• 8. Economic growth close

Main economic activities of the home of the survey of the year 2013.

"Wanderarbeiter aus Inamwanga auf dem Weg nach Kilossa" ("Migrant workers from Inamwanga on the way to Kilossa"). Seven men carrying loads on their shoulders are standing on the road. Behind them a lorry.; [66 black-and-white photographs]; Samuel Baudert (1879-1956), born as son of the missionary Samuel Baudert in South Africa, was mission director of the Herrnhuter Br��dergemeine from 1924 to 1927 and since 1929 their bishop.

Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.

Pie R Squared employees making pizzas

The "Survey on Visitation and Alimony 2002" is commissioned by the Ministry of Children and Family (BFD). It was followed by a similar survey in 2004. The purpose of these two surveys is to evaluate the alimony reform that was enforced the 1st of October 2003. The reform introduced new rules for calculation of the alimony. Whilst the contributions before 2003 were calculated as a percentage of the maintenance payer's income, the post-2003 calculation would also take the alimony recipient's economy and child-related expenses into consideration. The main objective of the survey in 2002 was to contribute to the evaluation of the reform by mapping put the situation before the implementation of the reform. The situation after the reform was investigated in the 2004 survey. The Ministry of Children and Family was particularly eager to learn of the extent which the new rules affected the respondents' economic situation and actual time spent with the children.

We examine how participation in social safety net programs differs by income-to-poverty levels, and how that relationship changed after the Great Recession. We define income-to-poverty based on the average of 2 years of merged CPS data, and investigate program participation among households with income less than 300 percent of poverty. We find changes in both the level and distribution of safety-net program participation during the Great Recession, with SNAP expanding most at the bottom, the EITC expanding most in the middle, and UI expanding most at the top of the income ranges that we investigate; TANF did not expand.

Credit spreads are large, volatile, and countercyclical, and recent empirical work suggests that risk premia, not expected credit losses, are responsible for these features. Building on the idea that corporate debt, while fairly safe in ordinary recessions, is exposed to economic depressions, this paper embeds a trade-off theory of capital structure into a real business cycle model with a small, exogenously timevarying risk of economic disaster. The model replicates the level, volatility and cyclicality of credit spreads, and variation in the corporate bond risk premium amplifies macroeconomic fluctuations in investment, employment, and GDP.

ABSTRACT This article aims to analyze the process of a planned organizational change within a family-owned company from the technological sector. More specifically, it looks at the internal and external issues intervening in this process. Furthermore, this article intends to understand how this organization change process shifted four critical organization dimensions: (1) people; (2) the organization structure; (3) the management processes, and; (4) the company’s strategy. In addition to endogenous factors, exogenous factors seemed to have influenced the process of change occurred within the company. Taking into account that these process are normally triggered by crisis, our research suggests the emergence of a more pro-active attitude of the company, which anticipated a deeper crisis and introduced changes that effectively contributed to propel its business.

The current market is dynamic and, consequently, industries need to be able to meet unpredictable market changes in order to remain competitive. To address the change in paradigm, from mass production to mass customization, manufacturing flexibility is key. Moreover, current digitalization of the industry opens opportunities regarding real-time decision support systems allowing the companies to make strategic decisions, and gain competitive advantage and business value. The main focus of this paper is to demonstrate a proof of concept Prescriptive System applied to Reconfigurable Manufacturing Systems. This system is capable of suggesting sequences of machines throughputs that best balance productivity, and the impact of the proposed throughput in the degradation of the equipment. The proposed solution is mainly composed of two modules, namely manufacturing environment simulation and optimizer. The simulation module is modeled based on Directed Acyclic Graphs and the second one on Genetic Algorithms. The results were evaluated against two metrics, variation of pieces referred as differential and availability of the system. Analysis of the results show that productivity in all testing scenarios improves, and in some instances, availability slightly increases showing promising indicators. However, further research should be conducted to be able to generalize the obtained results.

Datasets: DS0: Study-Level Files DS1: Round 1 Interview Info Data DS2: Round 1 Knowledge Data DS3: Round 1 Employment and Income Data DS4: Round 1 Access Food and Food Security Data DS5: Round 1 Safety Nets 1 Data DS6: Round 1 Safety Nets 2 Data DS7: Round 1 Interview Result Data DS8: Round 1 Roster Data DS9: Round 2 Interview Info Data DS10: Round 2 Access Food and Food Security Data DS11: Round 2 Access Health Data DS12: Round 2 Access to Finance Data DS13: Round 2 Access to Education Data DS14: Round 2 Education and Childcaring Data DS15: Round 2 Coping Mechanisms 1 Data DS16: Round 2 Coping Mechanisms 2 Data DS17: Round 2 Concerns Data DS18: Round 2 Interview Result Data DS19: Round 2 Roster Data DS20: Round 3 Interview Information Data DS21: Round 3 Knowledge Data DS22: Round 3 Employment and Income Data DS23: Round 3 Access Food and Food Insecurity Data DS24: Round 3 Access Health Data DS25: Round 3 Safety Net 1 Data DS26: Round 3 Safety Net 2 Data DS27: Round 3 Interview Result Data DS28: Round 3 Roster Data DS29: Round 4 Interview Information Data DS30: Round 4 Digital Transaction 1 Data DS31: Round 4 Digital Transaction 2 Data DS32: Round 4 Access to Health 1 Data DS33: Round 4 Access to Health 2 Data DS34: Round 4 Access to Education Data DS35: Round 4 Coping Mechanism 1 Data DS36: Round 4 Coping Mechanism 2 Data DS37: Round 4 Concerns Data DS38: Round 4 Interview Result Data DS39: Round 4 Roster Data DS40: Round 5 Interview Information Data DS41: Round 5 Employment and Income Data DS42: Round 5 Access Food and Food Insecurity Data DS43: Round 5 Access to Health 1 Data DS44: Round 5 Access to Health 2 Data DS45: Round 5 Subjective Welfare/Concerns Data DS46: Round 5 Safety Net 1 Data DS47: Round 5 Safety Net 2 Data DS48: Round 5 Interview Result Data DS49: Round 5 Roster Data DS50: Round 6 Interview Information Data DS51: Round 6 Employment and Income Data DS52: Round 6 Food Security Data DS53: Round 6 Access to Health 1 Data DS54: Round 6 Access to Health 2 Data DS55: Round 6 Coping Mechanism 1 Data DS56: Round 6 Coping Mechanism 2 Data DS57: Round 6 Safety-Net 1 Data DS58: Round 6 Safety-Net 2 Data DS59: Round 6 Interview Result Data DS60: Round 6 Roster Data DS61: Round 7 Interview Information Data DS62: Round 7 Employment and Income Data DS63: Round 7 Food Security Data DS64: Round 7 Access to Health 1 Data DS65: Round 7 Access to Health 2 Data DS66: Round 7 Access to Education Data DS67: Round 7 Access to Education Household Data DS68: Round 7 Interview Result Data DS69: Round 7 Roster Data DS70: PMT Results Data DS71: Rounds 1-6 Weight Data Household weights were first calculated independently by each initial survey and then combined altogether afterward. For this approach to be properly applied without potential bias, there should not be overlapped survey areas across different surveys. The household weights were calculated for both cross-section for each round and panel for all rounds of the survey. In each round of the survey, the initial sampling weight was calculated following the original sampling method of the survey from which the sampled households were drawn. A sampling weight trimming using the mean and standard deviation of the weights was then conducted to reduce weight variability. In particular, the weight trimming was applied to some outlier weights (only a small proportion of the samples), while keeping the total of the weights remaining the same. Afterwards, the weights were calibrated using a raking method to ensure the total estimates of the households with respect to designated variables were comparable with the population estimates of those variables from the National Household Survey of Socio-economic (SUSENAS) 2019. The designated variables included region (DKI Jakarta, Java Non-DKI Jakarta Urban/Rural, Outside Java Urban/Rural), gender of household's head, and level of education of household's head (junior secondary and lower, senior secondary, and tertiary). The attrition occurred when respondents were not able to be interviewed, which was mostly because their phones were unreachable or unanswered. A test for whether attrition was random showed that the dropped households were not associated with key households' characteristics, such as household head age, gender, education, region (DKI Jakarta, Java-non DKI Jakarta, and Outside Java), and wealth status. However, there was a weak association between households' participation and the area where they reside, as households in urban area were less likely to participate in the follow-up surveys than those in rural areas. The difference in participation rates between urban and rural samples was taken into account in the survey weight calculation. Therefore, for analysis requiring panel households, attrition bias is not a concern when interpreting changes between rounds. Three stages of sampling strategies were applied. For the first stage, districts (as primary sampling unit (PSU)) were selected based on probability proportional to size (PPS) systematic sampling in each stratum, with the probability of selection being proportional to the estimated number of households based on the National Household Survey of Socio-economic (SUSENAS) 2019 data. Prior to the selection, districts were sorted by provincial code. In the second stage, villages (as secondary sampling unit (SSU)) were selected systematically in each district, with probability of selection being proportional to the estimated number of households based on the Village Potential Census (PODES) 2018 data. Prior to the selection, villages were sorted by sub-district code. In the third stage, the number of households was selected systematically in each selected village. Prior to the selection, all households were sorted by implicit stratification, that is gender and education level of the head of households. If the primary selected households could not be contacted or refused to participate in the survey, these households were replaced by households from the same area where the non-response households were located and with the same gender and level of education of households' head, in order to maintain the same distribution and representativeness of sampled households as in the initial design. Response Rates: The actual sample of households in the first round was 4,338 households or 85 percent of the 5,100 target households. However, the response rates in the following rounds are higher than expected, making the sampled households successfully interviewed in Round 2 4,119 (95% of Round 1 samples), and in Rounds 3, 4, 5, and 6, 4,067 (94%), 3,953 (91%), 3,686 (85%), and 3,471 (80%) respectively. The number of balanced panel households up to Rounds 3, 4, 5, and 6 are 3,981 (92%), 3,794 (87%), 3,601 (83%), 3,320 (77%) respectively. This study covers the following topics: Knowledge and behavior (Rounds 1, 3) Employment and income loss (Rounds 1, 3, 5, 6, 7) Food security (Rounds 1, 2, 3, 4, 5, 6, 7) Access to health services (Rounds 2, 3, 4, 5, 6, 7) Digital transactions (Rounds 2, 4) Education (Rounds 2, 4, 7) Coping mechanisms (Rounds 2, 4, 6, 7) Concerns/Subjective Welfare (Rounds 2, 4, 5) Social safety-nets (Rounds 1, 3, 5, 6) Household roster (Rounds 1, 2, 3, 4, 5, 6, 7; Full updates only in R1 and R4) This study is part of an effort by the World Bank, which launched a quick-deploying high-frequency phone-monitoring survey of households to generate near real-time insights on the socio-economic impact of COVID-19 on households. The survey is part of a World Bank-supported global effort to support countries in their data collection efforts to monitor the impacts of COVID-19. computer-assisted telephone interview (CATI)For additional information on the COVID-19 High Frequency Phone Survey of Households study, please visit the World Bank website. The purpose of this study is to provide insights into the socio-economic impact of COVID-19 on households in Indonesia. Smallest Geographic Unit: district