rafmacalaba/datause-encoder-data huggingface dataset:
after the 1840s, drawing in small shopkeepers and more substantial peasants. Some aspects of this, such as extreme litigiousness, were malignant. Yet awareness of rights became a feature of all levels of Indian society and contributed to the rapid development of India’s wider critical public sphere discussed below.

Along with awareness of rights Indians in the mid-nineteenth century began to develop what I term a ‘benign sociology’, that is an understanding of their own society in a wide geographical sweep which adapted colonial ethnographic and statistical models to empower rather the subjugation its people. The colonial state may have attempted to categorize and count its Indian subjects, but it was soon faced with Indian intelligentsia who collated statistics on the colonial state’s failings, argued against the view that Indian society was immobilized by caste and religion and created for it a glorious civilizational past which would presage a free and enlightened future. A variety of Indian travelers and ‘auto-ethnographers’ helped achieve this aim even before the 1870s, notably Dadhabhai Naoroji, R. C. Dutt, Keshub Chunder Sen and Bholanauth Chunder (Chunder, 1869). They stressed India’s traditions of charity, its veneration for family and marriage and its aesthetic and moral sensibility, all of which were compared favorably with the West. Similar movements to reclaim a sense of self-respect through reversing colonial stereotypes were seen in other colonized countries. In Egypt this was achieved by Salama Musa (Musa, 1961). In Africa, Jomo Kenyatta’s _Facing Mount Kenya_ (Kenyatta, 1938) created a benign view of Kikuyu society. This amounted to a kind of developmental morale-raising and its importance in stimulating Indian nationalism, internationalism and aspirations to improve material life cannot be understated. For development to occur people need to have the belief that they can succeed and that their own societies are essentially benign. Many colonial and some post-colonial agencies supposedly devoted to improvement have helped to erode rather than raise morale by marginalizing indigenous public sphere institutions. The activities of international aid agencies after the recent Asian Tsunami are only the most recent example of this.

_The Indian ecumene: an indigenous public sphere_ 17
total population. There are about 31,000 public primary school teachers in Lao PDR, of which 51 percent are female and 77 percent are Lao-Tai, serving approximately 909,000 primary students (ESITC, 2011). Only 6.7 percent of the teachers in our sample were absent on the day of the interview and principals could explain the reasons for most (91 percent) of the absences.<sup>20</sup> Thirteen percent of teachers offer tutoring lessons to their students; eight percent do not charge any fee (Table 1). Teachers generally spend an average of 13 hours per week preparing for class and grading homework. Although Figure 1 shows that the distribution of teachers’ after-school work hours (solid line) has somewhat a long right tail or positive skewness compared to the normal distribution (dashed line), over-reporting of work hours does not appear to be a serious issue. More than half of all teachers report putting in at least 10 after-school work hours per week, and 6 percent report zero after-school work hours. We will return to the question of overreporting in a later section with robustness checks on estimation results.

Teachers report frequent delays in salary payments.<sup>21</sup> They report a delay of 1.8 months, on average, with a maximum of up to 7 months; 64 percent report a delay of one month or more.<sup>22</sup> Interestingly, there seems to be a relationship between these salary delays in a school and average number of students tutored after-school by teachers in those schools (Figure 2). For

> 20 A recent survey found that nineteen out of 20 teachers interviewed in a recent survey said they were satisfied with their overall work, and absenteeism was estimated at less than 9 percent (World Bank, 2008), a number which is much lower than in other countries (Chaudhury _et al_ ., 2006).

> 21 Indeed, delays in salary payment are serious enough that teachers have raised the issue through the hotline to a recent National Assembly sitting (Vaenkeo, 2010).

> 22 Authors’ calculations from the school survey. These numbers are generally consistent with those from another recent Public Expenditure and Tracking survey implemented by the World Bank (World Bank, 2008; Benveniste, Marshall, and Santibañez, 2008). Also note that teacher salaries in Lao PDR are low and tightly compressed. The average monthly salary for primary teachers, including the base salary, bonuses, and family allowances is about 390,000 Kip (US$39) and is based on such factors as educational qualification, duty location and assignment, and teaching experience. For example, teachers teaching in remote and mountainous areas can get supplements up to 20 percent of their net salaries, and teachers teaching multi-grade classes can get supplements up to 50 percent of their base salaries (Benveniste, Marshall, and Santibañez, 2008).

15
# <u>West</u>

<!-- Start of picture text --> 250 Chongqing (province) 2008 400 Guangxi (province) 2008<br>200<br>300<br>150<br>Nanning (capital)<br>200<br>100<br>50 100<br>0 0<br>3000 30000 7500<br>Prefecture GDP per capita (RMB, log scale) Prefecture GDP per capita (RMB, log scale)<br>Public health spending per capita Public health spending per capita<br>Highest/ Standard Coefficient Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance M ean Highest Lowest lowest deviation of variance<br>162.40 236.65 75.95 3.12 32.17 0.20 179.24 347.26 4.80 72.34 88.62 0.49<br>Inner Mongolia (province) 2008 Inner Mongolia (province) 2000‐2007<br>700 500<br>600<br>400<br>500<br>400 300<br>2007<br>300<br>200<br>200<br>100 Houhot (capital) 100 2000 Houhot (capital)<br>‐ 0 Houhot (capital)<br>10000 100000 1500 15000<br>Prefecture GDP per capita (RMB, log scale) Prefecturde GDP per capita (RMB, log scale)<br>Public health spending per capita Public health spending per capita (2007)<br>Highest/ Standard Coefficient Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance M ean Highest Lowest lowest deviation of variance<br>266.90 601.37 145.14 4.14 129.45 0.49 221.43 453.20 87.69 5.17 122.77 0.55<br>Public health spending per capita (2000)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>56.26 122.63 15.73 7.79 38.00 0.68<br>600 Sichuan (province) 2008* 300 Sichuan (province) 2008**<br>500 250<br>400 200<br>300 150 Chengdu (capital)<br>200 100<br>100 Chengdu (capital) 50<br>‐<br>‐<br>5000 50000<br>Prefecture GDP per capita (RMB, log scale) 5000 Prefecture GDP per capita (RMB, log scale)<br>Public health spending per capita Public health spending per capita<br>Highest/ Standard Coefficient Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance M ean Highest Lowest lowest deviation of variance<br>189.07 189.07 189.07 1.00 103.64 0.55 158.42 254.72 103.21 2.47 39.01 0.25<br>* The two points within the circled area contain prefectures, Aba ** This graph excludes thetwo prefectures, Aba 阿坝藏族羌族自<br>阿坝藏族羌族自治州 (population 894,077)and Ganzi 甘孜藏族自 治州 and Ganzi 甘孜藏族自治州 .<br>治州 (population 975,207). Each prefecture attributes to 1% of the<br>total population of 81,331,778. c<br>1200 Xinjiang (province) 2007<br>1000<br>800<br>600<br>400<br>200<br>Urumqi<br>0 (capital)<br>2000 20000<br>Prefecture GDP per capita (RMB, log scale)<br>Public health spending per capita<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>234.98 466.45 124.63 3.74 97.16 0.41<br>Source: China Statistical Yearbooks and Provincial Statistical Yearbooks<br>per capita (RMB) per capita (RMB)<br>Prefecture health spending Prefecture health spending<br>per capita (RMB)<br>per capita (RMB)<br>Prefecture health spending<br>Prefecture health spending<br>per capita (RMB) per capita (RMB)<br>Prefecture health spending Prefecture health spending<br>per capita (RMB)<br>Prefecture health spending<br><!-- End of picture text --> 32
Loening et al. (2008), Shiferaw (2009) and Klapper and Richmond (2011). These studies establish that managerial and technical skills, finance and social networks, the macroeconomic and business environment, as well as firm age and size are significant factors of enterprise survival. Furthermore, a significant proportion of enterprises do not exit the rural market because of economic or business reasons, but due to personal reasons “such as bad health or retirement, while others closed because better options became available” (Frazer, 2005, p. 590). Loening et al. (2008) also suggest that non-business factors can influence the time in operation or the exit of rural enterprises. Seasonality in agriculture, for example, can restrict enterprise operation throughout the year and force these enterprises to temporarily stop due to agriculturally-determined reasons. These studies have focused on urban-based and formal enterprises in Africa, hence a large gap remains in our understanding of the survival dynamics of informal household enterprises in rural Africa.

# **3 Data and Methodology**

In this section we first describe the LSMS-ISA database and present in the second part our estimation strategies for the empirical analysis.

## **3.1 Data**

The LSMS-ISA database is the result of nationally representative, cross-sectional and longitudinal surveys conducted by the World Bank in collaboration with national statistical offices in various countries in Sub-Saharan Africa.<sup>3</sup> The surveys cover six countries at the time of writing: Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda. Cross-sectional data is currently available for all countries, and panel data for Nigeria, Tanzania and Uganda.

The surveys consist of three parts: a community questionnaire, an agricultural questionnaire and a household questionnaire. The community questionnaire collects community-level information including the communities’ access to public services and infrastructure, social networks, governance, and retail prices. The agricultural questionnaire collects information on crop production, storage and sales, land holdings, farming practices, input use and technology adaption, access to and use of services, infrastructure and natural resources, livestock, and fishery. Finally, the household questionnaire captures household demographics, migration, education, health and nutrition, food consumption and expenditure, non-food expenditure, employment, nonfarm enterprises and further income sources, dwelling conditions, durable assets, and participation in projects and programs. The location of households has been georeferenced.

> 3 For additional information on the LSMS-ISA, see also the World Bank’s website `www.worldbank.org/lsms-isa`

7
happen in countries where households have very dissimilar consumption baskets. A diversity index measures how different consumption patterns are in a given country. <u>Chai et al. (2015)</u> propose an index based on whether the probability that two currency units drawn randomly from the expenditure of a household are spent on different expenditure categories is different from the same probability calculated at the country level.<sup>9</sup> According to this index, the country in the EMDE ECA sample with the least diversity in consumption expenditure across households is Moldova, and the country with the greatest diversity is North Macedonia (Figure 6).<sup>10</sup> In Moldova, households up to the eighth decile differ mainly in the consumption share of food and beverages; the remaining share of their consumption is split proportionally among other consumption groups. In contrast, in North Macedonia, consumption shares across groups change rapidly by decile.

# **6 Heterogeneity of inflation by household characteristics**

The evidence presented in the previous section shows that consumption shares differ across the populations of ECA countries and that prices of the main components of household consumption grew at different rates in 2022. These two factors point to the possibility of significant variability in inflation across households. This section explores the drivers of such variability by examining its correlation with household characteristics.

Assuming that the shares of consumption components do not change between the two time periods, we can construct an inflation index for individual households by applying (3) to the household consumption basket and the prices for consumption components.<sup>11</sup> COL inflation rates will differ across households because each household consumes a unique basket of goods and services.

Household-specific COL inflation indexes can be used as dependent variables in the descriptive regression analysis to explore the heterogeneity of the impact of price changes by household 9 This diversity index is calculated by the formula: where, sjk referes to the share in the total expenditures of the whole population of the expenditures of individual _j_ on K N good _k_ , and sj. = ∑k=1 sjk and s.k = ∑j=1 sjk, N is the total number of individuals and K is the total number of expenditure categories. This index can be interpreted as one minus the ratio of the probability that two currency units drawn randomly from the expenditures of an individual are spent on different expenditure categories to the probability that two currency units drawn from the expenditures of the whole population are spent on two different expenditure categories.

> 10 Microdata from the 2020 Household Budget Surveys in EU and EEA countries has not yet been released.

> 11 These are the standard assumptions for calculating the CPIs in most countries (Kints and Breunig 2021).

11
23 from general public goods. In particular, their marginal benefit measure suggests that general expenditures are pro-poor, while their equivalent variation measures suggest the reverse. While the assumptions underlying their results are no less restrictive than the studies in the Aaron and McGuire marginal benefit tradition, the CGE results illustrate the problems involved when marginal benefit measures are used to make inferences about large changes in public programs.

# V. Developing Country Benefit Incidence: Empirical **Results** Studies of Total Public Expenditure Benefits

Since the critical review by DeWulf, we are aware of only four comprehensive benefit incidence studies: Meennan (1979) for Malaysia, Foxley et al. (1979) for Chile, Bahl et al. (1986) for Korea, and Riboud (1990) for Costa Rica. Of these studies, Foxley et al., and Bahl et aW., examine all government expenditures, whereas Meerman and Riboud place most of their emphasis on specific government expenditures. The results of these full benefit incidence studies are presented in Table 2. All four studies find government expenditures to be pro-poor in percentage terms, while Meernan and Foxley et al., find government expenditures to be pro-rich in dollar terms and Riboud finds government expenditures to be neutral in dollar terms.

In Meerman's benefit analysis, only about 33 percent cf expenditures are allocable direcly to households based on their use of particular government services. Other specific expenditures are assigned to households in proportion to their income, but Meerman does not assign to households t2he benefits from general or unchargeable expenditures<sup>3</sup> 2<sup>2</sup> The total benefits based on use of services, have a pro-rich distribution, but less so than the benefits assigned in proportion to income.<sup>23</sup> Foxley et al., analyze Chilean public expenditures (and taxes) using a 1969 survey of households in Chile. They follow Meerman's methods for allocating specific services to households based on their utilization of the service, when information of the service utilization is available. Nonetheless, most of the analysis allocates expenditures to households based on consumer expenditure, household income and, in some cases, on a per household basis (see Poxley et al., Table 21). The results also show that the distribution of expenditures is more
conditions are worst in smaller ULBs in Chennai while those in Hyderabad non central cities are better than the lot.

- Also, conditions in the smaller ULBs in Hyderabad are better than the smaller ULBs of other UAs. The conditions are moderate in Pune, both for central city and non central city.

**Table 6  Roads in UAs**

|Urban<br>Agglomeration|Cent|ral City|No<br>(Max|n Central City<br>Average<br>imum, Minimum)|
|---|---|---|---|---|
||Pucca Road Length<br>(KM)|Road Length(KM) per<br>Thousand Population|Pucca Road<br>Length (KM)|Road Length (KM) per<br>Thousand Population|
|Hyderabad Urban<br>Agglomeration|7,130|0.19|186.40<br>(425,32)|1.42<br> (2.05,0.45)|
|Chennai Urban<br>Agglomeration|2,920|0.70|144.28<br>(421.7, 14)|0.85<br>(1.37, 0.06)|
|Kolkata Urban<br>Agglomeration|1,585|0.41|145.75<br> (476, 10)|1.17<br>(5, 0)|
|Delhi Urban<br>Agglomeration|NA|NA|NA|NA|
|Pune Urban<br>Agglomeration|760|0.34|214.43<br>(641.92, 40.8)|1.08<br>(2.34, 0.53)|

**Source: Census of India, 2001, Authors’ Computations**

In terms of these broad comparisons we can say that there is a considerable difference in the status of these services in the central city and smaller ULBs in each UA. However, this difference in terms of coverage is the minimum in Hyderabad, if central cities are compared with non central cities. In fact the indicators show better status for street lights and roads in the smaller ULBs than the central cities in Hyderabad. However, this may be caused by the smaller population and lower number of households in the smaller ULBs. However in the absence of comparable indicators on the supply and availability of these services in the ULBs comparisons are restricted to these coverage indicators only.

13
**Figure 14: Increase in Reserves in early 2000s was not sterilized fully as reflected in an increase in reserve money, after accounting for growth in nominal GDP**

<!-- Start of picture text --> Change in Net Foreign Assets Reserve Money (residual)<br>4000 0.2<br>3500<br>0.15<br>3000<br>2500 0.1<br>2000<br>0.05<br>1500<br>0<br>1000<br>500 -0.05<br>0<br>-0.1<br>-500<br>-1000 -0.15<br>bn Rs<br>1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14<br><!-- End of picture text --> Note: reserve money series (right axis) is the residual money supply after regressing log reserve money on log nominal GDP.

# **3.4 Monetary Policy Framework**

The major factors influencing monetary policy as well as the instruments of monetary policy have evolved in India.<sup>17</sup> The policy was primarily driven by the financing need of the government until the early 1980s. Besides fiscal dominance, financial repression, in the form of high Statutory Liquidity Requirement (SLR) and Cash Reserve Ratio (CRR), interest rate regulations and directed credit to various sectors limited the availability of credit to the private sector. The policy framework changed in the late 1980s, when price stability was formally accepted as the dominant objective of monetary policy, and a range of 5-7 percent was adopted as the target range for inflation. In the monetary framework from the mid-1980s until the late 1990s, described as “monetary targeting with feedback”, broad money supply was to be used as the monetary aggregate, to be set in line with projected GDP growth and inflation target. The move to this framework coincided with wider financial sector reforms--interest rates were initially rationalized and subsequently deregulated completely; and the automatic monetization of fiscal deficits was phased out in 1994-97.

17 See Mohan and Kapur (2009) and Patra and Kapur (2012) for a discussion of the evolution of monetary policy in India, from where I have drawn here as well.

18
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