rafmacalaba/datause-encoder-data huggingface dataset:
between the DIHOPIT index and household expenditures is 0.73, the exact same correlation as that between the principal components index and expenditures.

In a slightly different vein, another sophisticated approach integrates the estimation of indicator weights with the estimation of the determinants of outcomes. Lubosky and Wittenberg (2005) and Wittenberg (2005) build on the proposal of Montgomery et al. (2000) to enter each indicator variable separately, but add on a procedure to recombine the coefficient estimates in a way that allows interpreting their combined effect as the impact of the latent factor on the outcome. The authors argue the optimality of the approach in terms of minimizing the potential attenuation bias relative to alternative approaches—indeed since this is based on OLS estimates, their measure will be the linear combination of the indicators that best predicts the outcomes. However, the cost of the approach is that the estimate of the measure of the latent factor is dependent on the outcome of interest: a different outcome measure will imply a different measure of the latent factor. Lubotsky and Wittenberg (2005) and Wittenberg (2005) compare estimates of the impact of the latent factor on outcomes and find that, as expected, the principal components approach tends to understate the impact relative to their proposed measure, although the results are quite similar.

An alternative index is the actual value of assets owned: it is not unreasonable to think that the gold standard against which the validity of an asset index should be measured is the underlying value of the assets.<sup>9</sup> As described above Morriss et al. (2000) compare their index to the total reported current value of assets and find fairly high correlations. Mukherjee (2006) compares his preferred asset index to the value of assets using the median unit values (as derived from all recent purchases of that asset in the data) as an estimate of the price of each asset. He finds that the two are highly related, with a correlation of 0.82 in urban areas and of 0.89 in rural areas.

# _c) Indicators used in asset index_

The last set of issues raised in the literature relates to which indicators, or types of indicators, should be included in the asset index. Filmer and Pritchett (2001) experiment with excluding water and sanitation variables, housing and land ownership, and both of these sets of variables in their derivation of economic status ranking using data from India. They find that

> 9 However, as mentioned above this ignores the liabilities side of the net household wealth equation.

10
18 poorest farners quickly return to their landless state. If social support systems<sup>have been</sup> destroyed in the process of refonn, the poorest members of society may even be net<sup>losers</sup> (Fitzgerald, 1987). The allocation of benefits is clearly problematic in this context. Migration Many developing countries are vulnerable to brain drains or the loss of the<sup>investment in</sup> human capital when wages or other arnenities are higher in alternative<sup>countries (Lee and Tan,</sup> 1984). Government funded scholarships for attending universities in other<sup>countries often carry</sup> provisions that require the recipient to return to the home country and work for<sup>the govemment</sup> for a period of time. Enforcement, however, varies widely among countries. In addition, residents educated at domestic universities or high schools often can emigrate<sup>with few or no</sup> restrictions.

In addition to emigration from the country, public services will<sup>often induce migration</sup> from the rural to the urban areas. Better educational opportunities, better infrastructure<sup>and the</sup> prospect of a job in the modem sector could induce migration from the rural<sup>to the urban areas</sup> of a country (Todaro, 1969; Williamson, 1-988), and thus shift benefits<sup>between regions.</sup> Providing better public services in urban compared to rural areas can lead<sup>to lower relative wages</sup> between urban and rural areas if the public services differential induces migrtion from<sup>mral to</sup> urban areas. The wage effects of geographically specific public services may<sup>significantly offset</sup> the benefits from providing urban public services.

# IV. Developed Country Benefit Incidence: Empirical Results

The benefit incidence literature for developed countries focuses primarily on the<sup>direct</sup> benefits of govemment spending as proxied by their costs, largely ignoring<sup>the income and price</sup> effects of govermnent spending and making at most, niinor allowances for benefit<sup>shifting.</sup> Studies of this type for the United States include Gillespie (1965), Aaron and<sup>McGuire (1970),</sup> Herriot and Miller (1972), Musgrave, Case, and Leonard (1974), Maital (1973,<sup>1975), Reynolds</sup> and Smolensky (1977), and O'Higgins and Ruggles (1981a).<sup>20</sup> Other developed<sup>country studies</sup>
What applies to population, applies even more to economic assets and output. Cities are engines of growth and firms prefer to locate in urban centers with good access to labor markets, complementary inputs and customers. Increasing returns and specialization raise productivity to levels not achievable in rural areas. Each unit of area therefore generates far greater output and hosts a larger stock of economic assets. Estimates of GDP for cities are not widely available, except for a few countries and some of the larger world cities. These suggest that urban output per capita tends to be several times higher than in rural areas. This reflects the concentration and greater economic value of productive assets— as well as public infrastructure and private assets such as homes—in cities. Relative exposure of economic assets to natural hazards in cities will therefore be considerably higher than in rural areas.

These dynamics have profound implications for urban disaster risk profiles. With climate change, event probabilities may increase for hydro-meteorological hazards. Vulnerability—the characteristics of exposed assets or people that make them more or less likely to be damaged by a hazard event—may also increase initially as fast urban growth leads to rising slum populations in sub-standard housing. But the main driver of hazard risk in urban areas today and over the next few decades will simply be the greater accumulation of exposure, likely exceeding the contribution of climate change (e.g., ISDR 2009).

# _But some factors may reduce urban risk_

In contrast to these factors that might increase risk, urban areas also have characteristics that mitigate possible hazard impacts. First, urbanization tends to be associated with increasing incomes and better education. These generally reduce damages from hazards (Kahn 2005, ISDR 2009). Loss of life is much lower in rich countries. Economic damages tend to be larger, but when measured as a share of exposed wealth, they are smaller than in poor countries. With higher incomes, better quality housing can be built, mitigation measures are more affordable and better institutions lead to enforcement of rules and regulations aimed at reducing vulnerability. Secondly, there are scale economies in risk mitigation. For example, risk control measures benefit more people, 8
**<u>Table 5: Estate characteristics by size</u>**

||**All**|||**Size categ**|**ory in ha**|||
|---|---|---|---|---|---|---|---|
|||**<=5**|**5-10**|**10-50**|**50-100**|**100-500**|**>500**|
|**Estate ownership**||||||||
|Years run by the current owner|18.99|13.14|12.54|15.28|21.13|19.84|30.77|
|Owner is Malawian (%)|72.58|75.00|82.76|92.42|80.21|50.52|29.75|
|Owner is expatriate (%)|10.48|12.50|0.00|1.18|4.17|29.17|20.66|
|Owner is other (%)|10.94|12.50|13.79|4.50|6.25|12.50|33.88|
|Owner is government (%)|2.19|0.00|0.00|0.00|3.13|5.21|4.96|
|Owner is NGO (%)|3.23|0.00|3.45|1.90|6.25|2.60|6.61|
|**Labor demand**||||||||
|Hired perm. male labor (%)|64.40|37.50|58.62|52.13|52.08|82.29|91.74|
|No. of perm. male labor|28.76|1.50|4.92|3.66|7.17|49.43|108.24|
|No. of perm. male labor per ha|0.60|0.50|0.93|0.40|0.45|0.89|0.88|
|Hired perm. female labor (%)|27.19|25.00|17.24|12.09|23.96|47.92|52.07|
|No. of perm. female labor|13.05|0.83|0.96|1.26|1.87|13.67|65.84|
|No. of perm. female labor per ha|0.19|0.28|0.19|0.11|0.05|0.19|0.58|
|Hired temp. male labor (%)|70.28|37.50|65.52|63.51|68.75|76.56|88.43|
|No. of temp. male labor|45.05|154.67|7.08|13.06|15.48|72.80|139.95|
|No. of temp. male labor per ha|1.51|31.28|1.96|1.48|0.96|1.14|1.03|
|Hired temp. female labor (%)|55.76|12.50|44.83|46.92|54.17|64.58|79.34|
|No. of temp. female labor|23.58|2.00|8.15|6.86|10.91|34.73|79.53|
|No. of temp. female labor per ha|0.71|0.50|1.65|0.86|0.50|0.54|0.45|
|Total wage bill per ha (US$)|131.95|249.76|144.42|133.60|174.62|138.21|77.64|
|**Tenancy**||||||||
|Have tenants (%)|33.64|12.50|10.34|43.84|44.79|25.00|9.92|
|Number of tenants|4.98|0.63|1.03|4.65|6.36|4.90|6.36|
|No. of obs.|868|8|29|422|96|192|121|

_Source:_ Own computation from 2006/07 NACAL.

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- 6) **Government support of SMEs in financing is a delicate subject.** Under conditions of efficiency, researchers have concluded, the Government should be less active. However, the Government can facilitate SME access to credit by promoting the adoption of reliable financial statements and the preparation of clear businesses plans to facilitate the evaluation of the risk profile. Furthermore, the Government can be very important in promoting financial sector efficiency and in facilitating the development of financial instruments to mitigate risks and permit adequate collateralization of assets—but in most circumstances, not in a direct role to finance firms.<sup>11</sup>

The government can help to correct the inefficiencies and deficiencies of the market and of government regulations that accentuate the differences among companies of various sizes. In addition to benefiting from government interventions, the firms can and should take measures to improve their situation. SMEs should adopt measures to reduce their unit costs and increase their productivity, innovation and quality.

Given the SMEs’ limited access to information, public interventions should be proactive and coordinated in a package of integrated services. SMEs are usually not aware of the policies and programs designed to support them and have these translate into higher efficiency. To make this link clearer, the Government should develop an underlying philosophy that unifies assistance. If assistance is provided for basic operational needs, it should be limited in scope and time, to encourage graduation of individual firms and allow access to others with similar needs. If assistance is intended to be transformational (helping firms to dramatically change productivity and promote their adoption of innovations), it should be more integrated and sustained. In either case, assistance should be tracked to measure concrete changes at the firm level, and service delivery efficiency at the institutional level.

Interventions to develop markets for financial and non-financial services will succeed only if the longterm effects of development surpass the distortionary effects of the market. In turn, this depends on whether the intervention solves the underlying problems that constrain market development. This argument points to the need to understand in-depth the structure and performance of the existing markets and of taking advantage of pre-existing institutions and business networks. The argument also points to the importance of evaluating the impact of the interventions on institutional performance, the development of the market and, finally, the competitiveness of the SMEs.

> 11 See World Bank OP 8.30 Operational Policy on Financial Intermediary Lending, July 1998, Revised August 2004 and April 2007.

7
32 Lianos, T. (1997). “Factors determining migrant remittances: The case of Greece.” _International Migration_ _Review,_ 31, 72-87.

Lucas, R. and Stark, O. (1985). “Motivations to remit: Evidence from Botswana.” _The Journal of Political_ _Economy_ , 93, 901-918.

Niimi, Y. and Ozden, C. (2006). “Migration and remittances: causes and linkages.” Unpublished manuscript.

World Bank, Washington, DC.

Passel, J., Capps, R., Fix, M. (2004). “Undocumented immigrants: facts and figures.” Unpublished manuscript. Urban Institute, Washington, DC.

Rapoport, H. and Docquier, F. (2004). “The Economics of migrants’ remittances.” Unpublished manuscript.

Department of Economics, Bar-Ilan University, Israel.

Ratha, D. (2004). “Enhancing the developmental effect of workers’ remittances to developing countries.” In _Global Development Finance_ (pp. 169-173). Washington, DC: World Bank.

World Bank (2000). _World Development Report, 2000/01._ World Bank, Washington, DC.

World Bank (2006). _Development Data Platform database._ World Bank, Washington, DC.

World Bank (2006). _Global Economic Prospects_ . World Bank, Washington, DC.

World Bank (2006). _Povcal Net database._ World Bank, Washington, DC.
that Pana households, who belong to the SC group, are 18 percentage points more likely to be covered under TRIPTI than the non-SC/ST households. Similarly, jati-level analysis reported in Table 12(a) and Figure 8(a) in Tamil Nadu indicates that there is no differential coverage of the program for BCs relative to SC/ST households. However, Table 12(b) and Figure 8(b) show that the Adi-Dravidar among the SCs are significantly more likely to be members of SHG under PVP while all Table 12(c) and Figure 8(c) show that the MBC jatis are significantly less likely to be covered by PVP.

These results suggest that jati-level differences matter when it comes to the implementation of rural livelihoods programs. Within broad caste groups, some jatis disproportionately benefit from state-run programs. The results also show that women who access such programs are not necessarily the women who could benefit the most from the opportunity for empowerment. In Bihar, for example, upper-caste women who display lower levels of empowerment in the baseline surveys are less likely to benefit from an SHG program that explicitly seeks to expand opportunities for empowerment and economic opportunity. This is consistent with the findings of previous research studies that highlight the difficulty of poverty targeting (Galasso and Ravallion; 2005). This study suggests that some jatis may have better political, economic or social networks for harnessing state programs. Understanding the mechanisms behind this would be an important question for future research.

# **6. Conclusion**

This paper has examined the relationship between caste and gender inequality in three states of India: Bihar, Odisha and Tamil Nadu. We use detailed data on caste to understand its effects on three measures of female status: labor-force participation, decision-making autonomy and mobility.

When we group households using conventional, government-defined categories of caste, we find overall patterns that are consistent with the literature on India. Lower-caste women are more likely to participate in the labor market, have greater decision-making autonomy within their households and have greater freedom of movement. There is some evidence of regional variations. Women in Bihar appear to be significantly affected by broad caste-based divisions, women in Odisha appear to be more affected by location and micro-contexts, and women in Tamil Nadu seem to be affected by education and income rather than by caste status.

However, when we group households by the narrower categories of jati, where caste is lived and experienced at the local level, we find more variability across the three states. In all three states, the overall relationship between caste and female employment is driven by specific jatis (such as the Musahars in Bihar and the Adi-Dravidars in Tamil 19
income generation but investing to have rural population at less than 2 km may be considered as overinvestment (in our selected districts)<sup>22</sup> . It will be demonstrated in the next section that the 2 km buffer has a minimal positive impact on income due to low plot size; increased road density does not create an expanded transport requirement for most farmers. Moreover, sustainability of such investments is at stake and therefore, roads investment strategy should probably be better adjusted to farmers’ transport requirements.

# **3. What is the transport requirement from a farmer’s and trader’s perspective?**

In rural areas in Uganda, most farmers produce between 400 kilos and 3 tons (per year) depending on commodities, soil fertility, inputs and other factors. In the case of most crops, taking into account that the average grown area which is, in most cases, around 1 hectare in Uganda, not more than 100-200 kilos are to be transported per week; therefore, except in special cases, a farmer only requires transport by bicycle or motorcycle.<sup>23</sup> If crop selling price is low, the current production make walking and selling crops directly to local markets the most profitable option and then does not necessarily require roads investment for trucks, at least for the last mile.

# **_The farmer’s perspective_**

Analysis of transportation methods in SSA conclude that intermediate transport, such as bicycles, motorcycles, handcarts, and animals, are vital in connecting people with markets (Starkey 2001). This connection enables people in remote areas to bring (more of) their crops to market while decreasing the costs and time of transport.

> 22 This finding is consistent with World Bank (2009.2). This research centered on Malawi finds out that the optimal transport time for higher agricultural growth is 2.2 hours. Assuming walking or bicycle at 4 kilometers per hour, the optimal transport distance for agricultural production from a road would then be over 8 kilometers (from an economic point of view).

> 23 This information comes from our household surveys. The UNHS does not have information about individual travel or mode of travel with crops to market. The USHS has information about the assets owned by the household (including bicycles), but we do not know what mode they are using to or how they transport their crops or how far/long they travel. The UNHS does ask for the most common mode of transportation from the village to the market, but this is at the community level, so connecting it to the type of crops grown or the amount sold would be a generalization at best and unreliable.

25
**Figure 4.14.  Payment for infrastructure and energy services**

(% of household with access) <!-- Start of picture text --> Capital Other urban Rural<br>80<br>60<br>40<br>20<br>0<br>Any payment for Any payment for Any payment for Any payment for<br>central w ater central heating electricity natural gas<br>%<br><!-- End of picture text --> Note: average among 18 ECA countries for central water, 12 ECA countries for district heating and natural gas, 15 ECA countries for electricity.

Source: see Table 2.1

**Quality of services and payment levels.** Under-pricing of urban services leads to a situation where subsidies are necessary to maintain the provision of these services to the general public.  However, given the fiscal constraints in most of the transition countries, public funds are not available to cover even basic maintenance costs.  As shown in Figure 4.15 below, there is an observed positive correlation between low levels of payments for water in capital cities of several ECA countries and the quality of the service provision, that is estimated as the percentage of capital city residents receiving 24 hours water supply. Even if we do not know much about the reasons for (non)payment, still the correlation tells us clearly that better service reliability is related with better payments.  Presumably low payments rates and consequent uncovered costs lead to poor quality services.  One can also infer that in countries where there is not much enforcement for payment, services are becoming less reliable.

**Figure 4.15. Payment rates for water and reliability of service in ECA capitals**

<!-- Start of picture text --> 120<br>100 BG<br>BA KZ<br>80<br>GE<br>60<br>TM<br>40 TJ<br>AL<br>20<br>AM<br>0<br>0 20 40 60 80 100 120<br>Payment rates for water at capital cities<br>water for 24 hours<br>Ratio of households receving<br><!-- End of picture text --> AL=Albania; AM=Armenia; BA=Bosnia-Herzegovina; BG=Bulgaria; GE=Georgia; KZ=Kazakhstan; TJ=Tajikistan; TM=Turkmenistan Source: see Table 2.1 34
|Kahn and<br>Davis (2008)|United States<br>and Mexico|2001-2007|Annual vehicle trade from<br>the U.S. to Mexico by<br>model, vintage, and<br>manufacturer|OLS|Trade in used vehicle leads<br>average vehicle emissions<br>to decrease in both<br>countries. Total<br>greenhouse<br>gas emissions increase,<br>primarily because trade<br>gives new life to vehicles<br>that otherwise would have<br>been scrapped.|
|---|---|---|---|---|---|
|**_Congestion prici_**<br>|**_ng_**<br>|||||
|Parry et al<br>(2007)|Various|Various|Local air pollution, global<br>air pollution, oil<br>dependency, market power,<br>traffic accidents|Discussion on<br>implications of<br>externalities for the<br>appropriate design of<br>federal policy, with<br>emphasis on some<br>practical difficulties in<br>policy reform|Taxing all oil products,<br>including aviation fuel,<br>diesel fuel, home heating<br>oil, and petrochemicals,<br>would be more cost-<br>effective than taxing<br>gasoline alone|
|Olszewski &<br>Xie (2005)|Singapore|1975-1999|Singapore Electronic Road<br>Pricing (ERP) system<br>(charges for entering the<br>city center and using some<br>expressways during<br>different times in a day)|Calibration|Road pricing schemes<br>reduce congestion by about<br>10-30% and provide<br>significant benefits for the<br>priced area|
|Beevers &<br>Carslaw (2005)|London,<br>England|2002-2003|Air pollution concentration,<br>weather, traffic data<br>(congestion, speed, flows)|Road Traffic Emissions<br>Model|Schemes reduce<br>congestion by about 10-<br>30% and provide|
|Simenova et al<br>(2018)|Stockholm,<br>Sweden|2004-2010|residential location and<br>timing of all hospital<br>inpatient and acute<br>(unplanned) outpatient<br>visits, air pollution|Parallel trends approach|significant benefits for the<br>priced area|

48