rafmacalaba/data-use-mentions-extended huggingface dataset:
0.629***|0.106**|0.091***|
||(0.178)|(0.081)|(0.166)|(0.086)|(0.138)|(0.090)|(0.044)|(0.025)|
|p-value|0.000|0.000|0.000|0.000|0.000|0.000|0.015|0.000|
|Test T1 >= T2 (p-value)|0.221|0.064|0.220|0.117|0.196|0.051|0.276|0.189|
|Endline control mean|||2.2|22|||No|Yes|
|Additional controls|No|Yes|No|Yes|No|Yes|0.|152|
|(Pseudo)R-squared|0.054|0.525|0.012|0.121|0.019|0.221|0.015|0.271|
|Observations|2,300|2,300|2,300|2,300|2,300|2,300|2,078|2,078|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (3), (4), (7) and (8)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. 82 model farmers from treatment groups excluded. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: ***
|**Total**<br>**Table 23. Credit status**|
|---|
|**Totally paid**<br>21.9%|
|**In payment process**<br>65.0%|
|**Without using it**<br>13.1%|
|**Total**<br>**100%**|
|**Source: ENDESFI**|

# D. What is the problem with being unbanked in Mexico?

There are no formal studies of the costs paid by Mexicans as a result of having such a large percentage of the population outside of the banking system, but there are good reasons to believe that these costs could be significant. They must, for example, pay extra fees to pay their utility bills through a bank. The 83% who are paid by check must pay, direct commissions or through favors and time to cash salary checks. Focus group interviews and cost surveys carried out as part of a World Bank survey of Mexico City’s unbanked suggest that households earning below median income (about U.S. $2,500) can spend up to $100 – or 0.5% per year on such extra fees, not counting the time lost – a heavy burden for persons dependent on daily wages. In addition, the lack of a deposit account can make it difficult for households to build assets. The households may lack a safe place to guard any financial savings that they may have. More importantly, one’s access to formal-sector credit is linked to the maintenance of a bank account. And credit can be used to acquire such non-financial assets as a home or an automobile, to smooth consumer purchases over time, or to finance small business activities. The cost of credit outside the formal banking system is high, as the data in Box 1, below. Finally, all Mexicans may pay a price as
impact close to 0.4 and a surprisingly large long-run effect. Tax multipliers, on the other hand, are statistically insignificant. Yadav et al. (2012) estimate the impact of fiscal shocks on the Indian economy using quarterly data from 1997 Q1 to 2009 Q2. They argue that unexpected changes in tax revenue have a much larger effect on GDP on impact than unexpected changes in government spending. Jain and Kumar (2013) estimate the size of the expenditure multiplier in India at the center and the state level using annual data for the period from 1980 to 2011. The size of the multiplier for all categories of expenditure by state governments is estimated to be larger than that of the central government. Furthermore, capital spending has a higher multiplier than current spending. Depending on the model specification, the aggregate tax multiplier is found to be between 0.1 to 0.5, which is lower than the expenditure multiplier. Finally, Bose and Bhanumurthy (2015) present a structural macroeconomic model for the estimation of fiscal multipliers in India. Based on annual data from 1991 to 2012, they find a large capital expenditure multiplier of 2.5, a transfer payment and current spending multiplier of 1, and a tax multiplier of -1.

# **3. Data**

The dataset constructed for the analyses in this paper comprises annual data from 1987 to 2017 for the following six South Asian countries: Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka. We rely mostly on the World Development Indicators (WDI) from the World Bank. This is the case for real GDP values in US dollars for all the countries and for the tax revenue measured as percent of GDP for Bhutan, Pakistan, and Sri Lanka. The WDI tax series for Bhutan and Pakistan have some missing values. Since the correlation for overlapping years of the WDI and Asian Development Bank (ADB) series is 4
.1 reflects the potential of gas-fired generation which is efficient at small scale, has a low capital cost and fast build-time to enable new entrants to enter at lower risk than with other fuels. Hypothesis H.9.2 is based on the idea that countries (e.g. Chile, UK, Argentina, Norway) which have access to own primary energy resources such as hydroelectricity or natural gas seem to have been more likely to liberalise their energy sectors.

# **6 Conclusions and policy implications**

Although electricity sector reform has evolved over a number of years, it has largely remained a work in progress. There is a need to learn from the accumulated experience, as many developing countries are either contemplating to implement reform, or are at some intermediate stage. Our review of cross-country studies has revealed that there is 45
maintenance of a favorable microclimate). It has been well-documented at least for India's dry regions that CPRs are of particular importance for the rural poor. Based on a survey of 82 villages in 21 districts of seven states26, Jodha (1986, 1995) showed that 70-80 per cent of total fuel and grazing/ fodder requirements were met from CPRs for poor households, compared with no more than 30 per cent for non-poor households with better access to private land holdings. While 130-200 days of employment per year were met from CPRs for poor households, the corresponding figure for non-poor households was around 30-60 days. In total, an estimated 1423 per cent of household income was met from CPRs for poor households, compared with only 1-3 per cent for the non-poor.

Although the household was taken as the unit of analysis in Jodha's survey, other studies have shown that women (in non-poor as well as poor households) depend relatively more on commons than do men, owing to socially assigned roles for meeting household fuel and food requirements and for raising at least some livestock (Agarwal 1989). The degree of dependence on commons tends to be inversely related to the extent of private land holdings. Nonetheless, access to CPRs complements private land and other asset holdings such as livestock, and allows asset-poor households to diversify livelihoods and reduce risk, as Chopra et al. (1990) demonstrate for the lower Shivaliks of Haryana and Punjab.

Numerous studies have documented the dramatic rates of depletion and degradation of commons in rural India over recent decades (Jodha 1990; Chopra, Kadekodi and Murty 1990; Arnold and Stewart 1990). In Jodha's seven-state survey, it was found that the area of village commons declined on average by around 31-55 per cent over the period 1950-52 to 1982-84. The share of total village area accounted for by CPRs fell from an average of 18-41 per cent in 195052 to 10-24 per cent in 1982-84 (Jodha 1986). Given their relative and absolute dependence on CPRs, this harms the poor
in the that period, and 36 percent became displaced with zero violent incidents.<sup>19</sup> Along with the high violence experienced in large portions of the country, the significant share of households that became displaced prior to any violence occurring is one of the reasons that the average violence experienced by households before displacement was not significantly different than the violence experienced by non-displaced households before their latest survey in Tables 1 and 2.

However, even for households fleeing districts that were non-violent prior to displacement, the expectation of future violence might have still forced displacement. Figure 6 presents the average pre- and post- displacement violence for these households that fled districts with no violent incidents in the month of or six months prior to displacement and those that fled districts with one or more violent incidents. The figures demonstrate that although some households fled non-violent districts, the violence in those districts significantly escalated in the months after they became displaced. The increase was larger than the increase following the latest survey of non-displaced households; and for the number of violent incidents, the increase was larger than even for households being displaced from more violent districts.

There are a number of potential explanations for these patterns. First, it could be the case that the humanitarian situation, that was caused by the conflict, could have been driving a significant portion of displacement. Or second, it could have been the case that only households of sufficient means were able to either better predict violence or afford to become displaced prior to any violence actually occurring. But in either of these cases, there would be a significant difference in the financial well-being of households that became displaced prior to violence occurring and those becoming displaced only after significant violence occurred.

We empirically investigate this possibility, where we estimate the difference in all average food security outcomes collected in the most recent pre-displacement surveys for households that fled districts with no violence and for households that fled districts with violence. Food access measures- particularly diet diversity- are strongly correlated

> 19The timing of these
guarantees of free provision of medical care to citizens for 2017 and for the planning period 2018 and 2019."

- The average cost of drug provision at the outpatient stage, in rubles, calculated on the basis of the data of the analytical review of the Russian pharmacological market for 2017, prepared by DSM Group, for diseases of the circulatory system, respiratory system and malignant neoplasms. For diseases of diabetes mellitus, the average cost of drug provision is determined according to scientific research. For diseases of tuberculosis in an outpatient setting, drug therapy is included in the standard of financial costs.

In accordance with the Methodology, the size of the base rate for DRGs in the 24-hour and day hospitals was determined on the basis of the average standard of cost of hospitalization according to the Program of the state guarantees of free medical care to citizens for 2017 and for the planning period 2018 and 2019 (Rub 28,767.4), except for expenses on high-tech medical care, hemodialysis services (calculations were made according to the Federal compulsory health insurance Fund data received for performance of research work). Thus, the size of the base rate for the 24-hour hospital was 22,000 rubles and for the day hospitals, 10,000 rubles.

As a tariff for the cost of outpatient treatment, the average standard for the cost of outpatient visits in accordance with the Program of state guarantees for the provision of free medical care to citizens for 2017 and for the planning period of 2018 and 2019, equal to Rub 376.2 rubles, is taken.

Based on the results of the Global Adult Tobacco Survey (GATS),<sup>15</sup> the prevalence of smoking in Russia in 2016 was 30.5%.

To calculate the number of diseases associated with tobacco smoking, the generally accepted indicator in available literature, indicator of population attributable risk (PAR), was used. PAR is calculated based on the data on smoking prevalence and on the indicator of the relative risk of disease in
for transportation and marketing services. If perfect competition rules, the prices for these services will be their respective marginal costs, which improvements in the road network should lower. In remote and poor regions, however, it is quite possible that such improvements will lead to sharper competition among truckers and traders. In that event, the changes in prices confronting farmers will also reflect a fall in truckers’ and traders’ pure profits: in effect, there will be a transfer of ‘rents’ in favour of farmers. For this reason, it is useful to investigate the behaviour of unit transport costs and prices faced by farmers separately, even though the former reflects the organization of the transport industry.

# **3.1 The household survey**

The household survey covered marketing in considerable detail, but only for the _kharif_ crop of 2009 ( _t_ = _T_ ). Unit transport costs depend, of course, on the good in question, and all the villages marketed a diverse basket of crops, though most households had rather few transactions. We concentrate here on the marketing of paddy, which is the main commercial crop, in terms of the aggregate volume and value of transactions as well as the number of households selling a surplus.<sup>2</sup> A summary of the main results for the remaining crops of commercial importance for which there is sufficient variation in households’ transport connections, namely, cotton, various pulses and millets, is given at the close of this sub-section.

Of the 136 households selling paddy, 74 did so at the farm gate. Only for the remaining 62, therefore, are the transport costs actually observed. To deal with the censoring that results from households choosing to sell at the farm gate, we employ a Tobit specification: where _τijT_ is the (observed) unit transport cost incurred by household _i_ in village _j_ , _τijT_<sup>_∗_isanindicatorfunction,</sup><sup>_XijT_isavectorofregressorsand</sup><sup>_εijT_isthestochastic</
exports of goods that are traditionally labelled as complex, such as electronic devices, was particularly low as it relied heavily on imported components. Johnson and Noguera (2012, 2017) quantified the scope of production sharing in the global economy, finding amongst others an increasing share of service activities in manufacturing exports. We continue this line of work breaking down the value-added content of exports and revisiting the relation between export development and income growth from an activity perspective.

To do so, we perform two empirical exercises. First, we redo the analysis of Hausmann and Klinger (2007) and Hidalgo et al. (2007) who identified development paths using the concept of “proximity”. The degree of proximity between a pair of products is empirically inferred from cooccurrence of revealed comparative advantages (RCAs) for these products in a cross-country panel data set. Instead, we perform export analysis based on proximities of activity pairs rather than product pairs. The results show that countries first diversify predominantly through shifting export activities across industries and later in the development process through upgrading of activities within industries. The former diversification process is picked up in traditional industry analyses, see, e.g., Imbs and Wacziarg (2003). The latter process will not show up as export diversification when viewed from a product or industry-based perspective. Moreover, the activity perspective also provides a natural basis to probe further into the role of technology in export specialization as technological change is typically task specific rather than sector or product specific (Acemoglu and Autor, 2011). We find that proximity appears to be particularly predictive for new specializations in activities that are intensive in routine manual tasks, which differs across advanced and developing economies. For developing economies, a 1 standard deviation increase in average proximity relates to a 10.9 percentage point increase in the probability of specializing in a new routine manual activity. In advanced economies the effect is only 2.9 percentage points. In a next step, we also analyze trade specialization patterns of individual countries in the vein
Input subsidy schemes may also be responsible for unintended consequences over and above their distributional impacts. While not usually directed at a specific crop, such schemes encourage the production of crops that benefit most from the subsidized inputs. They also incentivize increased input use. This means that, as with output subsidies, regions that are suited to crops that benefit most from the subsidized inputs will gain the most from these subsidies. Second, the increased use of nitrogen fertilizer— a common target of input subsidy programs in many countries<sup>2</sup> —leads to important environmental spillovers. Exceptionally low prices encourage farmers to overuse fertilizers beyond optimal levels (Huang, Gulati and Gregory 2017; Cassou et al. 2017), which can cause nutrient imbalances in soils (Gautam 2015; Kurdi et al. 2020; Basu et al. 2022). This reduces soil fertility and increases nitrogen runoff into waterways, with potentially disastrous impacts on local ecosystems including algal blooms, hypoxia, and contaminated drinking water (James et al. 2005; Damania et al. 2019). According to the Food and Agriculture Organization of the United Nations (FAO), the most common chemical contaminant found in groundwater aquifers is nitrate from farming (Mateo-Sagasta et al. 2017).

This paper presents a new global analysis that examines these unintentional distributional and environmental impacts of output and input subsidies, with estimates of their spatial distribution in 16 and 23 countries, respectively. Despite the lack of comprehensive microdata on the spatial distribution of output and input subsidies, we are able to assess—through the creative use of crop production data and crop-level national statistics on subsidies—the broad contours of the spatial distribution of these subsidies, even if these are not precise estimates.

Next, we examine the spatial progressiveness or regressiveness of both types of subsidy within countries by overlaying our spatial subsidy maps with spatially disaggregated data on gross domestic product (GDP). We use Lorenz curves to quantify the spatial relationship between subsidies and incomes and determine the spatial progressiveness or
by export status that we see in the 2013-2016-2020 panel data, unweighted. Export status is defined as follows: (i) “always” are firms which exported in all three years (2013, 2016, and 2020); (ii) “new” are firms that did not export in 2013 but were exporting by 2020 (starting in either 2016 or 2020); (iii) “stopped” are firms that switched from exporting to not; and (iv) “never” are firms that did not export in any of the three years.

30
We set the government share in the oil sector = 0.7, which is the historical average of oil revenues (as share of oil GDP) in Angola over 2001-2013 (IMF-RR ττ WEC, most recent data available). Private investment is set to 20 percent of GDP to match the average observed in 2000-2017 (IMF-FAD). Due to exceptionally large investments in energy infrastructure, public investment has been historically very high in Angola (above 10 percent of GDP), but is expected to fall significantly over time, especially due to the likely decline in oil revenues. Accordingly, we assume that public investment falls from 6 percent of GDI in 2023 to 2 percent by 2050. The marginal propensity to spend under the BBR is set to B = 0.2, as public investment averaged 20 percent of total government expenditure from 2000 RR θθ to 2017 (IMF-FAD) (recall D = 0.2 and = 1). Finally, in the baseline we set the actual and structural price of oil to 50 dollars RR HHRR θθ θθ per barrel, which is the estimated unconditional mean estimated with data since 1960 (see Appendix Figure 5 for details). Also, we assume discoveries of 400 million barrels of oil per year in the baseline, equal to the 25<sup>th</sup> percentile over 19902017 (BP-Energy data, see Figure 6 panel A).

**Calibration to Angola (External Balance submodel).** For the External Balance submodel, we set the private savings rate and FDI to 19 and 1 percent of GDP, respectively, similar to their averages over the past 20 years. The public investment share in expenditure is set = 0.2 until 2050 (also the historical average). Finally, we assume that = 0, which means a balanced budget for the tt ηη BBR and a structural balance for the SSR. We assume the default private tt bb<sup>�</sup> investment crowd-in parameter (Chinn and Ito 2011).
West Bengal case, the most notable aspect of the reform process was not legislative change - many of the central provisions had been on the statute books since the 1950s - but political change at the state level, reinforced by effective institutions at local level. With popular support from panchayati raj institutions and local political representative bodies, well-publicized land settlement camps moved from village to village, updating land records and offering tenants the right to register their tenancies at the same time. This concerted effort between government and citizens' representative bodies helped to bring about a significant shift in the bargaining power of tenants in relation to landlords which was ultimately the key to success".

Tenancy reforms are clearly of continuing relevance in reducing poverty. Two elements are of particular importance, involving both legal and institutional reforms. First, deregulation of land-lease markets is important where attempts are made to ban tenancy outright, since this exacerbates tenure insecurity for tenants. Second is the registration and protection of informal, concealed tenancies along the lines of the West Bengal model. More generally, three critical ingredients of success in implementing reforms in land administration stand out: the importance of collective action at local level, the public nature of proceedings, and state power exercised on behalf of the socially excluded. This is as true of land consolidation in Uttar Pradesh as it is of tenancy reforms in West Bengal, and offers valuable lessons from which to learn in broader efforts to improve land settlement, adjudication and registration.

In the first quantitative study of its kind in India, Besley and Burgess (1998) investigated the relationship between land reforms and poverty reduction at state level, using panel data for the sixteen major states. Their main conclusion is that land reforms do indeed appear to have led to reductions in poverty in India. In their analysis, the authors controlled for other factors that may be associated with poverty reduction, in order to rule out the possibility that land reform activity merely serves as a proxy for other policies<sup>12</sup> . Their detailed
||Male|**A**<br>Age|**ppendix Table**<br>Single /<br>Divorced|**2. Orthogon**<br>Adults in<br>HH|**ality checks**<br>Completed a<br>universitydegree|Has active<br>credit|Has savings<br>account|Owns business /<br>Self employed|<br>HH monthly<br>salary (USD)|
|---|---|---|---|---|---|---|---|---|---|
|_Panel A: Savings auditors_||||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|High experience|-0.168|-0.653|-0.040|-0.028|0.051|0.031|-0.185|0.110|190.285|
||(0.187)|(2.934)|(0.183)|(0.560)|(0.133)|(0.175)|(0.133)|(0.166)|(153.402)|
|Investment|0.116|-1.890|0.076|0.790|-0.308**|-0.084|0.018|-0.138|164.670|
||(0.188)|(2.984)|(0.185)|(0.566)|(0.139)|(0.172)|(0.133)|
‐2.1|‐1.4|‐1.0|‐1.0|‐1.5|‐0.6|0.9|1.4|1.6|1.4|
|EU‐28*|‐4.6|‐3.8|‐2.7|‐1.8|‐1.7|‐1.6|‐1.9|‐0.9|0.2|0.9|0.8|0.7|
|EA‐19**|‐3.9|‐3.6|‐2.3|‐1.3|‐1.5|‐1.4|‐1.5|‐0.4|0.0|0.5|0.8|0.7|
|EU‐28**|‐3.9|‐3.6|‐2.2|‐1.2|‐1.6|‐1.8|‐1.8|‐1.2|0.0|0.5|0.6|0.7|

*: weighted average. **: median. Adjustment based on potential GDP excessive deficit procedure. Estimates start after 2014. Source: European Commission, Annual macro‐economic database.

**Table 6: General government consolidated gross debt as a share of GDP (%)**

|Greece|2000<br>104|2001<br>107|2002<br>105|2003<br>101|2004<br>103|2005<br>107|2006<br>104|2007<br>103|2008<br>109|2009<br>127|2010<br>146|2011<br>172|2012<br>159|2013<br>177|2014<br>179|
|-