rafmacalaba/data-use-mentions-extended huggingface dataset:
that are closely aligned with older models. This process has created significant tensions with respect to Russia’s NTM policies.

The WTO agreements have multiple provisions that relate to trade beyond tariffs. In particular, the family of General Agreement on Tariffs and Trade (GATT) 1994 agreements negotiated in the Uruguay Round, which established the WTO, covers such measures as trade-related investment policies (TRIMs), traderelated intellectual property policies (TRIPs), government procurement, and customs formalities. However, the largest number of trade frictions arises from regulatory measures applied to imports— namely, sanitary and phytosanitary standards (SPSs) and technical barriers to trade (TBTs). In a series of national surveys commissioned by UNCTAD, the share of trader complaints pertaining to SPS or TBT issues ranged from a low of 65% in India, Tunisia, and Uganda, to a high of 93% in Thailand (Basu et al 2012). SPS and TBT complaints also probably dominate matters taken up by WTO bodies.

Issues related to regulatory measures applied to imports are governed by the SPS and TBT Agreements of GATT 1994. These deal with the relationship between regulatory measures and trade in a parallel but somewhat different manner. Both require that national technical regulations ... shall be directed at legitimate objectives such as human health or safety; animal or plant life or health; national security requirements; the prevention of deceptive practices; or the environment; and shall not be applied in a manner which would constitute a disguised restriction on international trade or create unnecessary obstacles to trade.

TBT regulations _“_ shall take into account available scientific and technical information _”_ and SPS measures “shall be based on scientific principles and shall not be maintained without sufficient scientific ”<sup>13</sup> evidence, when such evidence is available _._ Each agreement refers to recognized international standards, which can be used as a basis for WTO-compliant regulations.

The agreements also provide for disputes about compliance to be referred to the Dispute Settlement Body (
**12**

In Table 4, we use the same specification as in Table 3, but using export crop income-earning, a subset of commercial agriculture, as an outcome variable (instead of income-earning from any type of crop). Results are very similar to crop income-earning results, and correlations are bigger in magnitude, given that outcome means are lower for both men and women. Symmetrically, Table 5 uses lower-value crops produced for the domestic as the outcome variable. There is no correlation whatsoever between income-earning from these type of crops and selfesteem. These results indicate that Table 3’s results are driven by export-oriented agriculture. This is expected, since most agricultural income is derived from export-oriented agriculture in our setting, and confirms that the "battle of the sexes" is occuring in competition over specialization in a lucrative sector.

**Table 4:** "Battle of the sexes" in export-oriented agriculture

||(1)<br>Inc|(2)<br>ome-earn<br>Female|(3)<br>ing in exp|(4)<br>ort-oriente|(5)<br>d agricul<br>Male|(6)<br>ture|
|---|---|---|---|---|---|---|
|Self-esteem (fem)|0.02<sup>_∗∗∗_</sup>||0.03<sup>_∗∗∗_</sup>|-0.06<sup>_∗∗∗_</sup>||-0.07<sup>_∗∗∗_</sup>|
||(0.01)||(0.01)|(0.01)||(0.02)|
|Self-esteem (male)||-0.02<sup>_∗_<
non-farm sector and hence increase income. The households that are short of land but rich in labor are more likely to participate in non-farm activity. The participation in non-farm activity may raise the income of poor households to a larger extent than that of rich households, and reduce the income inequality in rural areas. The LSMS data show that the households that participate in non-farm activity have higher (total) income: the average income of these households is 7% higher than that of the households that participate only in farm activity. But these households with higher total income may not be the households that have high farm income. In fact, the participation in non-farm activity is a long-term rational choice of the rural households. The households that choose to participate only in farm activity are usually those with comparative advantages in farming and with a higher expected farm income. If the access to the non-farm sources of income is absent, the poor households, usually those with low level of land resource per capita, are more likely to suffer from the binding constraint and face the cornered solution, because their opportunity costs of labor are lower and their ability to weather negative shocks are weaker, other things being equal. In addition, if those currently employed in the nonfarm sector were engaged in some alternative employment, such as being agricultural labor, agricultural wage rates might be lower and overall income inequality might rise. So rather than raising inequality, in this case, the non-farm sector actually prevents inequality from rising even further.<sup>33</sup> In conclusion, the results show that, the non-farm activity participation reduces rural income inequality in China by widening the occupation choice that disproportionately favors the poor households.

# **6 Conclusions**

In rural China, non-farm activity has grown rapidly since the economic reforms in the late 1970s. Although farm income is still a principal income source of rural households, non-farm income also plays a significant role. In the provinces of Hebei and Liaoning in 1995/1997, twothirds of the rural
These differences across shocks and regions in our sample relative to the reported incidence in EMDAT may reflect data availability and our restriction to studies in English.

Most studies use data at the micro level— household (35 percent) or individual (21 percent)— while 38 percent use data at the country level and 27 percent at the subnational level (Table 1). These shares are not mutually exclusive since some studies provide estimates using data at different levels. At the estimate level, 54 percent use data at the country level to estimate the poor’s exposure to climate shocks and almost 40 percent use country-level data to estimate the adverse impact of climate shocks on the poor (Table 2). The channels through which the poor are more adversely affected by climate shocks than the non-poor include income reduction (almost a third of estimates), aggregate human capital losses (17 percent of estimates), mortality, poor health, and food insecurity (about 6 percent of estimates each).

# _3.2 Climate shock exposure_

More than two-thirds (68 percent) of estimates found that the poor are statistically significantly more exposed to climate shocks (Table 2). Of the estimates that are not from the World Bank’s CCDRs, 59 percent documented greater exposure of the poor, especially to droughts, extreme heat, and floods (together about three-quarters of estimates). Compared with studies of other natural disasters, studies of heat were 22 percentage points more likely to find that the poor were more exposed than the average household or country. Studies on droughts were 19 percentage points more likely to find that the poor were more exposed and studies on floods were 29 percentage points more likely to find that the poor were more exposed (Table 4, columns 1-2). When the CCDRs are excluded, the results on exposure are qualitatively similar.

The prevalence of studies finding that the poor are more exposed to shocks may reflect the data used in these studies. For example, there is some evidence that the poor are more
_Figure 1: The Regressiveness of Indirect taxes on Consumption in 2001_ <!-- Start of picture text --> Consumption Tax Payments per Household as a Percentage<br>of Income<br>6<br>5<br>4<br>VAT<br>3<br>2 NON-VAT<br>1<br>0<br>Source: SA SAM 2003<br>Figure 2: The progressiveness of the complete tax system in 2001<br>Total Tax Payments per Household as a Percentage of Income<br>25<br>20<br>15<br>10<br>5<br>0<br>Household Deciles<br>2001<br>h_d0 h_d1 h_d2 h_d3 h_d4 h_d5 h_d6 h_d7 h_d8 h_d91 h_d921 h_d922 h_d923 h_d924<br>h_d0h_d1h_d2h_d3h_d4h_d5h_d6h_d7h_d8h_d91h_d921h_d922h_d923h_d924<br>percent<br><!-- End of picture text --> Source: SA SAM 2003 _Figure 2: The progressiveness of the complete tax system in 2001_ Source: SA SAM 2003 Devarajan and Panagariya (2000) show that a uniform value-added tax would closely mimic a lump-sum tax. However, when commodities are left out of the tax base, there is an efficiency loss. This is certainly the case for South Africa as the South African Value-Added Tax Act No.89 of 1991 makes allowances for exemptions, exceptions, deductions and adjustments that effectively lower the VAT liability. At inception various basic food items such as brown bread, maize meal, samp, mealie rice, dried mealies, dried beans, lentils, pilchards, milk powder, milk, rice, unprocessed vegetables and fruit, vegetable oil, and eggs were exempted from VAT (SA Tax, 2001:Schedule 2 Part B). To reduce the regressiveness of VAT even further, paraffin, an energy source used by most poor households, was exempted in 2001. However, suppliers absorbed most of the benefits and did not pass it on to the consumers as intended. Various other exclusions exist
impacts are found around DS=0.08, but negative effects occur at DS=0.15. As no statistically significant treatment effects are found for Bachelor-students in Area III, this points at movements mainly between Area I and II.

Appendix Table 4 shows the results from a similar analysis using the survey data. For other factors than the treatment variable, we find (i) gender is an important determinant of career choice. Females are 9% more likely to study Humanities and Social Sciences (Area I), 12% more likely to study Economics and Administration (Area II), and 21% less likely to study Engineering (Area III); (ii) the propability of enrollment into Engineering and Natural Sciences decreases by 1% per additional year of age of the candidate, while the likelihood of enrollment into Economics and Management increases by a similar magnitude; and (iii) Having attended a private upper secondary school increases enrollment into Engineering and Natural Sciences by 5 percent. All effects are statistical significant.

28
> 4 For instance, 47 percent of district population in Makwanpur is Tamang people. 30 out of 49 bidders who applied for road projects in the district were local―registered in Makwanpur―or come from other districts where the dominant ethnic group is also Tamang, such as Dhading and Nuwakot.

> 5 Our monthly precipitation data are based on 20 weather stations that are close to our surveyed roads. Thus, the precipitation variables that we use in this paper are district-specific but time-variant and thus contract-specific.
Third, the joint distribution between consumption and health outcomes is often missing because monetary and non-monetary outcomes are typically collected through separate surveys. The classical solution used by global multidimensional poverty measures is to ignore the data on one of these two key aspects of well-being. We identify three alternative methods to construct a hybrid index accounting for data coming from separate surveys. The first two methods consist in assuming a value for the missing joint distribution, while the last method assumes equal non-monetary outcomes at the local level. The first method assumes the overlap between the monetary and the non-monetary poverty head-count ratios. We show in one context that this method yields better wellbeing comparisons than the classical solution regardless of the exact value assumed for the (unobserved) overlap. Shockingly, “making up” overlap data can improve well-being comparisons. The reason is that it allows increasing the amount of data used. The second method consists in imputing consumption into the non-monetary survey. We argue that the relevant benchmark to evaluate the relevance of such imputation approach is not whether the joint distribution assumed is sufficiently correct but rather whether it improves well-being comparisons. The third method consists in constructing a “total consumption” variable that aggregates monetary consumption with monetary valuations of non-monetary outcomes, which are considered equally distributed at local level. We propose one way of constructing the “total consumption” variable that recognizes that each core dimension of well-being is essential.

The second source of potential improvements relates to better filtering the data used. We identify two ways in which this could be done. The first is to move beyond the default procedure that assigns equal weights to all dimensions. This procedure has the advantage of being applicable without information on the relevant preference, but runs the risk of making implausible trade-offs. Some information on the relevant preference can be obtained from subjective well-being data or from dedicated preference elicitation surveys. We succinctly present a procedure to select the weights from these data sources.<sup>3</sup> Importantly, this procedure accounts for the fact that the weights
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Botswana’s share of roads in good condition is among the highest in southern Africa**

<!-- Start of picture text --> South Africa<br>Mauritius<br>Swaziland<br>Malawi<br>Namibia<br>Botswana<br>Mozambique<br>Madagascar<br>Zimbabwe<br>Zambia<br>Angola<br>Lesotho<br>0% 25% 50% 75% 100%<br>Percentage Main Road Network Length<br>Good Fair Poor<br><!-- End of picture text --> _Source:_ AICD Road Sector Database on southern Sub-Saharan African countries.

**Table 2. Botswana’s road indicators benchmarked against Africa’s low- and middle-income countries**

|Indicator|Unit|Resource-<br>rich countries|**Botswana**|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|98|**31**|278|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|128|**43**|318|
|GIS rural accessibility|% of rural pop. within 2 km of all-season road|20|**15**|31|
|Main road network condition [2]|% in good or fair condition|68|**80**|86|
|Rural road network condition [3]|% in good or fair condition|61|**73**|65|
|Classified paved road traffic|AADT|1,408|**858**|2
on interventions targeted at the individual. Only a small number of papers look at community-level interventions. For instance, Imai and Eklund (2008) use survey data on a women’s communitybased organization in rural Papua New Guinea to assess the effectiveness of autonomous women’s groups compared to those that receive external support. Their analysis— using a Heckman Selection Model as well as Propensity Score Matching— shows that the autonomous groups are more effective in improving child welfare. Thus, community-level interventions targeted at women can generate significant benefits to children’s well-being.

One of the few analyses of _Mahila Samakhya_ uses data from the state of Bihar to evaluate the program’s effect on community-level trust and social capital (Janssens, 2010). The paper uses Propensity Score Matching to calculate Intent-to-Treat estimates of the program. Matching women from treated villages to those from untreated villages, results suggest that the program significantly increases trust and engenders social capital. Participants are more likely to contribute to local educational and infrastructural community projects. Significant spillovers also exist with nonparticipants; non-participant households in program villages exhibit higher levels of trust and are more likely to engage in community building activities than households in non-program villages.

Other work examines allied aspects of the program without explicitly evaluating it: Kandpal and Baylis (2013) ask whether participation affects the diversity of participants’ peer group, while Kandpal and Baylis (2011) examine whether friends’ participation improves female bargaining power and child welfare outcomes. None of these studies considers the causal model behind the mechanisms of community-level interventions. Next, we present a model that decomposes the effect of program participation in a community-level intervention.

# **4 Decomposing the Effect of Program Participation**

Participation in _Mahila Samakhya_ can have a direct effect by improving a woman’s job prospects, and an indirect effect that changes perceived social norms through information spillovers. We attempt to decompose the effect of participation in _Mahila Samakhya_ into these two effects. Participation in the program directly increases the woman’s educational attainment, which in turn
||
||||(0.229)|(0.228)|(0.227)|(0.227)|
|How Much Of An Obstacle:|||0.053|0.059|0.049|0.047|
|Labor Regulations?|||||||
||||(0.300)|(0.300)|(0.299)|(0.298)|
|Population (logs)||||-33.168***|-31.558***|-31.448***|
|||||(10.906)|(10.966)|(11.101)|
|Urbanization (%)||||1.805***|1.735***|1.709***|
|||||(0.390)|(0.391)|(0.386)|
|WBL Index||||15.274*|15.788*|26.692**|

84
|Number of observations|250|1166|1166|1166|1166|1164|1166|
|---|---|---|---|---|---|---|---|
||||Dire<br>|ct Income Pay<br>|ments<br>|||
|Support per hectare UAA|0.1788**|-0.0222|0.0326|0.0338|-0.0275|1|0.3095**|
|Number of observations|250|1168|1168|1168|1168|1168|1167|
|Support per AWU|0.041|-0.0138|0.0261|-0.005|0.2042**|0.3095**|1|
|Number of observations|250|1169|1169|1169|1169|1167|1169|

Note: (1) Symbol + indicates correlation statistically significant at 0.1 level; * indicates correlation statistically significant at 0.05 level; ** indicates correlation statistically significant at 0.01 level; (2) CAP support are the sum of payments for the period 2008-2013; (3) Unemployment rate data is at NUTS2 level; (4) EU15 are Belgium, Denmark, Greece, Finland, France, Germany, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Spain, Sweden and the UK; (5) Market price support is estimated to be all pillar 1 payments other than the direct aids payments

**Table 3** . Pearson correlation coefficients between socio-economic indicators and the level of market price support accruing to NUTS3 regions, selected New Member States

||No.<br>observat<br>ions|Unemploy<br>ment rate<br>(NUTS 2)|Poverty<br>rate|Share of<br>the<br>country<br
29 Thus, the impact of federal transfers on fiscal decentralization at the regional level was mixed. On one hand, federal aid was by no means concentrated in regions with the highest degree of fiscal decentralization, but on the other, larger federal transfers to regions resulted in larger transfers from regions to local governments, and thus indirectly promoted some decentralization. In the preceding section, we identified two distinct patterns of fiscal decentralization prevalent in Russian regions of different administrative type. In oblasts and krais, the downward shift of expenditure responsibilities is generally financed through increases in the share of local governments in consolidated regional revenues - "true decentralization". In autonomous okrugs and republics, however, relatively more funds for local budgets are provided by regional governments via interbudgetary transfers - "decentralization through transfers". Consequently, the results of the correlation analysis suggest that federal transfers to regions promoted the process of fiscal decentralization in autonomous okrugs and republics and slowed down it in oblasts and krais.

# Section 7. Decentralization and Economic Performance.

The panel-data regression analysis conducted in this section addresses the following specific issues related to the nature and impact of fiscal decentralization within Russian regions:

- What are the main social and economic determinants of the degree of fiscal decentralization within Russian regions?

- Is there a link between regional fiscal decentralization and public expenditures policy (measured through the structure of the consolidated budget expenditures)?

- Does the degree of fiscal decentralization have an influence over regional economic performance?

In the initial stages of our empirical investigation of these problems, we have considered the following set of social and economic variables as potential determinants of the degree of fiscal decentralization within Russian regions:

# A. Geographic and administrative characteristics:

- Geographic location (regions divided in 11 zones, so called "economic regions", based on their location)

- Territory of region as percent of the total territory of the Russian Federation (%)

- Population density in 1994 (persons per square kilometer) * Administrative type (republic, oblast and krai, autonomous okrug)
- [18] Maheshwari, S. R. [1977], President's Rule in India, Delhi: Macmillan.

- [19] Manor, J. [1995], "India's Chief Ministers and the Problem of Governability", in P. Oldenburg [ed.], India Briefing: Staying the Course, M.E. Shaxpe, London.

- [20] McCallum, B. [1978], "The Political Business Cycle: An Empirical Test", Southern Economic Journal, 44, 504-515.

- [21] Mundle, S. and Rao, M. G. [1992], "Issues in Fiscal Policy," in B. Jalan [ed.], The Indian Economy: Problems and Prospects, Penguin Books.

- [22] Nordhaus, W. D. [1975], "The Political Business Cycle", Review of Economic Studies, 42, 169-190.

- [23] Ozler, B., Datt, G. and Ravallion, M. [1996], "A Database on Poverty and Growth in India", Mimeo, Policy Research Department, The World Bank.

- [24] Persson, T. and Tabellini, G. [1990], Macroeconomic Policy, Credibility, and Politics, Chur, Switzerland: Harwood Academic Publishers

- [25] Reserve Bank of India Bulletin, Reserve Bank of India, 1960-1995.

- [26] Rogoff, K. [1990], "Equilibrium Political Budget Cycles", The American Economic Review, 80[1], 21-36.

- [27] Rogoff, K. and Sibert, A. [1988], "Elections and Macroeconomic Policy Cycles", Review of Economic Studies, 55, 1-16.

- [28] Tirole, J. [1994], "The Internal Organization of Government," Oxford Economic Papers, 46, 1-29.

- [29] Tufte, E. R. [1978], Political Control of the
# 2. Identification of Turning Points of the Global Business Cycle

# 2.1. Database

Multiple data sources are employed to construct world GDP growth at annual and quarterly frequencies over a long period. The annual GDP series covers 180 countries— 36 advanced economies and 144 EMDEs—over the period 1950-2019, though the country sample size varies by year.<sup>4</sup> The quarterly series covers 106 countries over the period 1960:1-2019:3.<sup>5</sup> Appendix 1 presents the list of countries used to compute global growth. In addition to data on GDP growth, a wide range of measures of global economic and financial activity are employed.<sup>6</sup> Indicators of economic activity include trade, industrial

> 4 Data for 2019 are estimates. Annual growth forecasts for 2020 are included in the database. All estimates and forecasts are from the World Bank’s Global Economic Prospects report published in January 2020, which covers 180 countries (World Bank 2020).

> 5 The quarterly data collected begin in 1950:1, but data availability is quite limited during the 1950s, so data prior to 1960 were excluded.

> 6 For these series, estimates are also used for 2019. Whenever data series at higher frequencies (e.g., monthly or quarterly) are available, they are used to compute 2019 series.

4