rafmacalaba/data-use-mentions-extended huggingface dataset:
information. The public may not really know or understand the motivation of the government. Governments may then have to take even stronger reforms than technically necessary in order to “signal” their true intent.

Given the political transition in Argentina, the signaling of a new approach might require a package of policies that reinforce each other in terms of credibility. One of the paths forward might be to consider packages of reforms, each of which comply with the three criteria set out in the introduction to this paper.

Another key consideration in the path forward is the macroeconomic dominance, discussed above in the historical context, which probably persists today. It will be difficult to immediately unwind microeconomic problems created by export taxes and artificially low public utility tariffs unless a credible path for the fiscal accounts is established. It will also be difficult to convince investors that expropriation will not take place unless the government re-establishes access to normal forms of finance from the markets. Finally, a simple step to re-establish confidence would be to publish accurate and externally validated macroeconomic data, starting with inflation.

Since assuming office, the new administration has taken a number of actions. On the data front, the statistical agency (INDEC) resumed the publication of trade data, and the government took steps to restore the technical capacity of INDEC to produce reliable inflation estimates, as well as improve the national accounts. The government moved quickly to reach an agreement with holdout creditors, and the agreement was passed by Congress. The Central Bank has regained its focus on inflation and has been absorbing excess liquidity. In addition, monetization of fiscal 33
simulate depressed and stagnant economies. CPI and GDP stand for lender-country consumer price inflation and real GDP growth and are obtained from the IFS.

Next, we also matched Dealscan with Worldscope to obtain data on a borrower’s equity-to-assets ratio (Borrower E/A).<sup>5</sup> This variable is calculated as the lagged book value of common equity over total assets. To exclude the impact of outliers we winsorized all continuous borrower and loan level variables (i.e., Volume, Spread, Maturity and Borrower E/A) at the 1st and 99th percentiles. After this adjustment the average borrower equity-toassets ratio is 0.387.

Next, several variables capture the relationships that international banks have developed with borrower countries through the ownership of local banks or through the prior provision of syndicated loans. Foreign-Owned Banks, borrower represents the assets of foreign-owned banks located in the borrower country as a share of total banking system assets (this variable is taken from Barth et al., 2013). On average, foreign-owned banks hold 16.5% of banking system assets in borrower countries. Alternatively, FDI is the number of subsidiaries in the borrower country owned by banks in the lender country based on data collected by Claessens and van Horen (2015). The average number of foreign-owned banks in a lender country-borrower country relationship is 1.6. A bank’s own experience in the borrowing country is captured by the experience variable, which is the natural logarithm of 1 + the number of loans extended by the relevant lender in the country of the borrower in the three years prior to the loan. In addition, subsidiaries is a dummy variable that equals one if

> <mark>QE3, as follows: December 2008 to March 2010, November 2010 to June 2011, and September 2012 to December 2013.</mark>

> 5 <mark>We thank Ferreira and Matos (2012) for sharing their link between Dealscan and Worldscope identifiers.</mark>

9
and a discussion with a trained enumerator. Comparing this method to rope and compass in southern Mali they find that on average plots areas were underestimated by 11 percent, with smaller plots being overestimated and larger plots underestimated, and measurement error being smaller for cotton fields than for cereals.

Farmer self-reported area estimates are influenced not only by plot and respondent characteristics, but also by a variety of cultural considerations and logistics of survey implementation. One major such factor is the prevalence of traditional or non-standard units. In many countries, respondents are often not familiar with standard measurement units such as acres, square meters, or hectares as they are used to express area measures in traditional units. Those units are not standardized and may vary in size by region, or even across villages or farms. In Ethiopia, for example, one of the most common non-standards units is the _timad_ , traditionally defined as the amount of land a pair of oxen can plough in one day. This measure will vary significantly by region, and even by farm. The soil texture and moisture content, plot slope and strength of the oxen will have an impact on the ease with which the oxen move and hence on the size of a timad. For Eastern Ghana, Goldstein and Udry (1999) report a correlation between selfreported and GPS measured plot size of just 0.15, which they attribute to the agricultural history of the region where local field measurements are traditionally based on length rather than area, and respondents are not accustomed to converting them to two dimensional area measures.

Recent studies emphasize not only the presence of large measurement error in self-reported measures, but also the systematic association of the magnitude and sign of the error with important plot characteristics. Carletto et al (2015), using LSMS-ISA data from Malawi (2010/11), Tanzania (2010/11), Niger (2011) and Uganda (2009/10), identify a common trend in the magnitude of measurement bias, defined as selfreported minus GPS area for plots for which
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welfare statistics such as poverty and inequality measures. Although welfare statistics can be obtained using household surveys, these are rarely sufficiently large to allow for statistics corrsponding to small areas (Tarozzi and Deaton, 2009). Small area estimation methods attempt to exploit the large sample size of census data and combine this with the information from household surveys in an attempt to obtain reliable statistics for small areas.

In its essence the small area estimation presented here relies on using household survey data to estimate a joint distribution of the variable of interest and observed correlates, and use the parameters to simulate welfare using census data (Demombynes et al., 2008). Throughout the text we focus on welfare imputation, since this is what the Elbers, Lanjouw, and Lanjouw (2003) methodology focused on, nevertheless it can be applied for other continuous measures aside from welfare.

The first step towards small area welfare simulation is a per-capita<sup>7</sup> welfare model, estimated via ordinary least squares ( _OLS_ ), or weighted least squares ( _WLS_ ): where ln _Y_ is a _N ×_ 1 matrix indicating the household’s welfare measure (usually in logarithms, but not necessarily),<sup>8</sup> _X_ is a _N ×K_ matrix of household characteristics, and _u_ which is a _N ×_ 1 matrix of disturbances. To achieve valid small area estimates it is necessary that the set of explanatory variables, _X,_ used in the

> 6For the 1st stage of the analysis. The second stage relies on Monte-Carlo simulations, and thus differences are likely to arise.

> 7Other household equivalization methods are also feasible, for example adult equivalent household welfare.

> 8Logarithmic transformation is usually preferred since it makes the data more symmetrical (Haslett et al., 2010).

3
It is important to first define the organized manufacturing sector of the Indian economy. The organized manufacturing sector is comprised of establishments with more than ten workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. The organized sector accounts for over 80% of India’s manufacturing output, while the unorganized sector accounts for a high share of plants and employment (Ghani et al. 2012). We focus on the organized sector in this study.

The organized manufacturing sector is surveyed by the Central Statistical Organization through the Annual Survey of Industries (ASI). Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. We use the provided sample weights to construct populationlevel estimates of organized manufacturing activity at the district and two-digit NIC level. Districts are administrative subdivisions of Indian states or union territories. As we discuss further below, we use district variation to provide more granular distances from the various highway networks.

ASI surveys record several economic characteristics of plants like employment, output, capital, raw materials, and land and building value. For measures of total manufacturing activity in locations, we aggregate the activity of plants up to the district or district-industry level. We also develop measures of labor productivity and TFP. Labor productivity is measured through output per employee at the plant level, with an average then taken across plants for a district. TFP is calculated through a residual regression approach. For every two-digit NIC industry and year, we regress log value-added (output minus raw materials) of plants on their log employment and log capital. The residual from this regression for each plant is taken as its TFP. We then take the average of these residuals across plants for a district.

As our data are repeated cross-sections, rather than panels with unique plant identifiers, there
# **1. Background and Introduction**

Integrated water resources management (IWRM) and organizing it primarily at the river basin level are two of the most common and widely repeated recommendations in the water resources literature of the last decade if not longer (Allee 1988; Galloway 1997; McDonald and Kay 1988; World Bank 1993). Basin management is often associated with the concept of decentralization, of managing water resources at the “lowest appropriate level.” (See, e.g., International Conference on Water and the Environment 1992; Mody 2001). Several conceptual arguments have been presented in favor of decentralization in water resource management, and basin-level management in particular, including that the whole array of resources and use patterns in the basin will be taken into account, management decisions will be based on better knowledge of local conditions, and incentives for stakeholders to actively participate in management will be stronger.

Empirical studies of river basin management systems provide opportunities to examine the claims made for basin-level integrated water resources management, and to explore factors that appear to influence its implementation and outcomes. In this research project the project team has searched for those factors and their relationships to river basin management in two ways: with a survey of river basin organizations throughout the world, and with case studies of eight river basins analyzed in greater detail. Some of those eight cases have long histories of basin-scale institutions for water resource management, such as the Guadalquivir river basin in Spain and the Murray-Darling river basin in Australia. Others have emerged recently, as in the Tárcoles basin of Costa Rica where a river basin commission, the Coordinating Commission for the Río Grande de Tárcoles (CRGT), was established in the early 1990s.

This case has been extremely valuable because the formation of the basin commission was a locally-initiated action that occurred in the fairly recent memory of many individuals who are still actively involved in water and government, and whose perspectives on the origin, growth, and recent difficulties of the basin management effort are both fresh and
**Technology sophistication measure (Technology readiness)** The pre-pandemic technology index used in this paper reflects the sophistication of the most widely used technology in a business function, as described in Figure A1. Following ( _17_ ), based on the experts’ assessment, we order the technologies in each function _f_ according to their sophistication, and assign each a rank _rf ∈_ 1 _,_ 2 _, ..., Rf_ , from least to most advanced. Because several technologies may have the same sophistication, the highest rank in a function _Rf_ may be smaller than the number of possible technologies _Nf_ . We define the relative rank of a technology as _r_ ˆ _f_ = _Rrff−−_ 11<sup>.Notethat</sup><sup>_r_ˆ</sup><sup>_f∈_[0</sup><sup>_,_1].Thetech-</sup> nology sophistication of business function _f_ in firm _j_ is a monotonic increasing function of the relative rank of _the most widely used technology_ of firm _j_ in function _f_ ( _r_ ˆ _f,j_ ). For example, our baseline sophistication measure is

The technology sophistication measure is scaled between 1 and 5. For example, if the firm is mostly using a frontier technology (e.g., ERP for business administration), it takes the value of 5, while if it is mostly using the most rudimentary technology (e.g., handwritten processes for business administration), it takes the value of 1. The information of the average index and its distribution across quintiles, combined with the standard deviation, provided in Table 1 may provide a good intuition of the specific technologies used by those firms. For example, the average of 1.1 for firms in Q1 with a standard deviation of 0.02 suggests that these firms are mostly relying on manual methods to perform GBFs with little variation among them. ( _17_ ) provides several robustness checks to show the measure
countries like Kenya, slightly increased its adjusted net savings during the boom but its polity score increased more than 6 points.

Figure 36. Adjusted net savings share in GNI vs democracy score in Sub-Saharan African countries, 2004-2014 average.

_Notes: Average adjusted net savings % of GNI vs Polity IV score in Sub-Saharan Africa change during the boom. It excludes Eswatini, Burundi and Liberia considered as outliers. Eswatini scored an average of -9 in polity IV, and Burundi and Liberia reached ANS of -32 % and -61 % of GNI, respectively. Source: authors’ calculations using World Bank data and the Center for Systemic Peace (CSP), Polity IV dataset version 2015._ During the boom, we see that higher adjusted net savings are related to higher Country Policy and Institutional Assessment (CPIA) ratings for non-resource-rich countries and for fossil fuel-rich countries significant at 95% confidence level. However, this relationship does not hold for mineral rich countries as shown in Figure 37. Although, while several mineral-rich countries that improved their control of corruption during the boom period, saw an increase of their adjusted net savings share in GDP, many oil and gas rich countries actually worsened their control of corruption with an increase of their adjusted net savings, as shown in Figure 38.

46
We then examine the mechanisms behind the contrast between the small _positive_ wage effects of globalization within manufacturing and the relatively large _negative_ wage effects we observe at the occupational level. We begin by showing that trade and offshoring are associated with a contraction in the manufacturing workforce. Then, using a large panel of CPS workers who are matched across surveys, we demonstrate that workers who switch industries within manufacturing experience almost no decline in wages. However, when workers relocate to the service sector, they experience a significant wage loss. The negative wage impact is particularly large among displaced workers who also switch occupations. We estimate wage losses of 2 to 4 percent among workers leaving manufacturing and an additional 4 to 11 percent wage loss among workers who also switch occupations. These effects are most pronounced for workers who perform routine tasks. This downward pressure on wages due to import competition and offshoring has been overlooked because it operates between and not within sectors.

This provides compelling evidence that the negative consequence of trade on workers is mediated through a reallocation of labor across sectors and into different occupations. While many models of trade posit that workers can move in a costless manner to new jobs in the face of pressure from foreign labor, we identify large and significant wage declines among workers forced to leave manufacturing, and the wage decline is particularly pronounced for those who are forced to switch occupations.
migrants seek at destination areas, i.e., more educated workers tend to seek less land-intensive work whereas less educated individuals are more likely to pursue more land-intensive activities. The result is also consistent with the observation that the least educated workers tend to be engaged in farming in Vietnam (Vijverberg _et al._ , 2006). Workers in the ethnic minority category are also found to be strongly pulled to the locations where land is abundant.

The coefficients for distance reveal expected negative signs for all the regressions except for the ethnic minority subset, confirming that individuals are usually more likely to choose destinations which are closer. The positive coefficients for distance for ethnic minority are likely to reflect a large migration flow from the northern remote provinces to the Central Highlands.<sup>48</sup> Finally, the regression model in this section is parsimonious and not intended to explain comprehensively the determinants of in-migration. For instance, the data limitation does not allow me to analyze how job opportunities in the emerging (domestically-owned) private enterprises <mark>(Van Arkadie and Mallon, 2003)</mark> influence migration pattern. In addition, some coefficients for the state job become unstable, suggesting that further research is needed to better understand the migration mechanism in the state sector.

# **VII. Concluding Remarks**

This paper investigates the differential impacts of different job opportunities as determinants of internal migration and destination choices using the _Vietnam Migration Survey_ 2004 and the _Vietnam Household Living Standards Survey_ 2004. A particular focus is given to analyze the role of foreign employment opportunities in influencing migration patterns. I first run a logit model relating a dichotomous outcome of migration (migrant vs. non-migrant) to a set of personal and origin characteristics. The results turn out to be broadly in line with the previous literature:

> 48 Amongst minority migrants reported in the VMS, 80.9 percent of them moved to the Central Highlands of which 42.0 percent came from the Northeast region.
# Box 1. Integrated Centers for Special Attention to Women

According to Brazil’s National Secretariat of Women’s Policies, these centers –known as CRAMs or CIAMs--should provide the necessary care and attention to help women overcome their situation, contribute to the empowerment of women and to restoring their sense of citizenship (National Policy to Combat Violence Against Women 2011). The activities performed by the centers (according to the Technical Guidelines) should include: 1) Counseling in times of crisis, 2) Psychosocial support, 3) Legal counseling and advice, 4) Preventive actions, 5) Professional Qualification, 6) Articulation of local public and private services integrating the GBV network, and 7) Survey of local data on women in situations of violence. The CIAMs receive victims either by spontaneous demand or referrals. Most cases get forwarded by Women's Police Stations, although some women come on their own by word of mouth, either through friends, neighbors or other public services.

Data were collected from four different service units in Rio de Janeiro state, selected for practical reasons, as those were the ones maintained by the state government, which facilitated the team’s engagement. In addition, five key informants were also interviewed (one government representative, responsible for anti-VAW policies in the state and 4 CIAM service coordinators). Potential interviewees were identified by the staff of the CIAMs, and first approached by them to evaluate their interest to participate in this research. Interviews lasted about 90 minutes and were conducted by a researcher trained on VAW research. The research instruments were developed by the research team and with inputs of the Subsecretariat for Women Policies in Rio de Janeiro, under the State Secretariat for Social Assistance and Human Development (Subsecretaria de Políticas para as Mulheres do Rio de Janeiro sob a Secretaria Estadual de Assistência Social e Desenvolvimento Humano, SEASDH-RJ). All data collection followed the recommendations on the ethical conduct of research on domestic violence issued by the World Health Organization (2001). Although the profile
defined above. The VMS includes information about the province from which each migrant originated and whether

> 27 While only 13.5 percent of the ethnic Vietnamese (Kinh) and Chinese were poor, 60.7 percent of ethnic minorities lived below the poverty line in 2004 (ADB, 2006). See Hung and Reilly (2009) for analyses of ethnic wage gap. 28 Rural workers of working age worked only about 79 percent of working hours in 2004 (GSO, 2006, p.57).
Symptoms of depression and distress, z-score<br>War experience index (0-14)<br>Summary index of income measures, z-score<br>Savings stock (USD)<br>Hours/week working in potentially illicit activities<br>Hours/week working (total)<br>Summary index of antisocial behaviors, z-score<br>Summary index of self-control skills, z-score<br>Patience in game play index (0-6)<br>N<br>Mean of dependent variable<br>R-squared<br>P-value for test of joint signifcance<br>_Notes:_ Columns (1)–(2) pool all endlin<br>on the baseline covariates. Columns (3)–(8<br>covariates, restricting the samples to people <br>from this table. Robust standard errors are|

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