rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **3. The GTAP model and database**

The standard Global Trade Analysis Project (GTAP) model of the global economy is used to provide insights into the effects of governments allowing GM technology adoption in some countries without and then with cotton trade and subsidy policy reform globally. See Hertel (1997) for comprehensive documentation of the GTAP model, which is a neo-classical multi-regional, static, applied general equilibrium model that assumes perfect competition, constant returns to scale and unchanging aggregate employment of all factors of production. We use the latest Version 6.05 of the GTAP database (see Dimaranan and McDougall, 2005), which draws on global economic structures, policies and trade flows of 2001. The GTAP model has been aggregated to depict the global economy as having 27 sectors and 38 regions (to highlight the main participants in the world’s cotton markets, two of which are newly disaggregated countries: Nigeria and Pakistan). Trade is modeled using a nested Armington structure in which aggregate import demand for each sector’s product is the outcome of allocating domestic absorption between domestic goods and aggregate imports, and then aggregate import demand is allocated across source countries to determine the bilateral trade flows.

This economy-wide GTAP model does not include environmental or human health externalities, so the welfare consequences of any such externalities are not measured. This unfortunate situation is a result of the uncertainty surrounding the relationships among various economic and environmental variables. What can be said, though, is that the net environmental effects of producing GM crops could be positive or
- Excellent technical capacity and strong relationships with local communities and local authorities in the PPA agencies, combined with an eagerness to engage at the national policy level and to commit resources for this purpose;

- A strong emphasis on ensuring Government ownership of the PPAs through the mechanism of the Poverty Working Group (PWG);

- Availability of high quality household survey data to corroborate and enhance analysis; and,

- Timing.

# **Policy and Institutional Context**

## **_National policy formulation_**

Poverty reduction and equitable growth are central and important goals of the Government of Vietnam. But the poverty that is often defined in planning and policy documents refers mostly to economic deprivation and limited access to social services. The phrase "poverty reduction" is often equated to Government's Hunger Eradication and Poverty Reduction (HEPR) Program, which is a framnework of targeted support to groups defined as vulnerable or poor by the Ministry of Labor, Invalids and Social Affairs (MoLISA). As such, poverty reduction is often seen more in terms of social safety nets rather than addressing constraints at the macro, structural and sectoral levels.

In theory, participation by poor households in the Government's planning process can take place through the role that the Party (Communist Party of Vietnam) and the People's Councils play as their representatives. In some localities with active and consultative leadership, this may work well. In many places - and this was very clear from the PPA research - people rarely felt _informed_ about the Government's plans, less still _consulteGd'_ during the formulation stage. Government recognizes the need for greater transparency in its operations and there have been steps forward in the last few years. The national budget was recently published in summary form, for the first time ever. A recent decree stipulates actions which local leadership must take in order to promote grassroots democracy. This insists that communes (the lowest level of the administration in Government) must discuss their plans, budgets and expenditure with households
revenue ministers on land reforms in 1985 reached a consensus that 'computerisation of land and crop-based statistics should be taken up on pilot basis at tehsilVrevenue-circle level' (GOI, 1985:54). Computerisation of land records (COLR) is now being implemented to redress this shortcoming. It is hoped that it will:

- facilitate easy maintenance and updating of changes that occur in the land data base (e.g. changes due to availability of irrigation, natural calamities, consolidation or on account of land transactions);

- make land records tamper-proof and indirectly reduce litigation and social conflicts over land;

- facilitate implementation of development programmes for which data about distribution of landholding is vital;

- assist in planning for infrastructural and environmental development;

- produce accurate records for land revenue purposes;

- facilitate speedy retrieval of land-related data;

- provide a database for the quinquennial agricultural census; and * issue updated copy of RORs to landholders quickly and more cheaply.

COLR is a central-sector scheme with full financial assistance from Government of India (GOI). Orissa is the only state in the country in which the scheme is being implemented in all districts. A sum of around Rs. 300 million has been made available by the GOI for three years to cover data entry in the local language (Oriya), civil construction, purchase of hardware, and operational costs during that period. GOO will take over full responsibility for the operation at the end of the three-year period.

The scheme is to be executed on a district-wise project basis through the Revenue department. Private firms have been contracted to enter the records in 10 districts<sup>27</sup> in the first instance. The target for data entry is 90

- 27 The districts are: Ganjam, Cuttack, Kendrapada, Jagatsinghpur, Bhadrak, Khurda, Bargarh, Dhenkanal, Angul, and Nayagarh.

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In the empirical analysis, we use state level stamp duty rates as an a summary indicator of such problems in access to land. Stamp duties are currently high in India, at 8- 10% of the value of property changing hands. One effect they have is to reduce the supply of land on the market by discouraging land transactions. High stamp duty also provides incentives to grossly under-declare the real value of land. This in turn adversely affects the use of land as collateral for construction financing. (see Alm et. al [2004] for a discussion on the distortionary effects of stamp duties) **.** Among states, there are considerable inter-state variations in stamp duty rates. For example, a hypothetical property valued at Rs. 1 million would be liable to pay as stamp duty Rs. 50,000 in Andhra Pradesh, Rs. 125,000 in Haryana, Rs. 38,750 in Maharashtra, and Rs. 145,000 in Uttar Pradesh.

_Finance_ **:** Approximately 27 percent of firms in the ICS rate access to finance as a major to severe obstacle to business operations or growth. For the analysis in this paper, we use data on industrial credit availability, measured as the per capita lending to local industry by financial institutions. This is derived from the publication “Profiles of Districts” (CMIE), and is defined as the per capita bank credit to industries derived from the information on scheduled commercial bank branches, deposits and credits Even during their initial phase, Indian SMEs have traditionally relied much more on debt financing—from banks and nonbank financial institutions (NBFIs)—than their counterparts elsewhere. But the shrinkage of the NBFI sector in response to policy and regulatory changes since 1997 has meant that SMEs no longer have access to finance from this source. And bank credit to SMEs has also dropped sharply since 1997. The limited debt 12
sectors. Wang et al. (2017) show a way to measure the contribution of services along the value chain and find that they frequently are part of complex value chains.

While this paper studies the impact on productivity of the sector from the foreign financing and its links, it takes a slightly different approach to that adopted by Cadestin et.al. (2018) and Meng et. al. (forthcoming, 2020). In those approaches, the inter-country input-output tables for OECD and
what extent? Of the firms selling or sourcing internationally, what regions are they mostly exposed to and what products are they producing? To answer these questions, we work with an exhaustive dataset of all formally registered firms in Rwanda that is introduced in the next section.

# **3. Data**

This project uses administrative, micro-level data for Rwanda that includes information on the universe of registered firms from three different sources of administrative data:

- Trade-transactions data from custom’s: Imports & exports by firm-year in both value and quantity terms, broken down by product (HS 8-digit) and partner country.  While this is recorded at the transaction level, it is aggregated to yearly totals.

- Payroll data: Number of employees and total wage bill.  These are the reports provided to the payroll authority for the purpose of reporting wages and paying payroll tax. This dataset should be seen as representative of the universe of registered firms that have payroll, i.e. formally employ workers beyond the owner.

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regimes: fixed (pegs), intermediate (limited flexibility, crawls, bands, managed floating), and flexible (free-floating). **13** The rest of our data-exchange rates, inflation, and country indicators, such as population and income level on which the sample selection is based-come<sup>from the</sup> World Bank-IMF databases.

We focus on industrial economies and middle-income developing countries. Within this broad group, sample coverage is dictated **by** the availability of<sup>adequate</sup> interest rate data.'<sup>4</sup> The sample that results comprises 47 countries **(18** industrial and **29** developing) in addition to the **US,** whose interest rate is used as an explanatory variable, and a total number of monthly observations exceeding 9,400.

# **3.** Interest Rate Sensitivity: Pooled Estimates

We first assess empirically the sensitivity of domestic interest rates to **US** interest rates **by** estimating equation **(1)** using the entire sample, as well as distinguishing between industrial and developing countries and considering subsamples defined<sup>**by**time</sup> period.

Table 1 presents the full-sample fixed-effects estimation results, using a fiveregime classification of currency arrangements. To avoid cluttering the table,<sup>here andin</sup> the rest of the paper we only report the coefficients of interest-i.e., the slope parameter

> **13** As a robustness check, we compared the results obtained with this condensed classification with those obtained using the classification of Ghosh et al. **(1996).** On the whole, the results were very similar.

> 14 We dropped country/regime episodes possessing less than one year of consecutive monthly observations. We also excluded countries with population under one million, countries without availability of long exchange
|national AROPE rate|32.2<br>19.9|15.9<br>34.4|
|---|---|---|

Note: Percentage of individuals (using survey weights). AROPE rates of 2022 are not anchored to 2019, as in Figure 1 and Figure 2. Children are defined as those younger than 18 and elderly, those 65+. Social aid is defined as those households that report incomes from social assistance captured in the SILC survey by social exclusion not elsewhere classified. This captures households receiving the main poverty-targeted programs (mean-tested) as well as other mean-tested programs. Households with social aid cannot be identified as a vulnerable group in the Romania EU-SILC. Pensioners are defined as those receiving old-age benefits. Statistics presented are unconditional AROPE means. Since households with disabled members cannot be identified in the EU-SILC surveys, we use as a proxy those receiving disability benefits.

Source: Own estimates based on 2022 EU-SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

**Multidimensional poverty is also higher among individuals who are deprived of other life domains, such as education, health, and housing, particularly in Bulgaria and Romania.** The AROPE rate for individuals living with household heads who have only achieved primary education<sup>45</sup> doubles the national rate in all countries (Table 5). This is also the case for individuals living in families with unmet medical needs and in overcrowded houses<sup>46</sup> in Bulgaria and Romania (i.e., in countries with higher overall AROPE rates). These results shed some light on the potentially close relationship between being at risk of poverty or social exclusion and being deprived in additional dimensions apart from those included in the AROPE indicator.

|**Table 5.**AROPE rates for individuals living in households with other deprivations (%) by country|
|---|
more details. Similar surveys were previously conducted under the leadership of the World Bank and other IFIs in Africa (RPED), and world-wide in 2000 (World Business Environment Survey).  Enterprise Surveys still go under different names in some regions, e.g. BEEPS in the Central Asia and Eastern European transition economies had first been launched in 1999 and was then modified to be comparable with the broader global initiative.

> 4 We lose 25 countries when using the share of working capital financed with external finance, mostly in Eastern and Central Europe and East Asia.  There are a total of 95 surveys but some countries enter the dataset in multiple years. Information collected is standardized across countries and covers formal establishments with 5 or more employees. However, Africa, East Europe and Central Asia, and Latin America include establishments with fewer than 5 employees. In Africa, there are an additional 25 standardized surveys covering micro-firms and targeting informal establishments.

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cost of childbearing, even though it decreased the overall number of children that eligible women would have.

The next sections explore how PEP shaped fertility among Venezuelan migrants and suggest possible mechanisms for the observed responses.

# **IV DATA: VENREPS**

Our main source of data is the Venezuelan Refugee Panel Study (VenRePS), a longitudinal study of irregular Venezuelan migrants in Colombia. The survey was conducted to examine PEP’s impacts on migrant well-being and consisted of two waves of data collection, starting in October 2020 and one year later. The data represents four geographical areas: Bogot ́a, Medell ́ın, Barranquilla, and a group of smaller cities that together comprise an area.<sup>8</sup> The first three cities are large urban centers in Colombia that host the most Venezuelan migrants in the country. In Figure 2, the location of each city in the VenRePS sample is compared with the location of Venezuelan migrants in Colombia based on the 2018 population census (the last one available).

Roughly half of the individuals interviewed in VenRePS were randomly selected from the RAMV survey. The other half originated from a “snowball” sample of referrals from local migrant organizations and respondents in the RAMV sample. Ibanez et al. (2022) show that migrants surveyed in VenRePS who were contacted through the RAMV survey or “snowball” referrals were comparable in terms of sociodemographic characteristics before the program’s rollout. All migrants in the survey had no passport, were at least 18 years old, provided documents to prove they were born in Venezuela, and had arrived in Colombia between January 2017 and December 2018. In other words, they were irregular migrants living in Colombia at the time of PEP’s implementation.

Table A.1 presents summary statistics distinguishing by gender. Panel A shows descrip-

> 8This includes migrants interviewed in ten municipalities including C ́ucuta, Villa del Rosario, Cali, Cartagena, Riohacha, Maicao, Uribia, Valledupar, Santa Marta, and Arauca.
sup>and contract enforcement to be</sup> used in our study was assembled by the IRIS Center<sup>of the University of Maryland from</sup> the International Country Risk Guide - a monthly publication<sup>of Political Risk Services</sup> (PRS). The five PRS indicators used in the study are Government<sup>Repudiation of</sup> Contracts, Risk of Expropriation, Corruption, Rule of Law,<sup>and Bureaucratic Quality.</sup> For all five measures, the lower (higher) values indicate worse<sup>(better) standing.</sup> 6
group thus include all enumeration areas located farther than 50 km radius from a mine (be it active or inactive). As the individual data are identified by their enumeration areas or districts or Townsville, it is feasible to relate their responses to their residential location at the time of the survey.

# **3.2. Dependent Variables**

The dependent variables are _(a)_ the residents’ appraisal of public goods and services, and _(b)_ their self-reported optimism about the future. The first set is operationalized from the following survey question.

_“How well or badly would you say the current government is handling the following matters, or haven’t you heard enough to say? “_ The different matters are: _(i)_ improving living standards of the poor, _(ii)_ creating jobs, _(iii)_ improving basic health services, _(iv)_ addressing educational needs, and _(v)_ providing water and sanitation services. For each of these items, the possible answers are on a four-step Likert-scale and range from ‘ _very badly’_ to ‘ _very well’_ . For policy matter, it is created a binary variable that takes the value of 1 if the individual responded _‘fairly well’_ or _‘very well’_ to the question and 0 for the responses _‘bad’_ or _‘very bad’._ Other responses such as ‘ _I don’t know_ ‘ or _‘haven’t heard about it’,_ 7
Figure 3 compares FDI and FAI from different types of parent country into the host countries in our sample, expressing each series in shares relative to the total. The key differences highlighted above—that FAI is reported by ultimate parent and reflects real investment, whereas FDI is reported by immediate parent and reflects financial flows— appear evident in the data. Over the sample period, low tax countries, defined as those with a statutory tax rate in the first quartile of the distribution (which means a statutory tax rate of less than about 20 percent), account for a greater share of FDI than of FAI (Panel (a)). The same is true, and even more marked, in relation to investment hubs, defined as countries with an average FDI/GDP ratio of above 150 percent (Panel (b)).

Importantly, many of the observations in the FAI dataset—around 34 percent—are zero.<sup>31</sup>

# **4 Empirical Strategy**

This section describes the approach taken to estimation.

## **4.1 Specification and Other Data**

The focus of interest to which the analysis in Section 2 points is the impact of the _I − METR_ and, more generally, of its key components: the rate differential ∆ _h_ , the host country _KF_ - _METR_ and—operating through some channel not captured in the modeling above— the parent country _KF_ - _METR_ . Taking 1 _/_ (1 + ∆ _h_ ) _≈_ 1 _−_ ∆ _h_ , and now with many parent countries and time periods, we take as our most general estimating equation: where _FAIhpt_ denotes bilateral investment _Ihpt_ in tangible capital goods in host country _h_ (of which there are 32) from ultimate parent country _p_ (of which there are 187) in year _t_ (running from 1998 to 2016), scaled by the lagged capital stock in _h_ attributable to investment from

> 31This is also the case for FDI, with around
panel correlations for fourteen ρρ countries in the EU-SILC database and the United States.

We standardize these empirical correlations to a two-year reference interval, since the correlations reported covered intervals of different duration. Subsequently, we draw a bootstrap

> 6 Dang & Lanjouw (2023) also present a method to obtain point estimates, which depends on cohort-level income correlations to infer household-level correlation. However, the robustness of this method is not generally accepted. Instead, we make use of documented empirical measurements of household-level correlation in other countries.

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are themselves subject to significant variation, especially during periods of crisis.

The coefficient on the liquidity channel retains its sign and statistical significance in the first two of the four specifications, and approaches statistical significance in the final two ( _p_ = 0 _._ 134 and _p_ = 0 _._ 159, respectively). Its magnitude is of the same order of significance as the benchmark: the range of coefficients is between -0.02 and -0.04. Overall, we regard the findings reported in this section as broadly corroborative of the benchmark results. More importantly, given the insignificance of the interest rate even when it is instrumented, we conclude that concerns about endogeneity arising from its omission from the benchmark specifications are not generally well founded.

# **6 Conclusion**

In this paper, we have examined the transmission channels for financial shocks that affect international bank lending to developing countries, using the 2007/09 crisis as a case study. Our main finding is that cross-border lending by international banks contracted primarily due to liquidity difficulties faced by these banks, and to heightened uncertainty. Another key message is that the reduction in aggregate lending to developing countries by high income banks during the crisis was not the result of changes in their sensitivity to liquidity shortages or changes in their tolerance of risk, but instead was a normal reaction to an abnormally large shock to liquidity and uncertainty.

The main shortcomings of our work here stem from our reliance on aggregate cross-border lending data. While such data encompasses much greater coverage of the total volume of lending to the developing world, our inferences were largely limited to broad averages—and, in Subsection 4.3, U.S. and EU exposures—which limits our ability to tease out idiosyncratic elements that may be of interest, such as whether solvency considerations were altogether unimportant, or whether they were more important for certain classes of financial institutions, such as those facing more severe balance-sheet difficulties. Future research along the lines of the work considered here can
in 1990) as well as hard-to-control factors like its stage of development (e.g., more agriculture means less forest cover), and its population (larger population for a given terrain means less forest cover). The actual forest cover less the predicted forest cover is the ‘excess’ forest cover that the country should get credit for. Using a conversion rate for how much carbon is stored per square km of forest, the total tons of carbon stored in the ‘excess’ forest can be obtained. It is important to note that the carbon absorption quality of forests varies across countries; the proposal accounts for these differences using the Global Forest

> 6 The point here is that countries with a natural propensity for forest cover should not receive credit for their natural endowments.

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