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«June 2015 Abstract Since 2011, Saudi Arabia has dramatically extended its labor market policies to address youth unemployment and low Saudi ...»

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Notes: This table gives the full set of results for the RKD estimates on the matched sample. For Saudi percentage, firm size, and exit rate the running variable is Saudization percentage point distance from the cuto↵. For Saudi employees the running variable is distance from the cuto↵ in terms of number of Saudis. For expatriate employees the running variable is distance from the cuto↵ in terms of number of expatriate workers. Bandwidth is based on ! 49 the units of the running variable. For the exit rate regressions the sample consists of all firms in the sample in July

2011. For all other outcomes the sample consists of firms that appear in both the July 2011 and October 2012 data.

Bandwidth is based on the units of the running variable. The largest and smallest 1% outliers in outcome variables are dropped for the Saudi and expatriate regressions and the largest 1% increases from the firm size regressions.

!

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Notes: This table reports the average change in Saudi percentage, number of Saudi employees, number of expatriate employees, total number of employees, and exit rates between July 2011 and October 2012 based on initial color band assignment. Comparisons are based on firms in the same industry and size category that were assigned to the Green band. For odd-numbered columns, the omitted comparison group is all firms that were initially in the Green color band. In even-numbered columns the comparison group is Green firms that were just above the quota cutoff, with no more than five Saudi employees more than were needed to meet the quota. Average changes for firms that were in the Green band but which were wellabove the quota are reported as coefficients on D(Green!5). All regressions include industry by size fixed effects. Standard errors are clustered at the industry by size level. The last column lists the number of matched firms in each category for the sample used in the first eight columns. The last two rows compare the implied total estimated effect on the relevant outcome variable with the full-compliance benchmark. In columns 1 and 2 this is the average change in private-sector Saudization; in columns 3 and 4 the increase in Saudi employees; in columns 5 and 6 the decrease in expatriate employees; in columns 7 and 8 the total change in the number of private sector workers, and in 9 and 10 the number of firms that exited as a result of the program.

*** p"0.01, ** p"0.05, * p"0.1 !

!

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Notes: This table reports the average change in Saudi percentage, number of Saudi employees, number of expatriate employees, and total number of employees for firms that entered between July 2011 and October 2012 relative to firms who entered during July 2011.

Comparisons are based on firms that entered the market in the first month of the data in the same industry by size category, and all regressions include industry by size fixed effects.

Standard errors are clustered at the industry by size level. There were 5,065 new entrants in July 2011 and 40,620 additional entrants between July 31, 2011 and October 13, 2012.

*** p"0.01, ** p"0.05, * p"0.1

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These dummy variables are constructed similarly for distance in terms of expatriates, and using percentagepoint bins for regressions where the distance is measured in terms of Saudization percentage. I then estimate:

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In this specification, all e↵ects are compared Green firms just at the cuto↵, i.e. the omitted category k = 0.

Results from these specifications are reported graphically, with the estimated coe cients plotted against distance from the cuto↵.

Figure A.1 plots the coe cients from this di↵erences-in-di↵erences specification. Panel (a) shows the results for Saudi employees. As in Figure VIIa, there is again evidence of a kink in Saudi hiring at the Nitaqat quota. There is also evidence of the “poaching” e↵ect described in the main paper, in which Green firms farther above the cuto↵ tended to reduce their numbers of Saudi employees.

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%&$ '()*+,$-*$./01,2$34$5)/6-$70893:,,;$ %#$ &$ #$ !&$ !%#$ !%&$ !"#$ "#$ %&$ %#$ &$ #$ !&$ !%#$ !%&$ !"#$

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%&$ '()*+,$-*$./01,2$34$567)82-)8,$50793:,,;$ %#$ &$ #$ !&$ !%#$ !%&$ !"#$ !"#$ !%&$ !%#$ !&$ #$ &$ %#$ %&$ "#$

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Notes: Panel (a) of this figure plots the coe cients of the regression of the change in the number of Saudi employees on distance (in terms of number of Saudi employees needed) from the green color band cuto↵.

Panel (b) plots the coe cients of the regression of the change in the number of expatriate employees on distance (in terms of number of surplus expatriate employees) from the green color band cuto↵. Bounds for the 95% confidence intervals are marked in grey and are based on standard errors clustered at the industry by size group level. Regressions include a full set of cell fixed e↵ects, and di↵erences are the changes between July 2011 and October 2012. Omitted category is zero.

B Appendix: Sector and Industry-Level Results (For Online Publication) The impact of the program varies by industry, and Figure B.1 presents the main RKD results by sector.





The strongest e↵ects, both in terms of compliance (hiring Saudis) and costs (exit and downsizing), were experienced by the secondary sector, which includes construction (the largest private-sector industry) and manufacturing. E↵ects were more muted in the services sector, with lower compliance rates as well as more muted kinks in the size and exit relationships. The primary sector (agriculture and extraction) shows little e↵ect of the program – there is no evidence of an increase in the number of Saudis employed at Yellow and Red firms, and no corresponding decrease in firm size or increase in exit. This pattern is likely the result of several factors, including the degree to which di↵erent industries rely on foreign labor as well as the degree of competitiveness in the included industries. The services sector is the least exposed to competition from imports, and while these firms did increase Saudi hiring as a result of the program the exit and downsizing e↵ects were lower than in the secondary sector. In construction in particular, Saudization rates are among the lowest, and the industry has historically relied heavily on low-cost foreign labor. It was critical that these firms improve their color band status in order to keep the bulk of their workforce, so compliance rates were very high. The costs were also very acutely felt, however, with a strong response to the program in terms of firm size and exit rates. In the retail sector, which is less reliant on expatriate labor, compliance rates were slightly lower and costs were more muted.

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(Production and Construction) (Services) −50 −40 −30 −20 −10 0 10 20 30 −50 −40 −30 −20 −10 0 10

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−50 −40 −30 −20 −10 0 10 20

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!

C Appendix: Results on Stock Market Data (For Online Publication) Because Nitaqat a↵ected firm exit rates, it likely also had e↵ects on the capital investments, profitability, and market value of surviving firms. Unfortunately, these types of indicators are only available for the firms listed on the Saudi Stock Exchange. This appendix presents the data and RKD figure for the change in market value of these firms. The sample is too small to draw any conclusions about the size of these e↵ects, however, so they are excluded from the main results.

Stock price and balance sheet data are available for all Nitaqat entities belonging to joint stock companies that are listed on the Saudi Stock Exchange. These companies are required to submit their balance sheets and auditors’ reports to the Ministry of Commerce and Industry on a quarterly basis. This data, along with the number of shares and stock price, are available through the Saudi Stock Exchange (Tadawul) on their website54 as well as through Bloomberg. Of the 158 firms listed during the period, 147 were matched to Nitaqat entities, and 255 entities were matched to listed firms. Of these, 156 had stock price information available for the period. When multiple entities were matched to a single firm, most were subsidiaries of a larger firm that were engaged in di↵erent economic activities or that were located in di↵erent geographic areas. Balance sheet items that were reliably reported included capital, total equity, liabilities, expenses, and inventories. Compared to the rest of the sample, these firms tend to be large and to have higher Saudization rates. Only 22 percent of these firms were in the Small size category; 39 percent fell in the Medium, 36 percent in the Large, and 3 percent in the Giant category. They also tend to be concentrated in di↵erent industries, with the largest groups of Tadawul firms in manufacturing (19 percent), insurance (17 percent), and retail (15 percent). Construction is notably not well-represented, with only 8 percent of these firms in that sector compared with 35 percent of all Nitaqat firms. Although they are certainly not a representative sample, there is still some variation in their color bands at baseline, with 18 percent beginning in the Red band, 10 percent in the Yellow band, 32 percent in the Green band and 40 percent in the Platinum band.

The RKD figure for the market value of the 147 publicly-listed firms is shown in Figure C.1. Unfortunately the small sample size makes it impossible either to detect a kink in market value or to find a su ciently precise zero. As suggested by the figure, parametric estimates of the kink are very noisy and usually not statistically significant. This is also the case for other balance sheet measures available in the Tadawul data for these firms.

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