Gender  Budgeting  Sates (2002-2014)..............................11
- 3.    State by State Comparison  of Education  Spending  and Gender Equality.....................12
- 4.    Impact  of Gender Budgeting  on Gender Equality, with Fixed Effects .........................21
- 5.    Impact  of Gender Budgeting  on Gender Equality, with Lagged Gender Budgeting  
         Dummy ..........................................................................................................................22
- 6.    Impact  of Gender Budgeting  on Gender Equality, with Expenditure  Specification .....24
- 7.    Impact  of Gender Budgeting  on Gender Equality, with GMM and Lagged  Dependent  
         Variable .........................................................................................................................25
- 8.    Impact  of Gender Budgeting  on Fiscal Spending, with Fixed Effects ..........................27
- 9.    Impact  of Gender Budgeting  on Fiscal Spending, with Lagged Gender Budgeting  
         Dummy ..........................................................................................................................29

*Source: _wp16227 - References  .............................................................................................................................33*

### 10.  Impact  of Gender Budgeting  on Fiscal Spending, with GMM and Lagged  Dependent

### 10.  Impact of Gender Budgeting on Fiscal Spending, with GMM and Lagged Dependent Variable

### Overview and data
- Focus: effect of gender budgeting efforts in Indian states on (a) gender equality (female-to-male school enrollment ratios) and (b) fiscal spending (real per capita total and sectoral spending).
- Data source: CEIC India Premium Database and Ministry of Women and Child Development (2015) (state-level panel).
- Coverage and samples:
  - Gender inequality sample: panel with up to 28 states; gender equality dependent variables: lower primary, upper primary, lower secondary, upper secondary school female-to-male enrollment ratios.
  - Fiscal spending sample: up to 27 states (Union territories dropped); expenditure variables measured in real per capita terms (thousand rupees, 2014=100).
  - 16 of 29 states adopted and sustained gender budgeting; 13 did not (Andhra Pradesh split; Telengana excluded because data only up to fiscal year 2014).
- Key covariates: gender budgeting dummy (GBit; equals 1 after adoption and thereafter), real transfers from center per capita (log), real income per capita (log), population (millions), agriculture/manufacturing/services shares of state GDP (percent of state GDP).
- Econometric approaches: OLS, fixed effects (one-way state FE and two-way state+year FE), generalized method of moments (GMM) with lagged dependent variable; GMM specifications use panel averaging (2-year or 3-year averages) to limit instruments.

### Main empirical specification
- Core reduced-form model:
  - Υit = β GBit + δ Xit + ηi + νt + εit
  - Υit: dependent variable (gender equality index or expenditure indicator)
  - GBit: gender budgeting dummy (contemporaneous or lagged (T-2))
  - Xit: vector of controls (real transfers per capita (log), real income per capita (log), population (millions), sector shares)
  - Fixed effects included; alternative specifications include lagged dependent variable and GMM.

### Findings — Gender equality (school enrollment ratios)
- Consistent positive effect on primary education gender equality:
  - Fixed effects (Table 4): gender budgeting indicator positive and significant for lower primary (panel one-way) 0.024* and for upper primary (panel one-way) 0.031* (robust standard errors in tables).
  - Lagged GB (T-2) (Table 5): stronger positive and significant effects for lower primary and upper primary:
    - Lower primary: 0.037** (panel one-way), 0.033** (panel two-way).
    - Upper primary: 0.034* (panel one-way), 0.029 (panel two-way).
  - GMM with lagged dependent variable (Table 7): consistent positive and significant gender budgeting indicator with larger coefficients:
    - Lower primary (System GMM, two-step): 0.046*** (column (a)); with time dummies included coefficient reported 0.049* (column (b)).
    - Upper primary and secondary specifications in GMM also show positive and in some cases significant GB coefficients (Table 7 shows 0.046*** across specifications for the GB indicator rows).
  - Magnitude interpretation: coefficients imply an improvement of roughly 2 to 3 percentage points in the primary-school female-to-male enrollment index in many specifications; GMM estimates somewhat larger but still plausible.
- No significant effect on secondary education:
  - For lower secondary and upper secondary school gender equality indices, the gender budgeting dummy is generally not significant across specifications (Tables 4–7).
- Other covariates for gender equality:
  - Real transfers from center: generally not significant in fixed effects; some significance for upper secondary when GB is lagged (Table 5 shows real transfers positive and significant for upper secondary in that specification).
  - Real income per capita: positive and significant in some secondary-school regressions (Table 4 shows 0.156*** for lower secondary in panel one-way), but income is muted in many specifications — suggesting income growth alone does not guarantee enrollment gender parity.
  - Population: positive and sometimes significant for lower primary and lower secondary (e.g., Table 4 shows 0.002** in lower primary panel one-way).

### Findings — Fiscal spending
- Overall pattern:
  - Gender budgeting dummy generally not significant for most per-capita spending categories, except infrastructure.
- Infrastructure spending:
  - Fixed effects (Table 8):
    - One-way panel: gender budgeting indicator 0.128** (infrastructure real expenditure per capita, log terms).
    - Two-way panel: gender budgeting indicator 0.099* (infrastructure).
  - Lagged GB (T-2) (Table 9):
    - Infrastructure: 0.170*** (panel two-way) and 0.171*** (panel one-way) in the lagged specification — indicating a stronger and consistent positive effect on infrastructure spending when GB is lagged two years.
  - GMM (Table 10): System GMM (two-step) shows positive and significant GB for infrastructure in one specification (infrastructure column (6) shows GB = 0.250* with Windmeijer-corrected SE; other GMM columns show mixed significance).
  - Interpretation: gender budgeting in Indian states appears to exert an expansive effect on infrastructure spending; qualitative literature suggests infrastructure priorities relevant to women (clean water, sanitation, safe transport) which may explain this focus.
- Total and sectoral spending elasticities:
  - Real transfers from center per capita (log) and real income per capita (log) are generally positive and often significant determinants of spending:
    - Table 8 (one-way panel, Total): transfers elasticity 0.293***; income elasticity 0.572***.
    - Elasticities vary across sectors: welfare and infrastructure show higher income elasticities in some specifications; two-way FE tend to imply smaller elasticities.
  - Population: often negative (economies of scale) but not uniformly significant; e.g., Table 8 panel two-way total shows population -0.009**.
  - Sectoral structure:
    - Agriculture share tends to dampen spending (negative coefficients occasionally significant, e.g., Table 8 agriculture GDP percent coefficient -0.008** for total in panel two-way).
    - Services share tends to increase spending (positive and sometimes significant, e.g., Table 8 services GDP percent 0.009** in one-way total).

### Robustness and econometric notes
- Fixed-effects choice:
  - Tests (F-test for fixed effects and Hausman) supported use of fixed effects over pooled OLS or random effects (test statistics available upon request).
- Endogeneity and dynamics:
  - GMM estimators used to account for lagged dependent variables and potential endogeneity; panel averaging used to limit number of instruments (2-year averages for gender equality GMM; 3-year averages for spending GMM).
  - GMM results for gender equality confirm positive GB effects on primary education; GMM for spending shows more limited GB significance (infrastructure most robust).
- Measurement of GB:
  - Gender budgeting measured as a dummy (1 after adoption and thereafter); GB adoption dates vary across states; inability to construct a graded GB intensity index due to limited systematic information across states and over time noted as limitation.

### Synthesis of empirical conclusions
- Gender budgeting in Indian states:
  - Positive and consistent association with improved gender equality in primary school enrollment (lower and upper primary).
  - No consistent significant association with secondary school gender equality (smaller sample).
  - Limited impact on aggregate fiscal spending levels, with the most consistent positive effect on infrastructure spending.
- Policy-relevant implications drawn by authors:
  - Gender budgeting can contribute to improvements in gender equality in education, particularly at the primary level.
  - The mechanisms may not be simply higher education spending overall — possible channels include focused spending within programs, changes in program structure or incentives, or investments in infrastructure that facilitate female enrollment.
  - Income growth and central transfers alone may be insufficient to equalize enrollment; targeted government actions (e.g., gender budgeting) appear important.
  - Further evaluation with more disaggregated fiscal and household-level data and a more detailed measure of gender budgeting intensity would help identify the channels through which GB operates and refine policy design.

*Source: Authors’ estimates based on CEIC India Premium Database and Ministry of Women and Child Development (2015); chapter 10, “Impact of Gender Budgeting on Fiscal Spending, with GMM and Lagged Dependent Variable.”*

### REFERENCES

### _wp16227 - REFERENCES

### References (selected works cited)
- Biswas, R., S. Marjit, and V. Marimoutou, 2010, “Fiscal Federalism, State Lobbying and Discretionary Finance: Evidence from India,” Economics and Politics, Vol. 22, pp. 68-91.
- Bradford, W., and W.E. Oates, 1971, “Towards a Predictive Theory of Intergovernmental Grants,” American Economic Review, Vol. 61, pp. 440-448.
- Cashin, P.A., and Sahay, R., 1995, “Internal Migration, Center-State Grants and Economic Growth in the States of India,” Staff Papers (International Monetary Fund), Vol. 43, pp. 123-171.
- Centre for Budget and Governance Accountability, 2012, Recognising Gender Biases: Rethinking Budgets (New Delhi: Centre for Budget and Governance Accountability).
- Chakraborty, L., 2010, “Determining Gender Equity in Fiscal Federalism: Analytical Issues and Empirical Evidence from India,” Levy Economics Institute Working Paper No. 590.
- Chakraborty, L., 2016, “Asia: A Survey of Gender Budgeting Experiences,” International Monetary Fund Working Paper 16/150 (Washington, DC: IMF).
- Chakraborty, P., 2016, “Restructuring Central Grants: Balancing Fiscal Autonomy and Fiscal Space,” Economic and Political Weekly, Vol. 51, Feb 6, 2016, pages 15-19.
- Dahlberg, M., E. Mörk, J. Rattsø, and H. Ågren, 2008, “Using a Discontinuous Grant Rule to Identify the Effect of Grants on Local Taxes and Spending,” Journal of Public Economics, Vol. 92, pp. 2320-2335.
- Das, S., S. Jain-Chandra, K. Kochhar, and N. Kumar, 2015, “Women Workers in India: Why So Few Among So Many,” International Monetary Fund, Working Paper No. 55 (Washington, DC: IMF).
- Duflo, E., 2012, “Women Empowerment and Economic Development,” Journal of Economic Literature, Vol. 50, pp. 1051-1079.
- Hindricks, J., and G. D. Myles, 2006, Intermediate Public Economics (Cambridge, MA: MIT Press).
- Hines, J. R., and R. H. Thaler, 1995, “Anomalies: the Flypaper Effect,” Journal of Economic Perspectives, Vol. 9, pp. 217-226.
- Jha, R., 2014, “India: Redesigning Fiscal Federalism after the Global Financial Crisis,” ASARC Working Paper 2014/07.
- Joshi, S. B., 2013, Assessing Gender Equality Investments: A Multi-State Perspective (New Delhi: UN Women and National Foundation for India).
- Kaur, B., and S. Misra, 2003, “Social Sector Expenditure and Attainments: An Analysis of Indian States,” Reserve Bank of India Occasional Papers, Vol. 24.
- Khera, P., 2016, “Macroeconomic Impacts of Gender Inequality and Informality in India,” International Monetary Fund Working Paper, 16 (Washington, DC: IMF).
- Ministry of Women and Child Development, 2015, Gender Budgeting Handbook (New Delhi: Government of India).
- Mundle, S., S. Chowdhury, and S. Sikdar, 2016, “Governance Performance of Indian States, 2001-02 to 2010-11,” National Institute of Public Finance and Policy Working Paper No. 164 (New Delhi: NIPFP).
- Rao, M. G., 2009, “Fiscal Federalism in India—Trends and Reform,” Chapter V in S. Ichimura and R. Bahl, eds., Decentralization Policies in Asian Development (Hackensack, NJ: World Scientific Press), pp. 107-140.
- Stotsky, J.G., 2016, “Gender Budgeting: Fiscal Overview and Current Outcomes,” IMF Working Paper 16/149 (Washington, DC: IMF).
- Vaishnav, M. and Swanson, R., 2015, “Does Good Economics Make for Good Politics? Evidence from Indian States,” India Review, Vol. 14, pp. 279-311.
- World Bank, 2011, World Development Report 2012: Gender Equality and Development (Washington, DC: World Bank).

### Appendix A — Data appendix: Key data and tables
- Table A1. Gender Budgeting States vs. Non-Gender Budgeting States (Source: Ministry of Women and Child Development (2015))
  - Gender budgeting states (Indian states and Year of implementation):
    - Arunachal Pradesh 2007
    - Bihar 2008
    - Chhattisgarh 2007
    - Gujarat 2006
    - Himachal Pradesh 2008
    - Jammu and Kashmir 2007
    - Karnataka 2006
    - Kerala 2008
    - Madhya Pradesh 2007
    - Maharashtra 2013
    - Nagaland 2009
    - Odisha 2004
    - Rajasthan 2011
    - Tripura 2005
    - Uttarakhand 2007
    - Uttar Pradesh 2005
  - Non-gender budgeting states:
    - Andhra Pradesh
    - Assam
    - Goa
    - Haryana
    - Jharkhand
    - Manipur
    - Meghalaya
    - Mizoram
    - Punjab
    - Sikkim
    - Tamil Nadu
    - Telangana
    - West Bengal

- Table A2. Variables Used in this Study (Source: CEIC India Premium Database)
  - Variables and Years:
    - Total expenditure 1991-2015
    - Expenditure: social services 1991-2015
    - Expenditure: education 1991-2015
    - Expentiture: health 1991-2015
    - Expenditure: welfare 1991-2015
    - Expenditure: infrastructure 1991-2015
    - Total revenue 1991-2015
    - Revenue: total tax 1991-2015
    - Revenue: tax (shared taxes from center) 1991-2015
    - Revenue: total non-tax 1991-2015
    - Revenue: non-tax (grants from center) 1991-2015
    - Nominal GDP 1981-2015
    - Real GDP 1981-2015
    - Agriculture GDP 1981-2015
    - Manufacturing GDP 1981-2015
    - Services GDP 1981-2015
    - Gender equality index: primary school 2001-2011
    - Gender equality index: secondary school 2006-2011
    - Population 1994-2015

- Table A3. Correlation Matrix of the Gender Inequality Sample (Sources: CEIC India Premium Database and Ministry of Women and Child Development (2015))
  - Note: * indicates significance at 5% or less.
  - Variables include: Gender budgeting indicator; Gender equality index: lower primary school; Gender equality index: upper primary school; Gender equality index: lower secondary school; Gender equality index: upper secondary school; Real transfers from center per capita (log terms); Real income per capita (log terms); Population (millions); Agriculture GDP (percent of state GDP); Manufacturing GDP (percent of state GDP); Services GDP (percent of state GDP).
  - Selected correlation values (as presented):
    - Gender budgeting indicator with Gender equality index: lower primary school 0.09971
    - Gender budgeting indicator with Real transfers from center per capita (log terms) 0.1696*
    - Gender equality index: upper primary school with Gender equality index: lower primary school 0.6632*
    - Gender equality index: lower secondary school with Gender equality index: upper primary school 0.7543*
    - (Full matrix presented in the source with significance markers.)

- Table A4. Correlation Matrix of the Fiscal Spending Sample (Sources: CEIC India Premium Database and Ministry of Women and Child Development (2015))
  - Note: * indicates significance at 5% or less.
  - Variables include: Gender budgeting indicator; Total expenditure (real per capita in log terms); Social expenditure (real per capita in log terms); Education expenditure (real per capita in log terms); Health expenditure (real per capita in log terms); Welfare expenditure (real per capita in log terms); Infrastructure expenditure (real per capita in log terms); Real transfers from center per capita (log terms); Real income per capita (log terms); Population (millions); Agriculture GDP (percent of state GDP); Manufacturing GDP (percent of state GDP); Services GDP (percent of state GDP).
  - Selected correlation values:
    - Social expenditure with Total expenditure 0.9710*
    - Education expenditure with Social expenditure 0.9511*
    - Health expenditure with Education expenditure 0.9548*
    - Welfare expenditure with Social expenditure 0.7888*
    - Infrastructure expenditure with Social expenditure 0.9119*
    - Real transfers from center per capita (log terms) with Social expenditure 0.7949*
    - (Full matrix presented in the source with significance markers.)

### Appendix B — Gender budgeting initiatives in Indian states (summarized initiatives and focal sectors)
- General overview:
  - Gender budgeting initiatives in ten Indian states focused on social services sectors such as women and child development, education, health, nutrition, welfare, and infrastructure.
  - Common measures across states included: development of a state policy for gender equality; adoption of gender budgeting statements in budget documents; identifying nodal departments and forming gender budgeting cells; implementing policies and programs related to gender objectives; and data collection and analysis.
- State-specific initiatives (high-level highlights):
  - Gujarat (adopted 2006): gender budget cell; nodal department; gender budget statement; state policy for gender equality; gender data bank; gender budgeting manual; training workshops.
  - Jammu and Kashmir (adopted 2007): gender budget cell; nodal department; focus on women’s needs in infrastructure, welfare, and employment.
  - Karnataka (adopted 2006): nodal department; gender budgeting statement; monitoring and evaluation schemes; capacity building; auditing gender budgeting activities.
  - Kerala (adopted 2008): nodal department; gender advisory committee; gender budget statement; flagship schemes on education, women safety and protection, and skill training; gender audit of selected flagship schemes; increased funding for flagship schemes; gender-disaggregated data collection; emphasis on infrastructure to improve status of women.
  - Madhya Pradesh (adopted 2007): nodal department; gender budget statement; “Directorate of Women Empowerment”; inclusion of women’s groups and civil society in budget consultations; mandated inclusion of gender objectives in project reports; micro-level sex-disaggregated data collection and gender analysis.
  - Nagaland (adopted 2009): nodal department; gender core committee; gender budgeting task force; gender budgeting manual; capacity building for gender awareness.
  - Odisha (adopted 2004): gender budget statement; “Women Component Plan” reserving 30 percent of funds in the “Annual Plan” for women; nodal department; budget document with gender section; capacity building workshops; gender budgeting handbook; state policy for girls and women focused on survival, health and nutrition, education, livelihood, asset ownership, decision making, participation and political representation, and safety, security, and protection.
  - Rajasthan (adopted 2011): high level committee; gender budget cell; nodal department; capacity building workshops; gender budget statement; gender appraisal of budget proposals across multiple departments; gender desks established across 71 government departments.
  - Tripura (adopted 2005): gender budget cells across 18 departments; nodal department; gender budget statement; schemes focused on education, social welfare, and job skills.
  - Uttarakhand (adopted 2007): gender budget statement; gender budget cell; nodal department; capacity building workshops; gender budget statements enabled tracking and a significant increase between 2007 and 2014 in spending on women-related goals.

### Appendix C — Probit analysis on the adoption of gender budgeting (methods and key result)
- Approach and data:
  - Probit analysis used to assess determinants of adoption of gender budgeting.
  - Restricted sample of 14 states due to data limitations: 9 gender budgeting states and 5 non-gender budgeting states.
  - Political variables from Vaishnav and Swanson (2015): BJP alliance (binary), INC alliance (binary), Left alliance (binary).
  - Governance Performance Index (GPI), literacy rate, and crime rate from Mundle et al. (2016) with linear interpolation to fill missing years.
  - Population data from CEIC India Premium Database; population density and female-to-male ratio computed from CEIC.
  - Coastal indicator is a dummy variable (1 if coastal, 0 if inland).
- Main interpretive result:
  - The only significant indicators of gender budgeting adoption are political party variables: the BJP alliance is positively linked to adoption; the Left alliance is negatively linked to adoption; the INC alliance is not significantly linked.
- Table C1. Probit Analysis on the Adoption of Gender Budgeting — reported coefficients and statistics (Robust standard errors in parenthesis; ***, **, * indicate significance at 1%, 5%, and 10% respectively)
  - BJP alliance 2.872*** (0.879)
  - INC alliance -0.544 (0.609)
  - Left alliance -1.642* (0.970)
  - Governance Performance Index (GPI) -2.690 (4.694)
  - Literacy rate -0.037 (0.074)
  - Crime rate 0.125 (0.083)
  - Population: female to male ratio 7.345 (6.862)
  - Population density (km2) -690.797 (1,271.304)
  - Population (millions) 0.020 (0.014)
  - Coastal indicator 1.556 (1.242)
  - Constant -8.094 (6.336)
  - Observations 112
  - Pseudo R2 0.59
  - Number of States 14
  - Year FE YES

*Source: _wp16227 - REFERENCES (extracted content).*

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