rafmacalaba/data-use-mentions-extended huggingface dataset:
of total output and fiscal revenues) became key performance indicators for their career advancement (Li and Zhou 2005; Wang, Zhang, and Zhou 2020). For example, Li and Zhou (2005) show that between 1979 and 1995, the chance of promotion for provincial PSs increased with their economic performance. At the prefecturecity level, promotion opportunities of PSs mainly include membership in the provincial Party Standing Committee, the vice provincial governorship, and the deputy ministership in the central government.<sup>14</sup>

# **2.2 GDP accounting system and target setting in China**

# **2.2.1 GDP compilation and statistical institutions**

> 10 There are 333 prefecture-level administrative units in China as of 2020: 15 deputy-provincial level cities, 278 prefecture-level cities, 7 prefectures ( _diqu_ ), 30 autonomous prefectures ( _zizhizhou_ ) and 3 leagues ( _meng_ ). We do not consider provincial- and county-level cities because county-level cities are already embedded in prefecture-level cities, while province-level cities are too large to be comparable.

> 11 Top Chinese local officials could of course be viewed as both politicians and bureaucrats. Since they are typically professional life-time careerists in the Chinese bureaucratic system, who are selected for their capacity and evaluated professionally, and aim at rising in rank and importance, it is likely better to view them as bureaucrats rather than politicians, which has the connotation of catering to voters, non-existent in China.

> 12 The ranks of China’s officials are similar to those of administrative units.

> 13 Appointments of PSs and mayors of deputy-provincial level cities are also subject to the oversight of the Central Committee. These deputy-provincial level cities are governed by provincial-level governments though.

> 14 For deputy-provincial-level ranked PSs, the positions for promotion include provincial secretaryship and governorship as well as ministership in the central government.

7
the host. The stability and efficiency trade-off of branches versus subsidiaries thus depends on specific conditions.

Finally, fintech is a critical driver and is rapidly changing the banking landscape. Financial innovations (e.g., cheaper and faster remittances & payments services and collateral registries using blockchain-based methodologies, peer-to-peer lending, credit scoring based on online platform data) can facilitate cross-border expansion of EMDE groups by helping them reach underserved segments of the household and firm population with a new range of products and services; previously, such expansion might have been impossible or cost-prohibitive. However, fintech also poses challenges for regulators to nurture innovation and competition, while keeping risks related to financial stability, consumer protection, and AML/CFT at bay.

# **3. Scale and characteristics of EMDE groups**

By end-2013, EMDE groups operated 319 overseas banking subsidiaries in EMDE hosts and 49 in AEs (Table 2, Panel B and Annex 2). This is slightly less than AE groups, which were active through 340 overseas banking subsidiaries in EMDE hosts. Through its EMDE group operations, the median EMDE home country was connected to four EMDE hosts. The most and the least interconnected regions are Sub-Saharan Africa (SSA) and South Asia (SAR), respectively. The most connected home country is South Africa, which has banking ties to 15 EMDE hosts through overseas activities of South African groups. EMDE groups are mostly active in their own regions, but notable exceptions exist – MENA’s groups are quite active in ECA and SSA, for example.

While cross-border bank activities of EMDE groups are often still small relative to most home and host countries’ financial sectors, their relevance to both home and host jurisdictions has grown (Figure 2):

- In the median <u>host country</u> of EMDE groups, assets from EMDE group overseas bank subsidiaries increased by more than 10 percentage points from 2.6 percent of total host bank assets in 2000 to 13 percent in 2013.

11
indicators has occurred in the past 10 years such as population (e.g Leyk et al., 2019). The advantages of gridded data as a common spatial unit of integration and the cross-entropy models are clear. These common units allow us to examine within-country characteristics, especially in the case of spatial data that do not conform with each other such as administrative boundaries and natural hazards to inform analyses with local estimates. We present a novel data set that disaggregates the national and regional accounts of the agriculture sector across areas as a result of a model where we use ancillary data including satellite data. This allows us to estimate especially in countries that have a relatively higher share of agricultural activity in the entire economy. Then, we examine the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP, where nearly 1.2 billion people live, and find an estimated US$432 billion of agricultural GDP in 2010.

> 19The freshwater case does not account for any variation, whereas the marine port locations incorporate variation on vessel holds.

25
of refugees are male and young. Even after 1 or 2 years in Switzerland, refugees’ employment rates are almost 80 percentage points lower than natives’ and it takes them more than 10 years to reach a level which is 18 to 20 percentage points lower than the employment rate of natives. Controlling for the economic conditions in the host country (using fixed effects for the year of observation) does not change these results (column (2)). In columns (3) and (4) we also control for heterogeneity among arrival cohorts and the age structure of refugees upon arrival in Switzerland (using fixed effects for year of arrival and age at arrival). Adding these controls has a small impact on coefficients, by reducing slightly the employment gap for the first five years after arrival.

In section 4.2 above, we discussed some problems of collinearity that arise in the context of the identification of the profiles of refugees’ labor market integration. The first collinearity problem occurs in columns (3) to (6) of Table 3, where we control for year of arrival, the number of years since migration and the current year, which are perfectly collinear for refugees. We identify the effects of years since migration and the year of arrival by assuming that the impact of the current year dummies on employment rates is identical for refugees and natives. Second, in columns (4) to (6) we introduce fixed effects for the refugees’ age at arrival. As there is perfect collinearity between the refugees’ age at arrival, their age and the number of years since migration, this is equivalent to assuming that the impact of age on employment rates might differ for refugees and natives.<sup>28</sup> These estimates do not take the lower education levels of refugees into account, since this variable is missing in our main sample. This information is available in the Structural Survey, which is carried out yearly since 2010 and covers each year approximately 2.5% of
the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include logarithms of population, GDP, the average exchange rate and a measure of concentration in the top 4 FDI sources (i.e., United States, Germany, United Kingdom, and France, weighted by their respective 2003 destination shares). Standard errors are clustered at the destination country. Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

35
observed that the linear projection of the time series of poverty measures for the developing world suggests that one billion people could be lifted out of poverty by 2027. However, the above discussion has also pointed to some concerns about whether the linearity will hold that long. This section approaches the problem of quantifying an optimistic trajectory in a rather different way, without relying on the time-series evidence.

> 14 These forecasts factor in historical gaps between growth rates in the survey means and growth rates in private consumption per capita in the NAS. For most countries, approximately 90 percent of the NAS growth rate is passed onto the survey means, but for India, it was only approximately half, consistent with the larger gap between the two growth rates for India. In addition, an allowance is made for rising inequality in both China and India. For further details, see World Bank (2008).

13
>Rolstad, S., Adler, J., & Rydén, A. (2011). “Response burden and questionnaire length: is shorter better? A review and meta-analysis.”</mark> _<mark>Value in Health</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>(8), 1101-1108.</mark>

- <mark>Silberstein, Adriana (1990). “First wave effects in the U.S. Consumer Expenditure Interview Survey.”</mark> _<mark>Survey Methodology</mark>_ <mark>16: 293-304.</mark>

- Silberstein, A. R., and C. A. Jacob. (1989). “Symptoms of Repeated Interview Effects in the Consumer Expenditure Survey.” In Panel Surveys, ed. D. Kasprzyk, G. Duncan, G. Kalton, and M. P. <mark>Singh,</mark> 289– 303. Hoboken, NJ: Wiley.

- <mark>Smith, L. C., Dupriez, O., & Troubat, N. (2014). “Assessment of the reliability and relevance of the food data collected in national household consumption and expenditure surveys.”</mark> _<mark>International Household Survey Network</mark>_ <mark>.</mark>

- <mark>Smith, L. C. (2015). “The great Indian calorie debate: Explaining rising undernourishment during India’s rapid economic growth.”</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>, 53-67.</mark>

- <mark>Sudman, S., Bradburn, N. M., Schwarz, N., & Gullickson, T.
system. Specifically, consider a mechanism activated at a certain _t_ that produces a pulse sequence _ζ_ is the magnitude of the value of the considered impact. If **e** is the vector with a unit in the positions where a shock occurs, the response by the system is represented by This system is inactive until _t_ = 0, after which it generates the sequence _{_ **Ψ** 0 **e** _,_ **Ψ** 1 **e** _, ...,_ **Ψ** _∞_ **e** _, }_ . The impulse travels through the entire SVARMA structure with speed depending on the spatial autoregressive and time autoregressive parameters. It is possible to trace all the routes by taking into account how the spatial autoregressive polynomial **H** ( _z_ ) is structured. Finally, confidence bands around the response can be obtained by repeating an experiment of identical impact, and drawing different parameters for the SVARMA structure randomly from their confidence bands. Trivially, the sequence eq. (24) converges to zero exponentially fast _a.s._ , for a stationary and ergodic model. Hence, even when the aggregate behavior of all parameters is not directly of interest, the IRF provides a useful tool to explore stability of the estimated model, which is important also for Granger-causal inference on the individual parameters.

# **4 Penalized Estimation**

To relax the Gaussian assumption that may not hold for data that exhibits extreme tail movement with high probability, often the case in the environmental-economic data, we discuss estimation in the context of the Students’ _t_ -estimation. In line with our discussion on uniqueness, we apply _L_<sup>2</sup> (Eucledian distance) penalties set on the moving average components that vanish with a weight of 1 _/√NT_ . Penalizing the _L_<sup>1</sup> norm (absolute sum), as
today’s African economies. Instead, SSA will need to undertake a long-term process of institutional and economic reform to bring growth and better jobs to the region.

Instead, the informal sector – broadly defined – is the main source of jobs in urban SSA. The informal economy can be characterized as “small-scale, semi-legal, often lowproductivity, frequently family-based, perhaps pre-capitalistic enterprises” (Maloney 2004) and the jobs within.<sup>5</sup> Drawing from Bandiera et al (2022), we might define them as being less-organized and operating in low value-added sectors. De Vreyer and Roubaud (2013) use data from 2000-2001 from eight principle Sub-Saharan African cities to estimate that 70

> 1 The percentage varies widely between 14 percent in Burundi to 90 percent in Gabon.

> 2 Urban population growth in Sub-Saharan Africa can be attributed to high birth rates as well as urban to rural migration (Lam et al 2019).

> 3 The literature on the economic benefits of urban agglomerations is well established. For example, urban areas provide more access to public and private infrastructure (water, electricity, sewerage) and services (security forces, banks, schools), greater diversity in production and thus opportunities for trade, larger markets of suppliers and clients, and knowledge spillovers. However, as argued in Lall et al (2017), the economic benefits characteristic of urban zones are often absent in African cities.

> 4 See, for example, Calderon (2022), Coulibaly et al. (2022), Abreha et al. (2021).

> 5 ILO (2018) finds that 5.5% of the African labor force is classified as informal employees (not receiving legally mandated social benefits) working in formal sector firms (registered with authorities). The statistic includes data from SSA and Northern Africa and uses national data (rural and urban).

2
production within countries. Furthermore, in countries with a high share of agricultural employment in the national workforce, they find this combined effect increases the likelihood of conflict onset. To better target rural development strategies for economic growth and poverty reduction, as well as conserve the natural resource base for long-term sustainable development, we need to accurately delineate the spatial distribution of agricultural resources and production activities (Wood et al., 1999).

One method to partially address spatial mismatch between administrative and other geographic units such as natural hazards uses the gridded (raster) data format by providing an intermediate and consistent unit for disaggregation and aggregation (e.g. UNISDR, 2011). Data-disaggregation methods can use detailed data to inform estimates of aggregated data from large areas at the local level (e.g. see review in Pratesi et al., 2015). Several spatial data products from global models are available to estimate population at a local level (see review in Leyk et al., 2019).

Previous evidence-based risk analyses take advantage of global data of hazards to estimate exposure of population and economic activity (e.g. Gunasekera et al., 2018; Rashmin 3
, Romero, C., Smith, E. N., Yeager, D. S., & Dweck, C. S. (2015). Mind-set interventions are a scalable treatment for academic underachievement.</mark> _<mark>Psychological science</mark>_ <mark>,</mark> _<mark>26</mark>_ <mark>(6), 784793.</mark> <mark>Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement.</mark> _<mark>Econometrica</mark>_ <mark>,</mark> _<mark>73</mark>_ <mark>(2), 417-458.</mark> <mark>Rockoff, J. E. (2004). The impact of individual teachers on student achievement: Evidence from panel data.</mark> _<mark>American economic review</mark>_ <mark>,</mark> _<mark>94</mark>_ <mark>(2), 247-252.</mark> <mark>Rothstein, J. (2014). Revisiting the impacts of teachers.</mark> _<mark>UC-Berkeley Working Paper</mark>_ <mark>.</mark> <mark>Sabarwal, S., & Abu-Jawdeh, M. (2018). What teachers believe: mental models about accountability, absenteeism, and student learning.</mark> _<mark>World Bank Policy Research Working Paper</mark>_ <mark>, (8454).</mark> <mark>Sansone, D. (2019). Beyond early warning indicators: high school dropout and machine learning.</mark> _<mark>Oxford bulletin of economics and statistics<
# DETERMINANTS OF PUBLIC EXPENDITURE ON INFRASTRUCTURE: TRANSPORTATION AND COMMUNICATION'

# **I.** WHY STUDY **THE DEirm[NANTS OF** PUBUC EXPEND1muRE **ON INFRASTRUCTURE?**

The scope for investment in infrastructure and public consumption goods to increase urban productivity and national economic development is the subject of renewed attention in developing and transitional economies. Recent studies document the fact that public investment in infrastructure, such as roads, communications, and utilities, stimulates private investment. Blejer and Khan (1984), using a cross-country data set, find that public investment in infrastructure complements private investment. Greene and Villanueva (1991), using a panel of 23 developing countries, and Serven and Solimano (1993), using a panel of 15 countries, similarly find that public investment in infrastructure "crowds in" private investment. Time-series studies from single developing countries, such as Musalem's for Mexico (1989), also document the positive link between public investment in infrastructure and private investment. The _World Development Report 1994,_ focussing on the link between infrastructure and development, expounds numerous ways in which policy can improve the _quality,_ not only the quantity, of infrastructure services in less developed countries.

Many infrastructure investments have characteristics of public goods -- non-exhaustive and non-exclusive in consumption -- and thus will be undersupplied by the private sector. Yet, infrastructure investments facilitate private investment by lowering private production costs and opening new markets, thereby creating new opportunities for profit. Roads reduce transportation costs. Ports reduce transactions costs and facilitate trade, reducing the cost of input supplies and exposing domestic firms to the innovative forces of international competition. Telecommunications networks reduce transactions costs by increasing the flow of information and, as noted by Leff (1984), have important side effects that make other economic institutions more efficient. Norton (1992), using pooled time-series, cross-national data from 47 countries from post-World War II to 1977, finds that telecommunications networks not only increase investment, but also increase the rate of
4|1.5|-12.9|14.2|-0.1|-1.1|-11.3|8.3|0.1|2.9|
|SEE-4|17.1|2.0|-15.8|-5.3|9.0|-2.6|-10.0|2.4|13.9|-7.7|-7.5|0.1|
|**_Memorandum_**:Shareinper<br>|centin<br>|Bulgari<br>|a's total<br>|export<br>|s<br>||||||||
|Bulgaria|37.7|17.1|28.3|16.9|35.2|27.1|21.5|16.2|34.0|32.2|18.9|14.9|

Notes: NR—natural resource intensive products; UL-unskilled labor intensive products; CI-capital intensive products; SLskilled labor intensive products. SEE-4 countries include Albania, Macedonia, Moldova and Serbia and Montenegro. Because of lack of data, Bosnian and Herzegovina is not included. Source: Derived from countries’ data in UN COMTRADE database.

The 1996-2003 period has seen shifts in factor content of Bulgaria’s exports vis-à-vis its regional peers that in most CEEC-8 took place during the initial stages of transition in 1991-95. First, exports of CEEC-8 moved towards capital intensive products: their share was 11 percentage points higher than in Bulgaria’s exports in 2000 and rose further to 15 percentage points in 2003. Similar change has occurred in skilled labor intensive products, with the gap widening from 8 percentage points in 1996 to 16 percentage points in 2003. This shift is also visible when compared with Lithuania, an economy with a heavy presence of natural resource intensive exports. Lithuania moved between 1996 and 2003 away from
# **4.3 Data**

The household-level panel data for the empirical analysis were collected over a period of five years from households that were targeted by the MiDA land titling pilot intervention as well as those households located just outside the intervention area, i.e. the short-term control group and the long-term control group.

The first of the three surveys waves was collected in 2010; the second in 2011; and the third in 2014.<sup>9,10</sup> Therefore, all three survey waves were collected after the land titling intervention was initiated in 2009. The first two survey waves allow us to understand the short-term effects of the land titling program immediately after program initiation and the third survey wave gives us some insight into medium-term program impacts. Each of the three survey waves comprised of seven survey modules each covering a different dimension of the socio-economic characteristics including: demographic characteristics of sampled households; paid employment engaged in by household members; individual and household assets; agricultural production and land titling; non-farm enterprises; marital history of household heads and spouse(s); and financial literacy training.<sup>11</sup> The survey team also collected data on household and plot locations using GPS.

9 During the first round of field survey in 2010, a total of 65 enumerators were trained over a period of six days. Out of this number, 54 enumerators were selected for field work. The selection was based on the outcome of a test conducted to examine enumerator competence with regards to the questionnaire administration. In addition, a language fluency examination was also undertaken to ensure that enumerators had command over the Twi language which was going to be the main medium of communication with respondents. From the set of 54 enumerators, three working teams of 18 members were created. Each team consisted of a supervisor, a field editor, 2 plot mapping experts and 14 enumerators. The supervisor was the team leader and was responsible for overseeing, monitoring and, where necessary, correcting
|||(0.0112)|(0.0116)|(0.0192)|(0.0190)|(0.0207)|
|**preneur**<br>**Model**|Ever been a business||0.195***|0.151**|0.149**|0.339***|
|**Entre**<br>**Role**|owner||(0.0561)|(0.0562)|(0.0563)|(0.0818)|
|**e skills**|Digitspan score|||0.0213<br>(0.0257)|-0.0258<br>(0.0353)|-0.0375<br>(0.0411)|
|<br>**itiv**|Raven test score|||-0.0278|-0.00645|-0.0529*|
|stics<br>**ogn**||||(0.0192)|(0.0164)|(0.0307)|
|teri<br>**n-c**|Personal intitative|||-0.106|-0.270***|-0.120|
|rac<br>**no**||||(0.136)|(0.0798)|(0.189)|
|Cha<br>**and**|Error competence|||0.0868|0.505**|0.356*|
|iner<br>**ive**||||(0.140)|(0.204)|(0.186)|
|Tra<br>**Cognit**|Prosocial orientation|||0.