rafmacalaba/data-use-mentions-extended huggingface dataset:
0182|0.0188|0.0262<sup>_∗∗_</sup>|||
||(0.0124)|(0.0133)|(0.0195)|(0.0131)|||
|Has good local language skills|0.0113<br>(0.0154)|0.0101<br>(0.0177)|0.00517<br>(0.0243)|-0.00190<br>(0.0166)|||
|Heath index|-0.00417<sup>_∗∗∗_</sup>|-0.00394<sup>_∗∗∗_</sup>|0.0000256|-0.00434<sup>_∗∗∗_</sup>|||
||(0.00135)|(0.00140)|(0.00247)|(0.00135)|||
|Mental health index (PHQ9)|-0.00235<sup>_∗∗_</sup><br>(0.00117)|-0.00357<sup>_∗∗∗_</sup><br>(0.00129)|-0.00438<sup>_∗_</sup><br>(0.00242)|-0.00322<sup>_∗∗∗_</sup><br>(0.00115)|||
|Has relative(s) in same site|0.00334|0.00151|0.0200|-0.00408|||
||(0.0107)|(0.0112)|(0.0192)|(0.0109)|||
|Has relative(s) in Western countries|-0.00655<br>(0.0127)
measures of readiness are generic, including whether the firm has identified a foreign distribution agent, but others are sector-specific, having to do with conformity to quality regulation in the foreign market. The tool helps the agency to focus its efforts on where the social payoff is likely to be greatest. Another tool used is database for international buyers that allows them to browse the offerings of exporters.

_6) Use of private sector competition to deliver hard and soft infrastructure._ In Costa Rica, over 30 privately-owned export processing zones compete to cater to the needs of exporters and investors. Some of the zones provide plug-and-play office and production space, as well as schooling for the children of employees, and all offer renewable energy. By relying on the competitive market to deliver infrastructure, the investment and export promotion agencies have enriched their offering, without additional expenditure.

# **Conclusion**

Most discussion of industrial policy these days has focused on actions by the largest economies, like China and the United States, and a narrow set of industrial policy instruments, specifically tariffs and investment subsidies. This discussion is not relevant to governments in countries with small domestic markets pursuing export-led development strategies that require them to abide by international trade rules, and who lack fiscal space for large subsidy programs. Yet, these countries nonetheless need an approach to target policy instruments that resolve market failures specific to certain export sectors, and exploit latent opportunities for productive diversification. Such policy instruments can be called industrial policy because they require sector-specific information to implement, and are targeted at specific sectors. I have provided an outline of the approach many countries are taking and examples from a rich toolkit of policy instruments available, some of which have been validated by research in specific contexts.

The phrase developing countries used to be a euphemism for poor governance, some of which was linked to support of the wrong sectors or people. Even so, because lower-income countries are productive in fewer sectors and have less technology, targeting sectors can be more valuable for them. With recent
**Figure 112**

<!-- Start of picture text --> Pakistan: Revealed Factor Intensity (RFI) of Exports<br>RFI 1997 RFI 2007<br>12 12<br>8 8<br>4 4<br>0 0<br>Physical Capital Physical Capital<br>0 60000 120000 180000 0 60000 120000 180000<br>12 12<br>8 8<br>4 4<br>0 0<br>Dead Exports by 2007 New Exports in 2007<br>Human Capital<br>Human Capital<br><!-- End of picture text --> Source: Authors’ calculations based on data from UNCTAD.

The product-space analysis indicates what merchandise goods Pakistan exports competitively, and whether the portfolio has undergone a structural transformation over the past decades. Using the tools pioneered by Hidalgo et al. (2007), the product space maps indicate all tradable products at the SITC 4-digit level, with the black dots representing exports in which Pakistan has a Revealed Comparative Advantage (RCA). The hypothesis is that firms that build up competence in producing a certain good can redeploy their human, physical and institutional capital more easily if they seek to produce goods that are “nearby” those that they are producing already. This is predicated on the idea that structural transformations are not smooth movements along a continuum but a messy process beset by market failures. When such market failures are binding, it is harder for firms to hop longer distances without government coordination and support.

In the analogy of Hidalgo et al. (2007) the product/dots are trees that group themselves to form dense and sparse parts of a forest. Location of firms in the denser parts of the forest creates more opportunities for technological upgrading and structural transformation because market failures are less binding when firms have to make smaller adjustments to move to produce “nearby” goods requiring similar capacities. The center of the product-space, for example, is quite dense
|0.51|
|Area of registered plots m<sup>2</sup>|||||
|2015|15,349|17,361|17,712|554|
|2019|15,554|17,597|17,928|565|
|**No. of registered transfers**|||||
|2000|0.03|0.03|0.05|0.00|
|2010|0.06|0.06|0.09|0.00|
|2015|0.34|0.36|0.40|0.20|
|2019|0.34|0.34|0.36|0.32|
|**No. of registered mortgages**|||||
|2000|0.01|0.01|0.01|0.00|
|2010|0.08|0.08|0.09|0.03|
|2015|0.19|0.19|0.32|0.07|
|2019|0.16|0.16|0.17|0.15|
|**Structures (based on 2016 imagery)**|||||
|No. of buildings|56|56|75|31|
|Built-up area (m<sup>2</sup>)|4,989|5,003|7,045|2,132|
|Number of blocks<br>|1,932|1,373|321|238|
|Block area m<sup>2</sup>|62,500|62,500|62,500|62,500|

_Source:_ LAA, land administration database and digitized records, 2020 22
# **5. Regressions Results**

In this section, we give the estimation results of the equations presented in section 3. Table 10 (in Appendix) first provides estimates for the emergency assistance and food consumption score equations without other covariates than pre-earthquake asset index, assetlosses, post-disaster housing damage, location and participation in associations. Second, Table 11 (in Appendix) presents estimates when adding other covariates such as gender, age and education of the head of the household, the number of infants, children, youths, adults and elderly people in the household, the fact of living in a private housing or not, the fact of having agricultural practice or not, the main income source, and other covariates which correspond to typical household coping strategies: aids and transfers from other Haitians in Haiti or abroad, type of transfers, debts, savings and banking accounts. We also add among the covariates a _reduced coping strategy score_ computed according to how frequently household members had to resort to survival strategies related to food consumption during the week before the survey.

# _(a) Emergency assistance equation_

It is shown in Tables 10 and 11 that asset losses have no significant effects on receiving either general or food assistance. However, adding more covariates in Table 11, the preearthquake assets index appears to have a statistically significant effect on food assistance and less so on general assistance. Destroyed housing has a significant and positive impact on getting general assistance, while it does not on getting food assistance. The place where households sleep appears to have a significant effect on assistance allocation. Sleeping in a camp does not appear to have a significant effect on getting food assistance (as it was the case according to the February survey). Furthermore, of the various association memberships, participation in agricultural organizations and participation in school committees have significant positive effects on receiving assistance, whereas participation in religious organizations has a small but significant negative effect. Only agricultural organization membership has a large and significant positive effect on food assistance.

Interestingly enough
above questions with implications for the other queries posed we review historical political events related to river flows (droughts and floods) identified by the BWI. In this context, we also highlight BWI‘s utility for research on conflict and cooperation over water.

# **BWI’S VALUE FOR RESEARCH ON CONFLICT AND COOPERATION OVER WATER**

The river flow data provided in the BWI provide hydrological and physical data required to investigate the links between water scarcity/variability and conflict and cooperation over water. To date, scholars have used national measures of water availability per capita as well as precipitation and drought to explore how physical water scarcity affects inter-state conflict (Toset et al. 2000; Hensel et al. 2006; Gleditsch et al. 2006). A similar methodology has been 20
poverty estimates to inaccessible areas based on already extracted satellite features and geo‐spatial data, given the objective of creating reliable and transparent poverty measures. The poverty imputation follows a process that is relatively similar to small area estimation, though only the point estimates were estimated and not higher moments of the outcome distribution (see for example: Elbers Chris et al., 2003; Guadarrama et al., 2016; Haslett, 2016). Poverty as measured in the 2016 wave of the HFS is regressed on a range of geo‐spatial characteristics such as distance to urban centers, distance to the electricity grid, annual rainfall, annual temperatures, urban‐rural status, IPC phase classification, and others. The estimated model is then used to calculate expected poverty rates across regions where the household survey data are not available, but where the geo‐spatial data are available. Poverty rates are predicted at the 100m*100m level across South Sudan. The poverty estimates then need to be weighted by local population counts to eliminate potential bias caused by vast uninhabited areas. Given the lack of reliable administrative data on settlements or population counts, local populations were in turn estimated using a set of covariates derived from geo‐referenced data such as urbanicity, roads, clinics, and buildings.

# Estimating Settlements Data

The aggregation of poverty estimates to the county and state levels needs to be calibrated against suitable population estimates. Naively aggregating poverty rates across broad geographic regions would result in extremely high poverty rates given the vast uninhabited expanses isolated from the rest of the country, in which a model would likely predict high poverty rates. Indeed, South Sudan is sparsely populated relative even to most other large African countries, in 2008 South Sudan had a population density of approximately 13 persons per kilometer squared compared to the Sub‐Saharan Africa average of 35.<sup>37</sup> Because an accurate high‐resolution map of population density is not available for South Sudan, the spatial distribution of settlements was used as a proxy for
..........................................................|51|
|VI. 2. Compliance With Labor Legislation .............................. ,.,.,,.....|53|
|VI. 3. Health and Safety Provisions ......|54|
|VI. 4. Knowledge of Standard Working Conditions, Wages and Benefits ..............|54|
|VI. 5. Role of Trade Unions and Workers' Aspirations .......................................|55|
|VII. CONCLUDING NOTES<br>...........................................|55|
|References ..........................................................|58|
|Survey of Women Workers Questionnaire ...........................................
_Tax Reforn in Transition_ 19 times average income. The second highest rate of 36 percent is reached by taxpayers earning as much as 7.1 times the average and the income level at which the rate of 32 percent applies is 4.3 times average income. The 1992 household budget survey shows that of the 2202 households surveyed, only one household has a per capita income that is greater than seven times average income, and no one reported income greater than 14 times the average. In sum, the 1992 tax brackets are extremely narrow and do not adequately tap the revenue potential of their respective income groups. Furthermore, these ratios are high compared to other Eastern European Countries. For instance, the income level where the maximum marginal rate is applied in Romania started with 3.44 times per capita GDP in 1992 (Shome, Haindl and Schenone (1993). This partially explains the relatively low potential revenue and effective tax rates under the 1992 tax system. In contrast, setting tax rates as low as possible, reducing the number of brackets and defining them as broadly, yet realistically as possible, and raising the exemption level, taking into account the underlying income level and distribution, would increase revenuesl°. For instance, all of the proposed tax systems extend the exemption level to cover the lowest 20 percent. Thus positive income tax payments start at a relatively higher income level, 60 percent of average per capita income, compared to 50 percent under the 1992 system. However, proposed tax rate becomes substantial (i.e., 30 percent) at incomes around mean per capita income. The proposed top rate of 40 percent requires an income nearly twice the average level (see panel b of Table 5).

A main factor responsible for the erosion of effective tax rates has no doubt been the weakness of tax administration, especially in a rapidly growing small-scale and self-employed private sector which is difficult to bring into the tax net. However, adjusting or modifying the income tax law so as to facilitate compliance
: DG AGRI (2017) Clearance Audit Trail System (CATS) database provided by the European Commission_ _Allocation of CAP funds based on regional characteristics_ To explore the characteristics of regions where the CAP funds tend to reach, we create four different clusters of regions at the NUTS1/NUTS2 levels based on the average holding size and the number of employees per holding. For each of these variables we create categories to group the existing regions. Table 4 below simplifies the clusters by characteristics, in which regions are grouped. Table 10
unit values from the NSS-CES for the respective commodity-types (PDS or non-PDS) in each successive round (61st and 66th). Thus, my estimate of _θj_ is the ratio of percentage changes between 2004-05 and 2009-10 of state mean unit values for PDS and non-PDS purchases of commodity _j_ . Median values of _θj_ for rice, wheat, and sugar across the 18 major states are all close to unity, which means that PDS prices were indeed quite stable over this five-year period.

The proportion of rice, wheat, and sugar consumption accounted for by the PDS generally increased between 2004-05 and 2009-10 (Appendix Figure C.2). No doubt, this reflects the 25
fishery and timber production to provide a prior estimate of gridded agricultural GDP. The estimate is then reconciled with national and sub‐national agricultural GDP values using an entropy‐based data fusion method. The second is a “spatial disaggregation” method that relies on imposing a data‐ generating process at the gridcell level with the variable of interest (in our present case, agricultural GDP, though the technique can be applied to many types of questions) – which is not measured at the gridcell level ‐‐ being defined by a combination of known gridded explanatory variables and a set of parameters to be estimated. The equations are aggregated up to the level at which the variable of interest is known, and a multiple regression is used to solve for the parameters, which in turn can be used to make predictions at the gridcell level.

In this paper, we use Brazilian agricultural GDP data to test these two new techniques against the traditional technique of using rural population to distribute the agricultural GDP. We have data available at the municipio level, with 5,564 units. However, we instead use the data at a more aggregated microregion level, with 558 units. Then we make predictions at the gridcell level and aggregate up to the municipio, at which level we compute two measures of predictive accuracy.

## Review of Previous Work

Previous analyses use two primary geographically‐referenced data types to locate GDP. The one that has been used the longest is nighttime light emissions captured by satellite imagery (Doll, Muller, and

> 5 It should be pointed out that gridded population data sets are technically not satellite data sets but GIS raster (gridded) data sets, but they may include satellite data sets as inputs to their construction.

2
First, the data available on non-monetary outcomes xij can be noisier than the data ˆ available on monetary outcomes. When selecting a variable xij capturing a non-monetary dimension j, there often is a trade-off between at least three conflicting goals. We present these three goals when assuming that health is the dimension j for which variables must be selected.

1. xˆij approximates xij with limited noise.

- Self-reported health surveys lack the objectivity of medical check-ups and thus noisily capture the health status of respondents. Many variables like vaccines administred or access to clinics relate to health inputs and thus noisily capture health outcomes.

2. xˆij offers a good coverage of dimension j.

Having received a vaccine or having delivered a baby in a clinic only capture very limited aspects of a person’s health. Indeed, they both ignore disabilities, nutritional status, chronic diseases, etc. Arguably, self-reported surveys like EQ5D provide a larger coverage of a person’s health (Szende et al., 2014).

3. xˆij is available or cheaply collected.

Medical check-ups could provide a broad coverage of a person’s health with limited noise, but their cost could be prohibitive.

In most cases, the variable xˆij that provides the best trade-off between these three conflicting goals is not cardinal, but rather ordinal or even categorical. As a result, the practitioner must rely on indicators that can accommodate ordinal data.

Second, the joint distribution between outcomes in different dimensions j and j<sup>′</sup> is often missing. The inter-personal comparisons presented in Figure 2 require not only partial distributions (xi1)i∈R and (xi2)i∈R but also the joint distribution. Without the joint distribution, an individual’s achievement vector (xi1, xi2) is not known and we thus ignore whether an individual cumulates low achievements in several dimensions. One problem is that non-monetary outcomes are often collected through different surveys than monetary outcomes. At global level, monetary outcomes are collected through LSMS-type surveys while non-monetary outcomes
conducted by the Bank of Tanzania (2007), this is grossly contradicted by the facts. Given our focus on services and the importance of tourism to the Tanzanian economy, we had to correct this problem.

In our first step, we reduced imports and exports in the "Transport and communication" sector by the value of tourism imports and tourism exports, respectively. This yields levels of traditional exports and imports for this sector which are roughly in line with balance of payments data from the Bank of Tanzania for these sectors.

In the second step, we had to reallocate the tourist expenditures subtracted from Transportation and Communication to other sectors. Since most of the tourism imports are reexported, e.g., international air travel, we chose to calculate net tourism expenditures. We allocated the net expenditures to the various sectors of our model based on the Bank of Tanzania survey results.

The survey revealed that around 28% of total expenditure of non-package tourists is transport, 29% is accommodation, 12% is shopping and 31% is other expenditure. We therefore allocated the cost share of tourism input expenditures to the following sectors: 29% to “Hotels & restaurants,” 28% to road transport, railway transport and air transport in proportion to the three sectors’ contribution to GDP. The remaining 43% is on goods. We allocate these expenditures to the goods producing sectors in proportion to their share in total exports of goods. Major examples of tourist expenditure on goods include clothing, gold items, and food products such as cashew nuts and coffee.

This allocation of tourist expenditures implies that our SAM gets additional accounts representing “Tourism”. An activity account (the tourism sector) contains the tourism expenditure and produces an aggregate tourism commodity. The tourism sector in our SAM does not generate value-added itself, but demands domestically produced services and goods (as detailed above). Additional exports of tourism therefore indirectly increases value-added in the Tanzanian economy.

An additional commodity account contains the aggregate tourism commodity (the output of
that PTAs serve as commitment mechanisms to foreign investors regarding the treatment of their assets that is more credible than domestic regulations. (Osnago, Rocha, & Ruta, 2015) find that deep trade agreements facilitate vertical FDI because they reduce the contractual uncertainty associated with the difference between PTA members’ institutions.

In terms of methodology, recent works have grasped the importance of accounting for the variation in content to distinguish the effects of deep versus shallow PTAs. Advances in data availability regarding the content of PTAs have enabled researchers to depart from the use of restrictive dummy variables and incorporate more sophisticated measures of PTA depth. Equipped with these new empirical tools, some papers have focused on the impact of PTA depth on trade flows, while others have addressed the same question for FDI.<sup>9</sup> For example, (Lesher & Miroudot, 2006) construct an index of investment disciplines in PTAs but limit their coverage to 24 North-South agreements. Several papers have attempted to build similar types of indexes based on careful analysis of the information contained in agreements’ treaties. However, none conducted this exercise on the universe of PTAs as well as their overall content, up until the work conducted by the World Bank for which experts accomplished a thorough mapping of all disciplines included in all PTAs notified to the WTO (see (Hofmann, Osnago, & Ruta, 2018) for a presentation of the content of PTAs data set). (Osnago, Rocha, & Ruta, 2015) were the first to use this data set to construct a comprehensive measure of PTA depth and use it to explore the impact of deep integration on vertical investments.

Several papers analyze the impact of specific policy areas on foreign investments

> 9 (Mattoo, Mulabdic and Ruta 2017) find that deep trade agreements increase gross trade and can have a positive spillover effect on trade with third countries if their design and implementation are non-discriminatory. (Orefice and Rocha 2014)  study trade in parts and components, and (Laget, et al. 2018