rafmacalaba/datause-encoder-data huggingface dataset:
# **I.   INTRODUCTION**

There is growing research interest on the topic of credit cycles, which shows the importance of credit fluctuations for the macroeconomy and their role in the propagation of shocks in the economy. Various studies have estimated cyclical components in credit in the advanced economies. Schularick and Taylor (2012) study the credit booms and leverage from 14 countries during 1870 to 2008 and show that there is a relation between credit growth and banking crisis. They show evidence of deeper recession following credit-intensive booms. Jorda et al. (2013, 2011 a, and 2011 b) provide empirical evidence for a deeper recession and sloppy recoveries after credit-intensive booms. Claessens et al. (2009, 2012) find that recessions that are linked to credit crunches are relatively deeper and more prolonged. Drehmann et al. (2012) using data for five advanced countries during 1960 to 2011 show that financial cycle peaks are related to the financial crisis and that after the changes in monetary policies and financial liberalization the length and amplitude of financial cycles incremented. Credit cycles have important implications for macro-prudential policies. The study of credit cycles has implications for policy coordination, as monetary policy affects banks' balance sheets and risk-taking on financial markets and thereby might interfere with macro-prudential policies (Adrian and Shin, 2011; Gertler and Karadi, 2011). While the literature on this topic is increasingly growing, most of the work has been done on the advanced countries.

In terms of methodology most studies that estimate the cyclical components in credit use univariate bandpass filters (e.g., Aikman, Haldane, & Nelson, 2015; Drehmann, Borio, and Tsatsaronis, 2012). There are other studies that have employed spectral methods (Schüler, Hiebert, and Peltonen, 2015) and autoregressive integrated moving average (ARIMA) models (Strohsal, Proaño, and Wolters, 2015) to assess cyclical properties of the series.

The general finding of these studies is related to the highly predictive linkage of credit cycles to the emergence of financial crisis, which leads to attaching high importance to their occurrence from a policy point of view. There are a number of indicators that regulators refer to such as the credit-toGDP ratio that are used to determine appropriate policy responses to address potential crisis. The regulations refer to this measure/indicator of the credit cycle, suggesting that the buffers that are built up as a mitigant should be released once the credit-to-GDP ratio exceeds its long-run trend by 2%. Also limits on loan-to-value and debt-service-to-income ratios are among the other policy measures at the disposal of regulators (Hanson, Kashyap, and Stein (2011), Cerutti, Claessens, and Laeven (2015), and Hartmann (2015)).

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# Table Al.2 - Basic US Census Listing for Grain Mill Products, 1977

|----------------------------------------------<br>Rank Code<br>of|----------------<br>Product Item|__---------<br>Unit|----__----<br>|---------------<br>Quantity|Product<br>Value|Dollar<br>Unit|
|---|---|---|---|---|---|---|
|Item<br>----------------------------------------------|-----------------|__-----|-------__--|-----------------|(mill.)|Value|
||WAt process corn byproducts:|mill.|lb.||||
|20460 71|Steepwater concentrate (50% solids basis)|||135.5|<br>4.1|30.26|
|6 20460 75|Corn gluten feed|||4,199.8|<br>226.6|53.95|
|12 20460 77|Corn gluten meal|||1,000.7|<br>122.2|122.11|
|13 20460 79|Other wet process corn byproducts|mill.|lb.|1,564.0|<br>117.9|75.38|
|20460 00|Other wet corn mill products. nsk, >-15 empl|.|||7.8||
|20460 02|Other wet corn mill products, nsk. <15 empl.||||6.4||
||TOTAL SPECIFIED 2041.2046||||5.624.7||

Source: US 1977 Census of Manufactures; Note: (a) Figure included under the figure for the total category 20411
|Rdper|R&D personnel|Number of researchers<br>(scientists and engineers)|(including electronic versions available<br>through the World Bank electronic<br>databases)<br> -OECD: Science and Technology<br>Indicators in electronic and printed<br>format<br>-RICYT<sup>7</sup><br>- Taiwan(China)Statistical Data book|
|---|---|---|---|
|Rdfinprod|R&D<br>expenditure<br>financed by the<br>productive<br>sector|Percentage of total R&D<br>expenditure|-UNESCO<br>Statistical<br>Yearbooks<br>(electronic and printed formats)<br>-OECD Main Science and Technology<br>indicators<br>(electronic<br>and<br>printed<br>formats)<br>-RICYT<br>-Taiwan (China) Statistical Data book<br>-Database developed byFurman et al.|
|Rdfinabro|R&D<br>expenditure<br>financed with<br>funds from<br>abroad|Percentage of total R&D<br>expenditure|-UNESCO Statistical Yearbooks<br>(electronic and printed formats)<br>-OECD Main Science and Technology<br>indicators<br>-RICYT<br>- Taiwan(China)Statistical Data book|
|Rdperfprod|R&D<br>expenditure<br>performed by<br>the productive<br>sector|Percentage of total R&D<br>expenditure|-UNESCO Statistical Yearbooks<br>-OECD Main Science and Technology<br>indicators<br>-RICYT<br>-Taiwan(China)Statistical Data book|
|Rdperfhe|R&D<br>expenditure<br>performed by<br>higher<br>education|Percentage of total R&D<br>expenditure|-UNESCO Statistical Yearbooks<br>-OECD Main Science and Technology<br>indicators<br>-RICYT<br>-Taiwan (China) Statistical Data book<br>- Database developed byFurman et al.|
|Rdperfpub|R&D<br>performed by<br>the public<br>sector|Percentage of total<br>R&D performed by the<br>public sector|-UNESCO Statistical Yearbooks<br>-OECD Main Science and Technology<br>indicators<br>-RICYT|
|stockpat, stockpatEPO|Stock of patents<br>granted by the<br>USPTO and<br>EPO|Sum of patents from the first<br>year that the patenting office<br>granted a patent to time “t”|Author’s calculations|

**3. Summary Statistics of Variables Used in Econometric Analyses Presented in Table 6.**

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# _Table 10: Double-difference estimates of VHI impact on components of per capita nonmedical household consumption_

||Treatment<br>effect|z-stat|F-test<br>consumption<br>quintile<br>differences:<br>Prob > F|<br> <br>Quintiles<br>with<br>significant<br>effects<br>where<br>significant<br>differences<br>exist|
|---|---|---|---|---|
|**Majority of household members insured**|||||
|Medical consumption|-28.898|-0.92|0.0847||
|Nonmedical consumption|319.728|4.20|0.5848||
|Food consumption|77.150|2.40|0.0002|Bottom|
|Total nonfood consumption|179.103|3.61|0.8064||
|Garbage collection expenditures|0.774|2.68|0.0108|Top|
|Water expenditures|1.454|0.93|0.0837||
|Electricity expenditures|5.490|1.34|0.6330||
|Education expenditures|42.357|2.84|0.2874||
|Imputed rent expenditures|3.030|0.19|0.4443||
|Other non-food expenditures|29.791|0.87|0.9347||
|Use value of durables|86.962|3.31|0.6749||
|**At least one household member insured**|||||
|Medical consumption|-34.793|-1.35|0.7917||
|Nonmedical consumption|229.767|3.52|0.5973||
|Food consumption|9.972|0.40|0.0000|Bottom|
|Total nonfood consumption|175.541|3.64|0.4876||
|Garbage collection expenditures|0.247|0.97|0.0001|Top|
|Water expenditures|2.103|1.48|0.0000|Top|
|Electricity expenditures|2.599|0.74|0.0000|Top|
|Education expenditures|55.872|3.27|0.0000|4<sup>th</sup>, Top|
|Imputed rent expenditures|11.866|1.28|0.5324||
|Other non-food expenditures|78.532|2.55|0.0412|Bottom|
|Use value of durables|36.057|1.65|0.8604||

# IX. **CONCLUSIONS**

The results in this paper lend support to recent research that argues that health insurance can improve health outcomes and can expand household consumption through the reduction of financial risk. The use of panel data collected before and after the insurance program’s introduction allows us to eliminate any confounding effects due to selection on time-invariant unobservables, and our coupling of propensity score matching with a double-difference estimator allows us to reduce the risk of biases due to inappropriate specification of the outcome regression model. The fact remains, however, that insurance was not assigned randomly, and there remains the possibility that people may have been selected into the program in part on the basis of timevarying unobservables. It is possible, in other words, that those in the program did not share the 19
Using data for 29 countries covering 19 years, we find that telecommunications reforms have been associated with significant changes in the European labor markets. More specifically, they have been accompanied by disproportionately higher employment creation and an increasing share of individuals working from home and part‐time among ICT‐intensive sectors. These results are robust to a number of alternative specifications. In contrast, we do not find evidence suggesting that the Internet has been associated with an increase in self‐employment, temporary work and second job holding.

We focus on Europe for several reasons. First, many European countries have witnessed a dramatic increase in alternative work arrangements, and while there is a large body of literature examining the drivers of these trends, there is no empirical evidence on the role played by the Internet. Second, while telecommunications reforms may be a noisy measure of policy‐driven Internet adoption when considering countries at very different levels of economic development, we expect that this concern would be attenuated when restricting the analysis to European economies. Finally, it allows us to use harmonized labor force surveys with detailed information on alternative work arrangements for 29 countries over a time span covering almost twenty years.

To our knowledge, this paper is the first to provide empirical evidence supporting a positive link between the expansion of the Internet and the rise of part‐time employment. The share of workers in part‐time jobs has dramatically increased in most European countries since the early 2000s, with more than 20 percent of workers having a part‐time job across many European countries in 2014 (see Figure 1). This trend has important welfare implications, as part‐time workers are more likely to be poor and a growing fraction of them find themselves in part‐time work involuntarily, as they cannot find a full‐time job (OECD [2016]; Horemans and Marx [2013]). Several hypotheses have been put forward to explain the rise of part‐time work in Europe, such as more flexible labor market regulations, an increase in female labor force participation and a shift from manufacturing to services (see Buddelmeyer et al.

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23 Section III examined cost structure, and in particular the food/non-food breakdown for 81 feeding programs, 5 food subsidy programs, and I micronutrient intervention. Non-food costs account for about 17% of the total for targeted food rations (median, 4 programs), 25% for feeding programs (median, 81 programs), 20-34% in food for work programs (2 programs) and 29% for micronutrient supplementation programs (1 program). Non-food costs are lower in sub-Saharan Africa than in other regions, in medium-size programs, and possibly in NGO-operated programs. A summary table (table 8) highlighted some of the main findings of sections II and III, and compared them to the previous "received wisdom" on the topic.

Only limited information could be obtained on cost-effectiveness (section IV). A total of 8 estimates were available, 2 on cost per death-averted, 5 on cost per child removed from moderate or severe malnutrition (from which cost per death-averted could be extrapolated), and I on cost per case of blindness prevented (from which cost per death-averted was estimated). Data from the Narangwal project were also cited, on cost per death-averted for different age groups, for nutrition and medical interventions alone and combined. These figures are given in tables 9 and 10.

Section V on financing presented information on share of nutrition interventions in health budgets and GNP for 5 countries, and share in health budgets for 2 donor agencies. Information on expenditures on feeding programs was cited, for 16 Latin American countries. Information on the share of external financing was presented for 81 feeding programs. Three country studies were undertaken, providing somewhat fragmentary evidence on local versus central government funding. In general nutrition expenditures seem to account for about 10% of the health budget, both for individual developing countries, and for external donors. The share of nutrition expenditures in GDP had a median of 0.16% of GDP. About 90% of domestic nutrition finance seemed to come from central government, and 10% local government. External finance accounted for a larger share of nutrition expenditures in poorer countries.
**Table 4: Impact of political turnover on market share – exploiting variation in the tenure of the last Suharto appointed mayors**

|||Depen<br>**Suhart**|dent variab<br>**o Family Fi**|le: market sh<br>**rms**|are||**Politically Co**|**nnected Fir**|**ms (Broad)**||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|PC family*post Suharto|-0.011||-0.014*|-0.013*|-0.011||||||
||(0.007)||(0.007)|(0.007)|(0.008)||||||
|PC family*post Suharto mayor||-0.008|0.003|-0.003|-0.012||||||
|||(0.007)|(0.004)|(0.018)|(0.018)||||||
|PC family*legacy||||0.003<br>(0.007)|||||||
|PC family*app yr=95*post Suharto mayor|||||0.020<br>(0.020)||||||
|PC family *app yr=96*post Suharto mayor|||||0.016<br>(0.021)||||||
|PC family *app yr=97*post Suharto mayor|||||0.011<br>(0.020)||||||
|PC*post Suharto||||||-0.015**||-0.011*|-0.009|-0.010*|
|||||||(0.006)||(0.006)|(0.006)|(0.006)|
|PC*post Suharto mayor|||||||-0.013**|-0.004|-0.025*|-0.008|
||||||||(0.006)|(0.006)|(0.013)|(0.013)|
|PC*legacy*post Suharto mayor|||||||||0.008*<br>(0.005)||
|PC*app yr=95*post Suharto mayor||||||||||-0.007<br>(0.015)|
|PC*app yr=96*post Suharto mayor||||||||||0.030*|
|||||||||||(0.015)|
|PC*app yr=97*post Suharto mayor||||||||||0.011<br>(0.013)|
|Observations|89,484|89,484|89,484|89,484|89,484|89,484|89,484|89,484|89,484|89,484|
|Firms|10,641|10,641|10,641|10,641|10,641|10,641|10,641|10,641|10,641|10,641|
|R-squared|0.793|0.793|0.793|0.793|0.793|0.793|0.793|0.793|0.793|0.793|

_Note_ : Table reports results of estimation of specification (2). The sample period spans 1993–96 and 2000–09. Districts for which we do not know the appointment year, districts that split over time, and districts in which the mayor changed or was re-appointed in 1998 are excluded. All specifications include firm, industry-year, district-year fixed effects, and Suharto-era firm controls interacted with either post Suharto dummy (columns 1 and 6) or post Suharto mayor dummy (columns 2 and 7) or both (columns 2-5 and 8-10). Firm controls include foreign and state ownership, the logarithm of firm age, and indicators for whether a firm imports or exports, all averaged over the Suharto era (1993-1996). Legacy is a measure of how long the last Suharto appointed mayor stayed in power after Suharto’s removal from office (defined as ( _Appointment year of the last Suharto appointed mayor_ +5)1998). Post Suharto mayor dummy variable indicates a time period after Suharto appointed mayor was changed. Appendix A defines all the variables. Standard errors are clustered by industry and district and presented in parentheses. * indicates significance at the 10 percent level, ** - at the 5 percent level, and *** - at the 1 percent level.

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cognitive skills keeps increasing (Costa Rica and Sri Lanka) alongside the non-routine skills. Understanding such different patterns of occupational developments – and with it the measured skill intensities – would be an important task for further research.

# **Concluding Remarks**

In this paper, we employed labor force and household surveys to compute skill intensities for 30 countries in the developing world at one point in time and for eight countries over time. Using the occupational structure derived from such surveys and matching them with a detailed skill mapping which had been developed for the United States, we derived such intensity measures for five different skills. Our cross section results indicate strong positive correlations between economic development and the skill intensity of non-routine cognitive analytical and interpersonal skills as well as routine cognitive skills while strong negative correlations with both routine and non-routine manual skills resulted. The dynamic analysis confirms this general pattern but showed significant variation, especially for the changing intensity of routine cognitive skills.

We pointed out a number of caveats related to this analysis which need to be taken into account when interpreting the results. First, and most important, is the application of the USspecific skill intensity per occupation to the entire sample of countries studied in this paper. While we were able to make some adjustments for individual categories to reflect labor market realities in developing and emerging economies, the use of the US skill intensities will most likely have led to an upward bias in the measured skill intensities for advanced skills – both nonroutine analytical as well as interpersonal skills. Second, while the skill intensity measures are an important data point, they cannot substitute for a proper measure of the quality of such available skills. For that, direct measurement techniques – as they are currently being developed – are necessary.

The observed skill intensity patterns are the result of the interaction between skill supply (i.e., the output of the education and skill building system, both in schools and workplaces alike) and demand (i.e., derived from public and private investment decisions as well as the business opportunities for self-employment in agriculture and urban areas). As such, they represent the

**20**
education—the demand for education could be controlled for more easily by including household and village characteristics. Middle schools in rural China are usually administered at the township level. The financing of the schools was provided by township governments which collected fees mainly from rural parents before 2001, and by county governments which have relied on intergovernmental transfers ever since (Liu et al. 2009). High schools have been managed by county governments. The key determinants of local educational attainment are thus county-level educational finance and local preferences for education, both of which are plausibly fixed at the county or county-year level. We therefore choose to use the full set of county-year fixed effects instead of the village fixed effects.

Another reason not to use village fixed effects in the outcome equation is that 74 percent of villages experienced no changes in the treatment status in the sample period. During the 21 years of CHNS coverage, 89 of the 174 villages had never implemented the water program, 40 of them had plant water in all the waves, and only 45 villages changed their treatment status (see Figure 3). The employment of village fixed effects implies that the estimation only exploits the variations in those 45 villages in relatively shorter periods of time.<sup>17</sup> More importantly, we believe a key source of variation is the within-county inter-village comparisons. In light of our findings presented later that the most pronounced effect of water treatment is observed for those youth who had their first exposure to water treatment between age zero and two, relying exclusively on the treatment-status-changing villages has an unfortunate consequence: we have to rely more on youth who got exposed to plant water at age older than 2 than in the case of keeping always-treated villages in the sample.<sup>18</sup> Indeed, having the always-treated villages in the sample confers important benefits—in such villages the youth are likely to be covered by this drinking water program for a few years before they first appear in our sample. Thus, many of the youth in these villages may start to use plant water before age two, and their inclusion would allow us to better capture the returns to treatment at early childhood. With this consideration in mind, our main empirical strategy is to hold time-variant educational financing and county policies constant, to control for key village characteristics and key household characteristics, and

> 17 Since the treatment happens in the middle of the sample period, the number of post-treatment years is more limited than the sample of “always treated”.  This poses another challenge for identifying the long-term effect of the treatment on education.

> 18 In particular, for data of wave 1997 and after, those individuals who got exposed to treated water at age zero to two would be excluded from the treated sample.

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**Table 15: Panel Regressions on Income and Employment when Members are i) Abroad or ii) Abroad and Send Remittances**

||**xt.Hous**<br>|**old Income**<br>|**xt.HH**<br>**Remi**<br>|**-Inc w/o**<br>**ttances**<br>|**xt.Share**<br>**(UZB on**<br>|**of Adults**<br>**ly) Working**<br>|**xt.Sha**<br>**(UZB +**<br>**Wo**<br>|**re Adults**<br>**Migrant)**<br>**rking**<br>|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Migrant Abroad|0.182**||-0.000***||-0.017**||0.134***||
||(0.076)||(0.000)||(0.008)||(0.008)||
|Receive Any Remittance||0.275**||0.000||-0.006||0.077***|
|||(0.115)||(0.000)||(0.008)||(0.008)|
|Constant|-0.024|-0.010|0.001***|0.001***|0.247***|0.244***|0.248***|0.264***|
||(0.016)|(0.010)|(0.000)|(0.000)|(0.003)|(0.003)|(0.003)|(0.003)|
|Number of observations|12,137|12,137|12,137|12,137|12,137|12,137|12,137|12,137|
|Calendar Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R2|0.005|0.010|0.008|0.007|0.058|0.057|0.108|0.073|
|r2_w|0.006|0.011|0.009|0.008|0.058|0.058|0.108|0.073|
|r2_o|0.003|0.007|0.010|0.005|0.027|0.021|0.045|0.034|
|r2_b|0.002|0.003|0.017|0.005|0.021|0.003|0.015|0.012|
|sigma_u|0.172|0.171|0.002|0.002|0.158|0.159|0.156|0.155|
|sigma_e|0.493|0.492|0.002|0.002|0.116|0.117|0.115|0.117|
|rho|0.109|0.108|0.400|0.401|0.648|0.650|0.651|0.639|

_note: .01 - ***; .05 - **; .1 - *; Panel regression; Standard errors clustered by household; Control for month of interview;_ 31
conceivable that response biases are at play. NSO (2005) reports, for example, that the 2004 DHS data point to an HIV prevalence in Zomba amongst men aged 15-49 that is roughly on par with the national average for men of that age (10.5% in Zomba versus 10.2% in Malawi as a whole). For reasons not addressed in the MNSO report, the 2004 DHS data suggest that prevalence amongst women in Zomba (24.6%) is nearly twice the national figure for this population group (13.3%). The SAE-based estimate, on the other hand, suggests that prevalence amongst women in this district is also on par with the national figure for women (13.4% versus 13.2%). These examples serve to remind that while the SAE methodology is certainly likely to introduce a degree of uncertainty into assessments of HIV prevalence, it is also the case that “gold-standard” estimates, based on direct measurements, deserve a degree of circumspection.

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