rafmacalaba/data-use-mentions-extended huggingface dataset:
Perceptions Index for 2002<sup>13</sup> . After eliminating observations with missing key data<sup>14</sup> , our sample includes 7760 firms in 80 developed and developing countries.

We measure property rights protection by survey responses to the question _“I am confident that the judicial system will enforce my contractual and property rights in business disputes”._ Firms are asked to respond on a six point scale, with one denoting the **highest** level of confidence. This property rights question is not meant to capture disputes between the state and the individual.

The four firm-level variables we study, **Size** , **Ownership, Legal Organization,** and **Sector** are also obtained from the survey. The sample includes firms of all sizes: small firms (between 5-50 employees) represent 39% of the sample, medium firms (51 to 500 employees) are 40% of the sample and the remaining 20% are large firms (more than 500 employees)<sup>15</sup> . The firms’ ownership structures are classified into 9 different categories - individual, family, conglomerate group, bank, board of directors, managers, employees, government and others. We also have information on the legal organization of the company, whether it was organized as a single proprietorship, partnership, cooperative, privately-held corporation, corporation listed on a stock exchange and a other category. The firms surveyed belong to five different sectors, agriculture, manufacturing, services, construction and other.

# **Insert Figure 1**

Figure 1 shows that there are differences in perceptions of property rights across firms with different characteristics. Overall, small-sized firms perceive their property rights as being less secure when compared to large and medium firms as shown in Figure 1a. On probing deeper, we find that there is significant variation across sizes even within the same country, i.e., holding all legal and political institutions constant. For
We further adjust the green dictionary so that it reflects the South African context. We reviewed the South African literature and policy reports for occupations that are expected to reduce the human-induced negative impact on the environment and climate change. We found discussion of industries that will adapt due to the greening of the economy - energy generation, resource efficiency, emissions control, and natural resource management – and industries that are most vulnerable to job loss –mining, petrochemical, electricity, agriculture, and tourism sectors (PCC 2021; ILO 2018; McLean 2018; Montmasson-Clair 2012; Maia et al. 2011; Rutovitz 2010; Nhamo 2010). None of these studies, however, takes an occupational approach, i.e., looks at the actual activities or tasks individuals do in jobs that could affect the human-induced impact on the environment. The only list of green occupations that we found was compiled by the Department of Higher Education and Training (DHET) through their organizing framework for occupations (OFO).<sup>20</sup> We use the DHET list to adjust the Granata and Posadas (2024) green dictionary.

We follow a three-step process to adapt the green dictionary for the South African context and connect it to the PALMS database. First, we adjusted a few terms in the green dictionary based on the DHET list of green occupations: _heat pump_ , _biotechno_ , _chemistry_ , _geograph_ and _geophysics._ These were in the Granata and Posadas (2024) green dictionary but were not identified as strict green terms. However, they should be classified as strict in the South African context since the OFO list includes occupations with those root terms: geophysicist, biotechnologist, geophysical technician, and heat pump installer (DHET 2013). After this adjustment, we ended up with 321 strict green terms and 37 additional terms for the broad green list. Second, we use the South Africa-adjusted green dictionary and follow the method used by Vona (2019) to calculate the GTI scores for each occupation as per Equation 1.
Policy Research Working Paper 8614

# **Abstract**

This paper studies the impact of transport infrastructure projects of the Belt and Road Initiative on shipment times and trade costs. Based on a new data on completed and planned Belt and Road transport projects, Geographic Information System analysis is used to estimate shipment times before and after the Belt and Road Initiative. Two sets of data are computed to address different research questions: a global database based on an analysis of 1,000 cities in 191 countries and 47 sectors and a regional database that focuses on more granular information (1,818 cities) for Belt and Road economies only. The paper uses sectoral estimates of “value of time” to transform changes in shipment times into changes in ad valorem trade costs at the country-sector level. The findings show that the Belt and Road Initiative will significantly reduce shipment times and trade costs. For the world, the average reduction in shipment time will range between 1.2 and 2.5 percent, leading to reduction of aggregate trade costs between 1.1 and 2.2 percent. For Belt and Road economies, the change in shipment times and trade costs will range between 1.7 and 3.2 percent and 1.5 and 2.8 percent, respectively. Belt and Road economies located along the corridors where projects are built experience the largest gains. Shipment times along these corridors decline by up to 11.9 percent and trade costs by up to 10.2 percent. The paper also shows that these effects are magnified by policy reforms that reduce border delays and improve corridor management.

This paper is a joint product of the Macroeconomics, Trade and Investment Global Practice; the Office of the Chief Economist, Middle East and North Africa Region; and the Development Research Group, Development Economics. 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
Table 8: OLS and IV Estimates of Mother’s Labor-Supply Models in Albania (2002 - 2012)

|Method||OLS|IV|IV|
|---|---|---|---|---|
|Instrument for Fertility:|||||
|_(Number of children ever born)_||—|_Same-sex_|_Two girls_|
|Survey Fixed-Efects||Yes|Yes|Yes|
|District Fixed-Efects||Yes|Yes|Yes|
|Controls||Yes|Yes|Yes|
|Dependent variable<br>Mean|Obs.|(1)|(2)|(3)|
|_Panel A: All data_|_bases (LS_|_MS and DHS)_|||
|_Worked in last_ 7 days<br>0.445|7404|-0.0063<br>(0.0073)|0.0474<br>(0.0474)|0.0360<br>(0.0340)|
|_Worked of-farm_<br>0.164|7404|-0.0291***<br>(0.0045)|0.0052<br>(0.0354)|0.0503**<br>(0.0258)|
|_Worked on-farm_<br>0.218|7404|0.0347***<br>(0.0066)|0.0125<br>(0.0396)|-0.0019<br>(0.0280)|
|_Self-employed_<br>0.067<br>_Panel B: On_|7404<br>_ly LSMS _|-0.0086**<br>(0.0037)<br> _databases_|0.0404<br>(0.0270)|-0.0107<br>(0.0182)|
|_Hours per week
disaggregate CAPEX by sector. However, evidence from BOOST allows the authors to investigate the evolution of public infrastructure spending by sector over the period 2009–16. During this time, infrastructure spending in the WSS sector began at about 0.3 percent of global GDP, climbing to a peak of about 0.6 percent in 2012, before falling back to 0.3 percent. The average expenditure for this period was around 0.4 percent of global GDP.

When combined with our estimates for OPEX, the use of the limited direct CAPEX data available results in total networked water and sewered sanitation subsidies in low- and middle-income countries in the range of 0.75–0.95 percent of GDP. While these estimates are below our estimate of 1.59–1.95 percent of GDP for low- and middle-income countries, such discrepancy is not unexpected given key differences between the two approaches followed.

First, the use of direct expenditure significantly underestimates the CAPEX subsidies provided to the sector for existing infrastructure due to the deferral of maintenance—a phenomenon especially common in low- and middle-income countries. It has been well-documented that low- and middle-income countries in particular struggle with revenue collection and limited fiscal capacity, and are thus prone to significant deferrals of maintenance, as well as major repairs and replacement of existing infrastructure. Yet, even if these expenditures are not currently being made—either through taxes, transfers, or tariffs—they will need to be covered by future generations to maintain existing WSS services over time. Since our model allocates such expenditure in equal installments across the design life of the asset, its estimates are significantly higher by reflecting these intergenerational subsidies.

Second, while our model accounts for the full costs of required major repairs and replacement of existing infrastructure, it does not account for expenditures towards infrastructure expansion. In a steady-state situation whereby infrastructure expansion is limited, both estimates should be reasonably similar since actual direct CAPEX would be exclusively and comprehensively covering the maintenance and replacement of existing infrastructure.
distribution, and rail transport services.<sup>2</sup> In line with many OECD countries, India has reformed its manufacturing sectors to allow for more FDI, but restrictions on foreign investments in services have remained relatively high compared to the OECD average (OECD Service Trade Restrictiveness Index).<sup>3</sup> The liberalization of trade and FDI increased the imports of services by the manufacturing sector. The import service intensity increased from around 5 percent in 1990 up to a high of 21 percent in 2017. Imports became particularly important for the Automotive and Electronics sectors, as can be seen in the right panel of Figure 5. In 2017, imports constituted nearly one-third of all services used in the electronics industry and more than a quarter of all services used in the automobile industry. These two industries have driven a large part of the upward trend in service imports in manufacturing. In Europe, Transport equipment is also a high user of imported services, in addition to Textiles and Coke and Chemicals (Veugelers, et al. 2017). Textiles firms in India on the other hand, import relatively few services compared to other sectors.

Figure 4: Imports and Exports by Domestic Firms and Foreign owned Firms <!-- Start of picture text --> Import Share (by Ownership Status)<br>Export Share (by Ownership status)<br>mean median<br>mean median<br>25.00<br>25.00<br>20.00<br>20.00<br>15.00<br>15.00<br>10.00<br>10.00<br>5.00 5.00<br>0.00 0.00<br>domestic minority majority wholly domestic minority majority wholly<br>foreign foreign foreign foreign foreign foreign<br>owned owned<br><!-- End of picture text --> Source: Prowess; Own calculations
identifiers, users may consult the following entries of the variable directories (Supplementary Tables 1 to 5): _plot_id_merge, hh_id_merge, indiv_id_merge, plot_manager_id_merge, respondent_id_merge, ea_id_merge_ .

Moreover, we include a common set of identifier and geographic variables, across the all four datasets of the HP. They are the EA and strata IDs, household weights, urban/rural status, geocoordinates and administrative-level codes and names, where applicable. It is important to note that the identification of administrative levels is sometimes only possible via numeric codes, which are not necessarily tracked through time (these cases are flagged in the variable directories).

# _Construction of the harmonized plot-crop and plot datasets_

This section describes plot-crop and plot-level datasets in the HP, which mostly contain data deriving from the agricultural questionnaires (see Figure 2). They consist of plot-crop and plot-level data from households which report cultivating crops in the main agricultural season, and therefore do not contain the entire population of households interviewed in each survey. Users can refer to the household or individual-level datasets (described below) for the entire sample of households.

The HP plot-crop dataset consists of a limited set of agricultural variables that were recorded at the plot and crop level of observation in the raw data, i.e. for each cultivated crop on each of the household’s agricultural plots. This dataset records production by crop on each plot, which can be used for more detailed analysis of production than the aggregated plot-level dataset can. The variables included are harvest output quantity and value, seed input quantity and value, harvest and planting months, use of improved seeds, pesticide use and crop shocks. The latter include a catch-all crop shock variable, along 9
compete with U.S. firms. 3 This paper argues that the traditional conflation of issues of formality and dualism is probably inappropriate and offers an alternate view of the role of the unregulated small firm sector. As in the industrialized countries, many workers may choose to start or work in small firms and in the developing country context the incentives to do so may be greater. First, the inefficiencies and rigidities that often accompany labor protections, and the implici **t** axation they imply in the absence of a binding minimum wage, may serve to reduce the attractiveness of formal sector employment. More generally, the low level of formal sector productivity for the mass of poorly educated workers in developing countries reduces the opportunity cost of being independently employed. Much of the informal sector may therefore reflect an efficient allocation of labor.

Despite the long tradition and a voluminous literature, support for the dualistic view is not strong and has rested largely on case studies and on comparisons of earnings across sectors adjusted for observable worker characteristics.4 This paper employs detailed panel data from Mexico to take a more comprehensive approach than has been previously feasible. It examines the earnings differentials and mobility patterns of individual workers transitioning among formal salaried employment and three modalities of informal work: The self-employed, owners of informal firms with or without additional employees; the infonnal salaried, those working for these informal firms and who are usually considered the least advantaged of the work force; and contract workers, those who do not receive a regular wage or salary, but who are paid as a percentage, by piece, on commission, _3_ See Portes, Castells and Benton (1989), Tokman (1992).The U.S. Department of Labor has sponsored two studies on the informal sector. See _Workers Without Protections: Case Studies of the Informal Sector in Developing Countries_ (1993) and **_The_** _Informnl Sector in Mexico (1992)._ 4 See Rosenzweig (1988).

2
|available for the country. Next, for any given<br>year and country, we take the average over the|
|---|
|1ast 10 years for which data are available (10|
|year moving average). This is done to avoid|
|too many missing values. Last, for the sample|
|of countries and years included in our sample,|
|we define a dummy variable equal to 1 if the|
|10-year moving average value is above the|
|median level and 0 otherwise. This dummy|
|variable is used in the regressions.|
|Source: World Value Surveys.|

43
_Source: World Bank Development Indicators (2014)_ <!-- Start of picture text --> Box 1: Demonstrating the impact of transport costs: The export fruit sector<br>The fruit industry makes a significant contribution to the South African economy; including R19.8bn in exports<br>to 36 ports, 165,000 direct employment positions at a total cost of R5.3bn and important downstream<br>linkages. Transport availability and cost is a major constraint to the fruit industry due to high port tariffs and<br>a reliance on road transport. The cost of road transport depends on distances to the port, levies and toll fees,<br>type of transport (i.e. refrigerated or conventional), cultivar weight, and payload (i.e. standard vs. high‐cube<br>pallets). 4 The export fruit industry spends R970m annually in road transport costs.<br>The below example of an orange producer in the Olifants River valley of the Western Cape gives an indication<br>of the extent to which transport and related costs can erode the margins of a fruit exporter. It shows that 54<br>percent of the export value goes towards logistics costs. The bulk of logistics costs goes to shipping and<br>insurance (R2,935), which reflects our distance from major markets as well as the concentration of market<br>power with a few shipping lines. However, around 8 percent of the total logistics costs is incurred to get the<br>oranges to the Cape Town port (it is much higher for citrus farmers in Tzaneen) and 23 percent for port costs<br>in South Africa.<br>Figure 3: Cost breakdown for an exporter of oranges based in Olifants River, Western Cape<br>R 7,840<br>R 8,000<br>R 7,000<br>R 6,000 R ‐1 507
education, the percentage of mothers who work, and the percentage of fathers who have professional/managerial jobs as well as the percentage who work in sales/service.

# **5 Results**

# **_5.1 Measures of consistency_**

In Table 1 we present mean proportions from the QAS tool and diagnostic research tool for each indicator. We also present mean class level and school level categorical and overall ratings, ranging from 1-4 (below minimum to achieved). An important initial finding is that, on a national level, the tools are generally showing similar results about quality for items, levels, and overall, based on similar means. Items with the exact same question in both tools tend to be more similar, as well as items that are easy to observe, such as inadequate light (10.9% in both tools), and class size below 36 (57.5% in the QAS tool, 56.7% in the diagnostic research tool). More subjective measures as well as measures based on different questions vary more, such as pedagogy or some infrastructure questions, for instance the different questions for adequate outdoor space (66.7% meet standard using data from the QAS tool and 80.1% in the diagnostic research tool).

Questions about children’s life skills do not yield similar means, but this may be because a different scale of responses was used for the two tools, even with the same questions. Foundational and multi-disciplinary skill questions, which are identical (questions and responses), do yield similar means. Differences in pedagogy and materials may also be related to observation time. For instance, the QAS tool captures a much lower level of having and using writing utensils (63.9% meet standard versus 84.6% for the diagnostic research tool data) and classroom management tools (41.6% meet standard versus 77.1% for diagnostic research tool data).

Turning now to the measures of agreement;
<br>32|
|Service exports in<br>% of imports|260|198|221|230|180|225|232|214|222|213|252|214|<br>238|
|Total exports in %<br>of imports|91|93|93|99|94|83|112|101|95|89|108|105|<br>107|

Source: Based on IMF Balance of Payments Statistics through World Bank WDI database.

Fifth, notwithstanding the continuing importance of exports of services as a source of foreign currency earnings, the loss of momentum in their growth in 1998-2004 is rather bewildering. While the value of their exports increased more than eight times between 1985 and 1998 and in terms of GDP doubled, exports of services stagnated in 1998-2004. Their share in total exports fell from 44 percent in 1998 to around 27 percent in 2002-04 (Table 2). This was in large part due to a very strong, double-digit growth of exports of goods.

To sum up, the level of integration of Turkey into global economy has significantly expanded over the last two decades. It is high both in terms of trade in goods and services as well
international’s Corruption Perception Index (CPI). The data cover 189 countries. The _Common Corruption_ dummy takes the value of 1 if both countries under consideration are relatively corrupt, i.e., the corruption indices of both countries for the last two years are less than the median value. Appendix Table A2 presents summary statistics of actual protests, news-based protests, polity IV, corruption perception index, and social media penetration in 2018.

We next present the test as to whether protests in period, _t-1,_ in the rest of world (i.e., for all other countries _j_ in the rest of the world) lead to protests in the current period, _t_ , in country _i_ . The number of protests per capita is skewed to the left, with a majority of countries having no protest (see Table 1).

The framework is an autoregressive spatial model following Lesage and Pace (2009). The ZeroInflated Poisson (ZIP) model mixes two zero generating processes. The first process generates zeros. The second process is governed by a Poisson distribution with an estimation as follows: ) (1) where is an N-by-1 vector that contains the number of protests tt tt−1 tt−ττ ii tt tt _per capita_ in N countries at yy = NNeepp (ρWWyy + δδIIyy + FFEE + FFEE + cc current month . is an N-by-1 vector that contains the number of protests _per capita_ in N tt yy countries in the past month. In subsequent robustness checks, we use quarterly frequency instead tt−1 of monthly. In addition to the Zero-Inflated Poisson estimator, we use the standard Poisson as an PP yy alternative estimator. We also use an alternative measure of protests based on the intensity of media chatter around protests. is an N-by-N spatial weight matrix that captures geographic proximity between countries. In the baseline specification, we measure the spatial spillovers using geographical distance. The WWdiagonal elements are zero. The off-diagonal elements are the inverse of the distance between 1 country capitals (i.e
turn, contribute to the rise in aggregate corporate debt during 2020. We then study how the risk that the credit program expansion entails is handled and shared between the banking system that grants the loans and the government that guarantees those loans. While Section 4 uses official aggregate data to display the patterns, here we compute the aggregate statistics from the micro data, allowing us to connect the micro and macro estimates.<sup>22</sup>

# **7.1 From Firm Indebtedness to Aggregate Indebtedness**

To determine how micro-level indebtedness reflects on the overall economy, we partition firms into four groups according to their predicted default risk, from high risk to low risk. The change in indebtedness in each risk group is obtained by multiplying the within-group change in the indebtedness of firms in each risk group by the weight of that group of firms in aggregate economic activity (measured by sales): We then obtain the aggregate change in indebtedness, relative to aggregate sales, by adding the contribution of leverage of the different risk groups: _G_ is a partition of firms according to risk, and _g_ indexes a specific group of firms. _Ygt−_ 1 = � _i∈g_<sup>_yit−_1is the sales of group</sup><sup>_g_of firms, where</sup><sup>_yit−_1denotes firm-level sales.</sup><sup>_Dgt_= �</sup> _i∈g_<sup>_dit_</sup> is the stock of debt of group _g_ of firms, where _dit_ denotes firm-level credit stock. _ωgt−_ 1 = _Ygt−_ 1 _/Yt−_ 1 is the weight of group _g_ in aggregate sales in year _t −_ 1. ∆ _Dt_ is the aggregate yearly change in debt between _t_ and _t −_ 1 (between 2020 and 2019). _Yt−_ 1 is aggregate sales in 2019.<sup>23</sup> Table 10 presents the results of this aggregation