rafmacalaba/datause-encoder-data huggingface dataset:
|a.|WinningRebates(inflation adjusted)|18466.3|21023.1|2198.4|2502.8|
|---|---|---|---|---|---|
|2.|Government Adm|inistrative C|ost Savings|||
|a.|Advertisement Cost Savings|779.5|985.9|92.8|117.4|
|b.|Tender Preparation Cost Savings|198.0|198.0|23.6|23.6|
|c.|SecurityCost Savings|42.6|42.6|5.1|5.1|
|d.|Staff-time savings|109.9|109.9|13.1|13.1|
||Total Government Savings|19596.2|22359.4|2332.9|2661.8|
|3.|Firm Transa|ction Cost S|avings|||
|a.|Staff-time savings|3.0|7.0|0.4|0.8|
|b.|Tender Preparation Cost Savings|-222.0|-189.1|-26.4|-22.5|
|c.|SecurityCost Savings|359.7|391.9|42.8|46.7|
||Total Private Sector Savings|140.6|209.8|16.7|25.0|

Note: lower bound estimates are produced by models employing CEM matching within years, while upper bound estimates are based on models using CEM matching within PEs.

# 9.9.3 FULL GOVERNMENT SAMPLE ESTIMATE

The annual expenditure in public procurement was estimated to US$24 billion in FY19, representing 45.2% of the annual budget and 8% of GDP.37 Based on the savings due increased winning rebate with an upper bound of 7.4%, the annual estimated savings if all procurement in Bangladesh is administered electronically could be as high as US$ 1.76 billion per annum, based on the same average higher rebate rate.38 9.9.4 POLICY RECOMMENDATIONS Based on the cost savings analysis, the following policy recommendations have been put forward:-

1. We observe that the majority of the savings was obtained from the transition to the e-procurement system was from the increased rebate rates and the initial investment in the system is recuperated multiple times over in a single year. Investing in the procurement functions, IT skills, capacity building of personnel, are tiny compared to the saving that can be obtained from it. Most of the training of procurement officials is legal in nature. Any training focusing on professional development of government officials involved in procurement would also be justified. Hiring of experienced procurement professionals can also be justified based on the savings that can be obtained.

> 37 Assessment of Bangladesh Public Procurement System Report 2020. (World Bank).

> 38 The other cost components have not been considered; the economic cost savings comprise more than 89% of the total government savings. Additionally, over 90% of tenders by number are currently administered electronically but only constitute about 65% of the total public procurement by value. As the economic cost savings is based on the value of the tender, whereas all the other cost components are primarily dependent on the number of tenders, the proportion of economic cost savings as a part of the whole will be even higher than 89%.

81
This section begins with some remarks on the setting of objectives for policy decisions. Several types of social cost-benefit analysis are then discussed: the Pareto criterion; the quasi-Pareto criterion, first order dominance, and rank dominance; abbreviated labor market well-being functions; minimizing a low-earnings index; and traditional social cost-benefit analysis. The section concludes with some comments on social cost-benefit analysis in practice.

# **<u>Specifying the Ultimate Objective or Objectives</u>**

When social cost-benefit analysis is used as a basis for allocating scarce resources among alternative uses, it is essential that an objective or a number of objectives be specified. These need to be the ultimate objective or objectives of policy, not intermediate objectives.

Think of familiar maximization problems in economics. An individual maximizes utility by choosing how much money to spend on each of a number of alternative products or by allocating one’s time between work and leisure. A firm maximizes profits by choosing how much output to produce, what production technology to use, and how much of each input to employ. A government or international agency seeks to minimize poverty by allocating an anti-poverty budget among the populace. In these and other decisions like them, what is being maximized or minimized - technically speaking, the maximand or minimand - is made explicit.

A great many studies purporting to offer “policy implications” from labor market research do not have a well-formulated maximand or minimand. Typically, inputs to or <u>components of the maximand or minimand are analyzed but not the maximand or</u> minimand itself. Minimum wage policy is sometimes analyzed in terms of the disemployment caused, active labor market policy by the penetration rate of the national employment service, pension policy by the social security replacement rate, and unemployment insurance by the percentage of workers who are eligible for benefits in the event of job loss.

Let us consider some more comprehensive alternatives. The interested reader is referred to Fields (2001, Chapter 8) for further details.

9
Bursztyn and Yang (2022), we establish an individual level measure of misperception based on how individuals’ perceptions compare to actual beliefs. The “true value” is determined by the average belief among respondents in a country who agree with a given norm. We assess the perceived community norm against this true value to calculate the proportion of “correct beliefs” among respondents. At the individual level, we can discern the fraction of the population that accurately assesses, overestimates, or underestimates the prevalence of a norm. Consequently, we report the proportion of respondents with accurate perceptions (those within 0.5 standard deviations of the truth); those who overestimate (respondents who hold beliefs that are at least 0.5 standard deviation greater than the truth); and those who underestimate (respondents who hold beliefs that are at least 0.5 standard deviation less than the truth).

We define _OverestimateNormij_ as a binary indicator that equals 1 for individuals whose perceptions exceed the true average belief by at least 0.5 standard deviations. Similarly, _UnderestimatesNormij_ equals 1 for individuals whose perceptions fall at least 0.5 standard deviations below the true average belief. The baseline category, _AroundNormij_ , includes individuals whose perceptions are within 0.5 standard deviations of the actual beliefs.

Since the reference group in our norm constructs is deliberately broad (i.e. neighbors), this may not directly correlate with individuals’ most influential social circles. The extent to which our behaviors are shaped by close personal connections versus the broader community remains an empirical question, hence our decision to keep a general reference group. Also since our norm constructs refer to “neighbors”, a group not synonymous with the Facebook user base itself. Therefore, while our analysis is useful for establishing broad patterns, we caution that any observed misperceptions might be attributable to sample selection rather than pluralistic ignorance.

# **2.4 Comparison of the Gender Equality at Home Survey with Other Data Sources**

The Gender Equality at Home Survey represents a general population of Facebook users and should not be considered representative of a country’s population. For example, demographic characteristics of the respondents on Facebook might differ from the average characteristics of a representative individual in the same country; or they may have different gender attitudes.

While our sample is not nationally representative, we examine how closely the gender attitude statements from our data correlate with comparable attitude statements collected in nationally representative global data sources: the World Values Survey (WVS)<sup>16</sup> and

> 16For our analysis, we utilized data from Wave 7 of the World Value Survey, spanning from 2017 to 2021.

10
the assistance provided through this program goes towards rehabilitating damaged housing. Although this program is more limited in scope, providing assistance to approximately 2,000 households each year since 1990, it may represent an important source of insurance in the event of unexpected disasters. If there is significant insecurity caused by the threat of droughts or floods, there may be some scope for expanding this or similar efforts rather than creating entirely new mechanisms.

# III. The Rationale for a New Income Security Scheme in Indonesia

Although some observers see the problem of income insecurity as a relatively recent phenomenon produced by the transition from a primarily rural and traditional society to an industrial and modem one, the description of underemployment in Indonesia presented in the first section of this paper suggests that this problem is not so much a product of the transition process as it is of not participating in that process. As people move out of rural areas and into jobs in the modem sector, underemployment and the income uncertainty it causes decline. And while it is impossible to determine any pattern over time from looking at one year of data, the structural nature of underemployment suggests that it should become less of problem over time.

Similarly, recent economic developments suggest that income insecurity should become less of a problem in the future. Rising average income levels, combined with an increasing multiplicity of employment opportunities (even those that are part-time and located in the informal sector), suggest that more households are becoming less vulnerable to fluctuations in the labor market all the time.

This optimistic story, however, still is consistent with the picture presented by the _SAKERNAS_ data and with the field observation that many Indonesians remain eager to accept wage employment at virtually any time of the year. Underemployment remains a common problem, especially in the informal sector, and average incomes remain low throughout rural Indonesia. These two circumstances, however, do not necessarily translate into income insecurity unless no mechanisms, whether generated by the household, the local community or the central government, are in place to spread out the risks of income loss. In Indonesia, household mechanisms appear to be functioning relatively well, and a patchwork of other community and government programs represent significant additional sources of insurance; as a result, the problem of income insecurity as a result of underemployment does not appear to be pervasive throughout the informal sector.

While this conclusion suggests that no new nationwide scheme for alleviating income insecurity is necessary at this time, it does not categorically reject that possibility that the Government of Indonesia could do something new to improve the living standards of workers in the informal sector. There are two possible grounds for new or expanded action to address the problem of underemployment in the informal sector. First, there almost certainly are locations where vulnerability to economic shocks at the household 22
“machinery, mechanical, and electrical equipment”sector (HS2 = 84-85).

The advantage of focusing on processing exporters is that we need not worry about their imports for ...nal consumption, as by de...nition, all imports in processing trade have to be used as intermediate inputs.<sup>52</sup> However, not all processing exporters import for their own use. Some of them import for other processing ...rms, which also implies that some processing ...rms must export more than what their imported materials can support. We develop systematic rules to identify processing ...rms that potentially import from and export for other ...rms. To this end, we merge the customs transaction data with the ...rm-level data from the Annual Surveys of Industrial Firms conducted by China’s National Bureau of Statistics (NBS hereafter). The surveys cover all state-owned enterprises (SOEs) and non-state-owned ...rms that have sales above 5 million yuan in a given year.<sup>53</sup> The NBS data contain detailed information for most of the standard balance sheet information, such as ...rm ownership, output, value added, industry code (480 categories), exports, employment, original value of ...xed asset, and intermediate inputs. Tables A2 and A3 present the percentages of ...rms and sales that are covered by the merged data. Table A4 presents the industry’s median of ...rm materials-to-sales ratios.

# 1.1.1 Transforming Chinese I/O Tables to One Based on UN Industry Code

1. Use the concordance from China’s National Bureau of Statistics to match multiple IO codes with multiple HS 6-digit codes (revision 2002).

2. Match multiple HS6 codes to multiple UN industry sector codes (20 of them).

> 52Manova and Yu (2013) examine how ...nancial constraints a¤ect exporters positions in global supply chains in China and thus their pro...ts. In this paper, we simply take advantage of the special features of the processing regime without getting into the details about ...rms’transition from one regime to another.

> 53The industry section in the o¢ cial statistical yearbooks of China is constructed based on the same data source. The unit of analysis is a ...rm, and not the plant, but other information in the survey suggests that more than 95% of all observations in our sample are single-plant ...rms. 5 million yuan is roughly exchanged to 600,000 US dollars during the sample period.

2
Given the small sample sizes involved in this pilot study, readers are cautioned against overinterpreting the results. Still, the differences that do (and do not) exist all provide insight into the state of teaching among the 16 project counties in Guangdong and have useful lessons for further teacher training and observation.

The results presented in this section focus on the applicability of CLASS within the context of the proposed Guangdong Compulsory Education project. For an analysis of breakdowns by years of teaching experience, method of observations, and more detailed breakdowns by county, see Appendix A.

# **3.1.1 Across domains**

Figure 2 shows the average scores across the three CLASS domains, separated by grade. Teachers observed in the three pilot counties scored highest in Classroom Organization. Elementary-school teachers scored higher than middle-school teachers across all three domains.

**Figure 2: Domain Results, Guangdong Pilot Counties**

<!-- Start of picture text --> 7<br>6.5<br>6.2<br>6<br>Elementary School<br>(Guangdong Pilot<br>5 Counties)<br>4.2<br>4 3.7 3.7 3.6<br>3<br>2<br>Middle School<br>(Guangdong Pilot<br>1 Counties)<br>0<br>Emotional Support Classroom Organization Instructional Support<br><!-- End of picture text --> Figure 3 adds four comparator values from the three studies, all using the Upper Elementary CLASS observation tool. The three studies are all from the United States, however, and therefore provide a limited international comparison. They are the largest upper elementary CLASS studies available.

It shows that, first, both in the Guangdong and MET studies, lower-grade classrooms scored higher than middle-school classrooms across all three domains. While the effect is less pronounced in the Guangdong scores, it is nevertheless consistent throughout. Second, despite popular discussion on the differences in classroom culture and the role of the teacher between China and the United States, overall scores are quite similar among the four studies, particularly on Emotional Support and Instructional Support. The largest difference between the three pilot counties and the U.S. studies is in Classroom Organization. Finally, Instructional Support is the weakest domain across all studies. Further work must be done to determine whether the results obtained in Guangdong for the domain of Instructional Support are related to difficulty of measuring some of the domains (particularly Analysis and Inquiry and Instructional Dialogue) or to an overall weakness on the part of teachers in providing adequate, student-centered instructional support. The approach of this pilot study does not allow for this to be assessed.

11
The destination information reported by Zambian customs must be interpreted with considerable caution, however. This becomes apparent in Table 4, where we list the ten main destination countries in value terms across three sub-periods. Astonishingly, Switzerland appears as Zambia’s main export destination in the most recent sub-period, 2008-2011, absorbing more than half of all Zambian exports. This cannot possibly reflect the true flow of goods. In Swiss import data, reported imports from Zambia are some 6,000 times smaller than reported Zambian exports to Switzerland. Moreover, Swiss import statistics do not suggest copper to feature among Switzerland’s main import products from Zambia. Hence, a large part of Zambian mining exports are evidently assigned to Switzerland in the statistics because the relevant multinational firms are headquartered there and not because the goods are destined for or even physically shipped to Switzerland. This is a major flaw in data quality, and it also potentially has considerable revenue implications for the Zambian authorities.

# _Main export products_

We now shift our attention to the product composition of Zambian exports. Table 5 shows export transactions and values across 16 product groups, averaged over the 1999-2011 sample period. The dominance of Metals and Mineral Products is evident, as they together accounted for 82% of the value of Zambian exports, dwarfing the main non-traditional sectors Foodstuffs (4.5%), Vegetable Products (3.3%) and Textiles (2.3%). In terms of transaction numbers, however, nontraditional exports are much more important, accounting for 64% of shipments. Given that administrative barriers to exports have a high fixed-cost component (i.e. are to a considerable extent unrelated to the value of the consignment), they should be of particular relevance when the aim is to facilitate non-traditional exports.

The customs data prior to 2006 report re-exports separately from “standard” exports of Zambiansourced goods. We show those data by product category in Table 6. Re-exports account for a quarter of all export transactions overall, and the majority of transactions in Transportation (88% 4
|<br>(8)|)<br>(-0.49∗∗∗)|-(0.13)∗∗∗|)<br>(-0.64∗∗∗)|-(0.03)∗∗∗<br><br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|-7804∗∗∗|-0.27∗∗∗<br>old+silver+livestock) and<br> Helmand and Kandahar<br>clude gender, age, marital<br>p<0.1.|
|---|---|---|---|---|---|---|---|
|ng Wealth<br>(7)|(-0.41∗∗∗|-(0.13)∗∗∗|(-0.89∗∗∗|-(0.03)∗∗∗<br>(-Yes∗∗∗)|(-Yes∗∗∗)<br>(-No∗∗∗)|-7804∗∗∗|-0.34∗∗∗<br>are GSL (g<br> residing in<br>controls in<br>∗p<0.05, ∗|
|Non-Housi<br>(6)|(-0.49∗∗∗)|-(0.13)∗∗∗|(-0.65∗∗∗)|-(0.02)∗∗∗<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-7804∗∗∗|-0.26∗∗∗<br>ent variables <br>es households <br>ousehold-head<br>: ∗∗∗p<0.01, ∗|
|(5)|(-0.37∗∗∗)|-(0.13)∗∗∗|(-0.61∗∗∗)|-(0.02)∗∗∗<br>(-No∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-7804∗∗∗|-0.10∗∗∗<br> two depend<br>mple exclud<br>Household/H<br>fcance levels|
|(4)|)<br>(-3.11∗∗∗)|∗<br>-(0.38)∗∗∗|)<br>(-0.37∗∗∗)|∗<br>-(0.08)∗∗∗<br><br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-Yes∗∗∗)|∗<br>-12060∗∗∗|-0.13∗∗∗<br>= β3 = 0. The<br>k-debt). The sa<br>n parentheses. <br>dwelling. Signi|
|SL<br>(3)|(-2.44∗∗∗|-(0.40)∗∗|(-0.48∗∗∗|-(0.09)∗∗<br>(-Yes∗∗∗)|(-Yes∗∗∗)<br>(-No∗∗∗)|-12060∗∗|-0.21∗∗∗<br>etting β2 <br>er+livestoc<br>errors are i<br>and type of|
|G<br>(2)|)<br>(-3.10∗∗∗)|∗<br>-(0.38)∗∗∗|)<br>(-0.36∗∗∗)|∗<br>-(0.08)∗∗∗<br>(-Yes∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|∗<br>-12060∗∗∗|-0.12∗∗∗<br>ation (2) after s<br>diture+gold+silv<br>Robust standard<br>per household,|
|(1)|(-3.00∗∗∗|-(0.30)∗∗|(-0.28∗∗∗|-(0.08)∗∗<br>(-No∗∗∗)|(-No∗∗∗)<br>(-No∗∗∗)|-12060∗∗|-0.00∗∗∗<br>ients of equ<br>rable expen<br>provinces. <br>ber of males|
|Variables|Variance of Income (log)||Income (log)|Household Controls|Province Fixed Efects<br>Confict|Observations|R-squared<br>The table shows the estimated coefc<br>Non-Housing Wealth (income-non-du<br>which are the two most confict-ridden<br>status, education, household size, num|

33
**Table 6 Descriptive Statistics**

|Variable|Observation|Mean|StandardDev.|
|---|---|---|---|
|_NetJobCr_|13895|0.001|0.059|
|_Export_|13873|0.265|-|
|_Export ( ≥ %10)_|13873|0.220|-|
|_Total Export (%)_|13873|10.750|24.801|
|_Direct Export (%)_|13873|9.263|23.059|
|_Foreign ( ≥ %10)_|13895|0.136|-|
|_log(Labor)_|13874|3.072|1.678|
|_log(Pro)_|10299|-2.096|1.145|
|_Growth_|13698|0.031|0.153|
|_Innovate_|13870|0.362|-|
|_Log(Wage)_|1667|1.368|1.105|
|_Age_|13887|14.833|17.335|
|_ExternalFin_|13895|10.989|26.574|
|_NonProdWrk_|13687|0.408|0.306|
|_Competition_|9506|0.826|0.379|
|_Senior_|13067|1.128|1.209|
|_Obstacle_|13540|0.248|0.432|
|_Inspect_|4015|0.441|0.659|
|_Multi-plant_|13884|0.259|-|

**Table 7 Correlation Matrix**

||_NetJobCr _|_Log_<br>_(labor)_|_Growth_|_Log(Pro)_|_Age_|_Foreign_|_External_<br>_Fin_|_Innovate_|_Log_<br>_(Wage)_|_NonProd_<br>_Wrk_|
|---|---|---|---|---|---|---|---|---|---|---|
|_Log(labor)_|-0.0655*||||||||||
|_Growth_|0.0339*|0.0855*|||||||||
|_Log(Pro)_|-0.0786*|-0.0718*|-0.0275*||||||||
|_Age_|-0.0522*|0.3686*|-0.1488*|0.0101|||||||
|_Foreign_|-0.0351*|0.1947*|0.0472*|0.0769*|-0.0531*||||||
|_ExternalFin_|-0.0365*|0.1289*|0.0567*|0.1622*|0.0225*|0.0259*|||||
|_Innovate_|-0.0308*|0.1831*|0.1340*|-0.0091|0.0294*|0.0844*|0.1037*||||
|_Log(Wage)_|-0.1692*|0.7343*|-0.0955*|0.2540*|0.2923*|0.0551*|0.0429|0.0482*|||
|_NonProdWrk_|0.0433*|-0.2399*|-0.0215*|0.0236*|-0.0849*|0.0391*|-0.0718*|-0.0106|-0.0157||
|Multi-plant|-0.0489*|0.3033*|0.0750*|0.0615*|0.0815*|0.1075*|0.0706*|0.1057*|0.1373*|-0.0305*|

* Shows significance of coefficients at 5%.

25
-i-

# Acknowledgements

Amongst our colleagues at the University of Groningen, we are particularly grateful to Harry van Ooststroom and Paul Wieringa who helped launch the investigation, and to Aerit Houben and Eddy Szirmai who helped us in the end phase. We also received useful comments from Tom Elfring, Kees van der Meer, Jan '?en and Hans Jflrgen Wagener.

We profited from discussions with colleagues who have been engaged in the analogous ICP studies, in particular, Irving Kravis, Alan Heston and Robert Summers of the University of Penns -vania, Hugo Krijnse Locker of EUROSTAT, Laszlo Drechsler of the UN Statistical Office, Sultan Ahmad, Jean Baneth, Arabinda Kundu, and Michael Ward of the World Bank. We are particularly grateful to De:rek Blades of OECD with whom we had very extensive discussions, particularly on chapters V and VI.

For help in interpreting and gathering data for particular countries, we are grateful to Robert Parker (Bureau of Economic Analysis) and Gaylord Worden (Bureau of the Census), of the US Department of Commerce, Jeromf, Mark, Arthur Neef, and Thomas Tibbetts of the US Bureau of Labor Statistics. In Brazil we received considerable help from Maria Alice Gusmao Veloso formerly of the Vargas Foundation and now with IBGE, Angelo de Souza and Ralph Zerkowsky of the Vargas Foundation. In Mexico we had the cooperation of Pedro Aspe Armella, and Jaime Alatorre Cordoba of INEGI, Jorge Ruben Morinelli of the UN team in INEGI and Victor Urquidi of El Colegio de Mexico.

We had a useful exchange of views with Robert Ballance, Stephen Davies, Arun Ghosh, Bryan Haig, Jagdish Kumar, Valentin Kudrov, Jaroslav Kux, Sigbert Prais, Uma Datta Roy Chaudhury, Anthony Smith, Anatoli Smyshlyaev, and in seminars in our faculty, in the World Bank and the International Association of Research in Income and Wealth.

We are also grateful to the World Bank both for intellectual cooperation, and for meeting some of our research expenses, particularly those for travel and translation.