rafmacalaba/data-use-mentions-extended huggingface dataset:
7

# volume of credit available for new or more viable enterprises.

In 1988/89 a process of gradual de-regulation of the financial sector was begun. The process for simplifying the administered structure of interest rates was initiated. In October, 1988 ceilings on interest rates on advances of commercial banks were removed and only floors were specified. Subsequently, this was extended to the interest rates on loans of the term lending institutions as well. In addition, uniformnity was introduced in interest rates across sectors and types of borrowers. Interest rates were adjusted more frequently in accordance with the rate of inflation. During 1988-89 the Credit Authorization Scheme was replaced with a Credit Monitoring Arrangement. Thus, banks were no longer required to obtain prior approval of the RBI for working capital advances and term loans, provided they were made according to certain norms. From October 1988 borrowers were permitted to transfer their accounts from one bank to another without seeking the approval of the original bank which introduced an element of competition into the system. Since 1991 the scope of the reform has been considerably widened. Unfortunately the period covered by our panel does not allow us to investigate the consequences of the more recent financial sector reforms.

# **III. The Data: Description of the Panel and Aggregate Descriptive Statistics.**

The data used in this study consist of selected variables from the annual balance sheets and profit and loss accounts of an unbalanced panel of public limited companies<sup>8</sup> for the period 1980/81 to 1989/909. The firms represented in the sample have submitted their balance sheets voluntarily and, therefore, the data do not allow any inference to be made regarding exit of firms. The fact that a firm is not present in the data set after a few years does not necessarily imply that the frm has left the

- 8 All the companies represented in the sample are non-financial companies in the private sector. 9 The financial year in India does not correspond to the calendar year.
girls to younger boys are more likely to reflect financial considerations, as liquidity-constrained households with older girls would gain relief earlier. We expect spillovers for nonfinancial reasons to be stronger when girls are younger, as parents would prefer older boys to accompany younger girls to school.

Unfortunately, the exam records do not identify which students belong to the same household, preventing us from testing these hypotheses using the same outcomes already considered. However, the Gambian Integrated Household Survey data allow us to explore the enrollment response using a richer set of individual and household characteristics. We use the 1992, 1998, 2003, and 2010 waves of the survey, limiting the sample to secondary school-aged boys (13–18) in regions 2–6. The data record all household members, allowing us to observe if boys live in the same household as scholarshipeligible girls (though we cannot distinguish siblings from other types of connections).

We run a series of linear probability models in which the outcome is an indicator for enrollment. Note that this outcome differs from others previously considered, because the sample is all boys in the age group, whereas previously our sample was Grade 12 boys taking the exit exam. In table 7, column (1), we find no statistically significant effect of the program on boys’ enrollment. The finding is consistent with Gajigo (2014), who found no increase in the boys’ enrollment rate in response to the program.

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investigate whether the cyclical component is an important driver of the results. The results in Figure 5 show that the age profile is basically unaffected when we dropped the cyclical component, in other words, the way the cyclical component is introduced in the model does not seem to be driving the results.

_Is the period of time long enough to capture life cycle effects?_ It could be argued that the period of time covered by the ESS – from 2002 to 2012 – may not be long enough to capture life cycle patterns. Preferences may change over a longer period of time, and 10 years cannot cover the same group of individuals from, for instance, age 20 to age 40. Our results rely on an assumption – common in this literature ‐ that the age profiles of different cohorts are the same. To test the robustness of this assumption, we need repeated cross‐sections covering a period of time longer than 10 years. To our knowledge, the only data set that satisfies this requirement is the World Value Survey (WVS) spanning the years from 1990 to 2011, that is, a period covering about 20 years. Unfortunately, the questions included in the WVS regarding immigrants are quite different from those of the ESS. Moreover, they are not available for all years, so we end up with only two countries from the Europe area: Spain (1990, 1995, 2000, 2007 and 2011) and Turkey (1990, 1996, 2001, 2007 and 2011).

The only question included in the WVS related to attitudes towards immigrants – and available for a sufficient number of survey years ‐ is: _On this list are various groups of people. Could you please mention any that you would not like to have as neighbors?_ The non‐mutually exclusive list of people includes immigrants/ foreign workers as well as drug addicts, people of a different race and heavy drinkers among others. We use this question to test the assumption that the 18
of the disinfectant by the household, or of the water tests that our enumerators used. It will be important to understand better why self-reported and measured disinfection diverge, especially in the context of evidence from Kenya that chlorine solution dispensers at water sources increase use and greatly reduce child mortality

> 15We used the standards defined by the Unicef IGROWUP macro for weight for age, see latest version at Unicef [2019].

> 16Children were weighed on their own if over two and with their mother if below two years of age.

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use annual averages of monthly household expenditures for the average household and for the bottom quintile household. The expenditure data are taken from the Indonesia Database for Policy and Economic Research (INDO-DAPOER, World Bank Group).<sup>9</sup> The air pollution data set contains estimates on mean annual (1999 to 2015) concentrations of fine particulate matter (PM2 _._ 5), coarse dust particles of 2.5 micrometers in diameter, that proxy a wider range of air pollutants. The data points are available at a 0.01-degree resolution and have been derived from a combination of satellite-, simulation- and monitor-based sources. The authors address several inconsistencies in satellite-derived PM2 _._ 5 data by calibrating their estimates with ground-based observations and reducing the noise of seasonal anomalies.

INDO-DAPOER contains key economic, social and demographic indicators at the districtlevel, primarily sourced from various surveys and the Indonesia Central Bureau of Statistics (BPS). We use the annual means of monthly per capita household expenditures (in IDR) and the same indicator for the average across the 20 percent poorest households, which are available from 1999 to 2014. We are primarily interested in distinguishing between average economic developments and changes in poor household income. The data set includes poverty rates and local estimates of GDP, but the coverage is poor.

We are primarily interested in the environmental-economic interactions in urban environments. To narrow the focus, we used a gridded population data set (Gridded Population of the World, v4 at 30 arc-seconds resolution) to distinguish urban from rural districts. We defined urban areas as a contiguous patch of pixels with population density higher than 300 per square kilometer and a population count higher than 5,000. This is similar to the approach followed by OECD and EC-DG Regio to define global Functional Urban Areas, scaling down the population counts to be relevant in a subnational context. Our approach identifies 219 areas with urban clusters. To establish a link between urban air pollution and the INDO-DAPOER database, we summarized the PM2
ownership on industry and firm productivity suffer from identification problem because the high growth industries and the better firms attract more foreign ownership.<sup>2</sup> The paper is organized as follows. Section 2 examines the literature on foreign ownership and growth, and discusses foreign direct investment in Korea in recent years. Section 3 presents a theoretical framework explaining foreign acquisitions. Section 4 describes the data. Section 5 examines the theory empirically. Section 6 concludes.

# **2. Foreign Direct Investment and Growth**

Theory suggests that technology diffusion through multinational companies might be an important source of growth for developing countries.<sup>3</sup> There is some empirical evidence that is consistent with the technology transfer argument. Using industry data, Caves (1974) and Globerman (1979) test whether the foreign share of employment contributes to labor productivity in a production function framework. They find evidence of positive spillovers from foreign direct investment (FDI) in Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson (1983) and Blomstrom (1986) find evidence of higher productivity and higher productivity growth in sectors with more FDI. Borensztein et. al. (1998) find evidence of positive spillovers from FDI in a cross section of countries, provided countries have a minimum level of human capital.

These results, however, may be generated by reverse causality because FDI may be determined by the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison (1993) and Aitken and Harrison (1999) find no evidence of positive spillovers in Morocco and 2Aitken and Harrison (1999) argue a similar point. Using plant level data on productivity in Venezuela, they find that foreign ownership is positively correlated with own-firm productivity, but not with productivity growth. They interpret this as evidence that foreigners invest in more productive plants, though they cannot rule out that there are own-firm positive level effects.

3Findlay (1978), Jovanovic and Rob
Senegal 10.2 38.2 36.4 1.65.8 6.11.7<br>Togo 13.0 22.4 50.4 3.9 4.4 3.62.3<br>Benin 21.3 14.0 57.1 2.42.71.31.1<br><!-- End of picture text --> Source: Own elaboration based on GMD data. Notes: Countries ordered by informality level, from lowest to highest. Rows do not sum to 100% due to rounding errors.

There is a strong negative correlation between informality and GDP per capita.<sup>43</sup> Whether measured by the share of the labor force that is informal, the share that is LE-SE or the share that is informal wage employees, countries with a higher share of informal sector employment tend to have lower levels of income per capita (Figure 5). With the exception of Gabon, that has an exceptionally high level of informality given its GDP per capita, the pattern is consistent across the region.<sup>44</sup>

> 43 This is consistent with a similar exercise in ILO (2018). 44 The estimated correlation is statistically significant, with an R2 = 0.53.

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# 3. Methodology

## 3.1. Poverty dynamics

This section summarizes the approach outlined in Dang et al. (2014). Our aim is to estimate joint and conditional probabilities of poverty dynamics. For example, we wish to know the likelihood that an individual is poor in survey round 1 and non-poor in round 2, a joint probability which we can write as follows, denoting per capita income by and the poverty line by : ii yy zz 1 a 2). ii1 ii2 PP (yy ≤zz aa yy > zz Alongside evidence on joint probabilities, we are also interested in conditional probabilities. For instance, we might wish to estimate the likelihood of moving out of poverty, conditional on being poor initially. Such a conditional probability can be written as and can be computed straightforwardly from the joint probabilities, as the right-hand side of the equation shows. In the absence of panel data, we do not observe incomes in round 1 and round 2 for the same unit and would normally not be able to estimate these quantities. However, the method we describe below draws on cross-sectional data and yields both upper and lower bounds for each of the four possible ii outcomes of the dynamic process, in both conditional and unconditional terms.

We model the logarithm of per capita income in each round as a linear function of time-invariant household characteristics and an individual error term. Schematically: and The basic idea of the method is to use time-invariant household characteristics to predict household per capita income for the year in which the actual observation is lacking. These characteristics will be ii xx 9
two years of active work) to analyze; a process that required deep reading, careful coding, reflection, debate, analysis and writing. This trade-off is limiting, and in the next section I will discuss the advantages and disadvantages of machine learning tools, and whether they could help us analyze narrative data at scale.

# C. <u>Machine Learning and Natural Language Processing</u>

The last decade has seen vast advances in using Machine Learning (ML) to analyze textual data, a field which is known as Natural Language Processing (NLP). ML and NLP pervade our everyday lives in ways that are seen and unseen – for instance they underlie how search engines work, target advertising from social media companies, filter spam emails, create virtual assistants to listen and respond to phone calls, and an infinity of other applications. The application of these methods by governments and firms have been shown to discriminate against minorities and women because they draw on training data sets that are largely populated by dominant groups and by men (Caliskan, 2021), which is not an indictment of the methods themselves but of how they have been used.

Researchers across a variety of fields, including Economics, have begun to use these tools to study a variety of questions (e.g. Gentzkow, Kelly and Taddy 2019). NLP vastly expands our ability make sense of high-dimensional textual data which opens whole new areas of research in the social sciences. In particular, it has the potential to revolutionize mixed-methods research by making very large amounts of narrative and textual data amenable to statistical analysis. The question is whether this can bring more reflexivity to economic analysis.

To answer this question, it might be helpful to contrast the “hand-made” analysis of village meetings discussed in the previous section, with a study that I was involved in (Parthasarathy et al, 2019) that used NLP methods to analyze 100 transcripts of village meetings recorded and transcribed in the Indian state of Tamil Nadu in 2014 (and, consequently
using country systems “when these provide assurances that aid will be used for agreed purposes.” Despite the lack of a consensus regarding the developmentally-optimal level of donors’ use of country systems overall, there is broad agreement that the optimal level varies positively with the quality of those systems. Jansen (2009: 23) provides an illustration of how weaknesses in a country’s financial management system can lead to rampant corruption and failure to achieve development objectives.

The existing evidence base is insufficient to identify the developmentally optimal level of use of country systems, conditional on varying degrees of the quality of those systems.

However, the Paris Declaration (and its 2008 follow-up “Accra Agenda for Action”) reflects a consensus belief that overall, donors’ use of country systems and their sensitivity to the quality of systems are both sub-optimal. In other words, development effectiveness would be enhanced if the mean use of country systems, and the elasticity of their use with respect to their quality, were both increased.

The three rounds of the Paris Declaration Monitoring Survey (PDMS) were designed to provide quantitative evidence of progress toward these goals and others set by the PD. Data were collected from donors and recipients pertaining to the years 2005 (as a baseline), 2007 (for measuring interim progress), and 2010 (the target year).

This study assesses donor behavior in terms of these premises underlying the PD agenda. Other studies report a weak relationship between quality of systems and donors’ use of them, casting doubt on whether donors are living up to one of their commitments under the PD. In contrast, this study finds a strong, significant and positive relationship between quality of PFM systems and donors’ reliance on them. Moreover, donors’ use of PFM systems overall increased over the 2005-2010 period far more than can be explained solely by improvements in the quality 3
# **_First Stage_**

In the “first stage” of analysis a model of consumption is developed using household survey data and those variables that have been selected in the zero stage. In previous application of this methodology the log of per capita consumption has commonly been used as dependent variable; in this application we modify the definition of the dependent variable and use the log of per adult equivalent consumption (see the implementation section for a detailed explanation).

> The log of adult equivalent household consumption, _ych_ is related to a set of observable

6 characteristics, _xch_ : Using a linear approximation, we model the observed log per adult equivalent consumption per household h as: where β is a vector of parameters, and **u** a vector of disturbances, distributed _F_ ( ,0 ∑) .

The model (2) is estimated by Generalized Least Squares using data from the 2002 Azerbaijan household budget survey. In order to estimate by GLS model, it is first necessary to produce an estimate of ∑ , the associated error covariance matrix. We model individual disturbances as:

> 6 This section summarizes the discussion in Elbers et al. (2002).

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by the relation between their observable characteristics in the absence of a shock, which is analogous to the predicting the poverty status of a household using a traditional PMT. In contrast, the vulnerable can be identified from the potential direct welfare effect of the wind speed associated with a new typhoon on future PCE and thus the possibility of falling below the poverty line.<sup>10</sup> One should bear in mind that an analogous similar modeling approach can be followed for the identification of the households vulnerable to other kinds of natural disasters, such as earthquakes, floods and droughts. Alternatively, if the interest is on identifying the vulnerable to all kinds of covariate and idiosyncratic shocks, and not just to a particular shock as in this paper, it would be worthwhile exploring the method proposed by Gunther and Harttgen (2009).<sup>11</sup>

> 8 Annex 3 provides an estimate of the evolution of the poverty rate in the Philippines, based on the household PCE measure adjusted by the year/month province specific CPI used in this study.

> 9 The 0.5 probability threshold is justified in the following arguments provided by Pritchett _et al._ (2000, p. 5) and by Suryahadi and Sumarto (2003, p. 48): “First, this is the point where the expected consumption coincides with the poverty line. Second, it is intuitive to say a household is “vulnerable” if it faces at least 50% probability of falling into poverty. Third, if a household is just at the poverty line and faces a mean zero shock, then this household has a one period ahead vulnerability of 0.5. This implies that, in the limit, as the time horizon goes to zero, then being _in current poverty_ and being _currently vulnerable to poverty_ coincide.”

> 10The potential effect of the shock may also be inclusive of the interaction of the shock with other household characteristics, if the term 3 b hb ) is included in equation
- Jacob, B. A. and Lefgren, L. (2003). Are idle hands the devil’s workshop? incapacitation, concentration, and juvenile crime. _American economic review_ , 93(5):1560–1577.

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capita real crop gross income|0.0324***<br>(0.00805)|0.0416***<br>(0.0103)|
|Log of per capita real livestock gross income|0.00573**<br>(0.00283)|0.00479<br>(0.00347)|
|Log of per capita real agricultural wage|0.00127<br>(0.00239)|0.00186<br>(0.00278)|
|Log of per capita real non- agricultural wage|0.00553**<br>(0.00271)|0.00505<br>(0.00359)|
|Log of per capita real self-employment income|0.00934***<br>(0.00246)|0.0106***<br>(0.00302)|
|Constant|10.28***<br>(0.140)|9.942***<br>(0.189)|
|R-squared|0.086|0.095|
|Number of HHID|2,644|1,853|

Source: author calculations using the UNPS 2006 and 2010. Notes: The dependent variable is log of real per capita consumption. Household, year and month of interview fixed effects are included but not shown. Robust standard errors in parentheses. Coefficient statistically significant at: ***1%, ** 5%, *10%

> and this may have resulted in a reduction in reported consumption. CAPI was not introduced in the nationally representative cross-sectional survey, the UNHS.

> 10 A nationally representative cross-section household survey conducted every three to four years to provide (among other things) data on poverty trends in the country.

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000***|0.28|
||[2,591,065]|[33,019]|[36,866]|[34,633]|[31,662]|||
|Thinks that it's difficult to know in advance|0.725|0.708|0.708|0.673|0.651|0.66|0.654|
|how much taxes she will have to pay|[0.447]|[0.464]|[0.464]|[0.471]|[0.478]|||
|Ratio tax/ annual profit for all businesses|0.128|0.076|0.047|0.066|0.058|0.000***|0.39|
||[0.221]|[0.105]|[0.083]|[0.104]|[0.088]|||
|Ratio tax/ annual profit for businesses|0.169|0.117|0.091|0.116|0.11|0.000***|0.813|
|paying taxes|[0.313]|[0.11]|[0.097]|[0.115]|[0.095]|||

Notes: sources: listing‐baseline survey March 2014 48