rafmacalaba/datause-encoder-data huggingface dataset:
orientation meetings for every bonus round. Teacher bonuses are also linked to own-school performance only, and not, for example, to own-school performance compared to other schools—the latter formulation could introduce added complexity and uncertainty for teachers (Fryer 2013). The mean number of teachers in program schools at baseline was three. It is likely easier to effectively counteract any incentives for freeridership in such small schools than in larger schools with an identical bonus design (Goodman and Turner 2013).

We posit three hypotheses for the absence of positive mean impacts on student exam scores. First, the binding constraint may be external to the link between teacher effort and student exam scores. Nonschool and school factors outside the control of teachers may make improving student exam scores difficult even with greater teacher effort. Although the public schools targeted by the program were those with the lowest mean student exam scores in the province, and thus have ample room for improvement, the targeting places the program under circumstances that may be especially handicapping. In the face of these external constraints, teachers may choose to withhold greater effort or see expended effort go to waste.

Second, the binding constraint may be that teachers lack the knowhow to improve student exam scores, essentially blocking the link between teacher effort and student exam scores. Teachers may not know what exact strategies to pursue to raise student exam scores, and whether what they intend to pursue is the most efficient way to raise student exam scores. In the face of this uncertainty, teachers may withhold greater effort or misdirect their effort (Fryer 2013).

Third, teachers may optimally choose to direct their effort at those incentivized margins—school enrollment, exam participation rates, and student exam scores—that maximize payoffs net of effort costs (Neal 2011). In this case, it appears that the net payoff-maximizing margin is school enrollment in urban areas, exam participation rates in rural areas, and not student exam scores anywhere. The extent to which movement on a given margin pays off would depend on how accurately movement is measured, and teacher effort would be sensitive to this (Baker 2002). Measurement error may be higher for student scores in PEC exams than enrollment information in the ASC surveys. Notwithstanding, if measurement error is large, PEC exam scores would be a highly imperfect measure of latent student academic achievement. The latter is one of the main targets of the program.

23
Bai et al. (2007) and Jin et al. (2008) confirm that Chinese food producers increasingly use voluntary standards. Based on surveys of Chinese firms, the authors argue that improved product quality and safety and access to foreign markets are the main incentives to use voluntary standards and apply for certifications. In the next section, we briefly introduce the institutional background on standardization in China.

# **3. Food Safety Standardization in China**

A number of government agencies are involved in regulating food safety in China: the Ministry of Agriculture, the Administration of Quality Supervision, Inspection and Quarantine (AQSIQ), the Food and Drug Administration, the Ministry of Health, the Administration for Industry and Commerce, and the Standardization Administration of the People‟s Republic of China (SAC). The large number of agencies with regulatory responsibilities in this area has been subject to criticism as it sometimes leads to an uncoordinated and confusing system (Asian Development Bank 2007). Other problems often cited include low levels of enforcement of standards and regulations by food safety officials and the lack of inspectors supervising an ever increasing number of food producers (Broughton, Walker 2010).

SAC is the agency responsible for approving and publishing Chinese standards and adopting international standards, including food standards. According to Wang Ping, the Executive Governor of the Science and Technology Committee of the China National Institute of Standardization, the standard-setting process in SAC takes place in 450 national technical committees in which stakeholders from government agencies, industry and research organizations participate. SAC nominates the participants and the technical committees are jointly run by SAC and some ministries (Ping 2010). SAC also represents Chinese interests in international organizations. China is actively involved in the Codex Alimentarius Commission (CAC), the organization responsible for setting international standards for food products.<sup>4</sup> Setting food safety standards is a general priority of the Chinese government. In the Eleventh Five-year Plan of Standardization development, agriculture and food safety were named key areas for standardization activities, and the Chinese standardization strategy encourages the adoption of international standards (Ping et al. 2010).

China‟s accession to the WTO in 2001 and the signing of the Agreement on Technical Barriers to Trade (TBT Agreement) and the Agreement on Sanitary and Phytosanitary Measures (SPS Agreement) provided further incentives to harmonize domestic standards. According to Article 2.4 of the TBT Agreement and Article 3.1 of the SPS Agreement, WTO members are encouraged to use international standards where they exist. Domestic standards that are harmonized to international standards are automatically presumed to

> 4 For example, SAC hosts the CAC sector committees for pesticide studies and food additives (WHO 2011)

5
factors is close to unity. They also show that the regularity properties of the translog model deteriorate rapidly when substitution diverges from unity. More recently, Feng and Serletis (2008), in their investigation of productivity trends in U.S. manufacturing, report disappointing results with the translog specification, in terms of theoretical regularity violations, even when local curvature is imposed using methods suggested by Ryan and Wales (2000). This is consistent with earlier evidence by Serletis and Shahmoradi (2007), in the context of consumer theory, in their study of the demand for money and liquid assets in the United States.

In this paper, we started with the translog functional form with the objective of also providing a comparison of our results with those in most of the existing empirical energy demand literature. We followed Ryan and Wales (1998) and Moschini (1999) and, as in Feng and Serletis (2008), treated the curvature property as a maintained hypothesis and built it into the model, very much like the homogeneity in prices and symmetry properties of neoclassical production theory. The results, however, with the energy data used in this paper were disappointing, in terms of theoretical regularity violations and inferences about the own-price elasticities and the Allen own elasticities of substitution, invalidating the use of the translog model.

These problems with the translog specification led us to use a locally flexible functional form for which the theoretical curvature conditions can be imposed globally. This is the normalized quadratic (NQ) cost function, introduced by Diewert and Wales (1987). See Diewert and Wales (1987) and also Barnett and Serletis (2008) for more details regarding the NQ form.

The NQ cost function is given by where we impose two restrictions on the B ≡ [] matrix Further, the  vector ( 0) is usually predetermined.

To obtain equations that are amenable to estimation, we apply Shephard’s (1953) lemma, to equation (4), and by dividing through by , we obtain the following input-output equations 5
|(9)<br>EFW|0.726***<br>(0.0609)<br>0.0342<br>(0.0958)<br>0.0616<br>(0.116)<br>-0.00437<br>(0.00657)<br>yes<br>0.001<br>0.210<br>0.997<br>216<br>36<br>62<br> a balanced sample<br>values reported for<br>umented for using<br>ariables in the ...rst|
|---|---|
|(8)<br>Polity2|0.625***<br>(0.0663)<br>0.514*<br>(0.286)<br>1.008***<br>(0.342)<br>0.884*<br>(0.523)<br>-2.889***<br>(0.550)<br>0.0459*<br>(0.0261)<br>yes<br>0.000<br>0.937<br>0.721<br>432<br>72<br>86<br>he sample is <br>lations. The <br>hey are instr<br>xplanatory v|
|(7)<br>CL|0.708***<br>(0.0582)<br>0.448**<br>(0.204)<br>0.357<br>(0.220)<br>1.805***<br>(0.401)<br>-2.073***<br>(0.553)<br>-0.00480<br>(0.0133)<br>yes<br>0.000<br>0.378<br>0.652<br>456<br>76<br>86<br> estimator. T<br>or serial corre<br>etermined. T<br>quations the e|
|(6)<br>PR|0.709***<br>(0.0600)<br>0.655**<br>(0.286)<br>0.553**<br>(0.269)<br>1.419**<br>(0.632)<br>-2.297***<br>(0.611)<br>0.0105<br>(0.0202)<br>yes<br>0.000<br>0.650<br>0.856<br>456<br>76<br>86<br>ystem GMM <br>o-Bond test f<br>ated as pre-d<br>or the level e|
|(5)<br>EFW|0.726***<br>(0.0609)<br>0.0342<br>(0.0958)<br>0.0616<br>(0.116)<br>-0.00437<br>(0.00657)<br>yes<br>0.001<br>0.210<br>0.997<br>216<br>36<br>62<br>s. One step s<br>es of Arellan<br>iables are tre<br> instruments f|
|(4)<br>Polity2|0.627***<br>(0.0627)<br>0.505*<br>(0.274)<br>0.994***<br>(0.350)<br>0.834*<br>(0.429)<br>-3.042***<br>(0.697)<br>0.0389<br>(0.0252)<br>yes<br>0.000<br>0.904<br>0.795<br>432<br>72<br>90<br> in parenthese<br> are the p-valu<br>y. All the var<br> also uses as|
|(3)<br>CL|0.664***<br>(0.0511)<br>0.516***<br>(0.198)<br>0.518**<br>(0.240)<br>1.106**<br>(0.446)<br>-2.275***<br>(0.804)<br>0.00389<br>(0.0157)<br>yes<br>0.000<br>0.551<br>0.803<br>456<br>76<br>90<br>d by country <br>) and AR(2) <br>ument validit<br> system GMM|
|(1)<br>PR|PRt�5<br>0.683***<br>(0.0546)<br>CLt�5<br>Polity2t�5<br>EFWt�5<br>Human capitalt�5<br>0.667**<br>(0.289)<br>Total emigration ratet�5<br>0.736***<br>(0.282)<br>Human capitalt�5*Soc. dummy<br>1.168***<br>(0.405)<br>Total emigration ratet�5*Soc. dummy<br>-2.804***<br>(0.589)<br>Human capitalt�5*Soc. dummy*d90<br>Total emigration ratet�5*Soc. dummy*d90<br>Log populationt�5<br>0.0238<br>(0.0219)<br>Time dummies<br>yes<br>AR(1) test<br>0.000<br>AR(2) test<br>0.564<br>Hansen J test<br>0.945<br>Observations<br>456<br>N. countries<br>76<br>N. Instruments<br>90<br>*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustere<br>comprising data at ...ve year interval between 1980 and 2005. AR(1<br>the Hansen J test are the p-values for the null hypothesis of instr<br>their own ...rst to second lags. In addition to these instruments, the <br>di¤erences lagged one period.|

22
# **Table 5: Results with Productive and Nonproductive Expenditures (panel OLS)**

## **_Dependent variable: Growth rate of GDP per capita_**

||**OLS**|
|---|---|
|Private investment<br>(% of GDP)|0.071<br>(1.235)|
|Initial human capital|0.007<br>(0.987)|
|Tax revenue|-0.201|
|(% of GDP)|(-1.877)*|
|Other revenue|-0.531|
|(% of GDP)|(-4.051)***|
|Productive expenditure (-1)|0.664|
|(% of GDP)|(4.314)***|
|Non-productive expenditure (-1)|0.312|
|(% of GDP)|(1.786)*|
|Budget surplus|0.450|
|(% of GDP)|(4.182)***|
|Inflation - consumer price index|-0.208<br>(-5.76)***|
|Growth rate of GDP per capita (-1)|0.377<br>(3.644)***|
|No. of observations|120|
|Adjusted R2|0.454|

Note: The estimation method is the ordinary least squares for panel data. Annual data are used. (-1) indicates variables lagged one period. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

32
the BRICS countries’ country-sector nodes are further disaggregated into: (i) activities carried out mostly by local firms, (ii) activities carried out by MNEs located abroad, and (iii) activities of MNE affiliates. This allows them to compare the GVC participation of MNEs with that of local firms. Their results suggest that MNEs are indeed more engaged in GVC trade, specifically in more complex GVC trade (as defined in Wang et al. 2017). In other words, most back-and-forth trade transactions are carried out between MNEs and their affiliates (although local firms still produce most value added within countries). In our paper, we are less concerned about whether the MNEs themselves are engaged in production sharing and GVC trade, and more focused on identifying the additional knock-on effects on productivity of firms in the host country- sector that receive foreign financing. In other words, we are more interested in the role of MNEs as financiers, rather than their direct participation in production.

# **FDI spillovers and GVC participation**

The recent emergence of the multinational input-output tables and indicators derived from them allow us to understand the production process more as a network or a long chain, which is very different from the typical production function of a firm that produces just one good. In this context, a firm in an emerging market can significantly increase its productivity if it can focus on the process it is best at, and there is some sort of ‘learning’ by inserting into the production process with foreign firms. The hypothesis is that those firms that export and import are learning more, but few papers clarify the channels.

The World Bank’s World Development Report (2020) and related background papers (see for example Fernandez et al., 2020) find that countries which have FDI, even at the broad (horizontal) level, also have greater GVC participation. But the concern tends to be on attracting FDI itself. The evidence seems to be country-specific, sector disaggregation is very broad, and the spillover mechanisms are not always clear.

Farole and Winkler (2015), in a comprehensive study of FDI spillovers in Africa, identify ‘expost’ the salient characteristics of firms that are inserted in GVCs, also using the WBES. They find that firms inserted into GVCs typically have good technology, the host country has good institutions and political stability (for non-resource-rich countries) and thus have the capacity to benefit from FDI spillovers (for example through their superior capacity to absorb new technologies). Winkler
– Table 10: Country income group Size heterogeneity in effects of different types of integration of SPS and TBT provisions Panel A: High-income countries

||Ex|port value of agricultu|ral products|Export value o|f any product|
|---|---|---|---|---|---|
||MR SPS|Harmonization SPS|Transparency SPS|Harmonization TBT|Transparency TBT|
|1<sup>_st_ </sup>tercile _×_ Prov|0.465<sup>_∗∗∗_</sup><br>(0.0723)|0.751<sup>_∗∗∗_</sup><br>(0.140)|0.184<sup>_∗∗∗_</sup><br>(0.0316)|-0.518<br>(1.143)|0.0377<br>(0.124)|
|2<sup>_nd_ </sup>tercile _×_ Prov|0.0637|0.196<sup>_∗∗_</sup>|0.0385<sup>_∗_</sup>|1.099<sup>_∗∗_</sup>|-0.00577|
||(0.0528)|(0.0841)|(0.0233)|(0.500)|(0.118)|
|3<sup>_rd_ </sup>tercile _×_ Prov|0.145|0.162|-0.000539|1.045<sup>_∗∗∗_</sup>|0.102|
||(0.155)|(0.108)|(0.0425)|(0.233)|(0.144)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|
|N|888246|888246|888246|2442665|2442665|

Panel B: Low and middle-income countries

||Ex|port value of agricultu|ral products|Export value o|f any product|
|---|---|---|---|---|---|
||MR SPS|Harmonization SPS|Transparency SPS|Harmonization TBT|Transparency TBT|
|1<sup>_st_ </sup>tercile _×_ Prov|0.478<sup>_∗_</sup><br>(0.256)|0.483<sup>_∗∗_</sup><br>(0.205)|0.159<sup>_∗∗∗_</sup><br>(0.0387)|0<br>(.)|0.260<sup>_∗∗∗_</sup><br>(0.0986)|
|2<sup>_nd_ </sup>tercile _×_ Prov|0.0122<br>(0.193)|-0.0686<br>(0.240)|0.0211<br>(0.0356)|0<br>(.)|-0.0348<br>(0.0723)|
|3<sup>_rd_ </sup>tercile _×_ Prov|-0.0426<br>(0.198)|-0.144<br>(0.235)|0.0236<br>(0.0578)|0<br>(.)|-0.0831<br>(0.0806)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|
|N|480787|480787|480787|2797370|2797370|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

40
<!-- Start of picture text --> 200 Per cent of GGDP Per cent of people of Age Pension age 400<br>180 80<br>160<br>60<br>140<br>oe ey 40<br>100 20<br>80 0<br>2007 2012 2017 2022 2027 2032 2037 2042 2047<br>—— Projected superannuation assets (LHS) Full pension (RHS)<br>------Part pension (RHS) —— No pension (RHS)<br><!-- End of picture text -->
The SAM for South Africa also reports taxes paid by institutions, commodity sales, and production activities. The tax instruments and their share in total revenue are summarized in Table 2.

_<u>Table 2: South African Taxes included in the CGE model</u>_

|**Tax**<br>**Category**|**Description**|**Model**<br>**parameter**|**Percent of total**<br>**government**|
|---|---|---|---|
||||**revenue collected**|
|**Institutional ta**<br>|**xes (direct taxes)**<br>|<br>||
|Corporate<br>Tax|Taxes Levied on<br>Firms|)<br>(_firms_<br>_tins_|20.5|
|Income Tax|Taxes Levied on<br>Individuals<br>(Households)|)<br>(_h_<br>_tins_|37.3|
|**Commodity ta**<br>|**xes (indirect taxes)**<br>|||
|VAT|Value Added Tax|)<br>(_c_<br>_tvat_|20.4|
|Fuel tax|Government levy<br>on fuel|)<br>(_c_<br>_tfuel_|5.3|
|Excise tax|Excise taxes on<br>goods|)<br>(_c_<br>_texcise_|3.1|
|Net taxes on<br>products|Other taxes on<br>products excluding<br>subsidies|)<br>(_c_<br>_tproducts_|2.0|
|Tariffs|Taxes levied on<br>imports of goods<br>and services|)<br>(_c_<br>_tm_|2.2|
|**Activity taxes**||||
|Net taxes on|Other taxes on|)<br>(_a_<br>_ta_|9.2|
|production|production|||
|Note: governm|entrevenue shares are|calculatedfromS|ASAM 2003.|

Consistent with other studies, we find that the VAT for South Africa is mildly regressive, see Figure 1. Low-income households pay a higher share of their income to VAT than do highincome households. It is also evident in Figure 1 that the VAT is more regressive or at least no better than the other indirect taxes in South Africa. For this reason, there is room for changes to the tax system that would make the VAT less regressive.

It should be emphasized that the whole tax system of South Africa is progressive. Indeed, Davis and Kay (1985) as well as Fourie and Owen (1993) stress that the progressiveness of the complete tax system should be taken into account and one should not look at the distributional impact of VAT in isolation. When evaluating total taxes paid, including direct taxes on households, it is evident that high-income households pay a larger percentage of their income to taxes than low-income households, see Figure 2.

5
# **Table S5.3 Probability that all meals are taken jointly in the household**

|VARIABLES|All meals<br>shared|
|---|---|
|decile_cons1|1.925*<br>|
||(0.753)|
|decile_cons2|1.083<br>|
||(0.356)|
|decile_cons3|0.868<br>|
||(0.262)|
|decile_cons4|1.322<br>|
||(0.422)|
|decile_cons5|0.845<br>|
||(0.235)|
|decile_cons6|1.012<br>|
||(0.282)|
|decile_cons7|<br>0.902|
||(0.235)|
|decile_cons8|0.793|
||(0.197)|
|decile_cons9|0.855|
||(0.204)|
|Dakar|0.670**|
||(0.122)|
|Other urban area|0.783|
||(0.148)|
|Nb of cells|1.312**|
||(0.144)|
|Nb of couples in the household|0.662***<br>|
||(0.0940)|
|2-cell hh: head+wife.|1.708***|
||(0.285)|
|2-cell hh : head + other|1.510*|
||(0.367)|
|3+-cell hh : head, wife(wives) + other|1.549*|
||(0.352)|
|Hh head is polygamous|1.074|
||(0.172)|
|Nb of children 0 – 4 y.o.|0.870**|
||(0.0556)|
|Nb of children 5 - 14 y.o.|0.969<br>|
||(0.0415)|
|Nb of women 15 - 65|0.893**|
||(0.0452)|
|Nb of elderly over 66 y.o.|0.867<br>|
||(0.111)|
|Constant|4.407***|
||(1.136)|
|Observations|1,762|

Source : « Pauvreté et Structure Familiale » (PSF) survey, authors’ calculations. Note : Dependent variable: dummy equals 1 if all meals are taken jointly by all household’s members. Logit estimates. Odd ratios reported. Coeff. standard errors in parentheses. Top consumption decile as a reference category. *** p<0.01, ** p<0.05, * p<0.

24
**Table 2: Decomposition of Start-up by Enterprise Size, 2005**

||P|ercentage d<br>|istribution<br>|byenterpr<br>|ise size|
|---|---|---|---|---|---|
|Category||N|umber of|workers||
||1|2|3|4|5+|
|Overall|63|22|6|4|5|
|_Formal_|54|25|5|6|10|
|_Informal_|66|21|6|3|4|
|Sectoral breakdown<sup>a/</sup>||||||
|_Trading_|65|22|6|4|3|
|_Services_|63|23|6|3|5|
|_Manufacturing_|62|20|2|1|15|
|_Agroprocessing_|58|18|8|3|13|
|_Construction_|64|16|2|2|16|
|_Mining and quarrying_|80|20|0|0|0|
|Two or more sectors|47|29|12|6|6|

Source: 2005 Tanzania RICS. a/ The breakdown is approximate due the small number of observations in the production sector, and the small number of observations for larger enterprises Start-ups of new rural nonfarm enterprises can indicate “good” or “bad” news. When agriculture is prospering and overall demand for nonfarm products or services is high, starting a business can mean prosperity. But when agriculture is languishing or population growth is high, startup jobs may simply reflect the news that firms are acting as a sponge, soaking-up excess workers in marginal activities (Liedholm, 2002; Barrett et al., 2001). In Tanzania enterprise start-up is closely related to agriculture (figure 6), with about 55% of start-up capital from agricultural production. Seventeen percent is from non-agricultural income sources and more than 13% from local friends or relatives.

**Figure 6: Sources of Start-up Capital, 2005**

<!-- Start of picture text --> Family or friends<br>13%<br>Non-agricultural<br>production<br>17%<br>Other informal<br>sources<br>7%<br>Other sources<br>4%<br>Agricultural Sale of assets<br>production 2%<br>55%<br>Private money<br>lenders<br>1%<br>Bank loan<br>1%<br><!-- End of picture text --> 13
Table 1: Company and Job Descriptive Statistics (Percentage)

||||Deg<br>|ree<br>|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Account.|Business|Comp. Sci.|Elec. Eng.|Mec. Eng.|Total|
|**Location**|||||||
|Kuala Lumpur|66.3|65.9|70.6|39.2|39.6|63.2|
|Rest of Malaysia|33.7|34.1|29.4|60.8|60.4|36.8|
|**Company Size**|||||||
|Up to 50 employees|44.1|46.5|45.8|39.2|33.7|44.5|
|51 employees or more|55.9|53.5|54.2|60.8|66.3|55.5|
|**Area of Job Specializa**|**tion**||||||
|Accounting/Finance|100.0|0.0|0.0|0.0|0.0|24.5|
|Admin/Human Res.|0.0|29.3|0.0|0.0|0.0|14.5|
|Information Tech.|0.0|0.0|89.6|0.0|0.0|11.8|
|Construc./Manufact.|0.0|0.0|0.0|2.7|12.9|1.0|
|Engineering|0.0|0.0|0.0|97.3|87.1|11.9|
|Sales/Marketing|0.0|54.0|0.0|0.0|0.0|26.7|
|Services|0.0|16.6|10.4|0.0|0.0|9.6|
|**Industry**|||||||
|Primary sector|1.8|2.3|0.0|1.1|6.1|2.1|
|Manufact./Industry|25.2|24.9|15.7|67.6|72.4|29.7|
|Healthcare|3.4|7.6|1.1|2.2|3.6|5.1|
|Real Estate, legal|7.9|5.4|2.1|0.6|0.0|4.9|
|Professional services|37.2|19.8|61.4|12.8|10.2|28.5|
|Retail|8.7|20.8|6.9|5.6|2.0|13.7|
|Transp./Telecomm.|3.9|3.6|5.3|2.8|0.0|3.6|
|Services|5.5|8.1|3.5|3.4|3.1|6.2|
|Others|6.3|7.5|4.0|3.9|2.6|6.2|
|**Processing Time**|||||||
|1-5 days|15.4|15.3|18.5|7.5|9.4|14.9|
|6-10 days|10.5|12.5|12.2|12.9|11.4|11.9|
|11-15 days|9.3|10.5|9.9|8.6|10.4|10.0|
|16-20 days|13.1|12.2|13.9|15.1|12.4|12.9|
|More than 20 days|51.7|49.4|45.6|55.9|56.4|50.4|
|**Salary**|||||||
|Less than 500|8.0|11.2|3.8|9.7|6.9|9.0|
|500-700|25.2|24.7|10.1|18.8|16.3|22.0|
|700-1000|21.6|25.8|22.8|22.6|24.8|24.1|
|1000-1500|2.7|5.8|15.7|3.2|2.5|6.0|
|More than 1500|42.4|32.5|47.6|45.7|49.5|38.9|
|Total|24.5|49.4|13.2|6.2|6.7|100.0|
|Note:<br>Descriptive statistics<br>criteria. All statistics are pr|for the fna<br>esented by d|l sample for<br>egree of spec|a total of 2,9<br>ialization. Col|95 job ads th<br>umn 1 is for A|at satisfy our <br>ccounting colu|inclusion<br>mn 2 for|
|Business Administration, co<br>Mechanical Engineering and<br>of job specialization, industr<br>in US dollars. To convert M<br>by the Central Bank of Ma<br>0.23.|lumn 3 for C<br>, column 6 fo<br>y, average ti<br>alaysian Ring<br>laysia https:|omputer Scie<br>r all of them. <br>me to proces<br>git to US do<br>//www.bnm.|nce, column 4 f<br> Descriptive st<br>s applications, <br>llars we use the<br>gov.my/exchan|or Electrical E<br>atistics for loca<br> average month<br> April average <br>ge-rates. Tha|,<br>ngineering, colu<br>tion, company <br>ly salary ofere<br>exchange rate p<br>t is, a exchang|mn 5 for<br>size, area<br>d per job<br>ublished<br>e rate of|

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