rafmacalaba/data-use-mentions-extended huggingface dataset:
sanction or reward public officials”.<sup>111</sup> Conceptualized as such, the most obvious mechanism for citizens to express their voice would be through free and fair elections; however, this condition would imply the existence of other non-electoral mechanisms for public participation, including civil participation in political parties, CSOs, NGOs, as well as other participatory institutions. Evidence that this condition is in place would include, _inter alia_ : evidence of political participation through free and fair elections; necessary oversight and electoral monitoring mechanisms; as well as political pluralism, including freedom of political parties, CSOs, and NGOs from discrimination, or persecution.

In order for this premise to be true, there would be a number of observable implications. First, we would expect to see higher levels of OGD produced in terms of sector performance data (e.g. in health and education) as well as higher levels of data on budget allocation and execution. Second, we would expect government accountability indicators to likewise increase over time, with a significant correlation to improvements in OGD. Finally, in line with the literature, we would expect publicity and political agency conditions to be positively and significantly correlated with increases in accountability. This premise would be falsified if either there was an inverse relationship between OGD and government accountability, or, indicators measuring publicity and political agency were not positively correlated with OGD and government accountability.

#  **_Premise 2: More accountable governments enable improved service delivery._**

According to this second premise, policy makers and elected representatives would be held accountable to respond to citizens “voice”—whether formally or informally expressed—or risk the consequences of inaction. As such, these policy makers must be able to discipline government agencies that execute the public policy directives, priorities, and initiatives that they are advocating. Moreover, each service delivery agency must be able to ensure that civil servants—including the frontline providers responsible for service delivery—are adequately rewarded/sanctioned for their performance in
strikes increase the user cost of IT related infrastructure because they create voltage surges and dips (Andersen et al., 2011, 2012); accordingly, we expect a negative first-stage coefficient.

Our exclusion restriction, therefore, postulates that lightning strikes affect radicalization only through their impact on internet access. However, as discussed by Manacorda and Tesei (2020), lightning strikes might be correlated with other climatic or topographical characteristics like rainfall, which in turn might affect economic development. Since the instrument varies only at the delegation level, controlling for delegation fixed effects is no longer feasible. We nonetheless include in the vector of control variables _Xdea_ a measure of delegation _d_ ’s income as proxied by (log) nighttime lights, its (log) area, and the (log) average monthly amount of rain that fell there.

**Addressing spatial correlation** The instrumental variable strategy makes salient that deployment of the internet, a network technology, is likely to be spatially correlated. Thus, for both error terms _εdea_ (or _ζdea_ for the fixed-effect estimations) and _νdea_ , we allow for spatial correlation. We first allow _εdea_ and _εde′a′_ to be correlated, by clustering standard errors at the delegation level, but given the potential spatial dependence in internet access and its instrumental variable, our preferred specifications use the procedure from Conley (1999) to accommodate spatial correlation.

# **3 Data**

To estimate equation (1) and its variants, we combine a dataset on Daesh foreign personnel that contains information on Tunisian recruits with socio-economic and other data on the geographic areas those recruits are coming from.

**Daesh foreign recruits** Our main source of data comes from Daesh administrative records on its foreign fighters. The dataset, believed to have been leaked by a defector, covers 3965 male foreign recruits, which constitutes an estimated 30 percent of foreign fighters who joined the organization from early 2013 through late 2014 (Dodwell et al., 2016a).<sup>3</
According to the 1990 Census, Indians remain the most marginalized group of Mexican society. More than 40 percent of the Indian population above fifteen years of age was illiterate; roughly three times the national rate. Thirty percent of Indian children between 6-14 years of age did not attend school. Indians also had significantly higher morbidity and mortality rates associated with infectious and parasitic illnesses, higher levels of nutritional deficiencies, and less access to such basic services such as indoor plumbing, piped water, and electricity.

# **6.  Poverty-Reduction Strategy for Rural Mexico**

Over the medium to long run, what is needed to alleviate the high levels of poverty is broad-based growth. However, this is not enough to alleviate poverty, particularly in the short run. Measures are needed to protect vulnerable groups and to ensure that the rural poor are able to take advantage of the greater opportunities in the economy. In order to address these latter needs, this paper has examined the profile of the poor in rural Mexico and the rural poverty correlates.

The government of Mexico has taken important steps to reduce poverty, for example though the continuation of _Oportunidades_ . However, it needs a rural poverty alleviation strategy that sets clear and appropriate priorities and goals for poverty reduction efforts within a framework of a continuation of economic policies that would promote growth. The challenge and test of the government’s resolve will be to what extent current and future policies and programs are governed by that strategy. In order to ensure that the poor reap the benefits, poverty measurement and monitoring are called for, including tracking changes and making appropriate adjustments in existing programs to reflect these changes.

## **A THREE-PRONGED POVERTY-REDUCTION APPROACH FOR MEXICO**

The differing characteristics of poor households call for multiple paths out of poverty aimed at: (i) small farm sector intensification, (ii) improved employment opportunities in dynamic commercial agriculture, (iii) growth of the rural non-farm sector, (iv) migration of the young, and (v) provision of safety
Policy Research Working Paper 6161

# **Abstract**

After 18 years of negotiations, Russia has joined the World Trade Organization. This paper assesses how the tariff structure of the Russian Federation will change as a result of the phased implementation of its World Trade Organization commitments between 2012 and 2020 and how it has changed as a result of its agreement to participate in a Customs Union with Kazakhstan and Belarus. The analysis uses trade data at the ten digit level, which allows the first accurate assessment of the impact of these policy changes. It finds that World Trade Organization commitments will progressively and significantly lower the applied tariffs of the Russian Federation. After all commitments are implemented, tariffs will fall from 11.5 percent to 7.9 percent on an un-weighted average basis, or from 13.0 percent to 5.8 percent on a weighted average basis. The average “bound” tariff rate of Russia under its World Trade Organization commitments will be 8.6 percent, that is, 0.7 percentage points higher than the applied tariffs. Russia’s commitments represent significant tariff liberalization, but compared with other countries that have acceded to the World Trade Organization, the commitments of the Russian Federation are not unusual, especially when compared with the Transition countries.

This paper is a product of the Trade and Integration Team, Development Research Group; and the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dtarr@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry
in the Republic of Korea.

Korea offers a unique setting for the purpose of this study.<sup>2</sup> First, an unprecedented regime change following the country’s impeachment of President Park Geun-hye in 2017 led to a dramatic shift toward more rigid labor market policies, including consecutive double-digit yearly minimum wage hikes and a reduction in the maximum allowed number of working hours per week by 16 hours (i.e., from 68 to 52 hours a week). The series of events around 2017 and in labor market were unan- subsequent changes regulations in the of the adverse labor cost shock as an ticipated eyes firms, yielding consequent effective source of exogenous variation for our empirical study. What is more, firm-level survey data from the Korea national statistical office provides a comprehensive set of operational information, including locations and industries of each firm’s foreign affiliates (if any), which enables us to identify MNCs and their responses to the labor cost shock through foreign internal networks.

Using the data set for the period 2013~2019, we investigate the differential impacts of the stricter labor market regulations across firms. We begin by conducting an event study analysis to track employment trends separately for Korean firms with foreign affiliates (i.e., MNCs) and for firms without foreign affiliates non-MNCs, finding that only the former group of firms experienced a decline in employment after 2017. To control for any potential time-varying confounding effects, we then estimate difference-in-differences as well as dynamic treatment effects by including industry-region-year fixed effects. In addition, to alleviate concerns about sample selection bias due to observable differences between MNCs and non-MNCs, we employ a propensity score matching (PSM) method. Overall, our empirical results suggest that, as a result of stricter labor market regulations, domestic employment declined by 3% in firms with foreign affiliates.<sup>3</sup> The negative employment effects for MNCs are consistent with task substitution between domestic workers and foreign factors of production through MNCs’ internal networks
**4.2 Life with the road**

We need to specify how the road improves the household’s environment in period 1, as described by the vector **Z** 1. Starting with unit transport costs for goods, Bell and van Dillen (2012) estimate that these are reduced by about 5 percent of the net price received for commodities marketed. Khandker _et al_ . (2009) obtain a similar estimate for rural roads in Bangladesh, once an allowance is made for greater volumes marketed. They also estimate that the farm-gate price of fertilizers declined by 5 percent. With a new road, therefore, let **p** 1 = (0 _._ 95 _,_ 1 _._ 05). Where schooling is concerned, Bell and van Dillen’s (2012) analysis of the Orissa survey yields substantial savings in travel-time for secondary-school pupils (just over 30 minutes a day) and far fewer days of involuntary absences for all grades (about 2 instead of about 9 for primary and 14 for secondary). These estimates imply _τ_ = 0 _._ 04 and ̄ _et_ = (1 _−_ 2 _/_ 180)(1 _−d_<sup>1</sup> _t_<sup>)</sup><sup>_/_(1+0</sup><sup>_._04) = 0</sup><sup>_._9508(1</sup><sup>_−d_1</sup> _t_<sup>).</sup> It is not to be expected that the provision of an all-weather road will have such strong effects on morbidity, at least in the very short run; and Bell and van Dillen (2012) find none in the Orissa villages. Households did, however, respond by switching away from local healers, quacks and primary clinics to hospitals, which certainly suggests that all-weather roads are valued in
availability of data on the policy interest rate, output, and inflation for the full sample period. We use the same data for each of the domestic variables as used in the analysis in earlier sections and as described in Appendix Table 2. The data for the global variables is also the same as that used in the previous section, with the addition of oil price growth, which is measured as the month-on-month growth rate of the nominal oil price (the simple average of Dubai, West Texas Intermediate, and Brent benchmarks) from the World Bank’s monthly Pink Sheet of commodity prices.

# V.2. Drivers of Rate Cycles

This section estimates the model described above in order to examine the importance of the seven different global and domestic shocks in driving rate cycles using standard forecast error variance decompositions. We use simple averages across countries in order to focus on the aggregated results of these decompositions and learn from the broader cross-country experience. We analyze the results over the full sample from 1970-2023, the five shorter sub-periods used in earlier sections, during the economy-specific easing and tightening phases identified in Section II and then for these two phases over shorter windows (the five sub-periods and for Highly Synchronized Tightening periods).

# V.2.1. Drivers of Rate Cycles over the Full Sample (1970-2023)

The left side of Table 2 and Chart 14 present the contributions of the seven global and domestic shocks to the variance of national policy interest rates over the full sample period from 1970-2023, including the combined contributions of all the global shocks and all the domestic shocks. On average, the global shocks together accounted for nearly 26 percent of the variation in interest rates in the five economies, ranging from 14 percent (in the United States) to 55 percent (in the euro area).<sup>57</sup> Among the global shocks, global demand shocks played the largest role in driving policy rates, accounting for 10 percent of the average variance in interest rates. The other global shocks (
over a long period when economic and financial structures have shifted and the relationships between variables have changed meaningfully. Measuring variables such as the neutral interest rate is also particularly difficult in real-time—especially today—which would limit our ability to analyze the current situation and compare it to historical precedents.<sup>7</sup> While there are multiple reasons to focus on policy interest rates as our main guide to identify cycles in monetary policy, they have several limitations. Although policy interest rates are currently the key tool for adjusting monetary policy, there are periods when other instruments have also been important (including for different goals)—such as adjusting reserve requirements, the money supply, credit controls, and the size and composition of the central bank’s balance sheet. Given our focus on monetary policy today, we incorporate information on QE and QT programs in our measure of rate cycles. For parts of the analysis, we also use the shadow interest rate to incorporate changes in balance sheet policy, but this measure is not available broadly for our sample and is less directly controlled by central banks. Finally, we do not take into account changes in monetary policy goals, frameworks and targets that occurred over the sample period (such as whether a central bank targets inflation, employment, the money supply, or the exchange rate), although we discuss the likely impact of some of these changes on rate cycles over time.<sup>8</sup> Our sample includes 24 advanced economies (including the euro area as a single entity for some parts of the analysis). We select these economies based on several criteria: (i) they are defined as advanced economies in the World Bank’s Global Economic Prospects report, January 2024 (World Bank 2024); (ii) they are independent countries with GDP of at least $100bn in 2023; (iii) they have data for GDP, inflation and interest rates from at least 1980; and (iv) they have not primarily
crossing the fixed standard of living captured by an absolute poverty line (and its associated vulnerability lines). Hence the rise in mobility captured in this way is likely real.

Alongside the rise in unconditional mobility, Table 3 also reveals that there was an increase in conditional mobility. Just under two-thirds of the poor in 1993/94 remained poor in 2004/05 (compared with three-quarters in the preceding interval), and 51–61 percent of the vulnerable (in panels B and A respectively) remained vulnerable (down from 64 percent). Downward mobility among the secure also declined, from about 45 percent in the 1987/88–1993/94 interval to less than a third in 1993/94–2004/05.

If we consider the 2004/05–2011/12 interval—a somewhat shorter period—we can see in Table 4 that mobility increased further. Alongside a striking reduction in poverty, from 35.5 percent to 24.8 percent, consumption mobility rose, with off-diagonal entries in panels A and B now representing 43–46 percent of the population. More than half of the poor in 2004/05 (about 54 percent) were no longer poor in 2011/12. Interestingly, however, although (conditional) mobility by the poor into the category of the secure did increase compared with the earlier intervals, it remained a rather rare event: only about 5 to 10 percent of the poor were able to make this transition across two welfare classes between 2004/05 and 2010/11. The picture of poverty decline emerging from this assessment is that although the poor did see improvements in living standards during the 2000s, they generally continued to face a heightened risk of falling back into poverty.<sup>17</sup>

> 17 As discussed earlier, the IHDS panel does not provide the perfect benchmark panel data to compare with the NSS given its much less detailed consumption aggregate and considerable attrition rate. Still, it could be useful to qualitatively gauge the
**Box 11: How development can reduce access to land**

Until 1981, coal mining companies could acquire substantial tracts of land with few restrictions under the Coal Bearing Areas Act. Under an agreement reached in 1981 between GOO and the coal companies, land is now required to be acquired by the Revenue Department at market rates (under the Land Acquisition Act) and each displaced family is to be assured of one permanent job in the mines.

Two major obstacles have frustrated the original intentions of this agreement:

- _the definition of family':_ households often consist of extended families with several adult members. Only one will be found a job although all are displaced.

- _inadequate land records:_ since land claims may not be accurately established in land records, intra-family struggles may ensue to establish a claim to the single job offered in compensation. All other household members are forced to seek alternative livelihoods.

The loss of common land is generally not compensated under this agreement, which adversely affects the landless. One study of people displaced by the Upper Indravati dam in Koraput district found that many of the displaced were landless people dependent upon _podu_ (shifting cultivation) on common lands (Mohapatra, 1983). Entitlement to government compensation rested upon private property rights only and ignored customary rights in common property.

villagers revealed that little usable common grazing land remains. Grazing commons have either been encroached upon or are so degraded that they are no longer used for grazing and the animals are now stall-fed.

In the absence of more reliable data, it is difficult to form an accurate view of the socio-economic status of those who encroach on common land. A series of focus group discussions with land owners and landless farmers in Laderpally village, Sambalpur district, suggest that 90 percent of all who encroach on village land in Laderpally own less than two hectares of private land. Medium and large farmers make up the remaining 10 percent. The estimated distribution of encroachers by size of class of existing land holding is as follows:

|Landless
Rest of the world**|97.74|98.64|97.07|97.14|97.25|97.86|

*As percentage of total export.

_Source_ : Calculated based on UN COMTRADE.

## **3.1. Bangladesh’s Trade with Neighboring Countries**

Intra-regional trade between South Asian countries has been low for decades. Prior to 1951, its share of the region‘s total trade was in double digits. By 1967, however, as South Asia became progressively more closed to the world market and political rivalry between India and Pakistan intensified, intra-regional trade fell to just 2 percent of the total. It rose through the 1990s and by 2002 had increased to 4.4 percent (Baysan _et al_ .). It peaked at 6.21 percent in 2004 and then fell back to 4.46 percent in 2010 (Figure 1a), though this decline in intensity is attributable more to South Asia‘s increased trade with the rest of the world than to intra-regional declines.<sup>15</sup> The region‘s outward trade may look healthy, but its internal activity, compared to other regions such as NAFTA, ASEAN, and EU, is very low (Figure 1b). South

> 15 During 2004 and 2008, South Asia‘s trade with the world more than doubled (from US$245 billion in 2004 to US$600 billion in 2008) and increased faster than intra-regional trade (based on IMF DOTS).

5
Eq 1) ln( _physicians per 1000 populationit_ ) = β0 + β1*ln( _GDP per capitait‐1_ ) + β2*ln( _GDP per capitait‐4_ ) + β3*ln( _GDP per capitait‐5_ ) +β4*ln( _OOPPCit‐2_ ) + β5*ln( _Pop65it‐3_ ) + _μi_ + _ξit_ where _μc_ represents a vector of country fixed effects, _ξct_ is the disturbance terms, and β coefficients are unknown parameters to be estimated from the model. Country fixed effects are included to account for time‐invariant unobservable heterogeneity across countries (i.e. differences in baseline characteristics between countries). Quadratic terms for income and health spending indicators to additionally account for nonlinearities were investigated but ultimately excluded due to multicollinearity.

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addition, having energy expenditure information in the EU-SILC allows for microsimulations that study energy price increases or potential mitigation measures.

**To improve the possibilities for statistical matching between EU-SILC and HBS, policymakers could push for greater harmonization between both surveys and include expenditure-based questions in a subsample of the EU-SILC.** In line with previous recommendations at the EU level (see Serafino and Tokin, 2017), we recommend better harmonization of surveys. In addition, including a roster of expenditure patterns for a subsample of the EU-SILC would increase the reliability of imputation and

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