China's Tax-for-Fee Reform and Village Inequality

Abstract

In the late 1990s, China enacted a rural tax reform known as the “Tax-for-Fee Reform” (TFR), largely driven by a desire to address farmers' complaints about what they perceived as a heavy and regressive tax burden. This study examines the impact of the TFR on inequality in rural villages in China. Our results suggest that the TFR plays an effective role in reducing inequality within villages. Its impact on a consumption-based measure of inequality took effect immediately; its impact on per capita household income inequality took somewhat longer. Our results also suggest that it was “rich” and/or “coastal” villages that exhibited a significant reduction of inequality as a result of the TFR, whereas “poor” and/or “inland” villages experienced no significant changes in inequality from the reform.

Keywords::

• H7
• I2
• I3
• O1
• O5
• P3

Notes

¹ As a further step towards reducing farmers' burdens, all agriculture-related taxes were completely eliminated in year 2006 by the central government. However, given data availability, we examine only the impacts of the TFR, rather than the abolition of agricultural taxes implemented in later years, after our sample information was collected.

² There are some notable exceptions. Of most relevance to our work, Sato et al. (2008) evaluate the redistributive impacts of rural taxation in China, with a special focus on the change in tax regressivity between 1995 and 2002. By comparing before- and after-tax Gini coefficients at an aggregated level, they conclude that the unfavourable redistributive impact of rural taxation was alleviated from 1995 to 2002. Our work here differs in several aspects. First, we are able to examine the impacts of the TFR at a more disaggregated (e.g. village) level. Second, we employ a DID propensity-score-matching approach to identify the causal effect of tax reform on inequality, with special focus on addressing the selection-bias problem in the sample. See also Shen & Yao (2008), Ha et al. (2009) and Shi et al. (2010) for studies of income inequality within villages in rural China. Also, Sato (2008a) uses a hierarchical linear model to demonstrate the importance of village-specific factors (e.g. physical infrastructure, human capital and social capital), as well as public management and public policy in influencing household-income determination. Park & Wang (2010) evaluate the policy effects of China's flagship poverty alleviation programme, begun in 2001, and they find that the programme did not increase the income or consumption of poorer households, while it did increase the income and consumption of richer households by 6.1–9.2%.

³ An exhaustive list and an estimated value of these illegal levies are very difficult to establish (Aubert & Li, 2002).

⁴ For a detailed analysis of the rising fiscal burden on farmers in the 1990s, see Lin & Liu (2007).

⁵ The township-pooling funds included education supplements, social help, family planning, collective transportation and militia exercises. The three village levies refer to the public accumulation fund, the public welfare fund and administrative fees.

⁶ For instance, if n dichotomous covariates are contained in X_i, then the possible number of matches will be 2ⁿ.

⁷ Recall that the propensity score is the probability that the TFR is implemented in village i, generated from the estimation of Equation (3).

⁸ There are various ways of defining common support. One method used by Dehajia & Wahba (1999, 2002) is to discard the comparison units with an estimated propensity score either less than the minimum or greater than the maximum estimated propensity score for treated units. We employ this method here.

⁹ Differences are expected before matching is performed. After matching, the covariates should be balanced in both treated and control villages.

¹⁰ Note that we also use a cross-section propensity-score-matching estimator, as defined in Equation (4). These results are the same as the results from our DID-matching estimators, so we do not report them here. All results are available on request.

¹¹ Even after conditioning on observed covariates, there are still various reasons why systematic differences may exist between the outcomes of treated and control villages (Smith & Todd, 2005). Such differences may arise because of the selection into treated based on unmeasured characteristics. To the extent that there are unobserved time-varying characteristics between treated and control villages, the DID-matching estimators are unable to account for these features. However, it is not clear what unobservable village characteristics could vary over time and across the two groups of villages.

¹² The 2002 CHIP is well known as one of the most representative household-level data-sets in China (Gustafsson et al.,2008). Studies that have used this data-set include Gustafsson & Li (2002), Li & Zahniser (2002), Sicular et al. (2007), Gustafsson et al. (2008), Meng & Zhang (2011) and many others.

¹³ “Total household net income” refers to household disposable income, which consists of wages earned by household labourers; income from household production (i.e. farming and non-farm activities); income from property; rental value of owner-occupied housing; remittance income sent back by household members who work elsewhere; cash and in-kind social benefits (including health, housing, food and other in-kind benefits); and other miscellaneous income. Household disposable income is net of costs for household production, depreciation of productive fixed capital and cash expenditure on taxes and fees. Total household consumption includes all food, non-durable and durable goods consumed during the year (e.g. clothing, transportation, communication, electronic appliances and cars); expenditures on education and health; housing expenses (e.g. imputed rents of owned dwellings, utilities); costs for household production; purchases of fixed capital for production; depreciation of productive fixed capital; interest payments on borrowing; cash expenditures on taxes and fees; and other expenditures.

¹⁴ We use the STATA command ineqdeco to calculate the Gini coefficients within a village.

¹⁵ Indeed, the village mean income for 2002 obtained from the village questionnaire has a strong positive correlation with the village mean income aggregated from the household survey (r = 0.809, n = 951) (Sato, 2008a).

¹⁶ These 22 provinces are Anhui, Beijing, Chongqing, Gansu, Guangdong, Guangxi, Guizhou, Hebei, Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Shananxi, Shangdong, Shanxi, Sichuan, Xingjiang, Yunnan and Zhejiang.

¹⁷ For the question of “net income per capita in the village”, the survey elicited answers for 1990, 1998 and 2002. Note that a common concern with retrospective information is measurement error, since respondents may simply report the same information for multiple years. However, the summary statistics for our data indicate significant variation for the net income data across the different years, so measurement error does not seem to be a major issue here.

¹⁸ A few villages that implemented tax reform before 1999 are excluded from the analysis, because we take information for the year 1998 as pre-treatment information, which by requirement should be independent of treatment status.

¹⁹ Note that we also estimated separate specifications with different combination of covariates using only “rich” villages, “poor” villages, “coastal” villages and “inland” villages, as these variables are defined later.

²⁰ Among the 658 treated villages in the full sample, 518 implemented the reform in year 2002, 86 in year 2001, 50 in year 2000 and 4 in year 1999. Given the relatively small number of villages that implemented in year 1999, we group those villages that implemented the reform in years 1999 and 2000 into the same subgroup.

²¹ As before, the DID-matching estimators for each village group are based on the specification that satisfies the balancing test and common support condition. We do not report the results of the balancing test and the common support condition, but all results are available on request.

²² Cai et al. (2010) use this argument to explain the counterintuitive phenomenon that consumption inequality in China parallels but stays above income inequality.

²³ Deltas (2003) addresses this small-sample bias by proposing an adjusted Gini coefficient, which is simply a multiplication by a factor (N/(N − 1)) of the original Gini coefficient. In our context, applying this same factor (N/(N − 1)) to the original Gini coefficients for the same village for both years would not affect our main results, given a constant and unchanged sample size N.

²⁴ The Atkinson index is computed with a value of “inequality aversion” equal to 0.5. We also use levels of “inequality aversion” equal to 1 and 2, with similar results. All results are available on request.

²⁵ In Table 8, the estimates of consumption-based measure of inequality for the first 2 post-reform years are statistically significant in a one-tail test at the 10% level.

²⁶ As noted in the data description, 80 (out of 961) villages contain only five households.

²⁷ As before, the propensity-score-matching estimator for each village group is based on the specification with the different combination of covariates that satisfies the balancing test and common support condition. To save space, we do not report the results of the probit estimation, the balancing test and the common support condition, but all results are available on request.

²⁸ The propensity-score-matching estimator for each village group is again based on the specification with the different combination of covariates that satisfies the balancing test and the common support condition. All results are available on request.

²⁹ Note that, due to small sample size, we are again unable to analyse the lagged effect of the TFR.

³⁰ The estimates of the consumption-based measure of inequality for coastal villages given in Table 10 are statistically significant in a one-tail test at the 10% level.

³¹ In fact, when we calculate the mean values of the Gini coefficient of per capita household net income for coastal versus inland villages and for rich versus poor villages in year 1998, these calculations indicate that rich villages have higher income inequality than poor villages before the TFR.

³² As pointed out by Lin & Liu (2007), because of the uneven level of industrialization between coastal and inland regions, the large non-agricultural tax base in coastal regions has contributed to a lower dependency on agricultural taxation there.

³³ For a detailed analysis of education in rural areas of China and its relationship with poverty, see Knight, Li et al. (2009, 2010); see also Wang et al. (2012) for an analysis of central government education reforms and their impacts on local government fiscal behaviour. Gong & Wu (2012) examine central government mandates on local governments, and they conclude that local governments were not always compliant.

³⁴ It has often been noted that intergovernmental fiscal transfers in China play little role in equalizing the fiscal disparity among regions, largely because the distribution of its main component (tax rebates) seems mainly to protect the interests of the richer provinces that existed prior to the fundamental tax-sharing system reform in 1994 (Zhang & Martinez-Vazquez, 2003). As a result, the lack of progressivity in the distribution of central transfers ensures that the richer provinces obtain a larger proportion of central transfers, while the poorer provinces with less fiscal capacity and fewer transfers are adversely affected. Although there is a specific transfer programme for rural regions with relatively lower fiscal capacities, the amounts are far from sufficient. For instance, the TFR led to a reduction of approximately RMB 150–160 billion in agricultural taxes and fees in 2005 alone, while in the same year the central transfers were only RMB 66.4 billion (Tao & Qin, 2007). In addition, the transfer payments from upper-level governments have typically been based on complicated and opaque procedures, which largely reduce their effectiveness.

³⁵ Note that we tried to use the detailed household income sources and consumption components from the previous waves of the survey (e.g. the 1995 CHIP) as a proxy for the information in 1998. However, as discussed by Gustafsson et al. (2008), the sampling procedure for the rural household survey was changed after 2000. For the 1995 CHIP, village selection was the responsibility of county bureaus; for the 2002 CHIP, provincial bureaus were responsible for drawing the village samples. This procedural change means that the villages and/or households included in the two waves are not comparable; indeed, it is quite unlikely that the villages and/or households are the same units. More importantly, it is impossible to link the specific villages in the two different surveys, because both data-sets blocked the names of the villages, indicating them only by a digit ranging from 1 to10, and not by any village-specific identifier.

³⁶ Brockmann et al. (2009) also attribute the decline of happiness in China for the past decade to the rising income inequality in China in the same period.

We are grateful to two anonymous referees and to the Editor for many helpful comments.