rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5.  Composition of Budgetary Expenditures of Rural Local Governments, 2004**<sup>16</sup>

|(Shares of total)|All Subnational<br>Gov'ts|Counties|Townships|
|---|---|---|---|
|1. Capital Investments|22%|17%|9%|
|2. Economic Services|13%|14%|14%|
|Agriculture|3%|4%|7%|
|3. Social Services|32%|37%|36%|
|Education|15%|21%|24%|
|Health|4%|5%|2%|
|4. Administration|21%|24%|29%|
|5. Subsidies|2%|1%|1%|
|6. Others|10%|7%|11%|
|Provision of Services(2 + 3 + 4)|66%|75%|80%|

Source: Compendium of Local Fiscal Statistics, 2005.

Local rural governments have had great difficulty in adequately assuming their expenditure responsibilities, although little information is collected systematically in China on the state of services delivered on the ground. However, the high average proportions of total expenditures devoted to service provision imply that many rural counties and townships at the lower end of the income distribution are unable to fund their expenditure responsibilities at adequate levels.<sup>17</sup> Even for the key national priority task of providing nine years of basic education, for example, the Ministry of Education found that at year-end 2002, 431 counties had not reached this target, nearly all of which are in remote mountainous areas. Together these counties had a population of 107 million – more than one-eighth of the rural population (Wang Zhan 2003).<sup>18</sup> This picture of poor localities straining but failing to provide adequate services can be glimpsed in anecdotal evidence collected during fieldwork for this study. The 2003 consolidated budget (including both the county and its townships) for Hua county, a nationally

> 16 Social services include basic education, public health, and social welfare.  Economic services include the operating expenses of the agriculture, forestry, animal husbandry, and industrial departments, and the agencies in charge of water conservancy.

> 17 This is consistent with the findings of empirical studies, such as World Bank (2002), Heberer (2001) and Chen and Han (2005).

> 18 The number of counties unable to provide 9 years of schooling has declined rapidly under the current campaign to extend compulsory education to all. By the end of 2004, the number of counties not achieving this target had been reduced to 284. Even in 2007, though, 93 counties still could not meet this target.

13
<!-- Start of picture text --> 60<br>47.9<br>50 46.7<br>39.0 39.8 38.5<br>40<br>35.3 34.9<br>33.7 33.3<br>31.4<br>30<br>20 17.4 15.8 17.9 15.6<br>13.0 13.3<br>10 5.6 6.8 6.6 7.7<br>0<br>Males Females Males Females<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

Figure A.4. Population shares of individuals (%) <!-- Start of picture text --> 50<br>46.3<br>44.9<br>45 43.1<br>40.4<br>40 37.8<br>33.7 33.5 33.9<br>35 32.6 31.8<br>30<br>25<br>20 15.217.8 16.5 17.9 16.1<br>13.3<br>15<br>10 6.3 7.0 5.8 6.2<br>5<br>0<br>Males Females Males Females<br>IDP Non-IDP<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

39
24 economic growth, petrol price, rate of exchange...The specific number of parameters should be picked by looking at the number of likely regulatory issues that are likely to emerge and for which regulator is likely to have to set conflicts. Safety, service quality and environmental issues are likely to arise and hence making sure that the appropriate elasticities are provided as part of the study for future uses.

# 7. **Conclusions**

Forecasting has long been a challenge. It will continue to be one for the foreseeable future. But the analytical instruments and the data processing capabilities provided by the latest technologies and softwares should allow much better forecasting than is observed in transport ministries or regulatory agencies. Privatization induces new needs for demand forecasting; it leads to pay more attention to risk than it is the case when investment is publicly financed. Furthermore the regulator has to be able to judge the traffic study made by the operators, and to find out the strategic behavior which influence these studies. For many governments and regulators the decision to avoid good demand modeling is driven by a lack of conviction that theory and models can do better than the "old-hands" of the sector. But this is particularly dangerous in situations in which the nature of the business is changing as a result of privatization. Another argument is that it is too costly. The fact is that for projects adding up to over US$ 100-200 million, a cost of US$ 100-200,000 should not be a reason to reject some effort to do reasonable modeling. In some cases, the forecasting firms are willing to sell their forecast with an insurance in case of significant gaps between forecasts and occurrences! These recent developments have to be balanced against the fact that bad forecast can result in rationing of or excess capacity which are not costless either. Subsidies are often a necessary outcome. Moreover, they can ruin the credibility of a whole concession program: a much costlier but often underestimated consequences of demand forecasting failures.

# **References**

AENA (1999), "Metodologia para el Calculo de la Prevision de la Demanda del Trafico Aero en los Aeropuertos Espanoles", Direccion Corporativa de Planificacion, Division de Planes Directores, Madrid, Spain Alexander, Estache and Oliveri (2000), "A few things transport regulators should know about risk and the cost of capital", _Utilities Policy,_ forthcoming Ben Akiva, M. _(1985), Discrete Choice Analysis: Theory and Application to Travel Demand,_ The MIT Press, Cambridge, MA..

Centre d'Etude sur les Reseaux, les Transports, l'Urbanisme et les Constructions Publiques (1998), _Comportement de deplacement en milieu urbain. les modeles de choix discret_ Coeck, C, A. Verbeke and W. Winkelmans (1996), " Back to the Future: an Evaluation of Traffic Forecasting Techniques used in Antwerp and Rotterdam", pp. 3-26, in Winkelmans, W. and T. Notteboom (eds), Crampes and Estache (1998), "Regulatory Trade-offs in Designing Concession Contracts for Infrastructure Networks", _Utilities Policy, 7,_ pp.<sup>1</sup> -<sup>1 3</sup>
clearly defining objectives (e.g. selecting the poorest, or any poor, or the food-insecure). We find that when measured consistently in comparable contexts, the variation in the performance of methods is relatively small. This result is different from Coady et al. (2004) and Devereux et al. (2017) whose reviews include countries that differ greatly in several ways. Finally, our results highlight how targeting studies based on simulations can be misleading. First, simulations are conducted on nationally representative surveys while most interventions are implemented on largely different samples, after geographical targeting has been applied. Second, program coverage rates often differ from the simulations conducted, having dramatic implications on the performance of targeting. The actual targeting performance is also affected by implementation issues.

Overall, our findings suggest that after geographical targeting is applied, the other targeting method employed is not likely to make a major difference in reducing poverty or food insecurity in low-income settings. That being said, targeting costs are also similar across methods and represent a small share of the total amount of funds distributed. Our findings imply that when making choices on targeting methods (or whether to target or not), other parameters – not considered in our paper because of data limitations - may be more important than targeting errors and costs. For instance, the legitimacy of targeting schemes, the program spillovers that targeting may generate on non-beneficiaries, or how targeting choices affect the program impact on final outcomes deserve greater attention.

Our study contributes in several ways to the literature. As far as we know, this is the first cross-country comparison of targeting performance based on a primary data analysis from actual programs. As such, it differs from cross-country analyses based on simulations (e.g. Brown et al., 2018), which focus on PMT performance but cannot assess CBT or measure actual performance in practice. Our study also differs from cross-country reviews that are based on actual programs, but not based on primary, micro-level data analysis (Coady et al., 2004; Devereux et al., 2017). Indeed, our methodology allows us to rely on multiple measures of performance that are comparable, and to benchmark our results against potential alternatives (random, geographic or universal targeting). Finally, we provide data on targeting costs, and discuss policy and methodological implications for the design and study of targeted programs in the future.

There are several caveats to our analysis. Our data do not allow us to consider the legitimacy of different targeting methods, or how they affect program impacts on household outcomes. Besides, we do not consider political economy and other issues (Sen, 1993; Devereux, 2016; Duchoslav et al., 2021). All these areas call for further research.

The next section discusses key measurement considerations in analyzing targeting methods. Section 3 describes the Sahel context and reviews the existing literature on targeting methods. Section 4 presents the 4
be related to the tax corruption series. They must also be as complete as possible in terms of both time and cross-section dimensions. A general corruption index is picked as the predictor, because it is the most related to the tax corruption ratio and at the same time their numbers of observations are mostly complete. The general corruption index used in the imputation process is “Control of Corruption” from the World Bank Institute’s Worldwide Governance Indicators Database. It is defined as “measuring perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ‘capture’ of the state by elites and private interests” (Thomas, 2010). After the imputation process, the tax corruption ratio has been transformed to its original scale.

While extending the tax corruption series by using the available information for predicted values of missing years, it should be noted that its statistical features have not been changed. The already available data points in the series are taken as is and the remaining data points are predicted. The descriptive statistics for the tax corruption series before and after data extension show that its average value was 23.1 before the extension and it is 22.1 after the extension. The median value of the tax corruption series was 18.7 before the extension of the series, and it becomes 18.1 after the extension. Similarly, the before-extension and afterextension standard deviations are very close as well: 19.1 and 18.8, respectively.

# **_2.3 Sample Selection_**

The distribution of the tax corruption series among countries indicates that some cultural perception issues play an important role in how firms define bribery or corruption in their countries. As presented in Table 1, while most Latin American countries have an unexpectedly low tax corruption ratio, some high-income or upper middle-income countries face a relatively high tax corruption ratio. These findings of the Enterprise Surveys appear to be contrary to anecdotal and other observations in these countries. Such low ratios may possibly be explained by an observation that in some countries gift demands by tax inspectors may not be considered corruption. Another explanation could be that our definition of tax corruption calculated from the Enterprise Surveys, i.e., the ratio of the number of "yes" answers to the “J5 question” to the total number of "yes" answers to the “J3 question”, would not cover cases 11
Table 1: Response Rate at Workshops

|Institution|Location|Year|Eligible<br>Attendees|Surveyed|Response<br>Rate|
|---|---|---|---|---|---|
|IDB|Washington, D.C.|2018|49|18 (18)|0.37 (0.37)|
|World Bank|Athens, Greece|2019|39|38|0.97|
|World Bank|Marrakesh, Morocco|2019|41|33|0.80|
|World Bank|Bangkok, Thailand|2023|30|27|0.90|
|World Bank|Dar es Salaam, Tanzania|2023|31|30|0.97|
|Total|||190|156|0.82|

The IDB rows include responses from the “pre” period and, in parentheses, the “post” period. The total is calculated using the total number of unique respondents across both rounds of the IDB survey (28). We exclude researchers from both the “eligible attendees” and “surveyed” counts, as too few researchers attended these workshops to be considered. Otherwise, all workshop participants are considered to have been eligible. Overall, response rates were very high.

## **2.2 IDB Sample**

Participants were also recruited from a workshop organized in May 2018 at the IDB headquarters in Washington, DC. Like the WB workshops, this workshop ran for approximately one week, was attended by policymakers and policy practitioners, and focused on impact evaluation methods. This workshop, however, did not include matching participants with researchers to design an impact evaluation as the World Bank workshops did.

Participants were emailed the survey link by the workshop organizers before the start of the workshop and again after the workshop (for a second, identical survey). We focus on the survey responses collected before the start of the workshop, as these may be more representative of the typical preferences held by policymakers and policy practitioners.<sup>4</sup>

# **3 Method**

A brief introduction informed participants that they would be asked questions that referred to conditional cash transfer programs. We also defined terms that some

> 4We do not have a sufficiently large sample to make before/after comparisons.

7
intervention by education, income and ethnic affiliation. Column 1 shows that participants with education above the median (completed bachelor’s and master) that were exposed to T3 experienced an increase of 0.55 standard deviation on their “use of public spaces index”. On the other hand, as shown in Column 2, the same intervention resulted in an increase of only 0.23 standard deviation for those with belowmedian education levels (completed primary and secondary). This difference is statistically different at the 5 percent confidence level.

Table 2.A. Subgroup Analysis: Willingness to Use Public Spaces (by education, income and ethnicity)

|VARIABLES<br>Index Public<br>Space (z-|(1)<br>Education<br>above<br>median|(2)<br>Education<br>below<br>median|(3)<br>F-test<br>(p-<br>value)|(4)<br>Income<br>above<br>median|(5)<br>Income<br>below<br>median|(6)<br>F-test<br>(p-<br>value)|(7)<br>Urdu<br>Speaker<br>s|(8)<br>Non-<br>Urdu<br>Speake|(9)<br>F-test<br>(p-<br>value)|
|---|---|---|---|---|---|---|---|---|---|
|score)|||(1)-(2)|||(4)-(5)||rs|(7)-(8)|
|[T1] Urban|0.41***|0.27***|0.23|0.47***|0.19**|0.01|0.34***|0.27**|0.60|
|Design|(0.100)|(0.067)||(0.085)|(0.075)||(0.066)|(0.112)||
|[T2] Diversity|0.01<br>(0.109)|0.07<br>(0.070)|0.63|0.01<br>(0.089)|0.07<br>(0.079)|0.62|0.05<br>(0.071)|-0.02<br>(0.108)|0.54|
|[T3] Urban|0.55***|0.23***|0.01|0.43***|0.24***|0.10|0.41***|0.10|0.01|
|Design +|(0.097)|(0.069)||(0.084)|(0.075)||(0.064)|(0.111)||
|Diversity||||||||||
|Observations|718|1,206||908|1,016||1,473|451||
|Control Mean|-0.16|0.09||-0.13|0.13||-0.03|0.10||
|Selected<br>Controls|X|X||X|X||X|X||

Notes: Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

Similarly, Columns 4-5 show that exposure to urban design elements generates an increase of 0.47 standard deviation for the richer participants (monthly household income above the median of 30,000 Pakistani rupees) and 0.19 for the poorer. This difference is statistically different at 5 percent confidence level. Finally, Columns 7-8 show that exposure to urban design elements and diversity is associated with an increase of 0.41 standard deviation on the “use of public spaces index” for Urdu speakers, but this finding does not hold for non-Urdu speakers. This difference is statistically different at 5 percent. In other words, even though the different social groups expressed that they like the improved public space and want to come back after having experienced the T1 and T3 interventions, there are group-based differences in the magnitude of these impacts. This raises an important question regarding inclusive urban design for vulnerable groups, which is discussed at the end of the paper.

15
The organized manufacturing sector is surveyed by the Central Statistical Organization every year through the Annual Survey of Industries (ASI), while unorganized manufacturing establishments are separately surveyed by the National Sample Survey Organization (NSSO) at approximately five-year intervals. Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. We use the provided sample weights to construct population-level estimates of total establishments and employment at the district and two-digit NIC level. We focus mostly on district and industry variation in our empirical analyses. Districts are administrative subdivisions of Indian states or union territories that are more appropriate spatial units for understanding the urbanization process.

These surveys identify for each establishment whether it is in an urban or rural location. Our study considers changes in urbanization over time, and thus the stability and comparability of this survey question over time are very important. To begin with, the statutory definition of an urban setting during our period of study is:

- (a) All statutory places with a municipality, corporation, cantonment board or notified town area committee, etc., or

- (b) A place satisfying the following three criteria simultaneously:

- i) A minimum population of 5,000; ii) At least 75% of male working population engaged in non-agricultural pursuits; and

- iii) A density of population of at least 400 per sq. km. (1,000 per sq. mile).

This definition has been mostly stable since the 1961 Census. One set of adjustments with the 1971 and 1991 Censuses focused on including ‘outgrowths’ (e.g., railway colonies, university campuses, industrial townships, and residential and commercial complexes) that lay beyond strict town or village boundaries within a combined urban agglomeration concept for the purposes of classifications. A second set of adjustments enacted since the 1981 Census focused on the definition of the workforce and agricultural sector.<sup>4</sup> Among our datasets, the 1989 survey follows the 1981 Census classification, the 1994 and 2000 surveys follow the 1991 classification, and the 2005 survey follows the 2001 Census classifications. Our primary focus is on urbanization changes since 1994, and the formal 4 India’s Ministry of Urban Development Report (2011), Kundu (2006, 2009, 2011a and 2011b), Himanshu (2007), Mohan and Dasgupta (2005), Planning Commission (2008), Nelson and Uchida (2008), Bhagat (2011), Ghani (2010), Sivaramakrishna and Kundu (2005) provide greater details. Desmet et al. (2011) examine spatial development of India. Chrandrasekhar (2011) and Mohanan (2008) examine the trends in workers commuting between rural-urban areas. Shaw and Pundit (2001) study segmentation of the informal sector within an industry acrosss urban zones. Gregson et. (2012) provide related evidence from Bangladesh.

5
<!-- Start of picture text --> 9<br>ov<br>ooo 8 .<br>|<br>ptN47 : os<br>» 6 ‘ , 1, |<br>|<br>2 ft, a: |<br>a Lh 4 ° es—e |<br>: .<br>| |<br>Sc 2 ; e<br>o 1 | ;<br>- |<br>1 2 | | |<br>log(1+floods |<br><!-- End of picture text -->
Islam N., Winkel J. (2017). Climate change and social inequality. DESA Working Paper No. 152 Key, N. and Sneeringer, S. (2014). Potential effects of climate change on the productivity of us dairies. _American Journal of Agricultural Economics_ , 96(4):1136–1156.

<mark>Nasri, K., & Belhadj, B. (2022). Household Vulnerability and Resilience in Tunisia: Evidence Using Fuzzy Sets and Multidimensional Approach.</mark> _<mark>S</mark> tudies in Microeconomic_ _<mark>s</mark>_ <mark>, 23210222221098836.</mark> Pörtner, H.-O., Roberts, D. C., Adams, H., Adler, C., Aldunce, P., Ali, E., Begum, R. A., Betts, R., Kerr, R. B., Biesbroek, R., et al. (2022). Climate change 2022: Impacts, adaptation and vulnerability. IPCC Sixth Assessment Report.

Stevanović, M., Popp, A., Lotze-Campen, H., Dietrich, J. P., Müller, C., Bonsch, M., Schmitz, C., Bodirsky, B. L., Humpenöder, F., and Weindl, I. (2016). The impact of high-end climate change on agricultural welfare. Science advances, 2(8):e1501452.

Svoboda, M., Hayes, M., Wood, D. A., et al. (2012). Standardized precipitation index user guide.

Verner, D., Treguer, D., Redwood, J., Christensen, J., McDonnell, R., Elbert, C., & Konishi, Y. (2018). Climate variability, drought, and drought management in Tunisia’s agricultural sector. World Bank, Washington, DC, World Bank.

Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23(7):1696–1718.

World Bank (2022). Tunisia’s Jobs Landscape. Available at: <u>https://www.worldbank.org/en/country/tunisia/publication/tunisia-s-jobs-landscape</u> World Bank (forthcoming). Tunisia Country Climate and Development Report (CCDR). Washington, D.C. : World Bank Group World Bank. (2015). Labor Policy to Promote Good Jobs in Tunisia: Revisiting Labor Regulation, Social Security, and Active Market Programs, 2015.

World, Bank, 2021. Climate Risk Profile: Tunisia (2021): The World Bank Group.

Zouabi, O., & Peridy, N. (2015). Direct and indirect effects of climate on agriculture: an application of a spatial panel data analysis to Tunisia. Climatic change, 133, 301-320.

16
# **FIGURE 1: TRADE WAR TARIFFS AND EXPORT GROWTH**

<!-- Start of picture text --> Panel A Panel B<br>Bystanders' Export Value to US Bystanders' Export Value to China<br>2015-17 2017-19 2015-17 2017-19<br>.2 .3<br>.2<br>.1<br>.1<br>0<br>0<br>-.1 -.1<br>0 .05 .1 .15 .2 0 .05 .1 .15 .2<br>∆ ln T(US,CH) ∆ ln T(CH,US)<br>Pre-period: β =-0.19 (0.10). Post-period: β =0.31 (0.10). Pre-period: β =0.07 (0.18). Post-period: β =0.01 (0.19).<br>Panel C Panel D<br>Bystanders' Export Value to RW Bystanders' Export Value to RW<br>2015-17 2017-19 2015-17 2017-19<br>.2 .2<br>.1 .1<br>0 0<br>-.1 -.1<br>-.2 -.2<br>0 .05 .1 .15 .2 0 .1 .2 .3<br>∆ ln T(US,CH) ∆ ln T(CH,US)<br>Pre-period: β =-0.14 (0.08). Post-period: β =0.20 (0.08). Pre-period: β =0.11 (0.08). Post-period: β =0.29 (0.08).<br>ln X(i,US) ln X(i,CH)<br>∆ ∆<br>ln X(i,RW) ln X(i,RW)<br>∆ ∆<br><!-- End of picture text --> Notes: The panels show binscatter plots of bystanders’ export growth (on the y-axes) against changes in tariffs due to the trade war (on the x-axes). Panel A is the regression: ∆ ln _Xiω_<sup>_US_</sup> = _α_ + _β_ ∆ ln _TCH_<sup>_US_+</sup><sup>_ε_</sup> _iω_<sup>_US_.Panel B is the regression:</sup> ∆ ln _Xiω_<sup>_CH_</sup> = _α_ + _β_ ∆ ln _TUS_<sup>_CH_+</sup><sup>_ε_</sup> _iω_<sup>_CH_.PanelCistheregression:∆ln</sup><sup>_X_</sup> _iω_<sup>_RW_</sup> = _α_ + _β_ ∆ ln _TCH_<sup>_US_+</sup><sup>_ε_</sup> _iω_<sup>_RW_</sup> . Panel D is the regression: ∆ ln _Xiω_<sup>_RW_</sup> = _α_ + _β_ ∆ ln _TUS_<sup>_CH_+</sup><sup>_ε_</sup> _iω_<sup>_RW_</sup> . Also reported are regressions with exports prior to the trade war from 2015-17. Below each panel are OLS coefficients (standard errors clustered by product).

15