rafmacalaba/data-use-mentions-extended huggingface dataset:
**Decoupling and recoupling**

depending on the specific issue at hand, stock holdings, capital flows or both may prove informative.

In addition, the investor behind a particular flow may also be relevant to understand FG. For example, a passive buy and hold portfolio investor may behave closer to real investors (FDI), with limited turnover (flows) for a given holding. By contrast, institutional or professional investors would tend to be more sensitive to expected return differentials, with both a larger turnover and a bigger impact in terms of price action its correlation across markets and with respect to economic fundamentals. Global mutual funds are a case in point in this regard. While, as a subset of cross-border holdings, they are generally a poorer proxy than other more comprehensive measures, they may shed some additional light on the role of the international investor as a financial transmission channel. To the extent that these funds tend to keep close to their benchmarks, they may introduce an additional source of market co-movement, particularly in the event of sharp swings in global risk aversion (when contributions and redemptions lead to purchases and sales in all markets at the same time).

In this light, we try to analyze different measures of de facto FG by their advantages and disadvantages of using them as proxies of financial globalization in emerging markets. More precisely, we look at two different sources: (i) US Treasury’s TIC monthly survey data on the market value of sales and purchases, and stocks holdings of foreign securities by US-domiciled investors (by the market where the security is issued); and (ii) EPFR’s monthly data on global fund flows and assets under management (AUM) (by the issuing country).

In our study, we are interested in asset prices on a monthly basis, and to address whether financial recoupling (or greater financial contagion) in the late 2000s was associated with financial globalization. At first sight, frequency and length problems arise with both Lane and Milesi-Ferretti’s
spatial integration using the multivariate Johansen approach to test for co-integration of the regional price series and explore the dynamics of the price transmission process. Then, they extract the trace statistic (the test statistic for the null of no co-integration) obtained from that first stage procedure and use it as a measure of market integration. The larger the value of that statistic, the stronger is the evidence for market integration. In the second stage, the authors regress the trace on measures of government intervention (purchases and sales of rice carried out by Bulog in each market), infrastructure (road density), market development (income per capita of the region) and a dummy that controls for the periods of self-sufficiency in rice production. The results for the whole period suggest that only the purchases of rice by Bulog had a significant effect on market integration. The rest of the variables do not significantly explain it. For the self-sufficiency period, sales of rice by Bulog also has a significantly positive effect, as well as per capita income.

Summarizing, there is scarce literature exploring determinants of market integration. Probably the most robust lesson that has been learnt is that spatial integration is weaker in distant provinces, and stronger in central ones. This paper attempts to contribute to this scarce literature by tackling the question of what are the factors that determine market integration in the context of Indonesian commodity markets.

# **C. DATASET: REVIEW AND DESCRIPTIVE STATISTICS**

In this paper we use consumer price time series for the period January 1993 − December 2007 for rice, sugar and cooking oil, and producer price time series for the period January 1992 − December 2006 for soybeans and maize. All price series were obtained from the National Bureau of Statistics of Indonesia (BPS). Data were also obtained from CEIC (CEIC Data Company Ltd) and BPS.

Table 2 presents mean, standard deviation, maximum and minimum of each of the variables used in this analysis, across provinces. „Distance‟ is the minimum distance in kilometers to
# **Annex 1. Data sources**

For Latin American Countries we use the SEDLAC dataset, which covers all countries in mainland Latin America and four of the largest countries in the Caribbean. Most household surveys included in the sample are nationally representative. For comparability purposes this dataset computes income using a common method across countries and years. In particular, it constructs a common household income variable that includes all the ordinary sources of income and estimates of the imputed rent from home ownership (see the Guide in the Web site of the SEDLAC for methodological details). Note that this is the only set of countries for which hours of work are available, allowing for further decomposition in the report.

For Bangladesh, Moldova, Romania, Peru, and Thailand, we use the Household Income and Expenditure Survey for each year. We make temporal and spatial adjustments for comparability reasons. For Ghana and Nepal we use the Rural Income Generating Activities (RIGA) data set, http://www.fao.org/economic/riga/en/.

<mark>Table A1. Data Sources</mark>

||Initial<br>Final|Initial<br>Survey<br>Name|Final<br>Survey<br>Name|
|---|---|---|---|
|In|come-Based Poverty|Measures||
|Argentina|2000<br>2010|EPH|EPH-C|
|Brazil|2001<br>2009|ECH|ECH|
|Chile|2000<br>2009|PNAD|PNAD|
|Colombia|2002<br>2010|CASEN|CASEN|
|Costa Rica|2000<br>2008|ECH|GEIH|
|Ecuador|2003<br>2010|ENFT|ENFT|
|El Salvador|2000<br>2009|EHPM|EHPM|
|Honduras|1999<br>2009|EPHPM|EPHPM|
|Mexico|2000<br>2010|ENIGH|
). It includes detailed information on IT use at the firm-level that, to the best of our knowledge, has not been previously used for research purposes.

We constructed a panel including firms that appeared in both surveys. Due to the sample design of this survey we were able to build a panel of 719 firms.<sup>6</sup> Additionally, firms from the trade, minining and

> 4Considering that these classifications are based on ISIC rev.3.1 we used the correspondences tables between ISIC rev 3.1 and ISIC 4 and then we used the NAICS-ISIC 4 correspondence table. As there are different levels of aggregation in this process, we also tested an IT intensity variable based on the sectoral descriptions rather than the classification codes, which proved to be more highly correlated with our endogenous variable (IT use).

> 5Considering that for some states there are great differences between the urban and rural sectors, different versions of these indicators were analyzed restricting the sample to localities with more than 15,000 inhabitants (urban).

> 6The ENTIC surveys were designed to gather information for firms with more than ten employees from the manufacturing, construction, services, electricity, mining, transportation and communications sectors according to the NAICS 2007 classification. The sample design is probabilistic and stratified by employment level. The sample is statistically representative at the national level by economic sector. Due to this probabilistic design it is easier to find that big firms are surveyed in both waves (2009 and 2013).

6
diversity measures used by Easterly and Levine are significant when the income variable is excluded from the regressions. However, they are not (nor do they have any explanatory power) when income is included. The variables are negative, suggesting that the manner in which they affect urbanization is by depressing income, which in turn reduces the level of urbanization.

Easterly and Levine also use a measure of racial tensions. Following the same logic as for SIVARD, we expect that racial tensions should result in higher urbanization. Cities tend to be centers where national identity is forged, and are less frequently dominated by one ethnic group. The racial tension data is only available for 1984, but to the extent that this is related to structural issues in a country, we use it as a proxy for racial tensions. over the whole period. The variable has a significantly positive effect on both the level and the rate of urbanization. In addition, it somewhat decreases the coefficient and level of significance of the Africa dummies. Thus, even if we control for level of income or of urbanization, racial tensions help explain higher than expected urbanization.

Regression 11 in Table 2 includes all significant variables,1<sup>3</sup> and shows that given the Africa dummies we can explain almost 90% of cross country variations in levels of urbanization. This specification, however, treats all observations equally and does not allow us to look at a country's individual urbanization process. We therefore run the regressions with country fixed effects, which enables us to determine how well we can explain countries' individual urbanization processes. Table 4 shows these results.

# _Countries' evolution over time: a fixed effect approach_

In the fixed effect specification, shown in Table 4, we find that income and income structure still are strongly correlated with a country's level of urbanization. The results are not substantially different than without fixed effects, although NOEDUC and NOEDUC2, CEREAL or black market premia are never significant. Secondary education and rural-urban wage
# **III. Tanzanian Insurance Companies**

The source of premium data for all Tanzanian insurance companies was the Commissioner of Insurance for Tanzania, _Annual Marketing Report_ , 2007. These data are for the year 2007 and were provided by a consultant as they were not publicly available. Based on these premium data, we calculated the market shares of each of the companies. We defined company market share as the share of total premium income in this market.

The source of the ownership shares of each of the companies is the 2009 report on the non-life insurance market of Axco entitled ‘Insurance Market Information: Non-Life.” From this source we obtained the share of ownership for all companies by country.

In Table B3, we list the result of these calculations.: Tanzania, 53.2; EU, 1.3; EAC, 39.6; SADC,5.9; ROW, 0.

105
Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|No|Yes|No|No|Yes|No|No|Yes|
|Education|No|No|Yes|No|No|Yes|No|No|Yes|
|Employment|No|No|Yes|No|No|Yes|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.090|0.092|0.103|0.173|0.192|0.225|0.255|0.309|0.354|
|Panel B Adj.R<sup>2</sup>|0.066|0.073|0.093|0.160|0.185|0.222|0.186|0.269|0.339|
|Nr. of countries|16|16|16|16|16|16|16|16|16|
|Nr. of hh|134624|134624|134624|134624|134624|134624|131294|131294|131294|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a dummy variable taking the value 1 if a household has access to a respective amenity. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector
<!-- Start of picture text --> Poverty headcount FGT(0)<br>Mo-10<br>Hi 11-20<br>Giz:(31 -30- 40<br>(41-50<br>(Js -60 .<br>1-70 Mundri<br>Gii71-80 Yambio<br>Wis: -90 e<br>M91 - 100<br>«+=» Disputed territories<br>. Nimule<br>@ Towns<br><!-- End of picture text --> <!-- Start of picture text --> JI<br>. ‘es * q va<br>mN, i Akobo<br>. {| eeteri,<br>“2 Ni,<br>states , Org<br>- \<br>------ Disputed territories = Terekeki<br>Poverty headcount FGT(0) r.°.<br>Value : 4 rT _Kapoe<br>100% XN ,. . rit.<br>| om €<br>el 0%, \. oD. iw<br><!-- End of picture text -->
|856,876|||||
|Baseline mean|0.295|0.284||0.289|0.278|||||

NOTES: Estimates of specification (2) in the text. The dependent variable is an indicator for birth to a given mother in a given year. Each column within a panel is a separate OLS regression. We use a mother-year data set for all mothers who have ever given birth, for each year from their year of marriage to the year of interview. Standard errors in parentheses are clustered by state. Baseline mean is the mean probability of birth in a given year during the pre-ultrasound period, by mothers with the specific socioeconomic status. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
analyses of export diversification.

It is important to note that a factor associated positively with export diversification would be expected to carry a positive sign in regressions with the first measure (specifi– – cations _D1 D3_ ) and a negative sign in the latter two measures (specifications _D4 D9_ ), given the nature of the indicators used as a dependent variable in the regressions. We include as controls the (natural logarithm) of the initial (year 2002) values of sector-specific density as given by (10), RCA as given by (5), PRODY as given by (6), trade volume, and the tariff rate. While we also experimented with taking changes in the variables as regressors, we consider the use of the initial values of each respective variable as a more accurate representation of the sector’s diversification potential throughout the period. This approach also has the added advantage of alleviating endogeneity concerns. That

> 20We also considered a cruder approach, with Probit regressions where the dependent variable was whether a sector became more diversified or not. The results obtained were qualitatively similar, and are available upon request.

> 21Utilizing the 6-digit HS to calculate diversification indicators at the 2-digit HS level allows us to maximize the level of disaggregation while still recovering sensible indicators at the aggregated level, since the number of exported 6-digit lines in any given 4-digit line is generally very small.

23
9.1%|...|
|VAT|3,904|4.3%|Yes|
|Excise Taxes|2,669|2.9%|Yes|
|Customs Duties|1,432|1.6%|No|
|Non-tax Revenue|354|0.4%|...|
|Grants|950|1.0%|...|

_Source: Uganda Annual Budget Performance Report 2016/17._ The tax system in Uganda is administered by the central government, through the Uganda Revenue Authority, and by local government regimes, and comprises both direct and indirect taxes. There are no social contributions funding social welfare or social security, or unemployment insurance (for example).

# 2.2. Indirect taxes

Over two-thirds of total tax revenue is collected through indirect taxes. Indirect taxes account for approximately 9.1 percent of GDP – a similar proportion as in Tanzania and South Africa (Figure 2), and greater than Kenya, Ghana and Ethiopia given the size of their economies (see Figure 3a). While _indirect taxes_ in Uganda are of a similar proportion of GDP as South Africa, South Africa compensates for this with significant social protection expenditures, thereby combatting undesirable impacts on poverty.

Indirect taxes include value added tax (VAT), excise taxes (mainly tobacco and alcohol) and custom duties (mainly petrol). The VAT on non-excisable goods and services is 18 percent. Goods and services such as

> 8 These are the countries for which there is a comparable CEQ fiscal incidence analysis. 9 These numbers coincide with the IMF Article IV – Feb 2019 figures (IMF, 2019).

8
corrects for this. Data for the monthly real effective exchange rate, starting in 1994, is obtained from the BIS. We adjust this measure for cumulative monthly discrepancies between official (INDEC) and private sector inflation estimates since November 2007 from PriceStats, thereby constructing an adjusted measure of the REER (see Figure 3).

We furthermore consider the case where CPI is possibly underestimated relative to PPI and adjust the CPI/PPI ratio upward by a cumulative 30% over 2008-2015, consistent with an estimated long-run relationship between the two price indices (average CPI inflation being higher than PPI inflation by 0.5%).<sup>24</sup> We also conduct a

> 20 We find other measures used in the literature, such as reserves as a percent of imports, to be I(0). 21 Real interest rate differentials between Argentina and countries such as Brazil and the US are found to be I(0), and therefore excluded from the cointegration estimation. Moreover, the presence of capital controls in Argentina is expected to mitigate the effects of this variable, as it violates the UIP condition that otherwise equalizes real interest rates.

> 22 Other papers have used this variable in log-differences but we find this measure to be I(0).

> 23 The official national and urban consumer price index (IPCNu) by INDEC, measured an annual inflation rate of 24% in 2014, lower than the implicit GDP deflator and much lower than inflation figures estimated by the private sector reaching close to 40% (see Figure 2). PriceStats−a specialist provider of inflation rates published by State Street, a financial services firm−estimates that since November 2007, cumulative inflation has reached 484% compared to 141% according to official estimates. “Congress” CPI inflation, which is calculated by averaging the measurements of eight independent consultants starting in 2011, estimates figures close to PriceStats and suggests that inflation averaged approximately 25% over the years 2011-2013.

24 On average, PPI historically tracks CPI closely
.owned affiliates. Because those sales include exports to third countries, this may suggest that domestic capacity growth underlies our earlier trade results to some degree. We emphasize, however, that this finding needs to be treated with caution, given the broadly aggregated nature of the BEA affiliate-sales data.

> 22Because the BEA data on affiliate sales are reported at highly aggregated levels, we pool all years in the sample to maintain a large sample, rather than consider five-year intervals as performed in the trade regressions.

33
Gains in labor productivity may be attained due to the reallocation of labor toward sectors with higher productivity. Such reallocation can help overcome the misallocation of factor inputs to comparatively unproductive sectors and firms.<sup>17</sup> Alternatively, labor productivity gains may occur due to workers becoming more productive within their sectors, e.g. due to labor-augmenting capital accumulation or technology improvements.<sup>18</sup> We compare the contribution of labor reallocation across sectors and the within-sectoral productivity gains to explain aggregate improvements in labor productivity for data extending until 2015. Over India’s two phases of high labor productivity growth, within-sector productivity improvement has been the key driver of growth in labor productivity (Figure 6). Until the early 2000s, reallocation contributed only approximately 1 percentage point to annual growth. Even though productivity increases driven by labor reallocation have grown in importance since the early 2000s, the contribution of labor reallocation to total labor productivity gain has remained relatively modest, at around 1.5 percent.<sup>19</sup>

**Figure 6: Labor Productivity Growth in India: Reallocation and within Sector Gains**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>Within Sector gains Reallocation of labour<br>Total change in productivity<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Central Statistics Office data. Employment statistics are estimates provided by the International Labor Organization, available for 1991 to 2017. Years refer to respective fiscal year.

# **2.5 Long-term Proximate drivers of growth**

In this section we discuss the proximate factors that have likely contributed to India’
and proxy activities across the agricultural production cycle. Thus, the main advantage that our survey design randomization offers is its ability to causally estimate survey design unbiased impacts of self/proxy reporting on child labor statistics and to assess potential drivers when these gaps emerge. Moreover, unlike some surveys that aim to capture two responses i.e., self - and proxy response for each child, the randomization of the survey instrument avoids potential strategic behavior where the last respondent (e.g., the child) tries to be consistent with earlier responses given by the proxy. Likewise, our controlled survey design instrument minimizes the self-selection of proxy respondents, a potential source of bias in standard survey designs.

The broader measurement error literature highlights the role of the salience of individuals and work activities, recall decay bias, and cognitive burdens of reporting when explaining recall bias relative to a known benchmark (e.g., Bound et al. 2001). Likewise, sources of measurement error in survey designs include question selection, sequencing and wording, and the data collection method. The use of a short module compared to detailed probing questions, for instance, has a statistically significant effect on child labor measurement (Dillon et al. 2012). Bias in statistics from surveys is also related to the representativeness of the sample due to non-interview rates and non-response rates. In this regard, the small literature on self/proxy reporting on labor statistics shows that self-respondents yield higher non-interview rates, while proxies yield higher item nonresponse rates (Biggs 1992).

Regular and predictable activities are easier to monitor and therefore recalling or counting the occurrence of events that are salient and regular is less costly for respondents. Thus, if farm labor performed by children is variable and irregular, proxy respondents are more likely to focus on information that is less prominent and perceptible, resulting in larger discrepancies of child labor reports. In the absence of recall-and-count information strategies, proxy respondents would 4
grow at the same rate, which implies that the saving rate is constant through that age range. The profiles we obtain are very similar to those measured using Taiwanese data in Deaton and Paxson (1994). They also show that the acceleration of saving after age 40 could be attributed to children exiting the household around that time, which is also a plausible explanation here.

_Figure 2 Age effects in income, consumption and saving rates_ <!-- Start of picture text -->
1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>252729313335373941434547495153555759616365<br>-0.2<br>Age of the household head<br>Saving rate Log consumption Log income<br>log-growth<br><!-- End of picture text -->

Turning to decompositions of direct measures of wealth, we find that households, on average, accumulate an amount of net worth equivalent to around 4.2 years’ (50 months’) worth of consumption between the ages of 25 and 65 (Figure 3). The plot shows the additional average household net worth relative to age 25. The accumulation is gradual, but it accelerates significantly in the second part of the life cycle. The slope of the household net worth curve in Figure 2 is 1.5 times steeper between ages 45 - 65 compared to 25-45: net worth increases by 20 months on average between ages 25 and 45, or 1 month per year and by 30 months between ages 45 and 65, or 1.5 months per year. The median behaves similarly to the mean, suggesting that outliers are not a concern. The percentiles 25 and 75 accumulate twice as slow and twice as fast, respectively.

13
, GCM MIROC-3.2, SLR 27 cm, land subsidence 2 mm/year); and most change (Scenario A2, GCM

> few coastal fish species may emerge gradually in inland water but their harvesting technology is costly and not affordable to the poor.

> 15 See Report of the Household Income and Expenditure Survey/HIES 2010.  Bangladesh Bureau of Statistics, Government of Bangladesh.

17